Soil moisture (SM) is a key state variable in the climate system through its control on evapotranspiration (ET) and ET-regulated lower atmospheric processes. The SM-ET coupling strength (SECS) is thus closely linked with land-atmosphere interactions and its reliability is crucial for Earth system modeling. However, acquiring global maps of unbiased SECS remains challenging given significant levels of error present in globally available SM and ET products. Triple collocation (TC) provides a possible solution; however, it is difficult to apply globally since it requires access to three independent SM-ET data pairs-a requirement that is difficult to meet in practice. Here, we generate a global SECS map based on a new two-system approach that requires only two independent SM-ET data pairs. This two-system approach is first validated over local ground sites versus a ground-inclusive benchmark SECS. Subsequently, it is applied to generate a global map of SECS with input from various independent globally available SM-ET data pairs (identified using the benchmark SECS). Results suggest that previous TC-based SECS estimates are generally negatively biased due to cross-correlated error present between RS products. Instead, our generated new SECS map is shown to provide more robust mapping of global SECS-thus offering an important reference for improving Earth system models.

Accurately characterizing dry‐season water availability ( W d ) is critical for projecting terrestrial carbon exchange and global water security. W d is commonly calculated as the minimum value of precipitation minus evapotranspiration within each calendar year. However, Earth System Model (ESM) projected W d contains substantial uncertainties and can disagree on even the sign. Based on a newly proposed framework, we disentangle the uncertainty sources in ESM‐based W d projections. Results demonstrate that ESM‐based W d uncertainties are dominated by land surface energy partitioning (summarized by evaporation fraction, denoted as EF) instead of precipitation or available energy. As such, EF alone can explain more than 83% of inter‐ESM variability in historical and future W d projections. Compared against data‐driven benchmarks, ESMs tend to overestimate dry‐season EF—suggesting that W d is likely to be underestimated in ESMs. Our analysis indicates that the ET resistance parameterization is the central error source in ESM‐based EF, which should be constrained to enhance the reliability of EF, and by extension, W d projections.

Soil moisture (SM) is a key variable in hydrometeorology and climate systems. With the growing interest in capturing fine-scale SM variability for effective hydroclimate applications, spaceborne L-band bistatic radar systems using Global Navigation Satellite System-Reflectometry (GNSS-R) technology hold great potential to meet the demand for high spatiotemporal resolution SM data. Although primarily designed for tropical cyclone monitoring purposes, the first GNSS-R satellite constellation - Cyclone Global Navigation Satellite System (CYGNSS) mission, has demonstrated the benefits of reliably monitoring diurnal SM dynamics through its initial stage of seven-year data record, thanks to its high revisit frequency at sub-daily intervals. Nevertheless, knowledge of SM retrieval from CYGNSS, particularly linked with its distinctive features, remains poorly understood, while numerous existing uncertainties and open issues can restrict its effective SM retrieval and practical applications in the next operating stages. Unlike other review papers, this work aims to bridge this knowledge gap in CYGNSS SM retrieval by highlighting noteworthy design properties based on analyses of its real-world data, while providing a synthesis of recent advances in eliminating external uncertainty factors and improving SM inversion methods. Despite its potential, CYGNSS SM retrieval faces both general and particular challenges arising from common issues in retrieval algorithms for conventional GNSS-R satellites and unique data limitations tied to its technical design. Scientific debates over the contributions of coherent and incoherent components in total CYGNSS signals and accurate partitioning of these two parts are defined as the key algorithm-related challenges to resolve, along with correcting attenuation effects of vegetation and surface roughness. The data-related challenges involve variations in CYGNSS's spatial footprint, temporal frequency, and signal penetration depth across different land surface conditions, inadequate consideration of CYGNSS incidence angle change, excessive dependence on a reference SM dataset for inversion model calibration/training or validation, and computational demands for processing rapid multi-sampling CYGNSS data retrieval. Future research pathways highlight leveraging cuttingedge machine learning/deep learning algorithms to enhance CYGNSS SM data quantity and quality and better interpret its complex interactions with other hydroclimate variables. Assimilating CYGNSS SM data streams into physical models to improve the prediction of related variables and climate extremes also presents a promising prospect.

