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The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study's findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance. [ABSTRACT FROM AUTHOR]

The spatial error structure of daily precipitation derived from the latest version 7 (v7) tropical rainfall measuring mission (TRMM) level 2 data products are studied through comparison with the Asian precipitation highly resolved observational data integration toward evaluation of the water resources (APHRODITE) data over a subtropical region of the Indian subcontinent for the seasonal rainfall over 6 years from June 2002 to September 2007. The data products examined include v7 data from the TRMM radiometer Microwave Imager (TMI) and radar precipitation radar (PR), namely, 2A12, 2A25, and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products were quantified based on performance metrics derived from the contingency table. For the seasonal daily precipitation over a subtropical basin in India, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with the 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition techniques performed to disentangle systematic and random errors verify that the multiplicative error model representing rainfall from 2A12 algorithm successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results verify that although the radiometer derived 2A12 rainfall data is known to suffer from many sources of uncertainties, spatial analysis over the case study region of India testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered. [ABSTRACT FROM PUBLISHER]

• Evaluation of precipitation from downscaled SM using SM2RAIN bottom-up approach. • Optical based SM downscaling method is used to downscale SMOS and ESA CCI SM. • Indirect validation of downscaled SM. • The framework is evaluated in Ganga Basin, India. Soil moisture (SM) and precipitation play an essential part in land surface and atmospheric processes. Availability of high-resolution SM and precipitation data are quintessential for regional and global studies in hydro climatology. There exists a strong interconnection between the varying amounts of SM and the precipitation over a given area, giving rise to the bottom-up approach of estimating precipitation from SM2RAIN. This study examines precipitation derived from SM, downscaled using a unique cognation of land surface temperature (LST), vegetation index (VI), and SM. For this work, MODIS optical data having one km spatial resolution has been used to downscale the SMOS (25 km) and ESA CCI (25 km) SM data to generate four fine-resolution SM products SMOS-LST-NDVI, SMOS-LST-EVI, ESA CCI-LST-NDVI, and ESA CCI-LST-EVI, respectively. In this study, the Ganga basin in India is used as a case study area to assess the bottom-up SM2RAIN approach. The correlation analysis of SM products with LST, NDVI, and EVI, shows that LST has a robust negative correlation, whereas NDVI and EVI have a significant positive correlation. Results obtained from the SM2RAIN simulation from downscaled SM managed to capture the spatial variation of the high precipitation event. Validation with IMERG GPM precipitation data shows that the SMOS-LST-NDVI precipitation product best captures the spatial variation of observed precipitation. The present study results provide convincing evidence that high-resolution precipitation can be estimated using satellite-derived SM data at basin level, as long as better calibration and validation data are available. [ABSTRACT FROM AUTHOR]

• Highlights the sampling effects of GPM orbital through hydrologic modeling. • A multidimensional error model is used to generate synthetic GPM orbital data. • The VIC model is used to scrutinize the impact of GPM orbital data sampling. • An insight into optimum integration time of number of satellites in a constellation. Global precipitation estimation depends on constellations of mainly passive microwave sensors along with satellite radars and constellations of geosynchronous earth orbit infrared sensors. With passive microwave constellations, a persisting challenge is the limited sampling, which impacts applications such as hydrological modeling. This study presents a framework to examine the impact of the Global Precipitation Measurement (GPM) orbital sampling on streamflow simulations. Orbital data from the GPM Dual Precipitation Radar (DPR) 2A-Ku is used within the Variable Infiltration Capacity (VIC) model to derive ensembles of runoff simulations. Ensembles of synthetic precipitation fields are generated at different sampling intervals which are representative of GPM orbital data products with the Satellite Rainfall Error Model (SREM-2D) over the Hirakud catchment in the Indian subcontinent over a period of two years (2015–2016). The NOAA-NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based gridded rainfall product is considered as the reference rainfall for our study. Sampling uncertainty in orbital precipitation data is shown to amplify in streamflow simulations. The statistical analysis indicates that increasing sampling rates tend to generate more realistic land surface fluxes. The results from our study indicate that streamflow simulations from VIC model using precipitation forcing at 6-hourly integration outperforms the simulations as compared to 3-hourly and 12-hourly forcing integration times. This is exemplified by the statistical indices, with mean relative error of 0.17 for simulations with 3-hourly forcing, 0.12 for simulation with 6-hourly forcing, and 0.57 for 12-hourly forcing. The relative Root-Mean-Square-Error (RMSE) for 3-hourly, 6-hourly, 12-hourly for 2015 are 0.31, 0.25, 1.43 and for 2016 are 0.25, 0.15, 1.06, respectively. It can be noticed that both mean relative error and relative RMSE show a similar pattern for both years. A generic framework is therefore proposed to study the manifestation of satellite constellation sampling uncertainty in hydrologic simulations, which are crucial for future missions such as the CubeSat-based Temporal Experiment for Storms and Tropical Systems, and the Time-Resolved Observations of precipitation structure and storm intensity with a constellation of smallsats. [ABSTRACT FROM AUTHOR]