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Fig. 1. Overview of the one-stop system. system (one-stop system) for supporting evacuation and emergency response activities leveraging satellite data and AI. Therefore, near real-time flood mapping system has been developed using CNN and optical satellite images to incorporate with Japanese national disaster monitoring program. The integrated system developed by SIP-Season2 is shown in Fig. 1. The main requirement for satellite data processing group is to process satellite data and upload them into the system within 30 minutes regardless of the extent of the data.

Figure 1 Overview of the one-stop system. system (one-stop system) for supporting evacuation and emergency response activities leveraging satellite data and AI. Therefore, near real-time flood mapping system has been developed using CNN and optical satellite images to incorporate with Japanese national disaster monitoring program. The integrated system developed by SIP-Season2 is shown in Fig. 1. The main requirement for satellite data processing group is to process satellite data and upload them into the system within 30 minutes regardless of the extent of the data.

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Table 1. Indicators for calculating DHI, DEI, and DVI.
Table 1. Indicators for calculating DHI, DEI, and DVI.
Fig. 1. Study area showing four administrative regions. Figure 1 shows the study area, which includes 27 provinces in Vietnam belonging to four administrative regions: the South Central Coast (Khanh Hoa, Ninh Thuan, and Binh Thuan); the Central Highland (Kon Tum, Gia Lai, Dak Lak, Dak Nong, Lam Dong); the South East (Ho Chi Minh, Ba Ria — Vung Tau, Binh Duong, Binh Phuoc, Dong Nai, Tay Ninh); and the Mekong Delta region (Long An, Tien Giang, Ben Tre, Tra Vinh, Vinh Long, Kien Giang, Hau Giang, Soc Trang, Bac Lieu, Ca Mau, Can Tho, An Giang, and Dong Thap). DATA AND METHODS Selection of indicators For calculating DRI, SPI and VHI data obtained from SWCEM products was extracted for the study area from global CDF data by using “Make net CDF raster layer” technique in ArcGIS (ESRI [date unknown]-b). This spatial data was classified and mapped to demonstrate monthly drought progression and then transformed into statistical data at the administrative level using Zonal statistics tool of ArcGIS (ESRI [date unknown]-c). The average statistical value of SPI and VHI for each province was used to identify the month with lowest average SPI and VHI value.
Fig. 1. Study area showing four administrative regions. Figure 1 shows the study area, which includes 27 provinces in Vietnam belonging to four administrative regions: the South Central Coast (Khanh Hoa, Ninh Thuan, and Binh Thuan); the Central Highland (Kon Tum, Gia Lai, Dak Lak, Dak Nong, Lam Dong); the South East (Ho Chi Minh, Ba Ria — Vung Tau, Binh Duong, Binh Phuoc, Dong Nai, Tay Ninh); and the Mekong Delta region (Long An, Tien Giang, Ben Tre, Tra Vinh, Vinh Long, Kien Giang, Hau Giang, Soc Trang, Bac Lieu, Ca Mau, Can Tho, An Giang, and Dong Thap). DATA AND METHODS Selection of indicators For calculating DRI, SPI and VHI data obtained from SWCEM products was extracted for the study area from global CDF data by using “Make net CDF raster layer” technique in ArcGIS (ESRI [date unknown]-b). This spatial data was classified and mapped to demonstrate monthly drought progression and then transformed into statistical data at the administrative level using Zonal statistics tool of ArcGIS (ESRI [date unknown]-c). The average statistical value of SPI and VHI for each province was used to identify the month with lowest average SPI and VHI value.
The mapping results for drought hazard, exposure, vulnerability, and the combined drought risk index are presented in Fig. 2. Fig. 2. Drought Hazard, Exposure, Vulnerability, and Risk Maps.
The mapping results for drought hazard, exposure, vulnerability, and the combined drought risk index are presented in Fig. 2. Fig. 2. Drought Hazard, Exposure, Vulnerability, and Risk Maps.
Table 1. Detail of all ten experiments performed in this study.
Table 1. Detail of all ten experiments performed in this study.
Minimum MSLP time series from all the 3DVar experiments along with CNTL simulation and IMD best- track data are provided in Fig. 3(a). Experiments L03 V02, L10V02, LO3V08 and CNTL have shown an under- estimation from the IMD, whereas L06V10, L10V06, L06V08, L10V10 and LO6V06 have depicted an over- estimation with a lag of 6-hours. L10V08 and L06V04 have equalled the IMD in terms of minimum MSLP though a lag of 6-hours exists with these experiments. However, MSLP error is found lowest with the LIOV08 experiment, shown in Fig. 3(b). The model has exhibited remarkable results as the MSLP error is less than 5 hPa up to 66 hours forecast. The average MSLP error for the entire simulation period is 3.4 hPa. Histogram plots for other experiments are not provided here.
Minimum MSLP time series from all the 3DVar experiments along with CNTL simulation and IMD best- track data are provided in Fig. 3(a). Experiments L03 V02, L10V02, LO3V08 and CNTL have shown an under- estimation from the IMD, whereas L06V10, L10V06, L06V08, L10V10 and LO6V06 have depicted an over- estimation with a lag of 6-hours. L10V08 and L06V04 have equalled the IMD in terms of minimum MSLP though a lag of 6-hours exists with these experiments. However, MSLP error is found lowest with the LIOV08 experiment, shown in Fig. 3(b). The model has exhibited remarkable results as the MSLP error is less than 5 hPa up to 66 hours forecast. The average MSLP error for the entire simulation period is 3.4 hPa. Histogram plots for other experiments are not provided here.
Fig. 3. (a) MSLP time series from all the 3DVar experiments along with CNTL simulation and IMD best- track; (b) MSLP error (hPa) histogram from L10V08 experiment calculated against the IMD best-track. Fig. 3. (a) MSLP time series from all the 3DVar Fig. 2. Track error (km) histogram from L10V08 experiment calculated against the IMD best-track.
Fig. 3. (a) MSLP time series from all the 3DVar experiments along with CNTL simulation and IMD best- track; (b) MSLP error (hPa) histogram from L10V08 experiment calculated against the IMD best-track. Fig. 3. (a) MSLP time series from all the 3DVar Fig. 2. Track error (km) histogram from L10V08 experiment calculated against the IMD best-track.
Similar to MSLP, the MSW time series is shown in Fig. 4(a). Some experiments have a time lead, whereas some have a time lag to attain their peak with underestimation or overestimation from the IMD. The MSW error (Fig. 4(b)) up to 66 hours forecast time is found lowest in the L10V08 experiment with an average of 2.8 m/s for the entire simulation hours.
Similar to MSLP, the MSW time series is shown in Fig. 4(a). Some experiments have a time lead, whereas some have a time lag to attain their peak with underestimation or overestimation from the IMD. The MSW error (Fig. 4(b)) up to 66 hours forecast time is found lowest in the L10V08 experiment with an average of 2.8 m/s for the entire simulation hours.
Fig. 4. (a) MSW time series from all the 3DVar error is found 45.7 km, whereas the average minimum sea level pressure (maximum sustained wind) error is found ave’ A kDa £9 7 tasla\ Anta eee tA: hE ALORA.
Fig. 4. (a) MSW time series from all the 3DVar error is found 45.7 km, whereas the average minimum sea level pressure (maximum sustained wind) error is found ave’ A kDa £9 7 tasla\ Anta eee tA: hE ALORA.
We use GEMS level 1C data at 0545 UTC 14 March 2021 after thinning and cloud screening. To prevent potential correlated errors of high-reso observation, simple thinning is applied, pixel every 100 km. For cloud screening, pixels that cloud fraction is more than 0.2 are removed. ution satellite selecting one After cloud screening, the number of observations d 1575 to 436 (Fig. 1). For this study, we channels (310.0 nm—310.8 nm with 0.2 nm interval and 311.0 nm—326.0 nm with 1.0 nm interval) and will extend ecreased from use just a few to the entire channels of GEMS in further study. Fig. 1. GEMS level 1C radiances at 310 nm on 0545 UTC 14 March 2021 with thinning and before (left) and after (right) cloud screening.
We use GEMS level 1C data at 0545 UTC 14 March 2021 after thinning and cloud screening. To prevent potential correlated errors of high-reso observation, simple thinning is applied, pixel every 100 km. For cloud screening, pixels that cloud fraction is more than 0.2 are removed. ution satellite selecting one After cloud screening, the number of observations d 1575 to 436 (Fig. 1). For this study, we channels (310.0 nm—310.8 nm with 0.2 nm interval and 311.0 nm—326.0 nm with 1.0 nm interval) and will extend ecreased from use just a few to the entire channels of GEMS in further study. Fig. 1. GEMS level 1C radiances at 310 nm on 0545 UTC 14 March 2021 with thinning and before (left) and after (right) cloud screening.
Figure 2 demonstrates the maximum DFS of each GEMS channel. The maximum DFS value is 0.9746 at 326.0 nm, and the minimum is 0.0003 at 312.0 nm. The value of DFS decreases from 310.0 nm, reaching a minimum at 312.0 nm, and then gradually increases. It turns out that the channels of 310.0—313.0 nm have much less information for control variables than those of 314.0— 326.0 nm. It may be due to lower absorption of troposphere trace gases (e.g. Chemical control variables: O03, NO2, SO2, and HCHO) over 310.0-313.0 nm. Fig. 2. The maximum DFS obtained by GEMS level 1C radiance assimilation. 326.0 nm. It may be due to lower absorption of
Figure 2 demonstrates the maximum DFS of each GEMS channel. The maximum DFS value is 0.9746 at 326.0 nm, and the minimum is 0.0003 at 312.0 nm. The value of DFS decreases from 310.0 nm, reaching a minimum at 312.0 nm, and then gradually increases. It turns out that the channels of 310.0—313.0 nm have much less information for control variables than those of 314.0— 326.0 nm. It may be due to lower absorption of troposphere trace gases (e.g. Chemical control variables: O03, NO2, SO2, and HCHO) over 310.0-313.0 nm. Fig. 2. The maximum DFS obtained by GEMS level 1C radiance assimilation. 326.0 nm. It may be due to lower absorption of
Fig. 3. A horizontal distribution of DFS obtained by GEMS level 1C radiance assimilation for (a) 312.0 nm and (b) 326.0 nm.
Fig. 3. A horizontal distribution of DFS obtained by GEMS level 1C radiance assimilation for (a) 312.0 nm and (b) 326.0 nm.
Table 1. Optimized tuning parameters — amplitude (0), decorrelation length (L) and time scale (t) for different experiments.
Table 1. Optimized tuning parameters — amplitude (0), decorrelation length (L) and time scale (t) for different experiments.
Fig. 1. Scatter plots of the ensemble mean spread and RMSE for (a) soil temperature (ST), (b) temperature at 1000 hPa (T1000), (c) soil moisture (SM), and (d) water vapor mixing ratio at 1000 hPa (Q1000). We investigate the ensemble spread and error using the scatter plot (Fig. 1). It shows that the experiment using the optimized tuning parameters generally increase ensemble spreads in all variables. As for the soil variables, the diurnally-varying tuning parameters (e.g., STP2 and SMP2) help to improve the desired linear relationship between the ensemble spread and error. On the other hand, when they propagate to the atmospheric model, the single (i.e., daytime only) tuning parameters (e.g., STP1 and SMP1) are more effective to increase the ensemble spread. The daytime amplitude and time scale, which show larger values in STP1 and SMP1, respectively, can generate larger random forcing.
Fig. 1. Scatter plots of the ensemble mean spread and RMSE for (a) soil temperature (ST), (b) temperature at 1000 hPa (T1000), (c) soil moisture (SM), and (d) water vapor mixing ratio at 1000 hPa (Q1000). We investigate the ensemble spread and error using the scatter plot (Fig. 1). It shows that the experiment using the optimized tuning parameters generally increase ensemble spreads in all variables. As for the soil variables, the diurnally-varying tuning parameters (e.g., STP2 and SMP2) help to improve the desired linear relationship between the ensemble spread and error. On the other hand, when they propagate to the atmospheric model, the single (i.e., daytime only) tuning parameters (e.g., STP1 and SMP1) are more effective to increase the ensemble spread. The daytime amplitude and time scale, which show larger values in STP1 and SMP1, respectively, can generate larger random forcing.
Fig. 2. The analysis increment (colored contours; positive in red, negative in blue, and zero in gray) and the background error against GFS analysis (shaded) for temperature (in K) in (a) CTRL and (b) STP1, and for water vapor mixing ratio (in g kg") in (c) CTRL and (d) SMPI1. Results are averaged from 850 hPa to 1000 hPa. variable perturbations to the atmosphere, through the sensible and latent heat fluxes, etc., is less effective than the single (i.e., daytime only) tuning parameter. variable perturbations to the atmosphere, through the
Fig. 2. The analysis increment (colored contours; positive in red, negative in blue, and zero in gray) and the background error against GFS analysis (shaded) for temperature (in K) in (a) CTRL and (b) STP1, and for water vapor mixing ratio (in g kg") in (c) CTRL and (d) SMPI1. Results are averaged from 850 hPa to 1000 hPa. variable perturbations to the atmosphere, through the sensible and latent heat fluxes, etc., is less effective than the single (i.e., daytime only) tuning parameter. variable perturbations to the atmosphere, through the
Fig. 1. Track of 120 typhoons approached through Japan during 2006-2019.
Fig. 1. Track of 120 typhoons approached through Japan during 2006-2019.
thresholds of 5, 10, 20 mm over the different regions of Figure | represents the tracks of the 126 typhoons that landfalled/passed near Japan during 2006-2019. It shows the typhoons approaching Japan mostly form in the region of 5N—-15N and move initially northwestward and, after recurving, northeastward to the southern coast of Japan. The tracks indicated that the typhoons mostly landfalled in the Pacific Ocean side of Japan during 2006-2019 and are more in numbers compared to those landfalled in the Sea of Japan side. Figure 2 shows the probabilities of the spell durations of precipitation exceeding various thresholds of 5, 10, 20 mm over the different regions of ea of Japan side. Figure 2 shows the probabilities of the
thresholds of 5, 10, 20 mm over the different regions of Figure | represents the tracks of the 126 typhoons that landfalled/passed near Japan during 2006-2019. It shows the typhoons approaching Japan mostly form in the region of 5N—-15N and move initially northwestward and, after recurving, northeastward to the southern coast of Japan. The tracks indicated that the typhoons mostly landfalled in the Pacific Ocean side of Japan during 2006-2019 and are more in numbers compared to those landfalled in the Sea of Japan side. Figure 2 shows the probabilities of the spell durations of precipitation exceeding various thresholds of 5, 10, 20 mm over the different regions of ea of Japan side. Figure 2 shows the probabilities of the
Fig. 1. Lightning density in 2009-2020 (stroke*km?* year!, log scale). The numbers are associated with proposed area division. The white circle marks Yakutsk city. The average lightning density decreased to the north: the linear fit was —0.0033 (with 95% confidence bounds: from —0.0035 to —0.0031) and R* = 0.93. Also, the strong density relation to orography can be noted (Fig. 1). The average lightning density was high (> 2 times) on the western slopes of the Verkhoyansk ridge (along Lena and Aldan rivers) and between Momskiy and Cherskiy ridges (northeast of Yakutia). Aldan rivers) and between Momskiy and Cherskiy ridges
Fig. 1. Lightning density in 2009-2020 (stroke*km?* year!, log scale). The numbers are associated with proposed area division. The white circle marks Yakutsk city. The average lightning density decreased to the north: the linear fit was —0.0033 (with 95% confidence bounds: from —0.0035 to —0.0031) and R* = 0.93. Also, the strong density relation to orography can be noted (Fig. 1). The average lightning density was high (> 2 times) on the western slopes of the Verkhoyansk ridge (along Lena and Aldan rivers) and between Momskiy and Cherskiy ridges (northeast of Yakutia). Aldan rivers) and between Momskiy and Cherskiy ridges
Fig. 2. Annual total lightning density in the parts according to the division indicated on Fig. 1 (stroke*km?*year"', log scale). different origin of storms that formed under the influence of air mass transfer from Okhotsk Sea.
Fig. 2. Annual total lightning density in the parts according to the division indicated on Fig. 1 (stroke*km?*year"', log scale). different origin of storms that formed under the influence of air mass transfer from Okhotsk Sea.
The coordinate of the WRF-3DVar is 38.1°N and 98.9°E. For assimilation data author used AMSU-A satellite radiance data (from ATOVS d ataset) and for upper air we have used data from NCAR archives. Model forecast runs occur at 0000, 0600, 1200, daily. The first experiment, Control (CN1 Global Forecast System (GFS) as initia and 1800 UTC TL), used NCEP and boundary conditions. And as observation data author used BMD Table 1. WRF model configure. where J(x) is the current state at (x) to the background state (x) which is summed and squared and also to the observations (y’) in which the inverse of the error covariance matrices is weighted to misfits. The forward observation operator H maps model state to observation space. An observation error (R) and the covariance matrices of background error (B) are Gaussian in nature. The WRF-Var analysis and WRF model configuration, along with the parameterization schemes used in the present study, are summarized in Table 1. The first experiment, Control (CNTL), used NCEP Global Forecast System (GFS) as initial and boundary conditions. Two hailstorm events were simulated and then assimilated. The physical schemes of the model were the same in all three experiments as outlined in Table 1. physical schemes of the model were the same in all three
The coordinate of the WRF-3DVar is 38.1°N and 98.9°E. For assimilation data author used AMSU-A satellite radiance data (from ATOVS d ataset) and for upper air we have used data from NCAR archives. Model forecast runs occur at 0000, 0600, 1200, daily. The first experiment, Control (CN1 Global Forecast System (GFS) as initia and 1800 UTC TL), used NCEP and boundary conditions. And as observation data author used BMD Table 1. WRF model configure. where J(x) is the current state at (x) to the background state (x) which is summed and squared and also to the observations (y’) in which the inverse of the error covariance matrices is weighted to misfits. The forward observation operator H maps model state to observation space. An observation error (R) and the covariance matrices of background error (B) are Gaussian in nature. The WRF-Var analysis and WRF model configuration, along with the parameterization schemes used in the present study, are summarized in Table 1. The first experiment, Control (CNTL), used NCEP Global Forecast System (GFS) as initial and boundary conditions. Two hailstorm events were simulated and then assimilated. The physical schemes of the model were the same in all three experiments as outlined in Table 1. physical schemes of the model were the same in all three
Table 2. Root mean square errors (RMSE) for rainfall, surface temperature and relative humidity for 27" February 2019 at Dhaka station. As it is found that the assimilation gives better agreement with the observational data, author used the assimilation technique to simulate the hailstorm event over Bangladesh which occurs on 27" February 2019 to investigate the associative atmospheric conditions responsible for the occurrence of hailstorms as sea level pressure, rainfall, accumulated hail (mm), reflectivity (dBZ), vertical profile of wind, vertical profile of CAPE, hail mixing ratio, graupel mixing ratio etc. and from the analyses it found that over the location where hailstorm occurred specially over Dhaka, Faridpur, Natore and Sirajganj these parameters were intense at the time of hail. As it is found that the assimilation gives better
Table 2. Root mean square errors (RMSE) for rainfall, surface temperature and relative humidity for 27" February 2019 at Dhaka station. As it is found that the assimilation gives better agreement with the observational data, author used the assimilation technique to simulate the hailstorm event over Bangladesh which occurs on 27" February 2019 to investigate the associative atmospheric conditions responsible for the occurrence of hailstorms as sea level pressure, rainfall, accumulated hail (mm), reflectivity (dBZ), vertical profile of wind, vertical profile of CAPE, hail mixing ratio, graupel mixing ratio etc. and from the analyses it found that over the location where hailstorm occurred specially over Dhaka, Faridpur, Natore and Sirajganj these parameters were intense at the time of hail. As it is found that the assimilation gives better
Fig. 1. Comparison of accumulated hail amount between WREDA and CNTL run from the model for 27" February 2019. As case study, high-resolution numerical simulation o the 27 February 2019, severe hailstorm event has been conducted using mesoscale model WRF ARW and 3DVAR data assimilation system over Bangladesh. The model was having been run with 02 nested domain of 09 km and 03 km horizontal resolutions using the appro- priate physics schemes. For improvement of extreme hai event,the radiance data assimilation and without assimi- ation (CNTL run) simulation of WRF was performed. By comparing the RMSE of the assimilation and simulation without assimilation, and the simulated data with the BMD observed data, it is found that the hail simulation with assimilation give better results than the CNTL simulation. The CNTL run cannot capture the hail ocation over Dhaka, but WRFDA can capture the exact ocation of the 27" February 2019 hailstorm event. The model simulated weather elements and microphysical parameters have been found in close agreement with the BMD observation during the hail event. In terms of other weather variables WRFDA performs better than without assimilation (CNTL run). As case study, high-resolution numerical simulation of
Fig. 1. Comparison of accumulated hail amount between WREDA and CNTL run from the model for 27" February 2019. As case study, high-resolution numerical simulation o the 27 February 2019, severe hailstorm event has been conducted using mesoscale model WRF ARW and 3DVAR data assimilation system over Bangladesh. The model was having been run with 02 nested domain of 09 km and 03 km horizontal resolutions using the appro- priate physics schemes. For improvement of extreme hai event,the radiance data assimilation and without assimi- ation (CNTL run) simulation of WRF was performed. By comparing the RMSE of the assimilation and simulation without assimilation, and the simulated data with the BMD observed data, it is found that the hail simulation with assimilation give better results than the CNTL simulation. The CNTL run cannot capture the hail ocation over Dhaka, but WRFDA can capture the exact ocation of the 27" February 2019 hailstorm event. The model simulated weather elements and microphysical parameters have been found in close agreement with the BMD observation during the hail event. In terms of other weather variables WRFDA performs better than without assimilation (CNTL run). As case study, high-resolution numerical simulation of
Table 1. CMIP5 models (underlined if no RCP4.5). FIGURES AND TABLES
Table 1. CMIP5 models (underlined if no RCP4.5). FIGURES AND TABLES
Table 2. Minimum central pressure (unit in hPa). REFERENCES Table 3. Additional sensitivity experiment (unit in hPa)
Table 2. Minimum central pressure (unit in hPa). REFERENCES Table 3. Additional sensitivity experiment (unit in hPa)
ACKNOWLEDGEMENTS
ACKNOWLEDGEMENTS
Fig. 1. Statistical Post-processing (SPP) procedure of probabilistic and single deterministic precipitation forecasts. In this study, we aim to produce well-calibrated 7-day accumulated precipitation forecasts through statistical Post-Processing (SPP), including probabilistic and single value (often called “deterministic”) forecasts. How to produce these two calibrated forecast products? Figure 1 shows the SPP procedure for precipitation forecasts. First, analog post-processing (AP; Hamill and Whitaker, 2006; Hamill et al., 2015) is used to get historical forecast analogs that most resemble the current ensemble forecast. That is, we search for the 20 past dates where the forecast is most similar to today’s forecast. Then we use the 20 observations corresponding to the forecast analogs as a calibrated forecast ensemble (called AP ensemble or APE hereafter). Probability-Matched mean (PM; Ebert, 2001) is then applied on APE to derive a single deterministic forecast (called APPM) on one hand, and frequency counting is used to obtain probabilistic forecasts on the other.
Fig. 1. Statistical Post-processing (SPP) procedure of probabilistic and single deterministic precipitation forecasts. In this study, we aim to produce well-calibrated 7-day accumulated precipitation forecasts through statistical Post-Processing (SPP), including probabilistic and single value (often called “deterministic”) forecasts. How to produce these two calibrated forecast products? Figure 1 shows the SPP procedure for precipitation forecasts. First, analog post-processing (AP; Hamill and Whitaker, 2006; Hamill et al., 2015) is used to get historical forecast analogs that most resemble the current ensemble forecast. That is, we search for the 20 past dates where the forecast is most similar to today’s forecast. Then we use the 20 observations corresponding to the forecast analogs as a calibrated forecast ensemble (called AP ensemble or APE hereafter). Probability-Matched mean (PM; Ebert, 2001) is then applied on APE to derive a single deterministic forecast (called APPM) on one hand, and frequency counting is used to obtain probabilistic forecasts on the other.
