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Figure 5: (a) Fuzzy logic-based refinement of NRCS values. Locations with a higher backscatter value are assigned with a low membership degree and thus, a low fuzzy value (close to 0). Locations with lower backscatter are assigned with a high fuzzy value (close to 1). Low fuzzy values mostly plot along edges and are likely to be water-lookalikes. Lower and medium fuzzy values in the center of potentially water patches are usually caused by rough water surfaces, e.g. from wind-induced waves resulting in higher backscatter values. (b) Categorical result of the fuzzy logic-based refinement. Locations with a certain fuzzy value are likely to be water whereas locations with very low fuzzy values are unlikely to be water. Those areas are to be excluded. Locations with medium fuzzy values are subject to region growing and may be re-included. output of our processing chain based on the initial crisis information on the basis of satellite imagery. We took advantage of this situation to check the accuracy of the processing chain and promote further thematic development.

Figure 5 (a) Fuzzy logic-based refinement of NRCS values. Locations with a higher backscatter value are assigned with a low membership degree and thus, a low fuzzy value (close to 0). Locations with lower backscatter are assigned with a high fuzzy value (close to 1). Low fuzzy values mostly plot along edges and are likely to be water-lookalikes. Lower and medium fuzzy values in the center of potentially water patches are usually caused by rough water surfaces, e.g. from wind-induced waves resulting in higher backscatter values. (b) Categorical result of the fuzzy logic-based refinement. Locations with a certain fuzzy value are likely to be water whereas locations with very low fuzzy values are unlikely to be water. Those areas are to be excluded. Locations with medium fuzzy values are subject to region growing and may be re-included. output of our processing chain based on the initial crisis information on the basis of satellite imagery. We took advantage of this situation to check the accuracy of the processing chain and promote further thematic development.

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Table 2: Total sectoral demand of mineral raw materials for 1,625 GW wind energy capacities from 2018 to 2030 and their global production in 2017.
Table 2: Total sectoral demand of mineral raw materials for 1,625 GW wind energy capacities from 2018 to 2030 and their global production in 2017.
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Figure 1: Global Electricity Generation by Fuel Type in 2018 (BP 2019). Hydroelectric is counted towards renewables in our definition.
Figure 1: Global Electricity Generation by Fuel Type in 2018 (BP 2019). Hydroelectric is counted towards renewables in our definition.
Table 3: Total sectoral demand of mineral raw materials for 2,621 GW photovoltaic capacities from 2018 to 2030 and their global refinery production in 2017. installed capacity and power generation). Taking the 539 GW installed capacity in 2017, an additional 1,625 GW would have to be added until 2030 (as- suming current installed capacities stay in operation without power loss — hence the estimate is just an inferior limit). Table 2 lists the volumes needed until 2030, at current mineral raw material requirements, to achieve this goal.
Table 3: Total sectoral demand of mineral raw materials for 2,621 GW photovoltaic capacities from 2018 to 2030 and their global refinery production in 2017. installed capacity and power generation). Taking the 539 GW installed capacity in 2017, an additional 1,625 GW would have to be added until 2030 (as- suming current installed capacities stay in operation without power loss — hence the estimate is just an inferior limit). Table 2 lists the volumes needed until 2030, at current mineral raw material requirements, to achieve this goal.
Estimated raw material demand for newly installed capacity of 99 GW in 2017 Figure 3: Global installed solar power capacity (SolarPower Europe, 2018) and mineral raw material demand of the newly installed capacity in 2017 (Muller, 2018, World Bank Group, 2017, BGR Database, own calculations). Photovoltaics had a global installed capacity of 402 GW in 2017, with 99 GW newly installed that year. Photovoltaics grew much faster than wind en- ergy over the past few years. We estimate that 1 MW of installed photovoltaics capacity requires 170 t of iron, 2.2 t of copper, 0.46 t of tin, 0.27 t of lead, 0.10 t
Estimated raw material demand for newly installed capacity of 99 GW in 2017 Figure 3: Global installed solar power capacity (SolarPower Europe, 2018) and mineral raw material demand of the newly installed capacity in 2017 (Muller, 2018, World Bank Group, 2017, BGR Database, own calculations). Photovoltaics had a global installed capacity of 402 GW in 2017, with 99 GW newly installed that year. Photovoltaics grew much faster than wind en- ergy over the past few years. We estimate that 1 MW of installed photovoltaics capacity requires 170 t of iron, 2.2 t of copper, 0.46 t of tin, 0.27 t of lead, 0.10 t
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Figure 4: Global minor metals production and their country concentration in 2016 (DERA, 2019). With the current NMC chemistry we estimate that 1 MWh of battery capacity requires on average 0.51 t of nickel, 0.22 t of cobalt, 0.2 t of manganese, 0.18 t of lithium and 0.57 t of graphite. The estimated raw material demand for the batteries of 1.3 million elec- tric vehicles registered in 2017 is shown in figure 5.
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Estimated raw material demand for batteries for electric vehicles registered in 2017 (1.3 m units) Figure 5: Global Electric vehicle registration from 2010 to 2017 (EV Volumes, 2019) and estimated raw material demand for EV-batterie in 2017 (BGR Database, own calculations). Global plug-in deliveries es deposit in Australia currently being the largest.
Figure 1: (A) The regional tectonic map shows the location of Borneo in the middle of active tectonic plates (plate boundaries after Bird, 2003) and the coloured dots are earthquakes; (B) shows the Google satellite image with three specific study locations in NW Borneo that are mapped in detail (See Figs. 2-4). It also shows the earthquake distribution across the region. Image extracted from IRIS Earthquake Browser on 25th March 2020. rates, which over the geological past has produced a network of active structures that largely dictate the topographic framework of the region. Thus, the on- shore and offshore geomorphology and topography preserve evidence and chronology of the geological past (Simons et al., 2007, Shah, 2013). The region is relatively far-off (e.g. ~800 km in the east) from the ac- tive plate margins (Fig. 1) and the evidence for active plate convergence at the Borneo-Palawan Trough is not satisfactory, which is why it is not considered a plate boundary in the plate tectonic map of the world (Bird, 2003). Therefore, the cause of active faulting in NW Borneo has largely remained divided into two major suggestions, which are related to gravity, and gravity plus tectonics (e.g. Ingram et al., 2004; Hesse et al., 2009; Sapin et al., 2013; Wang et al., 2017,
Figure 1: (A) The regional tectonic map shows the location of Borneo in the middle of active tectonic plates (plate boundaries after Bird, 2003) and the coloured dots are earthquakes; (B) shows the Google satellite image with three specific study locations in NW Borneo that are mapped in detail (See Figs. 2-4). It also shows the earthquake distribution across the region. Image extracted from IRIS Earthquake Browser on 25th March 2020. rates, which over the geological past has produced a network of active structures that largely dictate the topographic framework of the region. Thus, the on- shore and offshore geomorphology and topography preserve evidence and chronology of the geological past (Simons et al., 2007, Shah, 2013). The region is relatively far-off (e.g. ~800 km in the east) from the ac- tive plate margins (Fig. 1) and the evidence for active plate convergence at the Borneo-Palawan Trough is not satisfactory, which is why it is not considered a plate boundary in the plate tectonic map of the world (Bird, 2003). Therefore, the cause of active faulting in NW Borneo has largely remained divided into two major suggestions, which are related to gravity, and gravity plus tectonics (e.g. Ingram et al., 2004; Hesse et al., 2009; Sapin et al., 2013; Wang et al., 2017,
Figure 2: (A) Uninterpreted 30 m SRTM image with geology overlaid on it; (B) Interpreted image shows the various geologic and geomorphic features that are used as evidence for faulting. The contractional duplex feature is interpreted, and 3-dimensional illustrations are shown to explain the formation of the duplex with both normal (left illustration) and reverse (right illustration) fault geometries. We think the left illustration is representative of the fault zone.
Figure 2: (A) Uninterpreted 30 m SRTM image with geology overlaid on it; (B) Interpreted image shows the various geologic and geomorphic features that are used as evidence for faulting. The contractional duplex feature is interpreted, and 3-dimensional illustrations are shown to explain the formation of the duplex with both normal (left illustration) and reverse (right illustration) fault geometries. We think the left illustration is representative of the fault zone.
Figure 3: (A) Uninterpreted 30 m SRTM image with geology overlaid on it; (B) Interpreted image shows the mapped geologic and geomorphic features that support the existence of active faults. The truncation of syncline at the western portions is interpreted as a fault. The syncline is exposed as a circular basin with clear evidence for faulting. Small left-lateral strike-slip faults are also mapped (see the yellow rectangular area).
Figure 3: (A) Uninterpreted 30 m SRTM image with geology overlaid on it; (B) Interpreted image shows the mapped geologic and geomorphic features that support the existence of active faults. The truncation of syncline at the western portions is interpreted as a fault. The syncline is exposed as a circular basin with clear evidence for faulting. Small left-lateral strike-slip faults are also mapped (see the yellow rectangular area).
Figure 4: (A) Uninterpreted 30 m SRTM image with geology overlaid on it; (B) Interpreted image shows the mapped geologic and geomorphic features that support the evidence for active faults. The ~E-W reverse faults are truncated by the younger ~N-S trending normal faults, which suggests ~N-S convergence and ~E-W extension.
Figure 4: (A) Uninterpreted 30 m SRTM image with geology overlaid on it; (B) Interpreted image shows the mapped geologic and geomorphic features that support the evidence for active faults. The ~E-W reverse faults are truncated by the younger ~N-S trending normal faults, which suggests ~N-S convergence and ~E-W extension.
Figure 1: Map showing the geology of the relict (VV) and active (DV) mudflat region along the southern Saurashtra, Gujarat, western India
Figure 1: Map showing the geology of the relict (VV) and active (DV) mudflat region along the southern Saurashtra, Gujarat, western India
Figure 2: Enriched redox sensitive elements in VV (a) and DV (b) mudflat sections during 4710 to 4000 cal BP and 4100 to 3200 cal BF suggests anoxic conditions, while an intermittent euxinic environment during 4000 to 3200 cal BP prevailed in VV. Since 3000 cal BP, bot! regions (VV and DV) witnessed oxic conditions.
Figure 2: Enriched redox sensitive elements in VV (a) and DV (b) mudflat sections during 4710 to 4000 cal BP and 4100 to 3200 cal BF suggests anoxic conditions, while an intermittent euxinic environment during 4000 to 3200 cal BP prevailed in VV. Since 3000 cal BP, bot! regions (VV and DV) witnessed oxic conditions.
Figure 3: A schematic drawing of changing redox conditions at VV and DV locations wherein (a) the coastal lagoon with anoxic conditions prevailed due to high sea level and mangrove forest during 4710 to 4000 cal BP. (b) Gradual sea regression resulted in euxinic and anoxic conditions at VV and DV till 3200 cal BP, respectively. (c) An upliftment accompanied by sea regression transformed the coastal lagoon into a mudflat causing an oxic environment after 3200 cal BP. (d) A possible tectonic/sea regression after ~ 1500 cal BP terminated the sedimentation at VV.
Figure 3: A schematic drawing of changing redox conditions at VV and DV locations wherein (a) the coastal lagoon with anoxic conditions prevailed due to high sea level and mangrove forest during 4710 to 4000 cal BP. (b) Gradual sea regression resulted in euxinic and anoxic conditions at VV and DV till 3200 cal BP, respectively. (c) An upliftment accompanied by sea regression transformed the coastal lagoon into a mudflat causing an oxic environment after 3200 cal BP. (d) A possible tectonic/sea regression after ~ 1500 cal BP terminated the sedimentation at VV.
Figure 1: (A) Regional tectonic map of SE Asia that mainly shows the active tectonic plate boundary faults systems. The color fillec circles show significant earthquakes that have caused damage to life and property (modified after Shah et al., 2018). (B) Digital elevatior model (DEM) derived image with previously mapped fault data that are sourced from Wang et al. (2016), Mustafar et al. (2017) and JMC (Malaysian Government Agency). (C) The presence of active faults in Sabah (after Shah, 2016 and Wang et al., 2017). (D) The GPS vectors show a NW to W directed present day crustal movement, which suggests oblique extension on Crocker fault system (GPS dat after Mustafar et al., 2017).
Figure 1: (A) Regional tectonic map of SE Asia that mainly shows the active tectonic plate boundary faults systems. The color fillec circles show significant earthquakes that have caused damage to life and property (modified after Shah et al., 2018). (B) Digital elevatior model (DEM) derived image with previously mapped fault data that are sourced from Wang et al. (2016), Mustafar et al. (2017) and JMC (Malaysian Government Agency). (C) The presence of active faults in Sabah (after Shah, 2016 and Wang et al., 2017). (D) The GPS vectors show a NW to W directed present day crustal movement, which suggests oblique extension on Crocker fault system (GPS dat after Mustafar et al., 2017).