Successful surface soil moisture (SM) retrieval from space has been enabled by microwave satellite measurements of Earth's upwelling brightness temperature (TB). Nevertheless, correction for the impact of vegetation on TB emission remains a challenge for SM retrieval algorithms. Such correction is often performed in a simplified manner. For example, the Single Channel Algorithm (SCA) uses ancillary climatological normalized vegetation difference index values as a proxy for vegetation optical depth (z) - resulting in SM retrievals that do not account for interannual z variability. Official NASA Soil Moisture Active/Passive (SMAP) mission SM products are all based, to varying degrees, on the SCA. Here, we utilize an instrumental variable analysis and alternative SMAP SM retrievals derived from the Multi-Temporal Dual Channel Algorithm (MTDCA) - that better account for time variations in z - as a benchmark for examining SMAP Level 3 SM retrievals for the presence of signal crosstalk associated with the neglect of interannual z variability. Results suggest that failing to account for such variability introduces a spurious vegetation-based signal into monthly climatological SMAP SM anomalies. The SMAP Dual Channel Algorithm (DCA), which serves as the current SMAP baseline algorithm, reduces - but does not eliminate - this crosstalk. Results therefore suggest the need for caution when applying SMAP SM retrievals to science applications aimed at understanding SM coupling with the terrestrial biosphere. Plain language summary: Satellite observations of natural microwave emission from Earth's land surface can be converted into estimates of both surface soil moisture and vegetation water content. Such estimates have a variety of applications. However, the separation of the vegetation signal from the soil moisture signal is challenging and often performed using only approximate methods. This paper uses a novel approach to evaluate how accurately state-of-the-art soil moisture retrieval algorithms perform such partitioning. Results suggests that spurious vegetation signals remain in existing soil moisture products - with some approaches removing it more than others. Such "crosstalk" between soil- and vegetation-based signals limits the value of existing satellite soil moisture products for agricultural and ecohydrological applications and motivates the development of improved retrieval algorithms.

Remote sensing (RS) soil moisture retrievals are frequently assimilated into land surface models (LSMs) to enhance model estimates. However, soil moisture data assimilation (DA) efficiency is highly model‐dependent, making it imperative to investigate whether current LSMs can achieve expected DA efficiencies and identify potential model limitations for DA. Here, we examine soil moisture DA efficiency based on a typical LSM by benchmarking it against a reference soil moisture merging scheme (i.e., assigning weights to combine multiple products into a single one). Both the merged and DA soil moisture estimates are comparable since they are based on identical error estimation theory and the same RS soil moisture data sets. In theory, the DA soil moisture estimates should be superior to the merged results—since DA can characterize the temporal variation of model error and propagate DA benefits into subsequent forecast steps. However, ground‐based validation results indicate that DA soil moisture performs worse than simply merged results in regions where the LSM is less precise than RS retrievals. Further combing synthetic experiment, we confirm that the unexpected DA results are primarily attributable to land parameterization uncertainty, which leads to an unrealistic representation of soil moisture events (e.g., dry‐downs) and significantly hampers the DA application. Given this, soil moisture DA is likely to remain suboptimal in achieving its desired goals. Therefore, this study emphasizes the urgency and necessity of reducing model parameterization uncertainty in land DA systems.

To better monitor global water resources, and understand how they will vary in the future, it is critical to track the extent and intensity of irrigated agriculture. Within the past decade, there has been increased interest in the satellite-based detection of anomalous soil moisture (SM) and/or evapotranspiration (ET) signals associated with irrigation. However, little comparative information is available concerning the relative merits of available ET versus SM satellite products for this purpose. Such uncertainty has hampered the development of optimal monitoring strategies that appropriately integrate information acquired across a range of remote sensing resources. Here, using relatively more mature irrigation products derived from a combination of ground data and visible/near-infrared remote sensing as a reference, we compare the skill of microwave (MW) SM and thermal-infrared (TIR) ET satellite products for mapping coarse-scale (36-km) spatial variations in the proportion of land irrigated across the conterminous United States. Results suggest that, while spatial mapping skill exists in both products, the irrigation signal in satellite-based ET products is stronger, and easier to interpret, than the analogous signal in SM products. Since MW-based SM and TIR-based ET products appear to possess approximately equal spatial precision (i.e., spatial correlations with respect to true SM and ET, respectively), this difference is attributed to the stronger impact of irrigation on spatial ET patterns versus surface SM.Plain Language Summary: To better monitor global water resources, and understand how they will vary in the future, it is important to track the extent and intensity of irrigated agriculture. To do this, different satellite-based methods have been proposed to track irrigation from space. Unfortunately, there has been little comparison of these methods, and it is unclear which ones work best. Here, we compare methods for monitoring irrigation based on two different remote sensing techniques: thermal infrared and passive microwave. While information derived from both types of remote sensing is useful for irrigation monitoring, results show that thermal-infrared remote sensing contains more information. This insight will help improve future efforts to globally monitor irrigation using satellite-based sensors.

