Calibration parameters used to simulate streamflow from application of the Hydrologic Simulation Program-FORTRAN Model (HSPF) to mountainous basins containing coal mines in West Virginia

Abstract

This report presents the Hydrologic Simulation Program-FORTRAN Model (HSPF) parameters for eight basins in the coal-mining region of West Virginia. The magnitude and characteristics of model parameters from this study will assist users of HSPF in simulating streamflow at other basins in the coal-mining region of West Virginia. The parameter for nominal capacity of the upper-zone storage, UZSN, increased from south to north. The increase in UZSN with the increase in basin latitude could be due to decreasing slopes, decreasing rockiness of the soils, and increasing soil depths from south to north. A special action was given to the parameter for fraction of groundwater inflow that flows to inactive ground water, DEEPFR. The basis for this special action was related to the seasonal movement of the water table and transpiration from trees. The models were most sensitive to DEEPFR and the parameter for interception storage capacity, CEPSC. The models were also fairly sensitive to the parameter for an index representing the infiltration capacity of the soil, INFILT; the parameter for indicating the behavior of the groundwater recession flow, KVARY; the parameter for the basic groundwater recession rate, AGWRC; the parameter for nominal capacity of the upper zone storage, UZSN; the parameter for the interflow inflow, INTFW; the parameter for the interflow recession constant, IRC; and the parameter for lower zone evapotranspiration, LZETP.

Multiscale Analysis of Hydrology in a Mountaintop Mine‐Impacted Watershed

“…In contrast, base flow averaged 63% of storm flow for three forested headwater catchments in the Fernow Experimental Forest. It is plausible that deep mine drainage is responsible for the relatively large proportion of base flow during storms, as this region has a long history of deep mining with numerous known and unknown abandoned mines, portals, and seeps, and auger mines that have the potential to contribute large volumes of mine drainage to streams (Hobba, ; Atkins et al ., ). Therefore, the volume of Q qf may be smaller due to a large unaccounted‐for volume of mine drainage controlling streamflow.…”

Surface mining and reclamation effects on flood response of watersheds in the central Appalachian Plateau region

“…The Hydrologic Simulation Program‐Fortran (HSPF) is the core watershed simulation module of the BASINS 3.1 software [ Atkins et al , 2005]. We chose HSPF for our work because it has a rich history as a tool for predicting changes in watershed response due to changes in land cover [e.g., Brun and Band , 2000; Coon , 2003; Doherty and Johnston , 2003].…”

“…Georges Creek contains complex underground tunnel systems and drainages related to deep shaft mining, a situation common among watersheds in the CAP [e.g., Atkins et al , 2005]. One manifestation of underground mining activities in Georges Creek is the Hoffman Tunnel (HT), a 3.2‐km tunnel connected to over 8 km of underground diversion ditches and auxiliary tunnels.…”

“…We selected 11 parameters (Table 2) as most important for the calibration of hydrology in mined watersheds [ Lumb et al , 1994; U.S. EPA , 2000; Atkins et al , 2005]. These parameters influence partitioning of water flows, such as overland, interflow, and ground flow, within the HSPF model (Figure 3).…”

Using the HSPF and SWMM Models in a High Pervious Watershed and Estimating Their Parameter Sensitivity

“…However, due to the use of different values of the HSPF model parameters, the major factors affecting model output for each watershed may be different. Al-Abed and Whiteley [28] and Fonseca et al [35] found that LZSN is the major factor, INFILT is the most sensitive parameter in a complex geomorphological river basin (Kourgialas et al [36]) and in the South National watershed in Eastern Ontario (Iskra and Droste, [37]), and DEEPFR is the most sensitive parameter in mountainous basins containing coal mines (Atkins et al [38]). However, we found that UZSN and INFILT are more sensitive.…”