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Despite long‐standing efforts, hydrologists still lack robust tools for calibrating land surface model (LSM) streamflow estimates within ungauged basins. Using surface soil moisture estimates from the Soil Moisture Active Passive Level 4 Soil Moisture (L4_SM) product, precipitation observations, and streamflow gauge measurements for 617 medium‐scale (200–10,000 km2) basins in the contiguous United States, we measure the temporal (Spearman) rank correlation between antecedent (i.e., pre‐storm) surface soil moisture (ASM) and the storm‐scale runoff coefficient (RC; the fraction of storm‐scale precipitation accumulation converted into streamflow). In humid and semi‐humid basins, this rank correlation is shown to be sufficiently strong to allow for the substitution of storm‐scale RC observations (available only in basins that are both lightly regulated and gauged) with high‐quality ASM values (available quasi‐globally from L4_SM) in streamflow calibration procedures. Using this principle, we define a new, basin‐wise LSM streamflow calibration approach based on L4_SM alone and successfully apply it to identify LSM configurations that produce a high rank correlation with observed RC. However, since the approach cannot detect RC bias, it is less successful in identifying LSM configurations with low mean‐absolute error. Plain Language Summary: Accurately forecasting the fraction of rainfall that runs off into streams, as opposed to infiltrates into the soil, is critical for flash‐flood prediction, water‐resource monitoring, and tracking the transport of nutrients from agricultural fields into local waterways. Such forecasting is typically performed by hydrologic models that attempt to represent the physical processes responsible for surface runoff generation. However, to provide accurate streamflow forecasts, these models typically need to be calibrated against actual streamflow observations. This is problematic given the relatively poor, and declining, global availability of stream gauges. This paper presents a novel model calibration strategy that uses soil moisture from remote sensing and numerical modeling in place of streamflow observations during calibration. This transition has significant practical advantages because, unlike streamflow observations, the soil moisture data are continuously available across space. Our results demonstrate that this new approach can significantly improve hydrologic models within humid and semi‐humid basins lacking sufficient ground‐based instrumentation for traditional streamflow calibration. Key Points: Hydrologic models still perform poorly in ungauged basinsIn relative humid areas, the correlation between pre‐storm soil moisture and the storm‐scale runoff coefficient approaches unitySuch strong correlation can be exploited to improve the precision of runoff coefficient estimates provided by models in ungaged basins [ABSTRACT FROM AUTHOR]

Satellite imagery is commonly used to map surface water extents over time, but many approaches yield discontinuous records resulting from cloud obstruction or image archive gaps. We applied the Dynamic Surface Water Extent (DSWE) model to downscaled (250‐m) daily Moderate Resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine to generate monthly surface water maps for the conterminous United States (US) from 2003 through 2019. The aggregation of daily observations to monthly maps of maximum water extent produced records with diminished cloud and cloud shadow effects across most of the country. We used the continuous monthly record to analyze spatiotemporal surface water trends stratified within Environmental Protection Agency Ecoregions. Although not all ecoregion trends were significant (p < 0.05), results indicate that much of the western and eastern US underwent a decline in surface water over the 17‐year period, while many ecoregions in the Great Plains had positive trends. Trends were also generated from monthly streamgage discharge records and compared to surface water trends from the same ecoregion. These approaches agreed on the directionality of trend detected for 54 of 85 ecoregions, particularly across the Great Plains and portions of the western US, whereas trends were not congruent in select western deserts, the Great Lakes region, and the southeastern US. By describing the geographic distribution of surface water over time and comparing these records to instrumented discharge data across the conterminous US, our findings demonstrate the efficacy of using satellite imagery to monitor surface water dynamics and supplement traditional instrumented monitoring. Plain Language Summary: Daily satellite images can be used to create cloud‐free maps of surface water for most of the United States (US). An analysis of these maps over multiple decades reveals surface water change in large‐scale water features such as lakes, reservoirs, and major rivers. Results show that increasing surface water trends are more common across the central US while decreasing trends are present across much of the western and eastern regions over the past 17 years. More specifically, we observed certain seasonal trends including increasing spring flooding along the Mississippi River. Comparing these data to trends in streamgage discharge provides additional insights into certain regions where surface water changes are more directly related to dynamic surface water flows. Results from this study identify national and regional surface water variability over space and time that can contribute to more effective water resource management. Key Points: Monthly Moderate Resolution Imaging Spectroradiometer maps can be used to calculate and compare surface water area trends across US ecoregionsThe central United States (US) underwent an increase in surface water while most of the western/eastern US underwent a decline from 2003 through 2019Monthly trends in surface water were congruent with monthly trends in discharge for the majority of Level III Ecoregions in the US [ABSTRACT FROM AUTHOR]

