Assessment of the solar energy-agriculture-water nexus in the expanding solar energy industry of India: An initiative for sustainable resource management.

The rapid expansion of solar energy in India poses challenges in the context of cropland encroachment and water-resource scarcity. To help address these challenges, we used open access satellite observations and GIS technologies over Earth Engine platform to devise the Solar Panel Index (SPI) for efficient detection of solar farms, achieving a Land use/Land cover classification accuracy of 89 %. We have used cropland, water availability, power grid and land surface temperature data-sets for generation of land suitability map. Sentinel-2 data-sets along with SPI were used to develop solar farm locations and their estimated installed capacity within the study area. The outcomes from the study depicted that over 40 % of all solar farms in the country are located on agricultural land, with the highest seen in Karnataka (73.55 %) followed by Tamil Nadu (68.81 %). Furthermore, high installed capacity coincides with low groundwater depths, exacerbating local water strain. Madhya Pradesh was found to have almost no power plant located within 5 km of surface water bodies. Our findings emphasize the significance of the solar-agriculture-water (SAW) nexus for sustainable development. Using high-resolution Land Suitability Map (LSM) as a decision-making criteria for allocating land for power generation reveals that Rajasthan holds the highest potential for solar energy installation. Our integrated approach considering the interplay between solar-energy production, agriculture, and water-resource management contributes to the sustainable growth of the solar-energy sector and regional development.

Interactions of urbanisation, climate variability, and infectious disease dynamics: insights from the Coimbatore district of Tamil Nadu.

Climate change and shifts in land use/land cover (LULC) are critical factors affecting the environmental, societal, and health landscapes, notably influencing the spread of infectious diseases. This study delves into the intricate relationships between climate change, LULC alterations, and the prevalence of vector-borne and waterborne diseases in Coimbatore district, Tamil Nadu, India, between 1985 and 2015. The research utilised Landsat-4, Landsat-5, and Landsat-8 data to generate LULC maps, applying the maximum likelihood algorithm to highlight significant transitions over the years. This study revealed that built-up areas have increased by 67%, primarily at the expense of agricultural land, which was reduced by 51%. Temperature and rainfall data were obtained from APHRODITE Water Resources, and with a statistical analysis of the time series data revealed an annual average temperature increase of 1.8 °C and a minor but statistically significant rainfall increase during the study period. Disease data was obtained from multiple national health programmes, revealing an increasing trend in dengue and diarrhoeal diseases over the study period. In particular, dengue cases surged, correlating strongly with the increase in built-up areas and temperature. This research is instrumental for policy decisions in public health, urban planning, and climate change mitigation. Amidst limited research on the interconnections among infectious diseases, climate change, and LULC changes in India, our study serves as a significant precursor for future management strategies in Coimbatore and analogous regions.

Earthquake-induced soil landslides: volume estimates and uncertainties with the existing scaling exponents.

Quantifying landslide volumes in earthquake affected areas is critical to understand the orogenic processes and their surface effects at different spatio-temporal scales. Here, we build an accurate scaling relationship to estimate the volume of shallow soil landslides based on 1 m pre- and post-event LiDAR elevation models. On compiling an inventory of 1719 landslides for 2018 Mw 6.6 Hokkaido-Iburi earthquake epicentral region, we find that the volume of soil landslides can be estimated by γ = 1.15. The total volume of eroded debris from Hokkaido-Iburi catchments based on this new scaling relationship is estimated as 64-72 million m3. Based on the GNSS data approximation, we noticed that the co-seismic uplift volume is smaller than the eroded volume, suggesting that frequent large earthquakes (and rainfall extremes) may be counterbalancing the topographic uplift through erosion by landslides, especially in humid landscapes such as Japan, where soil properties are rather weak.

Substantial changes in Gaseous pollutants and health effects during COVID-19 in Delhi, India.

