Improved Hydrological Forecasting of Subseasonal Streamflow for the Irrawaddy and Mekong Rivers in Southeast Asia

Abstract

To provide a better subseasonal-to-seasonal (S2S) hydrological forecast, it is essential to investigate the factors that control streamflow prediction at time scales beyond that of traditional weather forecasts. Using a hydrological forecast framework built around NASA's Catchment-CN land model and GEOS S2S forecast meteorology, this study examines the predictive skill of subseasonal (∼30 days) streamflow in Southeast Asia and shows how that skill may be improved in combination with satellite-based rainfall information in areas for which the rain-gauge measurements are particularly poor. Initialized at four different times of a year, the prediction skill along the Irrawaddy River in Myanmar was significantly improved, going from no skill up to a correlation coefficient R of 0.65 during the wet season and up to 0.55 during the following transitional period by introducing Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite-based precipitation into our land initialization methodology. The streamflow forecast skill along the Mekong River was reasonably high (R of 0.6–0.7) during the dry season before and after the utilization of IMERG data, and the wet-season forecast skill modestly increased up to R of 0.8. The accurate land initialization is found to contribute dominantly to the predictive skill of subseasonal streamflow; however, low rainfall forecast skill occasionally offsets the positive contribution from the land initialization. Our findings suggest an alternative way to enhance S2S hydrological forecasting in other large river basins where rain gauge information is limited and illustrate the need for a careful application of forecast rainfall to hydrological prediction during the transitional seasons.