NASA's Soil Moisture Active Passive (SMAP) satellite mission provides an unprecedented opportunity to monitor global surface soil moisture (SM). The retrieval of SMAP official SM product relies on the inversion of a zerothorder tau-omega radiative transfer model constrained by climatological Normalized Difference Vegetation Index (NDVI) derived vegetation optical depth (VOD) and constant surface roughness. However, NDVI climatology cannot capture vegetation variation in response to climate extremes and agricultural practices, which can cause nonnegligible errors in SMAP SM products. To resolve this issue, we develop a new Dynamic Dual-Channel Algorithm (DDCA) by constraining the tau-omega model using VOD and surface roughness derived from the real-time dynamic NDVI observations acquired from MODIS and VIIRS, where surface roughness is estimated through the classic DCA with VOD determined via dynamic NDVI. Considering that NDVI is not a perfect proxy for VOD, its derived surface roughness may contain VOD information to some extent. To reduce uncertainties in surface roughness, four different parameterization schemes are considered, including daily-scale, monthly average, yearly average, and constant surface roughness. Validation results against in-situ measurements demonstrate that DDCA is typically superior to the SMAP baseline algorithm - Regularized Dual-Channel Algorithm (RDCA) - across different continents, land covers, and climates, especially when parameterized with surface roughness at relatively coarse time scales (i.e., monthly or annually), indicating that averaging daily surface roughness at monthly and yearly scales can effectively reduce its uncertainties. One exception is that daily-scale roughness works well for grassland, likely because NDVI can accurately approximate VOD in grassland and its derived surface roughness is of high quality. Further analysis demonstrates that the improvement of DDCA SM over the SMAP official SM (SMAP_L3_SMPE) is particularly remarkable in cases of drought and agricultural practices. Overall, these results highlight the necessity to account for accurate vegetation dynamics during SMAP SM retrieval.

Soil moisture (theta) thresholds associated with nonlinear changes in soil water loss rates can be used to detect transitions between land surface water flux regimes. Since such regimes are characterized by distinct soil water loss processes (e.g., energy- versus water-limited evapotranspiration), their identification is useful for applications reliant on the accurate parameterization of land surface water and energy fluxes. The most direct method for such regime detection is the time differencing of successive theta retrievals acquired from satellite-based, L-band microwave data records. The detection of regime transitions, and their associated theta thresholds, requires access to long-term historical theta data sets. As a result, traceable measurement requirements must be defined to ensure that future satellite missions can sample theta at temporal frequencies required for priority science applications reliant on the characterization of theta thresholds. Here, we perform a synthetic analysis to examine the impact of satellite theta temporal sampling frequency on water-flux regime detection skill based on the differencing of a retrieved theta time series. Globally, results suggest a sharp reduction in regime-detection skill when the inverse (tau) of this frequency exceeds a threshold value in the range of 2-4 days. Therefore, to directly contribute to land surface regime-detection applications, future satellite soil moisture missions must sample theta retrievals at tau <= 4 days.

Multi-source data merging via weighted average (WA) is widely applied to enhance large-scale precipitation estimates. However, these data sets usually contain substantial conditional biases with respect to extreme precipitation (EP) events-undermining their utility for extreme event analysis. Nevertheless, the main source of such EP biases remains unknown. Here, we demonstrate that WA algorithms are responsible for less than 1% of total EP biases. Instead, EP biases originate from the multi-source precipitation inputs, which are not adequately adjusted prior to WA. Specifically, current data-merging frameworks only correct the monthly means or statistical distributions of the remote sensing/reanalysis precipitation inputs prior to WA. Such procedures are insufficient for adjusting EP timing uncertainties, which eventually propagate into the WA-based merged data set as an EP bias. Therefore, developing algorithms that iteratively adjust EP timing and intensity errors should be prioritized in future precipitation merging frameworks.

