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Soil moisture is a key variable that contributes for flux partitioning into sensible and latent heat components. There is a pressing need to study the impacts of shallow groundwater table dynamics on antecedent soil moisture and corresponding feedbacks to atmosphere. In this study, we evaluate WRF‐NoahMP simulated soil moisture using two experimental configurations: one with the default, free drainage approach (CTL) and with Miguez‐Macho groundwater scheme (GW). Results are presented for the study conducted over Ganga River basin, India between the years 2008 and 2014 for all the three seasons (pre‐monsoon, monsoon, post‐monsoon). It was observed that the GW model runs, improve soil moisture in topmost and bottom‐most layers. We discovered that the simulations improved temporal negative bias of seasonal accumulation during monsoon by around 91 mm. In addition, the average negative bias in latent heat flux improved by around 28 W/m2. Compared to CTL, GW was found to contribute additional atmospheric moisture content ranging between 3% and 5%. Comparison with in‐situ ground water showed that model overestimates the ground water table depth. This can be attributed to the ability of the model to only simulate natural occurring variabilities and lack of inclusion of anthropogenic factors such as ground water pumping in the model. The groundwater trend in Ganga is declining at alarming rate. In addition, if over exploitation of groundwater is not monitored properly, water and food scarcity is an imminent threat to the country's economy. To the best of authors knowledge, this is the first study of its kind to be implemented over Ganga basin, which evaluates the role of shallow ground water table on regional climate using coupled atmosphere‐land surface‐groundwater model. In summary, we highlight that the groundwater component along with WRF‐NoahMP coupled model improves soil moisture and precipitation representation over the Ganga basin. [ABSTRACT FROM AUTHOR]

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]

L-band microwave radiometry is one of the globally accepted techniques to estimate surface soil moisture by measuring upwelling brightness temperature (TB) from the surface. L-band radiometers have been historically used for ground truth validation of space-borne missions. Existing devices are distinguished by using huge and bulky antennas to conduct precise multi-angular measurements at the cost of portability. The current work demonstrates that a portable ground-based L-band microwave radiometer can be used to measure high-resolution TB, soil temperature and emissivity in a case study area in India as an alternative to a traditional huge ground-based radiometer. To show the influence of surface soil moisture (SM) on TB, a controlled experiment was conducted to investigate the temporal variation of TB over a bare soil surface. Spatial analysis of the measured data shows that the compact radiometer can efficiently capture the linear relationship between emissivity and TB, while temporal analysis of TB over a small area shows the variation in TB dynamics with SM. [ABSTRACT FROM AUTHOR]

• The efficiency of SM products to improve unmodelled effects in LSM are examined. • Assimilation of ASCAT and CCI products effectively captures the dynamics of SM. • ASCAT based SM products have high skill to improve LSM simulations. • Blended SM products are more effective to capture the daily SM variability. Realistic representation of surface states using the land surface model (LSM) is extremely challenging owing to human-induced changes and uncertainty in forcing data. In this study, we focus on two crucial objectives pertaining to hydrology namely (a) to understand the ability of different soil moisture (SM) products to improve simulation of unmodeled irrigation processes through data assimilation process, and (b) to learn the feasibility of these SM products to correct the spatial surface soil moisture artifacts caused due to error in precipitation forcing. The utility of SM products evaluated in the present study are retrieved from different satellite sensors and algorithms such as the active satellite-based the Advanced Scatterometer (ASCAT), merged SM from European Space Agency Climate Change Initiative (ESA CCI V4.2) and the latest passive microwave-based SMOS INRA-CESBIO (SMOS-IC) SM product. The results presented for three years (2010, 2011 and 2012) suggest that assimilation of ASCAT and CCI based products effectively captures the SM changes due to irrigation. Similarly, it corrects the spatial artifacts caused due to precipitation errors. However, the single sensor based products have a limitation in spatial samples per day which is critical to capture dynamic SM products over a larger area. Hence, blended products are more effective on a larger area to capture the dynamics in a more effective manner at daily temporal resolutions. [ABSTRACT FROM AUTHOR]

• TVDI applied for the first time over Ganga Basin to examine the SM variation. • High TVDI values are observed in central plateau and hill agro-climatic zone. • TVDI developed using EVI shows better correlation with SM compared with the NDVI generated TVDI. • Spatial and temporal valuation of TVDI shows that its applicability can be used mainly in the summer season. • TVDI have high negative correlation with SM for the 10–40-cm soil depth range. Soil Moisture (SM) is a critical parameter for land–atmosphere interaction, measuring drought conditions in agricultural areas, and it can significantly affect surface water and agricultural production. Based on remote sensing observations, the temperature vegetation dryness index (TVDI) can be generated to evaluate SM at a large spatial scale. Land Surface Temperature (LST) and Vegetation Index (VI) plots are generated to obtain the triangular/trapezoidal space to calculate TVDI. For the present study, TVDI was calculated by using LST and normalized difference vegetation index (NDVI) or the enhanced vegetation index (EVI) for a period of three years (2017, 2018, and 2019) over the Ganga Basin. The applicability of the TVDI at soil depths of 0–10, 10–40, 40–100, and 100–200 cm was also examined. The result from the study indicates that, a better correlation is obtained between TVDI generated using EVI and SM compared to TVDI generated using NDVI. Temporal variation of TVDI with SM shows that TVDI (EVI) almost captures the maximum and minimum SM variation in most locations. Also, SM at 10–40 cm shows a better negative correlation (r close to −0.5) with TVDI (EVI) than 0–10 cm depth for the summer season. The result thus reveals the potential of TVDI in assessing SM especially in summer season, while using EVI as the vegetation index. [ABSTRACT FROM AUTHOR]

