TY - JOUR
T1 - Automatic calibration and improvements on an instream chlorophyll a simulation in the HSPF model
AU - Lee, Dong Hoon
AU - Kim, Jin Hwi
AU - Park, Mi Hyun
AU - Stenstrom, Michael K.
AU - Kang, Joo Hyon
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Accurate prediction of chlorophyll a (Chl a) concentration in surface water bodies such as lakes or rivers is crucial for water quality management. This study improved the predictive simulation of instream Chl a with the Hydrological Simulation Program-FORTRAN (HSPF) by adding automatic calibration and modifying the growth-temperature formulation of phytoplankton in the original HSPF model. A total of 62 model parameters, selected from a series of sensitivity analyses, were automatically calibrated in a stepwise manner for different variables in the order of flow, sediment, water temperature, ammonia/nitrate couple, and phosphate/Chl a couple. With finer temporal resolution (5–8 days) data than those of majority of the existing HSPF studies, the automatic calibration procedure provided the model with performance ratings of ‘satisfactory’ or better for all the variables including nutrients and Chl a: The percent bias values ranged from -18% - 54% and -20% – 62% for nutrients and Chl a, respectively. The original linear equation on the growth-temperature relationship of phytoplankton in simulating instream Chl a was modified using a quadratic equation and an exponential equation. The exponential equation outperformed the original linear and quadratic equations, particularly in simulating the excess concentrations of Chl a observed during summer seasons. For the validation data set, the exponential equation predicted 78% of the eutrophic cases while the linear and quadratic equation only predicted 53% and 13% of the eutrophic cases, respectively. The modified HSPF model offers an improved prediction of instream Chl a. This approach will be useful for providing early warning of algal blooms, facilitating the implementation of effective management of stream water quality.
AB - Accurate prediction of chlorophyll a (Chl a) concentration in surface water bodies such as lakes or rivers is crucial for water quality management. This study improved the predictive simulation of instream Chl a with the Hydrological Simulation Program-FORTRAN (HSPF) by adding automatic calibration and modifying the growth-temperature formulation of phytoplankton in the original HSPF model. A total of 62 model parameters, selected from a series of sensitivity analyses, were automatically calibrated in a stepwise manner for different variables in the order of flow, sediment, water temperature, ammonia/nitrate couple, and phosphate/Chl a couple. With finer temporal resolution (5–8 days) data than those of majority of the existing HSPF studies, the automatic calibration procedure provided the model with performance ratings of ‘satisfactory’ or better for all the variables including nutrients and Chl a: The percent bias values ranged from -18% - 54% and -20% – 62% for nutrients and Chl a, respectively. The original linear equation on the growth-temperature relationship of phytoplankton in simulating instream Chl a was modified using a quadratic equation and an exponential equation. The exponential equation outperformed the original linear and quadratic equations, particularly in simulating the excess concentrations of Chl a observed during summer seasons. For the validation data set, the exponential equation predicted 78% of the eutrophic cases while the linear and quadratic equation only predicted 53% and 13% of the eutrophic cases, respectively. The modified HSPF model offers an improved prediction of instream Chl a. This approach will be useful for providing early warning of algal blooms, facilitating the implementation of effective management of stream water quality.
KW - Algae
KW - Automatic calibration
KW - Chlorophyll a(chl a)
KW - Hydrological simulation program-fortran (HSPF)
KW - Phytoplankton
KW - Sensitivity analysis
UR - http://www.scopus.com/inward/record.url?scp=85074755948&partnerID=8YFLogxK
U2 - 10.1016/j.ecolmodel.2019.108835
DO - 10.1016/j.ecolmodel.2019.108835
M3 - Article
AN - SCOPUS:85074755948
SN - 0304-3800
VL - 415
JO - Ecological Modelling
JF - Ecological Modelling
M1 - 108835
ER -