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Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary: Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points: We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall testRecomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall testNo perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data [ABSTRACT FROM AUTHOR]

Aim: As climate change increases the frequency and severity of droughts in many regions, conservation during drought is becoming a major challenge for ecologists. Droughts are multidimensional climate events whose impacts may be moderated by changes in temperature, water availability or food availability, or some combination of these. Simultaneously, other stressors such as extensive anthropogenic landscape modification may synergize with drought. Useful observational models for guiding conservation decision‐making during drought require multidimensional, dynamic representations to disentangle possible drought impacts, and consequently, they will require large, highly resolved data sets. In this paper, we develop a two‐stage predictive framework for assessing how drought impacts vary with species, habitats and climate pathways. Location: Central Valley, California, USA. Methods: We used a two‐stage counterfactual analysis combining predictive linear mixed models and N‐mixture models to characterize the multidimensional impacts of drought on 66 bird species. We analysed counts from the eBird participatory science data set between 2010 and 2019 and produced species‐ and habitat‐specific estimates of the impact of drought on relative abundance. Results: We found that while fewer than a quarter (16/66) of species experienced abundance declines during drought, nearly half of all species (27/66) changed their habitat associations during drought. Among species that shifted their habitat associations, the use of natural habitats declined during drought while use of developed habitat and perennial agricultural habitat increased. Main Conclusions: Our findings suggest that birds take advantage of agricultural and developed land with artificial irrigation and heat‐buffering microhabitat structure, such as in orchards or parks, to buffer drought impacts. A working lands approach that promotes biodiversity and mitigates stressors across a human‐induced water gradient will be critical for conserving birds during drought. [ABSTRACT FROM AUTHOR]

Drought has a paramount impact on global agriculture and food security. However, the study on future cropland areas that can incur drought is inadequate. This paper uses input parameters from 7 CMIP6 models for 7 future scenarios (SSP1‐1.9, SSP1‐2.6, SSP4‐3.4, SSP2‐4.5, SSP4‐6.0, SSP3‐7.0, and SSP5‐8.5) to measure South Asian cropland exposure to drought and its underlying factors. Some defined epochs such as 2021–2040 (near‐term), 2041–2060 (mid‐term), 2081–2100 (long‐term), and 1995–2014 (reference period) are designed to explore diverse outlooks of the change. The Standardized Precipitation Evapotranspiration Index and the Run theory methods are applied to detect drought. Results indicate an intensified cropland (under SSP4‐3.4, SSP3‐7.0, and SSP5‐8.5) in the Indo‐Gangetic Plain region of South Asia, where mostly the variation occurs among scenarios and periods. Notably, the future cropland exposed to drought will increase in the 2021–2040, and 2041–2060 periods, but it intends to decline during the 2081–2100. Relatively, the exposed cropland will upturn highest by 49.2% (SSP3‐7.0) in the mid‐term period and decrease by −8.2% (SSP5‐8.5) in the end future. Spatially, distributed cropland in the central, south‐west, and portion of the northeast of South Asia are subjective to be exposed largely, but it can drop greatly across the eastern part by the end future. Importantly, the climate change effect plays a grounding role in future exposure change over the region during the near to mid‐term periods, while the cropland change effect is predominant in the long‐term perspectives. However, these findings signify the urgency of policymaking focusing on drought mitigation to ensure food security. Plain Language Summary: Estimating drought‐induced cropland exposure is the pivotal aspect of agricultural risk assessment for drought impacts. The purposes of this study are to explore (a) how much of the cropland area would be exposed to drought under SSP scenarios; and (b) the degree to which climate change and cropland change effects contribute to changes in exposure. In this regard, we conducted a multi‐model and multi‐scenario‐based analysis to reveal variations in the cropland area exposed to drought in South Asia. Among the seven scenario combinations, the highest percentage of cropland exposed to drought was found at 49.2% (SSP3‐7.0) in the mid‐term epoch (2041–2060). The climate change effect seems to be the key contributor in the near‐term to mid‐term period, whereas it is the cropland change effect long‐term. To reduce drought risk, we recommend focusing on strong climate change mitigation policy development in the near‐ and mid‐term periods at a global scale and strict land‐use management policy interventions in the long‐term period at the regional level. Key Points: Larger cropland area will experience drought in the 2040–2060 period, while it will decline by the end of 21st centuryThe exposed cropland will increase by 49.2% under SSP3‐7.0 than that of historical timeThe climate change effect is the pivotal contributor in exposure changes over South Asia [ABSTRACT FROM AUTHOR]

