Wednesday, 25 January 2017
4E (Washington State Convention Center )
Recent studies have shown that skillful streamflow forecasts can be utilized in water and energy management. The forecasting skill varies across different hydroclimatic regimes and geographical locations as well as over different seasons. For instance, snow water equivalent plays an important role in streamflow predictability in snow-dominated basins while precipitation plays an important role in governing streamflow in the rainfall-runoff dominated basins. Therefore, the objectives of this study are to: 1) developing statistical streamflow forecasts 1 to 6 months ahead for two basins in different hydroclimatic settings — Salt River near Roosevelt, Arizona (arid) and Mississippi River near Clinton, Iowa (snow melt driven regime) — and 2) evaluate the role of air temperature forecasts, wind speed and El Niño conditions on streamflow forecasting skill over 1 to 6 months ahead lead time. We will develop monthly updated streamflow forecasts based on climate forecasts from the ECAHM4.5 General Circulation Model using Principal Component Regression model. The model will be evaluated using retrospective climate forecasts. Finally, the role of adding air temperature forecasts, wind speed and El Niño conditions as additional predictors will be evaluated. Our results in the Salt River near Roosevelt, AZ, indicate that during late winter, spring and early summer (February through June), streamflow forecasts performed better than climatological forecasts (no-forecasts). Similar performance was also observed during the fall months (October and November), as indicated by positive Mean Square Skill Scores (MSSS). However, climatological forecasts are better than streamflow forecasts contingent on climate forecasts for January, July, September and December, the months with negative MSSS.
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