Lakes are often defined by seasonal cycles. The seasonal timing, or phenology, of many lake processes are changing in response to human activities. However, long‐term records exist for few lakes, and extrapolating patterns observed in these lakes to entire landscapes is exceedingly difficult using the limited number of available in situ observations. Limited landscape‐level observations mean we do not know how common shifts in lake phenology are at macroscales. Here, we use a new remote sensing data set, LimnoSat‐US, to analyze U.S. summer lake color phenology between 1984 and 2020 across more than 26,000 lakes. Our results show that summer lake color seasonality can be generalized into five distinct phenology groups that follow well‐known patterns of phytoplankton succession. The frequency with which lakes transition from one phenology group to another is tied to lake and landscape level characteristics. Lakes with high inflows and low variation in their seasonal surface area are generally more stable, while lakes in areas with high interannual variations in climate and catchment population density show less stability. Our results reveal previously unexamined spatiotemporal patterns in lake seasonality and demonstrate the utility of LimnoSat‐US, which, with over 22 million remote sensing observations of lakes, creates novel opportunities to examine changing lake ecosystems at a national scale. Plain Language Summary: Lakes have seasonal cycles that result in yearly peaks in algal growth. The size and timing of these peak periods depends on the amount of nutrients available and the timing of key events such as freezing and thawing. Bluer lakes with little algae typically have one peak in the spring, while greener, high algae lakes can have multiple peaks or longer duration peaks that span the summer months. The timing and duration of these peaks manifest in changes to overall lake color. Here, we look at how these seasonal cycles changed in over 26,000 lakes across the United States between 1984 and 2020. We show that seasonal cycles are changing with distinct regional patterns. Specifically, lakes are generally moving toward stable blue states in the Pacific Northwest, while high latitude lakes in the Northeast are increasingly showing seasonal cycles associated with high algae waterbodies. Lakes at high elevations and in catchments with large year‐to‐year fluctuations in temperature and population density are most prone to changes in seasonal cycles over time. Key Points: Summer lake color phenology can be generalized into five distinct seasonal patterns of greening and blueing eventsSince the mid‐1990s, the number of lakes with color patterns corresponding to eutrophic waterbodies has been increasingWe observe these patterns using a new U.S. lake remote sensing data set that contains over 22 million lake observations [ABSTRACT FROM AUTHOR]

Microwave brightness temperature observations from the NASA Soil Moisture Active Passive (SMAP) mission and gauge‐based precipitation data over the United States are used to reconstruct the soil water loss function and then historical (1979–2019) hydrological fluxes in the form of evapotranspiration (ET) and drainage (D) are quantified. Over the period of study, with the exception of snowy and hyper‐arid regions, we observe a correlation of R2 > 0.6 between SMAP‐precipitation derived drainage estimates and streamflow measurements from the U.S. Geological Survey (USGS). There is a bias between estimated drainage and USGS streamflow with an underestimation of about 1 mm day−1 in southwest United States to 3 mm day−1 in parts of the eastern United States. SMAP‐derived sensitivities of drainage and ET partitioning with respect to precipitation anomalies are also calculated. In parts of the Great Plains the drainage partitioning exhibits a near‐linear response, while in the southeast United States, the response is nonlinear. Partitioning also is examined for 6 four‐digit hydrologic unit basins wherein year‐to‐year variations in drainage partitioning are shown to be key mediators in translating precipitation anomalies into streamflow anomalies. Observation‐driven drainage and ET estimates are obtained without relying on full hydrologic and Land Surface Models (LSMs). This independence (isolation from model parameterization assumptions) provides a path toward using satellite‐derived landscape hydrological diagnostics to assess hydrologic models and LSMs as well as to guide their further development. Key Points: A large amount of hydrologic information is encoded within satellite remote sensing observations of soil moistureSMAP‐era soil moisture and precipitation information, without reliance on models, can characterize landscape hydrological partitioningHistorical remote sensing and precipitation‐based drainage estimates closely track streamflow measurements across the United States [ABSTRACT FROM AUTHOR]

