Search

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]

Highly fragile small island states experience disproportionate climate impacts given their limited capacity to implement cost‐effective tools for detecting emerging signals of drying conditions and monitoring systems for sensitive sectors such as agriculture, especially for uncertain, ‘creeping’ events such as droughts. Despite the existence of open‐source Google Earth Engine datasets, untapped potential remains for their full deployment in disaster management infrastructure. Given this gap, this paper explores the utility of the Enhanced Vegetation Index (EVI) for detecting spatio‐temporal variations of Mid‐Summer Drought (MSD) impacts on vegetation in the small island of Jamaica, with emphasis on major historical drought events. Geospatial analyses of EVI datasets from the Terra Moderate‐Resolution Imaging Spectroradiometer (MODIS) between 2000−2015 archived by the United States Geological Survey (USGS), were computed, and validated by station‐based precipitation and production data for selected parishes for historical case study MSD events. Results revealed highly asymmetrical drought impacts, with Jamaica's agriculturally intense Southern coastline displaying the most stressed vegetation (EVI < 0.5). North‐Western and North‐Eastern regions had the healthiest vegetation during the MSD (EVI > 0.6). A ‘fair’ to ‘moderate’ concurrent correlation was found between EVI and precipitation (R > 0.6), with lower correlations vis‐a‐vis agricultural production (R = 0.2–0.4). The results provide evidence of EVI's utility as a drought monitoring tool in a small island context. [ABSTRACT FROM AUTHOR]

Risk assessment for climate‐sensitive systems often relies on the analysis of several variables measured at many sites. In probabilistic terms, the task is to model the joint distribution of several spatially distributed variables, and how it varies in time. This paper describes a Bayesian hierarchical framework for this purpose. Each variable follows a distribution with parameters varying in both space and time. Temporal variability is modeled by means of hidden climate indices (HCIs) that are extracted from observed variables. This is to be contrasted with the usual approach using predefined standard climate indices (SCIs) for this purpose. In the second level of the model, the HCIs and their effects are assumed to follow temporal and spatial Gaussian processes, respectively. Both intervariable and intersite dependencies are induced by the strong effect of common HCIs. The flexibility of the framework is illustrated with a case study in Southeast Australia aimed at modeling "hot‐and‐dry" summer conditions. It involves three physical variables (streamflow, precipitation, and temperature) measured on three distinct station networks, with varying data availability and representing hundreds of sites in total. The HCI model delivers reliable and sharp time‐varying distributions for individual variables and sites. In addition, it adequately reproduces intervariable and intersite dependencies, whereas a corresponding SCI model (where hidden climate indices are replaced with standard ones) strongly underestimates them. It is finally suggested that HCI models may be used as downscaling tools to estimate the joint distribution of several variables at many stations from climate models or reanalyzes. Plain Language Summary: The management of hydroclimatic hazards such as droughts and heatwaves relies on the estimation of probabilities of occurrence for extreme events. Standard approaches are available for this task when one given hazard is studied at one particular location. However, this is often not sufficient. For instance, the impact of droughts or heatwaves strongly depends on their spatial extent, which requires analyzing them over many sites. It is also useful to analyze droughts and heatwaves together rather than separately, because their joint occurrence creates favorable conditions for other hazards such as bushfires to occur. In this paper, we propose a methodological framework to analyze in a probabilistic way data sets describing several hazards at many sites over many years. The principle of this approach is to identify unobserved processes called "hidden climate indices" that are pulling the strings that make data vary. This is illustrated with a case study analyzing "hot‐and‐dry" summers in Southeast Australia (https://vimeo.com/600898709). Key Points: We propose a general probabilistic model for describing the space‐time variability of multivariable dataThe model is based on hidden climate indices extracted from the target data, as opposed to predefined standard climate indicesThe model is general and flexible, and can handle a wide range of hydrometeorological data sets [ABSTRACT FROM AUTHOR]

