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Globally, land subsidence (LS) often adversely impacts infrastructure, humans, and the environment. As climate change intensifies the terrestrial hydrologic cycle and severity of climate extremes, the interplay among extremes (e.g., floods, droughts, wildfires, etc.), LS, and their effects must be better understood since LS can alter the impacts of extreme events, and extreme events can drive LS. Furthermore, several processes causing subsidence (e.g., ice‐rich permafrost degradation, oxidation of organic matter) have been shown to also release greenhouse gases, accelerating climate change. Our review aims to synthesize these complex relationships, including human activities contributing to LS, and to identify the causes and rates of subsidence across diverse landscapes. We primarily focus on the era of synthetic aperture radar (SAR), which has significantly contributed to advancements in our understanding of ground deformations around the world. Ultimately, we identify gaps and opportunities to aid LS monitoring, mitigation, and adaptation strategies and guide interdisciplinary efforts to further our process‐based understanding of subsidence and associated climate feedbacks. We highlight the need to incorporate the interplay of extreme events, LS, and human activities into models, risk and vulnerability assessments, and management practices to develop improved mitigation and adaptation strategies as the global climate warms. Without consideration of such interplay and/or feedback loops, we may underestimate the enhancement of climate change and acceleration of LS across many regions, leaving communities unprepared for their ramifications. Proactive and interdisciplinary efforts should be leveraged to develop strategies and policies that mitigate or reverse anthropogenic LS and climate change impacts. Plain Language Summary: Our article reviews existing research on the global issue of land subsidence (LS), or the relative sinking of the land surface. At the global scale, a variety of LS drivers and physical processes are present and interact with one another. The combination of climatic extremes (e.g., droughts, floods, wildfires, etc.) and LS can lead to devastating impacts on natural and built systems as well as feedbacks within our Earth system that enhance climate change. Yet, a review of the combined effects of extreme events, climate, subsidence, and their impacts, does not currently exist. In this article, we identify and characterize LS drivers, rates, and impacts throughout many different climates (e.g., arid and humid regions) and landscapes (coastal and inland locales, urban and agricultural areas). We also bring together the interplay among extreme events and LS and their connections to climate, human activities, and infrastructure. Integrating such relationships into LS analysis and monitoring frameworks is necessary to better understand cascading hazards and identify vulnerable populations, assess at‐risk infrastructure, and develop mitigation and adaptation strategies for LS. Key Points: Human activities led to the largest land subsidence (LS) rates and greater human exposure to anthropogenic LS is expected in the futureModeling the interplay of extreme events and LS is vital as feedbacks can amplify impacts on infrastructure, the environment, and societyProcesses such as permafrost thaw and soil organic matter oxidation drive LS and release greenhouse gases, enhancing climate change [ABSTRACT FROM AUTHOR]

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]

Drought indices do not always provide the most relevant information for water resources management as most of them neglect the role of snow in the land surface water balance. In this study, a physically based drought index, the Standardized Moisture Anomaly Index (SZI), was modified and improved by incorporating the effects of snow dynamics for drought characterization at multiple time scales. The new version of the SZI, called SZIsnow, includes snow in both the water supply and demand in drought characterization by using the water‐energy budgets from the Global Land Data Assimilation Systems product. We compared and evaluated the performance of SZIsnow and SZI in drought identification globally across various time scales using observed multicategory drought evidences from several sources. Results show that the SZIsnow agrees better with the observed changes in hydrological and agricultural droughts than the SZI, particularly over basins with high snow accumulation. Furthermore, the SZIsnow is more consistent with the residual water‐energy ratio than the SZI over snow‐influenced regions. Overall, the SZIsnow can be either a complement or an improvement over the SZI for identifying, monitoring, and characterizing hydrological and agricultural droughts at various scales (e.g., 1–48 months) over high‐latitude and high‐elevation regions that receive snow. Key Points: A global multi‐scalar drought index (SZIsnow) was developed to improve the drought characterization by incorporating snow processesThe SZIsnow generally performs better than the SZI for monitoring multi‐category droughts by considering snow dynamicsThe SZIsnow incorporates water‐energy budgets to address the influence of snow on both the water supply and demand in drought assessment [ABSTRACT FROM AUTHOR]

