https://cronfa.swan.ac.uk/Record/cronfa21107/Download/0021107-10052016114550.pdf

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

https://backend.orbit.dtu.dk/ws/files/161196347/CETHRY_JoH_manuscript_ORBIT.pdf

Identifying fit-for-purpose lumped surrogate models for large urban drainage systems using GLUE

Hydrological models demand large numbers of input parameters, which are to be optimally identified for better simulation of various hydrological processes. Identifying the most relevant parameters and their values using efficient sensitivity analysis methods helps to better understand model performance. In this study, the physically-based distributed model SHETRAN is used for hydrological simulation on the Netravathi River Basin in south India and the most important parameters are identified using the Morris screening method. Further, the influence of a particular model parameter on streamflow is quantified using local sensitivity analysis and optimal parameters are obtained for calibration of the SHETRAN model. The results demonstrate the capability of two-stage sensitivity analysis, combining qualitative and quantitative methods in the initial screening-out of insignificant model parameters, identifying parameter interactions and quantifying the contribution of each model parameter to the streamflow. The results of the sensitivity analysis simplified the calibration procedure of SHETRAN for the study area.

A two-stage sensitivity analysis for parameter identification and calibration of a physically-based distributed model in a river basin

The quality of precipitation (P) and potential evapotranspiration (PET) data greatly affects the hydrological modeling performance. Considerable attention has been paid to identifying the influence of biased P or PET inputs independently. However, few studies have explored the joint interaction of biases in P and PET inputs on hydrological simulations. Here, we investigate the mutual compensation of P and PET biases on the performance of two widely used conceptual hydrological models, the Xinanjiang model and the Probability Distributed Model. P and PET from HYREX (HYdrological Radar EXperiment) and CAMELS-GB (Catchment Attributes and Meteorology for Large-sample Studies in Great Britain) data sets are collected over five catchments with varying characteristics in Great Britain. Different biases are added to these original time series to generate 6560 biased input scenarios. The results suggest that there is a certain compensational relationship between the biases in P and PET inputs to reproduce desirable streamflow simulations. A new hydrological proxy named Compensational Interaction Angle (CIA) is identified and found to be stationary with various modeling periods, as well as stable with different hydrological models despite model equifinality. Further, the CIA highly relates to the long-term climate aridity ratio. The catchments with greater aridity have larger CIAs. This study offers a fresh perspective to analyze the input errors in hydrological modeling. The results can help to better understand P and PET interactions in hydrological modeling, and guide the selection/evaluation/bias-correction of P and PET data sets for hydrological applications. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2022WR033323

Interacting Effects of Precipitation and Potential Evapotranspiration Biases on Hydrological Modeling

Chance-constrained overland flow modeling for improving conceptual distributed hydrologic simulations based on scaling representation of sub-daily rainfall variability.

Practical experience has demonstrated that single objective functions, no matter how carefully chosen, prove to be inadequate in providing proper measurements for all of the characteristics of the observed data One strategy to circumvent this problem is to define multiple fitting criteria that measure different aspects of system behavior, and to use multi-criteria optimization to identify non-dominated optimal solutions Unfortunately, these analyses require running original simulation models thousands of times As such, they demand prohibitively large computational budgets As a result, surrogate models have been used in combination with a variety of multi-objective optimization algorithms to approximate the true Pareto-front within limited evaluations for the original model In this study, multi-objective optimization based on surrogate modeling (multivariate adaptive regression splines, MARS) for a conceptual rainfall-runoff model (Xin’anjiang model, XAJ) was proposed Taking the Yanduhe basin of Three Gorges in the upper stream of the Yangtze River in China as a case study, three evaluation criteria were selected to quantify the goodness-of-fit of observations against calculated values from the simulation model The three criteria chosen were the Nash-Sutcliffe efficiency coefficient, the relative error of peak flow, and runoff volume (REPF and RERV) The efficacy of this method is demonstrated on the calibration of the XAJ model Compared to the single objective optimization results, it was indicated that the multi-objective optimization method can infer the most probable parameter set The results also demonstrate that the use of surrogate-modeling enables optimization that is much more efficient; and the total computational cost is reduced by about 925%, compared to optimization without using surrogate modeling The results obtained with the proposed method support the feasibility of applying parameter optimization to computationally intensive simulation models, via reducing the number of simulation runs required in the numerical model considerably

Parameter identification and calibration of the Xin’anjiang model using the surrogate modeling approach

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Parameter identification and calibration of the Xin’anjiang model using the surrogate modeling approach