• Investigation of the national weather service soil moisture accounting models for flood prediction in the northeast floods of january 1996

      Hogue, Terri S.; Sorooshian, Soroosh; Department of Hydrology & Water Resources, The University of Arizona (Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1999-10)
      Extensive flooding occurred throughout the northeastern United States during January of 1996. The flood event cost the lives of 33 people and over a billion dollars in flood damage. Following the `Blizzard of `96 ", a warm front moved into the Mid-Atlantic region bringing extensive rainfall and causing significant melting and flooding to occur. Flood forecasting is a vital part of the National Weather Service (NWS) hydrologic responsibilities. Currently, the NWS River Forecast Centers use either the Antecedent Precipitation Index (API) or the Sacramento Soil -Moisture Accounting Model (SAC-SMA). This study evaluates the API and SAC -SMA models for their effectiveness in flood forecasting during this rain -on -snow event. The SAC -SMA, in conjunction with the SNOW-17 model, is calibrated for five basins in the Mid -Atlantic region using the Shuffled Complex Evolution (SCE-UA) automatic algorithm developed at the University of Arizona. Nash-Sutcliffe forecasting efficiencies (Ef) for the calibration period range from 0.79 to 0.87, with verification values from 0.42 to 0.95. Flood simulations were performed on the five basins using the API and calibrated SAC-SMA model. The SAC-SMA model does a better job of estimating observed flood discharge on three of the five study basins, while two of the basins experience flood simulation problems with both models. Study results indicate the SAC-SMA has the potential for better flood forecasting during complex rain-on-snow events such as during the January 1996 floods in the Northeast.
    • A multi-step automatic calibration scheme (MACS) for river forecasting models utilizing the national weather service river forecast system (NWSRFS)

      Sorooshian, Soroosh; Gupta, Hoshin; Hogue, Terri S.; Holz, Andrea; Braatz, Dean; Department of Hydrology & Water Resources, The University of Arizona (Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1999-10)
      Traditional model calibration by National Weather Service (NWS) River Forecast Center (RFC) hydrologists involves a laborious and time -consuming manual estimation of numerous parameters. The National Weather Service River Forecasting System (NWSRFS), a software system used by the RFCs for hydrologic forecasting, includes an automatic optimization program (OPT3) to aid in model calibration. The OPT3 program is not used operationally by the majority of RFC hydrologists who perform calibration studies. Lack of success with the traditional single - step, single-criterion automatic calibration approach has left hydrologists more comfortable employing a manual step-by-step process to estimate parameters. This study develops a Multistep Automatic Calibration Scheme (MACS), utilizing OPT3, for the river forecasting models used by the RFCs: the Sacramento Soil Moisture Accounting (SAC-SMA). and SNOW-17 models. Sixteen parameters are calibrated in three steps, replicating the progression of manual calibration steps used by NWS hydrologists. MACS is developed by minimizing different objective functions for different parameters in a step -wise manner. Model runs are compared using the MACS optimized parameters and the manually estimated parameters for six basins in the North Central River Forecast Center (NCRFC) forecast area. Results demonstrate that the parameters obtained via the MACS procedure generally yield better model performance than those obtained by manual calibration. The MACS methodology is a time-saving approach that can provide prompt model forecasts for NWS watersheds.