Papers by Prashant Srivastava
Evaluation of TRMM rainfall for soil moisture prediction in a subtropical climate
Environmental Earth Sciences, 2013
Applied Geomatics, 2011
Fresh and clean water is a vital commodity of need for the well-being of human societies, and dam... more Fresh and clean water is a vital commodity of need for the well-being of human societies, and damage of these aquifers is one of the most serious environmental problems of the past century. The regular monitoring and management of groundwater resources is very important for the sustainable development. The present study monitors the groundwater quality relation to the land use/land cover (LULC) using remote sensing and GIS techniques. Physicochemical analysis data of groundwater samples collected at different locations forms the attribute database for the study. LULC categories, such as agricultural and built-up area, associated with human activities, incorporated maximum change in groundwater quality. In this study, weighting analysis of Water Quality Index (WQI) and Land Cover Index (LCI) have been performed to map the Suitability Index (SI) of water for drinking purpose in the area. Spatial interpolation technique was used for generation of pollution potentiality map of the area. Cluster analysis was performed for delineating and grouping the similar pollution causing area. The overall view of the results indicates that most of the study area exhibited very low SI for the drinking purpose due to very high groundwater pollution.
Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics
Natural Hazards, 2014
WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables
Water Resources Management, 2015
Rainfall and Reference Evapotranspiration (ETo) are the most fundamental and significant variable... more Rainfall and Reference Evapotranspiration (ETo) are the most fundamental and significant variables in hydrological modelling. However, these variables are generally not available over ungauged catchments. ETo estimation usually needs measurements of weather variables such as wind speed, air temperature, solar radiation and dew point. After the development of reanalysis global datasets such as the National Centre for Environmental Prediction (NCEP) and high performance modelling framework Weather Research and Forecasting (WRF) model, it is now possible to estimate the rainfall and ETo for any coordinates. In this study, the WRF modelling system was employed to downscale the global NCEP reanalysis datasets over the Brue catchment, England, U.K. After downscaling, two statistical bias correction schemes were used, the first was based on sophisticated computing algorithms i.e., Relevance Vector Machine (RVM), while the second was based on the more simpleGeneralized Linear Model (GLM). The statistical performance indices for bias correction such as %Bias, index of agreement (d), Root Mean Square Error (RMSE), and Correlation (r) indicated that the RVMmodel, on the whole, displayed a more accomplished bias correction of the variability of rainfall and ETo in comparison to the GLM. The study provides important information on the performance of WRF derived hydro-meteorological variables using NCEP global reanalysis datasets and statistical bias correction schemes which can be used in numerous hydro-meteorological applications.
Meteorological Applications, 2014
A novel radio frequency interference (RFI) detection method is introduced for satellite-borne pas... more A novel radio frequency interference (RFI) detection method is introduced for satellite-borne passive microwave radiometer observations. This method is based on factor analysis, in which variability among observed and correlated variables is described in terms of factors. In the present study, this method is applied to the Tropical Rainfall Measuring Mission (TRMM)/TRMM Microwave Imager (TMI) and Aqua/Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) satellite measurements over the land surface to detect the RFI signals, respectively, in 10 and 6 GHz channels. The RFI detection results are compared with other traditional methods, such as spectral difference method and principal component analysis (PCA) method. It has been found that the newly proposed method is able to detect RFI signals in the C-and X-band radiometer channels as effectively as the conventional PCA method.
Ice cloud detection from AMSU-A, MHS, and HIRS satellite instruments inferred by cloud profiling radar
Remote Sensing Letters, 2014
ABSTRACT An algorithm for ice cloud detection aided by support vector machine (AID-SVM) is presen... more ABSTRACT An algorithm for ice cloud detection aided by support vector machine (AID-SVM) is presented. The AID-SVM algorithm is applied and tested for the Advanced Microwave Sounding Unit-A, microwave humidity sounder (MHS), and high resolution infrared radiation sounder (HIRS) instruments onboard NOAA-19 satellite. The algorithm is based on satellite brightness temperature measurements and developed as well as validated by using collocated ice/no-ice cloud information acquired from the CloudSat cloud-profiling radar. The algorithm is tested over both ocean and land surfaces. Overall, the results exhibit very promising potential to acquire ice/no-ice cloud information using the passive satellite sensors. It is found that infrared satellite sensor such as HIRS is more efficient in detecting ice clouds than the counterpart microwave satellite sensors. Furthermore, the combined measurements using microwave/infrared synergy perform no better than the infrared-only measurements.