Figure 2: The ongoing active deformation of NW Borneo region is controversial, and this is also reflected in the mapping of majo! structures. For example the West Baram Line (known as Tinjar Line onshore) is either considered a transform boundary, a fault, or @ structural discontinuity with unknown slip. And the exact location also remains a controversial topic.
Figure 2: The ongoing active deformation of NW Borneo region is controversial, and this is also reflected in the mapping of majo! structures. For example the West Baram Line (known as Tinjar Line onshore) is either considered a transform boundary, a fault, or @ structural discontinuity with unknown slip. And the exact location also remains a controversial topic.
Figure 3: The un-interpreted field photograph is shown at the top, and the Interpreted (bottom) shows the occurrence of NW and SE dipping normal faults in the region. The stereonet plots show the same, and suggest an older normal fault system with NW-SE directed regional extension. faulting event that struck Sabah in June 2015 was a common occurrence and similar earthquakes should have occurred in the past, as is clearly visible from the bedrock faulting. However, the age of recent ac- tivity on these faults is not known, which requires fur- ther work on the ground.
Figure 3: The un-interpreted field photograph is shown at the top, and the Interpreted (bottom) shows the occurrence of NW and SE dipping normal faults in the region. The stereonet plots show the same, and suggest an older normal fault system with NW-SE directed regional extension. faulting event that struck Sabah in June 2015 was a common occurrence and similar earthquakes should have occurred in the past, as is clearly visible from the bedrock faulting. However, the age of recent ac- tivity on these faults is not known, which requires fur- ther work on the ground.
Figure 4: (A) Gravitational-driven model. The model illustrates gravity alone could not explain the deformation in NW Borneo. The conceptual idea here is the strike slip systems must have initiated transtensional settings along the Sabah continental shelf. At depths of 10 km, the gravitational deformational seems ineffective without an active influence of additional forces that are usually tectonic in origin. (B) The oblique compression model of Shah et al (2018) shows the occurrence of a major strike slip system along the backbone of Bornec Island, which could be controlling the additional deformation that gravity alone cannot explain. Borneo, where active deformation is ongoing (Shah et al., 2018). Therefore, we argue that strike-slip fault- related events recorded in Borneo are related to ac- tive deformation onshore, and suggest that the Island is slowly accumulating strain that could be related to far-field tectonic stresses and not just sliding under the influence of gravity (Shah et al., 2018). Finally, the depth, scale, extent, and type of faulting in Borneo are challenging and cannot be simply explained by just gravity-driven deformation models. Therefore, we propose that gravity-driven deformation and tectonic stresses work together to explain the deformation in Borneo. Similar conclusions are drawn by some of the past studies where the amount of shortening and extension within NW Borneo has been interpreted to understand and quantify the deformation related to gravity-driven and far-field tectonic processes in northern Borneo since the late Pliocene (Hesse et al., 2009; King et al., 2010). The recent data have dem- onstrated that the amount of shortening within the 6. Acknowledgement
Figure 4: (A) Gravitational-driven model. The model illustrates gravity alone could not explain the deformation in NW Borneo. The conceptual idea here is the strike slip systems must have initiated transtensional settings along the Sabah continental shelf. At depths of 10 km, the gravitational deformational seems ineffective without an active influence of additional forces that are usually tectonic in origin. (B) The oblique compression model of Shah et al (2018) shows the occurrence of a major strike slip system along the backbone of Bornec Island, which could be controlling the additional deformation that gravity alone cannot explain. Borneo, where active deformation is ongoing (Shah et al., 2018). Therefore, we argue that strike-slip fault- related events recorded in Borneo are related to ac- tive deformation onshore, and suggest that the Island is slowly accumulating strain that could be related to far-field tectonic stresses and not just sliding under the influence of gravity (Shah et al., 2018). Finally, the depth, scale, extent, and type of faulting in Borneo are challenging and cannot be simply explained by just gravity-driven deformation models. Therefore, we propose that gravity-driven deformation and tectonic stresses work together to explain the deformation in Borneo. Similar conclusions are drawn by some of the past studies where the amount of shortening and extension within NW Borneo has been interpreted to understand and quantify the deformation related to gravity-driven and far-field tectonic processes in northern Borneo since the late Pliocene (Hesse et al., 2009; King et al., 2010). The recent data have dem- onstrated that the amount of shortening within the 6. Acknowledgement
Figure 1: Geological map of Snowy Mountains area highlighting the location of the sampled basanite dyke (modified after Molnar 1963). 2. Sample location and Petrography
Figure 1: Geological map of Snowy Mountains area highlighting the location of the sampled basanite dyke (modified after Molnar 1963). 2. Sample location and Petrography
Figure 2: Ternary Diagram describing the modal classification of our suite of xenoliths based on the modal proportions of Olivine (Ol), Orthopyroxene (opx) and Clinopyroxene (cpx). The samples consist of neoblasts and porphyrob- lasts of olivine + clinopyroxene + spinel + orthopy- roxene. The majority of these samples are devoid of orthopyroxene along with concomitant presence of apatite, amphibole and patches of carbonate. Minor proportions of apatite, sulphide and ilmenite precipi- tates are also found. Chemical equilibrium between the minerals and matrix in the samples is reasonably assumed from absence of compositional zoning in the minerals. The mineral grains in the entire suite are heavily fractured by basanite melt veins, carbon- ate patches and serpentine mesh, producing a range of textures.
Figure 2: Ternary Diagram describing the modal classification of our suite of xenoliths based on the modal proportions of Olivine (Ol), Orthopyroxene (opx) and Clinopyroxene (cpx). The samples consist of neoblasts and porphyrob- lasts of olivine + clinopyroxene + spinel + orthopy- roxene. The majority of these samples are devoid of orthopyroxene along with concomitant presence of apatite, amphibole and patches of carbonate. Minor proportions of apatite, sulphide and ilmenite precipi- tates are also found. Chemical equilibrium between the minerals and matrix in the samples is reasonably assumed from absence of compositional zoning in the minerals. The mineral grains in the entire suite are heavily fractured by basanite melt veins, carbon- ate patches and serpentine mesh, producing a range of textures.
Figure 3: Histogram of equilibration temperatures estimated for the sample suite at 20 kbars (top) and 15 kbars (bottom), as calculated from various experimentally-derived thermometers (2-pyroxene and Ca-in-orthopyroxene methods from Brey and KOhler (1990); olivine-spinel method from Balhaus et al. (1990)).
Figure 3: Histogram of equilibration temperatures estimated for the sample suite at 20 kbars (top) and 15 kbars (bottom), as calculated from various experimentally-derived thermometers (2-pyroxene and Ca-in-orthopyroxene methods from Brey and KOhler (1990); olivine-spinel method from Balhaus et al. (1990)).
Figure 4: Plot of MgO vs CaO for clinopyroxene in spinel peridotite. Note the mixing relationship between a pure carbonatite melt (Wallace and Green, 1988) and pre-metasomatic lithosphere to generate the carbonatized xenoliths and enrichment in CaO concentrations.
Figure 4: Plot of MgO vs CaO for clinopyroxene in spinel peridotite. Note the mixing relationship between a pure carbonatite melt (Wallace and Green, 1988) and pre-metasomatic lithosphere to generate the carbonatized xenoliths and enrichment in CaO concentrations.
Figure 5: Cl chondrite normalized plot for (a) amphiboles from this study and in Yaxley and Kamenetsky (1999) and (b) clinopyroxenes from this study. The amphiboles from this study are richer in HREE compared to amphiboles from those in Yaxley and Kamenetsky (1999) (Fig. 5). The amphiboles from this study are richer in HREE
Figure 5: Cl chondrite normalized plot for (a) amphiboles from this study and in Yaxley and Kamenetsky (1999) and (b) clinopyroxenes from this study. The amphiboles from this study are richer in HREE compared to amphiboles from those in Yaxley and Kamenetsky (1999) (Fig. 5). The amphiboles from this study are richer in HREE
Figure 1: Map of the Tien Shan (MTS Naryn profile, 76° E) across the Kyrgyz Tien Shan and its’ geoelectric cross-sections. (a) genera map (after Bielinski et al., 2003); (6) geoelectric 1D starting model. Inside the model nodes the resistivity is indicated in Ohm-m (afte Berdichevsky et al., 2010); (c) 2D inversion result model along the Naryn profile (adopted after Batalev et al., 2011). Inside the bodies the resistivity is indicated in Ohm-m. Abbreviations: A - Aksay Basin; IK - Issyk-Kul; KR - Kyrgyz range; KP - Kazakh Platform; N - Naryr Basin; TB - Tarim Basin.
Figure 1: Map of the Tien Shan (MTS Naryn profile, 76° E) across the Kyrgyz Tien Shan and its’ geoelectric cross-sections. (a) genera map (after Bielinski et al., 2003); (6) geoelectric 1D starting model. Inside the model nodes the resistivity is indicated in Ohm-m (afte Berdichevsky et al., 2010); (c) 2D inversion result model along the Naryn profile (adopted after Batalev et al., 2011). Inside the bodies the resistivity is indicated in Ohm-m. Abbreviations: A - Aksay Basin; IK - Issyk-Kul; KR - Kyrgyz range; KP - Kazakh Platform; N - Naryr Basin; TB - Tarim Basin.
Figure 2: Interpretative crustal cross-section of the Tien Shan (Naryn profile) adopted after Jourdon et al. (2018), as the combination of the surface geological data (Przhiyalgovskii et al., 2018; Bataleva et al., 2017), thermo-mechanical model (presented in Jourdon et al., 2018) with the addition of geophysical data (restyled accordingly to results after Berdichevsky et al., 2010 and Bielinski et al. 2003). Legend: Southern Tien Shan: 1 - Paleozoic cover, 2— Basement, 3- High pressure mica schists, 4 - Accretionary wedge; Middle Tien Shan: 5 - Paleozoic cover, 6 - Cenozoic cover, 7 - Permian granites, 8 - Basement; 9 — geoelectric bodies contours with apparent resistivity values, 10 — active faults (after Thompson et al., 2002); Northern Tien Shan: 11 — Ordovician plutons + basement, 12 — Paleozoic cover, 13 — Lower crust.
Figure 2: Interpretative crustal cross-section of the Tien Shan (Naryn profile) adopted after Jourdon et al. (2018), as the combination of the surface geological data (Przhiyalgovskii et al., 2018; Bataleva et al., 2017), thermo-mechanical model (presented in Jourdon et al., 2018) with the addition of geophysical data (restyled accordingly to results after Berdichevsky et al., 2010 and Bielinski et al. 2003). Legend: Southern Tien Shan: 1 - Paleozoic cover, 2— Basement, 3- High pressure mica schists, 4 - Accretionary wedge; Middle Tien Shan: 5 - Paleozoic cover, 6 - Cenozoic cover, 7 - Permian granites, 8 - Basement; 9 — geoelectric bodies contours with apparent resistivity values, 10 — active faults (after Thompson et al., 2002); Northern Tien Shan: 11 — Ordovician plutons + basement, 12 — Paleozoic cover, 13 — Lower crust.
Figure 1: (A) Regional map showing the two major active faults system in NW Himalaya. The faults are in red, and the background satellite image is derived from the GeoMap App. The earthquake hypocenters (color filled circles and moment tensor solutions (earthquake beach balls)) are also plotted. The 3D-illustrations are based on the fault plane solutions that are labelled as numbers, and are interpreted as the best possible representative events of active normal faulting in the region. (B) The earthquake structural details that are used for the 3D-illustrations.
Figure 1: (A) Regional map showing the two major active faults system in NW Himalaya. The faults are in red, and the background satellite image is derived from the GeoMap App. The earthquake hypocenters (color filled circles and moment tensor solutions (earthquake beach balls)) are also plotted. The 3D-illustrations are based on the fault plane solutions that are labelled as numbers, and are interpreted as the best possible representative events of active normal faulting in the region. (B) The earthquake structural details that are used for the 3D-illustrations.
Figure 2: The shuttle radar topography (SRTM) with the regional seismicity. The major faults are shown in red, and the rectangular regions represent the areas that are studied in details (see Fig. 3 to 5). crests are truncated by faults, triangular facets are widespread, and clear, and often Holocene filled gla- cial valleys are also cut by faults, which are narrow and spread across the region. This intersecting re- lationship of geological structures shows that ~NE- SW faults are older, and are truncated, and often cut by younger ~NW-SE faults. This suggests either the faults have formed together or, ~NW-SE trend- ing faults are younger (see Discussion for details). Similar structures are observed in other areas (e.g. Fig. 3) where clear evidence for faults is observed, and a range of active tectonic features suggest active faulting. The entire region shows a consistent pattern of faults (Figs. 3 to 5) where a number of ~NW-SW and ~NE-SW trending normal faults are also mapped. Together all these faults show evidence of Holocene movement, and therefore these are active.