In Senegal, agriculture is an important sector underpinning the socioeconomic fabric of the populace. Notably, the agricultural production in this region exhibits heightened sensitivity to climatic perturbations, particularly droughts and heat waves. This study aims to determine the resilience of different agronomic interventions for farmers practicing mixed farming that produce both crops (i.e., groundnut (Arachis hypogaea L.) and pearl millet (Pennisetum glaucum (L.) R. Br.)) and raise animals in the Groundnut Basin in Senegal, which holds historical and socioeconomic significance. To understand the current situation regarding demographics, economics, consumption behavior, and farm operations for smallholder farmers, data were comprehensively collected from government and nongovernment organizations (NGO) reports, scientific papers, organization databases, and surveys. Additionally, the Agricultural Production Systems sIMulator (APSIM) was used to understand how combinations of three planting dates, three plant densities, and six urea nitrogen (N) fertilizer rates affected the yield of pearl millet, which were used as the alternative scenarios to the baseline in the farm modeling and analyses. All the collected and generated data were used as inputs into the Farm Simulation Model (FARMSIM) to generate economic, nutritional, and risk data associated with mixed farming systems. The generated data were then used to determine the resilience of the alternative scenarios against the baseline. Initially, a multi‐objective optimization was employed to meet nutritional needs while maintaining a healthy diet at the lowest cost. Then, the scenarios that met the population's nutritional requirements were evaluated based on four economic indicators: net cash farm income (NCFI), ending cash reserves (EC), net present value (NPV), and internal rate of return (IRR). Lastly, those that passed the economic feasibility test were ranked based on risk criteria certainty equivalent (CE) and risk premium (RP). The analyses found N fertilizer rates of 0, 20, and 100 kg N ha−1 were generally economically not feasible. Additionally, medium (early‐July to late‐August) and late (late‐July to mid‐September) planting dates generally performed better than early (early‐June to late‐July) planting dates, while plant densities of 3.3 and 6.6 pL m−2 performed better than 1.1. The robust resilience approach introduced in this study is easily transferable to other regions. [ABSTRACT FROM AUTHOR]

We describe the bias adjustment and downscaling to a resolution of 1 × 1 km of daily temperature, precipitation and solar radiation of a multimodel ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs with the local observational data set BhutanClim for Bhutan and an analysis of the results for 1996–2100. The GCMs were selected based on their ability to reproduce local temporal and spatial precipitation patterns, resulting in a total of 14 models for each shared socioeconomic pathway (SSP). The results are shown for five global warming levels (GWLs) 1.5, 2, 3, 4 and 5°C, providing insight into the projected impacts of climate change at different warming levels. The daily temperature and precipitation data are publicly available, which is of great benefit for climate impact studies, such as ecological, hydrological or infrastructure‐related studies. In this paper, we discuss showcase climate indicators. Peak‐over‐threshold climate indicators such as hot days above 30°C are relevant for human health. In the capital city of Thimphu, these are currently rare, at 4 days per year, but might increase to more than 80 for GWL5.0. Precipitation increases in the summer months, but slightly decreases in the drier winter months. Accordingly, the 3‐month SPEI drought index is projected to increase in summer, but strongly decreases in the winter months, mostly pronounced in February. The increasing severity of late‐winter and spring droughts might have a negative impact on vegetation, wildfires and water supply. [ABSTRACT FROM AUTHOR]

Understanding of past droughts has been mostly shaped by meteorological data, with relatively less known about the human aspects of droughts, their socio‐economic impacts, as well as choices people make in response to droughts in different environmental and socio‐political contexts. The lack of data that systematically record and categorize drought impacts is an important reason for this disparity. In this paper, we present an Irish drought impacts database (IDID) containing 6094 newspaper reports and 11,351 individual impact records for the island of Ireland, covering the period 1733–2019. Relevant articles were identified through systematic searching of the Irish Newspaper Archives, and recorded impacts were categorized using a modified version of the classification scheme employed by the European drought impact inventory (EDII). Drawing on the wealth and diversity of content provided by the newspapers, the IDID database provides information on the documented temporal and geographical extent of drought events, their socio‐economic and political contexts, their consequences, mitigation strategies employed and their change over time. The IDID also facilitates analysis of long‐term patterns in drought incidence, individual impact categories, as well as detailed insight into the impacts of individual drought events over nearly three centuries of Ireland's history. In addition, by allowing an examination of the coherence between meteorological records and identified impacts, it advances our understanding of the influences that contemporary economic, political, environmental and societal events had on the human experience, perception and impact of droughts. This new open‐access database, therefore, provides opportunities for improving understanding of drought vulnerability and is an important step in developing greater capacity to cope with and respond to future droughts on the island of Ireland. [ABSTRACT FROM AUTHOR]

Aim: Shifting climates are reshaping ecosystems globally and are projected to intensify over the coming century. Understanding how biodiversity will respond to these shifts is crucial for developing effective climate adaptation measures. We generate predictive models built from long‐term data to hindcast historic fluctuations in small mammal abundances as they have responded to shifting rainfall and fire conditions. This data set serves as the basis for predicting historical variations (hindcasting) in small mammal abundances, allowing us to examine their responses to decadal changes in fire and rainfall conditions within our study landscape. Location: Australia (Victoria). Taxa: Small mammals (Mammalia). Time Period: 1970–2022. Methods: Small mammal abundance was surveyed at 36 long‐term trapping sites and modelled against coinciding fire history, vegetation productivity and rainfall using generalized additive mixed models. Six species were then used in predictive modelling against these variables for the decades preceding our monitoring programme (1970–2007). Results: All species abundances increased with higher rainfall. Time since fire was also an important variable in all but one species model, with species displaying varying responses to time since fire. Hindcasting predictions for small mammal abundances varied with some species showing marked declines over time. Clear trends emerged, indicating more volatile population fluctuations in response to intensified fire and rainfall extremes in the 21st century. This suggests that periods of higher rainfall and less frequent fire events in the decades preceding our monitoring period supported higher and more stable small mammal abundances. Conclusions: Native species show distinct sensitivity to the combined effects of drought and fire, which has occurred in recent times. Intensification of these drivers has caused increased volatility in small mammal abundances with low abundance extremes occurring more frequently. [ABSTRACT FROM AUTHOR]