Background: Coronavirus disease has affected the entire population worldwide in terms of physical and environmental consequences. Therefore, the current study demonstrates the changes in the concentration of gaseous pollutants and their health effects during the COVID-19 pandemic in Delhi, the national capital city of India. Methodology: In the present study, secondary data on gaseous pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ammonia (NH3), and ozone (O3) were collected from the Central Pollution Control Board (CPCB) on a daily basis. Data were collected from January 1, 2020, to September 30, 2020, to determine the relative changes (%) in gaseous pollutants for pre-lockdown, lockdown, and unlockdown stages of COVID-19. Results: The current findings for gaseous pollutants reveal that concentration declined in the range of 51%-83% (NO), 40%-69% (NOx), 31%-60% (NO2), and 25%-40% (NH3) during the lockdown compared to pre-lockdown period, respectively. The drastic decrease in gaseous pollutants was observed due to restricted measures during lockdown periods. The level of ozone was observed to be higher during the lockdown periods as compared to the pre-lockdown period. These gaseous pollutants are linked between the health risk assessment and hazard identification for non-carcinogenic. However, in infants (0-1 yr), Health Quotient (HQ) for daily and annual groups was found to be higher than the rest of the exposed group (toddlers, children, and adults) in all the periods. Conclusion: The air quality values for pre-lockdown were calculated to be "poor category to "very poor" category in all zones of Delhi, whereas, during the lockdown period, the air quality levels for all zones were calculated as "satisfactory," except for Northeast Delhi, which displayed the "moderate" category. The computed HQ for daily chronic exposure for each pollutant across the child and adult groups was more than 1 (HQ > 1), which indicated a high probability to induce adverse health outcomes.

Spatial integration framework of solar, wind, and hydropower energy potential in Southeast Asia.

Amid its massive increase in energy demand, Southeast Asia has pledged to increase its use of renewable energy by up to 23% by 2025. Geospatial technology approaches that integrate statistical data, spatial models, earth observation satellite data, and climate modeling can be used to conduct strategic analyses for understanding the potential and efficiency of renewable energy development. This study aims to create the first spatial model of its kind in Southeast Asia to develop multi-renewable energy from solar, wind, and hydropower, further broken down into residential and agricultural areas. The novelty of this study is the development of a new priority model for renewable energy development resulting from the integration of area suitability analysis and the estimation of the amount of potential energy. Areas with high potential power estimations for the combination of the three types of energy are mostly located in northern Southeast Asia. Areas close to the equator, have a lower potential than the northern countries, except for southern regions. Solar photovoltaic (PV) plant construction is the most area-intensive type of energy generation among the considered energy sources, requiring 143,901,600 ha (61.71%), followed by wind (39,618,300 ha; 16.98%); a combination of solar PV and wind (37,302,500 ha; 16%); hydro (7,665,200 ha; 3.28%); a combination of hydro and solar PV (3,792,500 ha; 1.62%); and a combination of hydro and wind (582,700 ha; 0.25%). This study is timely and important because it will inform policies and regional strategies for transitioning to renewable energy, with consideration of the different characteristics present in Southeast Asia.

Exploring Indigenous and Local Knowledge and Practices (ILKPs) in Traditional Jhum Cultivation for Localizing Sustainable Development Goals (SDGs): A Case Study from Zunheboto District of Nagaland, India.

Globally, shifting cultivation is known to be an important driver of tropical deforestation. However, in this paper, we argue that it can be sustainably managed if the environmental boundary conditions, laid by the traditional customs and practices, are fully respected. We narrate an empirical study from the Zunheboto district of Nagaland, India, where we deployed a mixed research method to explore the Indigenous and Local Knowledge and Practices (ILKPs) associated with shifting cultivation (aka Jhum), particularly concerning farm-level practices, forest and biodiversity conservation, and disaster risk reduction measures. The research method included analysis of primary data obtained through Focus Group discussions (FGDs), key informant interviews (n = 21), and a questionnaire survey (n = 153) with Jhum farmers from two different age groups, i.e., below 50 years (middle-aged farmers) and above 50 years (older farmers). From the qualitative inquiry, we identified 15 ILKPs, which were then validated from survey responses. We used the Mann-Whitney U test to examine differences in agreement between two groups of framers. Based on this analysis, we conclude that upholding of the ILKPs holds strong potential for the local implementation of several Sustainable Development Goals (SDGs), particularly, SDG-1(No poverty), SDG-2 (Zero hunger), and SDG-15 (Life on land). However, eight of the identified ILKPs showed a statistically significant difference between older and middle-aged farmers, underlining a declining trend. Finally, we suggest suitable policy measures to mainstream ILKPs to balance the trade-offs in food production and biodiversity conservation, and to ensure the future sustainability of Jhum cultivation in the region and beyond.

Temporary reduction in VOCs associated with health risk during and after COVID-19 in Maharashtra, India.

