Uncertainty and Nonstationarity in Streamflow Extremes under Climate Change Scenarios over a River Basin

Das, Jew; Umamahesh, N. V.

doi:10.1061/(asce)he.1943-5584.0001571

Cited by 32 publications

(17 citation statements)

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“…Therefore, it would be necessary to account nonstationary analysis in modelling the hydrological extremes (Parey et al ., 2010; Cooley, 2013; Salas and Obeysekera, 2014). Recently, many studies have approached the development of nonstationary models to understand the influence of natural variability in extremes (Das and Umamahesh, 2017; Bracken et al ., 2018; Galiatsatou et al ., 2018). In this study, we investigated the nonstationary behaviour of extreme precipitation over India considering ENSO, IOD and AMO as nonstationary influences.…”

Section: Introductionmentioning

confidence: 99%

“…The reasons behind the calling the results from standard/classical approach as possibly unreliable are, for example, non‐availability of sufficiently large and quality dataset, parameter uncertainty which is usually overlooked (Chandra et al ., 2015). Specifically, the classical approach uses the point estimate of the parameters obtained using different methods, namely L‐moments (Saf, 2009; Haddad et al ., 2011), method of moments (Lück and Wolf, 2016), and maximum likelihood estimation (MLE; Das and Umamahesh, 2017) and hence, not usually accounts for the uncertainty associated with the parameters. To overcome the issues and provide reliable estimates for risk assessment, Bayesian method offers a coherent framework and with advancement in the computational facility.…”

Section: Introductionmentioning

confidence: 99%

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Low frequency global‐scale modes and its influence on rainfall extremes over India: Nonstationary and uncertainty analysis

Jha

Das

Goyal

2020

Intl Journal of Climatology

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The variability in the extreme rainfall events is of growing concern in the context of climate change. Several high rainfall events have occurred in India in recent years and simulations from the Intergovernmental Panel on Climate Change suggest a rise in extremes. The low‐frequency global‐scale modes/oscillations are widely considered as the significant drivers of inter‐annual variability of the Indian rainfall pattern and extreme rainfall events. To account for climate external forcings, we assessed the influence of El Nino Southern Oscillation, Indian Ocean Dipole and Atlantic Multidecadal Oscillation on extreme precipitation over 24 major river basins of India using the nonstationary extreme value analysis. Moreover, the uncertainty in the parameters of the fitted nonstationary extreme value distribution is assessed using Bayesian inference. It was found that extreme precipitation events in the country are dominated by these oscillations, especially in central India. Moreover, the return levels of high rainfall were found to be intensifying with increasing return period. We also observed that uncertainty in return levels was significant in almost every river basin. The results presented here contribute to a better understanding of the large‐scale climate variability and its impact on high rainfall pattern, which would provide an essential understanding of the rainfall‐induced hazard prevention and enhance the risk management strategy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low frequency global‐scale modes and its influence on rainfall extremes over India: Nonstationary and uncertainty analysis

Jha

Das

Goyal

2020

Intl Journal of Climatology

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“…In recent research, an emerging feature of all aspects of climate change scenarios is the growing use of probabilistic terms such as probability density function (PDF) and cumulative distribution function (CDF) which can provide detailed quantitative descriptions of uncertainties of climate change scenarios. Many studies (e.g., Giorgi and Mearns, 2003;Wilby and Harris, 2006;Kay et al, 2009;Prudhomme and Davies, 2009;Paeth et al, 2013;Gillingham et al, 2015;Das and Umamahesh, 2017;Sung et al, 2018;Mackay et al, 2019) have carried out the quantification of uncertainties in climate change impact assessment using meteorological parameters and expressed in probabilistic terms. Sometimes, an ensemble approach is also applied to deal with the uncertainty in climate scenarios because a specific scenario cannot represent all future climate conditions (Sung et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Further, in climate change scenarios the risk and streamflow assessment is generally carried out through return periods under nonstationarity assumptions, as these assumptions enable to introduce time-varying concepts for better assessment (Cooley, 2013;Mondal and Mujumdar, 2016). The analysis of nonstationary approximations of the return levels under lower return periods may be more beneficial to design low-capacity hydraulic structures (Das and Umamahesh, 2017). The low flows are also significant parameters in hydrology (Kiely, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, some researchers (e.g., Xu et al, 2003;Huang et al, 2006;Das and Umamahesh, 2017) have used a 3layer variable infiltration capacity (VIC-3L) model for hydrological modelling to forecast the future streamflows of river basins. However, running the VIC model may seem to be a formidable task (https://vic.readthedocs.io/ en/master/Documentation/UserGuide/) as it involves complex process to follow.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainties and nonstationarity in streamflow projections under climate change scenarios and the ensuing adaptation strategies in Subarnarekha river basin, India

2020

Global NEST: The International Journal

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<p>The present study analyses the various uncertainties and nonstationarity in the streamflow projections of Subarnarekha river basin in Eastern India using two widely used hydrological climate models: 1) general circulation model (GCM), and 2) forcing climate change scenarios. These two climate models are used to force the ArcSWAT model. Subsequently this model is calibrated using SUFI-2 optimization technique. The downscaled and bias-corrected data from an ensemble of 10 climate projections with representative concentration pathways (RCP) 4.5 and 8.5 scenarios (five each) were used in first model, whereas in second model a total of 63 (7 perturbed precipitations and 9 perturbed temperatures) combinations of hypothetical climate change scenarios were used. The results show very good correlation during monthly calibration time steps and relatively good agreement between the observed and simulated streamflows in daily calibration time steps. The uncertainties are expressed in probabilistic terms using probability density function (PDF) and cumulative distribution function (CDF) as they provide significant information for decision process in climate change adaptation in the river basin. The uncertainties associated with climate models, return periods and streamflow extremes are also analysed in the present work. The RCP 8.5 scenarios seem more appropriate than RCP 4.5 scenarios in quantifying the uncertainties under nonstationarity assumptions. The mean values of water balance components and their percentage variation for both historic and future periods reveal that the water balance components get affected significantly due to climate change in a future period. Consequently, the streamflows are likely to decline in the river basin. The present study also highlights the comprehensive approaches that are being planned to facilitate adaptation to climate change as well as those that are specific to the water resources management in the study region. The findings in this work are useful for overall well-being of people in the study area.</p>

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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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Uncertainty and Nonstationarity in Streamflow Extremes under Climate Change Scenarios over a River Basin

Cited by 32 publications

References 49 publications

Low frequency global‐scale modes and its influence on rainfall extremes over India: Nonstationary and uncertainty analysis

Low frequency global‐scale modes and its influence on rainfall extremes over India: Nonstationary and uncertainty analysis

Uncertainties and nonstationarity in streamflow projections under climate change scenarios and the ensuing adaptation strategies in Subarnarekha river basin, India

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

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