A Framework for Estimating the Probability Distribution of Event Runoff Coefficient in Ungauged Catchments. Issue 1 (3rd January 2023)

• Record Type: Journal Article
• Authors: Zheng, Yanchen; Coxon, Gemma; Woods, Ross; Li, Jianzhu; Feng, Ping
• Is Part Of: Water resources research. Volume 59:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-03
• Subjects: event runoff coefficient -- ungauged catchments -- regionalization -- flood estimation -- catchment attributes -- probability distribution; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2022WR033227
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: The derived distribution method is a promising approach for flood estimation in ungauged catchments. In this approach, event runoff coefficient (ERC) is often adopted to behave as the runoff generation component since the behavior of ERC is closely related to flood generation mechanisms and its distribution reflects catchment climatological and landscape characteristics. However, there is a lack of understanding of how to transfer the information on ERC characteristics from gauged to ungauged catchments at catchment scale. Hence, here, we propose a generalized framework to estimate the probability distribution of ERC for ungauged catchments based on knowledge of the spatial and temporal controls on ERC from gauged catchments. Key components of the framework include cluster analysis based on ERC characteristics, linking clusters with catchment attributes, and constructing typical probability distribution of ERC conditioned on its temporal indicators. A total of 290, 743 rainfall‐runoff events observed in 431 GB catchments during the period 1990–2014 have been employed to verify the framework. Good estimations are obtained with the median value of coefficient of determination ( R 2 ) reaching 0.85 across all test catchments. The results indicate that similar pre‐event catchment conditions may cause distinct runoff response in different catchments, thus predicting the correct spatial cluster is crucial to the estimation accuracy. This work sheds light on constructing a stochastic generator model of ERC according to its spatial pattern and temporal dynamic, facilitating a new alternative for flood estimation method in ungauged catchments. Key Points: The framework has been verified in 431 Great Britain catchments achieving good estimation with a median value of R 2 reaching 0.85 Similar pre‐event conditions may result in various runoff responses across catchments, spatial clustering is crucial to estimation accuracy This framework facilitates a new alternative for flood estimation in ungauged catchments