Figure 2: The shuttle radar topography (SRTM) with the regional seismicity. The major faults are shown in red, and the rectangular regions represent the areas that are studied in details (see Fig. 3 to 5). crests are truncated by faults, triangular facets are widespread, and clear, and often Holocene filled gla- cial valleys are also cut by faults, which are narrow and spread across the region. This intersecting re- lationship of geological structures shows that ~NE- SW faults are older, and are truncated, and often cut by younger ~NW-SE faults. This suggests either the faults have formed together or, ~NW-SE trend- ing faults are younger (see Discussion for details). Similar structures are observed in other areas (e.g. Fig. 3) where clear evidence for faults is observed, and a range of active tectonic features suggest active faulting. The entire region shows a consistent pattern of faults (Figs. 3 to 5) where a number of ~NW-SW and ~NE-SW trending normal faults are also mapped. Together all these faults show evidence of Holocene movement, and therefore these are active.
Figure 3: (A) Satellite image with interpreted tectonic and other geomorphic features. (B) 3D-illustrations before and after faulting. It is representative of one of the major normal fault systems that we have mapped here. different ridges and valleys because of the progres- sive deformation that has formed these faults.
Figure 3: (A) Satellite image with interpreted tectonic and other geomorphic features. (B) 3D-illustrations before and after faulting. It is representative of one of the major normal fault systems that we have mapped here. different ridges and valleys because of the progres- sive deformation that has formed these faults.
Figure 4: (A) Satellite image with interpreted tectonic and other geomorphic features. A number of syn-tectonic valleys are filled with Holocene sediments. (B) 3D illustrations before and after faulting. It is representative of one of the major normal fault systems that we have mapped here. LHIGAL WWE GAVE HHA VeU Tree. 6). The new mapping shown above further demon- strates that such faulting is ubiquitous throughout the west and southwest of the Karakoram fault. We have mainly observed orogen parallel faults, and ~NE-SW and ~N-S trending normal faults throughout the study area (Figs. 2 to 6). These normal faults are active, have ruptured and displaced Holocene deposits, and are morphologically similar to the normal faulting pat- ern that is observed in Tibet (Shah et al., 2018). The opographic expression of faulting is mainly shown by a number of faults and these are interpreted as normal faults, which have formed syn- or postgenetic o the formation of the Karakorum fault because we have observed dextral offset of normal faults at KF Fig. 6D). The mapping further illustrates that ~NW- SE trending faults have truncated, and displaced the ~NE-SW trending faults (Figs. 2 to 5), which suggests the relative age relationship and confirms the earlier formation of ~NW-SE trending faults. The available earthquake moment tensor solutions (Fig. 1) strength- en our geomorphic mapping, and indicate the ~NW- SE to ~E-W directed crustal extension. This means on aregional scale that the ongoing lithospheric plate convergence is also absorbed by ~E-W extension, and that is strikingly similar to how Tibet is deforming
Figure 4: (A) Satellite image with interpreted tectonic and other geomorphic features. A number of syn-tectonic valleys are filled with Holocene sediments. (B) 3D illustrations before and after faulting. It is representative of one of the major normal fault systems that we have mapped here. LHIGAL WWE GAVE HHA VeU Tree. 6). The new mapping shown above further demon- strates that such faulting is ubiquitous throughout the west and southwest of the Karakoram fault. We have mainly observed orogen parallel faults, and ~NE-SW and ~N-S trending normal faults throughout the study area (Figs. 2 to 6). These normal faults are active, have ruptured and displaced Holocene deposits, and are morphologically similar to the normal faulting pat- ern that is observed in Tibet (Shah et al., 2018). The opographic expression of faulting is mainly shown by a number of faults and these are interpreted as normal faults, which have formed syn- or postgenetic o the formation of the Karakorum fault because we have observed dextral offset of normal faults at KF Fig. 6D). The mapping further illustrates that ~NW- SE trending faults have truncated, and displaced the ~NE-SW trending faults (Figs. 2 to 5), which suggests the relative age relationship and confirms the earlier formation of ~NW-SE trending faults. The available earthquake moment tensor solutions (Fig. 1) strength- en our geomorphic mapping, and indicate the ~NW- SE to ~E-W directed crustal extension. This means on aregional scale that the ongoing lithospheric plate convergence is also absorbed by ~E-W extension, and that is strikingly similar to how Tibet is deforming
Figure 5: (A) Satellite image with interpreted tectonic and other geomorphic features. The rugged topography is dissected by glaciers, which often occupy tectonically controlled valleys. (B) 3D-illustrations before and after faulting. It is representative of one of the major normal fault systems that we have mapped here.
Figure 5: (A) Satellite image with interpreted tectonic and other geomorphic features. The rugged topography is dissected by glaciers, which often occupy tectonically controlled valleys. (B) 3D-illustrations before and after faulting. It is representative of one of the major normal fault systems that we have mapped here.
Figure 6: (A) 3-D view of the satellite image shows the two major active fault systems in the NW Himalaya that are reflective of the oblique tectonic convergence between India and Eurasia. The illustrations (B to D) summarize the formation and possible evolution of normal faulting in the studied region. (B) The normal faults could have formed earlier or syntectonic with the oblique plate convergence. (C) This could have later formed strike-slip faults, which have displaced normal faults. (D) This could mean that the Karakorum fault has formed later than normal faults. However, the regional oblique plate tectonic convergence could form both the fault systems together in the region with some time lag between their development history, which seems a normal procedure during the mountain building processes.
Figure 6: (A) 3-D view of the satellite image shows the two major active fault systems in the NW Himalaya that are reflective of the oblique tectonic convergence between India and Eurasia. The illustrations (B to D) summarize the formation and possible evolution of normal faulting in the studied region. (B) The normal faults could have formed earlier or syntectonic with the oblique plate convergence. (C) This could have later formed strike-slip faults, which have displaced normal faults. (D) This could mean that the Karakorum fault has formed later than normal faults. However, the regional oblique plate tectonic convergence could form both the fault systems together in the region with some time lag between their development history, which seems a normal procedure during the mountain building processes.
Figure 2: Unmasked image of PCA (left) and masked image of PCA (right).
Figure 2: Unmasked image of PCA (left) and masked image of PCA (right).
Figure 3: Sea ice extent obtained from the analysis. 4. Results
Figure 3: Sea ice extent obtained from the analysis. 4. Results
Figure 5: Temporal plot of SCATSAT-1 SIE, OSI SIE and NSIDC SIE. The differences among the SIE are greatest in the period from January to April (winter to spring) and are
Figure 5: Temporal plot of SCATSAT-1 SIE, OSI SIE and NSIDC SIE. The differences among the SIE are greatest in the period from January to April (winter to spring) and are
Figure 4: Qualitative comparison of SIEs. the least during the period from August to October (summer to autumn). Between SCATSAT-1 and OSI SIE, the largest difference observed is ~1.09 - 10° km? during the winter period while during the summer pe- riod, the largest difference observed is 0.57 - 10° km?. These values for the SCATSAT-1 and NSIDC com- parison are 0.91 - 10° km? and 0.66 - 10° km?, respec- tively. Thus, the three estimations show convergence in summer rather than in winter. This should be in- vestigated in our future work to understand why the difference smaller during summer than during winter. A. TaD ated. .szmlcam afi fi OP ie thar. errr mreemimm mint mf Fiea
Figure 4: Qualitative comparison of SIEs. the least during the period from August to October (summer to autumn). Between SCATSAT-1 and OSI SIE, the largest difference observed is ~1.09 - 10° km? during the winter period while during the summer pe- riod, the largest difference observed is 0.57 - 10° km?. These values for the SCATSAT-1 and NSIDC com- parison are 0.91 - 10° km? and 0.66 - 10° km?, respec- tively. Thus, the three estimations show convergence in summer rather than in winter. This should be in- vestigated in our future work to understand why the difference smaller during summer than during winter. A. TaD ated. .szmlcam afi fi OP ie thar. errr mreemimm mint mf Fiea
Figure 6: Regression plot between SCATSAT-1 SIE and OSI SIE (left) and SCATSAT-1 SIE and NSIDC SIE. confidence level (95%), we have to accept the H, that the means that the data do not differ statistically. Another way is by considering the absolute value of the calculated t-value (t-stat). If it is less than t-crit (which is a standard value of the t value available in the standard table for given degrees of freedom and present confidence levels), we have to accept the null hypothesis. derived from the SCATSAT-1 will be useful to study the changes of arctic SIE throughout the life span of the SCATSAT-1 satellite. The method can also be ap- plied to calibrated data of other scatterometer satel- lites with SCATSAT-1. the changes of arctic SIE throughout the life span of plied to calibrated data of other scatterometer satel-
Figure 6: Regression plot between SCATSAT-1 SIE and OSI SIE (left) and SCATSAT-1 SIE and NSIDC SIE. confidence level (95%), we have to accept the H, that the means that the data do not differ statistically. Another way is by considering the absolute value of the calculated t-value (t-stat). If it is less than t-crit (which is a standard value of the t value available in the standard table for given degrees of freedom and present confidence levels), we have to accept the null hypothesis. derived from the SCATSAT-1 will be useful to study the changes of arctic SIE throughout the life span of the SCATSAT-1 satellite. The method can also be ap- plied to calibrated data of other scatterometer satel- lites with SCATSAT-1. the changes of arctic SIE throughout the life span of plied to calibrated data of other scatterometer satel-
Many natural sites in Morocco are geologically rich, especially in the Atlasic area. The potential of these geosites with their geologically diverse and complex features has been recognized by geologists world- wide. Yet, studies in geotourism and the sustainable development of these sites are generally lacking. Su- manapala and Wolf (2020), who reviewed the field of geotourism, found that although geotourism clearly constitutes an emerging sub-sector of nature-based tourism with great potential, only 26 studies have been published in this field in peer reviewed journals been published in this field in peer reviewed journals Figure 1: Geological Map of the Middle Atlas (after Mounir, 202( Proceedings of the 5" International YES Congress DOI: 10.2312/yes19.24 © CC-BY 4.0, except where otherwise noted. Here we contribute to filling this gap by discussing the Middle Atlas in Morocco as a potential geotourism site. We evaluate the socio-economic, cultural and environmental conditions as they provide opportuni- ties for development, and discuss the threats that im- pede such a development. ties for development, and discuss the threats that im-
Many natural sites in Morocco are geologically rich, especially in the Atlasic area. The potential of these geosites with their geologically diverse and complex features has been recognized by geologists world- wide. Yet, studies in geotourism and the sustainable development of these sites are generally lacking. Su- manapala and Wolf (2020), who reviewed the field of geotourism, found that although geotourism clearly constitutes an emerging sub-sector of nature-based tourism with great potential, only 26 studies have been published in this field in peer reviewed journals been published in this field in peer reviewed journals Figure 1: Geological Map of the Middle Atlas (after Mounir, 202( Proceedings of the 5" International YES Congress DOI: 10.2312/yes19.24 © CC-BY 4.0, except where otherwise noted. Here we contribute to filling this gap by discussing the Middle Atlas in Morocco as a potential geotourism site. We evaluate the socio-economic, cultural and environmental conditions as they provide opportuni- ties for development, and discuss the threats that im- pede such a development. ties for development, and discuss the threats that im-
Figure 2: The main approaches, methods and interventions in the heritage industry process.
Figure 2: The main approaches, methods and interventions in the heritage industry process.
