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    Comparison of HSPF and PRMS Model Simulated Flows Using Different Temporal and Spatial Scales in the Black Hills, South Dakota

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    %28ASCE%29HE.1943-5584.0001596.pdf (1.996Mb)
    Date
    2018
    Author
    Chalise D. R.;Haj A. E.;Fontaine T. A.
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    Abstract
    The hydrological simulation program Fortran (HSPF) [Hydrological Simulation Program Fortran version 12.2 (Computer software). USEPA, Washington, DC] and the precipitation runoff modeling system (PRMS) [Precipitation Runoff Modeling System version 4. (Computer software). USGS, Reston, VA] models are semidistributed, deterministic hydrological tools for simulating the impacts of precipitation, land use, and climate on basin hydrology and streamflow. Both models have been applied independently to many watersheds across the United States. This paper reports the statistical results assessing various temporal (daily, monthly, and annual) and spatial (small versus large watershed) scale biases in HSPF and PRMS simulations using two watersheds in the Black Hills, South Dakota. The Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (r), and coefficient of determination (R2) statistics for the daily, monthly, and annual flows were used to evaluate the models’ performance. Results from the HSPF models showed that the HSPF consistently simulated the annual flows for both large and small basins better than the monthly and daily flows, and the simulated flows for the small watershed better than flows for the large watershed. In comparison, the PRMS model results show that the PRMS simulated the monthly flows for both the large and small watersheds better than the daily and annual flows, and the range of statistical error in the PRMS models was greater than that in the HSPF models. Moreover, it can be concluded that the statistical error in the HSPF and the PRMS daily, monthly, and annual flow estimates for watersheds in the Black Hills was influenced by both temporal and spatial scale variability.
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    http://yetl.yabesh.ir/yetl1/handle/yetl/4249781
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