Parameter Estimation and Uncertainty Analysis: A Comparison between Continuous and Event-Based Modeling of Streamflow Based on the Hydrological Simulation Program–Fortran (HSPF) Model

Hydrologic modeling is usually applied to two scenarios: continuous and event-based modeling, between which hydrologists often neglect the significant differences in model application. In this study, a comparison-based procedure concerning parameter estimation and uncertainty analysis is presented b...

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Bibliographic Details
Published in:Water (Basel) 2019-01, Vol.11 (1), p.171
Main Authors: Xie, Hui, Shen, Zhenyao, Chen, Lei, Lai, Xijun, Qiu, Jiali, Wei, Guoyuan, Dong, Jianwei, Peng, Yexuan, Chen, Xinquan
Format: Article
Language:English
Subjects:
Base flow
Climate change
Computer simulation
FORTRAN
Gravity
Groundwater
Hydrologic models
Hydrology
Laboratories
Land use
Modelling
Nonpoint source pollution
Outflow
Parameter estimation
Parameter sensitivity
Parameter uncertainty
Precipitation
Rain and rainfall
Rainfall
Runoff
Sensitivity analysis
Simulation
Stormwater management
Stream discharge
Stream flow
Streamflow
Topography
Uncertainty analysis
Water outflow
Watershed management
Watersheds
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Summary:Hydrologic modeling is usually applied to two scenarios: continuous and event-based modeling, between which hydrologists often neglect the significant differences in model application. In this study, a comparison-based procedure concerning parameter estimation and uncertainty analysis is presented based on the Hydrological Simulation Program–Fortran (HSPF) model. Calibrated parameters related to base flow and moisture distribution showed marked differences between the continuous and event-based modeling. Results of the regionalized sensitivity analysis identified event-dependent parameters and showed that gravity drainage and storage outflow were the primary runoff generation processes for both scenarios. The overall performance of the event-based simulation was better than that of the daily simulation for streamflow based on the generalized likelihood uncertainty estimation (GLUE). The GLUE analysis also indicated that the performance of the continuous model was limited by several extreme events and low flows. In the event-based scenario, the HSPF model performances decreased as the precipitation became intense in the event-based modeling. The structure error of the HSFP model was recognized at the initial phase of the rainfall-event period. This study presents a valuable opportunity to understand dominant controls in different hydrologic scenario and guide the application of the HSPF model.
ISSN:2073-4441
2073-4441
DOI:10.3390/w11010171