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Recent advancements of space geodetic observations and remote sensing techniques allow to extract hydrological information reasonably well at the river basin scale. Combining this information with in-situ data provide a new opportunity to explore the feasibility of assessing hydrological models at the basin scale. Appreciating such possibilities, a group of researchers from the different institutions in India, has developed a project that envisioned to quantify each component of hydrological cycle at the basin scale and also planned to investigate crustal deformations due to the hydrological changes. The project was financially supported by National Geospatial Programme, Department of Science and Technology, India. A precise assessment of Terrestrial Water Storage (TWS), groundwater storage, soil moisture, evapotranspiration, severity of drought and hot –spells at basin scale and downscaling of the GRACE derived TWS to the sub-basin scale are carried out under this project. Several GPS stations co-located with soil moisture probes are installed in the Ganga river basin to validate and improve the hydrological models. It is planned to present the results over three major river basins of India studied under this project., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Transit time and water age characteristics are fundamental descriptors of catchment response, and their determination is vital for the implementation of sustainable strategies for managing nutrients and other contaminants in water environments – especially for groundwater where the deeper stores take decades to flush the dissolved solutes. The deterministic transit time models can be broadly categorized into 2 sorts – lumped models based on conceptual parameters and distributed models based on physical and quantifiable hydrodynamic parameters. Due to their simplicity, applicability and flexibility, lumped conceptual models are thus far widely and successfully used in modelling the groundwater flow, transport, and transit time of solutes. Usually, a bunch of parallel hydrological response units work in harmony to model the desired hydrological and solute concentration time-series. But sole reliance on calibration, non-scalability, leveraging on hydrologically passive mixing volumes, lack of forward modelling potential and ineffective scrutiny of the physical basis of the parameters of these conceptual models often generate skepticism in the research community. To address this issue, we devised a technique to determine the physical basis of these conceptual reservoirs, and to establish a mathematical connection between physical hydrodynamic parameters and lumped conceptual parameters. A lumped groundwater nitrate transit time model composed of two parallel stores (slow and fast) was previously calibrated (using GLUE) to generate the time series of baseflow and nitrate concentration time series in a groundwater dominated agricultural catchment in France. In this study, we generated synthetic 2D Dupuit-Forchheimer unconfined aquifers using a standard finite element code (FEFLOW 7.5) to replicate outputs of the lumped model. Furthermore, sensitivity tests were performed on these synthetic catchments and overall, a clear mathematical connection between physical and conceptual parameters was demonstrated. It was further observed that the difference between fast and slow stores can be explained using dual porosity – with drainable porosity affecting recession, and immobile porosity affecting the size of hydrologically passive mixing volumes. The spatial mean of the age distributions, the mean transit time and the half nitrate recovery time agreed with each other for both stores. Further sensitivity tests showed that lumped conceptual stores individually cannot acknowledge dispersivity – the difference in attenuation of different stores, in unison, produce a pseudo-dispersive behavior. Also, being purely depth-based, there is a scale issue in lumped models – an erroneous input of catchment dimension can yield the identical results for a completely different set of hydrodynamic parameters leading to equifinality. Therefore, transit times should always be normalized by catchment scale while cross-comparing catchments using lumped models. These finding can help reduce calibration reliance of lumped models, providing options to investigate parameter effectiveness, and offers these models a forward modelling potential which can be used to calculate the flow and transport behavior of catchments that lack long term observed time series but have proper measurements of hydrodynamic properties.

Processes controlling surface-groundwater interaction in terms of recharge and baseflow have been a topic of pursuit in the hydrological research community. The groundwater recharge and baseflow in hard-rock aquifers is significantly impacted by rainfall pattern, aquifer characteristics, weathering/soil condition, topography, land use and land cover. Analysis of the recharge and baseflow process in tropical semi-arid hard-rock aquifer regions of southern India is crucial due to heavily tailed monsoon system prevailing in the region, heterogeneity of aquifers in terms of fractures and lineaments and presence of several man-made irrigation tanks along with the drainage network. Process uncertainties in groundwater recharge simulation due to impact of climate change on vegetation and resulting changes in evapotranspiration can significantly impact groundwater projections. Poor representation of diffused and focused recharge pathways and inadequacy in capturing the feedback among climate, land use and groundwater systems are other factors introducing uncertainties. Finally, the gaps in long-term observational data make it challenging to assess the impact of climate change. A taluk scale study conducted in Karnataka State of India indicated a significant correlation between the rainfall intensity distribution and climatology on groundwater recharge in Hard-rock aquifers. The current study targets to add a baseflow component represented by a 2D groundwater model and extend the previous study to a larger scale. The primary objectives of the study are to estimate the long-term groundwater recharge and baseflow trends and evaluate their association with rainfall and climatological variability. As the projected climate scenarios reflect higher frequency of high-intensity rainfall, it becomes essential to evaluate the impacts of varying rainfall patterns on the surface-subsurface processes.