Figure 3: Examples of geological sites of special interest in the Middle Atlas. a-b. The Middle Atlas Heritage Interpretation Centre. c. Guigou meander (Taferdoust Kasbah). d. The Sources Oum er-Rbia Waterfall. e. Aguelmam Azigza National Park. f. The bathonian Theropod Trackways of the Central Middle Atlas. g. Karst reliefs and landforms. h. Azurite and malachite. i-j. The Plio-Quaternary volcanism of the Middle Atlas. The heritage process is subject to several stages of characterization, preservation, reclamation and geo-
Figure 3: Examples of geological sites of special interest in the Middle Atlas. a-b. The Middle Atlas Heritage Interpretation Centre. c. Guigou meander (Taferdoust Kasbah). d. The Sources Oum er-Rbia Waterfall. e. Aguelmam Azigza National Park. f. The bathonian Theropod Trackways of the Central Middle Atlas. g. Karst reliefs and landforms. h. Azurite and malachite. i-j. The Plio-Quaternary volcanism of the Middle Atlas. The heritage process is subject to several stages of characterization, preservation, reclamation and geo-
Figure 1: Map of Nigeria and the localities where sediment cores were retrieved. In Nigeria, several wetlands exist. However, the most significant is the mangrove swamp forests which are in the coastal states in Southern Nigeria such as Edo, Delta, Cross River, Rivers, Akwa-lbom, Ogun, Ondo and Lagos. Are there policies aimed at the con- servation, restoration, and sustainable use of these ecosystems in the face of climate change in Nigeria? Currently, there are no Nigerian laws which directly govern and/or regulate activities within wetlands. There are some laws such as the Forest Ordinance of 1901 and 1916, the Wild Animals Preservation Act of 1916, the Oil Pipelines Act of 1965, and the Fed-
Figure 1: Map of Nigeria and the localities where sediment cores were retrieved. In Nigeria, several wetlands exist. However, the most significant is the mangrove swamp forests which are in the coastal states in Southern Nigeria such as Edo, Delta, Cross River, Rivers, Akwa-lbom, Ogun, Ondo and Lagos. Are there policies aimed at the con- servation, restoration, and sustainable use of these ecosystems in the face of climate change in Nigeria? Currently, there are no Nigerian laws which directly govern and/or regulate activities within wetlands. There are some laws such as the Forest Ordinance of 1901 and 1916, the Wild Animals Preservation Act of 1916, the Oil Pipelines Act of 1965, and the Fed-
prospects as well as their stratigraphic counterparts Figure 1: Location of the study area (modified from Tuttle et al 1990). Proceedings of the 5" International YES Congress DOI: 10.2312/yes19.18 © CC-BY 4.0, except where otherwise noted. In the Niger Delta, growth faults and roll-over an- ticlinal structures have been documented to serve as structural traps for hydrocarbon accumulations (Merki, 1972; Orife and Avbovbo, 1982). The use of advanced technological tools in 3D seismic data in- terpretation and integration with other geological data has yielded great results in mapping such structural prospects as well as their stratigraphic counterparts
prospects as well as their stratigraphic counterparts Figure 1: Location of the study area (modified from Tuttle et al 1990). Proceedings of the 5" International YES Congress DOI: 10.2312/yes19.18 © CC-BY 4.0, except where otherwise noted. In the Niger Delta, growth faults and roll-over an- ticlinal structures have been documented to serve as structural traps for hydrocarbon accumulations (Merki, 1972; Orife and Avbovbo, 1982). The use of advanced technological tools in 3D seismic data in- terpretation and integration with other geological data has yielded great results in mapping such structural prospects as well as their stratigraphic counterparts
Table 1: Well logs from vertical wells available on the study areas within UKU field and QC.
Table 1: Well logs from vertical wells available on the study areas within UKU field and QC.
Figure 2: Well correlation of the H-1 and H-6 sands and the lateral discontinuity of the H-6 sands. 4. Results and discussion were mapped and subsequently used to generate th isochron and isopach maps via arithmetic operation in Petrel mapping software.
Figure 2: Well correlation of the H-1 and H-6 sands and the lateral discontinuity of the H-6 sands. 4. Results and discussion were mapped and subsequently used to generate th isochron and isopach maps via arithmetic operation in Petrel mapping software.
Figure 5: H1 Top depth map. Figure 6: H6 Top depth map.
Figure 5: H1 Top depth map. Figure 6: H6 Top depth map.
Figure 3: Chronostratigraphic correlation and stacking patterns.
Figure 3: Chronostratigraphic correlation and stacking patterns.
The amplitude maps were generated for the mapped horizons to aid structural trap interpretation. RMS amplitude maps show discontinuity of trapped hydrocarbons in H1 and H6 reservoir better than oth- er attribute maps. Hart et al. (1996) mentioned the concentration of high amplitude towards the crest of anticlines. Therefore, conformance of high amplitude o fault delineation and map contours was used to urther describe trap definitions in the process. Fig- ure 4 shows the root mean square amplitude map of H6 to apprehend the geological framework. The high amplitude zones seen on the map are constrained to he red to yellow color band while the medium to low amplitude are constrained to the blue to pink color bands. The high-amplitude values are indicative of hydrocarbon-rich sands and thus delineate the pos-
The amplitude maps were generated for the mapped horizons to aid structural trap interpretation. RMS amplitude maps show discontinuity of trapped hydrocarbons in H1 and H6 reservoir better than oth- er attribute maps. Hart et al. (1996) mentioned the concentration of high amplitude towards the crest of anticlines. Therefore, conformance of high amplitude o fault delineation and map contours was used to urther describe trap definitions in the process. Fig- ure 4 shows the root mean square amplitude map of H6 to apprehend the geological framework. The high amplitude zones seen on the map are constrained to he red to yellow color band while the medium to low amplitude are constrained to the blue to pink color bands. The high-amplitude values are indicative of hydrocarbon-rich sands and thus delineate the pos-
Figure 4: RMS amplitude overlay on H6. Time map@10 ms window.
Figure 4: RMS amplitude overlay on H6. Time map@10 ms window.
Table 2: Petrophysical parameters of the reservoir sands. Time and depth structural contour maps were pro- duced for the two horizons defined on top of sanc bodies, namely H1 and H6. The time and depth structure contour maps show a system of differently oriented growth faults (Figs. 5 and 6). A major curvy shaped growth fault, dipping NW-ward, extends ove! about 85 % of the mapped area. The two faults on the map give a fault dependent structure favorable for hy- drocarbon accumulation, which is somewhat uniform westward across the field but better developed to- wards the main structure-building fault. The trap type that developed for H1 is fault assisted while the tra type for H6 is a combination of structural and strati- graphic trap components (Figs. 5 and 6). Depth maps A buckle plot of water saturation (S,) against po- graphic trap components (Figs. 5 and 6). Depth maps Hydrocarbon potentials of the delineated sands were revealed in their Petrophysical properties. Table 2 shows reservoir properties that were calculated for H1 and H6 from UKU-1, UKU-4, and UKU-6. Sands are well sorted with low values of water saturation S,,). Effective porosities (greater than 0.3 or 30 % po- rosity units) are satisfactory for a reservoir to be ad- judged a producible reservoir (Shell, 2002). In these wells, all the reservoir sands have MHI values lower han 0.7; hence hydrocarbons were moved during in- vasion.
Table 2: Petrophysical parameters of the reservoir sands. Time and depth structural contour maps were pro- duced for the two horizons defined on top of sanc bodies, namely H1 and H6. The time and depth structure contour maps show a system of differently oriented growth faults (Figs. 5 and 6). A major curvy shaped growth fault, dipping NW-ward, extends ove! about 85 % of the mapped area. The two faults on the map give a fault dependent structure favorable for hy- drocarbon accumulation, which is somewhat uniform westward across the field but better developed to- wards the main structure-building fault. The trap type that developed for H1 is fault assisted while the tra type for H6 is a combination of structural and strati- graphic trap components (Figs. 5 and 6). Depth maps A buckle plot of water saturation (S,) against po- graphic trap components (Figs. 5 and 6). Depth maps Hydrocarbon potentials of the delineated sands were revealed in their Petrophysical properties. Table 2 shows reservoir properties that were calculated for H1 and H6 from UKU-1, UKU-4, and UKU-6. Sands are well sorted with low values of water saturation S,,). Effective porosities (greater than 0.3 or 30 % po- rosity units) are satisfactory for a reservoir to be ad- judged a producible reservoir (Shell, 2002). In these wells, all the reservoir sands have MHI values lower han 0.7; hence hydrocarbons were moved during in- vasion.
Figure 7: Reservoir Description and contacts for H1 top sand. FB1, OWC = -2640 ft, FB2, GOC = -2470 ft and OWC = -2640 ft; red: oil; green: gas.
Figure 7: Reservoir Description and contacts for H1 top sand. FB1, OWC = -2640 ft, FB2, GOC = -2470 ft and OWC = -2640 ft; red: oil; green: gas.
Figure 8: Reservoir description and contacts for H6 top sand. FBW, strat-trap = -3683 ft; FB Central = -3584 ft (gas) oil-strat- trap, South lobe GOC = -4048.8 ft oil-strat-trap; red: oil; green: gas.
Figure 8: Reservoir description and contacts for H6 top sand. FBW, strat-trap = -3683 ft; FB Central = -3584 ft (gas) oil-strat- trap, South lobe GOC = -4048.8 ft oil-strat-trap; red: oil; green: gas.
Figure 2: Description of satellite images used in the study. The geographical area extends from 20°49’ N to 21°41’ N in latitudes and 73°01’ E to 73°3’ E in lon- gitudes. The coastline of the study area is compara- tively uniform and broken by a few indentions. Sandy
Figure 2: Description of satellite images used in the study. The geographical area extends from 20°49’ N to 21°41’ N in latitudes and 73°01’ E to 73°3’ E in lon- gitudes. The coastline of the study area is compara- tively uniform and broken by a few indentions. Sandy
Figure 1: Administrative divisions of Southern Gujarat. and stakeholder preferences to make informed choic- es about equitable resource allocation. Coastal plan- ning and management in Gujarat is constrained by a dearth of data about interaction between develop- ment activities and coastal environment conditions. Information relating to causes of land use change will allow more cautious preparation and organisation in the area. It will provide us with an understanding of cause and effect relationships between interacting social, economic, and environmental components.
Figure 1: Administrative divisions of Southern Gujarat. and stakeholder preferences to make informed choic- es about equitable resource allocation. Coastal plan- ning and management in Gujarat is constrained by a dearth of data about interaction between develop- ment activities and coastal environment conditions. Information relating to causes of land use change will allow more cautious preparation and organisation in the area. It will provide us with an understanding of cause and effect relationships between interacting social, economic, and environmental components.
Figure 3: Datasource and methodology, The study uses both primary information and sec- ondary information available at various levels. Multi- temporal analysis has been done in this study by us- The study uses both primary information and sec-
Figure 3: Datasource and methodology, The study uses both primary information and sec- ondary information available at various levels. Multi- temporal analysis has been done in this study by us- The study uses both primary information and sec-
Figure 4: Maps showing land use and land cover from 1980 to 2019 growth, infrastructure development policy, topogra- phy, and climate change. In this study, the primary factors are determined by interpreting the interaction between the social-economic and ecological systems in the area and their relevance to the coastal degra- dation.
Figure 4: Maps showing land use and land cover from 1980 to 2019 growth, infrastructure development policy, topogra- phy, and climate change. In this study, the primary factors are determined by interpreting the interaction between the social-economic and ecological systems in the area and their relevance to the coastal degra- dation.
Figure 5: Trends of land use and land cover changes from 1980 to 2019. As it has already been noted that there has been a significant increase in urban areas, the population is the most important underlying factor because with population growth there is the corresponding need for urban land. The growth of population increases various human activities in the area, including resi-
Figure 5: Trends of land use and land cover changes from 1980 to 2019. As it has already been noted that there has been a significant increase in urban areas, the population is the most important underlying factor because with population growth there is the corresponding need for urban land. The growth of population increases various human activities in the area, including resi-
Figure 1: Location Map of Ranthambhore Tiger Reserve 2. Study Area In India, the Tiger corridor of Ranthambhore is lo- cated amid latitudes 25°52’071” N to 26 33’713” N to longitudes 75 85’84.0” E to 77 02’48.0” E at the inter- section of the Aravalis and Vindhyan mountains (Fig. 1). The corridor ensures a tiger habitat in an area of 6741.73 km? and it is home to over 60 tigers. Espe- cially the core area of the corridor is well known in Ranthambhore, of which the habitat is 1113.36 km? and the buffer area is 297.92 km? (Fig. 1). It is a total geographical area of 1411.28 km?. The Ranthambore is a hotspot of tiger population and marks the transi- tion zone between the real deserts and seasonally wet peninsular India. It is connecting from Keoladeo, Van Vihar, Ranthambhore, Sawai Mansingh, Ram- garh, Jawahar Sagar, and Dara sanctuaries. As per the biogeography classification (Rodgers & Panwar, 1988), it falls in 4 B (Semiarid zone and Gujarat-Ra- jwara biotic province).
Figure 1: Location Map of Ranthambhore Tiger Reserve 2. Study Area In India, the Tiger corridor of Ranthambhore is lo- cated amid latitudes 25°52’071” N to 26 33’713” N to longitudes 75 85’84.0” E to 77 02’48.0” E at the inter- section of the Aravalis and Vindhyan mountains (Fig. 1). The corridor ensures a tiger habitat in an area of 6741.73 km? and it is home to over 60 tigers. Espe- cially the core area of the corridor is well known in Ranthambhore, of which the habitat is 1113.36 km? and the buffer area is 297.92 km? (Fig. 1). It is a total geographical area of 1411.28 km?. The Ranthambore is a hotspot of tiger population and marks the transi- tion zone between the real deserts and seasonally wet peninsular India. It is connecting from Keoladeo, Van Vihar, Ranthambhore, Sawai Mansingh, Ram- garh, Jawahar Sagar, and Dara sanctuaries. As per the biogeography classification (Rodgers & Panwar, 1988), it falls in 4 B (Semiarid zone and Gujarat-Ra- jwara biotic province).
ambhore Tiger Reserve terrain is mostly formed by Pre-Cambrian metamorphic, igneous, and sedimen- tary rocks belonging to pre-Aravali Vindhyas. Pre- Aravali rocks units, quartzites, micashocts geneuses, and migmatites cover the north-eastern part of the tiger reserve between Lalsot and Karauli. The rocks of the Vindhyas super group are characterized by the Kaimur, Rewa and Bhander group, and consist
ambhore Tiger Reserve terrain is mostly formed by Pre-Cambrian metamorphic, igneous, and sedimen- tary rocks belonging to pre-Aravali Vindhyas. Pre- Aravali rocks units, quartzites, micashocts geneuses, and migmatites cover the north-eastern part of the tiger reserve between Lalsot and Karauli. The rocks of the Vindhyas super group are characterized by the Kaimur, Rewa and Bhander group, and consist
Figure 4: Anthropogenic Pressures on Ranthambore Tige Reserve.
