How Do Floods and Drought Impact Economic Growth and Human Development at the Sub-National Level in India?

Sources and uncertainties of future global drought risk with ISIMIP2b climate scenarios and socioeconomic indicators

The severity of potential drought impacts is influenced not only by physical characteristics, such as precipitation, soil moisture, and temperature but also by local socioeconomic conditions that influence a region's exposure and vulnerability. This study aims to demonstrate projected future global drought risk, which is quantified based on indicators representing three risk components, namely, hazard, exposure, and vulnerability. Drought hazard is evaluated using the standardized precipitation-evapotranspiration index. Drought exposure considers population and agricultural land use, and drought vulnerability accounts for gross domestic product, total water storage, and water consumption. This global-scale study was conducted for the historical and future periods of 1975-2005 and 2070-2099, respectively, and employed three combined scenarios consisting of Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) with datasets from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). To evaluate the proposed approach, the results obtained for the historical period were compared with drought records. The projections suggest that in addition to increasing drought hazards caused by climate change, populous regions, or areas heavily dependent on agriculture are at a higher risk than other regions because of high water consumption levels. The contributions analysis indicates that agricultural land use is the largest contributor to drought risk, except for Africa, where the population makes the largest contribution. Model uncertainty of the General Circulation Models (GCMs) and Hydrological Models (HMs) is dominant compared to the RCP and SSP scenarios, with uncertainty from the GCMs the most dominant. This study provides possible depictions and their uncertainties of future drought risks and can assist decision-makers in developing better adaptation and mitigation strategies for climatic, environmental, and socioeconomic changes.

Drought and society: Scientific progress, blind spots, and future prospects

Human activities have increasingly intensified the severity, frequency, and negative impacts of droughts in several regions across the world. This trend has led to broader scientific conceptualizations of drought risk that account for human actions and their interplays with natural systems. This review focuses on physical and engineering sciences to examine the way and extent to which these disciplines account for social processes in relation to the production and distribution of drought risk. We conclude that this research has significantly progressed in terms of recognizing the role of humans in reshaping drought risk and its socioenvironmental impacts. We note an increasing engagement with and contribution to understanding vulnerability, resilience, and adaptation patterns. Moreover, by advancing (socio)hydrological models, developing numerical indexes, and enhancing data processing, physical and engineering scientists have determined the extent of human influences in the propagation of drought hazard. However, these studies do not fully capture the complexities of anthropogenic transformations. Very often, they portray society as homogeneous, and decision-making processes as apolitical, thereby concealing the power relations underlying the production of drought and the uneven distribution of its impacts. The resistance in engaging explicitly with politics and social power-despite their major role in producing anthropogenic drought-can be attributed to the strong influence of positivist epistemologies in engineering and physical sciences. We suggest that an active engagement with critical social sciences can further theorizations of drought risk by shedding light on the structural and historical systems of power that engender every socioenvironmental transformation. This article is categorized under:Climate, History, Society, Culture > Disciplinary Perspectives.

Sentinel-1 Spatiotemporal Simulation Using Convolutional LSTM for Flood Mapping

The synthetic aperture radar (SAR) imagery has been widely applied for flooding mapping based on change detection approaches. However, errors in the mapping result are expected since not all land-cover changes are flood-induced, and those changes are sensitive to SAR data, such as crop growth or harvest over agricultural lands, clearance of forested areas, and/or modifications on the urban landscape. This study, therefore, incorporated historical SAR images to boost the detection of flood-induced changes during extreme weather events, using the Long Short-Term Memory (LSTM) method. Additionally, to incorporate the spatial signatures for the change detection, we applied a deep learning-based spatiotemporal simulation framework, Convolutional Long Short-Term Memory (ConvLSTM), for simulating a synthetic image using Sentinel One intensity time series. This synthetic image will be prepared in advance of flood events, and then it can be used to detect flood areas using change detection when the post-image is available. Practically, significant divergence between the synthetic image and post-image is expected over inundated zones, which can be mapped by applying thresholds to the Delta image (synthetic image minus post-image). We trained and tested our model on three events from Australia, Brazil, and Mozambique. The generated Flood Proxy Maps were compared against reference data derived from Sentinel Two and Planet Labs optical data. To corroborate the effectiveness of the proposed methods, we also generated Delta products for two baseline models (closest post-image minus pre-image and historical mean minus post-image) and two LSTM architectures: normal LSTM and ConvLSTM. Results show that thresholding of ConvLSTM Delta yielded the highest Cohen’s Kappa coefficients in all study cases: 0.92 for Australia, 0.78 for Mozambique, and 0.68 for Brazil. Lower Kappa values obtained in the Mozambique case can be subject to the topographic effect on SAR imagery. These results still confirm the benefits in terms of classification accuracy that convolutional operations provide in time series analysis of satellite data employing spatially correlated information in a deep learning framework.

Geospatial Assessment of Flood-Tolerant Rice Varieties to Guide Climate Adaptation Strategies in India

Rice is the most important food crop. With the largest rain-fed lowland area in the world, flooding is considered as the most important abiotic stress to rice production in India. With climate change, it is expected that the frequency and severity of the floods will increase over the years. These changes will have a severe impact on the rain-fed agriculture production and livelihoods of millions of farmers in the flood affected region. There are numerous flood risk adaptation and mitigation options available for rain-fed agriculture in India. Procuring, maintaining and distributing the newly developed submergence-tolerant rice variety called Swarna-Sub1 could play an important role in minimizing the effect of flood on rice production. This paper assesses the quantity and cost of a flood-tolerant rice seed variety- Swarna-Sub1, that would be required during the main cropping season of rice i.e., kharif at a district level for 17 major Indian states. The need for SS1 seeds for rice production was assessed by developing a geospatial framework using remote sensing to map the suitability of SS1, to help stakeholders prepare better in managing the flood risks. Results indicate that districts of Bihar, West Bengal and Uttar Pradesh will require the highest amount of SS1 seeds for flood adaptation strategies. The total estimated seed requirement for these 17 states would cost around 370 crores INR, less than 0.01 percent of Indian central government’s budget allocation for agriculture sector.