Indian agriculture relies on monsoon rainfall and irrigation from surface and groundwater. The interannual variability of monsoon rainfalls is high, which forces South Indian farmers to adapt their irrigated areas to local water availability. In this study, we have developed and tested a methodology for monitoring these spatiotemporal variations using Sentinel-1 and -2 observations over the Kudaliar catchment, Telangana State (~1000 km 2 ). These free radar and optical data have been acquired since 2015 on a weekly basis over continental areas, at a high spatial resolution (10-20 m) that is well adapted to the small areas of South Indian field crops. A machine learning algorithm, the Random Forest method, was used over three growing seasons (January to March and July to November 2016 and January to March 2017) to classify small patches of inundated rice paddy, maize, and other irrigated crops, as well as surface water stored in the small reservoirs scattered across the landscape. The crop production comprises only irrigated crops (less than 20% of the areas) during the dry season (Rabi, December to March), to which rain-fed cotton is added to reach 60% of the areas during the monsoon season (Kharif, June to November). Sentinel-1 radar backscatter provides useful observations during the cloudy monsoon season. The lowest irrigated area totals were found during Rabi 2016 and Kharif 2016, accounting for 3.5 and 5% with moderate classification confusion. This confusion decreases with increasing areas of irrigated crops during Rabi 2017. During this season, 16% of rice and 6% of irrigated crops were detected after the exceptional rainfalls observed in September. Surface water in small surface reservoirs reached 3% of the total area, which corresponds to a high value. The use of both Sentinel datasets improves the method accuracy and strengthens our confidence in the resulting maps. This methodology shows the potential of automatically monitoring, in near real time, the high short term variability of irrigated area totals in South India, as a proxy for Remote Sens. 2017, 9, 1119; doi:10.3390/rs9111119 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 1119 2 of 21 estimating irrigated water and groundwater needs. These are estimated over the study period to range from 49.5 ± 0.78 mm (1.5% uncertainty) in Rabi 2016, and 44.9 ± 2.9 mm (6.5% uncertainty) in the Kharif season, to 226.2 ± 5.8 mm (2.5% uncertainty) in Rabi 2017. This variation must be related to groundwater recharge estimates that range from 10 mm to 160 mm·yr −1 in the Hyderabad region. These dynamic agro-hydrological variables estimated from Sentinel remote sensing data are crucial in calibrating runoff, aquifer recharge, water use and evapotranspiration for the spatially distributed agro-hydrological models employed to quantify the impacts of agriculture on water resources.

No abstract

No abstract

Crystalline aquifers are among the most complex groundwater systems, requiring adequate methods for realistic characterization and suitable techniques for improving the long-term management of groundwater resources. A tool is needed that can assess the aquifer hydrodynamic parameters cost-effectively. A model is presented, based on a groundwaterbudget equation and water-table fluctuation method, which combines the upscaling and the regionalization of aquifer parameters, in particular specific yield (S y) in three dimensions (3D) and the recharge in two dimensions (2-D) from rainfall at watershed scale. The tool was tested and validated on the 53-km 2 Maheshwaram watershed, southern India, at a 685 m × 685 m cell scale, and was calibrated on seasonal groundwater levels from 2011 to 2016. Comparison between computed and observed levels shows an absolute residual mean and a root mean square error of 1.17 m and 1.8 m, respectively, showing the robustness of the model. S y ranges from 0.3 to 5% (mean 1.4%), which is in good agreement with previous studies. The annual recharge from rainfall is also in good agreement with earlier studies and, despite its strong annual variability (16 to 199 mm/y) at watershed scale, it shows that spatial recharge is clearly controlled by spatial structure, from one year to another. Groundwater levels were also forecasted from 2020 to 2039 based on the climate and groundwater abstraction scenarios. The results show severe water-level depletion around 2024-2026 but it would be more stable in the future (after 2030) because of a lower frequency of low-rainfall monsoons.

Indian Rainwater Harvesting System (RHS) is an essential source of irrigation water in upstream agricultural areas. It is composed of hundreds of thousands of Small Reservoirs (SR) often disconnected from any perennial rivers. This study aims at quantifying the RHS Maximum Water Storage Capacity (MWSC) in the Telangana State, South India. The true bathymetries of 545 dry SR, located from Sentinel-2 (SENT-MWAE) and Landsat Maximal Water Area Extent (GSW-MWAE), are extracted from four Very High Resolution (VHR) Pleiades Digital Elevation Model (DEM). The average water depth at full capacity ranges from 22 cm to 4.6 m (average 1.3 m, std 0.6). The MWSC estimated within the Pleiades ground-coverage, for 62% of the total SR, accounts for 37.2 mm on average. The estimated capacity highly depends on the MWAE data source, varying from 5 to 30%. The Telangana RHS MWSC based on the RHS GSW-MWAE (1.6% of the Telangana area) is estimated at 29.7 mm +/-9 mm. This capacity seems small compared to the large dam capacity (113mm for 126 registered dams in Telangana), but matters in upstream areas, far from irrigated command areas, to complement local groundwater pumping (from 62 to 295 mm). These preliminary results show the high interest of VHR DEM to evaluate uncertainties derived from MWAE products and medium to high resolution DEM to map water storage in RHS.