Key Points: Groundwater withdrawals are not actively monitored in most places of the world at a scale necessary to implement sustainable solutionsVarious multitemporal remote sensing data are integrated into a machine learning framework to effectively predict groundwater withdrawalsThe results over the High Plains Aquifer, Kansas, USA, show that this approach is applicable to similar regions having sparse in situ data Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests—a state of the art machine learning algorithm—to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the Years 2002–2019. Our modeled withdrawals had high accuracy on both training and testing data sets (R2 ≈ 0.99 and R2 ≈ 0.93, respectively) during leave‐one‐out (year) cross validation with low mean absolute error (MAE) ≈ 4.31 mm and root‐mean‐square error (RMSE) ≈ 13.50 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R2 ≈ 0.84) with MAE ≈ 9.72 mm and RMSE ≈ 24.17 mm. Therefore, the proposed machine learning approach should be applicable to similar regions for proactive water management practices. Plain Language Summary: Groundwater is an essential component of global water resources and is the largest source of Earth's liquid freshwater. It is extensively used for drinking water and to support global food production. Consequently, groundwater consumption has significantly increased owing to the pressing demands for water, food, and energy primarily driven by the increasing global population. Despite its critical role in the water‐food‐energy nexus, very few regions in the United States or elsewhere actively monitor their groundwater withdrawals (extraction or pumping) for implementing sustainable water management solutions. We develop a novel approach combining water balance components and land use measured using openly available remote sensing products with a machine learning model to estimate groundwater withdrawals. This framework automatically learns the interrelationships among these variables and groundwater withdrawals. Our study area is a portion of the High Plains Aquifer in Kansas (central United States) where overpumping has caused substantial groundwater storage loss. Also, a large amount of groundwater pumping data are available for validation. The results indicate good accuracy even for extreme drought years. Thus, this approach should be applicable to similar regions having sparsely or moderately available groundwater pumping data, enabling water managers to proactively implement sustainable solutions addressing water security issues. [ABSTRACT FROM AUTHOR]

Satellite estimates of inland water quality have the potential to vastly expand our ability to observe and monitor the dynamics of large water bodies. For almost 50 years, we have been able to remotely sense key water quality constituents like total suspended sediment, dissolved organic carbon, chlorophyll a, and Secchi disk depth. Nonetheless, remote sensing of water quality is poorly integrated into inland water sciences, in part due to a lack of publicly available training data and a perception that remote estimates are unreliable. Remote sensing models of water quality can be improved by training and validation on larger data sets of coincident field and satellite observations, here called matchups. To facilitate model development and deeper integration of remote sensing into inland water science, we have built AquaSat, the largest such matchup data set ever assembled. AquaSat contains more than 600,000 matchups, covering 1984–2019, of ground‐based total suspended sediment, dissolved organic carbon, chlorophyll a, and SDDSecchi disk depth measurements paired with spectral reflectance from Landsat 5, 7, and 8 collected within ±1 day of each other. To build AquaSat, we developed open source tools in R and Python and applied them to existing public data sets covering the contiguous United States, including the Water Quality Portal, LAGOS‐NE, and the Landsat archive. In addition to publishing the data set, we are also publishing our full code architecture to facilitate expanding and improving AquaSat. We anticipate that this work will help make remote sensing of inland water accessible to more hydrologists, ecologists, and limnologists while facilitating novel data‐driven approaches to monitoring and understanding critical water resources at large spatiotemporal scales. Key Points: AquaSat contains ∼600,000 paired observations of water quality and Landsat reflectance, the largest such matchup data setMatchups capture diverse water bodies across the USA for 1984–2019; we see clear water quality/reflectance relationshipsAquaSat and open source code developed here will enable better development of models for remote sensing of water quality [ABSTRACT FROM AUTHOR]

