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Traditional, mainstream definitions of drought describe it as deficit in water‐related variables or water‐dependent activities (e.g., precipitation, soil moisture, surface and groundwater storage, and irrigation) due to natural variabilities that are out of the control of local decision‐makers. Here, we argue that within coupled human‐water systems, drought must be defined and understood as a process as opposed to a product to help better frame and describe the complex and interrelated dynamics of both natural and human‐induced changes that define anthropogenic drought as a compound multidimensional and multiscale phenomenon, governed by the combination of natural water variability, climate change, human decisions and activities, and altered micro‐climate conditions due to changes in land and water management. This definition considers the full spectrum of dynamic feedbacks and processes (e.g., land‐atmosphere interactions and water and energy balance) within human‐nature systems that drive the development of anthropogenic drought. This process magnifies the water supply demand gap and can lead to water bankruptcy, which will become more rampant around the globe in the coming decades due to continuously growing water demands under compounding effects of climate change and global environmental degradation. This challenge has de facto implications for both short‐term and long‐term water resources planning and management, water governance, and policymaking. Herein, after a brief overview of the anthropogenic drought concept and its examples, we discuss existing research gaps and opportunities for better understanding, modeling, and management of this phenomenon. Plain Language Summary: This article reviews research and progress on the notion of anthropogenic drought broadly defined as drought events caused or intensified by human activities. Most commonly used drought definitions are based on deficit in hydrologic/meteorologic drivers such as precipitation and runoff. Within coupled human‐water systems, however, drought must be defined and understood as the complex and interrelated dynamics of both natural and human‐induced changes. This anthropogenic drought definition considers the full spectrum of dynamic feedbacks and processes (e.g., land‐atmosphere interactions and water and energy balance) within human‐nature systems. Ideally, anthropogenic drought and the corresponding human interactions should be incorporated in models that include land‐atmosphere interactions, water balance, and energy balance. In this article, we review existing research gaps and opportunities for better understanding, modeling, and management of this phenomenon. Key Points: Anthropogenic drought is primarily governed by the joint impacts of natural renewable water variability, climate change, and human decisionsAnthropogenic drought and water bankruptcy will become more ubiquitous under current development and climate change trajectoriesIdeally, human interactions should be incorporated in models that include land‐atmosphere interactions, water balance and energy balance [ABSTRACT FROM AUTHOR]

Existing major reservoirs in California, with average age above 50 years, were built in the previous century with limited data records and flood hazard assessment. Changes in climate and land use are anticipated to alter statistical properties of inflow to these infrastructure systems and potentially increase their hydrological failure probability. Because of large socioeconomic repercussions of infrastructure incidents, revisiting dam failure risks associated with possible shifts in the streamflow regime is fundamental for societal resilience. Here we compute historical and projected flood return periods as a proxy for potential changes in the risk of hydrological failure of dams in a warming climate. Our results show that hydrological failure probability is likely to increase for most dams in California by 2100. Noticeably, the New Don Pedro, Shasta, Lewiston, and Trinity Dams are associated with highest potential changes in flood hazard. Plain Language Summary: Dams are critical manmade infrastructure that provide resilience against extremes (e.g., droughts and floods) and regulate water resources. In 2017, California experienced a series of flooding events, which triggered incidents such as structural failure of the Oroville Dam's spillway. Because of the large social and economic impacts of such incidents and given the major dams in California have an average age of above 50 years, it is important to evaluate the risk of failure of dams over a planning period in the future. In this study, we inspected the possible impacts of climate change on the future flooding hazard for several major dams in California. Here we show that in the warmer future climate, the risk of dam failure most likely increases for most of the major dams in California. The insights gained from this study highlight the important role of adaptation strategies for the operational management of aging dams in a changing climate, together with adequate and timely maintenance. Key Points: Flood hazard is projected to increase at the upstream of major dams in California due to changes in precipitation extremesFailure probability is projected to increase for most of the California dams in a warming climateHistorical 100‐year flood is 5 times more likely for some biggest California dams under RCP8.5 (high greenhouse gas concentration levels) [ABSTRACT FROM AUTHOR]

Climate change may affect ocean-driven coastal flooding regimes by both raising the mean sea level (msl) and altering ocean-atmosphere interactions. For reliable projections of coastal flood risk, information provided by different climate models must be considered in addition to associated uncertainties. In this paper, we propose a framework to project future coastal water levels and quantify the resulting flooding hazard to infrastructure. We use Bayesian Model Averaging to generate a weighted ensemble of storm surge predictions from eight climate models for two coastal counties in California. The resulting ensembles combined with msl projections, and predicted astronomical tides are then used to quantify changes in the likelihood of road flooding under representative concentration pathways 4.5 and 8.5 in the near-future (1998-2063) and mid-future (2018-2083). The results show that road flooding rates will be significantly higher in the near-future and mid-future compared to the recent past (1950-2015) if adaptation measures are not implemented. [ABSTRACT FROM AUTHOR]

Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980-2012, including the Millennium Drought in Australia (2001-2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25-45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops. [ABSTRACT FROM AUTHOR]

The frequency, severity, and spatial extent of destructive wildfires have increased in several regions globally over the past decades. While direct impacts from wildfires are devastating, the hazardous legacy of wildfires affects nearby communities long after the flames have been extinguished. Post‐wildfire soil conditions control the persistence, severity, and timing of cascading geohazards in burned landscapes. The interplay and feedback between geohazards and wildfire‐induced changes to soil properties, land cover conditions, and near‐surface and surface processes are still poorly understood. Here, we synthesize wildfire‐induced processes that can affect the critical attributes of burned soils and their conditioning of subsequent geohazards. More specifically, we discuss the state of knowledge pertaining to changes in mineralogical, hydraulic, mechanical, and thermal properties of soil due to wildfire with a focus on advances in the past decade. We identify how these changes in soil properties alter evapotranspiration, interception, sediment transport, infiltration, and runoff. We then link these alterations to the evolution of different geohazards, including dry raveling, erosion, rockfalls, landslides, debris flows, and land subsidence. Finally, we identify research gaps and future directions to advance knowledge on how wildfires control the evolution of various earth surface processes and geohazards over time. Key Points: Post‐wildfire soil conditions control the persistence, severity, and timing of cascading geohazards in burned landscapesWe synthesize the interplay between geohazards and wildfire‐induced changes to soil properties, land cover, and near‐surface processesFuture directions are identified to advance knowledge on how wildfires affect surface processes and geohazards over time [ABSTRACT FROM AUTHOR]

The wheat belt along the Indo‐Gangetic Plains (IGP) in India is an emergent hotspot of climate change‐driven crop loss threatening local food security. Using statistical Generalized Linear Models, observed temperature and wheat production data, we show an increase in magnitude, frequency and areal extent of heat stress episodes in the IGP region during 1967–2018. Such episodes comprise of temperatures exceeding a senescence‐inducing threshold. Further, using a copula‐based multivariate framework, we probabilistically assess wheat production conditioned on heat stress indicators, both in the historical period and also in the Coupled Model Intercomparison Project (CMIP6) future projections. Probabilistic estimates of below‐average wheat production under scenario‐averaged heat stress conditions are expected to rise by 8%–27% under the SSP5‐8.5 scenario, with Punjab showing the largest increase. Quantitative links between heat stress indicators and loss of crop yield highlight increased agricultural vulnerability in India under climate change. Plain Language Summary: The three states of Punjab, Haryana and Uttar Pradesh in the Indo‐Gangetic Plains are the largest wheat producers in India, playing a crucial role in ensuring food security of this densely populated country. Wheat, a winter crop, is reported to be sensitive to heat stress because of rising temperatures and climate change. However, in previous studies, the sensitivity of wheat yield has been mainly explored with respect to the magnitude of temperature. Here, based on statistical analysis of observed temperature and actual wheat production data, we show that the magnitude, frequency and areal extent of agricultural heat stress events are increasing in India's wheat belt, with frequency showing the most pronounced trend. Further, we establish a quantitative link between crop loss and these three heat stress indicators in a multivariate, probabilistic framework. This probabilistic approach is used to obtain likelihoods of reduced wheat yield in future, using state‐of‐art climate model projections. Under climate change, chances of below‐average wheat production rise by 8%–27% in the worst‐case scenario. Thus, our results quantify rising agricultural vulnerability in India's wheat‐belt under climate change and variability. Key Points: Increase in magnitude, frequency and areal extent of heat stress on wheat cultivation in India; frequency has the most pronounced trendQuantitative links established between yield and heat stress magnitude, frequency and areal extent in observed and projected climateProbabilistic estimates of below‐average wheat production rise by 8%–27% in the worst case, with largest impact in Punjab [ABSTRACT FROM AUTHOR]

