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Disaster Risk Management (DRM) is complex due to interacting climate risks from interacting hazards and sectors. We develop a synthetic multi‐risk test case to explore the effects of these interactions on decision‐making under deep uncertainty. The test case accounts for changes in hazard impacts and occurrence frequency due to interactions between floods and droughts. Interactions between the shipping, housing and agricultural sectors are considered as well. We use this test case to explore the utility of the Dynamic Adaptive Policy Pathways for Multi‐Risk (DAPP‐MR) framework. DAPP‐MR has been introduced to assess DRM policies' effectiveness under deep uncertainties and to develop integrated adaptive strategies considering interactions across hazards, sectors and time. With the test case, we show that the stage‐wise approach of DAPP‐MR, which gradually increases the complexity of the analysis, can facilitate the evaluation process. Earlier stages of the analysis can be used to establish the direct cause‐effect relations, later stages allow us to identify whether additional interacting factors have a significant effect on the direct cause‐effect relations. As a result, decision‐makers can gain insights into dependencies and their relevance for developing short‐to long‐term strategies under deep uncertainty. We show that multi‐risk interactions can lead to non‐linear effects that influence the outcome of the policy analysis, for example, the indirect influence of a decision in one sector on another. Future work could investigate further improving the operationalization of this staged approach as well as extending the set of uncertainties, dynamics and decision‐rules accounted for in the multi‐risk test case. Plain Language Summary: This paper addresses the complex challenge that many regions face when dealing with multiple interconnected disasters, like floods, droughts and earthquakes. Traditional Disaster Risk Management methods often fall short, particularly in long‐term planning that considers factors such as climate change. We apply a step‐by‐step approach known as "Dynamic Adaptive Policy Pathways for Multi‐Risk (DAPP‐MR)" to assist decision‐makers in better understanding and planning for these intertwined risks over time. This paper employs a hypothetical test case where different sectors (e.g., agricultural sector) seek effective ways to prepare for and respond to flood and drought risks. The DAPP‐MR method facilitates this by breaking down the process into smaller, more manageable steps. This approach allows for an easier understanding of the direct effects of disasters and gradually incorporates more complex interactions between different risks. Our findings suggest that this step‐by‐step method can aid decision‐makers in grasping the connections between various factors and their significance in formulating both short‐ and long‐term strategies. The study also reveals that interactions between different risks can produce unexpected, nonlinear effects, adding complexity to the interpretation of policy outcomes. Key Points: A multi‐risk modeling case with interacting hazards and sectors is created to explore effects for decision‐making under deep uncertaintyDynamic Adaptive Policy Pathways for Multi‐Risk has proven effective for pathway analysis to grasp interactions and their relevance through a structured, stage‐by‐stage approachMulti‐risk interactions can alter the decision‐making outcome because of changes in performance or timings of potential pathways [ABSTRACT FROM AUTHOR]

Global hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change). [ABSTRACT FROM AUTHOR]

The simultaneous occurrence of droughts and floods in neighboring regions amplifies the threats posed by droughts and floods individually. Nonetheless, few studies have been conducted to investigate the simultaneous occurrence of drought and flood events. Here we explore the spatiotemporal characteristics and the shift pattern of droughts and pluvials over Eastern China from a three‐dimensional perspective, using the self‐calibrated Palmer Drought Severity Index and the Climate Research Unit data set as well as four regional climate model simulations. We find that Eastern China experienced droughts and pluvials simultaneously in different locations during boreal summer, and it is projected to simultaneously experience more frequent and more intense droughts and pluvials under a warming climate. Specifically, we investigate the pattern of more pluvials in Southeast China and more droughts in Northeast China for the historical period of 1975–2004. This pattern dynamically evolves under climate warming: the pluvial‐dominated regime shifts from Southeast to Northeast China, while the drought‐dominated regime shifts from Northeast to Southeast China. The weakening strength of the western Pacific subtropical high and a northward displacement of the monsoon rain belt may both contribute to the pattern of more pluvials in Northeast China and more droughts in Southeast China. These findings provide insights into the development of adaptation strategies and emergency response plans for enhancing society's resilience to the spatial co‐occurrence of dry and wet extremes. Plain Language Summary: The spatial co‐occurrence of drought and flood events has been receiving widespread attention in recent years since the superimposed condition of drought and flood has brought unprecedented challenges to disaster prevention, mitigation, and relief work. Eastern China is prone to droughts and floods, but their spatial interconnections are not yet well understood. Thus, we explore the historical evolution and the future change of simultaneous occurrence of droughts and floods over Eastern China under a warming climate. The spatial co‐occurrence of droughts and floods is projected to affect more land areas and become more frequent as climate warms. Eastern China experienced a pattern of more floods in Southeast China and more droughts in Northeast China during 1975–2004. We find that droughts tend to become more intense in Southeast China and the severity of floods is expected to increase in the northern region of East China, resulting in a shift pattern of more droughts in Southeast China and more floods in Northeast China. The shift pattern may be attributed to the weakening of western Pacific subtropical high strength and a northward displacement of the monsoon rain band in a changing climate. Key Points: Eastern China is projected to experience more frequent and more intense droughts and pluvials simultaneouslyThe spatial pattern of more pluvials in Southeast China and more droughts in Northeast China is expected to become an opposite pattern under a warming climateThe weakening strength of the western Pacific subtropical high and a northward displacement of the monsoon rain belt may contribute to the pattern of more pluvials in Northeast China and more droughts in Southeast China [ABSTRACT FROM AUTHOR]