A novel coronavirus has affected almost all countries and impacted the economy, environment, and social life. The short-term impact on the environment and human health needs attention to correlate the Volatile organic compounds (VOCs) and health assessment for pre-, during, and post lockdowns. Therefore, the current study demonstrates VOC changes and their effect on air quality during the lockdown. The findings of result, the levels of the mean for total VOC concentrations were found to be 15.45 ± 21.07, 2.48 ± 1.61, 19.25 ± 28.91 µg/m3 for all monitoring stations for pre-, during, and post lockdown periods. The highest value of TVOCs was observed at Thane, considered an industrial region (petroleum refinery), and the lowest at Bandra, which was considered a residential region, respectively. The VOC levels drastically decreased by 52%, 89%, 80%, and 97% for benzene, toluene, ethylbenzene, and m-xylene, respectively, during the lockdown period compared to the previous year. In the present study, the T/B ratio was found lower in the lockdown period as compared to the pre-lockdown period. This can be attributed to the complete closure of non-traffic sources such as industries and factories during the lockdown. The Lifetime Cancer Risk values for all monitoring stations for benzene for pre-and-post lockdown periods were higher than the prescribed value, except during the lockdown period. Supplementary Information: The online version contains supplementary material available at 10.1007/s10874-022-09440-5.

Alleviating Drought Stress in Brassica juncea (L.) Czern & Coss. by Foliar Application of Biostimulants-Orthosilicic Acid and Seaweed Extract.

Agricultural productivity is negatively impacted by drought stress. Brassica is an important oilseed crop, and its productivity is often limited by drought. Biostimulants are known for their role in plant growth promotion, increased yields, and tolerance to environmental stresses. Silicon in its soluble form of orthosilicic acid (OSA) has been established to alleviate deteriorative effects of drought. Seaweed extract (SWE) also positively influence plant survival and provide dehydration tolerance under stressed environments. The present study was conducted to evaluate the efficacy of OSA and SWE on mitigating adverse effects of drought stress on Brassica genotype RH-725. Foliar application of OSA (2 ml/L and 4 ml/L) and SWE of Ascophyllum nodosum (3 ml/L and 4 ml/L) in vegetative stages in Brassica variety RH 725 under irrigated and rainfed condition revealed an increase in photosynthetic rate, stomatal conductance, transpirational rate, relative water content, water potential, osmotic potential, chlorophyll fluorescence, chlorophyll stability index, total soluble sugars, total protein content, and antioxidant enzyme activity; and a decrease in canopy temperature depression, proline, glycine-betaine, H2O2, and MDA content. Application of 2 ml/L OSA and 3 ml/L SWE at vegetative stage presented superior morpho-physiological and biochemical characteristics and higher yields. The findings of the present study will contribute to developing a sustainable cropping system by harnessing the benefits of OSA and seaweed extract as stress mitigators.

Quantifying spatio-temporal variation in aquaculture production areas in Satkhira, Bangladesh using geospatial and social survey.

Despite Bangladesh being one of the leading countries in aquaculture food production worldwide, there is a considerable lack of updated scientific information about aquaculture activities in remote sites, making it difficult to manage sustainably. This study explored the use of geospatial and field data to monitor spatio-temporal changes in aquaculture production sites in the Satkhira district from 2017-2019. We used Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) to locate aquaculture ponds based on the terrain elevation and slope. Radar backscatter information from the Sentinel-1 satellite, and different water indices derived from Sentinel-2 were used to assess the spatio-temporal extents of aquaculture areas. An image segmentation algorithm was applied to detect aquaculture ponds based on backscattering intensity, size and shape characteristics. Our results show that the highest number of aquaculture ponds were observed in January, with a size of more than 30,000 ha. Object-based image classification of Sentinel-1 data showed an overall accuracy above 80%. The key factors responsible for the variation in aquaculture were investigated using field surveys. We noticed that despite a significant number of aquaculture ponds in the study area, shrimp production and export are decreasing because of a lack of infrastructure, poor governance, and lack of awareness in the local communities. The result of this study can provide in-depth information about aquaculture areas, which is vital for policymakers and environmental administrators for successful aquaculture management in Satkhira, Bangladesh and other countries with similar issues.

Implications of climate change and drought on water requirements in a semi-mountainous region of the Vietnamese Mekong Delta.