Evapotranspiration has long been understood to vary with soil moisture in drier regions and tobe relatively insensitive to soil moisture in wetter regions. A number of recent studies have quantified this behavior with various model and observational datasets. However, given the disparate approaches and datasets used, uncertainty persists in how the underlying relationships vary in space and time. Here we complement the existing studies by analyzing two datasets as yet untapped for this purpose: a satellite-based evapotranspiration E product retrieved using geostationary thermal imagery and a meteorological-station-based dataset of daily 2-m air temperature (T2M) diurnal amplitudes. Both datasets are analyzed synchronously with soil moisture from the Soil Moisture Active Passive (SMAP) satellite. We thereby derive maps of evaporative regimes that vary in space and time as one might expect, that is, the water-limited regime grows eastward across the conterminous United States as spring moves into summer, only to shrink again going into winter. The relationship between the E and soil moisture data appears particularly tight, which is encouraging given that the E data (like the T2M data) were not constructed using any soil moisture information whatsoever. The general agreement between the two independent sets of results gives us confidence that the generated maps correctly represent, to first order, evaporative regime behavior in nature. The T2M results have the added benefit of highlighting the significant connection between soil moisture and overlying air temperature, a connection relevant to T2M predictability.

The reliability of irrigated area (IA) information dominates the performance of irrigation water use and crop modeling accuracy. IA is typically mapped using Food and Agriculture Organization (FAO) agricultural census and remote sensing indices. Recent advances in machine learning and sampling techniques further improve IA mapping. However, the relative performances of different IA mapping approaches and their capability in capturing long-term IA temporal variability remain unknown. Here, 1861 county-level IA information from Government Censored Data (GCD) during 2000-2021 are collected, cross-validated, and employed to evaluate commonly used gridded IA data sets. Results show that IA data sets based on the direct interpolation of FAO agricultural census can accurately capture the spatial distribution of IA. However, FAO statistics are only available in a particular year, which cannot capture inter-annual irrigation variations. In contrast, IA products solely based on vegetation indices are prone to positive biases over humid regions due to the lack of contrast in vegetation dynamics. Overall, the latest GCD-based machine learning IA data sets are relatively more accurate, but they are also problematic in estimating IA trends due to the use of temporally static training samples. Such biases are tightly related to agricultural suitability (AS calculated using precipitation and potential evapotranspiration). This suggests that AS should be employed as an endogenous variable in future machine learning based IA mapping algorithms. Understanding how much land is irrigated (Irrigated Area or IA) is crucial for accurate water use and crop modeling. Traditional IA maps are developed using data from the Food and Agriculture Organization (FAO) and remote sensing indices. Recent improvements, like machine learning, have enhanced IA mapping. However, the relative performances of these methods and their reliability in capturing long-term changes in irrigated areas remain unknown. This study collects government-censored county-level IA data from 2000 to 2021, which is subsequently used for evaluating commonly used IA data sets. Results show that data sets directly using FAO data can reasonability capture the spatial distribution of IA but may miss year-to-year changes. On the other hand, data sets relying only on vegetation data tend to overestimate irrigated areas in humid regions. The latest machine learning-based IA data sets are relatively more accurate but still contain significant uncertainties in IA trends. These biases are closely linked to agricultural suitability (calculated using precipitation and potential evapotranspiration), which may be useful for enhancing future IA mapping algorithms. Remote sensing vegetation index is insufficient for identifying irrigated area over humid regions Machine learning based irrigated area is sensitive to the quality of training sample All the irrigated area data sets are biased in representing the temporal dynamics of irrigated area

Abstract Using data from the NASA Soil Moisture Active/Passive mission, Koster et al. (2023, https://doi.org/10.1038/s41467‐023‐39318‐3) conclude that, for medium‐scale basins in the contiguous United States, a quarter of interannual variability in springtime streamflow is explained by interannual anomalies in late‐fall soil moisture. This lagged relationship can be leveraged for seasonal hydrologic forecasting, but only if effectively captured by existing prediction models. Here, we extend the analysis in Koster et al. (2023, https://doi.org/10.1038/s41467‐023‐39318‐3) to diagnose systematic errors present in the United States National Water Model (NWM). Results demonstrate that the NWM tends to underestimate both the trans‐winter temporal memory of 0–1 m soil moisture as well as the correlation between 0 and 1 m soil moisture and streamflow—thereby reducing the NWM's ability to leverage vertically averaged soil moisture as a source of hydrologic predictability.