Fig. 2. Frequency distribution of rain rate using all the samples from the 22 years data set in this study. The blue, red and black curves are results from the AP ensemble mean, APPM, and corresponding observations. Figure 2 shows that the under-forecasting for heavy rainfall and over-forecasting for light rainfall, which results from the averaging process in the ensemble mean, is mitigated in APPM: its frequency distribution of rain rate is close to the truth. In other words, the rainfall pattern of the APPM displays more detailed fine scale variability that is present in nature, as compared with that of the AP ensemble mean.
Fig. 2. Frequency distribution of rain rate using all the samples from the 22 years data set in this study. The blue, red and black curves are results from the AP ensemble mean, APPM, and corresponding observations. Figure 2 shows that the under-forecasting for heavy rainfall and over-forecasting for light rainfall, which results from the averaging process in the ensemble mean, is mitigated in APPM: its frequency distribution of rain rate is close to the truth. In other words, the rainfall pattern of the APPM displays more detailed fine scale variability that is present in nature, as compared with that of the AP ensemble mean.
Fig. 3. Tukey boxplots of correlation for summer, winter, and Mei-yu season. The blue and red boxplots are the results from the raw and calibrated forecasts respectively. For the two adjacent boxplots, the left is week 1 and the right is week 2 forecast. These results suggest that the precipitation forecasts downscaled and calibrated using the AP and PM methods substantially improve forecast quality and value.
Fig. 3. Tukey boxplots of correlation for summer, winter, and Mei-yu season. The blue and red boxplots are the results from the raw and calibrated forecasts respectively. For the two adjacent boxplots, the left is week 1 and the right is week 2 forecast. These results suggest that the precipitation forecasts downscaled and calibrated using the AP and PM methods substantially improve forecast quality and value.
A total of 79 cm, 172 cm, and 406 cm rainfall recorded at CCPO, MACPO & HACPO locations respectively during the Indian Summer Monsoon (ISM) 2019. During this season 12, 58 and 48 BB events are observed. It is noted that before-BB a sufficient amount of precipitation of 2.2 cm (CCPO), 24.9 cm (MACPO) and 41.7 cm (HACPO) observed shown in Fig. 1. Maximum number of BBs occur at MACPO but the longest duration of events seen at HACPO location. The total rainfall contributed by BB in before-during period is highest (512 mm) at HACPO. At the time of BB events, 6 mm and 125 mm were noted at CCPO and MACPO. Fig. 1. Rainfall (mm) associated with before-BB and during-BB events at CCPO, MACPO and HACPO locations. CONCLUSION
A total of 79 cm, 172 cm, and 406 cm rainfall recorded at CCPO, MACPO & HACPO locations respectively during the Indian Summer Monsoon (ISM) 2019. During this season 12, 58 and 48 BB events are observed. It is noted that before-BB a sufficient amount of precipitation of 2.2 cm (CCPO), 24.9 cm (MACPO) and 41.7 cm (HACPO) observed shown in Fig. 1. Maximum number of BBs occur at MACPO but the longest duration of events seen at HACPO location. The total rainfall contributed by BB in before-during period is highest (512 mm) at HACPO. At the time of BB events, 6 mm and 125 mm were noted at CCPO and MACPO. Fig. 1. Rainfall (mm) associated with before-BB and during-BB events at CCPO, MACPO and HACPO locations. CONCLUSION
Further, to analyze the scenario of the DSD before-BB and during-BB we classify the rain rate into four different categories i.e, R1 (0.1 < R <1 mmh!), R2(1<R< 5 mmlr!), R3 (5<R<10mmb) and R4(R>10 mmh’!). The DSD spectra for all rain rates at each location are presented in Fig. 3. It is observed that at CCPO majority of rainfall occur in R1 rain rate category for during-BB, whereas before-BB the R1 and R2 rain rate contributes to total rainfall for these periods, however at MACPO and HACPO all rain rate categories contribute to surface rainfall. At each location number concentration of small drops (0.1—0.6mm) observed higher before BB, whereas large drops (greater than 3mm) are much more observable during BB. Fig. 3. Mean raindrop concentration (N(D), m? mm) versus drop diameter (D, mm) for before-BB (a—c) and During-BB (d—f) at CCPO, MACPO and HACPO near ground level for rain rate R1 to R4.
Further, to analyze the scenario of the DSD before-BB and during-BB we classify the rain rate into four different categories i.e, R1 (0.1 < R <1 mmh!), R2(1<R< 5 mmlr!), R3 (5<R<10mmb) and R4(R>10 mmh’!). The DSD spectra for all rain rates at each location are presented in Fig. 3. It is observed that at CCPO majority of rainfall occur in R1 rain rate category for during-BB, whereas before-BB the R1 and R2 rain rate contributes to total rainfall for these periods, however at MACPO and HACPO all rain rate categories contribute to surface rainfall. At each location number concentration of small drops (0.1—0.6mm) observed higher before BB, whereas large drops (greater than 3mm) are much more observable during BB. Fig. 3. Mean raindrop concentration (N(D), m? mm) versus drop diameter (D, mm) for before-BB (a—c) and During-BB (d—f) at CCPO, MACPO and HACPO near ground level for rain rate R1 to R4.
Fig. 2. Contoured frequency altitude diagrams (CFAD) of fall velocity (ms!) BBB (a-c) and DBB (d-f) events at CCPO, MACPO and HACPO.
Fig. 2. Contoured frequency altitude diagrams (CFAD) of fall velocity (ms!) BBB (a-c) and DBB (d-f) events at CCPO, MACPO and HACPO.
Fig. 1. SLP at (a) 0000 UTC on 9 August (b) 1800 UTC on 10 August 2011.
Fig. 1. SLP at (a) 0000 UTC on 9 August (b) 1800 UTC on 10 August 2011.
Figure 2 shows strong circulation at 950 hPa over West-Bengal and adjoining Bangladesh with a trough extended to east-southeastwards. There was_ strong southwesterly flow of winds from the Bay of Bengal through Bangladesh coast. The circulation was found to extend vertically to 500 hPa (not shown in the figure) at 0000 UTC on 9 August 2011 and continued to 10 August. The maximum wind speed at 950 was 18.75 ms? (67.5 km/hr) at Rajshahi at 0000 UTC on 9 August. Fig. 2. Simulated wind vectors and circulations at 950 hPa on 9-10 August 2011.
Figure 2 shows strong circulation at 950 hPa over West-Bengal and adjoining Bangladesh with a trough extended to east-southeastwards. There was_ strong southwesterly flow of winds from the Bay of Bengal through Bangladesh coast. The circulation was found to extend vertically to 500 hPa (not shown in the figure) at 0000 UTC on 9 August 2011 and continued to 10 August. The maximum wind speed at 950 was 18.75 ms? (67.5 km/hr) at Rajshahi at 0000 UTC on 9 August. Fig. 2. Simulated wind vectors and circulations at 950 hPa on 9-10 August 2011.
Fig. 4. Simulated 6 hourly reflectivity over Bangladesh on 9-10 August 2011. Fig. 5. Simulated 6 hourly CAPE over Bangladesh on 9— 10 August 2011.
Fig. 4. Simulated 6 hourly reflectivity over Bangladesh on 9-10 August 2011. Fig. 5. Simulated 6 hourly CAPE over Bangladesh on 9— 10 August 2011.
Fig. 3. Simulated 3 hourly rainfall in Bangladesh on 9 August 2011.
Fig. 3. Simulated 3 hourly rainfall in Bangladesh on 9 August 2011.
the centre of the depression. Figure 6(b) shows the cloud water mixing ratio at stations. At Khepupara, maximum cloud water mixing ratio of more than 600 mgm* was at 0000 UTC on 10 August 2011. Maximum cloud water mixing ratio of 600 mgm* was found at 0900 UTC on 9 August at Rajshahi. Cloud water mixing took place between 950 and 300 hPa and was maximum with greater depth near the centre of the depression.
the centre of the depression. Figure 6(b) shows the cloud water mixing ratio at stations. At Khepupara, maximum cloud water mixing ratio of more than 600 mgm* was at 0000 UTC on 10 August 2011. Maximum cloud water mixing ratio of 600 mgm* was found at 0900 UTC on 9 August at Rajshahi. Cloud water mixing took place between 950 and 300 hPa and was maximum with greater depth near the centre of the depression.
Those previous studies indicated that there is nteraction between CENS and the DCR. However, the Fig. 1. Mean of ERAS surface wind (vector) and meridional wind (shaded) in boreal winter from 2000- 2020. Red box indicates CENS indices and the purple box indicates Java Island as the study area. and it is further defined as the cross equatorial northerly surge (CENS) by Hattori et al. (2011). Previous studies (Wu et al., 2007; Hattori et al., 2011; Yulihastin et al., 2020) agreed that CENS is associated to heavy rainfall events over Java Island. It is also associated to the early morning peak over the northwestern coast of Java Island that causes severe flood (Yulihastin et al., 2020).
Those previous studies indicated that there is nteraction between CENS and the DCR. However, the Fig. 1. Mean of ERAS surface wind (vector) and meridional wind (shaded) in boreal winter from 2000- 2020. Red box indicates CENS indices and the purple box indicates Java Island as the study area. and it is further defined as the cross equatorial northerly surge (CENS) by Hattori et al. (2011). Previous studies (Wu et al., 2007; Hattori et al., 2011; Yulihastin et al., 2020) agreed that CENS is associated to heavy rainfall events over Java Island. It is also associated to the early morning peak over the northwestern coast of Java Island that causes severe flood (Yulihastin et al., 2020).
Fig. 2. Subset area for western, central, and eastern Java which are indicated by dashed box r.1, 1.2, and r.3 respectively.
Fig. 2. Subset area for western, central, and eastern Java which are indicated by dashed box r.1, 1.2, and r.3 respectively.
Fig. 3. Hovmoller diagram of DCR over Java Island in the three different regions; r.1 ((a) & (d)), r.2 ((b) & (e)), r.3 ((c) & (f)); during nCENS days (top) and CENS days (bottom). JS is for Java Sea, IO is for the Indian Ocean, and area within two dashed black line is the island.
Fig. 3. Hovmoller diagram of DCR over Java Island in the three different regions; r.1 ((a) & (d)), r.2 ((b) & (e)), r.3 ((c) & (f)); during nCENS days (top) and CENS days (bottom). JS is for Java Sea, IO is for the Indian Ocean, and area within two dashed black line is the island.
Fig. 1. Variation between MCRA and Tbs on 19 Dec 2017.
Fig. 1. Variation between MCRA and Tbs on 19 Dec 2017.
Fig. 2. Bias between MCRA, GSMaP, IMERG with actual rainfall on 19 Dec 2017. Fig. 3. Rainfall map by MCRA on 04 Nov 2017 (1200 UTC).
Fig. 2. Bias between MCRA, GSMaP, IMERG with actual rainfall on 19 Dec 2017. Fig. 3. Rainfall map by MCRA on 04 Nov 2017 (1200 UTC).
observed smaller between 1 mm and 3 mm. The hourly bias between the estimates by MCRA, GSMaP and IMERG with the actual rainfall is found to be smaller between —2 mm and 2 mm but the smallest is observed on 19 December 2017 (Fig. 2). Meanwhile, RMSE is observed to be consistent and closer to each other at some points where the values are closer to 0 mm and the highest is observed at 8 mm on 20" Jan. The hourly bias between the estimates by MCRA with the satellite’s derived products show smaller values in between —1 mm and 0 mm. The RMSE is generally observed smaller between 1 mm and 3 mm. between —1 mm and 0 mm. The RMSE is generally
observed smaller between 1 mm and 3 mm. The hourly bias between the estimates by MCRA, GSMaP and IMERG with the actual rainfall is found to be smaller between —2 mm and 2 mm but the smallest is observed on 19 December 2017 (Fig. 2). Meanwhile, RMSE is observed to be consistent and closer to each other at some points where the values are closer to 0 mm and the highest is observed at 8 mm on 20" Jan. The hourly bias between the estimates by MCRA with the satellite’s derived products show smaller values in between —1 mm and 0 mm. The RMSE is generally observed smaller between 1 mm and 3 mm. between —1 mm and 0 mm. The RMSE is generally
are observed similar with the actual Himawari’s enhance image of IR on channel 13 (Fig. 4). Fig. 4. Himawari’s actual image of IR on channel 13 on 04 Nov 2017 (1200 UTC). REFERENCES CONCLUSION The algorithm at the threshold rainfall value of 0.8 mm
are observed similar with the actual Himawari’s enhance image of IR on channel 13 (Fig. 4). Fig. 4. Himawari’s actual image of IR on channel 13 on 04 Nov 2017 (1200 UTC). REFERENCES CONCLUSION The algorithm at the threshold rainfall value of 0.8 mm
Since Lorenz (1963), chaotic solutions have been a focal point for several decades, yielding the statement of “weather is chaotic”. In fact, depending on the relative strength of heating, the L63 model also produces non- chaotic solutions such as steady-state and limit cycle solutions (e.g., Figure 1 of Shen et al., 2021b). Given a model configuration, only one-type of solution appears, referred to as monostability, as shown in Fig. 2 (left). Fig. 2. Monostability illustrated by the chaotic solution of the L63 model (left), and multistability by coexisting chaotic and steady-state solutions of the GLM (right).
Since Lorenz (1963), chaotic solutions have been a focal point for several decades, yielding the statement of “weather is chaotic”. In fact, depending on the relative strength of heating, the L63 model also produces non- chaotic solutions such as steady-state and limit cycle solutions (e.g., Figure 1 of Shen et al., 2021b). Given a model configuration, only one-type of solution appears, referred to as monostability, as shown in Fig. 2 (left). Fig. 2. Monostability illustrated by the chaotic solution of the L63 model (left), and multistability by coexisting chaotic and steady-state solutions of the GLM (right).
tiny change in an IC will eventually lead to a very different time evolution for a solution. Fig. 1. An illustration of SDIC. Control and parallel runs were performed using the same model and the same model parameters. The only difference is the inclusion of an initial tiny perturbation within the parallel run. Two runs initially produced almost the same result for t € [0, 25], as shown with the red curve. This feature is called CDIC. During longer time integrations, the appearance of two curves (in red and blue) indicates significant differences (i.e., the “rapid divergence”) of solutions for the two runs. Such a feature is then called SDIC.
tiny change in an IC will eventually lead to a very different time evolution for a solution. Fig. 1. An illustration of SDIC. Control and parallel runs were performed using the same model and the same model parameters. The only difference is the inclusion of an initial tiny perturbation within the parallel run. Two runs initially produced almost the same result for t € [0, 25], as shown with the red curve. This feature is called CDIC. During longer time integrations, the appearance of two curves (in red and blue) indicates significant differences (i.e., the “rapid divergence”) of solutions for the two runs. Such a feature is then called SDIC.
To illustrate SDIC, Lorenz (1993) applied the activity of skiing (left in Fig. 3) and developed an idealized skiing model for revealing the sensitive dependence of time- varying paths on starting points (middle in Fig. 3). The eft panel for skiing may indicate monostability when slopes are steep everywhere. By comparison, the right panel for kayaking is used to illustrate multistability. In the photo, both strong currents and a stagnant area outlined with a white box) can be identified, suggesting both instability and local stability. As a result, when two kayaks move along strong currents, their paths display SDIC. On the other hand, when two kayaks move into the stagnant area, they become trapped. The features of kayaking reveal the nature of multistability. Fig. 3. Skiing as used to reveal SDIC and monostability (left and middle, Lorenz, 1993), and kayaking as used to indicate multistability (right, Copyright: ©Carol- stock.adobe.com).
To illustrate SDIC, Lorenz (1993) applied the activity of skiing (left in Fig. 3) and developed an idealized skiing model for revealing the sensitive dependence of time- varying paths on starting points (middle in Fig. 3). The eft panel for skiing may indicate monostability when slopes are steep everywhere. By comparison, the right panel for kayaking is used to illustrate multistability. In the photo, both strong currents and a stagnant area outlined with a white box) can be identified, suggesting both instability and local stability. As a result, when two kayaks move along strong currents, their paths display SDIC. On the other hand, when two kayaks move into the stagnant area, they become trapped. The features of kayaking reveal the nature of multistability. Fig. 3. Skiing as used to reveal SDIC and monostability (left and middle, Lorenz, 1993), and kayaking as used to indicate multistability (right, Copyright: ©Carol- stock.adobe.com).
Fig. 1. SWCRF and LWCREF as a function of size resolved AOD at TOA. Blue and red dot represents the small and large particles respectively.
Fig. 1. SWCRF and LWCREF as a function of size resolved AOD at TOA. Blue and red dot represents the small and large particles respectively.
Fig. 2. Same as Fig. | but at the surface. IGP region is known for the elevated aerosol loading during both monsoon and pre-monsoon period, with increased concentration of coarse mode absorbing aerosols (Jose et a et al., 2012). The TOA and surface ., 2021; Mangla et al., 2020; Srivastava decreased SWCRF and LWCREF at the for large particle could be probably due to the aerosol induced heating inside the clouds resulting in the evaporation of cloud droplets. IGP region is known for the elevated aerosol loading
Fig. 2. Same as Fig. | but at the surface. IGP region is known for the elevated aerosol loading during both monsoon and pre-monsoon period, with increased concentration of coarse mode absorbing aerosols (Jose et a et al., 2012). The TOA and surface ., 2021; Mangla et al., 2020; Srivastava decreased SWCRF and LWCREF at the for large particle could be probably due to the aerosol induced heating inside the clouds resulting in the evaporation of cloud droplets. IGP region is known for the elevated aerosol loading
Table 1. Frequency distribution of model and sounding derived relative humidity (surface — 500 hPa). 1umidity do not match on a correct interval with large Table 1 shows frequency distribution of model and sounding derived relative humidity for points of Eastern Siberia and Russian Far East (number of cases is 13194).
Table 1. Frequency distribution of model and sounding derived relative humidity (surface — 500 hPa). 1umidity do not match on a correct interval with large Table 1 shows frequency distribution of model and sounding derived relative humidity for points of Eastern Siberia and Russian Far East (number of cases is 13194).
Table 2. Frequency distribution of model and sounding derived index Jice (number of cases is 13194). Values of Jice frequency are decreased as it moves away from correct icing category. Slight overestimation
Table 2. Frequency distribution of model and sounding derived index Jice (number of cases is 13194). Values of Jice frequency are decreased as it moves away from correct icing category. Slight overestimation
Table 3. Contingency table. Table 3 shows that Peirce Skill Score is 0.83; Heidke
Table 3. Contingency table. Table 3 shows that Peirce Skill Score is 0.83; Heidke
Fig. 2. Speciation result of bulk soil samples. Fig. 1. Total metal concentrations of soil samples.
Fig. 2. Speciation result of bulk soil samples. Fig. 1. Total metal concentrations of soil samples.
Fig. 3. Metal concentrations in different size fractions.
Fig. 3. Metal concentrations in different size fractions.
Fig. 4. Geo-Accumulation Index of soil samples.
Fig. 4. Geo-Accumulation Index of soil samples.
Fig. 5. Contamination Factor of soil samples. the metals; hence the risk cannot be overestimated as in the case of considering total metal concentration for risk estimation.
Fig. 5. Contamination Factor of soil samples. the metals; hence the risk cannot be overestimated as in the case of considering total metal concentration for risk estimation.
heterotrophic respiration. With each experimental plot, soil solutions at 10 cm depth in triplicate were regularly sampled each month using self-made zero-tension lysimeters in growing season (May to October) each year (Xu et al., 2021). Two composed mineral soils (0-10 cm depth) within each experimental plot were randomly sampled in both non-growing and growing seasons using a soil auger (3.1 cm in diameter) each year, for measuring soil properties such as water content, pH, extractable carbon and nitrogen pools, and microbial biomass carbon and nitrogen. Fig. 1. Apparatus for measuring soil respiration and its components in the field.
heterotrophic respiration. With each experimental plot, soil solutions at 10 cm depth in triplicate were regularly sampled each month using self-made zero-tension lysimeters in growing season (May to October) each year (Xu et al., 2021). Two composed mineral soils (0-10 cm depth) within each experimental plot were randomly sampled in both non-growing and growing seasons using a soil auger (3.1 cm in diameter) each year, for measuring soil properties such as water content, pH, extractable carbon and nitrogen pools, and microbial biomass carbon and nitrogen. Fig. 1. Apparatus for measuring soil respiration and its components in the field.
Fig. 1. The geographical locations of TCCON stations in North America. The color legend represents 5D values for each station, also showing in Fig. 5.
Fig. 1. The geographical locations of TCCON stations in North America. The color legend represents 5D values for each station, also showing in Fig. 5.
RESULTS Fig. 2. The monthly mean 6D of TCCON stations from 2011 to 2021 in North America, color-coded according to their latitudes.
RESULTS Fig. 2. The monthly mean 6D of TCCON stations from 2011 to 2021 in North America, color-coded according to their latitudes.
Comparison of correlation between humidity and 6D Fig. 4. The comparison of correlation between water columns and 6D for all TCCON stations in North America, each line is from the average value of the original observations as shown in Fig. 3, color-coded according to their latitudes. Figure 3 shows the correlations between humidity and 6D for all six TCCON stations in North America. All six graphs show positive correlations between the water columns and dD values. This correlation can be explained by the Rayleigh process, which relates to the isotopologues changes during the evaporation and condensation processes, or the air mixing process, which relates to the mixing of air masses with different 6D.
Comparison of correlation between humidity and 6D Fig. 4. The comparison of correlation between water columns and 6D for all TCCON stations in North America, each line is from the average value of the original observations as shown in Fig. 3, color-coded according to their latitudes. Figure 3 shows the correlations between humidity and 6D for all six TCCON stations in North America. All six graphs show positive correlations between the water columns and dD values. This correlation can be explained by the Rayleigh process, which relates to the isotopologues changes during the evaporation and condensation processes, or the air mixing process, which relates to the mixing of air masses with different 6D.
Fig. 3. The correlations between water columns and 6D for TCCON stations in North America. TCCON stations, which is shown as light blue line in the graph. The lowest 5D value is from Eureka station, which is shown as orange line in the graph. The 6D values from all stations fluctuates as the season changes. In summer, the 5D values reached the highest point in each year while they dropped and reached the lowest points when the season turns to winter. Compared to the 5D values in all stations, the 6D value tends to be higher when the latitudes of the stations are higher.
Fig. 3. The correlations between water columns and 6D for TCCON stations in North America. TCCON stations, which is shown as light blue line in the graph. The lowest 5D value is from Eureka station, which is shown as orange line in the graph. The 6D values from all stations fluctuates as the season changes. In summer, the 5D values reached the highest point in each year while they dropped and reached the lowest points when the season turns to winter. Compared to the 5D values in all stations, the 6D value tends to be higher when the latitudes of the stations are higher.
positive. When the humidity keeps constant, t 6D are lower as the latitude —500%o in all six stations, w subduction of air from higher hich may be re depleted in 6D. According to Fig. 5 shown be values are decreasing as the America. The slope of the linear correlation is calculated atitude increas becomes higher. When the humidity tends to zero, the values of 6D tend t atmosphere that is dry and he values o o be around ated to the ow, the dD es in North as —1.46, ignoring the station with the lowest latitude which is Pasadena. Therefore, when the latitude increases by | degree, the 5D value will decrease by about 1.46%o. Fig. 5. The correlation between 6D and latitude for the six TCCON stations in North America. The mean values of OD are adapted from Fig. 4 between 1000 and 2000 ppm for XH20. America. The slope of the linear correlation is calculated CONCLUSIONS
positive. When the humidity keeps constant, t 6D are lower as the latitude —500%o in all six stations, w subduction of air from higher hich may be re depleted in 6D. According to Fig. 5 shown be values are decreasing as the America. The slope of the linear correlation is calculated atitude increas becomes higher. When the humidity tends to zero, the values of 6D tend t atmosphere that is dry and he values o o be around ated to the ow, the dD es in North as —1.46, ignoring the station with the lowest latitude which is Pasadena. Therefore, when the latitude increases by | degree, the 5D value will decrease by about 1.46%o. Fig. 5. The correlation between 6D and latitude for the six TCCON stations in North America. The mean values of OD are adapted from Fig. 4 between 1000 and 2000 ppm for XH20. America. The slope of the linear correlation is calculated CONCLUSIONS
Table 1. ML models performance evaluation for SSL inflow prediction. Fig. 1. Taylor diagram for ML models performance evaluation.
