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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]

We used remote sensing data, field observations and numerical groundwater modelling to investigate long-term groundwater storage losses in the regional aquifer of the Ganga Basin in India. This comprised trend analysis for groundwater level observations from 2851 monitoring bores, groundwater storage anomaly estimation using GRACE and Global Land Data Assimilation System (GLDAS) data sets and numerical modelling of long-term groundwater storage changes underpinned by over 50,000 groundwater level observations and uncertainty analysis. Three analyses based on different methods consistently informed that groundwater storage in the aquifer is declining at a significant rate. Groundwater level trend indicated storage loss in the range − 1.1 to − 3.3 cm year−1 (median − 2.6 cm year−1) while the modelling and GRACE storage anomaly methods indicated the storage loss in the range of − 2.1 to − 4.5 cm year−1 (median − 3.2 cm year−1) and − 1.0 to − 4.2 cm year−1 (median − 1.7 cm year−1) respectively. Probabilistic modelling analysis also indicated that the average groundwater storage is declining in all the major basin states, the highest declining trend being in the western states of Rajasthan, Haryana and Delhi. While smaller compared to the western states, average groundwater storage in states further towards east—Uttar Pradesh, Bihar and West Bengal within the basin are also declining. Time series of storage anomalies obtained from the three methods showed similar trends. Probabilistic storage analysis using the numerical model vetted by observed trend analysis and GRACE data provides the opportunity for predictive analysis of storage changes for future climate and other scenarios. [ABSTRACT FROM AUTHOR]

National Capital Region (NCR, Delhi) in India is one of the fastest-growing metropolitan cities which is facing a severe water crisis due to increasing water demand. The over-extraction of groundwater, particularly from its unconsolidated alluvial deposits makes the region prone to subsidence. In this study, we investigated the effects of plummeting groundwater levels on land surface elevations in Delhi NCR using Sentinel-1 datasets acquired during the years 2014–2020. Our analysis reveals two distinct subsidence features in the study area with rates exceeding 11 cm/year in Kapashera—an urban village near IGI airport Delhi, and 3 cm/year in Faridabad throughout the study period. The subsidence in these two areas are accelerating and follows the depleting groundwater trend. The third region, Dwarka shows a shift from subsidence to uplift during the years which can be attributed to the strict government policies to regulate groundwater use and incentivizing rainwater harvesting. Further analysis using a classified risk map based on hazard risk and vulnerability approach highlights an approximate area of 100 square kilometers to be subjected to the highest risk level of ground movement, demanding urgent attention. The findings of this study are highly relevant for government agencies to formulate new policies against the over-exploitation of groundwater and to facilitate a sustainable and resilient groundwater management system in Delhi NCR. [ABSTRACT FROM AUTHOR]

The alarming decline in groundwater (GW) storage threatens the sustainable development of the water‐energy‐food linkages of a country. India has experienced severe dry spells during the years 2002, 2004, 2009, and 2012. However, the relative contribution of factors affecting GW depletion (such as variability in precipitation rate and groundwater extraction) still remains unknown. Here, we first evaluate the current GW condition by implementing reliability, resilience, and vulnerability analysis on the 5,988 monitoring well observations spanning over a period of two decades. Our study further improves the existing studies by isolating the quantity of GW depletion due to precipitation and nonprecipitation (GW extraction) factors using a multivariate regression model. Our study indicates that the nonprecipitation factor is most responsible for GW depletion in North India at a rate of 3.5 cm per year. Plain Language Summary: In many parts of India, groundwater (GW) is used as the main source of freshwater for agricultural production. Continuous extraction of GW over the years has led to a reduction in GW storage. This affects agricultural productivity and increases the energy consumption to pump out GW from deeper wells. GW is composed of excess precipitation that percolates through the topsoil and accumulates over the hard rock material that soaks with water. Therefore, low precipitation reduces the replenishment of extracted GW and increases the rate of GW depletion. During the past decade, India has experienced severe low rainfall seasons during the years 2002, 2004, 2009, and 2012. We implement a methodology to monitor the change in GW, caused due to precipitation and nonclimatic factors over India using Gravity Recovery and Climate Experiment satellite and 5,988 monitoring well observations. Our analysis reveals that the continuous extraction of GW and reduced GW refill during low rainfall seasons before 2009 has considerably affected GW over India. Our study further provides insight into the sustainability of GW resources of India, which will help to develop sustainable GW management schemes in India. Key Points: Groundwater (GW) storage depletion in India has major contributions from nonprecipitation factorsThe analysis of GW based on performance indices indicates low GW storage sustainability over the majority of Indian statesGrowing water demand has increased GW stress on the eastern coast of the Indian peninsula [ABSTRACT FROM AUTHOR]

Groundwater is utilized intensively as a source of fresh water for irrigation and human needs. Hence, it is necessary to monitor groundwater storage for water security of the region in the future. The present study aims to evaluate the groundwater resource over the Krishna basin in South India. The basin comprises of 210 major and medium irrigation projects, which makes it important to evaluate the groundwater balance for a sustainable groundwater draft. This study evaluates the trend in groundwater anomaly derived from GRACE mascon product. Results indicate that the Krishna basin is subjected to a strong decline in groundwater at a rate of 0.34 cm per year. Further, the study explores the seasonality of precipitation and its effect on groundwater by adopting an entropy-based approach. Results indicate the combined effect of delay in precipitation to attain peak and reduced duration of the wet season as a primary reason for the decline in the groundwater storage. The result shows that the reduction in groundwater storage affects the evapotranspiration over the region. [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]