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- W4324382664 abstract "Abstract. Seasonal forecast is an early warning system that contributes to anticipatory management by providing spatial and temporal information of the near future. This study first examined the skill of ECMWF system 5 (SEAS5) sub–seasonal–to–seasonal (S2S) forecasts over Mainland Southeast Asia (MSEA). We evaluated the SEAS5 skill of temperature and precipitation for 30 years (1985–2014) against two reference model datasets, WFDE5 and APHRODITE, using probabilistic forecast verification skill metrics at grid cells for each month. Then, the SEAS5 data was used to force the Variable Infiltration Capacity (VIC) hydrological model to predict runoff and streamflow. These hydrological results were compared against the WFDE5-driven streamflow reanalysis and observed station data, using the same probabilistic skill statistics. The results show a prediction potential for temperature beyond two months in advance. The skill of precipitation and streamflow forecasting is limited to the first month. Strong seasonal and regional dependence occurs. The model shows high forecast skills during the pre-monsoon (April–May) and post-monsoon (October–November), arguably the period when its usefulness is potentially highest. Conversely, poor skill is observed during the rainy monsoon season (June–August). In eastern and southern MSEA, i.e. in eastern Thailand, Cambodia, Vietnam and Malaysia, considerable skill levels are found. Year–to–year precipitation tercile plots highlight skill in predicting the anomalous seasonal conditions caused by the ENSO. Overall, SEAS5 and derived hydrological forecasts show useful skill that can potentially be used for hydrological and agricultural anticipatory management in this region." @default.
- W4324382664 created "2023-03-16" @default.
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- W4324382664 date "2023-03-15" @default.
- W4324382664 modified "2023-09-26" @default.
- W4324382664 title "Analysis of seasonal climate and streamflow forecasts performance for Mainland Southeast Asia" @default.
- W4324382664 doi "https://doi.org/10.5194/hess-2023-56" @default.
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