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

Lan, Tian; Lin, Kairong; Liu, Zhiyong; He, Yanhu; Xu, Chong‐Yu; Zhang, Hongbo; Chen, Xiaohong

doi:10.1029/2018wr023160

Cited by 39 publications

(30 citation statements)

References 82 publications

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“…Hence, seasonal climate variability is the main cause of the nonstationarity in the hydrological processes of the Xun River basin. This finding is in agreement with the work of Lan et al (2018), who also recognized and analyzed the seasonal hydrological dynamics of this study region.…”

Section: / 58supporting

confidence: 93%

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

Zhang¹,

Liu²

2019

Preprint

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Abstract. Although the parameters of hydrological models are usually regarded as constant, temporal variations can occur in a changing environment. Thus, effectively estimating time-varying parameters becomes a significant challenge. Following a survey of existing estimation methodologies, this paper describes a new method that combines (1) the basic concept of split-sample calibration (SSC), whereby parameters are assumed to be stable for one sub-period, and (2) the parameter continuity assumption, i.e., the differences between parameters in consecutive time steps are small. Dynamic programming is then used to determine the optimal parameter trajectory by considering two objective functions: maximization of simulation accuracy and maximization of parameter continuity. The efficiency of the proposed method is evaluated by two synthetic experiments, one with a simple two-parameter monthly model and the second using a more complex 15-parameter daily model. The results show that the proposed method is superior to SSC alone, and outperforms the ensemble Kalman filter if the proper sub-period length is used. An application to the Wuding River basin indicates that the soil water capacity parameter varies before and after 1972, which can be interpreted according to land use and land cover changes. Further application to the Xun River basin shows that parameters are generally stationary on an annual scale, but exhibit significant changes over seasonal scales. These results demonstrate that the proposed method is an effective tool for identifying time-varying parameters in a changing environment.

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Section: / 58supporting

confidence: 93%

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

Zhang¹,

Liu²

2019

Preprint

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“…Climatically, the Han Jiang basin is located in the monsoon region of the eastern Asia subtropical zone. The area is cold and dry in winter and warm and humid in summer (Lin et al, 2010), and there are seasonal changes in vegetation density and types (Fang et al, 2002). Subtropical vegetation affects temporal moisture con-ditions.…”

Section: Study Cases and Datamentioning

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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“…Therefore, the effects of the "compensation" between the parameters on the dynamics of hydrological model parameters are investigated using a control scheme i.e., Scheme 2. The most common approach for assessing the dynamics of the hydrological model parameters is that the calibration period is partitioned into different sub-periods based on the temporal dynamic catchment characteristics (Sarhadi et al, 2016;Merz et al, 2011;Lan et al, 2018;Xiong et al, 2019;Motavita et al, 2019;Deng et al, 2018;Dakhlaoui et al, 2017;Choi and Beven, 2007;Brigode et al, 2013;Kim et al, 2015;Kim and Han, 2017;Zhao et al, 2017;Pfannerstill et al, 2015;Me et al, 2015;Deng et al, 2016;Coron et al, 2014;Vormoor et al, 2018;Luo et al, 2012;Guse et al, 2016;Zhang et al, 2015;Ouyang et al, 2016;Zhang et al, 2011). The parameter set in each sub-period is optimized to obtain the dynamic parameter set.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have demonstrated that sub-period calibration based on the dynamic catchment behaviour accurately captures the temporal variations of the catchment characteristics, thereby compensating for structural inadequacy (Lan et al, 2018;Zhao et al, 2017;Kim and Han, 2017;Zhang et al, 2011;De Vos et al, 2010;Gupta et al, 2009;Choi and Beven, 2007;van Griensven et al, 2006;Freer et al, 2003). In the study of Choi and Beven (2007), the sub-periods were identified based on different hydrological characteristics using a clustering technique.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamics of hydrological model parameters: mechanisms, problems, and solution

Lan¹,

Lin²,

Xu³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. It has been demonstrated that the application of time-varying hydrological model parameters based on dynamic catchment behaviour 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 behaviour 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.

show abstract

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

Cited by 39 publications

References 82 publications

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

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

Dynamics of hydrological model parameters: mechanisms, problems, and solution

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