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Open AccessArticle

A CN-Based Ensembled Hydrological Model for Enhanced Watershed Runoff Prediction

1
Department of Agricultural Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan
2
Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Korea
3
Department of Civil and Environmental Engineering, Hanyang University, Ansan 15588, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Kwok-wing Chau
Water 2016, 8(1), 20; https://doi.org/10.3390/w8010020
Received: 2 October 2015 / Revised: 11 January 2016 / Accepted: 11 January 2016 / Published: 15 January 2016
(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)
A major structural inconsistency of the traditional curve number (CN) model is its dependence on an unstable fixed initial abstraction, which normally results in sudden jumps in runoff estimation. Likewise, the lack of pre-storm soil moisture accounting (PSMA) procedure is another inherent limitation of the model. To circumvent those problems, we used a variable initial abstraction after ensembling the traditional CN model and a French four-parameter (GR4J) model to better quantify direct runoff from ungauged watersheds. To mimic the natural rainfall-runoff transformation at the watershed scale, our new parameterization designates intrinsic parameters and uses a simple structure. It exhibited more accurate and consistent results than earlier methods in evaluating data from 39 forest-dominated watersheds, both for small and large watersheds. In addition, based on different performance evaluation indicators, the runoff reproduction results show that the proposed model produced more consistent results for dry, normal, and wet watershed conditions than the other models used in this study. View Full-Text
Keywords: hydrological model; pre-storm soil moisture; runoff prediction; variable initial abstraction hydrological model; pre-storm soil moisture; runoff prediction; variable initial abstraction
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MDPI and ACS Style

Ajmal, M.; Khan, T.A.; Kim, T.-W. A CN-Based Ensembled Hydrological Model for Enhanced Watershed Runoff Prediction. Water 2016, 8, 20.

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