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Addressing impacts of flash droughts (FDs) on the water‐food nexus requires a understanding of FD mechanisms and drivers at the watershed level. Examining climatic drivers, dry and wet spell lengths from 1980 to 2019, we analyzed FD spatial and temporal characteristics, emphasizing areal extent, onset time, and duration. Our findings reveal substantial variations in FDs among different watersheds. Notably, watersheds in the Southern Hemisphere are witnessing expanding, faster‐developing, and longer‐lasting FDs, aligning with climate variations in precipitation and temperature. Additionally, at the watershed scale, the onset and duration of FDs are more influenced by the intensity (magnitude and variability) of climatic drivers than the average length of wet and dry periods. FD‐extents, however, correlate with both climatic conditions and wet and dry periods, underscoring watershed connectivity. Ultimately, our results underscore the necessity for research to comprehend the interplay between FDs and watershed characteristics and how it manifests in overall water resource management. Plain Language Summary: Flash droughts (FDs), which are sudden and severe dry periods, are causing problems for our water and food systems and making it harder to prepare for disasters. To address these challenges effectively, it is crucial to gain a thorough understanding of the underlying mechanisms and factors driving FDs at the watershed level. In this study, we looked at climatic patterns alongside the lengths of dry and wet periods spanning from 1980 to 2019. Our primary focus was on three key aspects: the extent of FDs, when they begin, and how long they persist. Our research findings demonstrate considerable variations in FDs occurrences across different regions. Notably, in the Southern Hemisphere, FDs are expanding rapidly, developing more swiftly, and enduring for extended periods, closely mirroring shifts in precipitation and temperature patterns. Interestingly, the onset and duration of FDs seem to depend more on the intensity of climatic factors than on how long it's been dry or wet. The expansion of FDs in a region is linked to both the climatic and dry/wet periods, emphasizing the geophysical connectivity within a watershed. Key Points: Watersheds in tropical, temperate climates, and with savanna and grassland land cover are more susceptible to flash droughts (FDs)FDs extents are affected by intraannual precipitation variability, but not by intraannual air temperature variabilityThe onset and duration of FD at the watershed scale are influenced more by climate variables, than average wet/dry spell periods [ABSTRACT FROM AUTHOR]

The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data‐driven irrigation management strategies, and expanding incentive‐driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field‐scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community‐driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well‐established satellite‐based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web‐based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite‐driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT‐JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET. [ABSTRACT FROM AUTHOR]

Groundwater extraction in the United States (US) is unsustainable, making it essential to understand the impacts of limited water use on irrigated agriculture. To improve this understanding, we integrated a gridded crop model with satellite observations, recharge estimates, and water survey data to assess the effects of sustainable groundwater withdrawals on US irrigated agricultural production. The gridded crop model agrees with satellite‐based estimates of evapotranspiration (R2 = 0.68), as well as survey data from the United States Department of Agriculture (R2 = 0.82–0.94 for county‐level production and 0.37–0.54 for county‐level yield). Using the optimistic assumption that groundwater extraction equals effective aquifer recharge rate, we find that sustainable groundwater use decreases US irrigated production of maize, soybean, and winter wheat by 20%, 6%, and 25%, respectively. Using a more conservative assumption of groundwater availability, US irrigated production of maize, soybean, and winter wheat decreases by 45%, 37%, and 36%, respectively. The wide range of simulated losses is driven by considerable uncertainty in surface water and groundwater interactions, as well as accounting for the many aspects of sustainability. Our results demonstrate the vulnerability of US irrigated agriculture to unsustainable groundwater pumping, highlighting the difficulty of expanding or even maintaining irrigated food production in the face of climate change, population growth, and shifting dietary demands. These findings are based on reducing pumping by fallowing irrigated farmland; however, alternate pumping reduction strategies or technological advances in crop genetics and irrigation could produce different results. Plain Language Summary: Irrigated agriculture in the United States (US) is depleting groundwater in some regions, which will eventually reduce the amount of water available for growing crops. We used a computer model to assess the impacts of sustainable water use on US irrigated agriculture. We first compared simulated crop production and water use with observed data over 5 years to evaluate the accuracy of the computer model. Simulated yield and crop water use were in good agreement with reported yields from the United States Department of Agriculture and satellite evapotranspiration estimates. We then modeled different water restriction scenarios and found that under the most optimistic scenario, US production of irrigated maize, soybean, and winter wheat decreases by 20%, 6%, and 25%, respectively. However, under the most pessimistic scenario, US production of these irrigated crops will decrease by 45%, 37%, and 36%, respectively. Overall, our simulations of different water restriction scenarios show the vulnerability of US irrigated agricultural production to reduced groundwater pumping, and highlight the difficulty of expanding or even maintaining irrigated food production across the country. Key Points: We integrate a crop model with satellite observations and survey data to simulate sustainable groundwater use for agricultureOptimistic sustainable groundwater use may decrease US irrigated production of maize, soybean, and winter wheat by 20%, 6%, and 25%Pessimistic sustainable groundwater use may decrease US irrigated production of maize, soybean, and winter wheat up to 45%, 37%, and 36% [ABSTRACT FROM AUTHOR]

