Identification of hydrological models for operational flood forecasting in St. John's, Newfoundland, Canada

被引:69
作者
Wijayarathne, Dayal Buddika [1 ]
Coulibaly, Paulin [2 ,3 ]
机构
[1] McMaster Univ, Sch Geog & Earth Sci, 1280 Main St West, Hamilton, ON L8S 4K1, Canada
[2] McMaster Univ, Jointly Sch Geog & Earth Sci, 1280 Main St West, Hamilton, ON L8S 4K1, Canada
[3] McMaster Univ, Dept Civil Engn, 1280 Main St West, Hamilton, ON L8S 4K1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Waterford River watershed; Flood forecasting; Hydrological models; Deterministic forecast; STREAMFLOW;
D O I
10.1016/j.ejrh.2019.100646
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Study region: Waterford River watershed, St. John's, Newfoundland and Labrador (NL), Canada. Study focus: This study investigates five hydrological models to identify adequate model(s) for operational flood forecasting at Waterford River watershed. These models included three lumped conceptual models (SAC-SMA: Sacramento Soil Moisture Accounting, GR4J: modele du Genie Rural a 4 parametres Journalier, and MAC-HBV: McMaster University Hydrologiska Byrans Vattenbalansavdelning), a semi-distributed model (HEC-HMS: Hydrologic Engineering Center's Hydrologic Modeling System) and a fully distributed physically-based model (WATFLOOD: University of Waterloo Flood Forecasting System). The best model(s) were chosen by comparison of performance criteria. To verify the potential of the best performing hydrological models for operational use, deterministic hydrologic forecasts were performed. New hydrological insights for the region: All five models are capable of simulating streamflow reasonably well in both calibration and validation periods. The SAC-SMA and GR4J models perform equally well and perform better than the other three models for all low, medium, and peak flows. The SAC-SMA and GR4J models generally perform better for peak flows, followed by HEC-HMS. Streamflow forecast experiment using deterministic weather prediction shows that SAC-SMA, GR4J, and HEC-HMS models perform well for up to 1-3 days ahead forecasts and are recommended for operational use. However, due to the good performance of all five models, an ensemble forecasting using continuous, multiple hydrological models is also recommended.
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