Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas

Abstract

To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.

Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting

“…This error characteristic can be represented by allowing QPP model parameters to vary within a year (but not across years). This approach is common for predictions/forecasts at monthly to seasonal timescales (e.g., Bennett et al, 2017; Li et al, 2013; Woldemeskel et al, 2018) and has also been used in daily forecasting (e.g., Kim et al, 2018); Dynamic biases , that is, shifts in the mean of hydrological errors over longer time periods (e.g., month to year). These nonstationarities may be due to a range of factors including systematic errors in the hydrological model calibration data (e.g., Westra et al, 2014) and/or inability of hydrological model to capture changes in hydrological processes over longer time scales, for example, due to unusually wet/dry seasons, interannual climate variability, and/or changes in catchment properties (Coron et al, 2012; Fowler et al, 2016; Merz et al, 2011; Westra et al, 2014).…”

“…This error characteristic can be represented by allowing QPP model parameters to vary within a year (but not across years). This approach is common for predictions/forecasts at monthly to seasonal timescales (e.g., Bennett et al, 2017;Li et al, 2013;Woldemeskel et al, 2018) and has also been used in daily forecasting (e.g., Kim et al, 2018); 2. Dynamic biases, that is, shifts in the mean of hydrological errors over longer time periods (e.g., month to year).…”

Multiscale Postprocessor for Ensemble Streamflow Prediction for Short to Long Ranges

“…We then disaggregate the predicted multidaily flow to daily flow using the granular patterns of daily flow in the bias-corrected model-simulated daily flow. The above approach is motivated by the fact that the larger the temporal scale of aggregation is, the more skillful a b k,1 is likely to be (Kim et al 2018;Limon 2019). Similar approaches have also been used in postprocessing forecasts of precipitation (Kim et al 2018;Schaake et al 2007a) and streamflow (Regonda and Seo 2008).…”

“…The positive impact of postprocessing raw model simulations of streamflow in ensemble streamflow forecasting has been widely reported (Kim et al 2018(Kim et al , 2016Madadgar et al 2014). Recently, it has also been shown that EnsPost significantly increases skill in ensemble forecasts of outflow from a water supply reservoir in North Texas during significant releases, in addition to that in ensemble inflow forecasts (Limon 2019).…”

“…Ensemble forecasting has emerged in recent years as the methodology of choice for modeling and communicating forecast uncertainty (Cloke and Pappenberger 2009;Demargne et al 2014;Demeritt et al 2010;NRC 2006;Schaake et al 2007b). In the United States, the National Weather Service (NWS) has recently implemented the Hydrologic Ensemble Forecast Service (HEFS; Demargne et al 2014) at all River Forecast Centers (RFC) (Lee et al 2018) following experimental operation at selected RFCs (Hartman et al 2015;Kim et al 2018;Wells 2017). To reduce biases in precipitation and temperature forecasts, the HEFS uses the Meteorological Ensemble Forecast Processor (MEFP; Schaake et al 2007a;Wu et al 2011;NWS 2017a).…”