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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. 

Abstract A frequently expressed viewpoint across the Earth science community is that global soil moisture estimates from satellite L‐band (1.4 GHz) measurements represent moisture only in a shallow surface layer (0–5 cm) and consequently are of limited value for studying global terrestrial ecosystems because plants use water from deeper rootzones. Using this argumentation, many observation‐based land surface studies avoid satellite‐observed soil moisture. Here, based on peer‐reviewed literature across several fields, we argue that such a viewpoint is overly limiting for two reasons. First, microwave soil emission depth considerations and statistical considerations of vertically correlated soil moisture information together indicate that L‐band measurements carry information about soil moisture extending below the commonly referenced 5 cm in many conditions. However, spatial variations of effective depths of representation remain uncertain. Second, in reviewing isotopic tracer field studies of plant water uptake, we find a prevalence of vegetation that primarily draws moisture from these upper soil layers. This is especially true for grasslands and croplands covering more than a third of global vegetated surfaces. Even some deeper‐rooted species (i.e., shrubs and trees) preferentially or seasonally draw water from the upper soil layers. Therefore, L‐band satellite soil moisture estimates are more relevant to global vegetation water uptake than commonly appreciated (i.e., relevant beyond only shallow soil processes like soil evaporation). Our commentary encourages the application of satellite soil moisture across a broader range of terrestrial hydrosphere and biosphere studies while urging more rigorous estimates of its effective depth of representation. 

Root zone soil moisture (RZSM) is one of the least-monitored variables within the hydrologic cycle. Given the importance of RZSM to agriculture, more effort is needed to understand the potential impacts of the El Niño southern oscillation (ENSO), Pacific decadal oscillation (PDO), and Atlantic multidecadal oscillation (AMO) on this critical variable. This study focused on the CONtiguous United States (CONUS) RZSM (0 to 40 cm depth) over nearly three decades (1992 to 2018). Basic trend analysis with the Mann–Kendall test and wavelet transform coherence (WTC) was utilized. The RZSM product examined was Soil MERGE (SMERGE 2.0). More CONUS pixels exhibited drying (56 to 75%) versus wetting (25 to 44%) trends between 1992 and 2018. Seasonal wetting trends were observed particularly during winter in the Southwest and Northwest regions associated with El Nino and La Nina episodes, respectively. The noted long-term RZSM trends are more clearly attributable to oceanic-atmospheric teleconnections than global climate change. The most significant result was the strong drying trend in central CONUS reflected a shift to La Nina and cool PDO conditions during the 2000s, further amplified by a change to positive AMO corresponding with this period.