In situ gauge networks are often used in hydrologic model calibration, but these networks are limited or nonexistent in many regions. The upcoming Surface Water Ocean Topography (SWOT) mission promises to fill this observation gap by providing discharge estimates for rivers wider than 100 m. SWOT observation utility for model parameter selection in regions devoid of in situ gauges is assessed using proxy SWOT discharge estimates derived from an observing system simulation experiment and Monte Carlo methods. The sensitivity of the parameter selection to measurement error and observation frequency is also evaluated. Single‐ and multi‐point parameter selection are performed for ten sub‐basins within the Susitna and upper Tanana river basins in Alaska. SWOT is expected to observe Alaskan river points 4–7 times per 21‐day repeat cycle with 120‐km swath coverage. For an expected SWOT measurement error of 35%, parameter estimation is successful for 50% (90%) of sub‐basins using single‐ (multi‐) point parameter selection. Decreasing observation frequency to simulate lower latitudes resulted in success for only 10% of midlatitude and tropical sub‐basins for single‐point selection, whereas multi‐point selection was successful in 80% (60%) of midlatitude (tropical) sub‐basins. Single‐point parameter selection is more sensitive to measurement error than multi‐point parameter selection. The results strongly support the use of multi‐point over single‐point parameter selection, yielding robust results nearly independent of observation frequency. Most importantly, this study suggests SWOT can be used to successfully select hydrologic model parameters in basins without an in situ gauge network. Plain Language Summary: The Surface Water Ocean Topography (SWOT) mission will provide novel observations of streamflow on a global scale. In regions lacking in situ stream gauges, SWOT may supply the only observations available. This paper investigates the ability for SWOT observations to select parameters for hydrologic models, finding that the unique spatial coverage of SWOT observations enables successful parameter selection in 90% of sub‐basins using multi‐point parameter selection despite an expected 35% relative discharge error for SWOT observations. Key Points: Surface Water Ocean Topography (SWOT) observations are critical for calibrating hydrologic models in regions devoid of in situ observationsFor an expected SWOT discharge error of 35%, multi‐point parameter estimation is successful for 90% of polar and 60% of tropical sub‐basinsMulti‐point parameter selection is preferred over single‐point parameter selection, offering more robust results with less sensitivity [ABSTRACT FROM AUTHOR]

Though coarse in spatial resolution, the nearly all weather measurements from passive microwave sensors can help in improving the spatio‐temporal coverage of optical and thermal infrared sensors for monitoring vegetation changes on the land surface. This study demonstrates the use of vegetation optical depth (VOD) retrievals from the Soil Moisture Active Passive mission for capturing the vegetation alterations from the recent 2019 to 2020 Australian bushfires and drought. The impact of vegetation disturbances on terrestrial water budget is examined by assimilating the VOD retrievals into a dynamic phenology model. The results demonstrate that assimilating VOD observations lead to improved simulation of evapotranspiration, runoff, and soil moisture states. The study also demonstrates that the vegetation changes from the 2019 to 2020 Australian drought and fires led to significant modifications in the partitioning of evaporative and runoff fluxes, resulting in increased bare soil evaporation, reduced transpiration, and higher runoff. Plain Language Summary: Satellite remote sensing, provides the ability to obtain integrated assessments of vegetation changes on the land surface. In this study, we employ the vegetation optical depth (VOD) retrievals from the NASA SMAP mission for characterizing the recent 2019‐2020 Australian drought and bushfires. The results demonstrate that SMAP VOD retrievals are effective in capturing the vegetation alterations from the unprecedented fires and drought. The study also examined the associated impact of these changes on the hydrological cycle. The removal of vegetation impacted the components of evaporation, by reducing the transpiration and increasing the bare soil evaporation. The results show that the reduction in transpiration likely amplified the amount of precipitation that is converted to runoff. Key Points: Soil Moisture Active Passive vegetation optical depth (VOD) detects features of the 2019–2020 Australian bushfires and droughtVOD assimilation improves land surface states and fluxesThe vegetation changes from the 2019 to 2020 Australian fires and drought causes significant changes in the water budget partitioning [ABSTRACT FROM AUTHOR]

