Assessing water availability in a semi-arid watershed of southern India using a semi-distributed model

Perrin, José; Ferrant, Sylvain; Massuel, Sylvain; Dewandel, Benoı̂t; Maréchal, Jean-Christophe; Aulong, Stéphanie; Ahmed, Shakeel

doi:10.1016/j.jhydrol.2012.07.002

Cited by 97 publications

(94 citation statements)

References 33 publications

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“…Groundwater fluctuations are also strongly impacted locally by water extraction for agriculture. The best water extraction proxy is the extent of seasonal irrigated areas located around wells [15].…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…The quantification of irrigated and inundated areas from optical high resolution spatial remote sensing (SPOT, from French spatial agency CNES; ResourceSat from Indian Space Agency ISRO) in 2009-2010 provided spatial estimates of seasonal groundwater extraction for irrigation, using the daily irrigation practices associated with each seasonal crop type [15,19]. 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].…”

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%

See 3 more Smart Citations

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%

See 2 more Smart Citations

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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“…Foremost are the models SCS (Michaud and Sorooshian, 1994;Symeonakis and Drake, 2004), VIC (Abdulla and Lettenmaier, 1997;Stephen et al, 2010), KINEROS (Michaud and Sorooshian, 1994;Hernandez et al, 2000;Al-Qurashi et al, 2008), TOPMODEL (Candela et al, 2005), MIKE SHE (McMichael et al, 2006), CRUM (Reaney et al, 2007), HBV (Masih et al, 2010;Jia and Sun, 2012), IHACRES (Jakeman et al, 1990;Jakeman and Hornberger, 1993;Evans and Jakeman, 1998;Dye and Croke, 2003;Croke et al, 2006;McIntyre and Al-Qurashi, 2009), HEC-HMS (Halwatura and Najim, 2013), and SWAT (Hernandez et al, 2000;Yu et al, 2011;Perrin et al, 2012). Some of these, however, share certain problems that reduce their effectiveness in semiarid regions.…”

Section: Introductionmentioning

confidence: 99%

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

Jiang

Liu

et al. 2015

Environmental Modelling & Software

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a b s t r a c tWe present a study on the Hydro-Informatic Modelling System (HIMS) rainfall-runoff model for a semiarid region. The model includes nine parameters in need of calibration. A master-slave swarms, shuffling evolution algorithm based on self-adaptive dynamic particle swarm optimization (MSSE-SDPSO) is proposed to derive model parameters. In comparison with SCE-UA, PSO, MSSE-PSO and MSSE-SPSO algorithms, MSSE-SDPSO has faster convergence and more stable performance. The model is used to simulate discharge in the Luanhe River basin, a semiarid region. Compared with the SimHyd and SMAR models, HIMS model has the highest Nash-Sutcliffe efficiencies (NSE) and smallest relative errors (RE) of volumetric fitness for the periods of calibration and verification. In addition, the studies indicate that the HIMS model with all-gauge data improves runoff prediction compared with single-gauge data. A distributed HIMS model performs better than a lumped one. Finally, the Morris method is used to analyze model parameters sensitivity for the objective functions NSE and RE.

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Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions

et al. 2018

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Ecohydrology combines empiricism, data analytics, and the integration of models to characterize linkages between ecological and hydrological processes. A challenge for practitioners is determining which models best generalizes heterogeneity in hydrological behaviour, including water fluxes across spatial and temporal scales, integrating environmental and socio‐economic activities to determine best watershed management practices and data requirements. We conducted a literature review and synthesis of hydrologic, hydraulic, water quality, and ecological models designed for solving interdisciplinary questions. We reviewed 1,275 papers and identified 178 models that have the capacity to answer an array of research questions about ecohydrology or ecohydraulics. Of these models, 43 were commonly applied due to their versatility, accessibility, user‐friendliness, and excellent user‐support. Forty‐one of 43 reviewed models were linked to at least 1 other model especially: Water Quality Analysis Simulation Program (linked to 21 other models), Soil and Water Assessment Tool (19), and Hydrologic Engineering Center's River Analysis System (15). However, model integration was still relatively infrequent. There was substantial variation in model applications, possibly an artefact of the regional focus of research questions, simplicity of use, quality of user‐support efforts, or a limited understanding of model applicability. Simply increasing the interoperability of model platforms, transformation of models to user‐friendly forms, increasing user‐support, defining the reliability and risk associated with model results, and increasing awareness of model applicability may promote increased use of models across subdisciplines. Nonetheless, the current availability of models allows an array of interdisciplinary questions to be addressed, and model choice relates to several factors including research objective, model complexity, ability to link to other models, and interface choice.

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Assessing water availability in a semi-arid watershed of southern India using a semi-distributed model

Cited by 97 publications

References 33 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

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions

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