Drought risk assessment can identify high‐risk areas and bridge the gap between impacts and adaptation. It is thus vital to investigate changes in drought risk and exposed social economy under climate change. Here, the future global meteorological drought risk is projected for the 2021–2100 period under four combined scenarios of Representative Concentration Pathways and Shared Socioeconomic Pathways (SSPs), i.e., SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. And then the exposed population and Gross Domestic Product (GDP) under high risk are analyzed. Drought risk will further strengthen in the future under SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, with large increases distributed in South Asia, the Mediterranean, East Asia, Southeast Asia, and Central America. The strongest increasing trends in drought risk are concentrated in equatorial regions including South Asia, West Africa, and Central America. A large percentage of the population and economy are exposed to high drought risk. The exposed population under the highest risk level is the largest under SSP5‐8.5 and the smallest under SSP3‐7.0 for regions in North America, while being the largest under SSP3‐7.0 for other regions. The exposed GDP under the highest risk level is the highest under SSP5‐8.5 and the lowest under SSP3‐7.0, while the disparity among diverse scenarios is larger for the rich regions than the developing ones. Plain Language Summary: Drought increasingly affects society, economy, and ecosystems as a frequent natural disaster. Drought risk assessment can help understand the extent of drought threat to the human system. Therefore, we projected 0.5° × 0.5° future drought risk for the 2021–2100 period under four scenarios and investigated the exposed population and Gross Domestic Product (GDP) under high risk. We find that high‐risk regions mainly concentrate in South Asia, the Mediterranean, East Asia, Southeast Asia, Central America, eastern North America, North Europe, and West Africa, and global drought risk will increase in the future. The population exposed to high drought risk for South Asia, West Africa, and East Asia is much larger than other regions. GDP exposed to high drought risk is the largest for East and South Asia. Large exposed population and GDP occur under high forcing fossil‐fueled development pathways. Our findings help policymakers develop adaptive disaster prevention measures. Key Points: Drought risk will increase worldwide in the future, being the highest under SSP5‐8.5 and the lowest under SSP1‐2.6 for most regionsExposed population under high risk is the largest under SSP5‐8.5 for regions in North America while under SSP3‐7.0 for other regionsExposed Gross Domestic Product under high risk is significantly higher under SSP5‐8.5 than the other scenarios and the lowest under SSP3‐7.0 for all regions [ABSTRACT FROM AUTHOR]

Regular monitoring of drought events, watershed characterization, management, and development efforts is crucial for future disaster predictions and mitigation strategies. The drought susceptibility investigation has been carried out in the Ken River Basin of Bundelkhand region, one of the most drought‐prone areas in India. Proxies used in the present study are long‐term climatological data (rainfall, standardized precipitation index, and aridity index), satellite data (slope, drainage density [DD], distance to river, and normalized difference vegetation index), lithology, lineament density, and groundwater depth. By the analytical hierarchy process, weightage of each factor is assigned according to its importance. Study shows that nearly 48% of the area of the basin experiences moderate to severe drought conditions. In the last five decades, there have been 22 years of extreme drought, with the most extended period being 1972–1974 and the driest year being 2006–2007. Sensitivity analysis reveals that lithology, slope, and DD are the most significant parameters in the susceptibility analysis. Model validation through an artificial neural network demonstrates the model's high accuracy (0.9) and sensitivity with minor errors. An integrated study of drought susceptibility and morphometry is useful for identifying the drought risk hotspots in the basin. The investigation will be helpful in river basin management and disaster management strategies. [ABSTRACT FROM AUTHOR]