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The Indus River Basin (IRB), one of the major river basins in the Hindu Kush Himalaya, is primarily sustained by meltwater from the cryosphere. It caters to diverse sectors, including intense irrigation-supported agriculture, energy production, tourism and biodiversity. The cryosphere of the Upper Indus Basin (UIB) is mainly influenced by the western disturbances with subordinate contribution from the Indian Summer Monsoon. Increase in ambient temperature, shrinking cryospheric reserves, fluctuating surface run-off and enhanced frequency of extreme weather events are some of the noticeable indicators of climate change in the UIB. These changes will most likely adversely affect the water-dependent sectors in the upstream and downstream of IRB, posing serious threat to food security and livelihoods. Although the region has gained significant attention in recent years, there remains a noticeable knowledge gap pertaining to certain key issues with serious implications for the natural environment and the people. A national workshop was organized for stimulated deliberations to identify the major knowledge gaps and suggest a policy framework for climate change mitigation in the UIB. The workshop underscores the urgent need of multi-institutional, multidisciplinary, comprehensive, coordinated and time-bound collaboration to study the interplay of complex drivers on water resources of the UIB. [ABSTRACT FROM AUTHOR]

The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation's contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation. [ABSTRACT FROM AUTHOR]

A comprehensive assessment and bias corrections of two gridded daily precipitation products, based on gauge-only and multi-satellite observations, are undertaken in this study using independent rain gauge data in and around the Kabini River (KR) basin in South India. The KR basin, with complex terrain, highly variable precipitation and heterogeneous land use poses challenges in the development of accurate gridded precipitation products. The gauge-only gridded precipitation data from the India Meteorological Department (IMD) and multi-satellite gridded precipitation product from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA)-3B42 are evaluated at daily and monthly scales in this study. Both gridded precipitation products available at 0.25° spatial resolution are assessed using independent 57 rain gauge observations for the period of 2009–2016. Multiple visual and statistical metrics have been utilized to assess the error characteristics and capability of these gridded precipitation estimates in replicating extremes. Results indicate that gauge-only gridded product is generally better than the multi-satellite precipitation product. The multi-satellite product notably overestimates light precipitation and underestimates extreme precipitation over the study region. To mitigate the overestimation of dry days in the TMPA-3B42 estimates, a dry-day correction method is developed and is applied using nearby rain gauge observations. Furthermore, a quantile-based correction is also applied to both gridded precipitation products after confirming the stationarity of the data, which substantially improved both precipitation estimates for distributed hydrological modelling studies in the KR basin. [ABSTRACT FROM AUTHOR]

The accuracy of gridded precipitation data depends on the availability of a uniformly spaced rain gauge network and an appropriate spatial interpolation method that considers the rainfall variability and other factors that influence the precipitation patterns in the region of interest. In the current study, conceptually superior variants of a widely used spatial interpolation algorithm, Shepard's method, are proposed, formulated and evaluated to overcome one of the major limitations in neighbourhood selection, that is, arbitrary selection of rain gauges. The variants provide mechanisms to objectively select the rain gauges (control points) based on correlation (variant 1), distribution similarity (variant 2) and a combination of both (variant 3). The improved variants were used in the development of gridded rainfall data at a resolution of 5 km over the Kabini River basin in south India, and in the state of Kentucky, United States. Results from multiple experiments using the original Shepard's method and its variants indicate improvements in the accuracy of precipitation estimates. Also, these variants have preserved the site‐specific statistics and distributional characteristics of the rainfall data. A variant 1 that uses a correlation‐based neighbourhood selection criterion performed better for daily and monthly data compared to others and is suitable for generation of gridded rainfall data. The variant 1 when used with information from clustering of sites for selection of the neighbours has led to improvement in gridded precipitation data estimates. The proposed variant 1 can also be used for point data estimation useful for filling missing data at any site. [ABSTRACT FROM AUTHOR]

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m 2 to tens of km 2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it's data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR). [ABSTRACT FROM AUTHOR]

The scale difference between point in situ soil moisture measurements and low resolution satellite products limits the quality of any validation efforts in heterogeneous regions. Cosmic Ray Neutron Probes (CRNP) could be an option to fill the scale gap between both systems, as they provide area-average soil moisture within a 150-250 m radius footprint. In this study, we evaluate differences and similarities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). Six CRNPs located on five continents have been selected as test sites: the Rur catchment in Germany, the COSMOS sites in Arizona and California (USA), and Kenya, one CosmOz site in New SouthWales (Australia), and a site in Karnataka (India). Standard validation scores as well as the Triple Collocation (TC) method identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs. The potential of CRNPs for satellite soil moisture validation has been proven; however, biomass correction methods should be implemented to improve its application in regions with large vegetation dynamics. [ABSTRACT FROM AUTHOR]