Stochastic Environmental Research and Risk Assessment, 2015
Uncertainty analysis of radar rainfall enables stakeholders and users have a clear knowledge of t... more Uncertainty analysis of radar rainfall enables stakeholders and users have a clear knowledge of the possible uncertainty associated with the rainfall products. Long-term empirical modeling of the relationship between radar and gauge measurements is an efficient and practical method to describe the radar rainfall uncertainty. However, complicated variation of synoptic conditions makes the radar-rainfall uncertainty model based on historical data hard to extend in the future state. A promising solution is to integrate synoptic regimes with the empirical model and explore the impact of individual synoptic regimes on radar rainfall uncertainty. This study is an attempt to introduce season, one of the most important synoptic factor, into the radar rainfall uncertainty model and proposes a seasonal ensemble generator for radar rainfall using copula and autoregressive model. We firstly analyze the histograms of rainfall-weighted temperature, the radar-gauge relationships, and Box and Whisker plots in different seasons and conclude that the radar rainfall uncertainty has strong seasonal dependence. Then a seasonal ensemble generator is designed and implemented in a UK catchment under a temperate maritime climate, which can fully model marginal distribution, spatial dependence, temporal dependence and seasonal dependence of radar rainfall uncertainty. To test its performance, 12 typical rainfall events (4 for each season) are chosen to generate ensemble rainfall values. In each time step, 500 ensemble members are produced and the values of 5th to 95th percentiles are used to derive the uncertainty bands. Except several outliers, the uncertainty bands encompass the observed gauge rainfall quite well. The parameters of the ensemble generator vary considerably for each season, indicating the seasonal ensemble generator reflects the impact of seasons on radar rainfall uncertainty. This study is an attempt to simultaneously consider four key features of radar rainfall uncertainty and future study will investigate their impacts on the outputs of hydrological models with radar rainfall as input or initial conditions.
SWAT Model Calibration and Uncertainty Analysis for Streamflow Prediction in the Kunwari River Basin, India, Using Sequential Uncertainty Fitting
Environmental Processes, 2015
ABSTRACT The Kunwari River Basin (KRB) needs effective management of water resources for sustaina... more ABSTRACT The Kunwari River Basin (KRB) needs effective management of water resources for sustainable agriculture and flood hazard mitigation. The Soil and Water Assessment Tool (SWAT), a semi distributed physically based model, was chosen and set up in the KRB for hydrologic modeling. SWAT-CUP (SWAT-Calibration and Uncertainty Programs) was used for model calibration, sensitivity and uncertainty analysis, following the Sequential Uncertainty Fitting (SUFI-2) technique. The model calibration was performed for the period (1987-1999), with initial 3 years of warm up (1987-89); then, the model was validated for the subsequent 6 years of data (2000-2005). To assess the competence of model calibration and uncertainty, two indices, the p-factor (observations bracketed by the prediction uncertainty) and the r-factor (achievement of small uncertainty band), were taken into account. The results of SWAT simulations indicated that during the calibration the p-factor and the r-factor were reported as 0.82 and 0.76, respectively, while during the validation the p-factor and the r-factor were obtained as 0.71 and 0.72, respectively. After a rigorous calibration and validation, the goodness of fit was further assessed through the use of the coefficient of determination (R2) and the Nash-Sutcliffe efficiency (NS) between the observed and the final simulated values. The results indicated that R2 and NS were 0.77 and 0.74, respectively, during the calibration. The validation also indicated a satisfactory performance with R2 of 0.71 and NS of 0.69. The results would be useful to the hydrological community, water resources managers involved in agricultural water management and soil conservation, as well as to those involved in mitigating natural hazards such as droughts and floods.