Drought can cause significant disruption to forest ecosystems and may have long‐term impacts on the structure and function of ecosystems after the end of drought. This is the key to quantifying the ability of ecosystem to respond to disturbance events by comprehensively analyzing the impact of drought on vegetation, the lagged effect, and ecosystem resilience to drought. This article takes broad‐leaved forests and coniferous forests in multiple temperature zones of China as the object of study, using distributed lagged nonlinear model (DLNM) to construct a systematic method. Our results show that the main sensitive lagged time for coniferous forests and broad‐leaved forests is the first 3 months in various temperature zones, with the strongest lagged effect in the month when the drought incidents occur. Coping capacity represents ecosystems to remain stable during droughts, and we quantified the indicator by the ratio of the resistance (the difference between NDVI value before the drought and during the drought) to recovery (the difference between NDVI value after the drought and during the drought). When dealing with intensive drought events, the coping capacity of subtropical broad‐leaved forests (−0.67) and tropical broad‐leaved forests (−0.88) exhibit the strongest coping capacity (value tends to −1). Overall, vegetation growth in subtropical and tropical regions is less affected by drought compared to temperate and cold temperate zones. The research results help us understand the comprehensive impact of drought on vegetation and the strategies for different vegetation to cope with drought. [ABSTRACT FROM AUTHOR]

Scenarios have emerged as valuable tools in managing complex human‐natural systems, but the traditional approach of limiting focus on a small number of predetermined scenarios can inadvertently miss consequential dynamics, extremes, and diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by exploring a wide range of possible futures and identifying those that yield consequential vulnerabilities. However, vulnerabilities are typically identified based on aggregate robustness measures that do not take full advantage of the richness of the underlying dynamics in the large ensembles of model simulations and can make it hard to identify key dynamics and/or storylines that can guide planning or further analyses. This study introduces the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced "forensic"): a scenario discovery framework that addresses these challenges by organizing and investigating consequential scenarios using hierarchical classification of diverse outcomes across actors, sectors, and scales, while also aiding in the selection of scenario storylines, based on system dynamics that drive consequential outcomes. We present an application of this framework to the Upper Colorado River Basin, focusing on decadal droughts and their water scarcity implications for the basin's diverse users and its obligations to downstream states through Lake Powell. We show how FRNSIC can explore alternative sets of impact metrics and drought dynamics and use them to identify drought scenario storylines, that can be used to inform future adaptation planning. Plain Language Summary: Scenario analysis is a useful tool for assessing the impacts of future conditions or alternative strategies. Focusing on a small number of predetermined scenarios can, however, limit our understanding of key uncertainties, and fail to represent diverse stakeholder impacts. Approaches such as exploratory modeling have been developed to address these issues by exploring a wide range of possible futures and system perspectives. These approaches often involve large simulation experiments with their own interpretability challenges. So, on one hand, we recognize the need to utilize large ensembles of hypothesized changes, but on the other hand, each additional dimension considered makes it more difficult to convey actionable insights. We introduce the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced "forensic"), a scenario discovery framework that helps users identify scenario storylines that capture key system dynamics and as well as important outcomes. We demonstrate its application to the Upper Colorado River Basin, focusing on decadal droughts and their water scarcity implications for the basin's diverse users and its obligations to downstream states through Lake Powell. We explore alternative impact metrics and dynamics, identifying scenario storylines with significant impacts, which can be used in future planning efforts to adapt to these stressed conditions. Key Points: Introduce framework for discovery of scenario storylines describing diverse stakeholder impacts and consequential dynamicsDemonstrate the framework in the Upper Colorado River Basin with hundreds of stakeholders and complex human‐natural system interactionsThe framework improves understanding and selection of drought scenario storylines through their effects on user‐ and basin‐scale impacts [ABSTRACT FROM AUTHOR]

The C4 Poaceae are a diverse group in terms of both evolutionary lineage and biochemistry. There is a distinct pattern in the distribution of C4 grass groups with aridity; however, the mechanistic basis for this distribution is not well understood. Additionally, few studies have investigated the functional strategies of co‐occurring C4 grass species for dealing with aridity in their natural environments. We explored the coordination of leaf‐level gas exchange, water use, and morphology among five co‐occurring semiarid C4 grasses belonging to divergent clades, biochemical subtypes, and size classes at three sites along a natural aridity gradient. More specifically, we measured predawn and midday water potential, stomatal conductance, water use efficiency, and photosynthesis. Leaf tissue was also collected for the analysis of stable isotopes of carbon and oxygen as well as for measurement of specific leaf area (SLA) and leaf width. Species differences in responsiveness of stomata to changes in vapor pressure deficit (VPD) were also assessed. It was expected that NAD‐me species would maintain higher rates of photosynthesis, higher water use efficiency, and have more responsive stomata than other co‐occurring species based on observed biogeographic patterns and past greenhouse studies. We found that Aristidoideae and Chloridoideae NAD‐me‐type grasses had greater stomatal sensitivity to VPD, consistent with a more isohydric strategy. However, midgrasses had both greater apparent water access and water use efficiency, regardless of subtype or lineage. PCK‐type species had less responsive stomata and maintained lower levels of photosynthesis with increasing aridity. There were strong interspecific differences in δ13C, leaf width, and SLA; however, these were not significantly correlated with water use efficiency. C4 grasses in our study did not fit discretely into functional groups as defined by lineage, biochemistry, or size class. Interspecific differences, evolutionary legacy, and biochemical pathway are likely to interact to determine water use and photosynthetic strategies of these plants. Control of water loss via highly responsive stomata may form the basis for dominance of certain C4 grass groups in arid environments. These findings build on our understanding of contrasting strategies of C4 grasses for dealing with aridity in their natural environments. [ABSTRACT FROM AUTHOR]