Groundwater resources in the crystalline basement complex of India are crucial for supplying drinking water in both rural and urban settings. Groundwater depletion is recognised as a challenge across parts of India due to over-abstraction, but groundwater quality constraints are perhaps even more widespread and often overlooked at the local scale. Uranium contamination in basement aquifers has been reported in many parts of India, locally exceeding WHO drinking water guideline values of 30 μg/L and posing a potential health risk. In this study 130 water samples were collected across three crystalline basement catchments to assess hydrochemical, geological and anthropogenic controls on uranium mobility and occurrence in drinking water sources. Groundwaters with uranium concentrations exceeding 30 μg/L were found in all three study catchments (30% of samples overall), with concentrations up to 589 μg/L detected. There appears to be a geological control on the occurrence of uranium in groundwater with the granitic gneiss of the Halli and Bengaluru study areas having higher mean uranium concentrations (51 and 68 μg/L respectively) compared to the sheared gneiss of the Berambadi catchment (6.4 μg/L). Uranium – nitrate relationships indicate that fertiliser sources are not a major control on uranium occurrence in these case studies which include two catchments with a long legacy of intense agricultural land use. Geochemical modelling confirmed uranium speciation was dominated by uranyl carbonate species, particularly ternary complexes with calcium, consistent with uranium mobility being affected by redox controls and the presence of carbonates. Urban leakage in Bengaluru led to low pH and low bicarbonate groundwater hydrochemistry, reducing uranium mobility and altering uranium speciation. Since the majority of inhabitants in Karnataka depend on groundwater abstraction from basement aquifers for drinking water and domestic use, exposure to elevated uranium is a public health concern. Improved monitoring, understanding and treatment of high uranium drinking water sources in this region is essential to safeguard public health. • Groundwater uranium (U) > 30 μg/L in 30% of samples, maximum U conc. of 589 μg/L. • Geogenic sources control groundwater U with lower U in sheared granitic gneiss. • No evidence found for fertiliser U sources even in intensively farmed regions. • Abstraction may enhance oxidizing conditions in aquifers promoting U mobility. • Urban leakage may control U speciation and reduce U mobility in groundwater. [ABSTRACT FROM AUTHOR]

Agricultural intensification in the Ganges basin since the mid-1960s has led to the increased use of surface water and groundwater-fed irrigation. To understand the dynamics and future evolution of such a human-influenced system, models are required that represent both interactions between physical processes and agricultural practice describing the source, quantity and timing of irrigation.Here, we incorporated a (i) 2D lateral groundwater model, and (ii) flood irrigation into the VIC macroscale hydrological model, with the future aim of applying it to simulate the effects of agricultural practice, land use change, groundwater pumping, and surface water irrigation on ecological flows in the River Gandak and the wider Ganges. We included the 2D lateral groundwater model within the image version of VIC 5, by modifying the boundary condition at the base of the soil column to simulate varying recharge rates as the groundwater table fluctuates, similarly to that of the previously developed SIMGM model. This recharge formulation is both a function of soil moisture and the depth to the groundwater table, and includes drainage by gravity and capillary rise. Saturated lateral groundwater flow was simulated using a one layer 2D groundwater model. Replacing VIC's baseflow formulation, groundwater discharge to the land surface is described either through river baseflow, or through capillary rise of groundwater into the soil column and increased evapotranspiration and/or runoff.The VIC model was further modified to incorporate groundwater and surface water-fed irrigation. In the Ganges basin, crops are cultivated over Kharif and Rabi seasons which were modelled separately. The irrigation scheme used is flood irrigation, and for crops that are submerged, such as rice, paddy formulation was added. The VIC-groundwater model was applied to an idealised and homogenous system based on the upper Ganges catchment. We investigated the role of the groundwater discharge, aquifer properties and grid size, in controlling spatial variability in water table depth, groundwater recharge, discharge and evapotranspiration. Feedbacks between depth to the groundwater table and the land surface fluxes increase for aquifers with a high aquifer hydraulic conductivity, modelled on a fine grid resolution. Comparing models with different grid sizes and aquifer properties, we find that lateral groundwater flow and depth to the water table are especially dependent on grid size for aquifers with a high hydraulic conductivity, however less so for lower hydraulic conductivity. Therefore, it will be important when simulating groundwater abstraction and irrigation in aquifers with a high hydraulic conductivity, such as the Ganges base, to use a fine spatial model grid.The next objective is to combine the VIC-groundwater and the VIC-irrigation version, and drive it with a farmer behaviour model. This integrated model will be applied to study the effect of irrigation on the water and energy balances over Ganges Basin. [ABSTRACT FROM AUTHOR]