Probabilistic evaluation of vegetation drought likelihood and its implications to resilience across India

Jha, Srinidhi; Das, Jew; Sharma, Ashutosh; Hazra, Budhaditya; Goyal, Manish Kumar

doi:10.1016/j.gloplacha.2019.01.014

Cited by 77 publications

(32 citation statements)

References 79 publications

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“…Areas of Indo-Gangetic plains, north-western regions and upper peninsular India were found to be particularly affected in changing climate scenarios. Majority of river basins in the south India have been found to be non-resilient to vegetation drought by Jha et al (2019) and since propagation of meteorological drought to vegetation and hydrological drought is well documented, the non-resilience to worsening meteorological drought for these river basins may point to low resilience values for vegetation drought. However for river basins which have large anthropogenic alterations in the form of irrigation, the propagation of drought from meteorological to vegetation needs to be further analysed.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Jha et al . (2019) used copula based probabilistic model to understand the resilience of Indian river basins to vegetation drought. The understanding of meteorological drought is important to understand the propagation of drought through the hydrological cycle since deficits in rainfall usually manifest as droughts in vegetation and hydrological sectors.…”

Section: Introductionmentioning

confidence: 99%

“…Thus it becomes important to attribute special emphasis on these regions for drought frequency analysis and ascertain whether they are less resilient to exacerbating drought conditions than the rest of the regions. Recently, Jha et al (2019) used copula based probabilistic model to understand the resilience of Indian river basins to vegetation drought. The understanding of meteorological drought is important to understand the propagation of drought through the hydrological cycle since deficits in rainfall usually manifest as droughts in vegetation and hydrological sectors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Copula based analysis of meteorological drought and catchment resilience across Indian river basins

Jha

Gujrati

Singh

2020

Intl Journal of Climatology

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Drought analysis is an important part of risk management plan. Drought is usually characterized by variables such as severity and duration. Using standardized precipitation index (SPI) at an aggregated scale of 12 months, we construct different copula models for different river basins of India. Based on goodness of fit tests, suitable distributions are selected for duration and severity. These marginal distributions are then used to construct copula models from amongst-Frank, Gumbel, Clayton and Student's t copula. It is found that for some river basins Frank copula can capture the dependence structure between duration and severity whereas for others Gumbel copula is effective. Exceedance probability, conditional probability and joint return period of different drought events are calculated which allude to differing drought resilience and persistence conditions in river basins. The river basins in the western India have a longer joint return period and smaller exceedance probability compared to the river basins in south India. We explore the conjunctive use for joint return period and exceedance probability to qualitatively assess the resilience of river basins to meteorological drought. The results suggest that the drought events in the south Indian river basins are less severe and more frequent whereas the ones in Central and Western India are severe and longer. The results of this study can provide useful information for drought mitigation strategies at a national scale.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Copula based analysis of meteorological drought and catchment resilience across Indian river basins

Jha

Gujrati

Singh

2020

Intl Journal of Climatology

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“…Moreover, this dataset precisely captures the spatio‐temporal distribution of Indian monsoon and extreme precipitation regions (such as Western Ghats and north‐east). Because of these attributes, IMD4 dataset has been used in many recent studies (Jha, Das, Sharma, Hazra, & Goyal, 2019; Sharma & Goyal, 2020; Sinha, Sharma, Khan, & Goyal, 2018; Vezhapparambu, Madhusoodanan, Sharma, & Ramesh, 2020). Here, we use the potential evapotranspiration (PE) dataset by Climate Research Unit (CRU), that is, CRU TS v. 4.01(Harris, Jones, Osborn, & Lister, 2014).…”

Section: Methodsmentioning

confidence: 99%

“…dataset has been used in many recent studies (Jha, Das, Sharma, Hazra, & Goyal, 2019;Sharma & Goyal, 2020;Sinha, Sharma, Khan, & Goyal, 2018;Vezhapparambu, Madhusoodanan, Sharma, & Ramesh, 2020). Here, we use the potential evapotranspiration (PE) dataset by Climate Research Unit (CRU), that is, CRU TS v. 4.01 (Harris, Jones, Osborn, & Lister, 2014).…”

Section: Datasetsmentioning

confidence: 99%

Quantifying groundwater sensitivity and resilience over peninsular India

Kumar

Sinha

Madramootoo

et al. 2020

Hydrological Processes

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Groundwater in India plays an important role to support livelihoods and maintain ecosystems and the present rate of depletion of groundwater resources poses a serious threat to water security. Yet, the sensitivity of the hydrological processes governing groundwater recharge to climate variability remains unclear in the region. Here we assess the groundwater sensitivity (precipitation-recharge relationship) and its potential resilience towards climatic variability over peninsular India using a conceptual water balance model and a convex model, respectively in 54 catchments over peninsular India. Based on the model performance using a comprehensive approach (Nash Sutcliffe Efficiency [NSE], bias and variability), 24 out of 54 catchments are selected for assessment of groundwater sensitivity and its resilience. Further, a systematic approach is used to understand the changes in resilience on a temporal scale based upon the convex model and principle of critical slowing down theory. The results of the study indicate that the catchments with higher mean groundwater sensitivity (GWS) encompass high variability in GWS over the period (1988-2011), thus indicating the associated vulnerability towards hydroclimatic disturbances. Moreover, it was found that the catchments pertaining to a lower magnitude of mean resilience index incorporates a high variability in resilience index over the period (1993-2007), clearly illustrating the inherent vulnerability of these catchments. The resilience of groundwater towards climatic variability and hydroclimatic disturbances that is revealed by groundwater sensitivity is essential to understand the future impacts of changing climate on groundwater and can further facilitate effective adaptation strategies.

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A methodological framework for extreme climate risk assessment integrating satellite and location based data sets in intelligent systems

Jha

Goyal

Gupta

et al. 2021

Int J of Intelligent Sys

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Adaptation and resilience practitioners lack guidance on how to understand and manage extreme climate risk using the data available. We present a methodological framework to integrate the satellite as well as location based data sets to estimate extreme climate risk. The framework, in detail, has been demonstration using a study carried out to quantify extreme rainfall risks in India incorporating the influence of global (large scale oscillations) as well as local factors (population, infrastructure, economic activity) in a probabilistic model. We use nonstationary extreme value theory along with Bayesian uncertainty analysis to model the time varying influence of oscillations such as El Niño/Southern Oscillation, Indian Ocean Dipole, and North Atlantic Oscillation in augmenting high rainfall risks in 637 districts across 29 states of India. It is found that at least 50% of the districts in 8 out of 29 states are at high risk. Extreme risk is observed in 198 (~31%) and 249 (~39%) districts caused by heavy downpour and extremely long wet spells, respectively. This study provides a framework to identify local implications of global factors and is aimed at supporting policy makers in framing extreme rainfall‐induced disaster risk reduction strategies.

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Probabilistic evaluation of vegetation drought likelihood and its implications to resilience across India

Cited by 77 publications

References 79 publications

Copula based analysis of meteorological drought and catchment resilience across Indian river basins

Copula based analysis of meteorological drought and catchment resilience across Indian river basins

Quantifying groundwater sensitivity and resilience over peninsular India

A methodological framework for extreme climate risk assessment integrating satellite and location based data sets in intelligent systems

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