This paper reviews the Flash Drought concept, the uncertainties associated with FD prediction, and the potential of Machine Learning (ML) and Deep learning (DL) for future applications. For this, 121 relevant articles covering different aspects of FD ‐ definitions, key indicators, distinguishing characteristics, and the current methods for FD assessment (i.e., ‐ monitoring, prediction, and impact assessment) are examined. FD is typically a short‐term drought event ‐ characterized by the rapid progression of heat waves and precipitation deficits, causing cascading impacts on the land and surface hydrology. FD prediction is constrained by the lack of consistent FD definitions, key indicators, the limited predictability of FD at the subseasonal‐ to‐seasonal (S2S) timescale, and uncertainties associated with the current prediction methods. Some of the uncertainties in the current methods are associated with a lack of our understanding of the physical processes. They are also related to the error in the input datasets (imperfect representation of indicators), parameter uncertainty (parameterization scheme adopted by the prediction model), multicollinearity, nonlinear, and non‐stationary interactions among different indicators. Combining traditional methods and multisource fusion data with ML and DL methods shows promise to better understand FD evolution and improves prediction.

Accurate runoff projections are vital for developing climate adaptation strategies, yet significant uncertainties persist. The commonly employed approaches to constrain these uncertainties rely on the stationarity of climate biases and runoff sensitivity, which may not hold for climate‐sensitive regions (e.g., semi‐arid regions). This study investigates the validity of the stationarity assumption across 29 CMIP6 models, encompassing diverse climate biases (Dry Warm, Wet Warm, Dry Cold, and Wet Cold), utilising a semi‐arid region in central India as a testbed. The implications of this assumption on runoff projection uncertainties were comprehensively assessed across the runoff modelling chain for three time periods (the 2030s, 2060s and 2090s) based on the Soil and Water Assessment Tool (SWAT) simulations. The results highlight the non‐stationary nature of climate biases and runoff sensitivity under future scenarios, challenging the widespread applicability of common uncertainty‐constraining approaches. Moreover, the impact of non‐stationarity on runoff projection uncertainty was found to be strongly influenced by the choice of GCMs, preprocessing methods and climate change scenarios. In the 2030s, GCMs dominate runoff uncertainty, with dry models exhibiting ~10%–15% higher uncertainty compared to warm models, which is further amplified when interacting with warm biases. However, from the mid‐century onwards, the bias‐adjustment approaches and climate change scenarios significantly shape runoff projection uncertainties under non‐stationary conditions. These findings emphasise the potential of climate bias and runoff sensitivity‐based GCM selection for reducing runoff uncertainty in near‐future assessment (2030s). For mid‐term and long‐term runoff projections, addressing diverse climate biases through bias‐adjustment approaches is more viable. This study offers critical insights to prioritise the development of a non‐stationarity‐based approach for reliable runoff projections in climate‐sensitive regions.

The 2015 Paris Agreement outlined limiting global warming to 1.5°C relative to the preindustrial levels, necessitating the development of regional climate adaptation strategies. This requires a comprehensive understanding of how the 1.5°C rise in global temperature would translate across different regions. However, its implications on critical agricultural components, particularly blue and green water, remains understudied. This study investigates these changes using a rice-growing semiarid region in central India. The aim of this study is to initiate a discussion on the regional response of blue–green water at specific warming levels. Using different global climate models (GCMs) and shared socioeconomic pathways (SSPs), the study estimated the time frame for reaching the 1.5°C warming level and subsequently investigated changes in regional precipitation, temperature, surface runoff, and blue–green water. The results reveal projected reductions in precipitation and surface runoff by approximately 5%–15% and 10%–35%, respectively, along with decrease in green and blue water by approximately 12%–1% and 40%–10%, respectively, across different GCMs and SSPs. These findings highlight 1) the susceptibility of blue–green water to the 1.5°C global warming level, 2) the narrow time frame available for the region to develop the adaptive strategies, 3) the influence of warm semiarid climate on the blue–green water dynamics, and 4) the uncertainty associated with regional assessment of a specific warming level. This study provides new insights for shaping food security strategies over highly vulnerable semiarid regions and is expected to serve as a reference for other regional blue/green water assessment studies. Significance Statement: This study helps to drive home the message that a global agreement to limit the warming level to 1.5°C does not mean local-scale temperature (and associated hydrological) impacts would be limited to those levels. The regional changes can be more exaggerated and uncertain, and they also depend on the choice of the climate model and region. Therefore, local-scale vulnerability assessments must focus on the multidimensional assessment of a 1.5°C warmer world involving different climate models, climate-sensitive components, and regions. This information is relevant for managing vulnerable agricultural systems. This study is among the first to investigate the critical agricultural components such as the blue–green water over a semiarid Indian region, and the findings and methodology are expected to be transferable for performing regional-scale assessments elsewhere.