Model‐based estimates of soil moisture (SM)‐evapotranspiration (ET) coupling strength (ρ) vary widely and are prone to bias. Here we apply numerical modeling and remote sensing to identify the process‐level source of modeled ρ bias with the goal of improving the fidelity of current Earth system models. Results illustrate that modeled ρ is most strongly determined by soil evaporation (E) stress, and (generally positive) ρ modeling bias is attributable to the oversimplification of soil texture impacts on E stress. Based on new remotely sensed estimates of ρ, we demonstrate that removing ρ bias via a single optimized E stress parameter leads to improved ET accuracy and resolves a well‐known modeling bias in the partitioning of ET into E and T. As such, we highlight the importance of the stress function relating E and SM and its central role in regulating land‐atmosphere coupling processes impacting local climate. Plain Language Summary: The interaction of soil moisture (SM) and evapotranspiration (ET) has been identified as a key process for accurately capturing land‐atmosphere coupling. However, recent work suggests that the representation of SM‐ET coupling in existing Earth system models (ESMs) is biased and can reduce the reliability of ESM‐projected climate change scenarios. Based on remote sensing and land surface modeling, we identify soil evaporation (E) stress as the key contributor to SM‐ET coupling uncertainty, even in areas where transpiration dominates total ET, and note that the representation of E stress is typically oversimplified in ESMs. Therefore, our analysis suggests that a more realistic representation of land E stress should be a priority for future ESM development. Key Points: Remote sensing techniques are used to identify the source of modeled soil moisture ‐ evapotranspiration (ET) coupling strength biasSoil evaporation (E) dominates modeled soil moisture‐ET coupling strength biasAdjusting E stress parameterization simultaneously improves model‐based soil moisture‐ET coupling and land surface energy partitioning [ABSTRACT FROM AUTHOR]

The Surface Water Ocean Topography (SWOT) mission will launch in early 2022 to provide the first global inventory of terrestrial surface water. Although SWOT is primarily a research mission with key science objectives in both the oceanography and hydrology domains, SWOT data are expected to have application potential to address many societal needs. To identify SWOT applications, prepare for the use of SWOT data, and quantify SWOT impacts prior to launch, realistic proxy SWOT observations with representative measurement errors are required. This paper provides a step‐by‐step description of two methods for deriving proxy SWOT water surface elevations (WSEs) from an Observing System Simulation Experiment (OSSE) using the Weather Research and Forecasting hydrological extension package (WRF‐Hydro). The first, a basic method, provides a simple and efficient way to sample WRF‐Hydro output according to the SWOT orbit and add random white noise to simulate measurement error, similar to many previous approaches. An alternate method using the Centre National d'Etudes Spatiales (CNES) Large‐Scale SWOT Hydrology Simulator accounts for additional sources of measurement error and produces output in formats comparable to that expected from official SWOT products. The basic method is ideal for river hydrology applications in which a full representation of SWOT measurement errors and spatial resolution is unnecessary, whereas the CNES simulator approach is better‐suited for more rigorous scientific studies that require a comprehensive error budget. Plain Language Summary: The Surface Water Ocean Topography (SWOT) mission is a multinational satellite mission that is expected to launch in 2022 to observe global rivers, lakes, reservoirs, and wetlands. As the first of its kind to measure inland water, SWOT is expected to address many societal needs. To identify SWOT applications, prepare for the use of SWOT data, and quantify SWOT impacts prior to launch, realistic sample SWOT observations are needed. This paper provides a step‐by‐step description for deriving proxy SWOT measurements using a hydrologic model and a SWOT observation simulator. Key Points: Two approaches for generating proxy SWOT water surface elevations from a hydrology modeling framework are providedProxy SWOT data are well‐suited for societal applications and scientific studies prior to and following launchThe Large‐Scale SWOT Hydrology Simulator provides realistic proxy SWOT data for error budget studies [ABSTRACT FROM AUTHOR]