Abstract: GPS monitoring of solid Earth deformation due to surface loading is an independent approach for estimating seasonal changes in terrestrial water storage (TWS). In western United States (WUSA) mountain ranges, snow water equivalent (SWE) is the dominant component of TWS and an essential water resource. While several studies have estimated SWE from GPS‐measured vertical displacements, the error associated with this method remains poorly constrained. We examine the accuracy of SWE estimated from synthetic displacements at 1,395 continuous GPS station locations in the WUSA. Displacement at each station is calculated from the predicted elastic response to variations in SWE from SNODAS and soil moisture from the NLDAS‐2 Noah model. We invert synthetic displacements for TWS, showing that both seasonal accumulation and melt as well as year‐to‐year fluctuations in peak SWE can be estimated from data recorded by the existing GPS network. Because we impose a smoothness constraint in the inversion, recovered TWS exhibits mass leakage from mountain ranges to surrounding areas. This leakage bias is removed via linear rescaling in which the magnitude of the gain factor depends on station distribution and TWS anomaly patterns. The synthetic GPS‐derived estimates reproduce approximately half of the spatial variability (unbiased root mean square error ∼50%) of TWS loading within mountain ranges, a considerable improvement over GRACE. The inclusion of additional simulated GPS stations improves representation of spatial variations. GPS data can be used to estimate mountain‐range‐scale SWE, but effects of soil moisture and other TWS components must first be subtracted from the GPS‐derived load estimates. [ABSTRACT FROM AUTHOR]

The accurate specification of observing and/or modeling error statistics presents a remaining challenge to the successful implementation of many land data assimilation systems. Recent work has developed adaptive filtering approaches that address this issue. However, such approaches possess a number of known weaknesses, including a required assumption of serially uncorrelated error in assimilated observations. Recent validation results for remotely sensed surface soil moisture retrievals call this assumption into question. Here we propose and test an alternative system for tuning a soil moisture data assimilation system, which is robust to the presence of autocorrelated observing error. The approach is based on the application of a triple collocation approach to estimate the error variance of remotely sensed surface soil moisture retrievals. Using this estimate, the variance of assumed modeling perturbations is tuned until normalized filtering innovations have a temporal variance of one. Real data results over three highly instrumented watershed sites in the United States demonstrate that this approach is superior to a classical tuning strategy based on removing the serial autocorrelation in Kalman filtering innovations and nearly as accurate as a calibrated Colored Kalman filter in which autocorrelated observing errors are treated optimally. [ABSTRACT FROM AUTHOR]

Observation‐based long‐term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental conditions. In this study, three daily long‐term SWE products (Special Sensor Microwave Imager and Sounder [SSMI/S] SWE, GlobSnow‐2 SWE, and University of Arizona [UA] SWE) were evaluated by seasonal snow cover and land cover classifications over the conterminous United States from 1982 to 2017, using the historical airborne gamma radiation SWE observations (20,738 measurements). We found that there are similar patterns in SSMI/S and GlobSnow‐2 SWE when compared against the gamma SWE. However, GlobSnow‐2 SWE had better agreement with gamma SWE than SSMI/S SWE in some forested‐type classes and maritime and prairie snow classes. As compared to SSMI/S and GlobSnow‐2 SWE, UA SWE has much better agreement with gamma SWE in all land cover types and snow classes. Tree cover and topographic heterogeneity affect the agreement between the gamma and gridded SWE and accuracy of gamma SWE itself with the largest differences typically occurring when the percent tree cover was 80% or higher, the terrain slope was steeper than 2.5°, and the elevation range exceeded 100 m. The results demonstrate the reliability of the UA SWE products and the benefits of the gamma radiation approach to measure SWE, especially in forested regions. Plain Language Summary: Long‐term gridded snowpack data are essential for effective water resource management and flood risk assessments under a changing climate in the United States. However, the currently available snowpack data have large uncertainties, and the evaluations have been limited due to the lack of reliable independent data sets. Here, we evaluate three daily long‐term (>30 years) snow water equivalent (SWE) products—empirically based SWE from passive microwave, GlobSnow‐2 SWE, and University of Arizona SWE from 1982 to 2017 over the conterminous United States—using the historical airborne gamma SWE observations collected by the National Oceanic and Atmospheric Administration. Two key findings are that (1) the UA SWE product is the most reliable and (2) the long‐term airborne SWE record is of great value, especially in forested regions. Key Points: Long‐term airborne gamma radiation observations provide reliable SWE observations even over forest regionsUniversity of Arizona (UA) SWE has the strongest agreement with gamma SWE regardless of seasonal snow and land cover classLarge differences occur when the tree cover is 80% or higher, the slope is steeper than 2.5°, and the elevation range exceeds 100 m [ABSTRACT FROM AUTHOR]