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The potential of satellite soil moisture (SM) in improving hydrological modeling has been addressed in synthetic experiments, but it is less explored in real data cases. Here, we investigate the added value of Soil Moisture and Passive (SMAP) and Advanced Scatterometer (ASCAT) SM data to distributed hydrological modeling with the soil and water assessment tool (SWAT) in a highly human disturbed catchment (126, 486 km2) featuring a network of SM and streamflow observations. The investigation is based on the ensemble Kalman filter (EnKF) considering SM errors from satellite data using the triple collocation. The assimilation of SMAP and ASCAT SM improved the surface (0–10 cm) and rootzone (10–30 cm) SM at >70% and > 50% stations of the basin, respectively. However, the assimilation effects on distributed streamflow simulation of the basin are un-significant and not robust. SM assimilation improved the simulated streamflow at two upstream stations, while it deteriorated the streamflow at the remaining stations. This can be largely attributed to the poor vertical soil water coupling of SWAT, suboptimal model parameters, satellite SM data quality, humid climate, and human disturbance to rainfall-runoff processes. This study offers strong evidence of integrating satellite SM into hydrological modeling in improving SM estimation and provides implications for achieving the added value of remotely sensed SM in streamflow improvement. [ABSTRACT FROM AUTHOR]

Abstract The multi-satellite-retrieved (ESA CCI SM) and the Global Land Data Assimilation System-Noah-simulated (GLDAS-Noah) surface soil moisture (SM) datasets are compared for global drought analysis over a multi-decadal time period (1991–2015). Global drought events and their duration, frequency and severity are assessed on a grid basis with soil moisture anomaly percentage index (SMAPI). The results show that the ESA CCI SM and the GLDAS-Noah based SMAPI values are significantly (p < 0.05) correlated over most (83%) of the study region, of seasonally dependent. Both datasets show similar global patterns in drought duration, drought frequency and drought severity. The droughts present generally longer duration, higher frequency and more severity in arid and semi-arid regions than humid and sub-humid regions. The ESA CCI SM droughts are relatively higher in frequency and more intense in severity than the GLDAS-Noah SM droughts in many regions of the globe, while the two datasets show considerable differences in drought duration over arid, semi-arid and highly vegetated regions. For long-term trend detection, both datasets show high consistency in spatial pattern of SMAPI, with major significant drying trends in arid and semi-arid regions. Part (~20%) trends are confirmed by the Global Precipitation Climatology Centre (GPCC) precipitation dataset using the Standard Precipitation Index (SPI). The two SM datasets exhibit large disparity in trending drought duration, drought frequency and drought severity. Despite that, both show major significant increasing trends in arid and semi-arid regions. Both soil moisture datasets are capable of identifying extreme drought events reported in southern China, North America, Europe and southern Africa. The ESA CCI SM dataset is more effective in determining the severity and spatial pattern of drought excluding densely vegetated regions, while the GLDAS-Noah dataset is more powerful in detecting drought occurrence, even over densely vegetated regions. Overall, the ESA CCI SM and GLDAS-Noah SM show potential in global drought analysis, yet cautions should be paid to arid and semi-arid regions where drying trends are prevalent and large discrepancy presents between two datasets. Highlights • ESA CCI and GLDAS-Noah are significantly correlated in soil moisture drought index. • They show similar global pattern for drought duration, frequency and severity. • Two datasets exhibit similar spatial pattern and high consistency in drought trend. • Significant drying trends appear mainly in arid and semi-arid region. • ESA CCI is superior in drought detection except densely vegetated areas. [ABSTRACT FROM AUTHOR]

