Identification Of Hydrological Model Parameter Variation Using Ensemble Kalman Filter
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.
Publication Date
12-16-2016
Publication Title
Hydrology and Earth System Sciences
Volume
20
Issue
12
Number of Pages
4949-4961
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.5194/hess-20-4949-2016
Copyright Status
Unknown
Socpus ID
85006427730 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85006427730
STARS Citation
Deng, Chao; Liu, Pan; Guo, Shenglian; Li, Zejun; and Wang, Dingbao, "Identification Of Hydrological Model Parameter Variation Using Ensemble Kalman Filter" (2016). Scopus Export 2015-2019. 3321.
https://stars.library.ucf.edu/scopus2015/3321