Abstract
Abstract. Seasonal hydrologic forecasts derive their skill from knowledge of initial hydrologic conditions and climate forecast skill associated with seasonal climate outlooks. Depending on the type of hydrological regime and the season, the relative contributions of initial hydrologic conditions and climate forecast skill to seasonal hydrologic forecast skill vary. We seek to quantify these contributions on a relative basis across the Conterminous United States. We constructed two experiments – Ensemble Streamflow Prediction and reverse-Ensemble Streamflow Prediction – to partition the contributions of the initial hydrologic conditions and climate forecast skill to overall forecast skill. In ensemble streamflow prediction (first experiment) hydrologic forecast skill is derived solely from knowledge of initial hydrologic conditions, whereas in reverse-ensemble streamflow prediction (second experiment), it is derived solely from atmospheric forcings (i.e. perfect climate forecast skill). Using the ratios of root mean square error in predicting cumulative runoff and mean monthly soil moisture of each experiment, we identify the variability of the relative contributions of the initial hydrologic conditions and climate forecast skill spatially throughout the year. We conclude that the initial hydrologic conditions generally have the strongest influence on the prediction of cumulative runoff and soil moisture at lead-1 (first month of the forecast period), beyond which climate forecast skill starts to have greater influence. Improvement in climate forecast skill alone will lead to better seasonal hydrologic forecast skill in most parts of the Northeastern and Southeastern US throughout the year and in the Western US mainly during fall and winter months; whereas improvement in knowledge of the initial hydrologic conditions can potentially improve skill most in the Western US during spring and summer months. We also observed that at a short lead time (i.e. lead-1) contribution of the initial hydrologic conditions in soil moisture forecasts is more extensive than in cumulative runoff forecasts across the Conterminous US.
Highlights
Accurate seasonal hydrologic forecast information is a key aspect of drought mitigation (Hayes et al, 2005)
We examine the variation of relative contributions of the initial hydrologic conditions (IHCs) and forecast skill (FS) in the cumulative runoff (CR) and soil moisture (SM) forecast with each forecast initialization date for lead times of 1 to 6 months across the Conterminous United States (CONUS)
In the Western US, sub-regions such as Pacific Northwest (PNW), Great Basin (GB), Lower Colorado (LC), Upper Colorado (UC), CA, and Rio Grande (RG)-1, high skill due to the IHCs in lead 1–6 months CR forecasts is mainly apparent during spring (MAM) and summer months
Summary
Accurate seasonal hydrologic forecast information is a key aspect of drought mitigation (Hayes et al, 2005). Maurer and Lettenmaier (2003) evaluated the predictability of runoff throughout the Mississippi River basin spatially, by season and prediction lead time using a multiple regression technique to relate runoff and climate indices (El Nino-Southern Oscillation and the Arctic Oscillation) and components of the IHCs (SM and SWE) They found that initial SM was the dominant source of runoff predictability at lead-1 in all seasons except in June-July-August (JJA) in the western mountainous region, where SWE was most important. They concluded that knowledge of IHCs, especially when forecast initial conditions are dry, could provide useful predictability that can augment predictions of climate anomalies up to 4.5 months of lead time They found statistically significant correlations between 1 March SWE and March-April-May (MAM) runoff over parts of the Western US and Great Lakes regions and between 1 March SWE and June-July-August (JJA) runoff over the Pacific Northwest (PNW), the Far West, and the Great Basin. We seek in this study (1) to quantify the contributions of IHCs and FS to seasonal prediction of cumulative runoff (CR) and SM during each month of the year, and (2) to identify the months and sub-regions within CONUS, where improvement in simulating the IHCs and/or FS can have the greatest impact on seasonal hydrologic forecast skill
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