Environmental Processes, 2015
Remote sensing and GIS are important tools for studying land use/land cover (LULC) change and int... more Remote sensing and GIS are important tools for studying land use/land cover (LULC) change and integrating the associated driving factors for deriving useful outputs. This study is based on utilization of Earth observation datasets over the highly urbanized Allahabad district in India. Allahabad district has experienced intense change in LULC in the last few decades. To monitor the changes, advanced techniques in remote sensing and GIS, such as Cellular Automata (CA)-Markov Chain Model (CAMCM) were used to identify the spatial and temporal changes that have occurred in LULC in this area. Two images, 1990 and 2000, were used for calibration and optimization of the Markovian algorithm, while 2010 was used for validating the predictions of CA-Markov using the ground based land cover image. After validating the model, plausible future LULC changes for 2020 were predicted using the CAMCM. Analysis of the LULC pattern maps, achieved through classification of multitemporal satellite datasets, indicated that the socio-economic and biophysical factors have greatly influenced the growth of agricultural lands and settlements in the area. The two Environ. Process. Absorption Coefficient (LAC) were also used, which indicated a drastic change in the area in terms of urbanization. The predicted LULC scenario for year 2020 provides useful inputs to the LULC planners for effective and pragmatic management of the district and a direction for an effective land use policy making. Further suggestions for an effective policy making are also provided which can be used by government officials to protect this important land resource.
Environmental Earth Sciences, 2013
Human activities in many parts of the world have greatly changed the natural land cover. This stu... more Human activities in many parts of the world have greatly changed the natural land cover. This study has been conducted on Pichavaram forest, south east coast of India, famous for its unique mangrove bio-diversity. The main objectives of this study were focused on monitoring land cover changes particularly for the mangrove forest in the Pichavaram area using multi-temporal Landsat images captured in the 1991, 2000, and 2009. The land use/land cover (LULC) estimation was done by a unique hybrid classification approach consisting of unsupervised and support vector machine (SVM)-based supervised classification. Once the vegetation and non-vegetation classes were separated, training site-based classification technology i.e., SVM-based supervised classification technique was used. The agricultural area, forest/plantation, degraded mangrove and mangrove forest layers were separated from the vegetation layer. Mud flat, sand/beach, swamp, sea water/ sea, aquaculture pond, and fallow land were separated from non-vegetation layer. Water logged areas were delineated from the area initially considered under swamp and sea water-drowned areas. In this study, the object-based postclassification comparison method was employed for detecting changes. In order to evaluate the performance, an accuracy assessment was carried out using the randomly stratified sampling method, assuring distribution in a rational pattern so that a specific number of observations were assigned to each category on the classified image. The Kappa accuracy of SVM classified image was highest (94.53 %) for the 2000 image and about 94.14 and 89.45 % for the 2009 and 1991 images, respectively. The results indicated that the increased anthropogenic activities in Pichavaram have caused an irreversible loss of forest vegetation. These findings can be used both as a strategic planning tool to address the broad-scale mangrove ecosystem conservation projects and also as a tactical guide to help managers in designing effective restoration measures.
Arabian Journal of Geosciences, 2014
The knowledge about genetic origin of the chemical elements is important for the evaluation of hy... more The knowledge about genetic origin of the chemical elements is important for the evaluation of hydrogeochemistry of aquatic ecosystem. In the present study, preand post-monsoon samples were collected to identify the role of rain and seawater in the hydro-geochemical processes. Geochemical model and multivariate statistical methods of data analysis were jointly used to define the variations and the genetic origin of chemical parameters of water in mangrove ecosystem. The geochemical model, WATEQ4F, was executed to compute the saturation indices of the minerals with respect to surface water. The interpretation of the saturation indices for minerals shows that the majority of samples fall in the category of under saturation state except for fluorite. An increase in the concentration of various nutrients, namely, nitrate and phosphate, was observed. Suitability of water was checked on the basis of chemical categorisation by Aquachem software. Grouping of waters on the Piper diagram suggested a common composition and origins. Further results showed that pre-and post-monsoon samples mainly consist of Na-Cl and Ca-Cl water type indicating a significant contribution of cations and anions from terrestrial and marine inputs in the mangrove ecosystem.
