Evaluation of Estimation of Distribution Algorithm to Calibrate Computationally Intensive Hydrologic Model

Li, Zejun; Liu, Pan; Deng, Chao; He, Pinjia; Wang, Caijun

doi:10.1061/(asce)he.1943-5584.0001350

Cited by 23 publications

(12 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xunhe River basin has a drainage area of 6,448 km 2 . In this basin, mountains dominate the topography, and the elevations of the mountain peaks range from 2,000 to 3,000 m (Li et al, ; K. R. Lin et al, ). Daily streamflow and climatic data from 1980 to 1990 are used.…”

Section: Case Study and Datamentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Case Study and Datamentioning

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

View full text Add to dashboard Cite

show abstract

“…It is applicable to a variety of nonlinear problems (Evensen, 2003;Weerts and El Serafy, 2006) and has been widely applied to hydrological models (Abaza et al, 2014;DeChant and Moradkhani, 2014;Delijani et al, 2014;Samuel et al, 2014;Tamura et al, 2014;Xue and Zhang, Deng et al, 2015). Furthermore, the EnKF has been successfully used in time-invariant parameter estimations for hydrological models (Moradkhani et al, 2005;Wang et al, 2009;Zhang, 2010, 2013).…”

Section: Ensemble Kalman Filtermentioning

confidence: 99%

“…Two evaluation criteria, including the Nash-Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970) and the volume error (VE) are used to evaluate the runoff assimilation results for the synthetic experiment and the application to real catchments (Deng et al, 2015;Li et al, 2015).…”

Section: Evaluation Indexmentioning

confidence: 99%

Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.

show abstract

“…Located between 108°24'-109°26' E and 32°52'-33°55' N, the study area covers approximately 6448 km 2 . The Xun River is ~218 km long and has an average annual flow of 73 m 3 /s (Li et al, 2016). The basin has a subtropical monsoon climate.…”

Section: Study Area: Xun River Basinmentioning

confidence: 99%

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

Zhang¹,

Liu²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Evaluation of Estimation of Distribution Algorithm to Calibrate Computationally Intensive Hydrologic Model

Cited by 23 publications

References 32 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

Identification of hydrological model parameter variation using ensemble Kalman filter

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

Contact Info

Product

Resources

About