The water balance equation (WBE) describes how net water inflows into a system relate to storage changes over a time span (dt). This equation is fundamental in hydrologic studies, helping to determine water supply and elucidate the terrestrial water cycle. For land surface, the WBE links water fluxes like precipitation (P), evapotranspiration, drainage and surface runoff to soil moisture (SM) changes within a discrete soil layer (dSM). While traditional focus has been on the precision of the WBE-estimated fluxes, there has been increased recent interest in fitting the WBE using remotely-sensed P and SM data to infer water balance parameters, particularly the Delta Z that associates net fluxes and dSM within dt. However, obtaining physically interpretable WBE parameters like Delta Z is a potentially ill-posed problem due to simplifying assumptions imbedded in a WBE implementation, missing hydrologic constraints, and accuracy and revisit limitations in available remotely-sensed data. In addition, WBE parameters obtained from classical maximum likelihood estimation approaches can vary significantly. Here, using a Bayesian non-linear modeling approach, we demonstrate how these factors can impede the identification of useful Delta Z estimates. Specifically, we find that Delta Z estimates are likely to be spuriously biased due to limitations in the temporal resolution and accuracy of satellite-based dSM estimates - as well as the neglect of other hydrological components in the WBE. Maps showcasing the lower and upper bounds of the 94 % HDI from our Bayesian model further elucidate these biases. Results suggests that estimates of Delta Z are effective in nature and great care should be exercised when attempting to interpret them physically. Instead, they should be defined as effective parameters reflecting the characteristics of their fitted remotely-sensed data sets. Drawing from current progress, accurate AZ estimates, informed by more advanced remote sensing techniques and precise WBE, will enhance our understanding of terrestrial water cycle.

AbstractWhile remote sensing has provided extensive insights into the global terrestrial water, carbon, and energy cycles, space‐based retrievals remain limited in observing the belowground influence of the full soil moisture (SM) profile on ecosystem function. We show that this gap can be addressed when coupling 70 m resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station retrievals of land surface temperature (LST) with in‐situ SM profile measurements. These data sets together reveal that ecosystem water use decreases with depth with 93% of sites showing significant LST coupling with SM shallower than 20 cm while 34% of sites have interactions with SM deeper than 50 cm. Furthermore, the median depth of peak ecosystem water use is estimated to be 10 cm, though forests have more common peak interactions with deeper soil layers (50–100 cm) in 37% of cases. High spatial resolution remote sensing coupled with field‐level data can thus elucidate the role of belowground processes on land surface behavior.

Thermal infrared (TIR) remote sensing of the land-surface temperature (LST) provides an invaluable diagnostic of surface fluxes and vegetation state, from plant and sub-field scales up to regional and global coverage. However, without proper consideration of the nuances of the remotely sensed LST signal, TIR imaging can give poor results for estimating sensible and latent heating. For example, sensor view angle, atmospheric impacts, and differential coupling of soil and canopy sub-pixel elements with the overlying atmosphere can affect the use of satellite-based LST retrievals in land-surface modeling systems. A concerted effort to address the value and perceived shortcomings of TIR-based modeling culminated in the Workshop on Thermal Remote Sensing of the Energy and Water Balance, held in La Londe les Maures, France in September of 1993. One of the outcomes of this workshop was the Two-Source Energy Balance (TSEB) model, which has fueled research and applications over a range of spatial scales.In this paper we provide some historical context for the development of TSEB and TSEB-based multi-scale modeling systems (ALEXI/DisALEXI) aimed at providing physically based, diagnostic estimates of latent heating (evapotranspiration, or ET, in mass units) and other surface energy fluxes. Applications for TSEB-based ET retrievals are discussed: in drought monitoring and yield estimation, water and forest management, and data assimilation into – and assessment of – prognostic modeling systems. New research focuses on augmenting temporal sampling afforded in the thermal bands by integrating cloud-tolerant, microwave-based LST information, as well as evaluating the capabilities of TSEB for separating ET estimates into evaporation and transpiration components. While the TSEB has demonstrated promise in supplying water use and water stress information down to sub-field scales, improved operational capabilities may be best realized in conjunction with ensemble modeling systems such as OpenET, which can effectively combine strengths of multiple ET retrieval approaches.