Identifying areas expected to remain buffered from climate change and maintain biodiversity and ecological function (i.e., climate refugia) is important for climate adaptation planning. As structurally diverse transitional zones between terrestrial and aquatic environments, riparian areas are often biological hotspots and provide critical corridors for species movement, particularly in arid and semi‐arid regions. In our study region in the western and central USA, identifying riparian areas that could serve as climate refugia is a priority for wildlife managers. We mapped areas with connected riparian habitats that, based on landscape diversity and projected changes in summer temperatures and landscape runoff, are expected to serve as climate refugia. To incorporate uncertainty and balance the need for near‐ and long‐term planning, we mapped potential refugia for 2 future time periods (2040–2069, 2070–2099) based on 2 climate models that represented divergent but plausible climate outcomes. The approach we developed is not constrained by physiology or behavior of target species and can be used to identify areas expected to fare comparatively well under a wide range of future climate scenarios. Our approach can also be used to identify areas where restoration could increase riparian connectedness and climate resilience. [ABSTRACT FROM AUTHOR]

In this study, we present a machine‐learning model capable of predicting food insecurity in the Horn of Africa, which is one of the most vulnerable regions worldwide. The region has frequently been affected by severe droughts and food crises over the last several decades, which will likely increase in future. Therefore, exploring novel methods of increasing early warning capabilities is of vital importance to reducing food‐insecurity risk. We present a XGBoost machine‐learning model to predict food‐security crises up to 12 months in advance. We used >20 data sets and the FEWS IPC current‐situation estimates to train the machine‐learning model. Food‐security dynamics were captured effectively by the model up to 3 months in advance (R2 > 0.6). Specifically, we predicted 20% of crisis onsets in pastoral regions (n = 96) and 20%–50% of crisis onsets in agro‐pastoral regions (n = 22) with a 3‐month lead time. We also compared our 8‐month model predictions to the 8‐month food‐security outlooks produced by FEWS NET. Over a relatively short test period (2019–2022), results suggest the performance of our predictions is similar to FEWS NET for agro‐pastoral and pastoral regions. However, our model is clearly less skilled in predicting food security for crop‐farming regions than FEWS NET. With the well‐established FEWS NET outlooks as a basis, this study highlights the potential for integrating machine‐learning methods into operational systems like FEWS NET. Plain Language Summary: In the face of increasing droughts and food crises, this study explored the use of machine learning to provide predictions of food crises in the Horn of Africa, up to 12 months in advance. We used an algorithm called "XGBoost," which we fed with over 20 data sets of potential food security drivers. After training the model, we found that food security dynamics were accurately predicted up to 3 months in advance, especially in pastoral and agro‐pastoral regions. The model accurately predicted 20% of crisis onsets in pastoral areas and 20%–50% in agro‐pastoral regions with a 3‐month lead time. In agro‐pastoral and pastoral regions, our machine learning algorithm showed a similar performance to the established early warning system from FEWS NET. The machine‐learning model did not show good performance in crop‐farming areas. Nonetheless, this study underscores the potential of integrating machine‐learning methods into existing operational systems like FEWS NET. By doing so, it paves the way for improved early warning capabilities, crucial in mitigating the looming threat of food insecurity in the Horn of Africa. Key Points: A machine‐learning model is presented to predict food‐security crises in the Horn of AfricaThe model demonstrates high overall performance, and performs similarly to FEWS NET outlooks in the (agro‐) pastoral regionsThis study can be utilized to integrate machine learning into existing early warning systems, thereby creating hybrid solutions for the future [ABSTRACT FROM AUTHOR]

Historically, the precipitation trend over the past few decades in the Contiguous United States (CONUS) exhibits a "Dry‐West Wet‐East" pattern; this is manifested by recent droughts/floods in the western/eastern US. However, it remains elusive what atmospheric phenomenon has potentially driven such a remarkable, and impactful precipitation pattern. Here we found that a coupled climate mode—the Pacific Meridional Mode (PMM) exerted strong impacts on the precipitation pattern over the CONUS during the summer season. We discovered a significant association between the PMM index and precipitation across the majority of the CONUS; this was manifested as a zonal dipole pattern—negative correlations in the western U.S. along with positive correlations in the eastern and central U.S. Overall, the physical mechanisms based on observations were supported by using Atmospheric Model Intercomparison Project simulations available from the Coupled Model Intercomparison Project Phase 6. Plain Language Summary: Historically, the Contiguous United States (CONUS) was parched in the West and soaked in the East. Despite the importance of this distinct spatial pattern of precipitation variability, the drivers of this hydroclimatic phenomenon remain elusive. Therefore, there is big knowledge gap in understanding the zonal precipitation pattern during the historical period. Here we find that the Pacific Meridional Mode (PMM) can exert strong impacts on the zonal precipitation pattern over the CONUS during the summer season. We discovered a significant association between the PMM index and the precipitation across the majority of the CONUS; this was manifested as a zonal dipole pattern—negative correlations in the western U.S. along with positive correlations in the eastern and central U.S. Overall, the physical mechanisms based on observations were supported by the Atmospheric Model Intercomparison Project experiments of the Coupled Model Intercomparison Project (Phase 6). Our research results explain in large part the zonal precipitation pattern ("Dry‐West Wet‐East") in the CONUS, and perhaps more importantly, the viability of the PMM being used as a potential predictor/indicator for summertime precipitation over the U.S. Key Points: The Pacific Meridional Mode (PMM) exerted strong impacts on the precipitation pattern over the CONUS during the summer seasonThe modulation on precipitation is manifested as a zonal dipole pattern, tied to teleconnections associated PMM sea‐surface temperature anomaliesThe physical mechanisms based on observations were supported by CMIP6 climate model simulations [ABSTRACT FROM AUTHOR]

