Projected impacts of climate change on farmers' extraction of groundwater from crystalline aquifers in South India

Ferrant, Sylvain; Caballero, Yvan; Perrin, José; Gascoin, Simon; Dewandel, Benoı̂t; Aulong, Stéphanie; Dazin, Fabrice; Ahmed, Shakeel; Maréchal, Jean-Christophe

doi:10.1038/srep03697

Cited by 47 publications

(50 citation statements)

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“…In Telangana State, Hyderabad region, the areas of inundated rice fields vary widely with the crop seasons (government statistics and National Remote Sensing Center estimates published on the Bhuvan web portal). Eighty percent of groundwater abstraction is dedicated to rice [15,17]. The growing period is short (three months) and the rice areas represent a small proportion of the total.…”

Section: Introductionmentioning

confidence: 99%

“…Highly appropriate for building a processing chain, it seems suitable for automatically producing agro-hydrological variables at a large scale [23]. The seasonal land covers produced in this study have been used to estimate the seasonal Irrigated Water Demand (IWD), based on irrigation practices observed in previous studies [15,17,18,24].…”

Section: Introductionmentioning

confidence: 99%

“…This methodology, based on an agricultural land use assumed to be fixed, derived from a set of two seasonal land covers (2009)(2010) and allowed the simulation of groundwater fluctuations over a decade. It spatially assessed the groundwater availability and quantified theoretical groundwater shortages, i.e., when the crop water demand based on the satellite monitoring exceeded the groundwater storage [15,17]. These studies quantified the contribution to aquifer recharge of the small surface reservoirs spread along the non-perennial river network that constitutes a rainwater harvesting system maintained by farmers.…”

Section: Introductionmentioning

confidence: 99%

“…The agro-hydrological model SWAT (Soil Water Assessment Tool [20]) was adapted to simulate this daily extraction over several years, together with the aquifer recharge and runoff derived from the climate forcing variables [15,17]. This methodology, based on an agricultural land use assumed to be fixed, derived from a set of two seasonal land covers (2009)(2010) and allowed the simulation of groundwater fluctuations over a decade.…”

Section: Introductionmentioning

confidence: 99%

“…The growing period is short (three months) and the rice areas represent a small proportion of the total. For instance, they have been evaluated at around 10% and 7%, respectively, for the monsoon season (Kharif) 2009 and the dry season (Rabi) 2010 [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India

et al. 2017

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India

et al. 2017

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Quantifying storage changes in regional Great Lakes watersheds using a coupled subsurface‐land surface process model and GRACE, MODIS products

Niu

Shen

et al. 2014

Water Resources Research

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As a direct measure of watershed resilience, watershed storage is important for understanding climate change impacts on water resources. In this paper we quantify water budget components and storage changes for two of the largest watersheds in the State of Michigan, USA (the Grand River and the Saginaw Bay watersheds) using remotely sensed data and a process-based hydrologic model (PAWS) that includes detailed representations of subsurface and land surface processes. Model performance is evaluated using ground-based observations (streamflows, groundwater heads, soil moisture, and soil temperature) as well as satellite-based estimates of evapotranspiration (Moderate-resolution Imaging Spectroradiometer, MODIS) and watershed storage changes (Gravity Recovery and Climate Experiment, GRACE). We use the model to compute annual-average fluxes due to evapotranspiration, surface runoff, recharge and groundwater contribution to streams and analyze the impacts of land use and land cover (LULC) and soil types on annual hydrologic budgets using correlation analysis. Watershed storage changes based on GRACE data and model results showed similar patterns. Storage was dominated by subsurface components and showed an increasing trend over the past decade. This work provides new estimates of water budgets and storage changes in Great Lakes watersheds and the results are expected to aid in the analysis and interpretation of the current trend of declining lake levels, in understanding projected future impacts of climate change as well as in identifying appropriate climate adaptation strategies.

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A modeling study of irrigation effects on global surface water and groundwater resources under a changing climate

Leng

Huang

Tang

et al. 2015

J Adv Model Earth Syst

105

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This study investigates the effects of irrigation on global water resources by performing and analyzing Community Land Model 4.0 (CLM4) simulations driven by downscaled/bias-corrected historical simulations and future projections from five General Circulation Models (GCMs). For each climate scenario, three sets of numerical experiments were performed: (1) a CTRL experiment in which all crops are assumed to be rainfed; (2) an IRRIG experiment in which the irrigation module is activated using surface water (SW) to feed irrigation; and (3) a PUMP experiment in which a groundwater pumping scheme coupled with the irrigation module is activated for conjunctive use of surface water and groundwater (GW) for irrigation. The parameters associated with irrigation and groundwater pumping are calibrated based on a global inventory of census-based water use compiled by the Food and Agricultural Organization (FAO). Our results suggest that irrigation could lead to two major effects: SW (GW) depletion in regions with irrigation primarily fed by SW (GW), respectively. Furthermore, irrigation depending primarily on SW tends to have larger impacts on low-flow than high-flow conditions, suggesting increased vulnerability to drought. By the end of the 21st century, combined effect of increased irrigation water demand and amplified temporal-spatial variability of water supply may lead to severe local water scarcity for irrigation. Regionally, irrigation has the potential to aggravate/alleviate climate-induced changes of SW/GW although such effects are negligible when averaged globally. Our study highlights the need to account for irrigation effects and sources in assessing regional climate change impacts.Correspondence to: M. Huang, maoyi.huang@pnnl.gov Citation:Leng, G., M. Huang, Q. Tang, and L. R. Leung (2015), A modeling study of irrigation effects on global surface water and groundwater resources under a changing climate, J. Adv.

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Projected impacts of climate change on farmers' extraction of groundwater from crystalline aquifers in South India

Cited by 47 publications

References 50 publications

Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India

Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India

Quantifying storage changes in regional Great Lakes watersheds using a coupled subsurface‐land surface process model and GRACE, MODIS products

A modeling study of irrigation effects on global surface water and groundwater resources under a changing climate

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