Uncertainty of hydrologic simulation, and its impact on the design and the effectiveness of water conservation structures

Vema, Vamsi Krishna; Sudheer, K. P.; Chaubey, Indrajeet

doi:10.1007/s00477-020-01814-z

Cited by 7 publications

(3 citation statements)

References 73 publications

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“…Parameter uncertainties are considered important due to their nature in the hydrological modeling setup and their in uence on the forecasts (Vema et al, 2020). For instance, although the inputs are likely to change according to spatial-temporal coverage, the model parameters are often applied uniformly over the simulation period (Thornton et al, 2021).…”

Section: Parameter Uncertaintymentioning

confidence: 99%

“…In this respect, uncertainty in hydrological forecasting may evolve due to one or some of the following reasons: (1) measurement error (Tauro et al, 2018;Vema et al, 2020), (2) input error (Hrachowitz et al, 2013;Tauro et al, 2018;Blöschl et al, 2019), (3) errors in model conceptualization (McInerney et al, 2021), (4) initial setting of the parameters (Vema et al, 2020), (5) simulation error (Tran et al, 2020;Wang et al, 2017, Vema et al, 2020, (6) techniques and assumptions used in calibration procedures (Sivapalan et al, 2003), (7) assumptions used in the hydrological projections (Brigode et al, 2013), and (8) modeler's experience (Moges et al, 2021). Evaluation of recent studies suggests that much progress has been made in addressing the measurements, input, and conceptual uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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An overview of approaches for reducing uncertainties in hydrological forecasting: progress, and challenges

Panchanathan

Ahrari

Ghag

et al. 2023

Preprint

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In the present study, we review the methods and approaches used for uncertainty handling in hydrological forecasting of streamflow, floods, and snow. This review has six thematic sections: (1) general trends in accounting uncertainties in hydrological forecasting, (2) sources of uncertainties in hydrological forecasting, (3) methods used in the studies to address uncertainty, (4) multi-criteria approach for reducing uncertainty in hydrological forecasting and its applications (5) role of remote sensing data sources for hydrological forecasting and uncertainty handling, (6) selection of hydrological models for hydrological forecasting. Especially, a synthesis of the literature showed that approaches such as multi-data usage, multi-model development, multi-objective functions, and pre-/post-processing are widely used in recent studies to improve forecasting capabilities. This study reviews the current state-of-the-art and explores the constraints and advantages of using these approaches to reduce uncertainty. The comparative summary provided in this study offers insights into various methods of uncertainty reduction, highlighting the associated advantages and challenges for readers, scientists, hydrological modelers, and practitioners in improving the forecast task. A set of freely accessible remotely sensed data and tools useful for uncertainty handling and hydrological forecasting are reviewed and pointed out.

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Section: Parameter Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An overview of approaches for reducing uncertainties in hydrological forecasting: progress, and challenges

Panchanathan

Ahrari

Ghag

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…One of the steps in application of these models, is the estimation of their parameters, which is known as model calibration (Guinot et al., 2011; Madsen, 2000; Vema & Sudheer, 2020). Parameters of the hydrological models are estimated by comparing the simulated and observed variables of interest (usually streamflow) (Vema et al., 2020). The match between simulated and observed variables is expressed using statistical performance measures such as Nash Sutcliffe Efficiency (NSE) (Anil et al., 2021; Guo et al., 2018; Nash & Sutcliffe, 1970; Paul et al., 2019; R. Zhang et al., 2018; Tegegne et al., 2017), Kling‐Gupta Efficiency (KGE) (Becker et al., 2019; Brighenti et al., 2019; Garcia et al., 2017; Gupta et al., 2009; Mizukami et al., 2019), Root Mean Squared Error (RMSE) (Kim & Kim, 2020; Y. Zhang et al., 2016) and many others.…”

Section: Introductionmentioning

confidence: 99%

Formulation of Wavelet Based Multi‐Scale Multi‐Objective Performance Evaluation (WMMPE) Metric for Improved Calibration of Hydrological Models

Manikanta

Vema

2022

Water Resources Research

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Hydrological models are essential tools for assessing, analyzing and developing solutions for natural hydrologic systems (Mostafaie et al., 2018;Wagener et al., 2001). One of the steps in application of these models, is the estimation of their parameters, which is known as model calibration (Guinot et al., 2011;. Parameters of the hydrological models are estimated by comparing the simulated and observed variables of interest (usually streamflow) . The match between simulated and observed variables is expressed using statistical performance measures such as Nash Sutcliffe Efficiency (NSE)

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Uncertainty analysis in probabilistic design of detention rockfill dams using Monte-Carlo simulation model and probabilistic frequency analysis of stability factors

Riyahi

Riahi-Madvar

2022

Environ Sci Pollut Res

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The detention rock ll dams are of crucial signi cance in engineering ood control projects due to their minimal technical requirement, low cost, minimal environmental effects, self-automotive operation process. However, the reliable design of detention rock ll dams is a challenging topic because of the complex interaction between the coarse porous rock ll material and the ow, the non-Darcian ow in rock lls, their stability encountered with oods, reliable ood peak reduction, and safe ood discharge release to the downstream. The present study aims to investigate and evaluate the in uence of aleatory uncertainties (i.e., rock ll material, hydrologic, hydraulic and geometric uncertainties) in the reliable design of detention rock ll dams. In this study a reliable design framework of detention rock ll dams is developed with a focus on the importance of stability of rock ll dams against the oods. The developed framework is used to acquire future detention rock ll dam design and modeling requirements. The stability analysis, neglected in previous literature of rock ll dams, is performed under the uncertain design parameters. The uncertainty of input parameters in model design consideration is investigated including the uncertainties of inlet hydrograph, volume-elevation relation of reservoir, non-Darcian stage-discharge equation in coarse rock ll material, non-linear ood routing in detention rock ll dams. The effects of design uncertainty sources on the stability of dam, safety factors of overturning, sliding and bearing, along with the hydraulic performance of the dam were tested. The results in the present study revealed that the uncertainties in the input parameters of the model can effectively produce uncertainties in the hydraulic performance of detention rock ll dams ranges from − 53.54 to + 110.11 percent. Finally, the effects of input uncertainties on the structural stability and safety factors in detention rock ll dams is investigated. The results revealed the safety factor against the sliding (SFS) has maximum dependencies with the uncertainties ranging − 32.63 to 87.81 percent. Using the Monte-Carlo Simulation (MCS) and tting probability distribution functions to the safety factor histograms, the best PDF is selected by the goodness of tting tests to determine the improvement percentages in safety factors. The MCS and uncertainty quanti cations results in 88.3 percent in increasing the safety factors as a reliable methodology for stability design of detention rock ll dams. Thus, the study calls for reliable, certain and safe design of ood protection rock ll ponds.

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Uncertainty of hydrologic simulation, and its impact on the design and the effectiveness of water conservation structures

Cited by 7 publications

References 73 publications

An overview of approaches for reducing uncertainties in hydrological forecasting: progress, and challenges

An overview of approaches for reducing uncertainties in hydrological forecasting: progress, and challenges

Formulation of Wavelet Based Multi‐Scale Multi‐Objective Performance Evaluation (WMMPE) Metric for Improved Calibration of Hydrological Models

Uncertainty analysis in probabilistic design of detention rockfill dams using Monte-Carlo simulation model and probabilistic frequency analysis of stability factors

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