Forests are a large carbon sink and could serve as natural climate solutions that help moderate future warming. Thus, establishing forest carbon baselines is essential for tracking climate‐mitigation targets. Western US forests are natural climate solution hotspots but are profoundly threatened by drought and altered disturbance regimes. How these factors shape spatial patterns of carbon storage and carbon change over time is poorly resolved. Here, we estimate live and dead forest carbon density in 19 forested western US ecoregions with national inventory data (2005–2019) to determine: (a) current carbon distributions, (b) underpinning drivers, and (c) recent trends. Potential drivers of current carbon included harvest, wildfire, insect and disease, topography, and climate. Using random forests, we evaluated driver importance and relationships with current live and dead carbon within ecoregions. We assessed trends using linear models. Pacific Northwest (PNW) and Southwest (SW) ecoregions were most and least carbon dense, respectively. Climate was an important carbon driver in the SW and Lower Rockies. Fire reduced live and increased dead carbon, and was most important in the Upper Rockies and California. No ecoregion was unaffected by fire. Harvest and private ownership reduced carbon, particularly in the PNW. Since 2005, live carbon declined across much of the western US, likely from drought and fire. Carbon has increased in PNW ecoregions, likely recovering from past harvest, but recent record fire years may alter trajectories. Our results provide insight into western US forest carbon function and future vulnerabilities, which is vital for effective climate change mitigation strategies. Plain Language Summary: We investigated the role of western US forests as natural climate solutions by analyzing current forest carbon storage and trends from 2005 to 2019 across 19 forested regions. We found that the Pacific Northwest stores the most carbon, while the Southwest stores the least. Climate, wildfires, and human activities determined carbon amounts. For instance, climate is important in the Southwest and Lower Rockies, while wildfires impact the entire western US, but particularly the Upper Rockies and California. Human activities like harvesting and private ownership, decrease carbon, particularly in the Pacific Northwest. Since 2005, live carbon has declined in many western US areas, likely due to drought and fires. The study highlights the vulnerability of western US forests to climate‐related and human threats to carbon, providing crucial insights for effective climate change mitigation strategies. Understanding these dynamics is essential for scientists, policymakers, and educators working toward sustainable forest management and climate solutions in the region. Key Points: Live carbon has declined across much of western United States forests, likely due to drought and fire, resulting in an increase in dead carbonIn the Pacific Northwest (PNW), harvest led to reduced carbon, but recovery from past harvest likely caused carbon to increase since 2005Our results provide a baseline from which to evaluate future changes and inform management strategies [ABSTRACT FROM AUTHOR]

Drought is associated with adverse environmental and societal impacts across various regions. Therefore, drought monitoring based on a single variable may lead to unreliable information, especially about the onset and persistence of drought. Previous studies show vapor pressure deficit (VPD) data can detect drought onset earlier than other drought indicators such as precipitation. On the other hand, soil moisture (SM) is a robust indicator for assessing drought persistence. This study introduces a nonparametric multivariate drought index Vapor Pressure Deficit Soil moisture standardized Drought Index (VPDSDI) which is developed by combining VPD with SM information. The performance of the multivariate index in terms of drought onset detection is compared with the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) for six major drought events across the United States including three rapidly developing drought events (this term refers to flash droughts that develop on monthly scales) and three conventional drought events. Additionally, the performance of the proposed index in detecting drought persistence is compared with the Standardized Soil moisture Index (SSI), which is an agricultural drought index. Results indicate the multivariate index detects drought onset always earlier than SPI for conventional events, but VPDSDI detects drought onset earlier than or about the same time as SPEI for rapidly developing droughts. In terms of persistence, VPDSDI detects persistence almost identical to SSI for both rapidly developing and conventional drought events. The results also show that combining VPD with SM reduces the high variability of VPD and produces a smoother index which improves the onset and persistence detection of drought events leveraging VPD and SM information. Plain Language Summary: Drought has significant negative effects on the environment and society. Relying on a single variable for drought monitoring can provide unreliable information, particularly when it comes to determining when droughts begin and end. Previous research has found that vapor pressure deficit (VPD) data can identify the beginning of drought conditions earlier than measures like precipitation. In contrast, soil moisture (SM) has proven to be a reliable indicator for evaluating how long drought conditions last over time. In this study, we introduced a multivariate drought index that combines VPD with SM data, named Vapor Pressure Deficit Soil moisture standardized Drought Index (VPDSDI). We compared the performance of this index in detecting drought onset and persistence with SPEI/SPI and SSI, respectively. The results demonstrate that VPDSDI detects drought onset around the same time or earlier than SPEI/SPI. Moreover, VPDSDI shows similar detection capabilities to SSI for drought persistence. By combining VPD and SM, VPDSDI reduces variability and provides more reliable information for assessing and understanding drought events. Key Points: This study introduces a nonparametric agrometeorological drought index by combining vapor pressure deficit and soil moisture informationWe find this new index detects rapidly developing drought and conventional drought onset earlier or at the same time as Standardized Precipitation Evapotranspiration Index and Standardized Precipitation Index in evaluated events, respectivelyResults suggest that the proposed index shows drought persistence similar to Standardized Soil moisture Index in assessed events [ABSTRACT FROM AUTHOR]