This study explores whether incorporating a more sophisticated representation of groundwater, and human-groundwater interactions, improves predictive capability in a large-scale water resource model. The Global Water Availability Assessment model (GWAVA) is developed to include a simple layered aquifer and associated fluxes (GWAVA-GW), and applied to the Cauvery river basin in India, a large, human-impacted basin with a high dependence on groundwater. GWAVA-GW shows good predictive skill for streamflow upstream of the Mettur dam: Kling-Gupta Efficiency ≥ 0.3 for 91% of subcatchments, and improved model skill for streamflow prediction compared to GWAVA over the majority of the basin. GWAVA-GW shows some level of predictive skill for groundwater levels over seasonal and long-term time scales, with a tendency to overestimate depth to groundwater in areas with high levels of groundwater pumping. Overall, GWAVA-GW is a useful tool when assessing water resources at a basin scale, especially in areas that rely on groundwater.

Among the growing population (136.64 crs as per2019) many people experience scarcity of food and all. In the 2020 Global Hunger Index, India ranks 94th out of the 107 countries with sufficient data to calculate 2020 GHI scores. With a score of 27.2, India has a level of hunger that is serious (www.un.org/). The United Nations (UN) Food and Agriculture Organization (FAO) predicts that the on-going COVID-19 pandemic will increase this number as developing countries are double-hit by disease and hunger (www.fao.org/2019-ncov/q-and-a/) Disruptions in global supply chains, economic consequences (i.e., loss of jobs and incomes), the ban on the export of agricultural commodities, and price increases are the major reasons for this crisis. Although much attention is being given to the development of vaccines, therapeutic molecules, and preventive measures to combat COVID-19, the invisible threat to the lives and livelihoods of marginal populations through hunger and malnutrition remains largely unaddressed. The focus of the 2019 Global hunger index on ‘The Challenge of Hunger and Climate Change’ underlines the impacts of changing climates on agriculture that include crop failures owing to problems such as seasonal fluctuations, increased insect and pest attacks, and broad-spectrum infection by potential pathogens (www.globalhungerindex.org/).Supplying food grains is an immediate measure to aid the affected population, whereas devising long-term plans to prevent such challenges is the need of the hour. That said, the possibility of a second and third wave of COVID-19 in the near future should not be ignored. In such a case, the UN World Food Programme predicts (UN-WFP; https://insight.wfp.org/) that death due to lack of food would outnumber deaths caused by disease infection. The importance of crop diversity and of mainstreaming underutilized crops that could serve as functional foods has been pointed out before; however, identifying the best candidates of underutilized crops and deploying crop improvement strategies to release better varieties is still in a nascent stage. Mayes et al. Other plant species, including tubers, legumes, and leafy vegetables, also fall within the criteria of underutilized species; however, emphasis is given to small millets because they are capable of reducing the overdependence on major cereals. Three major cereals, namely rice, wheat, and maize, cater for up to 60% of the global food requirements, and this is one of the plausible causes of food and nutritional inadequacies in the hunger hotspots where these crops are largely imported for consumption. Millets, although cultivated marginally in those regions, have the potential to address these inadequacies if their area of cultivation is increased and crop improvement strategies are devised and deployed. View Article DOI: 10.47856/ijaast.2021.v08i9.018

Assessment of consumptive water use (CWU) and water productivity at the regional scale is important to diagnose vulnerable zones to improve water use efficiencies in cropland to achieve the sustain...

International audience; Abstract. Quantification of root-zone soil moisture (RZSM) is crucial for agricultural applications and the soil sciences. RZSM impacts processes such as vegetation transpiration and water percolation. Surface soil moisture (SSM) can be assessed through active and passive microwave remote-sensing methods, but no current sensor enables direct RZSM retrieval. Spatial maps of RZSM can be retrieved via proxy observations (vegetation stress, water storage change and surface soil moisture) or via land surface model predictions. In this study, we investigated the combination of surface soil moisture information with process-related inferred features involving artificial neural networks (ANNs). We considered the infiltration process through the soil water index (SWI) computed with a recursive exponential filter and the evaporation process through the evaporation efficiency computed based on a Moderate Resolution Imaging Spectroradiometer (MODIS) remote-sensing dataset and a simplified analytical model, while vegetation growth was not modeled and was only inferred through normalized difference vegetation index (NDVI) time series. Several ANN models with different sets of features were developed. Training was conducted considering in situ stations distributed in several areas worldwide characterized by different soil and climate patterns of the International Soil Moisture Network (ISMN), and testing was applied to stations of the same data-hosting facility. The results indicate that the integration of process-related features into ANN models increased the overall performance over the reference model level in which only SSM features were considered. In arid and semiarid areas, for instance, performance enhancement was observed when the evaporation efficiency was integrated into the ANN models. To assess the robustness of the approach, the trained models were applied to observation sites in Tunisia, Italy and southern India that are not part of the ISMN. The results reveal that joint use of surface soil moisture, evaporation efficiency, NDVI and recursive exponential filter represented the best alternative for more accurate predictions in the case of Tunisia, where the mean correlation of the predicted RZSM based on SSM only sharply increased from 0.443 to 0.801 when process-related features were integrated into the ANN models in addition to SSM. However, process-related features have no to little added value in temperate to tropical conditions.

