Hydrological Modelling in Data Sparse Environment: Inverse Modelling of a Historical Flood Event

Bàrdossy, András; Anwar, Faizan; Seidel, Jochen

doi:10.3390/w12113242

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…Here, the methodology is based on Random Mixing, and Whittaker-Shannon interpolation (Hörning et al, 2019) is used. A similar approach for the analysis of a single event was presented in Bárdossy et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Is Precipitation Responsible for the Most Hydrological Model Uncertainty?

et al. 2022

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Rainfall-runoff modeling is highly uncertain for a number of different reasons. Hydrological processes are quite complex, and their simplifications in the models lead to inaccuracies. Model parameters themselves are uncertain—physical parameters because of their observations and conceptual parameters due to their limited identifiability. Furthermore, the main model input—precipitation is uncertain due to the limited number of available observations and the high spatio-temporal variability. The quantification of model output uncertainty is essential for their use. Most approaches used for the quantification of uncertainty in rainfall-runoff modeling assign the uncertainty to the model parameters. In this contribution, the role of precipitation uncertainty is investigated. Instead of a standard sensitivity analysis of the model output with respect to the input variations, it is investigated to what extent realistic precipitation fields could improve model performance. Realistic precipitation fields are defined as gridded realizations of precipitation which reproduce the observed values at the observation locations, with values which reproduce the distribution of the observed values and with spatial variability the same as the spatial variability of the observations. The above conditions apply to each observation time step. Through an inverse modeling approach based on Random Mixing precipitation fields fulfilling the above conditions and reproducing the discharge output better than using traditional interpolated observations can be obtained. These realizations show how much rainfall runoff models may profit from better precipitation input and how much remains for the parameter and model concept uncertainty. The methodology is applied using two hydrological models with a contrasting basis, SHETRAN and HBV, for three different mesoscale sub-catchments of the Neckar basin in Germany. Results show that up to 50% of the model error can be attributed to precipitation uncertainty. Further, inverting precipitation using hydrological models can improve model performance even in neighboring catchments which are not considered explicitly.

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Section: Introductionmentioning

confidence: 99%

Is Precipitation Responsible for the Most Hydrological Model Uncertainty?

et al. 2022

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“…Simultaneous analysis of both input and parameter uncertainty can execute better result [16]. Figure 1 illustrates the types of data uncertainty in hydrological models, how these are generated and related examples.…”

Section: Data Uncertaintymentioning

confidence: 99%

Uncertainty in hydrological modelling: A review

Farzana

2023

ijhr

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Availability of hydrological data and various soft wares for developing models make easy way to answer frequently asked questions to hydrologists. A great deal of concentration has given to the development of models in the last decades. But the thorough study regarding uncertainty of simulations has not carried out in comparison with the development of models. Uncertainty in models emanates from input data, calibrated data, parameters and from the structure of models. The sources of uncertainty, cause of generation and how these can be dealt with are reviewed here. This also comprises a review about five different methods viz. Monte Carlo sampling, Bayesian approach, Generalized Likelihood Uncertainty Estimation, Bootstrap Approach and Machine learning methods which were applied in the estimation of the model and parameter uncertainty. This will indicate the comparison between the methods which were applied to measure the uncertainty of hydrological models and highlight the strengths and weaknesses of the methods in identifying the usefulness of the models. By the comparison of the methods the improvement of the model reliability, slackening of the prediction error of the hydrological models can be suggested. By a proper quantification of uncertainty of data applied for the building up and evaluation of models, model performance can be improved, cost can be reduced and unambiguous results can lead the proper water resources management.

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“…Consequently, input uncertainty can interfere with the quantification of predictive uncertainty. Comparing input and model parameter uncertainty particularly in a data sparse region, Bárdossy et al [46] showed the severity of input uncertainty over parameter uncertainty and suggested a simultaneous analysis of both uncertainties to have a meaningful result.…”

Section: Sources Of Hydrological Model Uncertaintiesmentioning

confidence: 99%

Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis

Moges

Demissie

Larsen

et al. 2020

Water

151

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Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.

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Hydrological Modelling in Data Sparse Environment: Inverse Modelling of a Historical Flood Event

Cited by 12 publications

References 28 publications

Is Precipitation Responsible for the Most Hydrological Model Uncertainty?

Is Precipitation Responsible for the Most Hydrological Model Uncertainty?

Uncertainty in hydrological modelling: A review

Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis

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