We study drying trends across the central latitude strip of Europe (47.5–52.5°N and 2.5–27.5°E) during 1980–2019 and their links to atmospheric circulation. Daily differences between potential evapotranspiration and precipitation (PET–P) calculated from the E–OBS data are used to characterize dryness, and atmospheric circulation is represented by circulation types classified using daily sea level pressure patterns from the NCEP/NCAR reanalysis. Circulation types favoring dry conditions in vegetation season (April–September) are identified based on daily PET–P, and their temporal changes, seasonal variations, and links to trends in dryness in individual European regions are analyzed. In the early vegetation season (AMJ), drying trends are observed mainly in Western and Central Europe while in the late vegetation season (JAS), they are located predominantly in Eastern Europe. The dry circulation types include all anticyclonic types in all regions, as well as northeast to south (southwest in Eastern Europe) directional types. Trends of the dry circulation types correspond to those of dryness: the largest increase is found during AMJ in Western and Central Europe but during JAS in Eastern Europe. The results show that trends in dryness in the central latitude strip of Europe in the warm half‐year were associated with changes in atmospheric circulation, as the largest increases in frequency of dry circulation types occurred in the regions and months affected by pronounced drying. The increasing frequency of anticyclonic types in AMJ and reduced inflow of moist air masses from the Atlantic are the key factors supporting intensification of dry conditions in European mid‐latitudes. Plain Language Summary: Drought is a major climatic hazard that threatens many aspects of society and ecosystems. The 2015–2018 dry period and the recent dry summer of 2022 in Europe are mere examples of many recent droughts developing worldwide. We studied how dryness severity progressed in Europe since 1980 and whether trends in dryness were linked to changes in atmospheric circulation. We found distinct regional patterns in drying trends: while Western and Central Europe recorded the largest increases in dryness during the early vegetation season (April–June), Eastern Europe, by contrast, had more distinct drying trends in the late vegetation season (July–September). We linked this pattern to changes in frequency of circulation types favorable to dryness and concluded that increased frequency of anticyclonic (clear‐sky) weather and reduced westerly flow of moist oceanic air contributed to drought intensification in European mid‐latitudes. Key Points: Significant drying trends in European mid‐latitudes since 1980s have been primarily linked to growing potential evapotranspirationDry conditions during early vegetation season have become more frequent and severe particularly in Western and Central EuropeIncreased frequency of circulation types favoring dry conditions corresponds both spatially and temporally to the drying trends [ABSTRACT FROM AUTHOR]

Global warming has intensified the intensity of compound drought‐hot extremes (CDHEs), posing more severe impacts on human societies and ecosystems than individual extremes. The vulnerability of global terrestrial ecosystems under CDHEs, along with its key influencing factors, remains poorly understood. Based on multiple remote sensing data, we construct a Vine Copula model to appraise vegetation vulnerability under CDHEs, and attribute it to climatic and biotic factors for five different vegetation types. High vulnerability is detected in central and southern regions of North America, eastern and southern regions of South America, Southern Africa, northern and western Europe, and northern and eastern Australia. The drier the climate, the higher will be the vulnerability. Furthermore, biodiversity and biomass are key biotic factors influencing the vulnerability of various vegetation types, such that ecosystems with richer biodiversity and higher biomass have lower vulnerability to CDHEs. The findings deepen understanding of terrestrial ecosystem response to CDHEs. Plain Language Summary: Drought and hot‐related extremes often coincide or follow one another, known as compound drought‐hot extremes (CDHEs), adversely affecting various vegetation processes. Thus, investigating the response relationship between vegetation dynamics and CDHEs is critical for maintaining the sustainable development of ecosystems under the background of climate warming. High ecosystem vulnerability is detected in central and southern regions of North America, eastern and southern regions of South America, Southern Africa, northern and western Europe, and northern and eastern Australia. Climatic factors (precipitation, arid index, temperature, and radiation) dominate the vulnerability of all vegetation types. The drier the climate, the higher will be the vulnerability of vegetation to CDHEs. Furthermore, biodiversity and biomass are key biotic factors influencing the vulnerability. The results provide us with essential insights for managing and adapting terrestrial ecosystems against climate change and are of interest to a wide range of audiences, including but not limited to ecologists, hydrologists, and climatologists. Key Points: A framework is developed to estimate the vegetation vulnerability in response to compound drought‐hot extremes and attribute it to various factorsVegetation vulnerability is higher for grasses and shrubs than for evergreen and deciduous forestsVegetation vulnerability is both affected by climate and biotic factors, but climate factors are more dominant [ABSTRACT FROM AUTHOR]

Currently, there are three main shortcomings in meteorological drought indices: first, they rely on historical climate probability functions; second, the timescale used in calculations has a certain degree of subjectivity; third, the same index value may correspond to vastly different levels of actual drought in different climate types of regions. The purpose of this article is to establish a meteorological drought index that does not rely on historical meteorological element probability functions. Through theoretical derivation, four drought‐level maintenance lines are established on the cumulative precipitation‐cumulative water surface evaporation coordinate plane, and the coordinate quadrant is divided into five drought‐level areas. Through forward daily rolling accumulation, the maximum distance point is selected from the dynamically changing coordinate points to determine the corresponding cumulative precipitation and cumulative evaporation. The meteorological drought index is established by the distance from the selected coordinate point to each drought‐level maintenance line. Using daily precipitation and evaporation data from meteorological observation stations, the index is calculated based on the established meteorological drought index model, and compared with actual drought evolution and drought disaster records. The results show that the index can capture the development of drought well, and its changes are very consistent with drought disaster records. The index is of great significance for drought monitoring or assessment, and can provide guidance for water resource allocation, crop layout, and urban planning. Furthermore, it can also provide a way of thinking that does not rely on historical element probabilities for future drought research. [ABSTRACT FROM AUTHOR]