As a key variable in the climate system, soil moisture (SM) plays a central role in the Earth's terrestrial water, energy, and biogeochemical cycles through its coupling with surface latent heat flux (LH). Despite the need to accurately represent SM/LH coupling in Earth system models, we currently lack quantitative, observation‐based, and unbiased estimates of its strength. Here we utilize the triple‐collocation (TC) approach introduced in Crow et al. () to SM and LH products obtained from multiple satellite remote sensing platforms and land surface models (LSMs) to obtain unbiased global maps of SM/LH coupling strength. Results demonstrate that relative to coupling strength estimates acquired directly from remote sensing‐based data sets, the application of TC generally enhances estimates of warm‐season SM/LH coupling, especially in the western United States, the Sahel, central Asia, and Australia. However, relative to triple‐collocation estimates, LSMs (still) overpredict SM/LH coupling strength along transitional climate regimes between wet and dry climates, such as the central Great Plains of North America, India, and coastal Australia. Specific climate zones with biased relations in LSMs are identified to geographically focus the reexamination of LSM parameterizations. TC‐based coupling strength estimates are robust to our choice of LSM contributing SM and LH products to the TC analysis. Given their robustness, TC‐based coupling strength estimates can serve as an objective benchmark for investigating model‐predicted SM/LH coupling. Plain Language Summary: Physical models describing land‐atmosphere coupling have been developed to help better understand the impact of local‐, regional‐, and global‐scale climate on weather and the water cycle. However, verifying the accuracy of these models is challenging over sparsely instrumented areas. Here the strength of land‐atmosphere coupling between soil moisture and terrestrial evapotranspiration is examined by combining multiple global‐scale remote sensing and modeling products into a unified analysis. This analysis is unique in that it can be conducted globally and is unbiased by the presence of random errors in the remote sensing products. As such it provides the first robust estimate of the degree to which soil moisture and evapotranspiration are linked. Results show strong soil moisture/evapotranspiration coupling over the western United States, the African Sahel, central Asia, and Australia. However, they also demonstrate that most existing models are still overpredicting this coupling along transitional regions between wet and dry climates (like the Central Great Plains of North America, India, and coastal Australia). This work will help improve the representation of land‐atmosphere coupling in models used to obtain future climate projections. Key Points: Multiple sources of remote sensing‐based soil moisture and latent heat flux products are integrated via triple collocationGlobal observation‐based estimates of coupling strength between surface soil moisture and latent heat flux are obtainedLand surface models overestimate the strength of this coupling along transitional climate regimes [ABSTRACT FROM AUTHOR]

With satellite soil moisture (SM) retrievals becoming widely and continuously available, we aim to develop a method to objectively integrate the drought indices into one that is more accurate and consistently reliable. The data sets used in this paper include the Noah land surface model‐based SM estimations, Atmosphere‐Land‐Exchange‐Inverse model‐based Evaporative Stress Index, and the satellite SM products from the Advanced Scatterometer, WindSat, Soil Moisture and Ocean Salinity, and Soil Moisture Operational Product System. Using the Triple Collocation Error Model (TCEM) to quantify the uncertainties of these data, we developed an optically blended drought index (BDI_b) that objectively integrates drought estimations with the lowest TCEM‐derived root‐mean‐square‐errors in this paper. With respect to the reported drought records and the drought monitoring benchmarks including the U.S. Drought Monitor, the Palmer Drought Severity Index and the standardized precipitation evapotranspiration index products, the BDI_b was compared with the sample average blending drought index (BDI_s) and the RMSE‐weighted average blending drought indices (BDI_w). Relative to the BDI_s and the BDI_w, the BDI_b performs more consistently with the drought monitoring benchmarks. With respect to the official drought records, the developed BDI_b shows the best performance on tracking drought development in terms of time evolution and spatial patterns of 2010‐Russia, 2011‐USA, 2013‐New Zealand droughts and other reported agricultural drought occurrences over the 2009–2014 period. These results suggest that model simulations and remotely sensed observations of SM can be objectively translated into useful information for drought monitoring and early warning, in turn can reduce drought risk and impacts. Key Points: Develop a viable approach to validate the satellite soil moisture productsTranslate model and satellite soil moisture into useful information for drought monitoringAn objective blended drought index is recommended [ABSTRACT FROM AUTHOR]