Table 1. ML models performance evaluation for SSL inflow prediction. Fig. 1. Taylor diagram for ML models performance evaluation.
Fig. 2. Sediment flushing curves are drawn for (a) SW and (b) NREB sites.
Fig. 2. Sediment flushing curves are drawn for (a) SW and (b) NREB sites.
Fig. 1. Flow chart of SVR method, Wavelets and EnKF approach. This study used daily RWT and related minimum, maximum, and mean AT data from January 1, 1989, to January 1, 2004. Based on the sensitivity analysis, the maximum AT was found to be a highly sensitive variable; hence we have taken the maximum AT as an input ence we have taken the maximum AT as an input maximum AT was found to be a highly sensitive variable; parameter for prediction. Results demonstrated improved forecasting accuracy of the hybrid WT-SVR model (NSE: 0.87 and RMSE: 0.92) compared to the standalone SVR model (NSE: 0.84 and RMSE: 0.99). It was discovered that RWT prediction ML models with a monthly time scale performed better than those with a daily time scale. To develop a continuous intelligence system, the EnKF data assimilation approach is applied to improvise the performance of the WT-SVR model in each simulation step. The WT-SVR-EnKF model’s findings with merged data at different simulation steps demonstrate better outcomes from simulation-1 (January 1, 2001, to January 1, 2002) (NSE: 0.890 and RMSE: 0.740) to simulation-2 (January 1, 2002, to January 1, 2003) (NSE: 0.91 and RMSE: 0.73). These outcomes reveal that the WT-SVR- EnKF model performs well with blended data in RWT prediction. Further improvement of RWT prediction can be investigated by using other types of WT coupled with the SVR algorithm. Because of a lack of a complete dataset, flow discharge was not included in this work. After training the models on local data, creating a global model using the federated ML approach is the future scope. maximum, and mean AT data from January 1, 1989, to This study used daily RWT and related minimum,
Fig. 1. Flow chart of SVR method, Wavelets and EnKF approach. This study used daily RWT and related minimum, maximum, and mean AT data from January 1, 1989, to January 1, 2004. Based on the sensitivity analysis, the maximum AT was found to be a highly sensitive variable; hence we have taken the maximum AT as an input ence we have taken the maximum AT as an input maximum AT was found to be a highly sensitive variable; parameter for prediction. Results demonstrated improved forecasting accuracy of the hybrid WT-SVR model (NSE: 0.87 and RMSE: 0.92) compared to the standalone SVR model (NSE: 0.84 and RMSE: 0.99). It was discovered that RWT prediction ML models with a monthly time scale performed better than those with a daily time scale. To develop a continuous intelligence system, the EnKF data assimilation approach is applied to improvise the performance of the WT-SVR model in each simulation step. The WT-SVR-EnKF model’s findings with merged data at different simulation steps demonstrate better outcomes from simulation-1 (January 1, 2001, to January 1, 2002) (NSE: 0.890 and RMSE: 0.740) to simulation-2 (January 1, 2002, to January 1, 2003) (NSE: 0.91 and RMSE: 0.73). These outcomes reveal that the WT-SVR- EnKF model performs well with blended data in RWT prediction. Further improvement of RWT prediction can be investigated by using other types of WT coupled with the SVR algorithm. Because of a lack of a complete dataset, flow discharge was not included in this work. After training the models on local data, creating a global model using the federated ML approach is the future scope. maximum, and mean AT data from January 1, 1989, to This study used daily RWT and related minimum,
Fig. 1. The methodological framework of the proposed reservoir inflow forecasting model.
Fig. 1. The methodological framework of the proposed reservoir inflow forecasting model.
Table 1. The specification of Soil Moisture and Ocean Salinity. Fig. 2. Soil Moisture and Ocean Salinity product over the catchment area. Where 7 is the soil porosity (dimensionless) or ratio of non-solid volume to the total volume of soil, L is the soil depth (m) specified in the parameter setting, (1-qi) is Gamma in RRI algorithm. Once F is found, the infiltration rate f (m/h) can be obtained by: solution based on approximate physical theory to include soil moisture content and soil characteristics, is one advantage of the RRI Model. The cumulative depth of infiltrated water F (m) is:
Table 1. The specification of Soil Moisture and Ocean Salinity. Fig. 2. Soil Moisture and Ocean Salinity product over the catchment area. Where 7 is the soil porosity (dimensionless) or ratio of non-solid volume to the total volume of soil, L is the soil depth (m) specified in the parameter setting, (1-qi) is Gamma in RRI algorithm. Once F is found, the infiltration rate f (m/h) can be obtained by: solution based on approximate physical theory to include soil moisture content and soil characteristics, is one advantage of the RRI Model. The cumulative depth of infiltrated water F (m) is:
Fig. 1. Study area (left) and landuse (right) of upper Brantas River basin in East Java, Indonesia. solution based on approximate physical theory to include
Fig. 1. Study area (left) and landuse (right) of upper Brantas River basin in East Java, Indonesia. solution based on approximate physical theory to include
(see Fig. 2) tends to give higher value. In order to obtain the value of soil water content, the time variation of infiltration is extracted using the Green-Ampt equation. This amount is needed for soil moisture value validation. Fig. 3. RRI Model result showing observed discharge (black) and simulated runoff (red).
(see Fig. 2) tends to give higher value. In order to obtain the value of soil water content, the time variation of infiltration is extracted using the Green-Ampt equation. This amount is needed for soil moisture value validation. Fig. 3. RRI Model result showing observed discharge (black) and simulated runoff (red).
Fig. 4. RRI Model result showing the spatial simulated runoff (left) and inundation (right). Fig. 5. Green-Ampt cumulative depth of infiltrated water.
Fig. 4. RRI Model result showing the spatial simulated runoff (left) and inundation (right). Fig. 5. Green-Ampt cumulative depth of infiltrated water.
Fig. 6. Downscaling result and the original SMOS product.
Fig. 6. Downscaling result and the original SMOS product.
Fig. 1. Schematic diagram of the water balance model developed for the INS-IBWT. Blue boxes highlight groundwater related components of the RF scheme.
Fig. 1. Schematic diagram of the water balance model developed for the INS-IBWT. Blue boxes highlight groundwater related components of the RF scheme.
obtained for The vertica strategy is s Fig. 2. Pareto-approximate water transfer strategies the RF (blue) and NRF (green) formulations. axis represent a variable of interest at donor D), recipient (R), and system (S) level, arranged to have the preferred value at the top. Each Pareto-approximate hown by a line crossing the vertical axis at the ocations denoting its consequence for the variable of interest. M EF — minimum environmental flows, RF: recharge formulation, NRF: no-recharge formulation. strategies also performs better at system as well as individual basin level in maintaining minimum environmental flows. Notably, the mean annual deficits are reduced upto 50% in the strategies allowing artificial groundwater recharge. However, we did not find any systematic reduction in the required transfer volumes. Although the median of the annual transfer volume is lower for RF, both RF and NRF entail similar range of annual transfers volumes. Thus, the additional water storage provided by artificial recharge in the recipient basin is successful in reducing the system-level water stress significantly but not reducing reliance on the water transfers. basin is successful in reducing the system-level water
obtained for The vertica strategy is s Fig. 2. Pareto-approximate water transfer strategies the RF (blue) and NRF (green) formulations. axis represent a variable of interest at donor D), recipient (R), and system (S) level, arranged to have the preferred value at the top. Each Pareto-approximate hown by a line crossing the vertical axis at the ocations denoting its consequence for the variable of interest. M EF — minimum environmental flows, RF: recharge formulation, NRF: no-recharge formulation. strategies also performs better at system as well as individual basin level in maintaining minimum environmental flows. Notably, the mean annual deficits are reduced upto 50% in the strategies allowing artificial groundwater recharge. However, we did not find any systematic reduction in the required transfer volumes. Although the median of the annual transfer volume is lower for RF, both RF and NRF entail similar range of annual transfers volumes. Thus, the additional water storage provided by artificial recharge in the recipient basin is successful in reducing the system-level water stress significantly but not reducing reliance on the water transfers. basin is successful in reducing the system-level water
Fig. 3. Seoul National University Gwanak campus (Seo et al., 2012; Tak et al., 2017). University Gwanak campus. Seoul National University,
Fig. 3. Seoul National University Gwanak campus (Seo et al., 2012; Tak et al., 2017). University Gwanak campus. Seoul National University,
In this study, the balanced urban water cycle was defined in terms of sustainability, safety, equity, and efficiency according to the theoretical studies. Following Fig. 2 illustrates the four states of the balanced urban water cycle. Sustainability, equity, safety, and efficiency were defined as the securing of water circulation management system adapted to future climate change, the absence of inequity in the movement of water spatially, less damage to property and life caused by water, and the securing of the right amount of clear water to anyone, respectively. Fig. 2. Definition of balanced urban water cycle. absence of inequity in the movement of water spatially, The process of quantifying the four indicators and
In this study, the balanced urban water cycle was defined in terms of sustainability, safety, equity, and efficiency according to the theoretical studies. Following Fig. 2 illustrates the four states of the balanced urban water cycle. Sustainability, equity, safety, and efficiency were defined as the securing of water circulation management system adapted to future climate change, the absence of inequity in the movement of water spatially, less damage to property and life caused by water, and the securing of the right amount of clear water to anyone, respectively. Fig. 2. Definition of balanced urban water cycle. absence of inequity in the movement of water spatially, The process of quantifying the four indicators and
Fig. 5. Observation results by ATMOS41.
Fig. 5. Observation results by ATMOS41.
Fig. 1. Box plots of 8!8O and SD at different surface water sampling areas Content. Samples of 5D and 8'8O from the Upper Indus River Basin are around the LMWL and the GMWL (Fig. 2) indicating that the surface water is of meteoric origin. However, the data points are on and below the LMWL, indicating that the surface water was affected by evaporation. Saline lake samples are below the GMWL suggested that these samples have been more intensely evaporated. suggested that these samples have been more intensely indicating that the surface water was affected by The d-excess values of mainstream ranged from 6.1% to 15.6%o with a mean value of 11.0%o. The values of d-excess for tributaries and glaciers ranged from 12.3% to 19.4%o with a mean of 15.3%, 14.7%o to 20.6%o with a mean value of 17.2%o respectively. Our finding is also consistent with prior records (Pande et al., 2000; Rai et al., 2016; Karim and Veizer, 2002). All of the d-excess values of the glacier/glacial meltwater and river water recharged by glacial/snow meltwater are large, which indicates the characteristics of the d values of the glacier and glacial/snow meltwater. Large and small-scale glaciers are developed at the relatively high altitude of the Upper Indus River and are the source of the tributaries and The d-excess values of mainstream ranged from 6.1%
Fig. 1. Box plots of 8!8O and SD at different surface water sampling areas Content. Samples of 5D and 8'8O from the Upper Indus River Basin are around the LMWL and the GMWL (Fig. 2) indicating that the surface water is of meteoric origin. However, the data points are on and below the LMWL, indicating that the surface water was affected by evaporation. Saline lake samples are below the GMWL suggested that these samples have been more intensely evaporated. suggested that these samples have been more intensely indicating that the surface water was affected by The d-excess values of mainstream ranged from 6.1% to 15.6%o with a mean value of 11.0%o. The values of d-excess for tributaries and glaciers ranged from 12.3% to 19.4%o with a mean of 15.3%, 14.7%o to 20.6%o with a mean value of 17.2%o respectively. Our finding is also consistent with prior records (Pande et al., 2000; Rai et al., 2016; Karim and Veizer, 2002). All of the d-excess values of the glacier/glacial meltwater and river water recharged by glacial/snow meltwater are large, which indicates the characteristics of the d values of the glacier and glacial/snow meltwater. Large and small-scale glaciers are developed at the relatively high altitude of the Upper Indus River and are the source of the tributaries and The d-excess values of mainstream ranged from 6.1%
streams. Glacial/snow meltwater recharges the river, so the d value of the sampling points in these places is the largest. This finding indicates that the surface water of the Upper Indus River Basin originated from glacial/ snow but also atmospheric precipitation. Fig. 2. Plots of 65D and 5!8O of Mainstream, Tributaries, Glaciers, and Groundwater. The groundwater samples of 5!8O and 5D of the Upper Indus River Basin were around and below the GMWL and LMWL, indicating that the groundwater is derived from glacial/snow meltwater and meteoric water. The value of d-excess of groundwater ranged from 10.3%o to 2.0%0 with a mean value of 11.2%o were positive, indicating that p g the groundwater may have originated from glacia Snow meltwater. the distributions of the surface water and groundwater samples are relatively concentrated, indicating that they may have a common source.
streams. Glacial/snow meltwater recharges the river, so the d value of the sampling points in these places is the largest. This finding indicates that the surface water of the Upper Indus River Basin originated from glacial/ snow but also atmospheric precipitation. Fig. 2. Plots of 65D and 5!8O of Mainstream, Tributaries, Glaciers, and Groundwater. The groundwater samples of 5!8O and 5D of the Upper Indus River Basin were around and below the GMWL and LMWL, indicating that the groundwater is derived from glacial/snow meltwater and meteoric water. The value of d-excess of groundwater ranged from 10.3%o to 2.0%0 with a mean value of 11.2%o were positive, indicating that p g the groundwater may have originated from glacia Snow meltwater. the distributions of the surface water and groundwater samples are relatively concentrated, indicating that they may have a common source.
Fig. 3. Shows the variation in 5D values of river water along the flow path of the Indus River. This figure were plotted from the Indus river samples of (Karim & Veizer, 2002, black circle) and samples of this study (red circles).
Fig. 3. Shows the variation in 5D values of river water along the flow path of the Indus River. This figure were plotted from the Indus river samples of (Karim & Veizer, 2002, black circle) and samples of this study (red circles).
Table 1. Calibrated model parameters. CQOF stands for the catchment overland flow runoff coefficient. Zing, and rir: ES SS RO RAD TE) pee eee EE oe re EE ae” rete CE ee ee CE | until the simulated runoff matched the observed values.
Table 1. Calibrated model parameters. CQOF stands for the catchment overland flow runoff coefficient. Zing, and rir: ES SS RO RAD TE) pee eee EE oe re EE ae” rete CE ee ee CE | until the simulated runoff matched the observed values.
The observed hydro-meteorological data required for the RR model were o btained from the Sri Lanka Departments of Meteorology and Irrigation. The catchment boundaries and river network were generated using Shuttle Radar Topography Mission (SRTM) data, with a resolution of 1 arcsecond (Reuter et al., 2007). Land use and land cover d Department of Sri Lanka. ata were obtained from Survey Fig. 1. Location of rainfall and river gauges in the basin. Methodology The RR model was calibrated for the period 2000- 2003 and validated for the period 2004—2007 using the input data described above. The catchment area was divided into two sub-catchments for model para- meterization based on the locations of the river gauge stations. The validated model was used to simulate two major 2017. 7 flood peak discharge and flood volume, we conducted [o evaluate the impacts of and-use change on t flood events that occurred in May 2003 and May ne flood simulations under two conditions, with and without land-use changes within the catchment area during t 1999-2017 period (Jayapadma, 2019). The RR mod performance was evaluated according to the Nas Sutcliffe efficiency (NSE) and he el _ volume ratio (Vr), calculated using Equations (1) and (2) below. NSE value varies from 0 to 1 where NSE of 1 agreement of observed values wit would suggest a perfect h the simulated values.
The observed hydro-meteorological data required for the RR model were o btained from the Sri Lanka Departments of Meteorology and Irrigation. The catchment boundaries and river network were generated using Shuttle Radar Topography Mission (SRTM) data, with a resolution of 1 arcsecond (Reuter et al., 2007). Land use and land cover d Department of Sri Lanka. ata were obtained from Survey Fig. 1. Location of rainfall and river gauges in the basin. Methodology The RR model was calibrated for the period 2000- 2003 and validated for the period 2004—2007 using the input data described above. The catchment area was divided into two sub-catchments for model para- meterization based on the locations of the river gauge stations. The validated model was used to simulate two major 2017. 7 flood peak discharge and flood volume, we conducted [o evaluate the impacts of and-use change on t flood events that occurred in May 2003 and May ne flood simulations under two conditions, with and without land-use changes within the catchment area during t 1999-2017 period (Jayapadma, 2019). The RR mod performance was evaluated according to the Nas Sutcliffe efficiency (NSE) and he el _ volume ratio (Vr), calculated using Equations (1) and (2) below. NSE value varies from 0 to 1 where NSE of 1 agreement of observed values wit would suggest a perfect h the simulated values.
The NSE values (> 0.6) suggested that the model accurately simulated runoff in the Gin River catchment area and was thus suitable for flood simulation in this region. Figs. 2(a) and 3(a) compare the observed and simulated discharges at an upstream gauge station (Thawalama) during model calibration and validation, respectively, and Figs. 2(b) and 3(b) compare those at a downstream gauge (Baddegama) station (Jayapadma, 2019). The model performed well in simulating both low and peak flows at the Baddegama gauge station, whereas peak flows at the Thawalama gauge station were underestimated, perhaps due to the effects of average catchment parameter approximation, observed data uncertainties, and coarser input data resolution (DHI, 2014a; Horritt and Bates, 2002; Moseley and McKerchar, 1993). Fig. 2. Comparison of river discharge during model calibration.
The NSE values (> 0.6) suggested that the model accurately simulated runoff in the Gin River catchment area and was thus suitable for flood simulation in this region. Figs. 2(a) and 3(a) compare the observed and simulated discharges at an upstream gauge station (Thawalama) during model calibration and validation, respectively, and Figs. 2(b) and 3(b) compare those at a downstream gauge (Baddegama) station (Jayapadma, 2019). The model performed well in simulating both low and peak flows at the Baddegama gauge station, whereas peak flows at the Thawalama gauge station were underestimated, perhaps due to the effects of average catchment parameter approximation, observed data uncertainties, and coarser input data resolution (DHI, 2014a; Horritt and Bates, 2002; Moseley and McKerchar, 1993). Fig. 2. Comparison of river discharge during model calibration.
Fig. 3. Comparison of river discharge during model validation.
Fig. 3. Comparison of river discharge during model validation.
Fig. 1. Marshyangdi Basin. Finally, trend analysis was performed by Mann Kendall’s and Sen’s slope (Mann, 1945; Sens’s Slope, 1968) method.
Fig. 1. Marshyangdi Basin. Finally, trend analysis was performed by Mann Kendall’s and Sen’s slope (Mann, 1945; Sens’s Slope, 1968) method.
Table 1. Ecological Implications of selected hydrologic indices (m?/s/year). Note: HA: Hydrologic Alteration; S: Significant; S: Significant, NS: Insignificant; M: Moderate
Table 1. Ecological Implications of selected hydrologic indices (m?/s/year). Note: HA: Hydrologic Alteration; S: Significant; S: Significant, NS: Insignificant; M: Moderate
degree of hydrologic alteration. CONCLUSION The hydrologic alteration was low with an overall mean of 30% in the Marshyangdi basin. However, in the future, alteration in a flow regime due to climatic and anthropogenic activities could impact river health. The mean annual streamflow in the basin is 222 m?/s which is insignificantly increasing with a trend of 0.6 m*/s/y. Hydrologic alterations (HA) in the basin vary from low to moderate within five groups. An increase in the median flow values during the period of monsoon and _ post- monsoon values (Fig. 2) and consequent statistically significant increasing trend in the 30-day and 90-day maximum values indicate the possibility of flood in the basin in the future (Table 1). Poff and Zimmerman (2010) had also mentioned that almost all published research found negative ecological changes to alteration in the flow variability. NE re ee SSE eee pee ei Satee ng ee Sai asec sa ste ete oz.
degree of hydrologic alteration. CONCLUSION The hydrologic alteration was low with an overall mean of 30% in the Marshyangdi basin. However, in the future, alteration in a flow regime due to climatic and anthropogenic activities could impact river health. The mean annual streamflow in the basin is 222 m?/s which is insignificantly increasing with a trend of 0.6 m*/s/y. Hydrologic alterations (HA) in the basin vary from low to moderate within five groups. An increase in the median flow values during the period of monsoon and _ post- monsoon values (Fig. 2) and consequent statistically significant increasing trend in the 30-day and 90-day maximum values indicate the possibility of flood in the basin in the future (Table 1). Poff and Zimmerman (2010) had also mentioned that almost all published research found negative ecological changes to alteration in the flow variability. NE re ee SSE eee pee ei Satee ng ee Sai asec sa ste ete oz.
Table 1. Classification of drought intensity based on SRI value and for DNBC.
Table 1. Classification of drought intensity based on SRI value and for DNBC.
Fig. 1. Location of the Han River basin with sub-basin ID number, elevation, and stream network. METHODOLOGY Standardized Runoff Index (SRI) was proposed by Shukla and Wood (2008) to characterize and monitor hydrological drought using streamflow data. The calculations of SRI are quite similar to Standardized Precipitation Index (SPI), in which log-normal distribution was fitted to streamflow data and cumulative probabilities of marginal distribution were transformed into standard normal variate with zero mean and unity standard deviation. In this study, daily data of streamflow was aggregated into monthly timescale and SRI was calculated at 3-month time scale.
Fig. 1. Location of the Han River basin with sub-basin ID number, elevation, and stream network. METHODOLOGY Standardized Runoff Index (SRI) was proposed by Shukla and Wood (2008) to characterize and monitor hydrological drought using streamflow data. The calculations of SRI are quite similar to Standardized Precipitation Index (SPI), in which log-normal distribution was fitted to streamflow data and cumulative probabilities of marginal distribution were transformed into standard normal variate with zero mean and unity standard deviation. In this study, daily data of streamflow was aggregated into monthly timescale and SRI was calculated at 3-month time scale.
Fig. 2. Different classes of hydrological drought assessed through DNBC during 2001—2013 and compared with the observed SRI in watershed a) #1003, (b) #1010, (c) #1012, (d) #1018. where P = {J,+ 1 =i/Y;} is the probability of hydrological drought classes in next month given hydrological drought classes in current month J;. Pi is the transition probability matrix for hydrological classes in next month condition on the hydrological drought class in current month.
Fig. 2. Different classes of hydrological drought assessed through DNBC during 2001—2013 and compared with the observed SRI in watershed a) #1003, (b) #1010, (c) #1012, (d) #1018. where P = {J,+ 1 =i/Y;} is the probability of hydrological drought classes in next month given hydrological drought classes in current month J;. Pi is the transition probability matrix for hydrological classes in next month condition on the hydrological drought class in current month.
Fig. 3. DNBC in the classification of hydrological drought classes. (a) accuracy and (b) precision. The accuracy estimated for class 1 varied from 50% to 63%; for Class 2 from 46% to 70%; for Class 3 from 59% to 67%; for Class 4 from 45% to 67%; and for Class 5 from 33% to 50%, while the precision estimated for class 1 varied from 45% to 58%, for class 2 it varied from 55% to 85%, for class 3 it varied from 46 % to 78%, for class 4 it varied from 24% to 50% and for class 5 it varied from 33% to 71%.
Fig. 3. DNBC in the classification of hydrological drought classes. (a) accuracy and (b) precision. The accuracy estimated for class 1 varied from 50% to 63%; for Class 2 from 46% to 70%; for Class 3 from 59% to 67%; for Class 4 from 45% to 67%; and for Class 5 from 33% to 50%, while the precision estimated for class 1 varied from 45% to 58%, for class 2 it varied from 55% to 85%, for class 3 it varied from 46 % to 78%, for class 4 it varied from 24% to 50% and for class 5 it varied from 33% to 71%.