Copyright of Disasters is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drought is one of the main natural disasters affecting economic development and food security. Drought monitoring and identification are the premise of disaster risk management, and the drought index is the main means of monitoring and identifying drought. The paper proposes a new drought index, the agricultural crop drought index (ACDI), which is suitable for rainfed agriculture in arid areas. ACDI calculates water deficit from precipitation and evapotranspiration and considers the weight of crop growth stage. The weight of growth stage and water deficit constitute the weighted data series. The fitting distribution is optimized by Kolmogorov–Smirnov test, and the data are standardized to obtain ACDI. The weight of growth stage is determined by the multiple correlation coefficient between meteorological factors and grain yield. A large weight is equivalent to a large coefficient, large coefficient means that meteorological factors in this growth stage will have a greater impact on grain yield, so the water deficit in this growth stage should play a more important role in defining drought index to better identify agricultural drought from the perspective of crops. Finally, Qiqihar, a semiarid area in Northeast China, is selected as the research area for the case study. Comparing ACDI with standard precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI) and crop water deficit index (CWDI), the standard deviation of ACDI is larger, which indicates that the difference between index values is larger and it is relatively good in distinguishing the degree of drought. In addition, the correlation coefficient between ACDI and climate grain yield passed the test with a significance level of 0.05, while the other three indexes did not pass the test, which shows that the relationship between ACDI and grain yield is higher than the other three drought indexes. For arid areas, grain yield can reflect the impact of drought. Therefore, ACDI looks more promising to identify agricultural drought from the perspective of crops. ACDI provides a reference for monitoring agricultural drought from the perspective of crop growth stage, and enriches the drought index theory. [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]

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]

The western United States (US) is a hotspot for snow drought. The Oregon Cascade Range is highly sensitive to warming and as a result has experienced the largest mountain snowpack losses in the western US since the mid‐20th century, including a record‐breaking snow drought in 2014–2015 that culminated in a state of emergency. While Oregon Cascade snowpacks serve as the state's primary water supply, short instrumental records limit water managers' ability to fully constrain long‐term natural snowpack variability prior to the influence of ongoing and projected anthropogenic climate change. Here, we use annually‐resolved tree‐ring records to develop the first multi‐century reconstruction of Oregon Cascade April 1st Snow Water Equivalent (SWE). The model explains 58% of observed snowpack variability and extends back to 1688 AD, nearly quintupling the length of the existing snowpack record. Our reconstruction suggests that only one other multiyear event in the last three centuries was as severe as the 2014–2015 snow drought. The 2015 event alone was more severe than nearly any other year in over three centuries. Extreme low‐to‐high snowpack "whiplash" transitions are a consistent feature throughout the reconstructed record. Multi‐decadal intervals of persistent below‐the‐mean peak SWE are prominent features of pre‐instrumental snowpack variability, but are generally absent from the instrumental period and likely not fully accounted for in modern water management. In the face of projected snow drought intensification and warming, our findings motivate adaptive management strategies that address declining snowpack and increasingly variable precipitation regimes. Key Points: Decadal low‐snow intervals, particularly during the 19th century, are part of the natural Oregon Cascade snowpack variabilitySnow "whiplash" events are a common although variable feature of the Oregon Cascades' reconstructed recordIn our 331‐year record, only one other event matches the severity of the Oregon Cascades 2014–2015 snow drought [ABSTRACT FROM AUTHOR]

Negative extreme anomalies in vegetation growth (NEGs) usually indicate severely impaired ecosystem services. These NEGs can result from diverse natural and anthropogenic causes, especially climate extremes (CEs). However, the relationship between NEGs and many types of CEs remains largely unknown at regional and global scales. Here, with satellite‐derived vegetation index data and supporting tree‐ring chronologies, we identify periods of NEGs from 1981 to 2015 across the global land surface. We find 70% of these NEGs are attributable to five types of CEs and their combinations, with compound CEs generally more detrimental than individual ones. More importantly, we find that dominant CEs for NEGs vary by biome and region. Specifically, cold and/or wet extremes dominate NEGs in temperate mountains and high latitudes, whereas soil drought and related compound extremes are primarily responsible for NEGs in wet tropical, arid and semi‐arid regions. Key characteristics (e.g., the frequency, intensity and duration of CEs, and the vulnerability of vegetation) that determine the dominance of CEs are also region‐ and biome‐dependent. For example, in the wet tropics, dominant individual CEs have both higher intensity and longer duration than non‐dominant ones. However, in the dry tropics and some temperate regions, a longer CE duration is more important than higher intensity. Our work provides the first global accounting of the attribution of NEGs to diverse climatic extremes. Our analysis has important implications for developing climate‐specific disaster prevention and mitigation plans among different regions of the globe in a changing climate. [ABSTRACT FROM AUTHOR]

