Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty
- 作者：Thorsten Wagener and Howard S. Wheater
- 关键词：Rainfall-runoff modelling ; Ungauged catchments ; Regionalization ; Parameter estimation ; Uncertainty ; Model structural error
- 刊名：Journal of Hydrology
- 出版年：2006
- 期刊代码：68_00221694
- 类别：abs
- 出版时间：30 March 2006
- 卷：320
- 期：1-2
- 页码：132-154
- 文件大小：779 K
摘要
The prediction of streamflow at ungauged sites is one of the fundamental challenges to hydrologists today. While major progress has been made in the regionalization of statistical flow properties (e.g. extreme values), and methods for synthesis of event response at ungauged sites are widely applied, the estimation of continuous streamflow time-series is still very uncertain. The challenge of predicting the response at ungauged sites is often met through a process of parameter regionalization. Little attention has so far been given to the impact of new insights into model identification at gauged sites, e.g. regarding the problem of structural error, on this regionalization process. Questions that are addressed in this paper are the following: (1) What is the relationship between local parameter identifiability and catchment characteristics? (2) How is the uniqueness of catchments reflected in regionalization? (3) What is the result of local model structural uncertainty on the regionalization result? (4) How can we propagate local parameter uncertainty into predictions in ungauged basins and what is the result? A case study of 10 catchments located in the southeast of England is utilized to deal with these questions. The main conclusions from this study are that the uncertainty in the locally estimated model parameters is a function of their importance in representing the response of a given catchment, model structural error hinders identification of a parameter to represent a certain process and therefore hinders the regionalization, and the uncertainty in the calibrated parameters can be propagated to ungauged sites using a weighted regression approach.