UK water resources face a number of challenges when planning for an uncertain future. Climate change impacts and what future droughts might look like can be a significant contributor to this uncertainty. Recent and potential future developments (e.g. ever‐finer resolutions) in climate modelling offer the potential for running bias‐corrected transient future scenarios through hydrological, hydrogeological and water supply models, providing users with droughts of differing severity, frequency, spatial extent and duration to those experienced historically, incorporating changes over time and an understanding of climate model uncertainty. The recent enhanced Future Flows and Groundwater (eFLaG) project sought to demonstrate a climate service using these transient scenarios, with the aim of enhancing the resilience of the water industry to drought events and complementing existing approaches. The project demonstrated the use of this transient climate change information within a water resource setting, using a variety of hydrological and water resource models to help illuminate potential gaps and issues with such an approach. If we are to realise the potential of transient scenarios, a number of barriers – both scientific and organisational – need to be overcome. We present a road map for the future based on outcomes from the eFLaG project, as well as ways the eFLaG projections could be used to improve system resilience in the present. [ABSTRACT FROM AUTHOR]

The research on the trends of mountainous aridity and the issue of elevation‐dependent aridity (EDA) has long been hindered by limitations in data validity. In this study, we present global monthly merged data sets of Vapor Pressure Deficit and Aridity Index (AI) from 1960 to 2020, aiming to detect and attribute elevation‐dependent patterns of atmospheric and soil aridity in six representative high mountainous areas worldwide. Our findings reveal that most of the mountainous regions experienced significant aridification after the 1970s (p < 0.05). The Rocky Mountains and Ethiopian Highlands are identified as the most vulnerable areas, with both atmospheric and soil aridity increasing by 13% across all elevation gradients. Significant humidification has been observed only in High Asian Regions at 4,000 m above sea level with an approximate decrease of 11% in soil AI. In both temporal and spatial patterns, soil aridity exhibits stronger heterogeneity compared to atmospheric aridity, with certain regions and seasons showing humidification, despite the overall aridification trend. Elevation‐dependent aridity is observed in two‐thirds of the mountains, but whether high altitude alleviates or amplifies aridity depends on the pattern of precipitation changes at different elevations. The rise in air temperature is the primary driving factor for soil and atmospheric aridification, contributing to over 50% of each. In two‐thirds of the regions, changes in precipitation exacerbate soil aridity. Simulations show that human activities are closely related to the ongoing prolonged atmospheric aridification. This study contributes to a comprehensive understanding of global mountainous aridity evolution projected under climate change. Plain Language Summary: Due to limitations in data validity, the conclusions regarding mountainous aridity changes and related elevation‐dependent aridity issues have often been contradictory. To address this issue, we generated a comprehensive data set of drought indices by integrating observed and simulated data, covering the period from 1960 to 2020. Our analysis revealed that the 1970s marked a significant turning point for aridity patterns in mountainous regions globally. Since then, except for the High Asian Regions, both soil and atmospheric aridity have exhibited a rapid increase in mountainous areas worldwide, with the most severe aridification observed in the Rocky Mountains and Ethiopian Highlands. We also identified a certain relationship between the severity of aridification and elevation gradients, although it is not a straightforward relationship where higher elevations necessarily experience more severe aridification. Importantly, our attribution analysis suggests that human activities may impact the observed atmospheric aridification in mountainous regions on a global scale. Key Points: The period from 1970 to 1980 witnessed a significant shift from humidification to aridification in most mountainous regions worldwideThe relationship between changes in precipitation and elevation gradients shapes the distinct patterns of elevation‐dependent aridityProlonged atmospheric aridification in mountainous regions may not be solely attributed to natural variability [ABSTRACT FROM AUTHOR]

Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below‐normal precipitation and terminated by average or above‐normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming‐driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts. Plain Language Summary: Regional warming associated with climate change is already making droughts worse in many places. This trend is expected to continue as global temperatures increase in response to continued emissions of greenhouse gases. With increasing temperatures, soils are projected to lose water to the atmosphere as it draws more and more moisture from the land surface, increasing drought risk. At the same time, climate change is also projected to shift precipitation patterns. This means it is important to understand how changes in rainfall will cause and end droughts in the future. Using state‐of‐the‐art climate models, we investigate how future warming will impact the influence of precipitation on soil moisture in the Mediterranean and Western North America. Our results suggest that current levels of monthly precipitation will be insufficient to alleviate drought conditions in a warmer world. Key Points: Warming causes declines in soil moisture in the Mediterranean and Western North AmericaThe probability of a moderate soil moisture drought increases during both low and high precipitation anomalies, even at 1°C of warmingThe ability of large positive precipitation anomalies to terminate soil moisture droughts will be substantially reduced in a warmer world [ABSTRACT FROM AUTHOR]