Water, food, and energy security are the major climate risks of global warming. The Paris Agreement proposed an ambitious target of limiting the rise in global mean surface temperature to well below 20C, and preferably to 1.50C, compared to the pre-industrial era. However, the implication of this policy discourse on the agricultural system is imperative for ensuring food security in the face of global warming. This research focuses on understanding the changes in water availability and rice productivity under 1.50C global warming over a global rice-exporting semi-arid watershed in Central India. Towards this goal, the mean climate under 1.50C of global warming was computed for 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate models (GCMs). For each GCM, the corresponding changes in blue-green water availability and rice productivity at 1.50C warming period were estimated under two global warming scenarios (SSP2-4.5 and SSP5-8.5) based on the semi-distributed Soil and Water Assessment Tool (SWAT). Results suggest that the green and blue water is projected to change by ~ -20% to 10 and ~ -50 to 20%, respectively. The rice yield is projected to reduce in the range of 5% to 50%, with an increase in local temperature (~10C) and a decrease in local precipitation (~20%) being the limiting factor. This study provides useful information on when the 1.50C global warming could reach and how it can affect the agricultural productivity of semi-arid watersheds across different global warming scenarios. This study will help develop appropriate strategies to reduce/alleviate the impacts of global warming and foster food security at the watershed-scale.   

Irrigation plays a crucial role in mitigating the impacts of climate variability and extreme climate events on agricultural productivity, helping to limit drought risks and reduce yield volatility. Given projected increases in the frequency and magnitude drought and water risks to crop production, expansion and intensification of irrigation will be an important component of agricultural adaptation to climate extremes. At the same time, there are significant concerns about the impacts that increased dependence of agriculture on irrigation could have for water resource sustainability and freshwater-dependent ecosystems.Understanding how climate extremes will alter the value of irrigation is critical for managing these trade-offs, and for supporting development of policies to deliver sustainable and efficient use of water in agriculture. Here, we develop a global gridded modeling framework based on the AquaCrop-OSPy model to estimate the impacts of climate change on the agronomic and economic return on investment from irrigation under multiple potential climate futures. Our initial analyses are focused on quantifying changes in the value added from irrigation for four major crop types – Maize, Wheat, Rice, and Soybean – across four shared socio-economic pathways— SSP245 (moderate), SSP370 (regional rivalry), and SSP5-8.5 (extreme) scenarios – drawing on downscaled climate projections from a range of CMIP6 models out to 2100.Our analysis highlights hotspots of change in variability of irrigation across major agricultural production systems and hydrologic basins. Our results show that the value of irrigation is likely to increase not only in regions that are currently water stressed but also in those where irrigation has historically been more limited. Furthermore, we demonstrate a significant increase in the inter-annual volatility of irrigation value across many production regions globally, in particular driven by greater variability in precipitation and drought-related production risks. Our findings provide insights to guide the development of irrigation expansion strategies, while also highlighting potential areas where future increases in irrigation could increase potential for conflict over limited water resources.