Due to recent advances in the development of land data assimilation systems (LDAS) and the availability of high-quality, satellite-based surface soil moisture (SSM) retrieval products, we now have unambiguous evidence that the assimilation of SSM retrievals, or their proxy, can improve the precision (i.e., correlation versus truth) of surface state estimates provided by a land surface model (LSM). However, this clarity does not yet extend to the estimation of LSM surface water fluxes that are key to hydrologic and numerical weather forecasting applications. Here, we hypothesize that a key obstacle to extrapolating realized improvements in water state precision into comparable improvements in water flux accuracy (i.e., mean absolute error) is the presence of water state-water flux coupling strength biases existing in LSMs. To test this hypothesis, we conduct a series of synthetic fraternal twin data assimilation experiments where realistic levels of state- flux coupling strength bias-involving both evapotranspiration and runoff-are systematically introduced into an assimilation LSM. Results show that the accuracy of the resulting water flux analysis is sharply reduced by the presence of such bias, even in cases where the precision of soil moisture state estimates (e.g., SSM) is improved. The rescaling of SSM observations prior to their assimilation (i.e., the most common approach for addressing systematic differences between LSMs and assimilated observations) is not always a robust strategy for addressing these errors and can, in certain circumstances, degrade water flux accuracy. Overall, results underscore the critical need to assess, and correct for, LSM water state-water flux coupling strength biases during the operation of an LDAS.

Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.

Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling.

The soil moisture active/passive (SMAP) mission represents a significant advance in measuring soil moisture from satellites. However, its large spatial-temporal data gaps limit the use of its values in near-real-time (NRT) applications. Considering this, the study uses NRT operational metadata (precipitation and skin temperature), together with some surface parameterization information, to feed into a random forest model to retrieve the missing values of the SMAP L3 soil moisture product. This practice was tested in filling the missing points for both SMAP descending (6:00 AM) and ascending orbits (6:00 PM) in a crop-dominated area from 2015 to 2019. The trained models with optimized hyper-parameters show the goodness of fit (R2 ≥ 0.86), and their resulting gap-filled estimates were compared against a range of competing products with in situ and triple collocation validation. This gap-filling scheme driven by low-latency data sources is first attempted to enhance NRT spatiotemporal support for SMAP L3 soil moisture.

The event runoff coefficient (i.e. the ratio between event runoff and precipitation that originated the runoff) is a key factor for understanding basin response to precipitation events. Runoff coefficient depends on precipitation intensity and duration but also on specific basin geohydrology attributes (including soil type, geology, land cover, topography) and last but not least, antecedent (or pre-storm) conditions (i.e., the amount of water stored in the different hydrological compartments, like the river, groundwater, soil and snowpack). The relation between runoff coefficient and basin pre-storm conditions is critical for flood forecasting, yet, the understanding of where, when and how much basin pre-storm conditions control runoff coefficients is still an open question. Here, we tested the control of basin pre-storm conditions on runoff coefficient for 60620 flood events across 284 basins in Europe. To do so, we derived basin pre-storm conditions from different proxies, namely: antecedent precipitation; surface and root zone soil moisture from hydrological models, reanalyses and land surface models also ingesting satellite observations; pre-storm river discharge, and pre-storm total water storage anomalies. We evaluated the coupling strength between runoff coefficient and pre-storm conditions proxies in relation to five classes of European basins, defined based on land use and soil type (as indexed by the Soil Conservation Service curve number CN), topography, hydrology and long-term climate and tested their ability to explain stormflow volume variability. We found that precipitation explains relatively well the stormflow volumes for both small and large events but not very well the peak discharge, especially for large floods. The runoff coefficient of events shows different distributions for the five different classes and correlates well with deep soil storages (such as root-zone soil moisture and pre-storm total water storage anomalies), pre-storm river discharge, and pre-storm snow water equivalent. Overall, these correlations depend on the class. Poor correlations are found against antecedent precipitation index despite its wide use in the hydrological community. Seasonal and interannual climate variability exert a key role on the coupling strength between runoff coefficient and pre-storm conditions by inducing sharp changes in the correlation with season and climate. These results increase our understanding of the coupling between pre-storm conditions and runoff coefficients. This will aid flood forecasting, hydrological and land surface model calibration, and data assimilation. Furthermore, these findings can help us to better interpret future flood projections in Europe based on expected changes in long and short-term climatic drivers.