The main challenge facing agricultural crop production in the current global climate change scenario is sustainability. Drought, as a yield‐limiting factor, has become a major threat to international food security. Tolerance to drought is a complex trait and its response is carried out by various genes, transcription factors, microRNAs, hormones, proteins, cofactors, ions and metabolites. The complexity of the trait has limited the development of drought‐tolerant sweet potato cultivars by traditional breeding. Advances in sweet potato breeding to exploit the full potential of the crop to contribute to improved and higher performing sweet potato cultivars, adapted to increasingly risky rainfed conditions, mainly drought, are key to making food production systems more efficient and more tolerant to pressure from drought and other stressors. Genetic gain for yield potential in sweet potato has improved mostly in African countries, mainly as a result of an accelerated breeding scheme. The focus on maximising the utilisation of molecular tools for sweet potato improvement and yield has been recently explored in breeding programmes in sub‐Saharan Africa (SSA). This article provides an update on the trends and gaps in sweet potato breeding in SSA, reviewing the relevant strategies used to improve sweet potato in the region. Finally, the perspectives of using new advanced tools including genetic engineering and genome editing, and the challenges associated with climate change for further improvement of the crop are highlighted. Collaborative efforts in African countries are driving advances in breeding methods through the incorporation of molecular tools to develop drought‐tolerant sweet potato varieties that are important to global food security despite challenges posed by the drastic change in climate. [ABSTRACT FROM AUTHOR]

Cultivated crops are generally expected to have less abiotic stress tolerance than their wild relatives. However, this assumption is not well supported by empirical literature and may depend on the type of stress and how it is imposed, as well as the measure of tolerance being used. Here, we investigated whether wild and cultivated accessions of Helianthus annuus differed in stress tolerance assessed as proportional decline in biomass due to drought and whether wild and cultivated accessions differed in trait responses to drought and trait associations with tolerance. In a greenhouse study, H. annuus accessions in the two domestication classes (eight cultivated and eight wild accessions) received two treatments: a well‐watered control and a moderate drought implemented as a dry down followed by maintenance at a predetermined soil moisture level with automated irrigation. Treatments were imposed at the seedling stage, and plants were harvested after 2 weeks of treatment. The proportional biomass decline in response to drought was 24% for cultivated H. annuus accessions but was not significant for the wild accessions. Thus, using the metric of proportional biomass decline, the cultivated accessions had less drought tolerance. Among accessions, there was no tradeoff between drought tolerance and vigor assessed as biomass in the control treatment. In a multivariate analysis, wild and cultivated accessions did not differ from each other or in response to drought for a subset of morphological, physiological, and allocational traits. Analyzed individually, traits varied in response to drought in wild and/or cultivated accessions, including declines in specific leaf area, leaf theoretical maximum stomatal conductance (gsmax), and stomatal pore length, but there was no treatment response for stomatal density, succulence, or the ability to osmotically adjust. Focusing on traits associations with tolerance, plasticity in gsmax was the most interesting because its association with tolerance differed by domestication class (although the effects were relatively weak) and thus might contribute to lower tolerance of cultivated sunflower. Our H. annuus results support the expectation that stress tolerance is lower in crops than wild relatives under some conditions. However, determining the key traits that underpin differences in moderate drought tolerance between wild and cultivated H. annuus remains elusive. [ABSTRACT FROM AUTHOR]

Drought has affected much of the western United States since about the year 2000, including the Upper Colorado River Basin (UCRB). Using a time series of UCRB streamflow derived from a tree‐ring based reconstruction of UCRB streamflow for the years 1 CE through 1905 CE, together with naturalized UCRB streamflow values for 1906 CE through 2021 CE, we identify 51 drought events, including the 2000–2021 drought. Although the recent 2000–2021 drought has been relatively severe, it is not the most severe of the UCRB drought events we identified. Results also indicate that natural variability combined with projected climate warming can result in UCRB drought events that are more severe than any drought since 1 CE. Plain Language Summary: A long and severe drought has affected much of the western United States since about the year 2000 CE, including the Upper Colorado River Basin (UCRB). Comparing this drought to past UCRB droughts (during 1 CE through 2021 CE), we find that the 2000–2021 drought is not the most severe UCRB drought. The results also suggest that natural variability combined with projected climate warming could result in UCRB drought events that are more severe than any drought since 1 CE. Key Points: We identify 51 drought events for the years 1 CE through 2021 CE for the Upper Colorado River Basin (UCRB)Twelve of the 51 UCRB droughts we identify were more severe than the 2000–2021 droughtNatural variability and/or a 1°C temperature increase can trigger UCRB droughts that are more severe than the 2000–2021 drought [ABSTRACT FROM AUTHOR]

Urban green infrastructure (UGI) is a prominent concept toward climate adaptation and urban resilience, but it is also affected by droughts and heat. Hence, this study aims to advance the multi‐assessment of drought and heat risks (DHRs) for UGI through the DHR assessment framework with conceptual and methodological features, paving the way toward knowledge creation and decision support. The framework was systematically developed, starting with defining the situation, analyzing concepts, and finally, constructing the framework. The situation is interpreted as a coupled human and natural system to represent the biophysical and immaterial elements, processes, and interrelations. Further, the concepts of risk, UGI, and ecosystem services lead to a risk system showing the compound hazards, the exposure, and the cascading vulnerabilities of the UGI. The DHR assessment framework distinguishes two stages, multi‐risk analysis and multi‐criteria risk evaluation. The analysis includes the definition and interpretation of the UGI situation under drought and heat conditions, analyzing the hazards, exposures, and vulnerabilities of the system, and translating the risk system into an indicator‐based information system. Hereby, the vulnerability analysis of the biophysical UGI aspects comprises the susceptibility and resilience of UGI entities, as well as the degree to which providing ecosystem functions and services can be affected. The multi‐criteria risk evaluation covers the assignment of thresholds and weights for indicators, in addition to the aggregation methods. The resulting framework intends to support local actors in the risk assessment of current and future conditions, fostering evidence‐based decisions and interventions to deal with compound DHRs. [ABSTRACT FROM AUTHOR]