Fig. 3. Simulating 169 climate change cases (13 precipitation * 13 temperature). Each climate change case is used to generate drought time series.
Fig. 3. Simulating 169 climate change cases (13 precipitation * 13 temperature). Each climate change case is used to generate drought time series.
Fig. 2. Spatial maps of 215 catchments presented in the figure. Catchments are classified based on Aridity Index.
Fig. 2. Spatial maps of 215 catchments presented in the figure. Catchments are classified based on Aridity Index.
Fig. 4. Classification and Regression Trees (CART) for a catchment: Critical threshold of precipitation and temperature are obtained from classification and regression tree for SPEI and PDSI.
Fig. 4. Classification and Regression Trees (CART) for a catchment: Critical threshold of precipitation and temperature are obtained from classification and regression tree for SPEI and PDSI.
Table 1. Statistical characteristics of SGSs. Results The best-fit probability distribution function of each D-day low flow sequence was identified for each SGS in the Kucuk Menderes River basins (Table 2). No probability distribution function was identified for D = 1, 7, 14 and 30-day low flows in D06A001 and E06A001, and for D = 1, 7, 14, 30 and 90-day low flows in D06A012. Why a probability distribution function does not exist is because of the total number of non-zero D-day low flows. We applied frequency analysis on D-day flow
Table 1. Statistical characteristics of SGSs. Results The best-fit probability distribution function of each D-day low flow sequence was identified for each SGS in the Kucuk Menderes River basins (Table 2). No probability distribution function was identified for D = 1, 7, 14 and 30-day low flows in D06A001 and E06A001, and for D = 1, 7, 14, 30 and 90-day low flows in D06A012. Why a probability distribution function does not exist is because of the total number of non-zero D-day low flows. We applied frequency analysis on D-day flow
Fig. 1. Location of Kucuk Menderes River Basin in Turkey and layout of SGSs.
Fig. 1. Location of Kucuk Menderes River Basin in Turkey and layout of SGSs.
Table 2. Best-fit probability distribution functions of D- day low flows. Parameters of the selected probability distribution
Table 2. Best-fit probability distribution functions of D- day low flows. Parameters of the selected probability distribution
Fig. 2. LFDF curves for SGS D06A001. Parameters of the selected probability distribution functions were determined and used in calculating the D- day low flows in every individual SGS for return periods of 2, 5, 10, 25, 50 and 100 years. SGS DO6A001 was selected to demonstrate the LFDF curves (Fig. 2). LFDF curves decrease quite fast towards very low discharges and even to zero values other than the 273-day LFDF curve. The D-day low flow time series have mostly zero values. It means that the river basin is prone to get dry. It is also important to notice that no curves were obtained for D = 1 and 7 days. The curves extend up to T = 5 years for D = 14 days, and to T = 10 years for D = 30 and 90 days. The duration-frequency curve is complete up to T = 100 years only for D = 273 days. This is because of the number of zero flows in each D-day low flow sequence. 100 years only for D = 273 days. This is because of the CONCLUSION Frequency analysis shows that Log-Pearson Type III probability distribution function fits the D-day low flows
Fig. 2. LFDF curves for SGS D06A001. Parameters of the selected probability distribution functions were determined and used in calculating the D- day low flows in every individual SGS for return periods of 2, 5, 10, 25, 50 and 100 years. SGS DO6A001 was selected to demonstrate the LFDF curves (Fig. 2). LFDF curves decrease quite fast towards very low discharges and even to zero values other than the 273-day LFDF curve. The D-day low flow time series have mostly zero values. It means that the river basin is prone to get dry. It is also important to notice that no curves were obtained for D = 1 and 7 days. The curves extend up to T = 5 years for D = 14 days, and to T = 10 years for D = 30 and 90 days. The duration-frequency curve is complete up to T = 100 years only for D = 273 days. This is because of the number of zero flows in each D-day low flow sequence. 100 years only for D = 273 days. This is because of the CONCLUSION Frequency analysis shows that Log-Pearson Type III probability distribution function fits the D-day low flows
"RMSE: root mean square error. >RMSPE: root mean squared percentage error. “NSE: Nash-Sutcliffe efficiency. 4N: number of samples Table 1. The statistical model validation.
"RMSE: root mean square error. >RMSPE: root mean squared percentage error. “NSE: Nash-Sutcliffe efficiency. 4N: number of samples Table 1. The statistical model validation.
Fig. 2. Conceptual diagram of ecological model. Fig. 1. The computational range of the model. The dotted line illustrates the outer boundaries. The red point is the model validation point. Model validation The model was validated by comparing the model outputs with the field measurements data (Saito et al., 2020). Three statistical variables were used to validate this model, namely root mean square error (RMSE), root mean squared percentage error (RMSPE), and Nash- Sutcliffe efficiency (NSE). The validation target position is near the estuary of the Kuma River, shown as the red point in Fig. 1.
Fig. 2. Conceptual diagram of ecological model. Fig. 1. The computational range of the model. The dotted line illustrates the outer boundaries. The red point is the model validation point. Model validation The model was validated by comparing the model outputs with the field measurements data (Saito et al., 2020). Three statistical variables were used to validate this model, namely root mean square error (RMSE), root mean squared percentage error (RMSPE), and Nash- Sutcliffe efficiency (NSE). The validation target position is near the estuary of the Kuma River, shown as the red point in Fig. 1.
And we validated the fitting results of the hydrodynamic module of this model, using salinity, temperature, and ot as objects, and the fitting results of the ecological module, using DIC, TA, and pCO as objects. The validation results are shown in Table 1, and all these values are in an acceptable range, so the model fit of this model was regarded as good. Fig. 3. Field measurement results (a), and simulation results (b) of ot distribution (top) and pCOz distribution (bottom), during the strong stratification period (August 2, 2019). objects. The validation results are shown in Table 1, and
And we validated the fitting results of the hydrodynamic module of this model, using salinity, temperature, and ot as objects, and the fitting results of the ecological module, using DIC, TA, and pCO as objects. The validation results are shown in Table 1, and all these values are in an acceptable range, so the model fit of this model was regarded as good. Fig. 3. Field measurement results (a), and simulation results (b) of ot distribution (top) and pCOz distribution (bottom), during the strong stratification period (August 2, 2019). objects. The validation results are shown in Table 1, and
We inspected the vertical pCO2 dynamics during the flood season. The inspection cross section is shown as the red line in Fig. 4, starting at the estuary of the Kuma River. Fig. 4. Inspection cross section of vertical pCO2 dynamics.
We inspected the vertical pCO2 dynamics during the flood season. The inspection cross section is shown as the red line in Fig. 4, starting at the estuary of the Kuma River. Fig. 4. Inspection cross section of vertical pCO2 dynamics.
Fig. 5. Vertical distribution at 12:00 on July 7, 2018. CONCLUSIONS
Fig. 5. Vertical distribution at 12:00 on July 7, 2018. CONCLUSIONS
Fig. 6. Vertical distribution at 12:00 on July 27, 2018. From the distribution of salinity at 12:00 on July 7, it can be seen that the freshwater spreads on the seawater surface. In addition, the distribution of pCO2 shows that freshwater with high pCO2 and seawater with low pCO: are stratified. Therefore, it was confirmed that the freshwater concentrates on the seawater surface immediately after flooding, which promotes CO2 emissions into the atmosphere.
Fig. 6. Vertical distribution at 12:00 on July 27, 2018. From the distribution of salinity at 12:00 on July 7, it can be seen that the freshwater spreads on the seawater surface. In addition, the distribution of pCO2 shows that freshwater with high pCO2 and seawater with low pCO: are stratified. Therefore, it was confirmed that the freshwater concentrates on the seawater surface immediately after flooding, which promotes CO2 emissions into the atmosphere.
Fig. 1. Calculation domain. METHODS This study uses the hydrodynamic model and lower- trophic ecosystem model by Delft3D, which are the same as Tadokoro and Yano (2019). Calculation domain is a combination of the Ariake Sea and the Yatsushiro Sea (Fig. 1). The horizontal grid is a rectangular grid with a Cartesian frame of reference and the vertical grid uses o coordinate system, where the number of o-layers is defined as 10 layers with 5% x 3, 10% x 4, 15% x 3 from surface to bottom. Open boundaries are located outside the inlet to the Ariake Sea and on the line connecting ABSTRACT: The Ariake Sea is considered as an estuary or inner bay-type region of freshwater influence (ROFD) located in Kyushu Island, western Japan, which is one of the most important shallow sea for fisheries and seaweed production. In recent years, extreme flooding events occurred frequently in Japan. Heavy rainfall may cause a large amount of freshwater inflow, which will have serious impact on the marine environment and ecosystem in the bay. For proposing the adaptation, this study conducted different scenarios of effluent patterns to evaluate the development of hypoxia by coupling the three-dimensional hydrodynamic model (Delft3D-FLOW) and the ecosystem model (Delft3D-WAQ) in the bay. The results indicate that the duration and peak discharge of effluent can significantly affect the development of hypoxia in the bottom layer.
Fig. 1. Calculation domain. METHODS This study uses the hydrodynamic model and lower- trophic ecosystem model by Delft3D, which are the same as Tadokoro and Yano (2019). Calculation domain is a combination of the Ariake Sea and the Yatsushiro Sea (Fig. 1). The horizontal grid is a rectangular grid with a Cartesian frame of reference and the vertical grid uses o coordinate system, where the number of o-layers is defined as 10 layers with 5% x 3, 10% x 4, 15% x 3 from surface to bottom. Open boundaries are located outside the inlet to the Ariake Sea and on the line connecting ABSTRACT: The Ariake Sea is considered as an estuary or inner bay-type region of freshwater influence (ROFD) located in Kyushu Island, western Japan, which is one of the most important shallow sea for fisheries and seaweed production. In recent years, extreme flooding events occurred frequently in Japan. Heavy rainfall may cause a large amount of freshwater inflow, which will have serious impact on the marine environment and ecosystem in the bay. For proposing the adaptation, this study conducted different scenarios of effluent patterns to evaluate the development of hypoxia by coupling the three-dimensional hydrodynamic model (Delft3D-FLOW) and the ecosystem model (Delft3D-WAQ) in the bay. The results indicate that the duration and peak discharge of effluent can significantly affect the development of hypoxia in the bottom layer.
Table 1. Calculation results of hypoxia for six effluent patterns. Fig. 3. Observation and calculation results of DO at Sta.B3 in 2020.
Table 1. Calculation results of hypoxia for six effluent patterns. Fig. 3. Observation and calculation results of DO at Sta.B3 in 2020.
Fig. 4. Relationship of normalized total effluent volume and duration of hypoxia at the bottom layer as well as the peak discharge in each case.
Fig. 4. Relationship of normalized total effluent volume and duration of hypoxia at the bottom layer as well as the peak discharge in each case.
Fig. 2. Conceptual diagram of lower-trophic ecosystem model.
Fig. 2. Conceptual diagram of lower-trophic ecosystem model.
Fig. 5. Isopleth of DO at Sta.B3 in six effluent patterns. The effluent period is extracted when the most severe hypoxia occurred. Figure 5 shows the isopleth of DO at Sta.B3 in six effluent patterns (Fig. 1). The duration of continuous hypoxia lasted for more than two months in case 1. Among the three months in summer (from Jun. to Aug.), the hypoxic water mass at the beginning of August was larger than the others and it distributed vertically in the entire water column. REFERENCES Figure 5 shows the isopleth of DO at Sta.B3 in six
Fig. 5. Isopleth of DO at Sta.B3 in six effluent patterns. The effluent period is extracted when the most severe hypoxia occurred. Figure 5 shows the isopleth of DO at Sta.B3 in six effluent patterns (Fig. 1). The duration of continuous hypoxia lasted for more than two months in case 1. Among the three months in summer (from Jun. to Aug.), the hypoxic water mass at the beginning of August was larger than the others and it distributed vertically in the entire water column. REFERENCES Figure 5 shows the isopleth of DO at Sta.B3 in six
Fig. 1. the Ramkhamhaeng polder, Bangkok, Thailand. Bangkok, the capital city of Thailand, is located in the Shao Phraya River basin. Chao Phraya River split down he aadddAleae at thse. cei Tha eraind larval ta Whatiyaan +O nO
Fig. 1. the Ramkhamhaeng polder, Bangkok, Thailand. Bangkok, the capital city of Thailand, is located in the Shao Phraya River basin. Chao Phraya River split down he aadddAleae at thse. cei Tha eraind larval ta Whatiyaan +O nO
Fig. 2. Real-time flood forecasting system framework. In the central part of Bangkok, the Ramkhamheng area is representative of urbanization. Bangkok Metropolitan Administration (BMA) has a department of drainage and sewerage (DDS) working on flood problems in Bangkok. Several studies from DDS have shown that the drainage capacity of the existing system is still not perfect enough. The Ramkhamhaeng polder has an area of 11.444 sq. km. The north boundary is the Saen Saeb canal. South is the East Railway. The east is Khlong Tooyor and Khlong Hua Mak, and the west is the Saen Saeb Canal and Khmer Canal. The surface ground level is between 0.00 to +0.50 m.MSL, some areas are below sea level, such as Ramkhamhaeng University. Real-time flood forecasting system divided into three main parts. The first part is the rainfall input data process. This research measured rainfall data with 10 minutes of rainfall from a radar station near this study area. The forecasted rainfall data with 30 minutes of rainfall duration collected from the forecasting system. This research assigned the TITAN software to track and forecast the storm and analyze movement by determining the linear movement of the storm groups, calculating the overlapping area between the storm boundary and the study area to calculate the amount of precipitation entering the study area in advance. The optimal success of CSI (Critical Success Index) of 66% was obtained at a forecasted time of 30 min as shown in Fig. 3. Real-time flood forecasting system divided into three
Fig. 2. Real-time flood forecasting system framework. In the central part of Bangkok, the Ramkhamheng area is representative of urbanization. Bangkok Metropolitan Administration (BMA) has a department of drainage and sewerage (DDS) working on flood problems in Bangkok. Several studies from DDS have shown that the drainage capacity of the existing system is still not perfect enough. The Ramkhamhaeng polder has an area of 11.444 sq. km. The north boundary is the Saen Saeb canal. South is the East Railway. The east is Khlong Tooyor and Khlong Hua Mak, and the west is the Saen Saeb Canal and Khmer Canal. The surface ground level is between 0.00 to +0.50 m.MSL, some areas are below sea level, such as Ramkhamhaeng University. Real-time flood forecasting system divided into three main parts. The first part is the rainfall input data process. This research measured rainfall data with 10 minutes of rainfall from a radar station near this study area. The forecasted rainfall data with 30 minutes of rainfall duration collected from the forecasting system. This research assigned the TITAN software to track and forecast the storm and analyze movement by determining the linear movement of the storm groups, calculating the overlapping area between the storm boundary and the study area to calculate the amount of precipitation entering the study area in advance. The optimal success of CSI (Critical Success Index) of 66% was obtained at a forecasted time of 30 min as shown in Fig. 3. Real-time flood forecasting system divided into three
The schematic of the real-time flood forecasting system was designed based on the optimum rainfall duration, which contained 40 minutes including 10 minutes measured and 30 minutes forecasted rainfall durations. Moreover, it should be included 10 minutes for the computer processing time. Fig. 4. The schematic of the real-time flood forecasting system. duration, which contained 40 minutes including 10 The second part of the real-time flood forecasting system is the main process for simulating flood maps. The PCSWMM software assigned to simulate as a hydraulic model which consist of flows in pipe and overland flows. A database centers the system for storing input and output data. The database system is responsible for keeping and importing the preparation data via a script command from the python and automatically running through the window task scheduler. system was designed based on the optimum rainfall
The schematic of the real-time flood forecasting system was designed based on the optimum rainfall duration, which contained 40 minutes including 10 minutes measured and 30 minutes forecasted rainfall durations. Moreover, it should be included 10 minutes for the computer processing time. Fig. 4. The schematic of the real-time flood forecasting system. duration, which contained 40 minutes including 10 The second part of the real-time flood forecasting system is the main process for simulating flood maps. The PCSWMM software assigned to simulate as a hydraulic model which consist of flows in pipe and overland flows. A database centers the system for storing input and output data. The database system is responsible for keeping and importing the preparation data via a script command from the python and automatically running through the window task scheduler. system was designed based on the optimum rainfall
Fig. 3. Critical success index, CSI (%), of rainfall forecasting results.
Fig. 3. Critical success index, CSI (%), of rainfall forecasting results.
Table 1. Comparison of continuous incidents that were processed by the real-time flood alert system. The real-time flood alert system can provide effective notification from the comparison results when taking continuous events. It was found that the rain data obtained from the system can inform approximately 20 minutes early warning. This system can alert and provide location and depth information of flood levels, and it can be advance and effective and can be used to make informed decisions of the authorities in the area drainage management. REFERENCES This research tested t data from the RTF system, rain data from the automatic rain gauge, comparing t and rain da he test resu hree types of rainfall data: rain ta from the rain radar. When ts from different rainfall data sources, it was found that rainfall data from the RTF system could be notified automatic ra faster than the rain data from an in detector in 30 minutes, and the rain data from the rain radar in 20 minutes as shown in Table 1 below. For t he accurate evaluation of the RTF system, it was performed by selecting 116 historical rainfall events in ten days duration. The results showed that the RTF system provided 71 out of 116 pre-warming events (61.2%) with rainfall forecasting accuracy above 69.1%.
Table 1. Comparison of continuous incidents that were processed by the real-time flood alert system. The real-time flood alert system can provide effective notification from the comparison results when taking continuous events. It was found that the rain data obtained from the system can inform approximately 20 minutes early warning. This system can alert and provide location and depth information of flood levels, and it can be advance and effective and can be used to make informed decisions of the authorities in the area drainage management. REFERENCES This research tested t data from the RTF system, rain data from the automatic rain gauge, comparing t and rain da he test resu hree types of rainfall data: rain ta from the rain radar. When ts from different rainfall data sources, it was found that rainfall data from the RTF system could be notified automatic ra faster than the rain data from an in detector in 30 minutes, and the rain data from the rain radar in 20 minutes as shown in Table 1 below. For t he accurate evaluation of the RTF system, it was performed by selecting 116 historical rainfall events in ten days duration. The results showed that the RTF system provided 71 out of 116 pre-warming events (61.2%) with rainfall forecasting accuracy above 69.1%.
Table 1. Status of formulation of BCP and other emergency response plans. Meanwhile, 29 companies have defined priority recovery operations and their target recovery time in their plans, which must be defined in the BCP, and 19% of the companies have plans that can be considered BCPs (Table 2).
Table 1. Status of formulation of BCP and other emergency response plans. Meanwhile, 29 companies have defined priority recovery operations and their target recovery time in their plans, which must be defined in the BCP, and 19% of the companies have plans that can be considered BCPs (Table 2).
Table 2. Status of BCP formulation.
Table 2. Status of BCP formulation.
Table 3. Degree of external dependence on management resources. Furthermore, when asked what resources related to
Table 3. Degree of external dependence on management resources. Furthermore, when asked what resources related to
Table 4. Difficulty in collecting information and making decisions due to external dependence on management resources. Meanwhile, when asked what kind of cooperation with stakeholders is envisioned in BCPs and other disaster response plans, and what kind of cooperation with stakeholders was used in past disasters and other emergencies, there were no responses that envisioned or cooperated with stakeholders related to social infra- structure. Meanwhile, when asked what kind of cooperation with stakeholders is envisioned in BCPs and other disaster
Table 4. Difficulty in collecting information and making decisions due to external dependence on management resources. Meanwhile, when asked what kind of cooperation with stakeholders is envisioned in BCPs and other disaster response plans, and what kind of cooperation with stakeholders was used in past disasters and other emergencies, there were no responses that envisioned or cooperated with stakeholders related to social infra- structure. Meanwhile, when asked what kind of cooperation with stakeholders is envisioned in BCPs and other disaster
Fig. 1. Empirical and estimated distribution functions of extreme rainfalls X; (mm) for different durations for London CS station.
Fig. 1. Empirical and estimated distribution functions of extreme rainfalls X; (mm) for different durations for London CS station.
Fig. 2. Probability plots between the observed and estimated short-duration ERs of station #7-Ottawa CDA for the calibration period 1961-1990. Yellow circle markers represent the empirical probability plots while red cross markers and dash lines show the at-site frequency analysis plots. The gray lines and boxplots show the derived distributions of short-duration ERs based on the proposed approach and data from 21 GCMs. Fig. 1. Boxplots comparing different indices (CC, MADr, and RMSEr) computed based on the difference between observed and adjusted data or observed and regional data for (a) calibration and (b) validation periods.
Fig. 2. Probability plots between the observed and estimated short-duration ERs of station #7-Ottawa CDA for the calibration period 1961-1990. Yellow circle markers represent the empirical probability plots while red cross markers and dash lines show the at-site frequency analysis plots. The gray lines and boxplots show the derived distributions of short-duration ERs based on the proposed approach and data from 21 GCMs. Fig. 1. Boxplots comparing different indices (CC, MADr, and RMSEr) computed based on the difference between observed and adjusted data or observed and regional data for (a) calibration and (b) validation periods.
84 stations located in Ontario, Canada. The jackknife technique was used to represent the ungaged site condition at 15 chosen sites among them. In general, based on different indices, the results produced by the proposed approach highly agree with the observed data. More specifically, the low values of RMSEr and MADr and high values of CC indicate the accuracy of the new method in the estimation of short-duration extreme design rainfalls for an ungaged location in the context of a changing climate. Fig. 1. Boxplots comparing different indices (CC, MADr,
84 stations located in Ontario, Canada. The jackknife technique was used to represent the ungaged site condition at 15 chosen sites among them. In general, based on different indices, the results produced by the proposed approach highly agree with the observed data. More specifically, the low values of RMSEr and MADr and high values of CC indicate the accuracy of the new method in the estimation of short-duration extreme design rainfalls for an ungaged location in the context of a changing climate. Fig. 1. Boxplots comparing different indices (CC, MADr,
LUCC spatial analysis, change and future scenario models The study is carried out in two water bodies environmentally differentiated: the reservoir of As Conchas in the province of Orense, Northwest Spain and the Albufera of Valencia, a Mediterranean coastal lagoon, in the province of Valencia, East Spain (Fig. 1). Fig. 1. Study areas location: 1 As Conchas Reservoir, 2 Albufera de Valencia Lagoon. Information on land use/cover changes (LUCC) has Spatial and temporal analysis of LUCC dynamics should imply the application of quantitative models to establish the projection of historical land covers to future scenarios (Belda-Carrasco et al., 2019) and to foresee how LUCC affect ecosystem services performance, including those services related to water regulation in the interaction of LUCC with soil functions. Land Use/Cover Changes related to water quality and quantity processes
LUCC spatial analysis, change and future scenario models The study is carried out in two water bodies environmentally differentiated: the reservoir of As Conchas in the province of Orense, Northwest Spain and the Albufera of Valencia, a Mediterranean coastal lagoon, in the province of Valencia, East Spain (Fig. 1). Fig. 1. Study areas location: 1 As Conchas Reservoir, 2 Albufera de Valencia Lagoon. Information on land use/cover changes (LUCC) has Spatial and temporal analysis of LUCC dynamics should imply the application of quantitative models to establish the projection of historical land covers to future scenarios (Belda-Carrasco et al., 2019) and to foresee how LUCC affect ecosystem services performance, including those services related to water regulation in the interaction of LUCC with soil functions. Land Use/Cover Changes related to water quality and quantity processes
Fig. 2. Percentage of land cover for the years 1990, 2006. 2018 and the 2030 and 2050 scenarios in As Conchas. urban covers (U10) and the agricultural mosaic rainfed (A21) the ones that grow the most, with clear reduction in the rest of the agricultural ones.