Observations of land surface temperature (LST) are crucial for the monitoring of surface energy fluxes from satellite. Methods that require high temporal resolution LST observations (e.g., from geostationary orbit) can be difficult to apply globally because several geostationary sensors are required to attain near-global coverage (60°N to 60°S). While these LST observations are available from polar-orbiting sensors, providing global coverage at higher spatial resolutions, the temporal sampling (twice daily observations) can pose significant limitations. For example, the Atmosphere Land Exchange Inverse (ALEXI) surface energy balance model, used for monitoring evapotranspiration and drought, requires an observation of the morning change in LST-a quantity not directly observable from polar-orbiting sensors. Therefore, we have developed and evaluated a data-mining approach to estimate the midmorning rise in LST from a single sensor (two observations per day) of LST from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Aqua platform. In general, the data-mining approach produced estimates with low relative error (5 to 10%) and statistically significant correlations when compared against geostationary observations. This approach will facilitate global, near-real-time applications of ALEXI at higher spatial and temporal coverage from a single sensor than currently achievable with current geostationary data sets. [ABSTRACT FROM AUTHOR]

The health of the Chesapeake Bay ecosystem has been declining for several decades due to high levels of nutrients and sediments largely tied to agricultural production systems. Therefore, monitoring of agricultural water use and hydrologic connections between crop lands and Bay tributaries has received increasing attention. Remote sensing retrievals of actual evapotranspiration (ET) can provide valuable information in support of these hydrologic modeling efforts, spatially and temporally describing consumptive water use by crops and natural vegetation and quantifying response to expansion of irrigated area occurring with Bay watershed. In this study, a multisensor satellite data fusion methodology, combined with a multiscale ET retrieval algorithm, was applied over the Choptank River watershed located within the Lower Chesapeake Bay region on the Eastern Shore of Maryland, USA to produce daily 30 m resolution ET maps. ET estimates directly retrieved on Landsat satellite overpass dates have high accuracy with relative error (RE) of 9%, as evaluated using flux tower measurements. The fused daily ET time series have reasonable errors of 18% at the daily time step - an improvement from 27% errors using standard Landsat-only interpolation techniques. Annual water consumption by different land cover types was assessed, showing reasonable distributions of water use with cover class. Seasonal patterns in modeled crop transpiration and soil evaporation for dominant crop types were analyzed, and agree well with crop phenology at field scale. Additionally, effects of irrigation occurring during a period of rainfall shortage were captured by the fusion program. These results suggest that the ET fusion system will have utility for water management at field and regional scales over the Eastern Shore. Further efforts are underway to integrate these detailed water use data sets into watershed-scale hydrologic models to improve assessments of water quality and inform best management practices to reduce nutrient and sediment loads to the Chesapeake Bay. [ABSTRACT FROM AUTHOR]

The fate of the terrestrial biosphere is highly uncertain given recent and projected changes in climate. This is especially acute for impacts associated with changes in drought frequency and intensity on the distribution and timing of water availability. The development of effective adaptation strategies for these emerging threats to food and water security are compromised by limitations in our understanding of how natural and managed ecosystems are responding to changing hydrological and climatological regimes. This information gap is exacerbated by insufficient monitoring capabilities from local to global scales. Here, we describe how evapotranspiration (ET) represents the key variable in linking ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, and highlight both the outstanding science and applications questions and the actions, especially from a space-based perspective, necessary to advance them. [ABSTRACT FROM AUTHOR]

[i] Studies that have assimilated remotely sensed soil moisture (SM) into land surface models (LSMs) have generally focused on retrievals from microwave (MW) sensors. However, retrievals from thermal infrared (TIR) sensors have also been shown to add unique information, especially where MW sensors are not able to provide accurate retrievals (due to, e.g., dense vegetation). In this study, we examine the assimilation of a TIR product based on surface evaporative flux estimates from the Atmosphere Land Exchange Inverse (ALEXI) model and the MW-based VU Amsterdam NASA surface SM product generated with the Land Parameter Retrieval Model (LPRM). A set of data assimilation experiments using an ensemble Kaiman filter are performed over the contiguous United States to assess the impact of assimilating ALEXI and LPRM SM retrievals in isolation and together in a dual-assimilation case. The relative skill of each assimilation case is assessed through a data denial approach where a LSM is forced with an inferior precipitation data set. The ability of each assimilation case to correct for precipitation errors is quantified by comparing with a simulation forced with a higher-quality precipitation data set. All three assimilation cases (ALEXI, LPRM, and Dual assimilation) show relative improvements versus the open loop (i.e., reduced RMSD) for surface and root zone SM. In the surface zone, the dual assimilation case provides the largest improvements, followed by the LPRM case. However, the ALEXI case performs best in the root zone. Results from the data denial experiment are supported by comparisons between assimilation results and ground-based SM observations from the Soil Climate Analysis Network. [ABSTRACT FROM AUTHOR]