Figure 4: Anthropogenic Pressures on Ranthambore Tige Reserve.
Figure 3: Administrative Division of Ranthambhore Tiger Reserve mainly of various types of sandstones, and lime- stones. The geologic structure of the Ranthambhore Tiger Reserve area has a strong influence on the soil types, which in turn also determines the vegetation type. In these particular areas where quartzite forms the uppermost strata, the soil is impoverished and unhealthy for agricultural land. These areas have a very thin layer of coarse-grained soil in the slate and shale areas of Gwalior areas and the soils are fine clayey and shallow. Ranthambhore soils have very little fertility due to numerous physical and man-made
Figure 3: Administrative Division of Ranthambhore Tiger Reserve mainly of various types of sandstones, and lime- stones. The geologic structure of the Ranthambhore Tiger Reserve area has a strong influence on the soil types, which in turn also determines the vegetation type. In these particular areas where quartzite forms the uppermost strata, the soil is impoverished and unhealthy for agricultural land. These areas have a very thin layer of coarse-grained soil in the slate and shale areas of Gwalior areas and the soils are fine clayey and shallow. Ranthambhore soils have very little fertility due to numerous physical and man-made
Figure 5: Map of Irrigations & Hydrology of Ranthambhore Tiger Reserve. Causes.
Figure 5: Map of Irrigations & Hydrology of Ranthambhore Tiger Reserve. Causes.
Figure 7: Challenges and Issues of Ranthambhore Tiger Reserve)
Figure 7: Challenges and Issues of Ranthambhore Tiger Reserve)
Figure 6: Images of Cultural Resource, Ranthambhore Tiger Reserve.
Figure 6: Images of Cultural Resource, Ranthambhore Tiger Reserve.
Figure 1: View of some environmental forms in the Wayimirya Valley.
Figure 1: View of some environmental forms in the Wayimirya Valley.
Figure 4: Frequency rosette orientation of fractures (left) and schistosity (right). lations limestones), the ortho-gneissic complex (with
Figure 4: Frequency rosette orientation of fractures (left) and schistosity (right). lations limestones), the ortho-gneissic complex (with
Figure 2: The Wayimirya Valley is a waste deposit site.
Figure 2: The Wayimirya Valley is a waste deposit site.
Figure 3: Project hydrograph at the confluence of the Wayimirya and Kimemi flows.
Figure 3: Project hydrograph at the confluence of the Wayimirya and Kimemi flows.
Table 1: Synthesis of laboratory analysis results.
Table 1: Synthesis of laboratory analysis results.
Figure 5: Stress tensor orientation of fractures (left) and schistosity (right). percentage of fine particles is less than 30. The Wayimirya Stream has its source in the Vule- ma-Vuhika Hills. It crosses moderate slopes but its low water flow measured at its mouth is large enough to reach 144 I/s with a total length of 2990.27 m. It pours into the Kimemi flow. It should be noted that the pours into the Kimemi flow. It should be noted that the We estimate it is a mixture of of 38 % fine particles and 62 % large particles. The soil is silty with 4 % gravel. In view of the Atterberg limits (Table 1), the soil has a mediocre bearing capacity. The consist- ency index is 2.8. With the plasticity index of 10, the percentage of fine particles is less than 30. The Wayimirya Stream has its source in the Vule-
Figure 5: Stress tensor orientation of fractures (left) and schistosity (right). percentage of fine particles is less than 30. The Wayimirya Stream has its source in the Vule- ma-Vuhika Hills. It crosses moderate slopes but its low water flow measured at its mouth is large enough to reach 144 I/s with a total length of 2990.27 m. It pours into the Kimemi flow. It should be noted that the pours into the Kimemi flow. It should be noted that the We estimate it is a mixture of of 38 % fine particles and 62 % large particles. The soil is silty with 4 % gravel. In view of the Atterberg limits (Table 1), the soil has a mediocre bearing capacity. The consist- ency index is 2.8. With the plasticity index of 10, the percentage of fine particles is less than 30. The Wayimirya Stream has its source in the Vule-
Table 2: Results of the permeability and in situ penetrometer test. The urban area of the Wayimirya Valley has an es- timated waterproofing rate of 80 %. The runoff hydro- gram, which shows the evolution of the flow, is shown in Figure 3. It was calculated based on the surfaces of the sub-basins and the values of various runoff coef- ficients. Since we have a uniform flow in the section, Table 2 shows how the speed and the tension vary according to the flow. After calculations, the width of the stable section was estimated at 1251.497 m. Hydraulic structures are needed to stabilize the Way- imirya flow bed.
Table 2: Results of the permeability and in situ penetrometer test. The urban area of the Wayimirya Valley has an es- timated waterproofing rate of 80 %. The runoff hydro- gram, which shows the evolution of the flow, is shown in Figure 3. It was calculated based on the surfaces of the sub-basins and the values of various runoff coef- ficients. Since we have a uniform flow in the section, Table 2 shows how the speed and the tension vary according to the flow. After calculations, the width of the stable section was estimated at 1251.497 m. Hydraulic structures are needed to stabilize the Way- imirya flow bed.
Figure 6: Mohr circle of open fractures (left) and of schistosity (right). The values of the field penetrometer test are sum- marized in Table 2. From a petrographic and struc- tural point of view, the Wayimirya Field is based on granitic and metamorphic formations, susceptible to tectonic deformation. The orientations of the open fractures and shale planes form the two geological structures encountered on the ground are presented in Figure 4. The opened ruptures are generally ori- ented in the first and second quadrants. There are others oriented in the third and fourth quadrants, but are less important. The schistosity planes are mainly oriented in the third and fourth quadrants.
Figure 6: Mohr circle of open fractures (left) and of schistosity (right). The values of the field penetrometer test are sum- marized in Table 2. From a petrographic and struc- tural point of view, the Wayimirya Field is based on granitic and metamorphic formations, susceptible to tectonic deformation. The orientations of the open fractures and shale planes form the two geological structures encountered on the ground are presented in Figure 4. The opened ruptures are generally ori- ented in the first and second quadrants. There are others oriented in the third and fourth quadrants, but are less important. The schistosity planes are mainly oriented in the third and fourth quadrants.
Figure 1: Example of how geophysical methods are often used to portray the subsurface. The signals show a change in color as a result of the different layering of earth materials below the surface. (Binley et al., 2015; figure licensed under CC-BY-NC-ND 4.0) However, the vadose zone can become increasing- ly contaminated and hence the degree of purification decreases. Urban environments are a source of nu- merous contaminants such as cattle dung, salinization contamination, dumping and irrigation that negatively impacts the water quality, thus posing new challenges to monitoring and treatment schemes. The contami- nant mass discharge method is frequently used to evaluate the environmental effect of contaminated sites on groundwater quality (Cai et al., 2011; Guil- beault et al., 2005; Kubert & Finkel, 2006; Trold-borg et al., 2010, 2012). Moreover, at large contaminated sites, portrayed by high source diversity such as land- fills, it is often not economically practicable to install the number of multilevel wells required to establish an adequately dense sampling grid (Troldborg et al., 2010). The difficulties of access and low dynamics show significant challenges for deep vadose zone reclamation, even though the techniques for reduc- ing the flux of contaminants within the deep vadose zone are effectively the same as those applicable to shallow vadose zone or groundwater remediation (Wellman et al., 2012). Monitoring methods for com- plicated subsurface environments, such as deep va- dose zones, are currently not well developed and are critical constituents of reclamation strategies, includ- ing those for flux-based methods (Bunn et al., 2012). 3. Hydrogeophysics
Figure 1: Example of how geophysical methods are often used to portray the subsurface. The signals show a change in color as a result of the different layering of earth materials below the surface. (Binley et al., 2015; figure licensed under CC-BY-NC-ND 4.0) However, the vadose zone can become increasing- ly contaminated and hence the degree of purification decreases. Urban environments are a source of nu- merous contaminants such as cattle dung, salinization contamination, dumping and irrigation that negatively impacts the water quality, thus posing new challenges to monitoring and treatment schemes. The contami- nant mass discharge method is frequently used to evaluate the environmental effect of contaminated sites on groundwater quality (Cai et al., 2011; Guil- beault et al., 2005; Kubert & Finkel, 2006; Trold-borg et al., 2010, 2012). Moreover, at large contaminated sites, portrayed by high source diversity such as land- fills, it is often not economically practicable to install the number of multilevel wells required to establish an adequately dense sampling grid (Troldborg et al., 2010). The difficulties of access and low dynamics show significant challenges for deep vadose zone reclamation, even though the techniques for reduc- ing the flux of contaminants within the deep vadose zone are effectively the same as those applicable to shallow vadose zone or groundwater remediation (Wellman et al., 2012). Monitoring methods for com- plicated subsurface environments, such as deep va- dose zones, are currently not well developed and are critical constituents of reclamation strategies, includ- ing those for flux-based methods (Bunn et al., 2012). 3. Hydrogeophysics
Figure 1: The Bukavu City with its three municipalities (data from the GeoRiskA project of the Royal Museum for Central Africa, MRAC). The combination of permanent and active natural hazards with social vulnerability is crucial to disturb the urban balance. For J.-C. Thouret and R. D’Ercole The combination of permanent and active natural
Figure 1: The Bukavu City with its three municipalities (data from the GeoRiskA project of the Royal Museum for Central Africa, MRAC). The combination of permanent and active natural hazards with social vulnerability is crucial to disturb the urban balance. For J.-C. Thouret and R. D’Ercole The combination of permanent and active natural
Figure 2: Location of study areas in the riparian space of Bukavu (background map: GeoRiskA project of the Royal Museum for Central Africa, MRAC). 3. Materials and Methods Work in this study is primarily based on the explora- tion of bibliographic sources concerning the intensifi- cation of the built-up zone in the riparian area and its impact on population vulnerability concerning natural hazards. We compared GoogleEarth satellite images of the riparian built-up zone of Bukavu between 2004 and 2021 to establish a diachronic analysis. This work allows to understand the urban growth and the exposure of the built-up zone to risks in the riparian area of Bukavu.
Figure 2: Location of study areas in the riparian space of Bukavu (background map: GeoRiskA project of the Royal Museum for Central Africa, MRAC). 3. Materials and Methods Work in this study is primarily based on the explora- tion of bibliographic sources concerning the intensifi- cation of the built-up zone in the riparian area and its impact on population vulnerability concerning natural hazards. We compared GoogleEarth satellite images of the riparian built-up zone of Bukavu between 2004 and 2021 to establish a diachronic analysis. This work allows to understand the urban growth and the exposure of the built-up zone to risks in the riparian area of Bukavu.
Figure 3: Map of ground deformation (C. Kalikone et al. 2017) and urban growth evolution between 1984 and 2013 (data from the GeoRiskA project of the Royal Museum for Central Africa, MRAC).
Figure 3: Map of ground deformation (C. Kalikone et al. 2017) and urban growth evolution between 1984 and 2013 (data from the GeoRiskA project of the Royal Museum for Central Africa, MRAC).