The assimilation of satellite soil moisture (SM) products with coarse resolution is promising in improving rainfall-runoff modeling, but it is largely impacted by the data assimilation (DA) strategy. This study performs the assimilation of a satellite soil moisture product from the European Space Agency (ESA) Climate Change Initiative (CCI) in a physically based semidistributed hydrological model (SWAT) in the upper Huai River basin in China, with the objective to improve its rainfall-runoff simulation. In this assimilation, the ensemble Kalman filter (EnKF) is adopted with full consideration of the model and observation error, the rescaling technique for satellite SM, and the regional applicability of the hydrological model. The results show that the ESA CCI SM assimilation generally improves the streamflow simulation of the study catchment. It is more effective for low-flow simulation, while for very high-flow/large-flood modeling, the DA performance shows uncertainty. The less-effective performance on large-flood simulation lies in the relatively low dependence of rainfall-runoff generation on the antecedent SM as during which the SM is nearly saturated and the runoff is largely dominated by precipitation. Besides, the efficiency of DA is deteriorated by the dense forest coverage and the complex topography conditions of the basin. Overall, the ESA CCI SM assimilation improves the streamflow simulation of the SWAT model in particular for low flow. This study provides an encouragement for the application of the ESA CCI SM in water management, especially over low-flow periods. [ABSTRACT FROM AUTHOR]

• Identify soil moisture (SM) droughts in a 3-D domain. • Severe droughts mainly distributed in northeast and southwest of Southeastern Asia. • SM droughts were more serious in cold (aggravated) than warm season (alleviated). • The drought clusters shared two predominant routes of westward and southwest. • SM droughts propagated synergistically with precipitation and PET. Droughts evolve in space and time simultaneously, but its spatio-temporal propagation remains less explored. As one of the hotspot regions in global soil moisture (SM) drought, the Southeastern Asia supports billions of population and trillions of dollars in gross domestic product. Its high drought risk initiated a deep understanding of drought propagation in multiple dimensions for effective prevention. This study investigated the SM droughts with a merged dataset of remote sensing and land surface modeling over 1979–2016. Our results showed that 369 drought clusters and 145 drought events occurred. Spatially, severe droughts appeared mainly in southwest and northeast of the study region. Temporally, the droughts were more severe in cold season (winter and spring) than warm season (summer and autumn). In last four decades, the SM droughts alleviated in warm season but aggravated in cold season significantly, posing a challenge for drought mitigation, especially for spring crop growth vigorously. Furthermore, the drought clusters shared two predominant routes of westward and southwest. The westward route is for the droughts started in winter in south region, while the southwest route is for the droughts started in summer across the Yangtze River basin from northeast to southwest. At a drought event level, the SM drought propagated synergistically with precipitation and potential evapotranspiration (PET). Precipitation was generally the primary driver and PET the secondary, particularly for SM droughts lasting in summer and winter in south region. The intensity of PET usually enhanced in spring and summer with temperature increasing and crop growing. The findings in drought propagation provide essential spatiotemporal structure for mechanism exploration and important clues for drought prediction. [ABSTRACT FROM AUTHOR]

Remote sensing and land surface models promote the understanding of soil moisture dynamics by means of multiple products. These products differ in data sources, algorithms, model structures and forcing datasets, complicating the selection of optimal products, especially in regions with complex land covers. This study compared different products, algorithms and flagging strategies based on in situ observations in Anhui province, China, an intensive agricultural region with diverse landscapes. In general, models outperform remote sensing in terms of valid data coverage, metrics against observations or based on triple collocation analysis, and responsiveness to precipitation. Remote sensing performs poorly in hilly and densely vegetated areas and areas with developed water systems, where the low data volume and poor performance of satellite products (e.g., Soil Moisture Active Passive, SMAP) might constrain the accuracy of data assimilation (e.g., SMAP L4) and downstream products (e.g., Cyclone Global Navigation Satellite System, CYGNSS). Remote sensing has the potential to detect irrigation signals depending on algorithms and products. The single-channel algorithm (SCA) shows a better ability to detect irrigation signals than the Land Parameter Retrieval Model (LPRM). SMAP SCA-H and SCA-V products are the most sensitive to irrigation, whereas the LPRM-based Advanced Microwave Scanning Radiometer 2 (AMSR2) and European Space Agency (ESA) Climate Change Initiative (CCI) passive products cannot reflect irrigation signals. The results offer insight into optimal product selection and algorithm improvement. [ABSTRACT FROM AUTHOR]