Core Ideas Long‐term observatories allow the study of global changes to water resources in India. Crystalline rock aquifers are highly heterogeneous. Management of crystalline aquifers necessitates solving several scientific questions. A multidisciplinary approach is necessary for improving water management in India. Multiscale and long‐term work is needed to tackle the scientific challenges found in areas vulnerable to climate change and anthropic pressure. This is the case in the semiarid and drought‐prone regions of southern India where freshwater is scarce and agriculture near fast‐growing cities is triggering high water demand. The Indo‐French Center for Groundwater Research (IFCGR) was established in 1999 between the Indian National Geophysical Research Institute (NGRI) and the French Geological Survey (BRGM) at the NGRI campus in Hyderabad, India. For almost 20 yr, the IFCGR has studied the hydrodynamic properties and associated hydrological processes in crystalline aquifers. To that end, the Center set up two sites for observing groundwater in crystalline rock aquifers: (i) the Maheshwaram basin for the study of groundwater management at catchment scale, and (ii) the Choutuppal experimental site for the detailed study of hydrogeological processes at local scale (between wells). Multiscale approaches allow the characterization of hydrodynamic and transport properties of the shallow weathered part of such crystalline aquifers and the implications for groundwater management under overexploitation conditions. The objective is to provide suitable characterization of aquifer properties for developing modeling and management tools applicable to such heterogeneous aquifers.

Indian agriculture relies on monsoon rainfall and irrigation from surface and groundwater. The inter-annual variability of monsoon rainfalls is high, which forces South Indian farmers to adapt their irrigated area extents to local water availability. We are developing and testing an automatic methodology for monitoring spatio-temporal variations of irrigated crops in near real time based on Sentinel-1 and -2 data feed over the Telangana State, South India. These freely available radar and optical data are systematically acquired worldwide, over India since 2016, on a weekly basis. Their high spatial resolution (10-20 m) are well adapted to the small size field crops that is common in India. We have focused first on drought prone areas, North of Hyderabad. Crop fraction remains low and varies widely (from 10 to 60%, ISRO-NRSC, Bhuvan). Those upstream areas, mainly irrigated with groundwater, are composed by less than 20% of irrigated areas during the dry season (Rabi, December to March) and up to 60% of the surface is used for crop production during the Kharif (June to November), which includes rainfed cotton and drip irrigated maize crops and inundated rice. A machine learning algorithm, the Random Forest (RF) method, was automatically used over 6 growing seasons ) from the Sentinel-1&2 data stacked for each season, to create crop mapping at 10m resolution over a study area located in the north of Hyderabad (210 by110km). Six seasonal land cover field surveys were used to train and validate the classifier, with a specific effort on rice and maize field sampling. The lowest irrigated area extents were found for driest conditions in Rabi 2016 and Kharif 2016, accounting for 3.5 and 5% with moderate classification confusion. This confusion decreases with the increase of irrigated crops areas during Rabi 2017. For this season, 22% of rice and 9% of irrigated crops were detected after heavy rainfall events in September 2017, which have filled surface water tanks (3.4% of the surface area) and groundwater (Central Groundwater Board observations). From this abundance situation, the surface water detected for each season decreased regularly to less than 0.3% together with the rice and irrigated area extents respectively from 22 to 11% and 10 to 3%, despite a good monsoon 2017. Groundwater level show similar trends, with a drop from 20 meters depth in October 2016 and 2017 to more than 30 m in June 2018 (more recent available data). The deficit of the monsoon 2018 will certainly bring this situation to a hydrological drought at the beginning of 2019, probably similar to the Rabi 2016 situation. The estimated Irrigated Water Demand (IWD) varies from 51 to 310 mm/season, depending on water availability. This methodology shows the potential of automatically monitoring, in near real time, with standard computers, irrigated area extents presenting fast high resolution variability. As it is based on standard global satellite acquisitions, it is foreseen to be used for other regions, for any studies on farmer's adaptation to cli...

Heat as a tracer in fractured porous aquifers is more sensitive to fracture-matrix processes than a solute tracer. Temperature evolution as a function of time can be used to differentiate fracture and matrix characteristics. Experimental hot (50 • C) and cold (10 • C) water injections were performed in a weathered and fractured granite aquifer where the natural background temperature is 30 • C. The tailing of the hot and cold breakthrough curves, observed under different hydraulic conditions, was characterized in a log-log plot of time vs. normalized temperature difference, also converted to a residence time distribution (normalized). Dimensionless tail slopes close to 1.5 were observed for hot and cold breakthrough curves, compared to solute tracer tests showing slopes between 2 and 3. This stronger thermal diffusive behavior is explained by heat conduction. Using a process-based numerical model, the impact of heat conduction toward and from the porous rock matrix on groundwater heat transport was explored. Fracture aperture was adjusted depending on the actual hydraulic conditions. Water density and viscosity were considered temperature dependent. The model simulated the increase or reduction of the energy level in the fracture-matrix system and satisfactorily reproduced breakthrough curves tail slopes. This study shows the feasibility and utility of cold water tracer tests in hot fractured aquifers to boost and characterize the thermal matrix diffusion from the matrix toward the flowing groundwater in the fractures. This can be used as complementary information to solute tracer tests that are largely influenced by strong advection in the fractures.