Figure 4: Google Earth satellite images (upper row: 2004, lower row: 2021) showing evolution of the built-up zone in the riparian area in Bukavu Muhanzi (A), Bassin du Collége (B) and Panzi (C). 5. Conclusions WD. VYVLTIVIUSIVITS We see a strong urban growth in the city of Bukavu which gained momentum between 2005 and today. This urban growth is not only linked to internal growth but also to rural exodus, which is significantly inten- sified by the insecurity that reigns in the rural envi- ronment. As in most cities in sub-Saharan Africa, the rate of urban growth is increasing every year. Un- fortunately, this urban growth in Bukavu has not fol- lowed an adequate development. Rural migration or rural exodus leads to the populating of outskirts and increases urban vulnerability. This vulnerability varies from one point to another in the urban environment. C. Michellier et al. (2020) show that social ecological system thinking is consistent with the necessity to in- tegrate the capacity of human societies to transform their environment to understand disasters. We see a strong urban growth in the city of Bukavu
Figure 4: Google Earth satellite images (upper row: 2004, lower row: 2021) showing evolution of the built-up zone in the riparian area in Bukavu Muhanzi (A), Bassin du Collége (B) and Panzi (C). 5. Conclusions WD. VYVLTIVIUSIVITS We see a strong urban growth in the city of Bukavu which gained momentum between 2005 and today. This urban growth is not only linked to internal growth but also to rural exodus, which is significantly inten- sified by the insecurity that reigns in the rural envi- ronment. As in most cities in sub-Saharan Africa, the rate of urban growth is increasing every year. Un- fortunately, this urban growth in Bukavu has not fol- lowed an adequate development. Rural migration or rural exodus leads to the populating of outskirts and increases urban vulnerability. This vulnerability varies from one point to another in the urban environment. C. Michellier et al. (2020) show that social ecological system thinking is consistent with the necessity to in- tegrate the capacity of human societies to transform their environment to understand disasters. We see a strong urban growth in the city of Bukavu
Table 1: Specifications of the selected stations
Table 1: Specifications of the selected stations
sumes that the observations Y are expressed as the Figure 1: Flowchart of the methodology. Figure 2: Study area.
sumes that the observations Y are expressed as the Figure 1: Flowchart of the methodology. Figure 2: Study area.
Figure 3: Optimal fingerprint analysis of various forces contributing to the (a) Rx1day and (b) Rx5day changes in southwestern Iran between 1951 and 2005.
Figure 3: Optimal fingerprint analysis of various forces contributing to the (a) Rx1day and (b) Rx5day changes in southwestern Iran between 1951 and 2005.
Figure 5: Comparison of the averages of the median percent changes of the Rx5day in southwestern Iran attributable to the (a) GHG and (b) ANT.
Figure 5: Comparison of the averages of the median percent changes of the Rx5day in southwestern Iran attributable to the (a) GHG and (b) ANT.
igure 6: The range of the GHG and ANT effects on the frequency of (a) Rx1day and (b) Rx5day in the frequency histogram betwee 951 and 2005. from averaging two Dezful stations and the APHRO- DITE datasets. The median percent change at the Dezful station is bigger than that of the APHRODITE dataset. be kept in mind that given the available uncertainties and including data especially from global models, it cannot be expected to specify certain values for shar- ing of climate change. It can be concluded from the results that the GHG effects are within the range of extreme precipitation, while other components of the ANT have had a less severe effect on values close to median. In addition, the starting point of the range at- tributable to the ANT are much shorter and the uncer- tainty of the results is low, while the uncertainty range of results related to the GHG is very long and covers the range close to the extreme precipitation up to the most extreme precipitation values.
igure 6: The range of the GHG and ANT effects on the frequency of (a) Rx1day and (b) Rx5day in the frequency histogram betwee 951 and 2005. from averaging two Dezful stations and the APHRO- DITE datasets. The median percent change at the Dezful station is bigger than that of the APHRODITE dataset. be kept in mind that given the available uncertainties and including data especially from global models, it cannot be expected to specify certain values for shar- ing of climate change. It can be concluded from the results that the GHG effects are within the range of extreme precipitation, while other components of the ANT have had a less severe effect on values close to median. In addition, the starting point of the range at- tributable to the ANT are much shorter and the uncer- tainty of the results is low, while the uncertainty range of results related to the GHG is very long and covers the range close to the extreme precipitation up to the most extreme precipitation values.
Figure 1: Dry events and dry periods in Iran from 10000 to 2000 BP. Red bands indicate dry events known in the paleoclimate literature (the 6200 BP event is introduced by the first author), and yellow bands illustrate dry climatic periods identified from the results of paleoclimate research in Iran. Lake Neor in Ardebil (Sharifi et al., 2015), Jazmourian Playa in Kerman (Vaezi et al., 2019), Katalekhor Cave in Zanjan (Andrews et al., 2020), Lake Kongor in Gorgan Plain (Shumilovskikh et al., 2016), Konar Sandal in Kerman (Safaierad et al., 2020) and Gol-e Zard Cave (Carolin et al., 2019). hybrid) can also be effective in the mitigation of global warming and reducing air pollution in large and indus- trial cities.
Figure 1: Dry events and dry periods in Iran from 10000 to 2000 BP. Red bands indicate dry events known in the paleoclimate literature (the 6200 BP event is introduced by the first author), and yellow bands illustrate dry climatic periods identified from the results of paleoclimate research in Iran. Lake Neor in Ardebil (Sharifi et al., 2015), Jazmourian Playa in Kerman (Vaezi et al., 2019), Katalekhor Cave in Zanjan (Andrews et al., 2020), Lake Kongor in Gorgan Plain (Shumilovskikh et al., 2016), Konar Sandal in Kerman (Safaierad et al., 2020) and Gol-e Zard Cave (Carolin et al., 2019). hybrid) can also be effective in the mitigation of global warming and reducing air pollution in large and indus- trial cities.
Figure 1: The location of water samples on the map of structural zones of Iran (Stécklin 1968). Red circle = samples from the Zagros orogeny (folded Zagros and Sanandaj-Sirjan), blue circle = samples from Alborz Mountain and its extension, green circle = samples from Kopet-Dagh, and pink circle = samples from Central Iran unit. The number of studied samples per geological unit is proportional to the number of producers in each spring sources in Chahar Mahal Bakhtiari, Ilam, Ker- manshah, Fars and Kohgilouyeh provinces is thought to be influenced by Zagros sedimentary rocks. This unit is dominantly composed of carbonate rocks with interlayers of shale, sandstone, chert, anhydrite and gypsum. The brands of bottled mineral water, sold in the markets of Tehran, Alborz, Ardebil, Zanjan, Golestan, Guilan and Mazandaran provinces, pass through several structural subdivisions of Alborz with different lithological components of sedimentary rocks as well as andesitic to basaltic volcanic rocks (St6ck- lin, 1968). Even though Central Iran is dominated by metamorphic and magmatic host rocks, the selected Kerman water has its source in Oligo-Miocene car- bonates of this unit. The source of the bottled mineral water produced in Khorasan Razavi province is lo- cated in a thick sequence of limestone and dolomite of Kopet Dagh basin in north-eastern Iran. of Kopet Dagh basin in north-eastern Iran.
Figure 1: The location of water samples on the map of structural zones of Iran (Stécklin 1968). Red circle = samples from the Zagros orogeny (folded Zagros and Sanandaj-Sirjan), blue circle = samples from Alborz Mountain and its extension, green circle = samples from Kopet-Dagh, and pink circle = samples from Central Iran unit. The number of studied samples per geological unit is proportional to the number of producers in each spring sources in Chahar Mahal Bakhtiari, Ilam, Ker- manshah, Fars and Kohgilouyeh provinces is thought to be influenced by Zagros sedimentary rocks. This unit is dominantly composed of carbonate rocks with interlayers of shale, sandstone, chert, anhydrite and gypsum. The brands of bottled mineral water, sold in the markets of Tehran, Alborz, Ardebil, Zanjan, Golestan, Guilan and Mazandaran provinces, pass through several structural subdivisions of Alborz with different lithological components of sedimentary rocks as well as andesitic to basaltic volcanic rocks (St6ck- lin, 1968). Even though Central Iran is dominated by metamorphic and magmatic host rocks, the selected Kerman water has its source in Oligo-Miocene car- bonates of this unit. The source of the bottled mineral water produced in Khorasan Razavi province is lo- cated in a thick sequence of limestone and dolomite of Kopet Dagh basin in north-eastern Iran. of Kopet Dagh basin in north-eastern Iran.
Table 1: An overview of general statistics of parameters determined in the analyzed bottled waters (n=36) together with guideline values established by Iranian legislation for drinking water (ISIRI, 1997) and by the WHO (2017).
Table 1: An overview of general statistics of parameters determined in the analyzed bottled waters (n=36) together with guideline values established by Iranian legislation for drinking water (ISIRI, 1997) and by the WHO (2017).
Figure 1: Workflow of the automated Sentinel-1 processing chain.
Figure 1: Workflow of the automated Sentinel-1 processing chain.
Figure 2: Selected tiles (green) have a low mean backscatter value and a high standard deviation of children mean backscatter values. Two subsets show the effect of these conditions. Subset a1 has a high mean backscatter value and a unimodal backscatter distribution a2, not allowing to separate the histogram into two classes. In contrast to that, subset b1 has a low mean backscatter value and also shows two distinct brightness regimes as can also be seen in the bimodal backscatter distribution b2. Therefore, subset b1 serves as a suitable candidate for threshold computation. The separating threshold is marked with a dashed line. Pre-processing requires a radiometric calibration to sigma naught, thus, converting the digital numbers of the input image to physical units, decibels (dB). Ina second step, we apply a Range-Doppler terrain cor- rection on the basis of data from the Shuttle Radar Topography Mission (SRTM). All of these steps are performed through the software snappy, a Python wrapper around ESA's Sentinel Application Platform SNAP (ESA 2020). The image output of this step is the normalized radar cross section (NRCS) giving in- formation about the radar backscatter in dB for each image pixel. Data is retrieved either through constantly re- questing new data from the Sentinel-1 Data Hub of the European Space Agency (ESA) or through data ingestion directly from one of the facilities of the German Aerospace Center (DLR) in Neus- trelitz, receiving data directly via an antenna. The entire processing chain is event-driven, i.e. the computation starts as soon as new data is available.
Figure 2: Selected tiles (green) have a low mean backscatter value and a high standard deviation of children mean backscatter values. Two subsets show the effect of these conditions. Subset a1 has a high mean backscatter value and a unimodal backscatter distribution a2, not allowing to separate the histogram into two classes. In contrast to that, subset b1 has a low mean backscatter value and also shows two distinct brightness regimes as can also be seen in the bimodal backscatter distribution b2. Therefore, subset b1 serves as a suitable candidate for threshold computation. The separating threshold is marked with a dashed line. Pre-processing requires a radiometric calibration to sigma naught, thus, converting the digital numbers of the input image to physical units, decibels (dB). Ina second step, we apply a Range-Doppler terrain cor- rection on the basis of data from the Shuttle Radar Topography Mission (SRTM). All of these steps are performed through the software snappy, a Python wrapper around ESA's Sentinel Application Platform SNAP (ESA 2020). The image output of this step is the normalized radar cross section (NRCS) giving in- formation about the radar backscatter in dB for each image pixel. Data is retrieved either through constantly re- questing new data from the Sentinel-1 Data Hub of the European Space Agency (ESA) or through data ingestion directly from one of the facilities of the German Aerospace Center (DLR) in Neus- trelitz, receiving data directly via an antenna. The entire processing chain is event-driven, i.e. the computation starts as soon as new data is available.
Figure 3: (a) The threshold t is computed from the backscatter distribution and marks the class break between water and non-water. Wher applying the threshold to the entire scene i... describes the mean backscatter value in the water regime. (b) The mean of all water pixels Later describes locations with a high classification certainty whereas the threshold t describes locations with a low classification certainty This is due to the fact that pixel values close to T may also be misclassified as the underlying backscatter value is caused both by the presence of water and non-water.
Figure 3: (a) The threshold t is computed from the backscatter distribution and marks the class break between water and non-water. Wher applying the threshold to the entire scene i... describes the mean backscatter value in the water regime. (b) The mean of all water pixels Later describes locations with a high classification certainty whereas the threshold t describes locations with a low classification certainty This is due to the fact that pixel values close to T may also be misclassified as the underlying backscatter value is caused both by the presence of water and non-water.
Figure 4: Overview of the study area. Background: 4 Sentinel-1A scenes from descending and ascending path (2 each), acquired on 2: August 2018, VV polarization (© Copernicus data/ESA 2018).
Figure 4: Overview of the study area. Background: 4 Sentinel-1A scenes from descending and ascending path (2 each), acquired on 2: August 2018, VV polarization (© Copernicus data/ESA 2018).
Figure 6: (left) water either detected by Sentinel-1, Sentinel-2, or both. Further subsets show the corresponding Sentinel-2 patch (middle) and Sentinel-1 patch (right). The upper row clearly shows the limitation of radar systems being sensitive to higher surface roughness resulting in non-detected water areas. In contrast to that, the lower row shows the limitation of optical systems where areas are not specifically cloud-covered (see black cloud mask). However, the occurrence of very fine clouds in the center of the path results in a significant change of the spectral signature and thus non-detected water areas.
