Quantifying groundwater sensitivity and resilience over peninsular India

Kumar, Nikhil; Sinha, Jhilam; Madramootoo, Chandra A.; Goyal, Manish Kumar

doi:10.1002/hyp.13945

Cited by 9 publications

(6 citation statements)

References 55 publications

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“…The NSE statistics has been used extensively for defining model performance 39 and therefore enables us to compare our results with earlier studies. For abcd model of 54 catchments, the calibration is carried out in previous study 40 and results are discussed here briefly. In abcd model, 81.5% of the catchments show more than bad performance limit (NSE > 0.50) in calibration period, however only 60% of catchments performed above this range (Fig.…”

Section: Resultsmentioning

confidence: 99%

Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India

Kumar

Patel

Singh

et al. 2023

Sci Rep

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Climate change significantly impacts the global hydrological cycle, leading to pronounced shifts in hydroclimatic extremes such as increased duration, occurrence, and intensity. Despite these significant changes, our understanding of hydroclimatic risks and hydrological resilience remains limited, particularly at the catchment scale in peninsular India. This study aims to address this gap by examining hydroclimatic extremes and resilience in 54 peninsular catchments from 1988 to 2011. We initially assess extreme precipitation and discharge indices and estimate design return levels using non-stationary Generalized Extreme Value (GEV) models that use global climate modes (ENSO, IOD, and AMO) as covariates. Further, hydrological resilience is evaluated using a convex model that inputs simulated discharge from the best hydrological model among SVM, RVM, random forest, and a conceptual model (abcd). Our analysis shows that the spatial patterns of mean extreme precipitation indices (R1 and R5) mostly resemble with extreme discharge indices (Q1 and Q5). Additionally, all extreme indices, including R1, Q1, R5, and Q5, demonstrate non-stationary behavior, indicating the substantial influence of global climate modes on extreme precipitation and flooding across the catchments. Our results indicate that the random forest model outperforms the others. Furthermore, we find that 68.52% of the catchments exhibit low to moderate hydrological resilience. Our findings emphasize the importance of understanding hydroclimatic risks and catchment resilience for accurate climate change impact predictions and effective adaptation strategies.

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Section: Resultsmentioning

confidence: 99%

Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India

Kumar

Patel

Singh

et al. 2023

Sci Rep

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“…Recent years have noticed a significant increase in the number of rain gauge stations, especially during the period between 2008 and 2010, where 1,350 automatic rain gauge stations were installed by IMD. This dataset has been used in a wide variety of studies including many evaluations, hydrological modelling as well as ecological studies (Himanshu et al ., 2018; Sharma and Goyal, 2018; Kumar et al ., 2020; Das et al ., 2020; Jha et al ., 2019).…”

Section: Dataset and Methodsmentioning

confidence: 99%

Evaluation of precipitation datasets available on Google earth engine over India

Dubey

Gupta

Goyal

et al. 2021

Intl Journal of Climatology

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Monthly mean precipitation estimates of seven products (TerraClimate, TRMM, CHIRPS, PERSIANN‐CDR, GPM‐IMERG, ERA5 and CFSR) available on Google earth engine (GEE) are evaluated against gridded gauge‐based precipitation product available from Indian Meteorological Department (IMD) for their skills and presence of systematic biases (during 2001–2018). All these products represent the climatological features reasonably well. Presence of systematic biases in these products is also observed from their evaluation. Biases across the periphery of the country are relatively on the higher side in comparison to the central regions. The magnitude of spatial variability is represented better for winter precipitation in comparison to summer precipitation. During both winter and summer, ensemble mean of various products outperforms individual products in terms of both RMSE and correlation. Performance of these products is also assessed across various Indian states, elevation bands and climate zones. The ability of these products to represent the seasonality was observed to be highest for the states with mid‐ranged peaks (10–20 mm·day−1) which tend to decrease with both increasing and decreasing peaks. Ability of the precipitation products to resemble the annual cycle does not vary with the amount of precipitation, although individual disparity among the products exists. Additionally, an alternative approach for data evaluation using Multiple Triple Collocation (MTC) was performed for the period 2001–2015 using an additional dataset obtained from soil‐moisture‐based rainfall estimates (SM2RAIN). Results from MTC convey that ERA5 performs relatively poor in comparison to the other products for central India followed by CFSR. In brief, the comprehensive evaluation of precipitation products reported herein will act a valuable reference for the researchers as well as decision makers to select the optimal product for their intended application and will inform the users about the various uncertainties in the foundations and specification of these products.

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“…distance to springs, elevation difference from springs, slope, lithology, fracture density, and fracture length density, and (2) an interpolation of VES curves after scoring them. [53] Peninsular India GIS, conceptual water balance model, a convex model

[37] Hotan River Basin, China GIS, Dempster-Shafer theory

[88] Durmitor, Montenegro GIS

[74] Groundwater storage Several aquifers in the world GRACE Determining the excess of water consumption from the available renewable water in the aquifer, renewable groundwater stress ratio was employed as a criterion for groundwater resilience appraisal. [64] , [65] Several aquifers in the world GRACE Resilience equals number of times a satisfactory condition follows an unsatisfactory condition per total number of unsatisfactory conditions [78] Central Valley, California [77] …”

Section: Groundwater Resilience Evaluationsmentioning

confidence: 99%

“…The same equations were used to compute groundwater resilience over peninsular India [37] and Hotan River Basin, China [88] . The considered method simulated groundwater resilience over the region auspiciously.…”

Section: Groundwater Resilience Evaluationsmentioning

confidence: 99%

“…With regard to climate change, resilience is the capability of the system to absorb turbulence, maintain similar functioning, and modify the external changes [32] . In fact, resilience is either the system's ability to buffer the long-term pressures or the recovery time of the system subsequent to a failure [25] , [37] , [57] . In other words, resilience is the system's ability to withstand a continuous variable environment, and absorb or adapt to recurring exterior stresses without any major changes in the system's operation [46] , [18] , [19] .…”

Section: Introductionmentioning

confidence: 99%

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