Fig. 2. Percentage of land cover for the years 1990, 2006. 2018 and the 2030 and 2050 scenarios in As Conchas. urban covers (U10) and the agricultural mosaic rainfed (A21) the ones that grow the most, with clear reduction in the rest of the agricultural ones.
Fig. 3. Percentage of land cover for the years 1990, 2006, 2018 and the 2030 and 2050 scenarios in Albufera of Valencia.
Fig. 3. Percentage of land cover for the years 1990, 2006, 2018 and the 2030 and 2050 scenarios in Albufera of Valencia.
Fig. 4. LUCC-Soils water retention capacity in As Conchas Reserovir. Fig. 5. LUCC-Soils water retention capacity in Albufera of Valencia. CONCLUSIONS LUCC analysis linked to the geospatial model for the generation of future scenarios allows relating water parameters with historic trends and future evolution of land cover. The LUCC scenarios obtained with the model
Fig. 4. LUCC-Soils water retention capacity in As Conchas Reserovir. Fig. 5. LUCC-Soils water retention capacity in Albufera of Valencia. CONCLUSIONS LUCC analysis linked to the geospatial model for the generation of future scenarios allows relating water parameters with historic trends and future evolution of land cover. The LUCC scenarios obtained with the model
REFERENCES
REFERENCES
To overcome this problem, K-water (2016) proposed a unique disaggregation method to disaggregate the annual drought frequency inflow into a monthly scale. Figure 1 below illustrates the KDM process. The solid curve indicates the annual frequency inflow estimated by the frequency analysis. The dotted curve is the sum of monthly frequency inflows estimated by the monthly frequency analysis. In order to satisfy the additivity condition, annual drought inflow is distributed according to the same sum of the combination of monthly frequency inflows. For example, the return period of 100-year in the solid curve can be disaggregated according to the ratio of the return period 16.5-year monthly inflows. By using KDM, the annual sum of the drought inflow
To overcome this problem, K-water (2016) proposed a unique disaggregation method to disaggregate the annual drought frequency inflow into a monthly scale. Figure 1 below illustrates the KDM process. The solid curve indicates the annual frequency inflow estimated by the frequency analysis. The dotted curve is the sum of monthly frequency inflows estimated by the monthly frequency analysis. In order to satisfy the additivity condition, annual drought inflow is distributed according to the same sum of the combination of monthly frequency inflows. For example, the return period of 100-year in the solid curve can be disaggregated according to the ratio of the return period 16.5-year monthly inflows. By using KDM, the annual sum of the drought inflow
is shown in Fig. 2. With the negative inflow issue related to the August 2011 flood event, Method 2 was ruled out. Fig. 2. Comparison of return period 20-year drought inflows in Boryeong Dam (m?3/s). The results all followed the same pattern: high during
is shown in Fig. 2. With the negative inflow issue related to the August 2011 flood event, Method 2 was ruled out. Fig. 2. Comparison of return period 20-year drought inflows in Boryeong Dam (m?3/s). The results all followed the same pattern: high during
Fig. 1. Trend in was measured hydro-meteorological parameters. The variation in the streamflow was found sensitive to both climate and anthropogenic activities in the watershed. The contribution of the climate change in the variation of streamflow remains primary factor accounting 70% to 76% in the total change. However, the anthropogenic actives contribute 24% to 30% in the tota change in the watershed streamflow (Table 1). The fractional contribution of anthropogenic activities observed gradually increase in every year in the pos change period. This increase in the impact o anthropogenic activities may result in the hydrologica disaster in the watershed. The variation in the streamflow was found sensitive to
Fig. 1. Trend in was measured hydro-meteorological parameters. The variation in the streamflow was found sensitive to both climate and anthropogenic activities in the watershed. The contribution of the climate change in the variation of streamflow remains primary factor accounting 70% to 76% in the total change. However, the anthropogenic actives contribute 24% to 30% in the tota change in the watershed streamflow (Table 1). The fractional contribution of anthropogenic activities observed gradually increase in every year in the pos change period. This increase in the impact o anthropogenic activities may result in the hydrologica disaster in the watershed. The variation in the streamflow was found sensitive to
Learning from the Central Sulawesi disastrous event in 2018 (Cipta et al., 2020), we investigate the interior of Jakarta Basin to better understand the subsurface structure that may have hidden under the thick sediment. This technique is able to prove the existence of Cisadane, Ciliwung and Kali Bekasi faults that firstly have been identified by Moechtar & Poedjoprajitno (2003). The Citarik fault which was identified by Sidarto (2008), could be a southwest continuation of the Kali Bekasi fault, has also been captured by the HVSR method. This method was able to detect the Baribis fault as well (Fig. 1). Fig. 1. (a) West-east and (b) south-north cross section showing a quite deep of Jakarta basin, reaching > 500 m (Vs of basement = 800 m/s) and suspected faults crossing in and around the basin. (c) Location of the cross sections.
Learning from the Central Sulawesi disastrous event in 2018 (Cipta et al., 2020), we investigate the interior of Jakarta Basin to better understand the subsurface structure that may have hidden under the thick sediment. This technique is able to prove the existence of Cisadane, Ciliwung and Kali Bekasi faults that firstly have been identified by Moechtar & Poedjoprajitno (2003). The Citarik fault which was identified by Sidarto (2008), could be a southwest continuation of the Kali Bekasi fault, has also been captured by the HVSR method. This method was able to detect the Baribis fault as well (Fig. 1). Fig. 1. (a) West-east and (b) south-north cross section showing a quite deep of Jakarta basin, reaching > 500 m (Vs of basement = 800 m/s) and suspected faults crossing in and around the basin. (c) Location of the cross sections.
Fig. 2. (a) and (b) are S-N cross sections showing Baribis- Kendeng Fault and other suspected fault south of the Baribis- Kendeng. (c) soft sediment (Vs = 150 m/s) thickness inferred from HVSR inversion is compared with (d) subsidence rate in Semarang city (Kuehn et al., 2009). Location of cross-sections and data points (black dots) are showing in (c).
Fig. 2. (a) and (b) are S-N cross sections showing Baribis- Kendeng Fault and other suspected fault south of the Baribis- Kendeng. (c) soft sediment (Vs = 150 m/s) thickness inferred from HVSR inversion is compared with (d) subsidence rate in Semarang city (Kuehn et al., 2009). Location of cross-sections and data points (black dots) are showing in (c).
Table 1. ABM Parameter Values. We ran the model 10 times for each of the scenarios, with 150 steps each run. The averages of the results were then computed, and the results for the no vaccination scenario were validated with the COVID-19 data from March 21 to May 21, 2021 (60 days).
Table 1. ABM Parameter Values. We ran the model 10 times for each of the scenarios, with 150 steps each run. The averages of the results were then computed, and the results for the no vaccination scenario were validated with the COVID-19 data from March 21 to May 21, 2021 (60 days).
Table 2. Simulation Results.
Table 2. Simulation Results.
We implemented the agent-based model using Mesa (Project Mesa Team, 2021) and MesaGeo (Corvince, 2018), two Python application libraries that are both publicly available (see Fig. 1). We then used this implementation to simulate the four vaccination scenarios of interest in this study. Fig. 1. GIS visualization of agent-based model.
We implemented the agent-based model using Mesa (Project Mesa Team, 2021) and MesaGeo (Corvince, 2018), two Python application libraries that are both publicly available (see Fig. 1). We then used this implementation to simulate the four vaccination scenarios of interest in this study. Fig. 1. GIS visualization of agent-based model.
Fig. 2. Validation of the death (left) and recovery (right) age distributions for the no vaccination scenario. Furthermore, our model also demonstrates the bias of COVID-19 against the elderly which was already pointed out by Bongolan et al. (2021) as early as 2020. In our simulations, we observed that older individuals (60+) generally die when hit by the disease and the younger ones recover. Ground-truthed with March 21 to May 21, 2021 (60 days) COVID-19 data, this captured age dynamics validates with actual observation (see Fig. 2).
Fig. 2. Validation of the death (left) and recovery (right) age distributions for the no vaccination scenario. Furthermore, our model also demonstrates the bias of COVID-19 against the elderly which was already pointed out by Bongolan et al. (2021) as early as 2020. In our simulations, we observed that older individuals (60+) generally die when hit by the disease and the younger ones recover. Ground-truthed with March 21 to May 21, 2021 (60 days) COVID-19 data, this captured age dynamics validates with actual observation (see Fig. 2).
Table 1. Variation of soil properties with magnitude of seismic shear strain according to Ishihara (1996). 'Department of Science and Technology — Philippine Institute of Volcanology and Seismology (DOST — PHIVOLCS), Quezon Cit 1101, Philippines. "Department of Education (DEPED), Pasig City 1605, Philippines. 3Department of Science and Technology (DOST), Taguig City 1631, Philippines. Microtremor survey is conducted within the confines of public schools situated along the coast of Manila Bay in Greater Metro Manila. Quaternary alluvium classified with saturated and unconsolidated material of varying
Table 1. Variation of soil properties with magnitude of seismic shear strain according to Ishihara (1996). 'Department of Science and Technology — Philippine Institute of Volcanology and Seismology (DOST — PHIVOLCS), Quezon Cit 1101, Philippines. "Department of Education (DEPED), Pasig City 1605, Philippines. 3Department of Science and Technology (DOST), Taguig City 1631, Philippines. Microtremor survey is conducted within the confines of public schools situated along the coast of Manila Bay in Greater Metro Manila. Quaternary alluvium classified with saturated and unconsolidated material of varying
Table 2. Liquefaction Potential Index and descriptions.
Table 2. Liquefaction Potential Index and descriptions.
Table 3. Strain range with SPT and SDS LPIs.
Table 3. Strain range with SPT and SDS LPIs.
Table 4. Strain range with SPT and SDS LPIs.
Table 4. Strain range with SPT and SDS LPIs.
thickness is distributed along the near ground surface along the coastal lowlands. Figure 1 shows liquefaction susceptibility increasing towards Manila Bay where soil thickness and groundwater saturation are believed to increase as well. Fig. 1. Liquefaction hazard map of Greater Metro Manila showing all experimental sites and relative features.
thickness is distributed along the near ground surface along the coastal lowlands. Figure 1 shows liquefaction susceptibility increasing towards Manila Bay where soil thickness and groundwater saturation are believed to increase as well. Fig. 1. Liquefaction hazard map of Greater Metro Manila showing all experimental sites and relative features.
Meycauayan City West Central Elementary School (MCWCS), Bulacan Fig. 3. Distribution of predominant period, relative ampli- tude, and seismic shear strain in MCWCS, Bulacan. Also shown are the locations of SPT and SDS boreholes.
Meycauayan City West Central Elementary School (MCWCS), Bulacan Fig. 3. Distribution of predominant period, relative ampli- tude, and seismic shear strain in MCWCS, Bulacan. Also shown are the locations of SPT and SDS boreholes.
Table 5. Strain range with SDS LPIs.
Table 5. Strain range with SDS LPIs.
Table 6. Strain range with SDS LPIs.
Table 6. Strain range with SDS LPIs.
Fig. 4. Distribution of predominant period, relative ampli- tude, and seismic shear strain in TNCHS. Also shown are the locations of SDS boreholes.
Fig. 4. Distribution of predominant period, relative ampli- tude, and seismic shear strain in TNCHS. Also shown are the locations of SDS boreholes.
Bagbag Elementary School II & Bagbag National High School (BES), Cavite Fig. 5. Distribution of predominant period, relative ampli- tude, and seismic shear strain in BES. Also shown are the locations of SDS boreholes.
Bagbag Elementary School II & Bagbag National High School (BES), Cavite Fig. 5. Distribution of predominant period, relative ampli- tude, and seismic shear strain in BES. Also shown are the locations of SDS boreholes.
Fig. 6. Non-linear curve fitting of y (x 10°) vs. LPIs from SDS and SPT.
Fig. 6. Non-linear curve fitting of y (x 10°) vs. LPIs from SDS and SPT.
Fig. 1. Disease transmission through respiratory droplets.
Fig. 1. Disease transmission through respiratory droplets.
Table 1. Validation results.
Table 1. Validation results.
Fig. 2. Methodological Chart.
Fig. 2. Methodological Chart.
Figure 3 clearly shows the movement droplets near the mouth region of a human. At initial time step (t = 0.5 sec), the droplet particle emerged out from a human travel over a longer distance in the supplement of velocity. The larger (100 um) and medium-sized (30 um) particles lose their inertia and settle rapidly under the influence of gravity. But in the case of smaller droplets, requires larger time to lose its inertia and remains in the air, travels over a longer distance. The distribution of droplets near the hip area is also reported in this study. Figure 4 indicates that the medium sized particle concentration (30 um) accumulates more within 2-4 meters. In case of smaller particles, it needs more time to gain its weight and settle down. The 1 um particle concentration is most often located at a distance of 5 metres. Similarly, the concentration of 0.1 um droplet particles is more noticeable at a distance of 6 meters. Fig. 3. Spatial spread of respiratory droplet near mouth region.
Figure 3 clearly shows the movement droplets near the mouth region of a human. At initial time step (t = 0.5 sec), the droplet particle emerged out from a human travel over a longer distance in the supplement of velocity. The larger (100 um) and medium-sized (30 um) particles lose their inertia and settle rapidly under the influence of gravity. But in the case of smaller droplets, requires larger time to lose its inertia and remains in the air, travels over a longer distance. The distribution of droplets near the hip area is also reported in this study. Figure 4 indicates that the medium sized particle concentration (30 um) accumulates more within 2-4 meters. In case of smaller particles, it needs more time to gain its weight and settle down. The 1 um particle concentration is most often located at a distance of 5 metres. Similarly, the concentration of 0.1 um droplet particles is more noticeable at a distance of 6 meters. Fig. 3. Spatial spread of respiratory droplet near mouth region.
Fig. 4. Spatial spread of respiratory droplet near hip region.
Fig. 4. Spatial spread of respiratory droplet near hip region.
Sedimented droplets are most often occupied near the foot area. The spatial spread of droplets near foot region is shown in Fig. 5. The large droplet (100 um) settles down quickly at a distance of 2 meters and the time taken is also less than 5 seconds. The distance covered by medium-sized particles (30 um) is 2-3 m. Further, the smaller droplets take longer time to settle down. The concentration level of these droplets is low in this region.
Sedimented droplets are most often occupied near the foot area. The spatial spread of droplets near foot region is shown in Fig. 5. The large droplet (100 um) settles down quickly at a distance of 2 meters and the time taken is also less than 5 seconds. The distance covered by medium-sized particles (30 um) is 2-3 m. Further, the smaller droplets take longer time to settle down. The concentration level of these droplets is low in this region.
Fig. 6. Spatial spread of respiratory droplet in XY direction at Initial time step. Fig. 7. Spatial spread of respiratory droplet in XY direction at Final time step.
Fig. 6. Spatial spread of respiratory droplet in XY direction at Initial time step. Fig. 7. Spatial spread of respiratory droplet in XY direction at Final time step.
In the LBM method, respiratory droplets are regarded as a particle that undergoes collision and propagation in a discrete domain space. The above study considered XY plane for simulating the spread of droplets. At an initial stage, the droplet particle is concentrated in the centre region. Concurrently, it undergoes collision and stream- ing process. The above process creates an unstable condition and make the particles move to the neighbour- ing cells. At the final stage, the particle loses its velocity and starts to settle down. Figures 6 and 7 indicate the spatial spread of respiratory droplet in XY direction at Initial time step and final step.
In the LBM method, respiratory droplets are regarded as a particle that undergoes collision and propagation in a discrete domain space. The above study considered XY plane for simulating the spread of droplets. At an initial stage, the droplet particle is concentrated in the centre region. Concurrently, it undergoes collision and stream- ing process. The above process creates an unstable condition and make the particles move to the neighbour- ing cells. At the final stage, the particle loses its velocity and starts to settle down. Figures 6 and 7 indicate the spatial spread of respiratory droplet in XY direction at Initial time step and final step.
Average Wet Season (May-Oct) Low Sea-Level Rise Scenario
Average Wet Season (May-Oct) Low Sea-Level Rise Scenario
Fig. 1. Average wet season (May—Oct) SLR scenario and its impact on roadway vulnerability. Groundwater map was adopted from Obeysekera et al. (2019). (A) Low SLR scenario (B) High SLR scenario (C) Detailed view of High SLR scenario. Blue line indicates concrete pavement. Table 1. Vulnerability classification based on ground- water condition.
Fig. 1. Average wet season (May—Oct) SLR scenario and its impact on roadway vulnerability. Groundwater map was adopted from Obeysekera et al. (2019). (A) Low SLR scenario (B) High SLR scenario (C) Detailed view of High SLR scenario. Blue line indicates concrete pavement. Table 1. Vulnerability classification based on ground- water condition.
be utilized for establishing the cumulative event rainfall— duration (ED) thresholds. As for the rainfall data, the rainfall event variables, such as cumulative event rainfall, rainfall duration, and three-day antecedent rainfall, are split by over 12 hours non-rainfall based on hourly rainfall data using Python programming. station, the Automated Agriculture Only one gauging Observing System (AAOS), is available for soil moisture observation data around this study area. Noah LSM requires the atmospheric forcing data, including air temperature, specific humidity, surface pressure, direction, rainfall, downward so downward longwave radiation. Among them, ERA-Land hourly data is used for downward downward longwave radiation data. 1 Automated Synoptic Observing System (ASOS) has al required meteorological data, so we wind speed, wind ar radiation, and solar radiation and The only Chuncheon can derive one soi moisture data with four layers from running Noah LSM. Fig. 1. Geomorphic setting of study area.
be utilized for establishing the cumulative event rainfall— duration (ED) thresholds. As for the rainfall data, the rainfall event variables, such as cumulative event rainfall, rainfall duration, and three-day antecedent rainfall, are split by over 12 hours non-rainfall based on hourly rainfall data using Python programming. station, the Automated Agriculture Only one gauging Observing System (AAOS), is available for soil moisture observation data around this study area. Noah LSM requires the atmospheric forcing data, including air temperature, specific humidity, surface pressure, direction, rainfall, downward so downward longwave radiation. Among them, ERA-Land hourly data is used for downward downward longwave radiation data. 1 Automated Synoptic Observing System (ASOS) has al required meteorological data, so we wind speed, wind ar radiation, and solar radiation and The only Chuncheon can derive one soi moisture data with four layers from running Noah LSM. Fig. 1. Geomorphic setting of study area.
We designed the Landslide Early Warning System (LEWS) as in Fig. 2. We defined a normal level as when it starts in the summer season and under the Cumulative event rainfall-duration thresholds. We proposed watch levels using the ED thresholds of the local area, which were presented in our previous study (Lee et al., 2021). Then, we made warning levels and severe warning levels using the Bayesian results of landslide probability based on rainfall and soil moisture data. Fig. 2. Landslide Early Warning System (LEWS).
We designed the Landslide Early Warning System (LEWS) as in Fig. 2. We defined a normal level as when it starts in the summer season and under the Cumulative event rainfall-duration thresholds. We proposed watch levels using the ED thresholds of the local area, which were presented in our previous study (Lee et al., 2021). Then, we made warning levels and severe warning levels using the Bayesian results of landslide probability based on rainfall and soil moisture data. Fig. 2. Landslide Early Warning System (LEWS).
Fig. 3. Comparison of soil moisture variation in the Noah LSM model and observation data in the landslide triggering year 2013.
Fig. 3. Comparison of soil moisture variation in the Noah LSM model and observation data in the landslide triggering year 2013.
landslide scarp, in cyan, is facing south to SE along its extent. The profile also shows that the boundary between the zones of depletion and accumulation is marked by a local uplift less than 5 m in height. The landslide toe farther downslope, which signifies the lower boundary of the zone of accumulation, is represented by blue-cyan color which reflects the distinct change in aspect and SRR. Fig. 1. The PC image composition with section line, corresponding elevation profile and color slice, and interpretation of the surface morphology of the landslide in Parasanon. White hachured lines represent field- mapped landslide scarps.
landslide scarp, in cyan, is facing south to SE along its extent. The profile also shows that the boundary between the zones of depletion and accumulation is marked by a local uplift less than 5 m in height. The landslide toe farther downslope, which signifies the lower boundary of the zone of accumulation, is represented by blue-cyan color which reflects the distinct change in aspect and SRR. Fig. 1. The PC image composition with section line, corresponding elevation profile and color slice, and interpretation of the surface morphology of the landslide in Parasanon. White hachured lines represent field- mapped landslide scarps.
morphological characteristics through color contrasts with the surrounding areas. Such analysis can be performed prior to field surveys for targeted landslide mapping. Fig. 2. The PC image composition with section line, corresponding elevation profile, and an oblique view of the landslide in Manghulyawon. morphological characteristics through color contrasts
morphological characteristics through color contrasts with the surrounding areas. Such analysis can be performed prior to field surveys for targeted landslide mapping. Fig. 2. The PC image composition with section line, corresponding elevation profile, and an oblique view of the landslide in Manghulyawon. morphological characteristics through color contrasts
Fig. 1. Lag between GRACE and GPCC data.
Fig. 1. Lag between GRACE and GPCC data.
Fig. 2. GRACE Onset in day numbers of the year.
Fig. 2. GRACE Onset in day numbers of the year.
Fig. 3. GPCC Onset in day numbers of the year.
Fig. 3. GPCC Onset in day numbers of the year.
Fig. 5. GPCC Demise in day numbers of the year.
Fig. 5. GPCC Demise in day numbers of the year.
Fig. 6. GRACE Monsoon duration in days. scales to understand the association between precipitation (GPCC) and mass anomalies (GRACE) on a national Pe eS Te: Eo See eee eae Me Lee ey Per tere
Fig. 6. GRACE Monsoon duration in days. scales to understand the association between precipitation (GPCC) and mass anomalies (GRACE) on a national Pe eS Te: Eo See eee eae Me Lee ey Per tere
Fig. 7. GPCC Monsoon duration in days. CONCLUSION There is an offset of 20 days between GRACE and GPCC data on average and it’s not consistent and varies spatially. The onset from GPCC is early than GRACE ~20 to 30 days. Similarly, GPCC demise falls early by GRACE with a similar gap. Even though the onset and demise don’t agree for GRACE and GPCC but monsoon duration is almost similar. We can safely assume that the GRACE data contains the information pertaining to monsoon but only for a very large area. Further, the information carried by GRACE data is lagged by 20-30 day compare to real-time monsoon. There is an offset of 20 days between GRACE and
Fig. 7. GPCC Monsoon duration in days. CONCLUSION There is an offset of 20 days between GRACE and GPCC data on average and it’s not consistent and varies spatially. The onset from GPCC is early than GRACE ~20 to 30 days. Similarly, GPCC demise falls early by GRACE with a similar gap. Even though the onset and demise don’t agree for GRACE and GPCC but monsoon duration is almost similar. We can safely assume that the GRACE data contains the information pertaining to monsoon but only for a very large area. Further, the information carried by GRACE data is lagged by 20-30 day compare to real-time monsoon. There is an offset of 20 days between GRACE and
Fig. 2. Locations of base station and verified control points (benchmarks) of the Geospatial Information Authority of Japan (GSI). This map is based on the Digital Map (Basic Geospatial Information 200000) published by the GSI. Fig. 1. System overview of this study, modified from figure of Shinmura and Nasu (2020).
Fig. 2. Locations of base station and verified control points (benchmarks) of the Geospatial Information Authority of Japan (GSI). This map is based on the Digital Map (Basic Geospatial Information 200000) published by the GSI. Fig. 1. System overview of this study, modified from figure of Shinmura and Nasu (2020).