The estimation of exceedance probabilities for extreme climatic events is critical for infrastructure design and risk assessment. Climatic events occur over a greater space than they are measured with point‐scale in situ gauges. In extreme value theory, the block maxima approach for spatial analysis of extremes depends on properly modeling the spatially varying Generalized Extreme Value marginal parameters (i.e., trend surfaces). Fitting these trend surfaces can be challenging since there are numerous spatial and temporal covariates that are potentially relevant for any given event type and region. Traditionally, covariate selection is based on assumptions regarding the topmost relevant drivers of the event. This work demonstrates the benefit of utilizing elastic‐net regression to support automatic selection from a relatively large set of physically relevant covariates during trend surface estimation. The trend surfaces presented are based on 24‐hr annual maximum precipitation for northeastern Colorado and the Texas‐Louisiana Gulf Coast. Plain Language Summary: Extreme climatic events (e.g., extreme rainfall, wind) by their nature occur over a much larger space than they are measured with point‐scale in situ gauges. Geographic and climatic variables (aka, covariates; e.g., elevation, temperature, pressure) that are associated with the extreme event type can be used to assist in properly modeling how the event varies over a given space. However, the selection of which geographic and climatic covariates should be included in spatial analysis of extremes is often left to manual selection and, in the case of extreme precipitation, is limited to only a few covariates (i.e., latitude, longitude, mean precipitation). Moreover, the use of a larger set of variables is often avoided due to the computational burden that is introduced. We demonstrate the use of elastic‐net regularization for automating the selection of geographical and climatic covariates for modeling the distribution of extreme event parameters spatially, and for reducing the computational time needed for selecting the best performing set of covariates. Key Points: Elastic‐net regularization can be used to automate covariate selection and reduce computational burden during trend surface developmentAdditional physical covariates can improve trend surface results for certain regions [ABSTRACT FROM AUTHOR]

Most attribution studies tend to focus on the impact of anthropogenic forcing on individual variables. However, studies have already established that many climate variables are interrelated, and therefore, multidimensional changes can occur in response to climate change. Here, we propose a multivariate method which uses copula theory to account for underlying climate conditions while attributing the impact of anthropogenic forcing on a given climate variable. This method can be applied to any relevant pair of climate variables; here we apply the methodology to study high temperature exceedances given specified precipitation conditions (e.g., hot droughts). With this method, we introduce a new conditional probability ratio indicator, which communicates the impact of anthropogenic forcing on the likelihood of conditional exceedances. Since changes in temperatures under droughts have already accelerated faster than average climate conditions in many regions, quantifying anthropogenic impacts on conditional climate behavior is important to better understand climate change. Plain Language Summary: Most studies investigating human impacts on climate conditions focus on characterizing changes in individual variables such as precipitation or temperature. However, since many climate conditions are interconnected, these individual variables do not comprehensively represent the many changes that can occur in response to human activity. Here, we introduce a method that takes into account underlying climate conditions while quantifying the impact of human activity on a given climate variable. This method can be used to study pairs of climate variables and here we provide an example application to examine high temperature occurrences during dry precipitation conditions using climate models. For example, we show that regions such as the Amazon have a 4.1 times higher likelihood of experiencing high temperatures under dry climate conditions as a result of human activity. Given our knowledge of future climate change, we anticipate that the relationships between key climate variables may continue to change, which makes the study of human impacts on conditional climate behavior important for a more complete understanding of climate change. Key Points: We propose a multivariate method to conduct an attribution analysis of one variable conditioned on another related climate variableWe show how this method can be applied to high temperature exceedances given specified precipitation conditionsLand regions between 60°N and 60°S show increased risk of conditional temperature extremes under dry conditions due to anthropogenic forcing [ABSTRACT FROM AUTHOR]

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil‐to‐atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS), is one of the largest carbon fluxes in the Earth system. An increasing number of high‐frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open‐source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long‐term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS, the database design accommodates other soil‐atmosphere measurements (e.g. ecosystem respiration, chamber‐measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package. [ABSTRACT FROM AUTHOR]

Abstract: Compound extremes correspond to events with multiple concurrent or consecutive drivers (e.g., ocean and fluvial flooding, drought, and heat waves) leading to substantial impacts such as infrastructure failure. In many risk assessment and design applications, however, multihazard scenarios of extremes and compound events are ignored. In this paper, we review the existing multivariate design and hazard scenario concepts and introduce a novel copula‐based weighted average threshold scenario for an expected event with multiple drivers. The model can be used for obtaining multihazard design and risk assessment scenarios and their corresponding likelihoods. The proposed model offers uncertainty ranges of most likely compound hazards using Bayesian inference. We show that the uncertainty ranges of design quantiles might be large and may differ significantly from one copula model to the other. We also demonstrate that the choice of marginal and copula functions may profoundly impact the multihazard design values. A robust analysis should account for these uncertainties within and between multivariate models that translate into multihazard design quantiles. [ABSTRACT FROM AUTHOR]

Key Points: Editors‐in‐chief of AGU journals stress urgent need for greening economyCross‐sector solutions‐based science must supplement basic researchAGU is adding solutions‐based community science journal to portfolio [ABSTRACT FROM AUTHOR]