Figure 6: (left) water either detected by Sentinel-1, Sentinel-2, or both. Further subsets show the corresponding Sentinel-2 patch (middle) and Sentinel-1 patch (right). The upper row clearly shows the limitation of radar systems being sensitive to higher surface roughness resulting in non-detected water areas. In contrast to that, the lower row shows the limitation of optical systems where areas are not specifically cloud-covered (see black cloud mask). However, the occurrence of very fine clouds in the center of the path results in a significant change of the spectral signature and thus non-detected water areas.
appointed by the central government, other the lower LST is the existence of dense forest cover in this part of the study area. Mullikkulam, Karadik- kuli, Marichchukaddi and Palalikuli areas, which are adjoining to Mannar district from Northern and West- ern edges of the forest reserve, also indicate exten- sive LST increases in the year 2018 compared to the year 2013. Total forest cover in WNP and adjacent forested areas has reduced from 204708 ha in 2013 to 197849 ha in 2018 (Fig 3). The total change in for- ested area due to various uses in the study area dur- ing this period is a loss of 6859 haa. It is visible that ar- eas with a higher increase in the LST are associated with high forest clearance areas (Fig. 3). ficers, range forest officers, public officials relevant government officers and residents in the area.
appointed by the central government, other the lower LST is the existence of dense forest cover in this part of the study area. Mullikkulam, Karadik- kuli, Marichchukaddi and Palalikuli areas, which are adjoining to Mannar district from Northern and West- ern edges of the forest reserve, also indicate exten- sive LST increases in the year 2018 compared to the year 2013. Total forest cover in WNP and adjacent forested areas has reduced from 204708 ha in 2013 to 197849 ha in 2018 (Fig 3). The total change in for- ested area due to various uses in the study area dur- ing this period is a loss of 6859 haa. It is visible that ar- eas with a higher increase in the LST are associated with high forest clearance areas (Fig. 3). ficers, range forest officers, public officials relevant government officers and residents in the area.
Figure 2: Land Surface Temperature Distribution over Wilpattu Forest Complex in its function of regulating micro-climatic conditions.
Figure 2: Land Surface Temperature Distribution over Wilpattu Forest Complex in its function of regulating micro-climatic conditions.
Figure 3: Forest Distribution in the Study Area in 2013 and 2018.
Figure 3: Forest Distribution in the Study Area in 2013 and 2018.
Figure 1: Geographical Map of Iran. In a next step, the images were imported to the ArcMap environment. The different images for each
Figure 1: Geographical Map of Iran. In a next step, the images were imported to the ArcMap environment. The different images for each
Figure 2: Land Cover Map of Iran in 2007.
Figure 2: Land Cover Map of Iran in 2007.
Figure 3: Land Cover Map of Iran in 2017.
Figure 3: Land Cover Map of Iran in 2017.
Figure 4: Land Cover Change Map of Iran in 2007 and 2017. to grow valuable (especially tree) species. At the mo- ment, based on the available results, most of these areas are covered by annual plants. It seems that further studies in areas where the dominant vegeta- tion is one-year vegetation can be used to evaluate the potential of planting higher quality plants to allow for more forestry where possible. On the other hand, due to the severe impacts of land cover and climate change, it is necessary to take the risk of converting vegetated areas into arid areas more seriously and to prevent this happening by clever management, es- pecially by using new techniques like remote sensing for monitoring. Buck, O., Niyogi, D.K. and Townsend, C.R., 2004. Scale-depen- dence of land use effects on water quality of streams in agricul- tural catchments. Environmental Pollution 130(2), pp. 287-299. httnc://doi ora/10 1016/1 anvnol 2002 10 018.
Figure 4: Land Cover Change Map of Iran in 2007 and 2017. to grow valuable (especially tree) species. At the mo- ment, based on the available results, most of these areas are covered by annual plants. It seems that further studies in areas where the dominant vegeta- tion is one-year vegetation can be used to evaluate the potential of planting higher quality plants to allow for more forestry where possible. On the other hand, due to the severe impacts of land cover and climate change, it is necessary to take the risk of converting vegetated areas into arid areas more seriously and to prevent this happening by clever management, es- pecially by using new techniques like remote sensing for monitoring. Buck, O., Niyogi, D.K. and Townsend, C.R., 2004. Scale-depen- dence of land use effects on water quality of streams in agricul- tural catchments. Environmental Pollution 130(2), pp. 287-299. httnc://doi ora/10 1016/1 anvnol 2002 10 018.
Figure 1: Geological map of the Federal Capital Territory, Abuja (modified after NGSA, 2006). made up of granites, mica schists, hornblende and feldspathic schists, and migmatites. They are highly fractured and jointed, showing essentially three line- ament patterns with a NE-SW, N-S, and NW-SE ori- entation (Fig. 1). Dense fracture networks are pre- sent, which may be interconnected and linked to the regional NE-SW trending Ilfewara-Zungeru fault line. These networks take the form of cracks and tensional joints resulting from stresses occasioned by alternat- ing heating-up and cooling sequences of rocks. Both horizontal and vertical fractures are very common, with the fracture widths generally decreasing with depth. This may be an indication that the fracture sys- tems are young, still evolving structures (Avci, 1983). We note the detailed geological report of the study
Figure 1: Geological map of the Federal Capital Territory, Abuja (modified after NGSA, 2006). made up of granites, mica schists, hornblende and feldspathic schists, and migmatites. They are highly fractured and jointed, showing essentially three line- ament patterns with a NE-SW, N-S, and NW-SE ori- entation (Fig. 1). Dense fracture networks are pre- sent, which may be interconnected and linked to the regional NE-SW trending Ilfewara-Zungeru fault line. These networks take the form of cracks and tensional joints resulting from stresses occasioned by alternat- ing heating-up and cooling sequences of rocks. Both horizontal and vertical fractures are very common, with the fracture widths generally decreasing with depth. This may be an indication that the fracture sys- tems are young, still evolving structures (Avci, 1983). We note the detailed geological report of the study
Figure 2: Schematic of the formation of thermal anomalies (modified after Shah et al., 2018). and tectonic settings of the FCT. The report indicates that the most evident structural features in the base- ment complex rocks of the FCT are northerly to north- easterly-southwesterly trends of the basement com- plexes near the eastern and western boundaries, and the easterly trends in the central part. These reflect the presence of a north-trending shear zone along the eastern limb of the FCT. A smaller north-trending shear zone appears just west of Abuja. Many faults and fractures trending NW cut these structures and are therefore of younger origin. They are believed o be the results of at least four major orogenic cy- cles of deformation, metamorphism and remobiliza- ion corresponding to the Liberian (2,700 Ma), the Eburnean (2,000 Ma), the Kibaran (1,100 Ma), and he Pan-African cycles (600 Ma) (Gauff and Adejumo, 1979; Black, 1980). These show that the young tec- onic structures may still be developing. The eastern shear zone represents a major regional tectonic fea- ure persisting for many tens of kilometers. Near the northeast corner of the FCT the shear zone is only a ew kilometers wide. In the southern part of the east- ern shear zone there are, at several places, individu- al shear zones in schists or granitic gneisses cutting other minor rocks such as amphibolites. Therefore, it is Supposed that there have been some tectonic movements causing regional metamorphism which ook place in Precambrian times (Gauff and Adejumo, 1979), which could have been reactivated along the aults in the weak/sheared zones. Shear zones are known to be weak zones, which abundantly underlie he FCT (Gauff and Adejumo, 1979). Hence, parts of he FCT may not be immune to tectonic movements and activities that may have caused the reported earth tremor of 6th September 2018. Figure 2: Schematic of the formation of thermal anomalies (modified after Shah et al., 2018). the eastern limb of the FCT. A smaller north-trending
Figure 2: Schematic of the formation of thermal anomalies (modified after Shah et al., 2018). and tectonic settings of the FCT. The report indicates that the most evident structural features in the base- ment complex rocks of the FCT are northerly to north- easterly-southwesterly trends of the basement com- plexes near the eastern and western boundaries, and the easterly trends in the central part. These reflect the presence of a north-trending shear zone along the eastern limb of the FCT. A smaller north-trending shear zone appears just west of Abuja. Many faults and fractures trending NW cut these structures and are therefore of younger origin. They are believed o be the results of at least four major orogenic cy- cles of deformation, metamorphism and remobiliza- ion corresponding to the Liberian (2,700 Ma), the Eburnean (2,000 Ma), the Kibaran (1,100 Ma), and he Pan-African cycles (600 Ma) (Gauff and Adejumo, 1979; Black, 1980). These show that the young tec- onic structures may still be developing. The eastern shear zone represents a major regional tectonic fea- ure persisting for many tens of kilometers. Near the northeast corner of the FCT the shear zone is only a ew kilometers wide. In the southern part of the east- ern shear zone there are, at several places, individu- al shear zones in schists or granitic gneisses cutting other minor rocks such as amphibolites. Therefore, it is Supposed that there have been some tectonic movements causing regional metamorphism which ook place in Precambrian times (Gauff and Adejumo, 1979), which could have been reactivated along the aults in the weak/sheared zones. Shear zones are known to be weak zones, which abundantly underlie he FCT (Gauff and Adejumo, 1979). Hence, parts of he FCT may not be immune to tectonic movements and activities that may have caused the reported earth tremor of 6th September 2018. Figure 2: Schematic of the formation of thermal anomalies (modified after Shah et al., 2018). the eastern limb of the FCT. A smaller north-trending
Figure 3: Land Surface Temperature images from (a) 4th to (f) 8th September 2018. 2.3. Research Data and Methodology and -2.099 respectively. This transformation was car- ried out in order to reposition the observed anoma- lies on their respective causative sources. Anomalies resulting from near-surface features were filtered by upward continuation of the TMI map to a height of 200 m. Thereafter, various edge enhancement tech- niques, comprising Horizontal Gradient Magnitude (HGM), First Vertical Derivative (FVD), and Euler De- convolution (ED), were applied on the filtered data. These edge enhancement filters were applied in or- der to enhance magnetic signatures from subsurface geologic linear structures (Such as contact, fractures and faults) and estimate their respective depth of occurrence. The LST and aeromagnetic data were processed using Oasis Montaj and ArcMap (version 10.3) software. Grids INC OCAIUIT MAA CGH WIS LEVI Y MODIS data derived from the NASA official network (NASA's Land Processes Distributed Active Archive Center (DAAC), https://lpdaac.usgs.gov/products/ modiitaivO06/) were analyzed for this study. The remote sensing imagery has 1 km spatial resolu- tion. Land Surface Temperature (LST) was extracted from the MODIS data. The Scientific Data sets in the MOD11A1 product include LST 1 km Day/Night, QC Day/Night, View Time Day/Night, View Angle Day/ Night, Emissivity Band 31/Band 32, Clear Sky Cover- age Day/Night. The daytime data was used. We ad- justed preprocessing according to the acquired data. The original data contain Land Surface Temperature (LST) imagery, but the map projection is sinusoidal i.e. LST products are provided in intergerized sinusoi- dal projection. The MODIS reprojection Tool (MRT) was used in order to reproject and mosaic the tiles of the product. The aeromagnetic data used in this study was acquired by Fugro Airborne Survey on behalf of the Nigerian Geological Survey Agency (NGSA) between the years 2005 and 2007. The air- craft was flown at a mean altitude of 80 m with 500 m line spacing. The total magnetic field intensity was measured using a triaxial magnetic gradiometer that sampled at an interval of 10 Hz (~7.5 m). The TMI anomaly map was reduced to the magnetic equator using magnetic inclination and declination of -6.401 MODIS data derived from the NASA official network
Figure 3: Land Surface Temperature images from (a) 4th to (f) 8th September 2018. 2.3. Research Data and Methodology and -2.099 respectively. This transformation was car- ried out in order to reposition the observed anoma- lies on their respective causative sources. Anomalies resulting from near-surface features were filtered by upward continuation of the TMI map to a height of 200 m. Thereafter, various edge enhancement tech- niques, comprising Horizontal Gradient Magnitude (HGM), First Vertical Derivative (FVD), and Euler De- convolution (ED), were applied on the filtered data. These edge enhancement filters were applied in or- der to enhance magnetic signatures from subsurface geologic linear structures (Such as contact, fractures and faults) and estimate their respective depth of occurrence. The LST and aeromagnetic data were processed using Oasis Montaj and ArcMap (version 10.3) software. Grids INC OCAIUIT MAA CGH WIS LEVI Y MODIS data derived from the NASA official network (NASA's Land Processes Distributed Active Archive Center (DAAC), https://lpdaac.usgs.gov/products/ modiitaivO06/) were analyzed for this study. The remote sensing imagery has 1 km spatial resolu- tion. Land Surface Temperature (LST) was extracted from the MODIS data. The Scientific Data sets in the MOD11A1 product include LST 1 km Day/Night, QC Day/Night, View Time Day/Night, View Angle Day/ Night, Emissivity Band 31/Band 32, Clear Sky Cover- age Day/Night. The daytime data was used. We ad- justed preprocessing according to the acquired data. The original data contain Land Surface Temperature (LST) imagery, but the map projection is sinusoidal i.e. LST products are provided in intergerized sinusoi- dal projection. The MODIS reprojection Tool (MRT) was used in order to reproject and mosaic the tiles of the product. The aeromagnetic data used in this study was acquired by Fugro Airborne Survey on behalf of the Nigerian Geological Survey Agency (NGSA) between the years 2005 and 2007. The air- craft was flown at a mean altitude of 80 m with 500 m line spacing. The total magnetic field intensity was measured using a triaxial magnetic gradiometer that sampled at an interval of 10 Hz (~7.5 m). The TMI anomaly map was reduced to the magnetic equator using magnetic inclination and declination of -6.401 MODIS data derived from the NASA official network
Figure 4: Daily average temperature anomaly from 4th to 8th September 2018. and faults) and estimate their respective depth of
Figure 4: Daily average temperature anomaly from 4th to 8th September 2018. and faults) and estimate their respective depth of
The Mpape, FCT tremor was recorded on the 6th of September, 2018 and the data acquired included the 4th to 8th of September, 2018. This is to incor- porate the LST for days before, during, and after the events were reported, allowing us to establish spatial thermal infrared variation and the temporal anomaly. Hence, we processed LST data sets for each day and produced the spatial anomaly maps. The LST data produced the spatial anomaly maps. The LST data Figure 6: The Inferred Lineament Map of the Study Area. Hence, we processed LST data sets for each day and
The Mpape, FCT tremor was recorded on the 6th of September, 2018 and the data acquired included the 4th to 8th of September, 2018. This is to incor- porate the LST for days before, during, and after the events were reported, allowing us to establish spatial thermal infrared variation and the temporal anomaly. Hence, we processed LST data sets for each day and produced the spatial anomaly maps. The LST data produced the spatial anomaly maps. The LST data Figure 6: The Inferred Lineament Map of the Study Area. Hence, we processed LST data sets for each day and
Figure 5: (a) Horizontal Gradient Magnitude (HGM) map; (b) HGM peaks overlain on HGM. 3. Results are presented as maps (Fig. 3), and averaged tem- perature values for each day were plotted (Fig. 4) to observe the trend of temperature fluctuations over the days under review. The aeromagnetic structural mapping identified some demarcated region suspect- ed to be sheared zones (Fig. 5), and faults/fractures hat could likely trigger the tremor (Fig. 6). These aults/fractures are labeled F1-F7 (Fig. 6). The faults rend in approximately NE-SW and E-W directions. Faults F4, F5, and F6 are deep-seated faults whose depths were estimated at a range > 1000 m, while he depths of F1, F2 and F3 were undetermined. F1 and F2 are regional faults that pass through Abuja and Nasarawa, and possibly extend to Kaduna. F7, which exists between depths of 700 and 1000 m (Fig. 5), had been identified by Eluyemi et al. (2019) as the Romanche Fault/Fracture. The Romanche Fault was found to emanate from the Gulf of Guinea and strikes in the same direction as the Pan African Orogeny.
