Reliability of lumped hydrological modeling in a semi-arid mountainous catchment facing water-use changes

Hublart, P.; Ruelland, Denis; Cortázar-Atauri, I. García de; Gascoin, Simon; Lhermitte, Stef; Ibacache, Antonio

doi:10.5194/hess-20-3691-2016

Cited by 22 publications

(19 citation statements)

References 90 publications

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“…Accurate streamflow prediction provides valuable information for agricultural irrigation, ecosystem management, disaster management, hydropower generation, and rural and urban water supply (e.g., Delorit et al, ; Hublart et al, ; Liu et al, , ; Turner et al, ; Wu et al, ). In this regard, it is essential to develop more efficient and reliable forecasting techniques (e.g., Mendoza et al, ; Miao et al, ; Wani et al, ; Wright et al, ; Wu et al, ).…”

Section: Introductionmentioning

confidence: 99%

A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate‐Land Surface Variations

Lan

Lin

Liu

et al. 2018

Water Resources Research

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One model structural deficiency is that some dynamic characteristics (such as seasonal dynamics) in catchment conditions are not explicitly represented by hydrological models. This study integrates data mining techniques to develop a clustering preprocessing framework for the subannual calibration of hydrological models to simulate seasonal dynamic behaviors. The proposed framework aims to solve the problems caused by missing processes and deficiencies of hydrological models, providing guidance for future model development. A set of climatic‐land surface indices is provided and preprocessed using the maximal information coefficient and the principal component analysis. Two clustering operations are performed based on the preprocessed climatic index and land‐surface index systems. Hydrological data are clustered into subannual periods for calibration. The parameters are independently optimized for each subperiod using a modified parallel calibration scheme and are then combined to generate a continuous simulation. The framework is applied in calibrating the TOPMODEL. The results show that the performance of the model with a clustering preprocessing framework in the middle‐ and low‐flow conditions is significantly improved without reducing the simulation accuracy for high flows. The transposability of the model parameters from the calibration to validation period has been improved significantly as well. The anomalous parameter values may be attributed in part to the convergence problem when using an optimization algorithm. Though well applied in the TOPMODEL, the framework has the potential to be used in other hydrological models.

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Section: Introductionmentioning

confidence: 99%

A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate‐Land Surface Variations

Lan

Lin

Liu

et al. 2018

Water Resources Research

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“…The performance of scheme 2 is only slightly better than that of scheme 1, which indicates only a slight increase in the model performance. Scheme 3 has the worst model Note that the flow duration curve (FDC) is usually split into different segments to describe different flow characteristics of a catchment (Cheng et al, 2012;Coopersmith et al, 2012;Kim and Kaluarachchi, 2014;Pugliese et al, 2014;Pfannerstill et al, 2014). The RMSE with quadratic character is usually used to evaluate poor model performance due to the strong sensitivity to extreme positive and negative error values.…”

Section: Model Performancementioning

confidence: 99%

“…Hydrological modeling is an essential tool for understanding the hydrological processes of a catchment and forecasting streamflow (Liu et al, 2015(Liu et al, , 2018Turner et al, 2017;Delorit et al, 2017;Fenicia et al, 2014Fenicia et al, , 2018Hublart et al, 2016;Höge et al, 2018;Sarrazin et al, 2016;Wi et al, 2015;Herman et al, 2013;Wagener et al, 2001Wagener et al, , 2003Madsen, 2000;Osuch et al, 2019;Zhang et al, 2019). Unfortunately, the paucity of progress in model development is partly due to structural inadequacy.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of hydrological-model parameters: mechanisms, problems and solutions

Lan

Lin

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. It has been demonstrated that the application of time-varying hydrological-model parameters based on dynamic catchment behavior significantly improves the accuracy and robustness of conventional models. However, the fundamental problems for calibrating dynamic parameters still need to be addressed. In this study, five calibration schemes for dynamic parameters in hydrological models were designed to investigate the underlying causes of poor model performance. The five schemes were assessed with respect to the model performance in different flow phases, the transferability of the dynamic parameters to different time periods, the state variables and fluxes time series, and the response of the dynamic parameter set to the dynamic catchment characteristics. Furthermore, the potential reasons for the poor response of the dynamic parameter set to the catchment dynamics were investigated. The results showed that the underlying causes of poor model performance included time-invariant parameters, “compensation” among parameters, high dimensionality and abrupt shifts in the parameters. The recommended calibration scheme exhibited good performance and overcame these problems by characterizing the dynamic behavior of the catchments. The main reason for the poor response of the dynamic parameter set to the catchment dynamics may be the poor convergence performance of the parameters. In addition, the assessment results of the state variables and fluxes and the convergence performance of the parameters provided robust indications of the dominant response modes of the hydrological models in different sub-periods or catchments with distinguishing catchment characteristics.

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“…It was selected for its good performance across a large spectrum of river flow regimes (e.g. Hublart et al 2016, Poncelet et al 2017).…”

Section: Rainfall-runoff Modellingmentioning

confidence: 99%

Water restrictions under climate change: a Rhone-Mediterranean perspective combining ‘bottom up’ and ‘top- down’ approaches

Sauquet¹,

Richard²,

Devers³

et al. 2018

Preprint

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Abstract. Drought management plans (DMPs) require an overview of future climate conditions for ensuring long term relevance of existing decision-making processes. To that end, impact studies are expected to best reproduce decision-making needs linked with catchment intrinsic sensitivity to climate change. The objective of this study is to apply a risk-based approach through sensitivity, exposure and sustainability assessments to evaluate the vulnerability of current DMPs operating in the Rhône-Méditerranée (RM) district to future climate projections. After inspection of legally-binding water restrictions (WR) from the DMPs in the RM district, a framework to derive WR durations was developed based on harmonized low-flow indicators. Whilst the framework could not perfectly reproduce all WR ordered by state services, as deviations from socio-political factors could not be included, it enabled to identify most WRs under current baseline, and to quantify the sensitivity of WR duration to a wide range of perturbed climates for 106 catchments. Four classes of responses were found across the RM district. Using the drought of 2011 to define a critical threshold of acceptable WR, the analysis showed that catchments in mountainous areas, highly sensitive to temperature changes, are also the most predisposed to future restrictions under projected climate changes considering current DMPs whilst catchments around the Mediterranean Sea, mainly sensitive to precipitation changes, were less vulnerable to projected climatic changes. The tools developed enable a rapid assessment of the effectiveness of current DMPs under climate change, and can be used to prioritize review of the plans for those most vulnerable basins.

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Reliability of lumped hydrological modeling in a semi-arid mountainous catchment facing water-use changes

Cited by 22 publications

References 90 publications

A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate‐Land Surface Variations

A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate‐Land Surface Variations

Dynamics of hydrological-model parameters: mechanisms, problems and solutions

Water restrictions under climate change: a Rhone-Mediterranean perspective combining ‘bottom up’ and ‘top- down’ approaches

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