We characterize the sensitivity of atmospheric river (AR)‐derived seasonal snowfall estimates to their atmospheric reanalysis‐based detection over Sierra Nevada, USA. We use an independent snow data set and the ARs identified with a single detection method applied to multiple atmospheric reanalyses of varying horizontal resolutions, to evaluate orographic relationships and contributions of individual ARs to the seasonal cumulative snowfall (CS). Spatial resolution differences have relatively minor effects on the number of ARs diagnosed, with higher‐resolution data sets identifying four more AR days per year, on average, during the 1985–2015 winters. However, this can lead to ~10% difference in AR attribution to the mean domain‐wide seasonal CS and differences up to 47% snowfall attribution at the seasonal scale. We show that identifying snow‐bearing ARs provides more information about the seasonal CS than simply knowing how many ARs occurred. Overall, we find that higher‐resolution atmospheric reanalyses imply greater attribution of seasonal CS to ARs. Plain Language Summary: While it is known that elongated moisture‐rich atmospheric features, known as atmospheric rivers (ARs), play an important role in the water resources of the mountainous western United States, less is known about how the atmospheric data sets used to diagnose the presence of ARs influence the amount of AR‐attributed snowfall estimated each winter. Nonetheless, this missing information can be important for managing water resources and improving seasonal snowfall forecasts for areas depending on AR‐derived snowfall. We show that using a single AR detection algorithm, applied to multiple atmospheric reanalyses to identify ARs, higher‐resolution atmospheric reanalyses diagnose up to four more ARs per winter implying 10% greater AR attribution to the mean seasonal snowfall across Sierra Nevada, USA. Understanding how different atmospheric reanalyses play a role in our interpretation of AR impacts for hydrologic studies and water resources management, as we investigate here, is important especially as more ARs are projected to occur in a warmer future atmosphere. Key Points: Atmospheric rivers (ARs) diagnosed with four atmospheric reanalyses often yield consistent mean seasonal snowfall patterns in the Sierra NevadaHigher‐resolution atmospheric reanalyses diagnose up to four more ARs per winter implying 10% greater AR attribution to the mean seasonal snowfallKnowing how many snowy ARs occur per winter yields more information about the total seasonal snowfall than only knowing how many ARs occur [ABSTRACT FROM AUTHOR]

This study investigates the extent to which the diagnosed contribution of atmospheric rivers (ARs) to the seasonal cumulative snowfall (CS) is related to the AR detection approach utilized. Using both satellite integrated water vapor (IWV)-based and reanalysis integrated vapor transport (IVT)-based methodologies, the corresponding AR-derived CS distributions were characterized over the Sierra Nevada (USA) from 1998 to 2015. AR detection methods indicated that ARs yield greater orographic enhancement of the seasonal CS than non-AR storms above ~2,100-2,300 m for the IWV-based approach and over all elevations for the IVT-based detection approach across the western (i.e., windward) Sierra Nevada. Due to differences in the methodologies, the IWV-based approach diagnosed 2.1 times fewer ARs than the IVT-based approach. As a result, the ARs diagnosed using the IWV-based detection method yielded an average 33% of the total range-wide CS annually as opposed to 56% from the IVT-based detection method. [ABSTRACT FROM AUTHOR]

A detailed picture of how snowfall varies across high-elevation mountain ranges in both space and time remains a knowledge gap in understanding the montane hydrologic cycle. Previous studies generally used point-scale snow measurements in an attempt to represent the spatial variability of snowfall across a range; however, these traditional approaches provide incomplete insight into the cumulative snowfall (CS) distribution from the basin-scale to the mountain range-scale. In this study, a high-resolution, spatially distributed snow reanalysis was utilized to characterize 31 winters (water years 1985-2015) of snowfall distributions, snowfall accumulation rates, and snowstorms across the Sierra Nevada (USA). The CS data set (quantified in units of snow water equivalent) was verified against over 2600 station years of in situ observations. The seasonal CS was found to have mean and root-mean-squared differences of −4 and 12 cm, respectively, and a correlation of 0.96 with snow pillow observations. Using this novel CS information, results indicated that the CS accumulates rapidly across all 20 basins examined with, on average, at least 50% of the integrated CS accumulating in less than or equal to 6 days or three snowstorms over each basin. The largest (or leading) snowstorms each season yield ∼27% of the CS, on average, and most frequently last 4 days. Across the range, over 40% of the leading snowstorms occur in February. This study showed that the hydroclimatology of the Sierra Nevada is driven by hydrological extremes as manifested in the high interannual variability of its seasonally integrated CS, 4.4-41.3 km3, during the 31 years. [ABSTRACT FROM AUTHOR]