Fluoride contamination mapping of groundwater in Northern India integrated with geochemical indicators and GIS
Water Science & Technology: Water Supply, 2013
Modeling mineral phase change chemistry of groundwater in a rural-urban fringe
Water Science & Technology, 2012
This research paper aims to determine the genetic origin of the chemical elements in groundwater.... more This research paper aims to determine the genetic origin of the chemical elements in groundwater. It deals with the results of physicochemical parameters, to evaluate the hydro-geochemistry of groundwater in rural-urban fringe of district Bareilly, India. Pre- and post-monsoon sampling has been carried out, which reveals inter-seasonal variability effect on the hydro-geochemical processes. Geochemical modeling especially computation of saturation index was undertaken using the WATEQ4F model. Majority of samples fall in the category of undersaturation, which further suggests that groundwater still has potential to dissolve more minerals. Chemical categorizations of groundwater samples were performed with the help of the Aquachem model. Grouping of groundwater on the Piper diagram reveals a common composition and origin. In most of the area, water facies is of Ca(2+)-HCO(3)(-) type in both the seasons. It also indicates that in pre-monsoon, ion exchange is the dominant process, whereas in post-monsoon, both ion exchanges as well as reverse ion exchanges are reported in the groundwater of the study area.
Water Resources Management, 2013
Many hydrologic phenomena and applications such as drought, flood, irrigation management and sche... more Many hydrologic phenomena and applications such as drought, flood, irrigation management and scheduling needs high resolution satellite soil moisture data at a local/regional scale. Downscaling is a very important process to convert a coarse domain satellite data to a finer spatial resolution. Three artificial intelligence techniques along with the generalized linear model (GLM) are used to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) derived soil moisture, which is currently available at a very coarse scale of~40 Km. Artificial neural network (ANN), support vector machine, relevance vector machine and generalized linear models are chosen for this study to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) with the SMOS derived soil moisture. Soil moisture deficit (SMD) derived from a hydrological model called PDM (Probability Distribution Model) is used for the downscaling performance evaluation. The statistical evaluation has also been made with the day-time and night-time MODIS LST differences with the mean day and night-time PDM SMD data for the selection of effective MODIS products. The accuracy and robustness of all the downscaling algorithms are discussed in terms of their assumptions and applicability. The statistical performance indices such as R 2 , %Bias and RMSE indicates that the ANN (R 2 =0.751, %Bias=−0.628 and RMSE=0.011), RVM (R 2 = 0.691, %Bias = 1.009 and RMSE = 0.013), SVM (R 2 =0.698, %Bias=2.370 and RMSE=0.013) and GLM (R 2 =0.698, %Bias=1.009 and RMSE=0.013) algorithms on the whole are relatively more skillful to downscale the variability of the soil moisture in comparison to the non-downscaled data (R 2 =0.418 and RMSE=0.017) with the outperformance of ANN algorithm. The other attempts related to growing and non-growing seasons have been used in this study to reveal that season based downscaling is even better than continuous time series with fairly high performance statistics.
Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model
Water Resources Management, 2013
ABSTRACT
Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate
Journal of Hydrology, 2013
ABSTRACT
Journal of Hydrology, 2014
Hydrological model uncertainty s u m m a r y
Integrated framework for monitoring groundwater pollution using a geographical information system and multivariate analysis
Hydrological Sciences Journal, 2012
ABSTRACT
Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India
Water International, 2010
Page 1. Water International Vol. 35, No. 2, March 2010, 233245 ISSN 0250-8060 print/ISSN 1941-17... more Page 1. Water International Vol. 35, No. 2, March 2010, 233245 ISSN 0250-8060 print/ISSN 1941-1707 online © 2010 International Water Resources Association DOI: 10.1080/02508061003664419 http://www.informaworld.com ...
Soil characterization based on land cover heterogeneity over a tropical landscape: An integrated approach using Earth Observation datasets
Geocarto International, 2014
ABSTRACT Soil is a vital part of the natural environment and is always responding to changes in e... more ABSTRACT Soil is a vital part of the natural environment and is always responding to changes in environmental factors, along with the influences of anthropogenic factors and land use changes. The long-term change in soil properties will result in change in soil health and fertility, and hence the soil productivity. Hence, the main aim of this paper focuses on the analysis of land use/land cover (LULC) change pattern in spatial and temporal perspective and to present its impact on soil properties in the Merawu catchment over the period of 18 years. Post classification change detection was performed to quantify the decadal changes in historical LULC over the periods of 1991, 2001 and 2009. The pixel to pixel comparison method was used to detect the LULC of the area. The key LULC types were selected for investigation of soil properties. Soil samples were analysed in situ to measure the physicochemical soil properties. The results of this study show remarkable changes in LULC in the period of 18 years. The effect of land cover change on soil properties, soil compaction and soil strength was found to be significant at a level of
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Papers by Prashant Srivastava