In the last 15 years, the growth in the modern wireless communication systems has motivated enlarged exploration on inexpensive circularly polarized dual band slot antennas. A simple circularly pol...

Understanding the water metabolism of developing country cities is challenging because of insufficient knowledge of how social, infrastructural and hydrological dimensions are coupled. Using Bengal...

Central India faces a freshwater shortage due to its diverse terrain, sudden change in precipitation patterns and crystalline rock covered subsurface. Here, we investigate the patterns in terrestrial water storage anomaly (TWSA) over the last two decades, and also study the influence of the COVID-19 lockdown on TWSA in the drought-prone regions of central India, mostly covering the Vidarbha region of the Indian state of Maharashtra. The Vidarbha region is arguably the most drought-affected region in terms of farmer suicides due to crop failure. Our forecast data using multiple statistical approaches show a net TWSA rise in the order of 3.65 to 19.32 km3 in the study area in May 2020. A short-term rise in TWSA in April–May of 2020 is associated with lockdown influenced human activity reduction. A long-term rise in TWSA has been observed in the study region in recent years; the rising TWSA trend is not directly associated with precipitation patterns, rather it may be attributed to the implementation of water management policies.

Wildfires burn 4 % of the Earth’s vegetated land surface annually and thus are key to global biochemical cycles such as the terrestrial organic carbon (OC) cycle. The incomplete combustion of biomass during fires produces a thermochemical-altered and environmentally more persistent form of OC than its unburnt precursor material, namely pyrogenic carbon (PyC). PyC accounts for one of the largest and oldest terrestrial OC pool and constitutes substantial parts of the total soil organic carbon (SOC) pool. Tropical ecosystems are a key component of the terrestrial OC cycle, with highest annual primary production and largest pool of OC in vegetation biomass; however, these ecosystems are prone to disturbances such as global warming, land use changes or wildfires. In addition, tropical soils store up to one third of the terrestrial OC, and substantial amounts are held in deeper soil horizons (>30cm). However, our understanding of the quantities, ages and dominant processes of PyC (and SOC) cycling in tropical soils and in the landscape (e.g. watersheds) are still limited.We sampled soils (0-60 cm depth) in tropical semi-arid watersheds under shrubland (Maddur and South Gundal watershed) and an experimental, sub-humid watershed under dry deciduous forest (Mule Hole Critical Zone observatory) in southwestern Karnataka, India. We included prevalent soil types (Lixisol, Vertisol and Ferralsol) under different geology in order to determine PyC (and SOC) stocks and radiocarbon ages. Along soil cores, we sampled bulk density cores to calculate accurate PyC (and SOC) stocks, and used the chemo-thermal oxidation (PyCCTO) and hydrogen pyrolysis (PyCHyPy) methods to separate the PyC. The bulk SOC and the PyCHyPy were further radiocarbon dated. Our aim was to quantify PyC (and SOC) stocks and identify major processes of PyC (and SOC) in prevalent soil types under shrubland and forest and in the landscape of in tropical watersheds.SOC stocks were lowest for Lixisols (58-98 Mg ha-1), intermediate for Ferralsols (85-129 Mg ha-1) and highest for Vertisols (118-169 Mg ha-1), and all soils stored substantial proportions of OC in subsoils (up to 40 % below 30cm depth). Radiocarbon data of bulk SOC indicates fast turnover of OC in these tropical topsoils (modern ages in 0-15cm for shrublands and forests: between 99.7 to 107.0 % modern 14C (± 0.5 %)); however, the 14C of bulk SOC shows much older ages in subsoils (>40cm), from 1530 to 8490 calibrated radiocarbon years BP in Ferralsols and from 1155 to 8125 calibrated radiocarbon years BP in Vertisols with increasing depth.PyCCTO stocks accounted for 0.3-0.9 Mg ha-1 (Lixisols), 0.4-0.7 Mg ha-1 (Ferralsol) and 0.9-1.9 Mg ha-1 (Vertisol). This means that PyCCTO represented approximately 1.1-3.4 % of SOC in the topsoil (0-15 cm) and increased with depth to 2.7-5.6 % in 45-60 cm depth on average over all sites. Conclusively, PyC can make up a substantial proportion of the SOC in tropical soils, and it may represent one of the oldest OC pools of bulk SOC.