Figure 5: (a) Horizontal Gradient Magnitude (HGM) map; (b) HGM peaks overlain on HGM. 3. Results are presented as maps (Fig. 3), and averaged tem- perature values for each day were plotted (Fig. 4) to observe the trend of temperature fluctuations over the days under review. The aeromagnetic structural mapping identified some demarcated region suspect- ed to be sheared zones (Fig. 5), and faults/fractures hat could likely trigger the tremor (Fig. 6). These aults/fractures are labeled F1-F7 (Fig. 6). The faults rend in approximately NE-SW and E-W directions. Faults F4, F5, and F6 are deep-seated faults whose depths were estimated at a range > 1000 m, while he depths of F1, F2 and F3 were undetermined. F1 and F2 are regional faults that pass through Abuja and Nasarawa, and possibly extend to Kaduna. F7, which exists between depths of 700 and 1000 m (Fig. 5), had been identified by Eluyemi et al. (2019) as the Romanche Fault/Fracture. The Romanche Fault was found to emanate from the Gulf of Guinea and strikes in the same direction as the Pan African Orogeny.
Figure 1: Above: The Geological and structural map of the Brunei-Muara district, which is modified after Wilford (1982), Morley et | (2003), and Aaisyah et al. (2020). Field evidence of selected outcrops is shown in subsets A to D where the average dip amount of t sedimentary layers varies from steep to gentle angles.
Figure 1: Above: The Geological and structural map of the Brunei-Muara district, which is modified after Wilford (1982), Morley et | (2003), and Aaisyah et al. (2020). Field evidence of selected outcrops is shown in subsets A to D where the average dip amount of t sedimentary layers varies from steep to gentle angles.
Figure 3: The high-resolution image (4000x3000 pixels) shown on left was captured by the drone, DJI Mavic Pro, and it shows high imag quality with clear evidence for faulting (right side) at the “Lion King” outcrop in Jerudong, which is one of the best outcrops to study th faulted sedimentary rocks in Brunei. cline in the north and the Belait syncline in the south. Prior to conducting any field excursion in the region, we created a base map of the study area by updat- ing the latest Brunei-Muara District geological maps (Wilford 1982; Morley et al., 2003). We used QGIS®
Figure 3: The high-resolution image (4000x3000 pixels) shown on left was captured by the drone, DJI Mavic Pro, and it shows high imag quality with clear evidence for faulting (right side) at the “Lion King” outcrop in Jerudong, which is one of the best outcrops to study th faulted sedimentary rocks in Brunei. cline in the north and the Belait syncline in the south. Prior to conducting any field excursion in the region, we created a base map of the study area by updat- ing the latest Brunei-Muara District geological maps (Wilford 1982; Morley et al., 2003). We used QGIS®
Figure 2: (A-C) Still images of large outcrops captured to show the whole overview of the outcrop that could not be achieved using hand: held cameras. (D-F) Exposed outcrops that were not accessible by foot were surveyed using the UAV by taking still images and videos. 2. REGIONAL SETTING Several faults are also mapped in the region (Fig. 1). The geological investigations presented here started in Brunei-Muara district, which is one of the adminis- trative divisions of the country and hosts most of the population. The field area covers the Berakas syn- cline and the Jerudong anticline which expose rocks of the Setap Shale, Belait, Miri, and Liang Forma- tions, along with the cover sequences of mainly Holo- cene sediments (Fig. 1). Morley et al. (2003) mapped three major faults in the region, which are named as Muara, Sengkurong, and the Bandar Seri Begawan faults (Fig. 1).
Figure 2: (A-C) Still images of large outcrops captured to show the whole overview of the outcrop that could not be achieved using hand: held cameras. (D-F) Exposed outcrops that were not accessible by foot were surveyed using the UAV by taking still images and videos. 2. REGIONAL SETTING Several faults are also mapped in the region (Fig. 1). The geological investigations presented here started in Brunei-Muara district, which is one of the adminis- trative divisions of the country and hosts most of the population. The field area covers the Berakas syn- cline and the Jerudong anticline which expose rocks of the Setap Shale, Belait, Miri, and Liang Forma- tions, along with the cover sequences of mainly Holo- cene sediments (Fig. 1). Morley et al. (2003) mapped three major faults in the region, which are named as Muara, Sengkurong, and the Bandar Seri Begawan faults (Fig. 1).
Figure 5: The drone field photograph shows the outcrop coverage at Kg Tungku (4°55'30.2"N, 114°53’51.0"E). The same outcrop was photographed after 5 months (right side). The tropical weather conditions in Brunei facilitate the process of weathering, and erosion, which is usually aided by the frequent rainfall events. This deteriorates the freshness of geological layers and makes observations difficult, which can be captured and preserved via drone footage. ed models such as the Wingtra drone (starting price The terrain map provided in Google Earth Pro shows that Brunei Darussalam is part of a folded se- quence of rocks that are exposed as plunging folds. The Brunei-Muara region mainly belongs to the ~ N plunging Berakas syncline. The onshore exposed portion of the ~ NE-SW trending right limb of the fold is ~ 29 km long while as the ~ N-S trending left limb is fordable cost (€ 700) compared to more sophisticat-
Figure 5: The drone field photograph shows the outcrop coverage at Kg Tungku (4°55'30.2"N, 114°53’51.0"E). The same outcrop was photographed after 5 months (right side). The tropical weather conditions in Brunei facilitate the process of weathering, and erosion, which is usually aided by the frequent rainfall events. This deteriorates the freshness of geological layers and makes observations difficult, which can be captured and preserved via drone footage. ed models such as the Wingtra drone (starting price The terrain map provided in Google Earth Pro shows that Brunei Darussalam is part of a folded se- quence of rocks that are exposed as plunging folds. The Brunei-Muara region mainly belongs to the ~ N plunging Berakas syncline. The onshore exposed portion of the ~ NE-SW trending right limb of the fold is ~ 29 km long while as the ~ N-S trending left limb is fordable cost (€ 700) compared to more sophisticat-
Figure 4: Comparison of aerial images of the “Lion King” outcrop in Jerudong (4°53'36.25"N, 114°49’59.01”E): (A) DJI Mavic Pro (resolution 4000x3000 pixels); (B) Ortho-photo obtained from Survey Department, Ministry of Development, Brunei (resolution: 1085x715 pixels); (C) Satellite image obtained from Google Earth Pro (2019) (resolution: 1175x758 pixels). The images from the DJI Mavic Pro produce muct clearer and sharper details.
Figure 4: Comparison of aerial images of the “Lion King” outcrop in Jerudong (4°53'36.25"N, 114°49’59.01”E): (A) DJI Mavic Pro (resolution 4000x3000 pixels); (B) Ortho-photo obtained from Survey Department, Ministry of Development, Brunei (resolution: 1085x715 pixels); (C) Satellite image obtained from Google Earth Pro (2019) (resolution: 1175x758 pixels). The images from the DJI Mavic Pro produce muct clearer and sharper details.
main selection criteria were: motivation, experience, and relevance of the application. A numerus clausus was Set up by the chairs based on their availability to efficiently supervise the participants’ workload. Then, the final selection was made to allow an equitable dis- tribution of participants by country of residence, and amongst students and ECPs. The selected partici- pants included 36 students and 56 ECPs. The partici- pants were affiliated with non-governmental and gov- ernmental organizations from 26 countries (Fig. 1).
main selection criteria were: motivation, experience, and relevance of the application. A numerus clausus was Set up by the chairs based on their availability to efficiently supervise the participants’ workload. Then, the final selection was made to allow an equitable dis- tribution of participants by country of residence, and amongst students and ECPs. The selected partici- pants included 36 students and 56 ECPs. The partici- pants were affiliated with non-governmental and gov- ernmental organizations from 26 countries (Fig. 1).
Among the 118 reviewers (i.e. participants and chairs), 41 completed the survey representing a 35% rate of participation. The survey demonstrated that the main goals driving the involvement of ECSs in the review were related to capacity building (35 %), the opportunity to expand their literature knowledge (22 %), the opportunity for international collaboration (20 %), and the desire to gain a better understand- ing of the structure and role of the IPCC (14 %) (Fig. 2a). Distribution charts presented in Figure 2b show that most ECSs dedicated between 1 to 3 h towards preparation on the training guide and webinars before the beginning of the review process. The difference in the amount of time dedicated to preparation by students and early career professionals was not sig- nificant neither for the time spent reviewing the guide (chi-squared = 3.101, p-value = 0.3763) nor for the time spent watching the workshops (chi-squared = 6.9272, p-value = 0.07426) (see methods in Casado et al, 2020). ECSs were willing to dedicate more time than they actually needed to proceed with the review (Fig. 2c). Again, no significant difference was found between students and professionals regarding the
Among the 118 reviewers (i.e. participants and chairs), 41 completed the survey representing a 35% rate of participation. The survey demonstrated that the main goals driving the involvement of ECSs in the review were related to capacity building (35 %), the opportunity to expand their literature knowledge (22 %), the opportunity for international collaboration (20 %), and the desire to gain a better understand- ing of the structure and role of the IPCC (14 %) (Fig. 2a). Distribution charts presented in Figure 2b show that most ECSs dedicated between 1 to 3 h towards preparation on the training guide and webinars before the beginning of the review process. The difference in the amount of time dedicated to preparation by students and early career professionals was not sig- nificant neither for the time spent reviewing the guide (chi-squared = 3.101, p-value = 0.3763) nor for the time spent watching the workshops (chi-squared = 6.9272, p-value = 0.07426) (see methods in Casado et al, 2020). ECSs were willing to dedicate more time than they actually needed to proceed with the review (Fig. 2c). Again, no significant difference was found between students and professionals regarding the
Figure 3: a) Distribution of submitted comments as a function of the participants’ position and the quality of submitted comments for each participants’ position based on the classification (major comments, minor comments and unfit for submission) of Casado, et al. (2020); b) the review experience of participants depending on their position.
Figure 3: a) Distribution of submitted comments as a function of the participants’ position and the quality of submitted comments for each participants’ position based on the classification (major comments, minor comments and unfit for submission) of Casado, et al. (2020); b) the review experience of participants depending on their position.
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