دورية أكاديمية

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

التفاصيل البيبلوغرافية
العنوان: A CN-Based Ensembled Hydrological Model for Enhanced Watershed Runoff Prediction
المؤلفون: Muhammad Ajmal, Taj Ali Khan, Tae-Woong Kim
المصدر: Water, Vol 8, Iss 1, p 20 (2016)
بيانات النشر: MDPI AG, 2016.
سنة النشر: 2016
المجموعة: LCC:Hydraulic engineering
LCC:Water supply for domestic and industrial purposes
مصطلحات موضوعية: hydrological model, pre-storm soil moisture, runoff prediction, variable initial abstraction, Hydraulic engineering, TC1-978, Water supply for domestic and industrial purposes, TD201-500
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2073-4441
العلاقة: http://www.mdpi.com/2073-4441/8/1/20Test; https://doaj.org/toc/2073-4441Test
DOI: 10.3390/w8010020
الوصول الحر: https://doaj.org/article/0d2eab2b06a442a995ea0f71fb74ea8bTest
رقم الانضمام: edsdoj.0d2eab2b06a442a995ea0f71fb74ea8b
قاعدة البيانات: Directory of Open Access Journals