|Amin, Mostofa - Pennsylvania State University|
|Veith, Tameria - Tamie|
|Karsten, Heather - Pennsylvania State University|
Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/6/2016
Publication Date: 7/16/2016
Citation: Amin, M., Veith, T.L., Collick, A.S., Karsten, H. 2016. Simulating hydrological and geochemical processes in a karstic watershed of the Upper Chesapeake Bay. Journal of Hydrology. 180(B):212-223. doi:10.1016/j.agwat.2016.07.011.
Interpretive Summary: Karst, or cave-like, geology increases the complexity of hydrologic and water quality modeling because both surface and groundwater flows must be represented accurately. Topo-SWAT, a modified version of the Soil and Water Assessment Tool that helps identify critical source areas within the watershed, was used to simulate runoff, erosion, and nitrogen and phosphorus loadings to three USGS gage locations within the Spring Creek Watershed in Centre County, Pennsylvania. After careful parameterization using a local data available, Topo-SWAT was found to predict losses similarly to measured losses during both calibration and corroboration stages. This work provides a base model setup for evaluating the impacts of differing land management within the karst watershed, and supports efforts by the Chesapeake Bay Modeling Program to continue to minimize agricultural nutrient loadings to the Bay.
Technical Abstract: Water quality improvement in the Chesapeake Bay is a grave concern. An initiative to reduce the nutrient loads to the streams in the watershed has been undertaken to attain a target total maximum daily load (TMDL) at Chesapeake Bay. A general guideline with a list of best management practices (BMPs) has been in place for the TMDL goal. For more effective allocations of the BMPs a field-scale watershed-specific implementation plan is needed. A hydrological and geochemical watershed model can be used cost-effectively to make an efficient implementation plan. To develop such a model for a watershed with karstic geology may be a challenge. The Soil and Water Assessment Tool (SWAT, both regular and a modified version) was used to develop a model for the karstic Spring Creek Watershed of Centre County, Pennsylvania. The SWAT model was chosen because it is capable of assessing the effects of BMPs on water quality under inter-annual and seasonal variations of temperature and precipitation. Recorded weather, streamflow, water quality, 10-m digital elevation map, SSURGO and FAO soil data, CDL (a crop-specific land cover map), and information from the local Agronomic Guide and stakeholders’ meetings were used to calibrate and corroborate the models. Surface water-groundwater interactive parameters, such as curve number, surface water lag time, surface water to groundwater transport delay factor, base-flow factor, and lateral flow lag time were the most sensitive calibration parameters. Both regular SWAT and a modified (Topo-SWAT) model described the hydrology of the watershed adequately with monthly Nash-Sutcliffe efficiencies (NSE) at the three UGSG locations ranging 0.83 to 0.87 for calibration and 0.79 to 0.83 for corroboration, respectively. Monthly R^2 ranges were also similar among the models but much narrower for calibration (0.84 to 0.87 for calibration and 0.70 to 0.88 for corroboration). Because Topo-SWAT has the additional capability of identifying critical source areas, this version of SWAT was used to predict the nutrient and sediment loads. Topo-SWAT predicted nutrients and sediment load satisfactorily also (percent bias for nitrogen was -9.2, for phosphorous 6.6 and sediment 5.4). Pathways of the nutrient transport/cycle such as leaching, runoff, and mineralization were also simulated well. In addition to nutrient losses to the stream, they continually accumulated in soil (organic pool was increased by 1.8% for nitrogen and 3.8% for phosphorous per year) with the current nutrient application rates, suggesting the importance of additional nutrient management practices to reduce nutrient loads to the streams. The model can thus be used for predicting total nutrient and sediment loads for different what-if scenarios to meet the target total maximum daily load.