Fig. 3. Rover station of this study. RTK-GNSS receiver (NEO-M§8P-0) is in a small white box in right side and Rtkgps+ (an application for RTK) on a smartphone is processing FIX data of precise positioning data. AS mentioned above, at site 5, there was a large difference between the data obtained from the first and second measurements, and between the data obtained from the first measurement and the survey data from GSI corrected to current period data. So the positioning results may change as time changes, and it is necessary to confirm how much accuracy would be maintained. Therefore, we carried out local-area RTK-GNSS measurements at a fixed point in the university almost every day except weekends for about two and a half months from the middle of November 2019 to January 2020. We set up a portable measurement system using a smartphone, a receiver (NEO-M8P-0) and the antenna to perform continuous position measurement and evaluate its portability (Fig. 3). The antennas were manually put where we defined as a fixed point at every measurement. Throughout the entire period, the latitude was north 32.8038943742 x 10°7° (about 2 cm), the longitude was East 130.7294967843.0 x 10°7° (about 3 cm), and the altitude was 52.6648 cm, and the measurements were concentrated closer to the mean value (Fig. 4). As described above, in about two months, fluctuations in all directions were accurate to within a few centimeters. The difference from the mean value tended to be larger when the weather was particularly bad. the weather was particularly bad.
Fig. 3. Rover station of this study. RTK-GNSS receiver (NEO-M§8P-0) is in a small white box in right side and Rtkgps+ (an application for RTK) on a smartphone is processing FIX data of precise positioning data. AS mentioned above, at site 5, there was a large difference between the data obtained from the first and second measurements, and between the data obtained from the first measurement and the survey data from GSI corrected to current period data. So the positioning results may change as time changes, and it is necessary to confirm how much accuracy would be maintained. Therefore, we carried out local-area RTK-GNSS measurements at a fixed point in the university almost every day except weekends for about two and a half months from the middle of November 2019 to January 2020. We set up a portable measurement system using a smartphone, a receiver (NEO-M8P-0) and the antenna to perform continuous position measurement and evaluate its portability (Fig. 3). The antennas were manually put where we defined as a fixed point at every measurement. Throughout the entire period, the latitude was north 32.8038943742 x 10°7° (about 2 cm), the longitude was East 130.7294967843.0 x 10°7° (about 3 cm), and the altitude was 52.6648 cm, and the measurements were concentrated closer to the mean value (Fig. 4). As described above, in about two months, fluctuations in all directions were accurate to within a few centimeters. The difference from the mean value tended to be larger when the weather was particularly bad. the weather was particularly bad.
Fig. 5. KGUMAP of Kumamoto Gakuen University. Left: Main page of this system to search for classrooms and other facilities. Upper right: output result 1, Lower right: output result 2 (the layout of buildings in the university and the location of a user on Google Map and Google Earth quoted by Google). Fig. 5. KGUMAP of Kumamoto Gakuen University. Left: Fig. 4. Results of continuous positioning of a fixed point using handy local-area RTK-GNSS system in Kumamoto Gakuen University, Japan (modified from figure of Shinmura and Nasu, 2021). Legends: White: clear; black: cloudy or rainy, triangle: GPS and BeiDou received; circle: GPS, BeiDou and QZSS received.
Fig. 5. KGUMAP of Kumamoto Gakuen University. Left: Main page of this system to search for classrooms and other facilities. Upper right: output result 1, Lower right: output result 2 (the layout of buildings in the university and the location of a user on Google Map and Google Earth quoted by Google). Fig. 5. KGUMAP of Kumamoto Gakuen University. Left: Fig. 4. Results of continuous positioning of a fixed point using handy local-area RTK-GNSS system in Kumamoto Gakuen University, Japan (modified from figure of Shinmura and Nasu, 2021). Legends: White: clear; black: cloudy or rainy, triangle: GPS and BeiDou received; circle: GPS, BeiDou and QZSS received.
RESULTS Drought events In this study, we used SPEI-9 to represent drought magnitude as it revealed a maximum correlation with crop yield anomaly. We calculated the number of drought years during 36 years of the analysis period (1981-2016) for each crop given by their different crop growing seasons based on moderate to severe drought events (SPEI < -1). Results show that the regions with more severe drought events include North-west China, Mongolia, North-east India, Southern China, Borneo, Myanmar and northern Vietnam. Regression coefficient is used as a proxy of crop sensitivity
RESULTS Drought events In this study, we used SPEI-9 to represent drought magnitude as it revealed a maximum correlation with crop yield anomaly. We calculated the number of drought years during 36 years of the analysis period (1981-2016) for each crop given by their different crop growing seasons based on moderate to severe drought events (SPEI < -1). Results show that the regions with more severe drought events include North-west China, Mongolia, North-east India, Southern China, Borneo, Myanmar and northern Vietnam. Regression coefficient is used as a proxy of crop sensitivity
SPEI is multiplied by —1 to get an absolute drought index value. The monthly drought index was then transformed to the annual drought index, corresponding with crop yield anomaly data based on harvesting month of crop growing season (Kim et al., 2019). While crop yield anomaly was modelled using the global dataset of historical yield (GDHY) (lizumi & Sakai, 2020) for the four major crops (Table 1). Then we calculated crop yield anomaly using a 5-year centered moving average (Kim et al., 2019). The minus value (%) of the crop yield anomaly indicates crop yield loss. Finally, we calculated Pearson correlation and linear regression to understand crop sensitivity to drought, represented by the regression coefficient. A steeper negative slope means drought induces crop yield loss with higher sensitivity. Fig. 1. Flow chart of the method used in this study. Table 1. Data used in this study.
SPEI is multiplied by —1 to get an absolute drought index value. The monthly drought index was then transformed to the annual drought index, corresponding with crop yield anomaly data based on harvesting month of crop growing season (Kim et al., 2019). While crop yield anomaly was modelled using the global dataset of historical yield (GDHY) (lizumi & Sakai, 2020) for the four major crops (Table 1). Then we calculated crop yield anomaly using a 5-year centered moving average (Kim et al., 2019). The minus value (%) of the crop yield anomaly indicates crop yield loss. Finally, we calculated Pearson correlation and linear regression to understand crop sensitivity to drought, represented by the regression coefficient. A steeper negative slope means drought induces crop yield loss with higher sensitivity. Fig. 1. Flow chart of the method used in this study. Table 1. Data used in this study.
Fig. 3. Correlation between SPEI and the Nifio 3.4. Fig. 2. Regression slope of linear correlation between crop yield loss and the drought.
Fig. 3. Correlation between SPEI and the Nifio 3.4. Fig. 2. Regression slope of linear correlation between crop yield loss and the drought.
Fig. 3. Overview of extents and depth estimation system.
Fig. 3. Overview of extents and depth estimation system.
Fig. 4. Inundation depth estimation methodology.
Fig. 4. Inundation depth estimation methodology.
testing phase mean IoU for each class, precision, recall and F-score were estimated as accuracy matrix. Fig. 2. Test data set and ground surveyed data points. METHODOLOGY
testing phase mean IoU for each class, precision, recall and F-score were estimated as accuracy matrix. Fig. 2. Test data set and ground surveyed data points. METHODOLOGY
Table 1. Validation accuracy comparison of models (1: No data, 2: Other, 3: Inundated area). Fig. 5. Comparison of model output with GSI data. REFERENCES Table 2. Test accuracy comparison of models for inundation area extraction. As shown in Fig. 5. both models were successfully extracted the flooded areas. However, there is a higher tendency of false extraction from ResNet101 model in comparison to MobileNetV2. In contrast, MobileNetV2 has relatively less false extraction and few missed extractions especially in the areas covered with thin clouds and shadows. Further, it has been found that the ResNet101 model can extract inundation extents of 1 km? with 0.64 s whereas mMobileNetV2 consumes only 0.38 s. Therefore, MobileNetV2 model is preferable for near real-time applications.
Table 1. Validation accuracy comparison of models (1: No data, 2: Other, 3: Inundated area). Fig. 5. Comparison of model output with GSI data. REFERENCES Table 2. Test accuracy comparison of models for inundation area extraction. As shown in Fig. 5. both models were successfully extracted the flooded areas. However, there is a higher tendency of false extraction from ResNet101 model in comparison to MobileNetV2. In contrast, MobileNetV2 has relatively less false extraction and few missed extractions especially in the areas covered with thin clouds and shadows. Further, it has been found that the ResNet101 model can extract inundation extents of 1 km? with 0.64 s whereas mMobileNetV2 consumes only 0.38 s. Therefore, MobileNetV2 model is preferable for near real-time applications.
Fig. 6. Comparison of estimated and ground truth Inundation depths. More than 170 ground surveyed data points were collected. Among them only 30 points were overlaid with the estimated inundation depths raster. The estimated inundation depths ware ranging from 0—12.8 m in which the maximum depths were found to be along river and a false extracted area due to cloud. However, according to the ground surveyed inundation depths (which was carried out on 18/07/2018, disaster occurred on 05-— 08/07/2018 and satellite data captured on 09/07/2018) maximum inundation depth was reported as 5.38 m. However, from the comparison of estimated inundation depths with ground truth data found that there is a relatively well agreement of estimated depths with field surveyed measurements.
Fig. 6. Comparison of estimated and ground truth Inundation depths. More than 170 ground surveyed data points were collected. Among them only 30 points were overlaid with the estimated inundation depths raster. The estimated inundation depths ware ranging from 0—12.8 m in which the maximum depths were found to be along river and a false extracted area due to cloud. However, according to the ground surveyed inundation depths (which was carried out on 18/07/2018, disaster occurred on 05-— 08/07/2018 and satellite data captured on 09/07/2018) maximum inundation depth was reported as 5.38 m. However, from the comparison of estimated inundation depths with ground truth data found that there is a relatively well agreement of estimated depths with field surveyed measurements.
Table 1. Classes used to split the grid points. The grid points of the geographical domain were splitted into different classes (Table 1 and Fig. 1), each one characterized by elevation, soil and vegetation types. The minimum number of grid points for each class is 7 (1% of total grid points).
Table 1. Classes used to split the grid points. The grid points of the geographical domain were splitted into different classes (Table 1 and Fig. 1), each one characterized by elevation, soil and vegetation types. The minimum number of grid points for each class is 7 (1% of total grid points).
The simulations were performed using the UTOPIA on all grid points of the global gridded database GLDAS2.0 (Global Land Data Assimilation System, version 2.0). Vegetation and soil code were extracted also from GLDAS2.0 and adapted to those of UTOPIA, while the initial values of soil temperature and moisture were deduced from the monthly mean values of January and July temperature, and from the mean relative humidity of the first day of simulation, using the formulations in Cassardo (2015). Fig. 1. The analyzed geographical domain. The different colors refer to areas characterized by different altitudes. Light brown color: h < 500, dark brown color: 500 < h < 2000, black color: h > 2000, where h is the elevation. Fig. 1. The analyzed geographical domain. The different the first day of simulation, using the formulations in
The simulations were performed using the UTOPIA on all grid points of the global gridded database GLDAS2.0 (Global Land Data Assimilation System, version 2.0). Vegetation and soil code were extracted also from GLDAS2.0 and adapted to those of UTOPIA, while the initial values of soil temperature and moisture were deduced from the monthly mean values of January and July temperature, and from the mean relative humidity of the first day of simulation, using the formulations in Cassardo (2015). Fig. 1. The analyzed geographical domain. The different colors refer to areas characterized by different altitudes. Light brown color: h < 500, dark brown color: 500 < h < 2000, black color: h > 2000, where h is the elevation. Fig. 1. The analyzed geographical domain. The different the first day of simulation, using the formulations in
Fig. 2. Annual cycle of sensible heat flux SHF (red line), latent heat flux LHF (green line), conductive soil heat flux CHF (blue line) and net radiation RN (black line). Class ID: 1. Fig. 3. Annual cycle of precipitation PR (black line), evapotranspiration ET (red line), surface runoff SR (blue line). Class ID: 1.
Fig. 2. Annual cycle of sensible heat flux SHF (red line), latent heat flux LHF (green line), conductive soil heat flux CHF (blue line) and net radiation RN (black line). Class ID: 1. Fig. 3. Annual cycle of precipitation PR (black line), evapotranspiration ET (red line), surface runoff SR (blue line). Class ID: 1.
Fig. 4. Annual cycle of soil temperature values at a depth of 0.25 m. Class ID: 1.
Fig. 4. Annual cycle of soil temperature values at a depth of 0.25 m. Class ID: 1.
Fig. 5. Annual cycle of soil saturation ratio values at a depth of 0.25 m. Class ID: 1.
Fig. 5. Annual cycle of soil saturation ratio values at a depth of 0.25 m. Class ID: 1.
Table 1. Experiment names, parameters, and their ranges (minimum, default, maximum) where the minimum and maximum values are —20% and +20% of the default value, respectively. Values of Way vary depending on the land use types: L1 indicates the evergreen needleaf forest, deciduous broadleaf forest, and mixed forests; L2 indicates the closed shrublands, and woody savannas; L3 indicates the open shrublands; L4 indicates the savannas, grasslands, croplands, urban, built-up, = and cropland/natural vegetation mosaic; LS indicates the permanent wetlands; L6 indicates the barren or sparsely vegetated, snow, ice, and barren tundra; L7 indicates water; and L8 indicates the wooded tundra and mixed tundra. REFERENCES Sensitivities of 2-m Temperature to Snow-related Parameters permanent wetlands; L6 indicates the barren or sparsely Figure 1 ranks the sensitivities of T2, to the snow- elated parameters. We have designed the cost function to ind the maximum sensitivity as follows:
Table 1. Experiment names, parameters, and their ranges (minimum, default, maximum) where the minimum and maximum values are —20% and +20% of the default value, respectively. Values of Way vary depending on the land use types: L1 indicates the evergreen needleaf forest, deciduous broadleaf forest, and mixed forests; L2 indicates the closed shrublands, and woody savannas; L3 indicates the open shrublands; L4 indicates the savannas, grasslands, croplands, urban, built-up, = and cropland/natural vegetation mosaic; LS indicates the permanent wetlands; L6 indicates the barren or sparsely vegetated, snow, ice, and barren tundra; L7 indicates water; and L8 indicates the wooded tundra and mixed tundra. REFERENCES Sensitivities of 2-m Temperature to Snow-related Parameters permanent wetlands; L6 indicates the barren or sparsely Figure 1 ranks the sensitivities of T2, to the snow- elated parameters. We have designed the cost function to ind the maximum sensitivity as follows:
Fig. 1. Averaged cost function of Tz,, (°C) during the experimental period (Nov 2009—May 2010). fractional snow cover, and the others are related to the snow albedo. It turns out that the snow density parameters do not lead a change of T>,, significantly. CONCLUSIONS As a preliminary CNOP-P test, we found that the five parameters, out of the nine parameters, to which T>,, is most sensitive Ty are Ps, Wax: C,@max,cofp and A. Asa next step, the CNOP-P method should be refined to obtain the randomly generated perturbations in parameters. Based on these results, we will optimize the most sensitive parameters using the optimization algorithm (e.g., micro- genetic algorithm) to improve both snow and T>,, prediction skills.
Fig. 1. Averaged cost function of Tz,, (°C) during the experimental period (Nov 2009—May 2010). fractional snow cover, and the others are related to the snow albedo. It turns out that the snow density parameters do not lead a change of T>,, significantly. CONCLUSIONS As a preliminary CNOP-P test, we found that the five parameters, out of the nine parameters, to which T>,, is most sensitive Ty are Ps, Wax: C,@max,cofp and A. Asa next step, the CNOP-P method should be refined to obtain the randomly generated perturbations in parameters. Based on these results, we will optimize the most sensitive parameters using the optimization algorithm (e.g., micro- genetic algorithm) to improve both snow and T>,, prediction skills.
Fig. 1. Ghana Coast and 6 Fishery port sites. Keywords: Swell-driven, Extreme Waves, GHANA Coast. METHODOLOGY ABSTRACT: In order to determine the near-shore extreme waves along swell-dominated coasts, this paper applies three different runs (i.e. run 1 with 50 yr waves & yearly 1% winds, run 2 with 50 yr winds & yearly 1% waves, and run 3 with 50 yr waves & winds for sensitivity analysis) for wave boundary and wind forcing in the wave modelling. Eventually Run | method is recommended for the GHANA Coastal extreme wave transformation as its results are more reasonable.
Fig. 1. Ghana Coast and 6 Fishery port sites. Keywords: Swell-driven, Extreme Waves, GHANA Coast. METHODOLOGY ABSTRACT: In order to determine the near-shore extreme waves along swell-dominated coasts, this paper applies three different runs (i.e. run 1 with 50 yr waves & yearly 1% winds, run 2 with 50 yr winds & yearly 1% waves, and run 3 with 50 yr waves & winds for sensitivity analysis) for wave boundary and wind forcing in the wave modelling. Eventually Run | method is recommended for the GHANA Coastal extreme wave transformation as its results are more reasonable.
Table 1. Selected combination of waves and winds input parameters. MODEL RESULTS As an example, extreme (1/50 years) wave fields of run 1 case is presented in Fig. 5. It can be mainly concluded that:
Table 1. Selected combination of waves and winds input parameters. MODEL RESULTS As an example, extreme (1/50 years) wave fields of run 1 case is presented in Fig. 5. It can be mainly concluded that:
Measured wave parameters (Hs, Tp) in nearshore (at —16 m) from Jan to Jul of 2018 are adopted to calibrate and validate the wave model. Figure 4 presents the time series comparison between measured and simulated Hs, which shows the simulated data fit well with the measure ones. Fig. 4. Simulated vs measured time series Hs.
Measured wave parameters (Hs, Tp) in nearshore (at —16 m) from Jan to Jul of 2018 are adopted to calibrate and validate the wave model. Figure 4 presents the time series comparison between measured and simulated Hs, which shows the simulated data fit well with the measure ones. Fig. 4. Simulated vs measured time series Hs.
Fig. 2. Hs ~ Tp scatter (upper) and Hs ~ wind speed time- series (lower). Wave Model Establish
Fig. 2. Hs ~ Tp scatter (upper) and Hs ~ wind speed time- series (lower). Wave Model Establish
Fig. 3. Model extent and bathymetry. The wave model is developed with Mike 21 SW (DHI, 2019), and adopts a nested-mesh wave modelling scheme as shown in Fig. 3. The wave model covers a part of Gulf of Guinea and entire Ghana coast, finest mesh resolution is about 20~60 m in the target region. Model Calibration/Validation
Fig. 3. Model extent and bathymetry. The wave model is developed with Mike 21 SW (DHI, 2019), and adopts a nested-mesh wave modelling scheme as shown in Fig. 3. The wave model covers a part of Gulf of Guinea and entire Ghana coast, finest mesh resolution is about 20~60 m in the target region. Model Calibration/Validation
Fig. 5. Wave field for extreme conditions (for run 1). Table 2 summarizes the simulated extreme waves rom all three runs. It can be concluded that:
Fig. 5. Wave field for extreme conditions (for run 1). Table 2 summarizes the simulated extreme waves rom all three runs. It can be concluded that:
Fig. 1. The identified best fitting model for annual maxima series at every station. Symbols square, triangle, circle, and diamond show the best fitted model of MO, M1, M2, and M3, that correspond to stationary model, ENSO, IOD, and MJO, respectively. The color show the estimated shape parameter categorized as Frechet, Gumbel, and Weibull. insignificantly. Overall, only ENSO influences tend to affect more on ER in the Makassar Island significantly.
Fig. 1. The identified best fitting model for annual maxima series at every station. Symbols square, triangle, circle, and diamond show the best fitted model of MO, M1, M2, and M3, that correspond to stationary model, ENSO, IOD, and MJO, respectively. The color show the estimated shape parameter categorized as Frechet, Gumbel, and Weibull. insignificantly. Overall, only ENSO influences tend to affect more on ER in the Makassar Island significantly.
Table 1. Horizontal Resolution for OISST and CMIP6 ensemble member. RESULTS
Table 1. Horizontal Resolution for OISST and CMIP6 ensemble member. RESULTS
depending on regions (Fig. 1). More than 60% of the CMIP6 model showed a dipole pattern of the MHW intensity bias in the Kuroshio extension, and were overestimated in the north and underestimated in the south. Compared to the OISST, it is possible that the Kuroshio Current simulated by the CMIP6 model was shifted northward, resulting in model errors in the simulation of the intensity of MHW. Fig. 1. Biases in the climatological mean of MHW mean intensity, averaged from 1982 to 2014 and obtained from (a—n) each of the ensemble members, and (0) the multi- model ensemble mean (MME). The biases were obtained against the OISST reanalysis.
depending on regions (Fig. 1). More than 60% of the CMIP6 model showed a dipole pattern of the MHW intensity bias in the Kuroshio extension, and were overestimated in the north and underestimated in the south. Compared to the OISST, it is possible that the Kuroshio Current simulated by the CMIP6 model was shifted northward, resulting in model errors in the simulation of the intensity of MHW. Fig. 1. Biases in the climatological mean of MHW mean intensity, averaged from 1982 to 2014 and obtained from (a—n) each of the ensemble members, and (0) the multi- model ensemble mean (MME). The biases were obtained against the OISST reanalysis.
according to warming compared to the OISST, implying that actual MHWs in the future are more likely to be stronger than the projections. Therefore, it is necessary to study the effects of underestimating MHW trends on future changes in MHWs. Fig. 2. Changes in MHW characteristics for warming. Warming was calculated as the spatial average of SST anomaly in NPO, and the base period is 1982-2011.
according to warming compared to the OISST, implying that actual MHWs in the future are more likely to be stronger than the projections. Therefore, it is necessary to study the effects of underestimating MHW trends on future changes in MHWs. Fig. 2. Changes in MHW characteristics for warming. Warming was calculated as the spatial average of SST anomaly in NPO, and the base period is 1982-2011.
Fig. 1. Predicted Fe vapor evolution at equilibrium from Mare regolith sample 24999 (FactSage 7.2 software). Figure | shows the predicted amount of Fe in the vapor phase in a sample of lunar regolith versus pressure and temperature. This modelling, completed with a feed composition matching that of the Luna 24 mission returned regolith sample #24999 (Papike et al., 1982), shows the direct correlation between pressure, temperature, and iron oxide dissociation amount. 16.2 wt.% Fe equates to all the Fe present in the sample, and thus complete thermal decomposition of all iron oxide species.
Fig. 1. Predicted Fe vapor evolution at equilibrium from Mare regolith sample 24999 (FactSage 7.2 software). Figure | shows the predicted amount of Fe in the vapor phase in a sample of lunar regolith versus pressure and temperature. This modelling, completed with a feed composition matching that of the Luna 24 mission returned regolith sample #24999 (Papike et al., 1982), shows the direct correlation between pressure, temperature, and iron oxide dissociation amount. 16.2 wt.% Fe equates to all the Fe present in the sample, and thus complete thermal decomposition of all iron oxide species.
Fig. 2. Schematic representation of a proposed lunar ISRU reactor (Shaw et al., 2021b). CONCLUSIONS
Fig. 2. Schematic representation of a proposed lunar ISRU reactor (Shaw et al., 2021b). CONCLUSIONS
As one of the international cooperation payloads of the Chang’e-4 mission, the Longjiang-2 satellite carries an optical camera payload developed by the King Abdulaziz Science and Technology City in Saudi Arabia. It is mainly used to shoot color images in the visible spectrum of targets such as the Moon and the Earth. Figure 2 is an Earth and Moon image captured by Saudi optical camera payload. Fig. 2. Earth and Moon image captured by Saudi optical camera payload. used to shoot color images in the visible spectrum of
As one of the international cooperation payloads of the Chang’e-4 mission, the Longjiang-2 satellite carries an optical camera payload developed by the King Abdulaziz Science and Technology City in Saudi Arabia. It is mainly used to shoot color images in the visible spectrum of targets such as the Moon and the Earth. Figure 2 is an Earth and Moon image captured by Saudi optical camera payload. Fig. 2. Earth and Moon image captured by Saudi optical camera payload. used to shoot color images in the visible spectrum of
Fig. 1. Earth’s noise spectrum detected by Longjiang-2. ABSTRACT: As the world’s first microsatellite that completed lunar transfer, lunar insertion and lunar orbiting independently, Longjiang-2 has explored a new mode of low-cost deep space exploration and international cooperation. This article firstly expounds the design overview of the microsatellite. Then the tasks’ execution and flight results are presented with introductions of onboard payloads, including the low frequency radio interferometer, the Saudi optical camera, the VHF/UHF radio and the student CMOS camera. Thirdly, the technical difficulties of the microsatellite, including orbit design and control, measurement and suppression of onboard electromagnetic interference, and wide-field 3D baseline interferometry, are reviewed. Finally, future deep space microsatellite missions will be introduced.