Based on the standardized precipitation evaporation index and reanalysis data, this article reveals that the relationship between the North Pacific Oscillation (NPO) and the drought over southern China in summer shifts from an insignificant correlation to a significant negative correlation after 2000. During the significant period (2001–2018), the positive NPO phase corresponds to an anomalous anticyclonic circulation dominating the mid‐low latitude North Pacific, which suppresses the moisture transport into southern China. Meanwhile, the easterly anomalies south of the anomalous anticyclonic circulation prevailing in the equatorial Pacific are coupled with a cooling of sea surface temperature in the central tropical Pacific, which enhances the Walker circulation and further induces an anomalous meridional circulation with the descending anomalies over southern China. Such moisture and dynamical conditions favor the occurrence of drought in southern China. During the former period (1983–2000), however, the NPO‐related atmospheric circulations over the North Pacific shift northward. In this case, the NPO shows insignificant relations with the moisture and dynamical conditions over southern China, thereby diluting the linkage of the NPO to the drought in southern China. The interdecadal strengthening in the relationship between the NPO and above atmospheric conditions is attributed to the westward (southward) extension of the NPO northern (southern) center. The larger interannual variability of spring sea ice in the Bering Sea and the southward movement of summer storm track over the North Pacific are hypothesized to account for the expansion of the NPO structure. Plain Language Summary: Southern China is an economically developed region with a large population. In recent decades, droughts frequently occurred in this region and severely affected agriculture production, forest‐fire prevention, and human lives. Therefore, it is very necessary to understand the physical mechanisms underlying the variability of droughts in southern China. In this study, we addressed the relationship of summer drought in southern China with the North Pacific Oscillation (NPO), which is characterized by the north–south seesaw pattern in sea level pressure over the North Pacific. We revealed that their relationship exhibits an interdecadal change around 2000, from an insignificant correlation to a significant negative correlation. Such an interdecadal strengthening is due to the expansion of the NPO structure, which may result from the changes of the spring sea ice in the Bering Sea and the summer storm track over the North Pacific. These findings are expected to deepen our understanding for the variation of drought in southern China and are also important for disaster prevention and mitigation. Key Points: Relationship between the summer drought in southern China and the North Pacific Oscillation (NPO) has strengthened after 2000Such a strengthening is attributed to the westward (southward) extension of the NPO northern (southern) centerThe expansion of the NPO structure is related to changes of spring sea ice in the Bering Sea and summer storm track over the North Pacific [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]

Crop production security is an essential guarantee for the prosperity of country and the well‐being of people. In the context of frequent occurrence of extreme weather events, how to comprehensively evaluate the risk of multiple hazards and quantitatively describe spatial differences will be one of the significant topics in agricultural disaster mitigation. In this study, based on the gridded meteorological data and other datasets (crop, socioeconomic, disaster loss, and actual planting location) from 2000 to 2020, multihazard risk model for different growth period was built to assess the comprehensive risk of winter wheat in the upper Huaihe River basin (UHRB), China. The conclusions were as follows: (a) The spatial distribution of continuous rain risk increased gradually from north to south; the area of the highest lodging risk was located in the east; the spatial trend of drought risk was the highest in the south and the lowest in the northeast. (b) The weights of continuous rain, lodging, and drought disasters were 41, 31, and 28%, respectively, which indicated that winter wheat in UHRB was most affected by continuous rain during the harvest period. (c) The spatial distribution of comprehensive disaster risk assessment showed that the higher risk areas were located in the central and southern parts (Huaibin, Linquan, Luoshan). After verification by historical disasters information, the results are credible, providing a concrete scientific basis for implementing regional agricultural disaster response measures. [ABSTRACT FROM AUTHOR]

Droughts and heatwaves are the most detrimental climatic threats to global wheat production, their spatiotemporal coexistence (compound drought and heatwave events [CDHEs]) can cause synergistic and amplified impacts on wheat yield, which call for an improved understating of their characteristics and variations that related to wheat growth. Past decade has witnessed a surge in the study of CDHEs in terms of their causative mechanisms, variabilities and impacts; however, changes in CDHEs occurring within crops' growing seasons are barely discussed, especially on global scale. In this study, we proposed a novel approach to identify and summarize CDHEs by taking "crop growing season" as accumulated period, and investigated the spatiotemporal changes of CDHEs occurring within wheat growing seasons over global wheat‐producing areas during 1981–2020, including their spatial extent, frequency and duration. Results show that the spatial extent of CDHEs experienced rapid expansion during wheat growing seasons of 1981–2020, meanwhile, droughts and heatwaves increasingly tended to occur simultaneously. More than 92% of wheat‐producing areas experienced at least one CDHE in each wheat growing season, the frequency of CDHEs increased significantly in 28.2% of wheat‐producing areas, and the total duration of CDHEs increased significantly in 33.2% of wheat‐producing areas. Europe, eastern China, western US and northern Argentina are the hotspot regions with the biggest contradiction between fast‐increasing CDHEs and wheat production, pointing to the urgency of marking spatially targeted adaptation measures for disaster reduction and risk management. [ABSTRACT FROM AUTHOR]