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

Abstract

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.

Simulating the Hydrological Processes of a Meso-Scale Watershed on the Loess Plateau, China

“…Uncertainties of the SWAT model were within acceptable limits, meaning that all source uncertainties were captured by parameter uncertainty in our study [15,17,21,23,54]. However, this does not neglect the significance of the spatiotemporal variability of precipitation [38,53] and model structures [2,55,56] during the whole calibration processes.…”

“…The larger the P-factor value, the greater the contribution of parameter uncertainty to the uncertainty of the simulation [2]. The lower P-factor means that input or structure may dominate model simulation uncertainty [2,38]. Our uncertainty analysis results during the validation period (P-factor = 87%, R-factor = 0.9) were better than those during the calibration period (P-factor = 77%, R-factor = 0.85), implying that parameter uncertainty contributed the most.…”

“…However, there are many key parameters in a watershed that describe the bio-geophysical characteristics and hydrological processes of the watershed, among which some parameters are hard to measure directly and can be gained only by empirical estimation and literature references, resulting in the increased uncertainties of the modeling system [11,17,21,36]. Several studies showed that using more informative data, modified model structures, or more parameter sets could reduce uncertainties [2,37,38]. In addition, the SUFI-2 method is capable of assessing the uncertainties from all sources for the SWAT model [21].…”

Net Ecosystem Production of a River Relying on Hydrology, Hydrodynamics and Water Quality Monitoring Stations

“…This approach increased reliability under uncertainty of new hydrologic scenarios [19][20][21] and supported the use of an optimal solution [20,22,23] as the best set of parameters for the HSPF model. To guarantee satisfactory predictions, HSPEXP+ 2.0 was used to assess the calibrated HSPF model, as in the works of Xie et al [20] and Lampert and Wu [24]. In this way, the streams module of the HSPF model can provide reliable predictions of the flow and velocity variables at specific locations along the river.…”

“…Given that there was a set of unknown parameters governing the HSPF model, our study integrated a process for parameters calibration using the non-sorted genetic algorithm II (NSGA-II) [17,18]. This approach increased reliability under uncertainty of new hydrologic scenarios [19][20][21] and supported the use of an optimal solution [20,22,23] as the best set of parameters for the HSPF model. To guarantee satisfactory predictions, HSPEXP+ 2.0 was used to assess the calibrated HSPF model, as in the works of Xie et al [20] and Lampert and Wu [24].…”