As the backbone of Vietnam's economy, the country has recently established a number of policies for promoting and investing in smart agriculture in the Mekong Delta, the country's largest agricultural hub, to foster overall socio-economic development. However, water remains a critical constraint for crop production, with progress being hindered by water scarcity and quality issues, and compounded by socio-economic transformation and climate change. Considering these challenges, this study used the CROPWAT model and a wide spectrum of climate change scenarios to investigate future total water demands in the 2030s and 2050s as well as drought levels in two underdeveloped semi-mountainous reservoir catchments, i.e., O Ta Soc and O Tuk Sa in An Giang province. The results suggest that the usable storage capacity of the O Ta Soc reservoir will increase to 650,000 m3 to meet water supply demands under all climate change scenarios and the medium-term, moderate drought conditions. The useable storage capacity of the O Tuk Sa reservoir will also be increased to 880,000 m3 and the irrigation area would see a marked 70% reduction compared to its design irrigation. Under these circumstances, the O Tuk Sa reservoir will continue to supply water under all climate change scenarios and medium-term droughts. As a core element for strategic planning and to ensure efficient management of water resources, the results highlight the importance of estimating potential runoff and rainfall in semi-mountainous reservoir catchments under various drought conditions in order to propose the suitable expansion of the useable water storage capacities.

The role of remote sensing during a global disaster: COVID-19 pandemic as case study.

Remotely sensed imagery is used as a tool to aid decision makers and scientists in a variety of fields. A recent world event in which satellite imagery was extensively relied on by a variety of stakeholders was the COVID-19 pandemic. In this article we aim to give an overview of the types of information offered through remote sensing (RS) to help address different issues related to the pandemic. We also discuss about the stakeholders that benefited from the data, and the value added by its availability. The content is presented under four sub-sections; namely (1) the use of RS in real-time decision-making and strategic planning during the pandemic; how RS revealed the (2) environmental changes and (3) social and economic impacts caused by the pandemic. And (4) how RS informed our understanding of the epidemiology of SARS-CoV-2, the pathogen responsible for the pandemic. High resolution optical imagery offered updated on-the-ground data for e.g., humanitarian aid organizations, and informed operational decision making of shipping companies. Change in the intensity of air and water pollution after reduced anthropogenic activities around the world were captured by remote sensing - supplying concrete evidence that can help inform improved environmental policy. Several economic indicators were measured from satellite imagery, showing the spatiotemporal component of economic impacts caused by the global pandemic. Finally, satellite based meteorological data supported epidemiological studies of environmental disease determinants. The varied use of remote sensing during the COVID-19 pandemic affirms the value of this technology to society, especially in times of large-scale disasters.

Imminent threat of rock-ice avalanches in High Mountain Asia.

Upsurge of glacier-related hazards in High Mountain Asia (HMA) has been evident in recent years due to global warming. While many glacial-related hazards are instantaneous, some large landslides were preceded by slow gravitational deformation, which can be predicted to evade catastrophes. Here, we present robust evidence of historical deformation in 2021 Chamoli rock-ice avalanche of Himalaya using space imaging techniques. Multi-temporal satellite data provide evidence of a precursor event in 2016 and expansion of a linear fracture along joint planes, indicating 2021 rock-ice avalanche is a retrogressive wedge failure. The deformation history shows that the fracture propagated at a velocity of ~0.07 m day-1 until September 2020, and with an accelerated velocity (~0.14 m day-1 on average) lately. Analysis of recent similar cases in HMA supported our inference on global warming-induced glacier retreat and thermomechanical effects in enhancing the weakening of fractured rock masses in tectonically active mountain belts. Recent advances in Earth observation and seismic monitoring system can offer clues to the location and timing of impending catastrophic failures in high mountain regions.

Genetic Analysis for Resistance to Sclerotinia Stem Rot, Yield and Its Component Traits in Indian Mustard [Brassica juncea (L.) Czern & Coss.].

Understanding the mode of gene action that controls seed yield and Sclerotinia stem rot resistance in Indian mustard is critical for boosting yield potential. In a line × tester mating design, ten susceptible lines and four resistant testers were used to conduct genetic analysis. The significance of general combining ability (GCA) and specific combining ability (SCA) variances revealed that both additive and non-additive gene actions were involved in the inheritance of Sclerotinia stem rot resistance and yield attributing traits. In addition to 1000-seed weight and number of primary and secondary branches/plant, the genotypes RH 1569 (line) and DRMR 2035 (tester) appeared to be the strongest general combiners for Sclerotinia stem rot resistance. RH 1657 × EC 597317 was the only cross among several that demonstrated a significant desired SCA value for Sclerotinia rot resistance. Regarding SCA effects for yield and component traits, the cross RH 1658 × EC 597328 performed best, with a non-significant but acceptable negative SCA effect for resistance. DRMR 2035, RH 1222-28, RH 1569, RH 1599-41, RH 1657, RH 1658, and EC 597328 are promising genotypes to use as parents in future heterosis breeding and for obtaining populations with high yield potential and greater resistance to Sclerotinia stem rot disease in Indian mustard, based on GCA effects of parents, per se performance, and SCA effects of hybrids. Days to 50% flowering, number of primary branches/plant, main shoot length, and 1000-seed weight all had a high genotypic coefficient of variability (GCV), broad-sense heritability (h2bs), and genetic advance as percent of the mean (GAM) values, as well as significant and desirable correlations and direct effects on seed yield. As a result, these traits have been recognized as the most critical selection criterion for Indian mustard breeding programs.