Fig. 1. Earth’s noise spectrum detected by Longjiang-2. ABSTRACT: As the world’s first microsatellite that completed lunar transfer, lunar insertion and lunar orbiting independently, Longjiang-2 has explored a new mode of low-cost deep space exploration and international cooperation. This article firstly expounds the design overview of the microsatellite. Then the tasks’ execution and flight results are presented with introductions of onboard payloads, including the low frequency radio interferometer, the Saudi optical camera, the VHF/UHF radio and the student CMOS camera. Thirdly, the technical difficulties of the microsatellite, including orbit design and control, measurement and suppression of onboard electromagnetic interference, and wide-field 3D baseline interferometry, are reviewed. Finally, future deep space microsatellite missions will be introduced.
While the satellite was in orbit, 50 ground stations in 17 countries successfully received 20,945 packets of GMSK data and 883 packets of JT4G data [5]. With the help of multiple GPS-synchronized ground station receivers, several VLBI experiments have been carried out. Compared with the S-band data, the differential range and velocity data preliminarily verified the feasibility of using VLBI for orbit determination in this frequency band. Figures 3 and 4 show differential range and velocity data in first lunar orbit UHF VLBI experiment compared with S-band data [6]. The student miniature CMOS camera is independently developed by undergraduates of Harbin Institute of Technology. The camera is integrated with the VHF/UHF communication module. During the one-year-flight of the satellite, a total of 135 images were taken and transmitted, including a picture of Moon and Earth published by “Science” magazine, and a picture observes total solar eclipse in South Africa taken on lunar orbit. Figure 5 is The student miniature CMOS camera is independently
While the satellite was in orbit, 50 ground stations in 17 countries successfully received 20,945 packets of GMSK data and 883 packets of JT4G data [5]. With the help of multiple GPS-synchronized ground station receivers, several VLBI experiments have been carried out. Compared with the S-band data, the differential range and velocity data preliminarily verified the feasibility of using VLBI for orbit determination in this frequency band. Figures 3 and 4 show differential range and velocity data in first lunar orbit UHF VLBI experiment compared with S-band data [6]. The student miniature CMOS camera is independently developed by undergraduates of Harbin Institute of Technology. The camera is integrated with the VHF/UHF communication module. During the one-year-flight of the satellite, a total of 135 images were taken and transmitted, including a picture of Moon and Earth published by “Science” magazine, and a picture observes total solar eclipse in South Africa taken on lunar orbit. Figure 5 is The student miniature CMOS camera is independently
Fig. 3. VLBI-S differential range data.
Fig. 3. VLBI-S differential range data.
Earth and Moon image captured by student CMOS camera [7]. Fig. 5. Earth and Moon image captured by student CMOS camera.
Earth and Moon image captured by student CMOS camera [7]. Fig. 5. Earth and Moon image captured by student CMOS camera.
This document briefly summarizes the configuration of MINERVA-II system as well as the operational results attained by the rovers on the asteroid surface. Fig. 1. Rover 1A (left) and Rover 1B (right). Two rovers were packed into one container (MINERVA-II1) to mount to the spacecraft.
This document briefly summarizes the configuration of MINERVA-II system as well as the operational results attained by the rovers on the asteroid surface. Fig. 1. Rover 1A (left) and Rover 1B (right). Two rovers were packed into one container (MINERVA-II1) to mount to the spacecraft.
Table 1. Components of MINERVA-H. TWIN ROVERS
Table 1. Components of MINERVA-H. TWIN ROVERS
Table 2. Specifications of twin rovers. The solar cells were the electric source of the rovers and were attached on every faces of the body which enabled the power generation at any attitude. The residual power was charged into the onboard double layer capacitors. The solar cells were the electric source of the rovers
Table 2. Specifications of twin rovers. The solar cells were the electric source of the rovers and were attached on every faces of the body which enabled the power generation at any attitude. The residual power was charged into the onboard double layer capacitors. The solar cells were the electric source of the rovers
Fig. 2. Images obtained by Rover 1A in one ballistic motion on 7th day. of the target asteroid in 2018. The accomplishment by the rovers was the World’s first mobile exploration over Solar system small body.
Fig. 2. Images obtained by Rover 1A in one ballistic motion on 7th day. of the target asteroid in 2018. The accomplishment by the rovers was the World’s first mobile exploration over Solar system small body.
Table 1. The initial parameters of two simulations. ‘Institute of Astronomy, National Central University, Taoyuan City, Taiwan (R.O.C.). Department of Space science and Engineering, National Central University, Taoyuan City, Taiwan (R.O.C.). To find out the association between Vatira-class and Atira-class asteroids, there are the 20 effective samples with 16.3 < H < 25.0 in the second set (Run B) of simulation, and generated 100 clone particles per known Atira-class asteroid by the covariance matrix from JPL SBDB. Also, each known Atiras-class asteroid was under three kinds of directions of the Yarkovsky force (e.g. 0, 90, 180 degrees).
Table 1. The initial parameters of two simulations. ‘Institute of Astronomy, National Central University, Taoyuan City, Taiwan (R.O.C.). Department of Space science and Engineering, National Central University, Taoyuan City, Taiwan (R.O.C.). To find out the association between Vatira-class and Atira-class asteroids, there are the 20 effective samples with 16.3 < H < 25.0 in the second set (Run B) of simulation, and generated 100 clone particles per known Atira-class asteroid by the covariance matrix from JPL SBDB. Also, each known Atiras-class asteroid was under three kinds of directions of the Yarkovsky force (e.g. 0, 90, 180 degrees).
Table 2. Summary of dynamical half-lifetimes of NEOs from Run A simulation.
Table 2. Summary of dynamical half-lifetimes of NEOs from Run A simulation.
Figure 1 shows the time variation of the NEOs from which the curve can be fitted by a combination of a short- ife population with a half-life of 2.71 Myr and a long-life population with a half-life of 23.84 Myr. The results can be compared with the median half-lifetime of ~10 Myr by 178 real NEOs samples in the result of Gladman et al. 1997). Note that one of the sub-groups of NEOs, namely he Amor-class, has the shorter short-term half-lifetime of ~0.14 Myr than the other classes of NEOs. This is because the Amor-class asteroid is located at the boundary of the NEOs region can be easily ejected out. Fig. 1. The dynamical half-lifetime of near-Earth objects in Run A simulation, including Atira-class, Aten-class, Apollo-class, and Amor-class asteroids. The green dash- line is for the short-term half-lifetime, while the red dash- line is for the long-term half-lifetime. Fig. 1. The dynamical half-lifetime of near-Earth objects
Figure 1 shows the time variation of the NEOs from which the curve can be fitted by a combination of a short- ife population with a half-life of 2.71 Myr and a long-life population with a half-life of 23.84 Myr. The results can be compared with the median half-lifetime of ~10 Myr by 178 real NEOs samples in the result of Gladman et al. 1997). Note that one of the sub-groups of NEOs, namely he Amor-class, has the shorter short-term half-lifetime of ~0.14 Myr than the other classes of NEOs. This is because the Amor-class asteroid is located at the boundary of the NEOs region can be easily ejected out. Fig. 1. The dynamical half-lifetime of near-Earth objects in Run A simulation, including Atira-class, Aten-class, Apollo-class, and Amor-class asteroids. The green dash- line is for the short-term half-lifetime, while the red dash- line is for the long-term half-lifetime. Fig. 1. The dynamical half-lifetime of near-Earth objects
The simulation results from De la Fuente Marcos et al. (2020) and Greenstreet (2020), both indicated 2020 AV2 was an Atira-class asteroid before entering the orbital region of the Vatira-class asteroid. Therefore, in order to investigate the relationship between the Vatira-class and Atira-class asteroids, we plot the time-weighted distribution to record the cumulative patterns of orbital elements over the dynamical evolution of test particles shown in Fig. 2. This time-weighted distribution could be used to track the specific orbital element that the test particle occupied in most evolution time, as well as to calculate the transfer probability of test particles. calculate the transfer probability of test particles. Fig. 2. The time-weighted distribution in a-e phase space for the clones and nominal of known Atira-class asteroids under the outward Yarkovsky effect (i.e. a > 0). The color bar represents the fraction of test particles. The bin size for semi-major axis and eccentricity is 0.01. The solid lines in a-e space are used to identify the boundaries of different classes of near-Earth objects and the inner- Venus object: Atira-class (0.718 < Q < 0.983 AU), Aten- class (a < 1.0 AU, O> 0.983 AU), Apollo-class (a > 1.0 AU, g < 1.017 AU), Amor-class (1.017 < q < 1.3 AU), and Vatira-class (0.307 < O< 0.718 AU). particle occupied in most evolution time, as well as to shown in Fig. 2. This time-weighted distribution could be
The simulation results from De la Fuente Marcos et al. (2020) and Greenstreet (2020), both indicated 2020 AV2 was an Atira-class asteroid before entering the orbital region of the Vatira-class asteroid. Therefore, in order to investigate the relationship between the Vatira-class and Atira-class asteroids, we plot the time-weighted distribution to record the cumulative patterns of orbital elements over the dynamical evolution of test particles shown in Fig. 2. This time-weighted distribution could be used to track the specific orbital element that the test particle occupied in most evolution time, as well as to calculate the transfer probability of test particles. calculate the transfer probability of test particles. Fig. 2. The time-weighted distribution in a-e phase space for the clones and nominal of known Atira-class asteroids under the outward Yarkovsky effect (i.e. a > 0). The color bar represents the fraction of test particles. The bin size for semi-major axis and eccentricity is 0.01. The solid lines in a-e space are used to identify the boundaries of different classes of near-Earth objects and the inner- Venus object: Atira-class (0.718 < Q < 0.983 AU), Aten- class (a < 1.0 AU, O> 0.983 AU), Apollo-class (a > 1.0 AU, g < 1.017 AU), Amor-class (1.017 < q < 1.3 AU), and Vatira-class (0.307 < O< 0.718 AU). particle occupied in most evolution time, as well as to shown in Fig. 2. This time-weighted distribution could be
Table 3. The transfer probabilities and errors of asteroids from the Atira-class to the other classes of near-Earth objects at the end of Run B simulation. The number of kilometer-sized inner Venus objects ([VOs) and Atiras from known NEOs population ([VOs) and Atiras from known NEOs population Before the discovery of the first observed Vatira-class asteroid Ip et al. (2020), Granvik et al. (2018) and Greenstreet et al. (2012) have predicted inner Venus objects to exist according to their NEOs model. Here, we estimated the number of kilometer-sized [VOs based on the transfer probability from Run A simulation and the known NEOs population. First, in the JPL database, there are 25188 cateloged NEOs with the absolute magnitude H on March 30 in 2021. Among them, considering the uncertainty of the absolute magnitude H of 2020 AV2 measured by Popescu et al (2020), NEOs with 15.625 < H < 17.175 occupied ~2% in the known NEOs population. Next, we estimated the transfer probability of NEOs population to become Vatira-class asteroids of ~0.5% from 752 NEOs calculation (Run A). As for Atira-class asteroids, the transfer probability from NEOs population is ~4.19%. Moreover, the statistic takes the spectral type of 2020 AV2 into account to the number of kilometer- sized IVOs and Atira-class asteroids. According to the result of Lin et al. (2018), the fractional abundances of the taxonomic complex from 92 NEO samples measured at Lulin Observatory in Taiwan are A-type ~3%, C-type ~6.5%, D-type ~8%, Q-type ~26%, S-type ~37%, V-type ~6.5%, and X-type ~13%. Finally, we find that the number of S-type of kilometer-sized would be ~0.9 + 0.80 and ~7.8 + 4.47 for [VOs and Atira-class asteroids to date, respectively. And the results are similar to the known remained in the same class and migrated outward instead of inward. From the result in Table 3, the transfer probability of the remaining Atira-class asteroid by the rotation obliquities are ~37, ~40, and ~42%, respectively. t indicates that the outward Yarkovsky force (i.e. a > 0) causes Atira-class asteroids to have more probability to become Aten-class asteroids, and the inward Yarkovsly force (i.e. d< 0) efficiently converts asteroids from Atira-class to Vatria-class. It suggests that the non- gravitational Yarkovsky force plays some role in asteroid transportation in long-term evolution of the inner solar system.
Table 3. The transfer probabilities and errors of asteroids from the Atira-class to the other classes of near-Earth objects at the end of Run B simulation. The number of kilometer-sized inner Venus objects ([VOs) and Atiras from known NEOs population ([VOs) and Atiras from known NEOs population Before the discovery of the first observed Vatira-class asteroid Ip et al. (2020), Granvik et al. (2018) and Greenstreet et al. (2012) have predicted inner Venus objects to exist according to their NEOs model. Here, we estimated the number of kilometer-sized [VOs based on the transfer probability from Run A simulation and the known NEOs population. First, in the JPL database, there are 25188 cateloged NEOs with the absolute magnitude H on March 30 in 2021. Among them, considering the uncertainty of the absolute magnitude H of 2020 AV2 measured by Popescu et al (2020), NEOs with 15.625 < H < 17.175 occupied ~2% in the known NEOs population. Next, we estimated the transfer probability of NEOs population to become Vatira-class asteroids of ~0.5% from 752 NEOs calculation (Run A). As for Atira-class asteroids, the transfer probability from NEOs population is ~4.19%. Moreover, the statistic takes the spectral type of 2020 AV2 into account to the number of kilometer- sized IVOs and Atira-class asteroids. According to the result of Lin et al. (2018), the fractional abundances of the taxonomic complex from 92 NEO samples measured at Lulin Observatory in Taiwan are A-type ~3%, C-type ~6.5%, D-type ~8%, Q-type ~26%, S-type ~37%, V-type ~6.5%, and X-type ~13%. Finally, we find that the number of S-type of kilometer-sized would be ~0.9 + 0.80 and ~7.8 + 4.47 for [VOs and Atira-class asteroids to date, respectively. And the results are similar to the known remained in the same class and migrated outward instead of inward. From the result in Table 3, the transfer probability of the remaining Atira-class asteroid by the rotation obliquities are ~37, ~40, and ~42%, respectively. t indicates that the outward Yarkovsky force (i.e. a > 0) causes Atira-class asteroids to have more probability to become Aten-class asteroids, and the inward Yarkovsly force (i.e. d< 0) efficiently converts asteroids from Atira-class to Vatria-class. It suggests that the non- gravitational Yarkovsky force plays some role in asteroid transportation in long-term evolution of the inner solar system.
Fig. 1. Definitions of the scattering plane and the sky plane in the polarimetric observations.
Fig. 1. Definitions of the scattering plane and the sky plane in the polarimetric observations.
Fig. 2. The phase-polarization curves from CASLEO (solid-line) and Lulin (dashed-line). The empty circles mark the higher phase-angle data of M-type polarimetric measurements of Gil-Hutton (2007) from which we can re-draw the phase-polarization curve of M-type asteroids. where Ao, A; and Az obtained from best-fitting to the observational data are constant coefficients for each asteroid (see Fig. 2).
Fig. 2. The phase-polarization curves from CASLEO (solid-line) and Lulin (dashed-line). The empty circles mark the higher phase-angle data of M-type polarimetric measurements of Gil-Hutton (2007) from which we can re-draw the phase-polarization curve of M-type asteroids. where Ao, A; and Az obtained from best-fitting to the observational data are constant coefficients for each asteroid (see Fig. 2).
Fig. 3. The rotational phase variations on Dec. 14, 2015 of the P, values at g’ r’ and i’ of (16) Psyche. polarization and position angle. The target asteroids are all bright objects with known taxonomic classification for the B-, C-, Ch-, M- and S-type asteroids and good agreements with previous result are found. This work thus demonstrated that TRIPOL is a reliable instrument for polarimetric observations of asteroids.
Fig. 3. The rotational phase variations on Dec. 14, 2015 of the P, values at g’ r’ and i’ of (16) Psyche. polarization and position angle. The target asteroids are all bright objects with known taxonomic classification for the B-, C-, Ch-, M- and S-type asteroids and good agreements with previous result are found. This work thus demonstrated that TRIPOL is a reliable instrument for polarimetric observations of asteroids.
Fig. 4. The rotational phase variations on Dec. 13, 2020 of the P, values at g’ r’ and i’ of (16) Psyche. Rotationally resolved photo-polarimetric measure- ments of the largest M-type asteroid, (16) Psyche, were made using the TRIPOL instrument. Our measurements show small and intriguing variability in the polarization degree as a function of the rotational phase. However, the observation uncertainty does not permit the confirmation of the sinusoidal variation, reported by Broglia & Manara (1992).
Fig. 4. The rotational phase variations on Dec. 13, 2020 of the P, values at g’ r’ and i’ of (16) Psyche. Rotationally resolved photo-polarimetric measure- ments of the largest M-type asteroid, (16) Psyche, were made using the TRIPOL instrument. Our measurements show small and intriguing variability in the polarization degree as a function of the rotational phase. However, the observation uncertainty does not permit the confirmation of the sinusoidal variation, reported by Broglia & Manara (1992).
had a metric (m) component starting at 18:17 to 18:48 UT. Fig. 1. (a) Wind/WAVES type II and type IT radio bursts with Fermi/LAT > 100 MeV light curve (white curve). (b) Proton intensity from GOES (black) and STEREO (STA, red; STB, blue) and the > 100 MeV SGRE flux (orange).
had a metric (m) component starting at 18:17 to 18:48 UT. Fig. 1. (a) Wind/WAVES type II and type IT radio bursts with Fermi/LAT > 100 MeV light curve (white curve). (b) Proton intensity from GOES (black) and STEREO (STA, red; STB, blue) and the > 100 MeV SGRE flux (orange).
Fig. 2. (a) Scatter plot of SGRE duration with type II burst ending frequency (a) and duration (b) for 19 events with duration > 3 h reported in Gopalswamy et al. (2019b). The green data point corresponds to the 2014 January 7 SGRE event. The blue data marks the 2014 September 1 SGRE event from a backside eruption (not included in the correlation). The shaded areas correspond to 95% and 99% confidence intervals. The 2014 January 7 event lies well within the 95% confidence interval. The red lines are linear fits to the data points (see text).
Fig. 2. (a) Scatter plot of SGRE duration with type II burst ending frequency (a) and duration (b) for 19 events with duration > 3 h reported in Gopalswamy et al. (2019b). The green data point corresponds to the 2014 January 7 SGRE event. The blue data marks the 2014 September 1 SGRE event from a backside eruption (not included in the correlation). The shaded areas correspond to 95% and 99% confidence intervals. The 2014 January 7 event lies well within the 95% confidence interval. The red lines are linear fits to the data points (see text).
Fig. 3. Height (Heme), speed (Veme), acceleration (Acme) as a function of time using GCS fit to SOHO, STEREO, and the Solar Dynamics Observatory’s Atmospheric Imaging Assembly (AIA) images of the CME. The GOES flare light curve is shown for comparison.
Fig. 3. Height (Heme), speed (Veme), acceleration (Acme) as a function of time using GCS fit to SOHO, STEREO, and the Solar Dynamics Observatory’s Atmospheric Imaging Assembly (AIA) images of the CME. The GOES flare light curve is shown for comparison.
Gopalswamy et al., 2019b). The initial acceleration peaked at ~4 km s (18:15 UT), which is consistent with the average acceleration (~1.86 km s”) obtained from the flare duration and average CME speed (Gopalswamy et al., 2016). Figure 3 shows that the CME kinematics are similar to those of typical CMEs underlying GLE events. Note that the CME starts driving a shock indicated by the metric type II burst and attains its peak speed within the impulsive phase of the flare. One thing peculiar about the CME is that its nose is at position angle 231°, even though it originated close to the disk center (Fig. 4). The highly deflected motion has been reported before to be due to a combination of a coronal hole and the active region field not participating in the eruption (Gopalswamy et al., 2014; M@stl et al., 2015). Fig. 4. (left) LASCO image at 18:36 UT showing the nose at a height of 5.67 Rs and at position angle 231°. (right) GOES 13 proton intensity showing the SEP event starting at 19:55 UT above the background of a preceding event.
Gopalswamy et al., 2019b). The initial acceleration peaked at ~4 km s (18:15 UT), which is consistent with the average acceleration (~1.86 km s”) obtained from the flare duration and average CME speed (Gopalswamy et al., 2016). Figure 3 shows that the CME kinematics are similar to those of typical CMEs underlying GLE events. Note that the CME starts driving a shock indicated by the metric type II burst and attains its peak speed within the impulsive phase of the flare. One thing peculiar about the CME is that its nose is at position angle 231°, even though it originated close to the disk center (Fig. 4). The highly deflected motion has been reported before to be due to a combination of a coronal hole and the active region field not participating in the eruption (Gopalswamy et al., 2014; M@stl et al., 2015). Fig. 4. (left) LASCO image at 18:36 UT showing the nose at a height of 5.67 Rs and at position angle 231°. (right) GOES 13 proton intensity showing the SEP event starting at 19:55 UT above the background of a preceding event.
Kis the number of objects (examples), N is the dimension of the training data. Fig. 1. Undercomplete Autoencoder architecture. The results of processing the neutron monitor (NM) lata are shown in Fig. 2.
Kis the number of objects (examples), N is the dimension of the training data. Fig. 1. Undercomplete Autoencoder architecture. The results of processing the neutron monitor (NM) lata are shown in Fig. 2.
Figure 3 shows the results of detecting Forbush effects in NM data (www.nmdb.eu) based on the proposed method and Autoencoder. Data were processed sequentially. First, the data were processed based on the Algorithm for constructing an approximating scheme. Then the Anomaly Detection Algorithm based on the use of an Autoencoder, CWT and threshold functions was applied. Alternatively, the data were processed by the Autoencoder network, then the Anomaly Detection Algorithm was applied. Forbush effects were detected on September 4, 6, 8, 10, 11, 12, 2014 (http://spaceweather.izmiran.ru/). Fig. 3. NM data processing results.
Figure 3 shows the results of detecting Forbush effects in NM data (www.nmdb.eu) based on the proposed method and Autoencoder. Data were processed sequentially. First, the data were processed based on the Algorithm for constructing an approximating scheme. Then the Anomaly Detection Algorithm based on the use of an Autoencoder, CWT and threshold functions was applied. Alternatively, the data were processed by the Autoencoder network, then the Anomaly Detection Algorithm was applied. Forbush effects were detected on September 4, 6, 8, 10, 11, 12, 2014 (http://spaceweather.izmiran.ru/). Fig. 3. NM data processing results.
Figure 4 shows the results of processing the network of stations of high-latitud le ground-based neutron monitors Inuvik (INVK), South Pole (SOPO), Tixie Bay (TXBY), Thule (THUL) are presented (www.nmdb.eu). Anomalies were detected at each registration station. The results of detecting Forbush e ffects in NM data based on the proposed method based on the Algorithm for constructing an approxima threshold functions was app detected on March 19, 2021. ing scheme, CWT and lied. Forbush effect were CONCLUSIONS The results of the research confirmed the effectiveness of the developed method. The application of the method to the neutron-monitor data showed that it can be used to detect Forbush effects in cosmic-ray variations. Numerical implementation of the method will allow it to be used in real-time analysis in space-weather forecasting problems, which makes its application important for research. to the neutron-monitor data showed that it can be used to Numerical implementation of the method will allow it to
Figure 4 shows the results of processing the network of stations of high-latitud le ground-based neutron monitors Inuvik (INVK), South Pole (SOPO), Tixie Bay (TXBY), Thule (THUL) are presented (www.nmdb.eu). Anomalies were detected at each registration station. The results of detecting Forbush e ffects in NM data based on the proposed method based on the Algorithm for constructing an approxima threshold functions was app detected on March 19, 2021. ing scheme, CWT and lied. Forbush effect were CONCLUSIONS The results of the research confirmed the effectiveness of the developed method. The application of the method to the neutron-monitor data showed that it can be used to detect Forbush effects in cosmic-ray variations. Numerical implementation of the method will allow it to be used in real-time analysis in space-weather forecasting problems, which makes its application important for research. to the neutron-monitor data showed that it can be used to Numerical implementation of the method will allow it to
Fig. 1. An ionogram containing three key features: the background, the contamintation, and the ionospheric signal. The colorbar on the right corresponds to the amplitude of the signal in decibels (dB).
