Applying the NWS’s Distributed Hydrologic Model to Short-Range Forecasting of Quickflow in the Mahantango Creek Watershed

Anthony R. Buda, Seann M. Reed, Gordon J. Folmar, Casey D. Kennedy, David J. Millar, Peter J.A. Kleinman, Douglas A. Miller, Patrick J. Drohan

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Accurate and reliable forecasts of quickflow, including interflow and overland flow, are essential for pre-dicting rainfall–runoff events that can wash off recently applied agricultural nutrients. In this study, we examined whether a gridded version of the Sacramento Soil Moisture Accounting model with Heat Transfer (SAC-HT) could simulate and forecast quickflow in two agricultural watersheds in east-central Pennsylvania. Specifically, we used the Hydrology Laboratory– Research Distributed Hydrologic Model (HL-RDHM) software, which incorporates SAC-HT, to conduct a 15-yr (2003–17) simulation of quickflow in the 420-km2 Mahantango Creek watershed and in WE-38, a 7.3-km2 headwater interior basin. We directly calibrated HL-RDHM using hydrologic observations at the Mahantango Creek outlet, while all grid cells within Mahantango Creek, including WE-38, were calibrated indirectly using scalar multipliers derived from the basin outlet calibration. Using the calibrated model, we then assessed the quality of short-range (24–72 h) deterministic forecasts of daily quickflow in both watersheds over a 2-yr period (July 2017–October 2019). At the basin outlet, HL-RDHM quickflow simulations showed low biases (PBIAS = 10.5%) and strong agreement (KGE′′ = 0.81) with observations. At the headwa-ter scale, HL-RDHM overestimated quickflow (PBIAS = 69.0%) to a greater degree, but quickflow simulations remained satisfactory (KGE′′ = 0.65). When applied to quickflow forecasting, HL-RDHM produced skillful forecasts (>90% of Peirce and Gerrity skill scores above 0.5) at all lead times and significantly outperformed persistence forecasts, although skill gains in Mahantango Creek were slightly lower. Accordingly, short-range quickflow forecasts by HL-RDHM show promise for informing operational decision-making in agriculture.

Original languageEnglish (US)
Pages (from-to)1257-1280
Number of pages24
JournalJournal of Hydrometeorology
Issue number8
StatePublished - Aug 2022

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


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