Integrated approach for spatial flood susceptibility assessment in Bhagirathi sub-basin, India using entropy information theory and geospatial technology.

Globally, floods as dynamic hydraulic hazard have caused widespread damages to both socioeconomic conditions and environment at various scales. Managing flood and management of water resource is a global challenge under the changing climatic condition. This study assessed flood susceptibility in the Bhagirathi sub-basin, India using entropy information theory and geospatial technology. Twelve flood susceptibility parameters such as land use/land cover, normalized difference vegetation index (NDVI), slope, elevation, geology, geomorphology, normalized difference water index (NDWI), soil, drainage density, average rainfall, maximum temperature, and humidity during monsoon season were utilized to examine flood susceptibility. Receiver operating characteristics (ROC) curve and Leave-One-Out Cross-Validation (LOOCV) techniques were carried out to validate flood susceptibility map. Kappa statistics was also used to check the reliability of the flood susceptibility model. Findings of the study revealed that nearly 45% area of the sub-basin was highly susceptible to flood followed by moderate (29.3%), very high (19%), low (6.9%), and very low (0.2%). These findings also revealed that nearly 92% area in the eastern, north-eastern, and deltaic sub-basin was susceptible to floods. ROC analysis indicated high success (0.932) and prediction (0.903) rates for the susceptibility map while LOOCV (R2 being 0.97) and Kappa (k = 0.934) have shown substantial prediction of the model. Hence, the susceptibility maps are useful for the local planners and government organization in designing the early flood warning system, and reducing the human and economic losses. The methodology used in this study is applicable for analyzing flood susceptibility at spatial scales in similar systems.

Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis.

Peatlands in Indonesia are subject to subsidence in recent years, resulting in significant soil organic carbon loss. Their degradation is responsible for several environmental issues; however, understanding the causes of peatland subsidence is of prime concern for implementing mitigation measures. Here, we employed time-series Small BAseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) using ALOS PALSAR-2 images to assess the relationship between subsidence rates and land use/land cover (LULC) change (including drainage periods) derived from decadal Landsat data (1972-2019). Overall, the study area subsided with a mean rate of -2.646 ± 1.839 cm/year in 2018-2019. The subsidence rates slowed over time, with significant subsidence decreases in peatlands after being drained for 9 years. We found that the long-time persistence of vegetated areas leads to subsidence deceleration. The relatively lower subsidence rates are in areas that changed to rubber/mixed plantations. Further, the potential of subsidence prediction was assessed using Random Forest (RF) regression based on LULC change, distance from peat edge, and elevation. With an R2 of 0.532 (RMSE = 0.594 cm/year), this machine learning method potentially enlarges the spatial coverage of InSAR method for the higher frequency SAR data (such as Sentinel-1) that mainly have limited coverage due to decorrelation in vegetated areas. According to feature importance in the RF model, the contribution of LULC change (including drainage period) to the subsidence model is comparable with distance from peat edge and elevation. Other uncertainties are from unexplained factors related to drainage and peat condition, which need to be accounted for as well. This work shows the significance of decadal LULC change analysis to supplement InSAR measurement in tropical peatland subsidence monitoring programs.

Migrating rivers, consequent paleochannels: The unlikely partners and hotspots of flooding.

