Comprehensive Performance Evaluation for Hydrological and Nutrients Simulation Using the Hydrological Simulation Program–Fortran in a Mesoscale Monsoon Watershed, China

Abstract

The Hydrological Simulation Program–Fortran (HSPF) is a hydrological and water quality computer model that was developed by the United States Environmental Protection Agency. Comprehensive performance evaluations were carried out for hydrological and nutrient simulation using the HSPF model in the Xitiaoxi watershed in China. Streamflow simulation was calibrated from 1 January 2002 to 31 December 2007 and then validated from 1 January 2008 to 31 December 2010 using daily observed data, and nutrient simulation was calibrated and validated using monthly observed data during the period from July 2009 to July 2010. These results of model performance evaluation showed that the streamflows were well simulated over the study period. The determination coefficient (R2) was 0.87, 0.77 and 0.63, and the Nash-Sutcliffe coefficient of efficiency (Ens) was 0.82, 0.76 and 0.65 for the streamflow simulation in annual, monthly and daily time-steps, respectively. Although limited to monthly observed data, satisfactory performance was still achieved during the quantitative evaluation for nutrients. The R2 was 0.73, 0.82 and 0.92, and the Ens was 0.67, 0.74 and 0.86 for nitrate, ammonium and orthophosphate simulation, respectively. Some issues may affect the application of HSPF were also discussed, such as input data quality, parameter values, etc. Overall, the HSPF model can be successfully used to describe streamflow and nutrients transport in the mesoscale watershed located in the East Asian monsoon climate area. This study is expected to serve as a comprehensive and systematic documentation of understanding the HSPF model for wide application and avoiding possible misuses.

Integral Application of Chemical Mass Balance and Watershed Model to Estimate Point and Nonpoint Source Pollutant Loads in Data-Scarce Little Akaki River, Ethiopia

“…This explanation is supported by the study conducted by Li et.al. [35], who used hydrological and water quality model, HSPF, where a similar trend with this study between the model simulated and observed flow was observed. The spatial location of rain meters and the heterogeneity among rainfall stations could also determine the deviation.…”

“…Though the estimation of point source load is relatively easy, it is challenging to quantify diffuse source loads, specifically in developing countries, where the estimation is often based on simple empirical equations with limited hydro-meteorological and water quality data. To fill such gaps, many watershed models have been developed and studies were conducted to determine pollutant loads from diffuse sources at catchment scale such as the hydrological simulation program-FORTRAN (HSPF) [35], agricultural nonpoint source pollution model (AGNPS) [36], pollution load (PLOAD) [37], soil and water assessment tool (SWAT) [38], and storm water management model (SWMM) [25]. However, most of the models developed so far are complex and require a large number of data, and hence are not feasible for data-scarce areas like Ethiopia.…”

A combined catchment‐reservoir water quality model to guide catchment management for reservoir water quality control

“…Albek et al (2019) used the HSPF to assess the inflow impacts in the Sarısu catchment under climate change scenarios. The application of the HSPF in China (e.g., Li et al, 2017) and the USA (e.g., Ouyang et al, 2015) has also been reported.…”

Impact of aerosols on reservoir inflow: A case study for Big Creek Hydroelectric System in California

“…The Nash‐Sutcliffe efficiency (NSE) (Nash and Sutcliffe ) is the most widely used indicator in hydrology because of its flexibility to apply to different types of mathematical models and intuitive interpretability (McCuen, Knight, & Cutter, ; Ritter & Muñoz‐Carpena, ; Schaefli & Gupta, ). It has been used widely in streamflow and run‐off predictions (Criss & Winston, ; Krause, Boyle, & Bäse, ; Li, Luo, Jiang, Wan, & Li, ; Moriasi et al, ; Noh et al, ; Schaefli & Gupta, ). It is a normalized measure comparing the mean square error generated by a model simulation to the variance of the observed values.…”