In the present study, soil moisture assimilation is conducted over the Indian subcontinent, using the Noah Land Surface Model (LSM) and the Soil Moisture Operational Products System (SMOPS) observations by utilizing the Ensemble Kalman Filter. The study is conducted in two stages involving assimilation of soil moisture and simulation of brightness temperature (Tb) using radiative transfer scheme. The results of data assimilation in the form of simulated Surface Soil Moisture (SSM) maps are evaluated for the Indian summer monsoonal months of June, July, August, September (JJAS) using the Land Parameter Retrieval Model (LPRM) AMSR-E soil moisture as reference. Results of comparative analysis using the Global land Data Assimilation System (GLDAS) SSM is also discussed over India. Data assimilation using SMOPS soil moisture shows improved prediction over the Indian subcontinent, with an average correlation of 0.96 and average root mean square difference (RMSD) of 0.0303 m3/m3. The results are promising in comparison with the GLDAS SSM, which has an average correlation of 0.93 and average RMSD of 0.0481 m3/m3. In the second stage of the study, the assimilated soil moisture is used to simulate X-band brightness temperature (Tb) at an incidence angle of 55° using the Community Microwave Emission Model (CMEM) Radiative transfer Model (RTM). This is aimed to study the sensitivity of the parameterization scheme on Tb simulation over the Indian subcontinent. The result of Tb simulation shows that the CMEM parameterization scheme strongly influences the simulated top of atmosphere (TOA) brightness temperature. Furthermore, the Tb simulations from Wang dielectric model and Kirdyashev vegetation model shows better similarity with the actual AMSR-E Tb over the study region. [ABSTRACT FROM AUTHOR]

• Demonstration of CTP HI framework to understand triggering of deep convection. • Over India it was observed that during monsoon season, atmospherically controlled cases dominated. • Lower values of CTP HI were observed in Northern and North-Eastern regions which could be attributed to steep topography. Soil moisture plays a significant role in partitioning surface energy fluxes into latent and sensible heat components and is fundamental for process level understanding of land-atmosphere interactions. Realistic simulations of such interactions by models are constrained due to intrinsic model assumptions. This study examines convective triggering based on soil moisture by employing a framework of coupling diagnostics called Convective triggering potential (CTP) and humidity index (HI Low) to classify land regions into various feedback regimes for each season between 2005 and 2015. Results show that atmospherically controlled cases prevail in most of the regions for all seasons. Temporal regime probability values (TRP) suggested a dominant wet regime in southern India and a dominant dry regime in central and western India. Precipitation events triggered in wet advantage conditions were found to have larger accumulations than similar events triggered in dry advantage conditions for all the seasons. This study highlights separate seasonal regime classification and emphasizes on how potential regime hotspots vary for each season. [ABSTRACT FROM AUTHOR]

• 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]

• Evaluated performance of precipitation forcing data (TMPA, GDAS, CHIRPS, MSWEP) to simulated soil moisture. • Validation data was used from ESA-CCI soil moisture and CTP-SMTMS dataset for years 2010–2012. • Evaluated graphical and statistical comparison of results. • Simulated soil moisture forced with GDAS precipitation showed overall superior performance. Land surface processes considerably influence the global weather and climate patterns which makes its quantification significant to scientists, hydrologists as well as policymakers alike. Considering the lack of available in-situ measurement, retrieval of the land surface fluxes mostly relies on remotely sensed satellite retrieval and through simulations from land surface models (LSMs). Hence, it is essential to quantify the uncertainties present in the output of these land surface models which are mainly due to errors in forcing data, model parameters and model structure. Precipitation is one of the key input forcing data used in LSMs. With the advancement of remote sensing techniques, multiple sources of precipitation products are made available to the user community. This study examines the effect of precipitation uncertainty in LSM simulated soil moisture. For this study, four precipitation products are used namely, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RT v7, Global Data Assimilation System (GDAS), Climate Hazards Infrared Precipitation with Stations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP). These data products are used as meteorological forcing in Noah 3.6 LSM for the simulation of soil moisture. The uncertainty inherent in the precipitation products are examined using two approaches a) By evaluating the precipitation products against the gridded India Meteorological Department (IMD) precipitation dataset and b) By using the precipitation products for simulating soil moisture outputs. These were validated over the Indian subcontinent using validation data from the European Space Agency-Climate Change Initiative (ESA-CCI) soil moisture and Central Tibetan Plateau Soil Moisture and Temperature Monitoring Network (CTP-SMTMN) dataset for the years 2010 to 2012. The study utilizes various graphical as well as quantitative evaluation methods to determine the best performing precipitation product. Our study indicates that the simulated soil moisture forced with GDAS and MSWEP precipitation product performed consistently superior among all the other simulation outputs over India. [ABSTRACT FROM AUTHOR]