Furious floods have become an omnipresent reality with the dawn of climate change and its transition to adulthood. Since climate change has now become an accepted reality, analysing the factors that favour or disfavour floods are an urgent requirement. Here we showcase the role of paleochannels, a product of migrating rivers, in a catastrophic flood in the south-western part of the Indian Peninsula. This study exposes whether these geomorphic features facilitate or impede floods. For the purpose of extracting paleochannels and floodwater mapping, we utilized multiple satellite datasets and took advantage of diversified feature selection algorithms. Paleochannels were demarcated viz., initial identification of a few paleochannels from literature and confirmation through high-resolution Google Earth (GE) images, followed by Principal Component Analysis (PCA) of Sentinel-2 images using Google Earth Engine (GEE), and a supervised classification of the principal bands 1, 2, and 3. False-positives were eliminated using Object-Oriented Analysis (OOA), which reduced the 964,254 polygons to 23,254. These polygons were visually affirmed using GE images that resulted in 115 paleochannels as the final collection. A few locations were verified through Vertical Electrical Sounding (VES) using the Schlumberger method. The features were analysed with the floodwaters of the 2018 catastrophic flood, extracted from Synthetic Aperture Radar (SAR) data, which was delineated for different temporal limits including the day of peak flood of August 17, 2018. During the peak flood, the inundation of the study area extended to 534.86 km2 with all the paleochannels getting immersed in floodwater. After 44 days of peak flood, the post-flood analysis revealed that when the floodwater receded 50%, the paleochannels emptied 87.39%, with the midland paleochannels discharging more than those of lowlands. Thus, such geomorphic features can be flood hotspots, but can be considered for discharging floodwater to mitigate flood risk in case of unprecedented rain.

Genetic and Proteomic Basis of Sclerotinia Stem Rot Resistance in Indian Mustard [Brassica juncea (L.) Czern & Coss.].

Sclerotinia stem rot is one of the utmost important disease of mustard, causing considerable losses in seed yield and oil quality. The study of the genetic and proteomic basis of resistance to this disease is imperative for its effective utilization in developing resistant cultivars. Therefore, the genetic pattern of Sclerotinia stem rot resistance in Indian mustard was studied using six generations (P1, P2, F1, F2, BC1P1, and BC1P2) developed from the crossing of one resistant (RH 1222-28) and two susceptible (EC 766300 and EC 766123) genotypes. Genetic analysis revealed that resistance was governed by duplicate epistasis. Comparative proteome analysis of resistant and susceptible genotypes indicated that peptidyl-prolyl cis-trans isomerase (A0A078IDN6 PPIase) showed high expression in resistant genotype at the early infection stage while its expression was delayed in susceptible genotypes. This study provides important insight to mustard breeders for designing effective breeding programs to develop resistant cultivars against this devastating disease.

Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet.

Digitalization provides access to an integrated network of unexploited big data with potential benefits for society and the environment. The development of smart systems connected to the internet of things can generate unique opportunities to strategically address challenges associated with the United Nations Sustainable Development Goals (SDGs) to ensure an equitable, environmentally sustainable, and healthy society. This perspective describes the opportunities that digitalization can provide towards building the sustainable society of the future. Smart technologies are envisioned as game-changing tools, whereby their integration will benefit the three essential elements of the food-water-energy nexus: (i) sustainable food production; (ii) access to clean and safe potable water; and (iii) green energy generation and usage. It then discusses the benefits of digitalization to catalyze the transition towards sustainable manufacturing practices and enhance citizens' health wellbeing by providing digital access to care, particularly for the underserved communities. Finally, the perspective englobes digitalization benefits by providing a holistic view on how it can contribute to address the serious challenges of endangered planet biodiversity and climate change.

Basin-wide flood depth and exposure mapping from SAR images and machine learning models.

Recent years recorded an increasing number of short duration - high-intensity rainfall events in the Indian subcontinent consequent with urban and riverine flash floods. Rapid assessments of flooded areas are key for effective mitigation strategies and disaster risk plans, as well as to prepare operative policies for future events. Herein, we present an integrated methodology for rapidly mapping the flood extent, and depths based on Synthetic Aperture Radar (SAR) images and a digital elevation model (DEM). Incessant rain during August 2019 brought heavy riverine flooding in southern India, killed at least 280 people, and displaced about one million inhabitants from low-lying areas. We used SAR images by Sentinel-1 before, and during the flooding, and the MERIT DEM which enabled us to map the flood extent and flood depth of the inundation zones. Because the coverage of Sentinel-1 scene was limited to the Kabini river section during the flood period, flood extent and depth maps for the adjacent basin was generated by mapping the susceptibility for flooding using the training set obtained from the flood time Sentinel-1 images, and a set of predictive variables derived from DEM using random forest model. Qualitative analysis and cross-comparison with a numerical flood model proved the proposed approach is highly reliable with an accuracy value of 90% and 86% respectively for training and validation data, thus allowing a precise, simple, and fast flood mapping. The methodology presented here could be applied to other flooded areas having incomplete inventory in the context of flood risk assessment.