HR: 10:40h
AN: H32C-02 INVITED    [Abstracts]
TI: MERGING MULTIPLE CLIMATE MODEL FORECASTS FOR SEASONAL HYDROLOGIC PREDICTIONS
AU: * Luo, L
EM: lluo@princeton.edu
AF: Princeton University, Environmental Engineering and Water Resources, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544,
AU: * Luo, L
EM: lluo@princeton.edu
AF: Princeton University, Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ 08543,
AU: Wood, E F
EM: efwood@princeton.edu
AF: Princeton University, Environmental Engineering and Water Resources, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544,
AU: Pan, M
EM: mpan@princeton.edu
AF: Princeton University, Environmental Engineering and Water Resources, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544,
AU: Li, H
EM: haibinli@princeton.edu
AF: Princeton University, Environmental Engineering and Water Resources, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544,
AB: Skillful seasonal hydrologic predictions are required in water resource management, preparation for drought and its impacts, energy planning, and many other related sectors. In this study, a seasonal hydrologic ensemble prediction system is developed and evaluated over the Eastern U.S., with focus on the Ohio River basin. The seasonal hydrologic prediction system utilizes a hydrologic model (in this case the Variable Infiltration Capacity model) as the central element for producing ensemble hydrologic predictions of soil moisture, snow and streamflow with lead times up to 6 months. The uniqueness of this forecast system is in the method for generating ensemble atmospheric forcings for the forecast period. It merges seasonal climate predictions from multiple climate models with observed climatology in a Bayesian framework such that the uncertainties related to the atmospheric forcings can be reduced and better quantified. This framework also downscales the climate model forecasts to scales appropriate for hydrologic prediction and uses a rank structure of selected historical forcings to ensure that generated ensembles of daily meteorological forcings have reasonable patterns in space and time. Three types of forecasts were performed in the study: those using information from NCEP's Climate Forecast System (CFS), those using information from CFS and the European Union funded multi-model prediction project called DEMETER, and those based the Extended Streamflow Prediction (ESP) approach. Forecasts (CFS, CFS+DEMETER and ESP) were made with the system for the summer periods (May to October) for 1981 - 1999, and represent forecast information from one climate model, eight climate models and none, respectively. The differences in forecast skills between CFS, CFS+DEMETER and ESP reflect the improvement with the new forecast method against the current hydrological operational approach, which is based on ESP. The forecast for the summer 1988 shows very promising skill in precipitation, soil moisture and streamflow forecast over the Ohio river basin, especially with the CFS+DEMETER forecast. The evaluation over all 19 summer forecasts shows significant skill improvement with the new multi-model method during the first two months of the forecasts. The improvement is marginal to moderate when only CFS forecast is used. This study validates the approach of using seasonal climate predictions from dynamic climate models in hydrological predictions. It also shows the need for international collaborations to develop multi-model seasonal predictions.
UR: http://hydrology.princeton.edu/forecast
DE: 1812 Drought
DE: 1847 Modeling
DE: 1854 Precipitation (3354)
DE: 1860 Streamflow
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: 2007 Fall Meeting