Fig. 1. An ionogram containing three key features: the background, the contamintation, and the ionospheric signal. The colorbar on the right corresponds to the amplitude of the signal in decibels (dB).
As previously mentioned in the introduction, the critical frequency is an important parameter to be considered in ionogram recovery. In Fig. 2, the distribution of the labelled F2 layer of the ionograms are shown. Fig. 2. Distributions of the maximum (blue) and minimum (orange) frequencies of the labelled F2 layer of the ionograms.
As previously mentioned in the introduction, the critical frequency is an important parameter to be considered in ionogram recovery. In Fig. 2, the distribution of the labelled F2 layer of the ionograms are shown. Fig. 2. Distributions of the maximum (blue) and minimum (orange) frequencies of the labelled F2 layer of the ionograms.
Fig. 3. (a) The histogram of the pixel intensity J of the entire ionogram (inserted on the right). (b) The histogram of the pixel intensity of the useful signal of the ionogram (inserted on the right). Based from the statistical distribution (histograms) of the signal amplitudes (pixel colors J), we can define a signal-to-noise ratio (SNR). This new definition of SNR is used to evaluate the level of contamination of the ionograms. In Fig. 3(a), the histogram of an entire ionogram is shown.
Fig. 3. (a) The histogram of the pixel intensity J of the entire ionogram (inserted on the right). (b) The histogram of the pixel intensity of the useful signal of the ionogram (inserted on the right). Based from the statistical distribution (histograms) of the signal amplitudes (pixel colors J), we can define a signal-to-noise ratio (SNR). This new definition of SNR is used to evaluate the level of contamination of the ionograms. In Fig. 3(a), the histogram of an entire ionogram is shown.
Fig. 5. The scatter plots of IoU vs C/A ratio for the F2 and Es layers.
Fig. 5. The scatter plots of IoU vs C/A ratio for the F2 and Es layers.
Figure 1 shows E layer, Fl layer and F2 layer of ionosonde. Fig. 1. The ionogram image.
Figure 1 shows E layer, Fl layer and F2 layer of ionosonde. Fig. 1. The ionogram image.
Graphic 1. Method of whole work process. Architecture of Dense Fully Convolutional Network LEARNING RATE
Graphic 1. Method of whole work process. Architecture of Dense Fully Convolutional Network LEARNING RATE
Fig. 2. The architecture of self-designed DFCN. The abbreviation ‘Conv’, ‘DRB’, ‘PL’, ’Conv_T’, ‘Pr_L’ and attention for Convolutional layers, Dense Residual Blocks, Pooling Layers, Transpose Convolutional Layers and Process Layers, respectively.
Fig. 2. The architecture of self-designed DFCN. The abbreviation ‘Conv’, ‘DRB’, ‘PL’, ’Conv_T’, ‘Pr_L’ and attention for Convolutional layers, Dense Residual Blocks, Pooling Layers, Transpose Convolutional Layers and Process Layers, respectively.
The learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between 0.0 and 1.0. Actually, learning rate controlled how quickly the model is adapted to the problem. So, out of below result, our model had a quickly and good learning rate. It means our model was very good adapted to the problem. Figure 5 shows Model Prediction Results. 1*t column pictures are Original ionograms, 2" column pictures are Ground Truth (Target), 3" column pictures are illustrate well and robust predicted results of DFCN. Figure 5 shows Model Prediction Results. 1*t column
The learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between 0.0 and 1.0. Actually, learning rate controlled how quickly the model is adapted to the problem. So, out of below result, our model had a quickly and good learning rate. It means our model was very good adapted to the problem. Figure 5 shows Model Prediction Results. 1*t column pictures are Original ionograms, 2" column pictures are Ground Truth (Target), 3" column pictures are illustrate well and robust predicted results of DFCN. Figure 5 shows Model Prediction Results. 1*t column
Fig. 5. A pictorial example to show the robustness of the proposed method on the challenging ionogram images.
Fig. 5. A pictorial example to show the robustness of the proposed method on the challenging ionogram images.
Table 2 shows the results of IoU and Accuracy for 9 class. Out of from this results table of IoU, model cannot distinguished between eo and ex. Also, we can see the highest IoU and accuracy results of es layer, foo layer and fbx layer. Out of from this results table of IoU, model cannot Fig. 6. Failed prediction cases of DFCN model.
Table 2 shows the results of IoU and Accuracy for 9 class. Out of from this results table of IoU, model cannot distinguished between eo and ex. Also, we can see the highest IoU and accuracy results of es layer, foo layer and fbx layer. Out of from this results table of IoU, model cannot Fig. 6. Failed prediction cases of DFCN model.
It is planned t hat the first IE probe to be launched wound 2027 will make a Jupiter flyby while the second E probe to be launched around 2030 will make a swing- oy of Neptune a equirement that fter a Jupiter flyby. Because of the scientific returns of planetary *xploration must be optimized for this long-term space sroject, the mode payload now has a set of remote- sensing experiments including an optical camera, an nfrared imager, an ultraviolet imager and a THz imager, is Shown in Fig. 1 . It should be added that the energetic veutral atom (ENA) analyzer is a powerful instrument to study planetary magnetospheres at a flyby trajectory. For xample, the Io interaction and Europa interaction with he Jovian corotating plasma can be imaged by the ENA inalyzer providing a global picture of the Jovian nagnetospheric dynamics (Krimigis et al., 2002; Futaana t al., 2015). Fig. 1. The scientific payload of the Interstellar Express spacecraft. Courtesy of H. Li (NSSC).
It is planned t hat the first IE probe to be launched wound 2027 will make a Jupiter flyby while the second E probe to be launched around 2030 will make a swing- oy of Neptune a equirement that fter a Jupiter flyby. Because of the scientific returns of planetary *xploration must be optimized for this long-term space sroject, the mode payload now has a set of remote- sensing experiments including an optical camera, an nfrared imager, an ultraviolet imager and a THz imager, is Shown in Fig. 1 . It should be added that the energetic veutral atom (ENA) analyzer is a powerful instrument to study planetary magnetospheres at a flyby trajectory. For xample, the Io interaction and Europa interaction with he Jovian corotating plasma can be imaged by the ENA inalyzer providing a global picture of the Jovian nagnetospheric dynamics (Krimigis et al., 2002; Futaana t al., 2015). Fig. 1. The scientific payload of the Interstellar Express spacecraft. Courtesy of H. Li (NSSC).
As summarized in Fig. 2, even without dedicated missions, a very significant program of outer planets research can be generated by the Interstellar Express mission of CNSA if careful consideration is given to the target selection. The observational results obtained from close encounters with Neptune, Triton, Centaur asteroids and TransNeptunian objects would energize a new generation of planetary scientists. From a long-term point of view, both the vertical probe of the Interstellar Express mission and the 1000 AU mission of NASA could be the prelude to a new era of space exploration not yet imaginable. Fig. 2. The potential scientific targets in the outer solar system are all reachable by the Interstellar Express mission n the 2030s in addition to a Jupiter Orbiter mission. Fig. 2. The potential scientific targets in the outer solar of view, both the vertical probe of the Interstellar Express
As summarized in Fig. 2, even without dedicated missions, a very significant program of outer planets research can be generated by the Interstellar Express mission of CNSA if careful consideration is given to the target selection. The observational results obtained from close encounters with Neptune, Triton, Centaur asteroids and TransNeptunian objects would energize a new generation of planetary scientists. From a long-term point of view, both the vertical probe of the Interstellar Express mission and the 1000 AU mission of NASA could be the prelude to a new era of space exploration not yet imaginable. Fig. 2. The potential scientific targets in the outer solar system are all reachable by the Interstellar Express mission n the 2030s in addition to a Jupiter Orbiter mission. Fig. 2. The potential scientific targets in the outer solar of view, both the vertical probe of the Interstellar Express
Figure | shows the magnetic field lines and current density Jz contour at T = 120 in the case of 7 =a = 0.0005. The Petschek-like structure extends from the X-point, i.e., origin, to the plasmoid. In fact, the Alfvenic reconnection jet generated from the X-point collides with the plasmoid and the positive current density region appears as the blue contour, which means the rapid deceleration of the jet at the plasmoid. Hence, this reconnection process is relatively fast. EQUILIBRIUM PISule 42 suOws Ue Udsl Ol If ~ U.UUYS AH GQ ~~ U.U. Hence, this is the case of usual uniform resistivity (Refs. 2, 3, 5). In comparison with Fig. 1, Fig. 2 looks
Figure | shows the magnetic field lines and current density Jz contour at T = 120 in the case of 7 =a = 0.0005. The Petschek-like structure extends from the X-point, i.e., origin, to the plasmoid. In fact, the Alfvenic reconnection jet generated from the X-point collides with the plasmoid and the positive current density region appears as the blue contour, which means the rapid deceleration of the jet at the plasmoid. Hence, this reconnection process is relatively fast. EQUILIBRIUM PISule 42 suOws Ue Udsl Ol If ~ U.UUYS AH GQ ~~ U.U. Hence, this is the case of usual uniform resistivity (Refs. 2, 3, 5). In comparison with Fig. 1, Fig. 2 looks
Sweet-Parker like. It means that the reconnection process is relatively slow. Fig. 3. Total kinetic energy of simulation box.
Sweet-Parker like. It means that the reconnection process is relatively slow. Fig. 3. Total kinetic energy of simulation box.
Fig. 4. Equilibrium of magnetic annihilation in 1D sheet.
Fig. 4. Equilibrium of magnetic annihilation in 1D sheet.
Fig. 1. The statistical distributions of the IMF and solar wind conditions of midday gap events (blue bars), non- gap events (yellow bars), and the background (gray bars). The blue and yellow curves show the relative occurrences of gap and non-gap events, respectively. The occurrence rates are estimated by dividing the event number by the background for each bin. Fig. 1. The statistical distributions of the IMF and solar
Fig. 1. The statistical distributions of the IMF and solar wind conditions of midday gap events (blue bars), non- gap events (yellow bars), and the background (gray bars). The blue and yellow curves show the relative occurrences of gap and non-gap events, respectively. The occurrence rates are estimated by dividing the event number by the background for each bin. Fig. 1. The statistical distributions of the IMF and solar
mre REED VEALED UE EEE RIVER BOY CORENS REED OUEEEE VV ARES OPO Figures 1(d) and 1(e) indicate that the IMF By and the flow speed both have outstanding effects in controlling the midday auroral emission. In order to investigate their respective effects, we carried out further analyses by dividing the non-gap events into four subsets based on the magnitudes of the solar wind speed. Figures 2(a) and 2(b) show the IMF B, distributions and the relative occurrence rates of the non-gap events observed with the solar wind speed greater than 300 km/s, 400 km/s, 500 km/s, and 600 km/s, respectively. The occurrence rates here are calculated in the same way as in Fig. 1. Figure 2(a) shows that when the V increases from 300 km/s up to 600 km/s, a minimal non-gap occurrence is always observed near the IMF By, = 0. Also, a minimal occurrence rate is also observed for all speed ranges near the By = 0. These strongly imply that a weak IMF B, is not favorable for the non-gap occurrence, even with high solar wind speed. Fig. 2. Overlapping of the IMF B, distributions of the non- gap observed with solar wind speed greater than 300 km/s (gray), 400 km/s (blue), 500 km/s (yellow), and 600 km/s (red) are shown in (a). (b) gives the relative occurrence rates of the four subsets, which are calculated in the same way as in Fig. 1. minimal non-gap occurrence is always observed near the
mre REED VEALED UE EEE RIVER BOY CORENS REED OUEEEE VV ARES OPO Figures 1(d) and 1(e) indicate that the IMF By and the flow speed both have outstanding effects in controlling the midday auroral emission. In order to investigate their respective effects, we carried out further analyses by dividing the non-gap events into four subsets based on the magnitudes of the solar wind speed. Figures 2(a) and 2(b) show the IMF B, distributions and the relative occurrence rates of the non-gap events observed with the solar wind speed greater than 300 km/s, 400 km/s, 500 km/s, and 600 km/s, respectively. The occurrence rates here are calculated in the same way as in Fig. 1. Figure 2(a) shows that when the V increases from 300 km/s up to 600 km/s, a minimal non-gap occurrence is always observed near the IMF By, = 0. Also, a minimal occurrence rate is also observed for all speed ranges near the By = 0. These strongly imply that a weak IMF B, is not favorable for the non-gap occurrence, even with high solar wind speed. Fig. 2. Overlapping of the IMF B, distributions of the non- gap observed with solar wind speed greater than 300 km/s (gray), 400 km/s (blue), 500 km/s (yellow), and 600 km/s (red) are shown in (a). (b) gives the relative occurrence rates of the four subsets, which are calculated in the same way as in Fig. 1. minimal non-gap occurrence is always observed near the
Fig. 1. Location of trajectory of the fireball and three VLF/LF paths.
Fig. 1. Location of trajectory of the fireball and three VLF/LF paths.
waves obliquely propagated from the end point (25 km altitude) up to the D-region height (90 km altitude) at the southside of the RKB receiver. Fig. 3. The estimated location of the variations in the VLF/LF amplitude along the paths.
waves obliquely propagated from the end point (25 km altitude) up to the D-region height (90 km altitude) at the southside of the RKB receiver. Fig. 3. The estimated location of the variations in the VLF/LF amplitude along the paths.
level). The details of the trends of the wind derived from the GOCE satellite can be found in Liu et al. (2016). We explore the advantage of this satellite to observe thermospheric neutral wind at the bottomside equatorial F region in the dusk sector or sunset period (~18 local solar time/LST) for the equatorial region of SEA (90-130°E; 5—15°N). Fig. 1. Scatter plots for relations of (a) the eastward wind vs. the PRE, (b) the EEJ vs. the PRE, and (c) the eastward wind vs. the EEJ. region in the dusk sector or sunset period (~18 local solar
level). The details of the trends of the wind derived from the GOCE satellite can be found in Liu et al. (2016). We explore the advantage of this satellite to observe thermospheric neutral wind at the bottomside equatorial F region in the dusk sector or sunset period (~18 local solar time/LST) for the equatorial region of SEA (90-130°E; 5—15°N). Fig. 1. Scatter plots for relations of (a) the eastward wind vs. the PRE, (b) the EEJ vs. the PRE, and (c) the eastward wind vs. the EEJ. region in the dusk sector or sunset period (~18 local solar
Fig. 1. The result of the spatial correlation of Shmax and De. It shows that we can characterize the possibility of earthquake potential hazards existence around Batu- Siberut Island. Fig. 2. The result of the estimated PGA around Batu- Siberut Island. The result of the PGA estimation at Batu- Siberut Island at the base rock ~0.5 to 0.7 g. The estimated PGA at the surface ~0.7 to 1.47 g, reaching the possible maximum MMI scale.
Fig. 1. The result of the spatial correlation of Shmax and De. It shows that we can characterize the possibility of earthquake potential hazards existence around Batu- Siberut Island. Fig. 2. The result of the estimated PGA around Batu- Siberut Island. The result of the PGA estimation at Batu- Siberut Island at the base rock ~0.5 to 0.7 g. The estimated PGA at the surface ~0.7 to 1.47 g, reaching the possible maximum MMI scale.
as areas with high stress levels. Therefore, finding a spatial correlation between the Shmax and high De could help understand the possible seismic hazard. The result shows that the level PGA at the base rock around Batu- Siberut Island is about 0.5 to 0.7 g. The PGA estimated around Batu-Siberut Island at the surface is around 0.7 to 1.47 g, and it probably could reach the possible maximum MMI scale.
as areas with high stress levels. Therefore, finding a spatial correlation between the Shmax and high De could help understand the possible seismic hazard. The result shows that the level PGA at the base rock around Batu- Siberut Island is about 0.5 to 0.7 g. The PGA estimated around Batu-Siberut Island at the surface is around 0.7 to 1.47 g, and it probably could reach the possible maximum MMI scale.
Fig. 1. 238 events (shown by red circles with blue ellipsoid indicate of horizontal errors) and location of historical significant events (yellow stars) in Aceh Segment (fault shown by yellow lines).
Fig. 1. 238 events (shown by red circles with blue ellipsoid indicate of horizontal errors) and location of historical significant events (yellow stars) in Aceh Segment (fault shown by yellow lines).
To overcome this challenge we determined the source parameters which are of basic interest in this study include seismic moment (Mg), moment magnitude (My,), corner frequency (f,), and stress drop (a) using spectral analysis method of S-wave spectrum. We concentrate our research on the distribution and variability of source parameter estimations in the Aceh Segment of Northern Sumatra, using 238 events that were relocated using NonLinLoc (Lomax and Curtis, 2001) to have a good constraint and quality of location (Fig. 1). The results of the study are expected to make a positive contribution to the earthquake hazard assessment in the study area. Fig. 2. (A) Waveform example of 20091003 event with few stations recorded (Yellow areas indicate a window of S-wave. Dash lines indicate P- and S-wave. (B) Spectra of few stations with determined corner frequency (f,) (showed by thick grey line) and other source parameters. (showed by thick grey line) and other source parameters.
To overcome this challenge we determined the source parameters which are of basic interest in this study include seismic moment (Mg), moment magnitude (My,), corner frequency (f,), and stress drop (a) using spectral analysis method of S-wave spectrum. We concentrate our research on the distribution and variability of source parameter estimations in the Aceh Segment of Northern Sumatra, using 238 events that were relocated using NonLinLoc (Lomax and Curtis, 2001) to have a good constraint and quality of location (Fig. 1). The results of the study are expected to make a positive contribution to the earthquake hazard assessment in the study area. Fig. 2. (A) Waveform example of 20091003 event with few stations recorded (Yellow areas indicate a window of S-wave. Dash lines indicate P- and S-wave. (B) Spectra of few stations with determined corner frequency (f,) (showed by thick grey line) and other source parameters. (showed by thick grey line) and other source parameters.
Table 1. Obtained spectral source parameters for S waves of the analyzed event. Fig. 3. Corner frequencies (f,) and moment magnitudes (My,) distribution with stations which recorded the event. Star symbol indicates event, and triangles indicate seismic stations. Indonesia for supporting this research through PMDSU scheme. We also thanks to MCGA for the waveform and catalog data and Mr. Claudio Satriano for his software SourceSpec used in this study. Indonesia for supporting this research through PMDSU The result obtained using spectra of S wave are given
Table 1. Obtained spectral source parameters for S waves of the analyzed event. Fig. 3. Corner frequencies (f,) and moment magnitudes (My,) distribution with stations which recorded the event. Star symbol indicates event, and triangles indicate seismic stations. Indonesia for supporting this research through PMDSU scheme. We also thanks to MCGA for the waveform and catalog data and Mr. Claudio Satriano for his software SourceSpec used in this study. Indonesia for supporting this research through PMDSU The result obtained using spectra of S wave are given
Fig. 1. Topographical map showing the survey area. Green dots show the AMT sites. Three red triangles represent the craters of the 2018 eruption. Kagami-ike- kita and Kagami-ike are two pyroclastic cones of Mt. Motoshirane.
Fig. 1. Topographical map showing the survey area. Green dots show the AMT sites. Three red triangles represent the craters of the 2018 eruption. Kagami-ike- kita and Kagami-ike are two pyroclastic cones of Mt. Motoshirane.
Fig. 2. East-west cross-section through the Kagami-ike- kita crater of the optimal 3-D resistivity model. Red triangles represent the craters of the 2018 eruption. White dashed line indicates a rough boundary between R1 and Cl, which could correspond to the depth of basement rocks. Missing parts of Cl are shown by black circles where explosion might occur. Relatively high resistive region surrounded by C1 could be a fluid reservoir. Figure 2 shows the east-west cross-section of the model crossing the pre-existing Kagami-ike-kita crater. We can successfully obtain the detailed structure of the shallow part that has not been resolved well due to the lack of high-frequency data in the previous study. The optimal model exhibited the similar tendency to the apparent resistivity distribution. The resistivity is high near the surface and decreases as getting deeper. Low resistivities become more prominent at altitudes deeper near the surface and decreases as getting deeper. Low
Fig. 2. East-west cross-section through the Kagami-ike- kita crater of the optimal 3-D resistivity model. Red triangles represent the craters of the 2018 eruption. White dashed line indicates a rough boundary between R1 and Cl, which could correspond to the depth of basement rocks. Missing parts of Cl are shown by black circles where explosion might occur. Relatively high resistive region surrounded by C1 could be a fluid reservoir. Figure 2 shows the east-west cross-section of the model crossing the pre-existing Kagami-ike-kita crater. We can successfully obtain the detailed structure of the shallow part that has not been resolved well due to the lack of high-frequency data in the previous study. The optimal model exhibited the similar tendency to the apparent resistivity distribution. The resistivity is high near the surface and decreases as getting deeper. Low resistivities become more prominent at altitudes deeper near the surface and decreases as getting deeper. Low
-orrespond retrieved tephra classes (c—d) at 2-mins after the
-orrespond retrieved tephra classes (c—d) at 2-mins after the
Fig. 2. Left panel (a) is the sampling points digitized from Setidjadji et al. (2019). Right panel (b) is the extracted GSD (in ¢=log2D[mm]) from the sampling points labeled in red square. Fig. 3. Top two rows panel are the temporal evolution of spatial tephra dispersal by mean EPS compared to radar observed, as indicated in bottom left corner of each graph, presented is the C, of M06. Bottom panel presents the POD for M05 (left) and M06 (right).
Fig. 2. Left panel (a) is the sampling points digitized from Setidjadji et al. (2019). Right panel (b) is the extracted GSD (in ¢=log2D[mm]) from the sampling points labeled in red square. Fig. 3. Top two rows panel are the temporal evolution of spatial tephra dispersal by mean EPS compared to radar observed, as indicated in bottom left corner of each graph, presented is the C, of M06. Bottom panel presents the POD for M05 (left) and M06 (right).
radar-GSD fell within the range of in-situ data points and confirmed the conclusion of Setidjadji et al. (2019), who found that MOS had coarser GSD than M06 (Fig. 2). This result is reasonable as M06 has greater magnitude than M05 (Budi-Santoso et al., 2018; Gonnermann, 2015, Fig. 2). Although generally, the forecast skill got poorer as time increased, the EPS reduced the uncertainty of the forecast (Fig. 3). Significantly, the framework for tephra tracking in this study manages to capture the spatial and temporal evolution of tephra, which matches the in-situ pyroclastic deposit area. It should be noted that our approach is based solely on X-MP radar data, and the identified tephra regimes are limited at coarse ash to lapilli regime. identified tephra regimes are limited at coarse ash to
radar-GSD fell within the range of in-situ data points and confirmed the conclusion of Setidjadji et al. (2019), who found that MOS had coarser GSD than M06 (Fig. 2). This result is reasonable as M06 has greater magnitude than M05 (Budi-Santoso et al., 2018; Gonnermann, 2015, Fig. 2). Although generally, the forecast skill got poorer as time increased, the EPS reduced the uncertainty of the forecast (Fig. 3). Significantly, the framework for tephra tracking in this study manages to capture the spatial and temporal evolution of tephra, which matches the in-situ pyroclastic deposit area. It should be noted that our approach is based solely on X-MP radar data, and the identified tephra regimes are limited at coarse ash to lapilli regime. identified tephra regimes are limited at coarse ash to
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