Global Hydrological Seasonal Prediction with an ESM: Impact of land data assimilation on streamflow forecast skill

Hydrological forecasts provide essential information to prevent flood and drought disasters, as well as to manage water, agriculture, and hydropower generation. Global forecast approaches are crucial in regions lacking hydrological in situ registers to develop and evaluate forecast systems. In this direction, in our work “Global Streamflow Seasonal Forecast by a Novel two-way AOGCM/Land/River Coupling” presented at the 2024 EMS annual meeting, we demonstrated the ability of the operational Météo-France seasonal prediction system (SYS8) to deliver skilful global streamflow forecasts. We also highlighted the impact of soil moisture initialisation on atmospheric forecasts, which is associated with land-atmosphere coupling.

Given the strong dependence of land-atmosphere water and energy exchanges on vegetated land cover, we explore the impact of Leaf-Area-Index (LAI) assimilation in the initialisation run on streamflow predictions. The control forecast of SYS8 derives land initial conditions from a historical run where the atmosphere/ocean is nudged to the ERA5/GLORYS re-analysis and climatological LAI (control initialisation). This study improves the initialisation run by taking the soil moisture and temperature from an offline land simulation where the LAI, derived from the THEIA satellite-based product, is assimilated (LDAS initialisation). Daily streamflow ensemble hindcasts of 25 members are generated, with a lead time of up to 4 months initialised on 1st of February/May/August/November between 1993 and 2017. Forecast performance is assessed against observed streamflow in flow-gauged basins worldwide and compared with the hindcasts from the control land initialisation. The performance metrics are the Kling-Gupta Efficiency (KGE) and its components (correlation, simulated-to-observed discharge mean and deviation ratios).

The LDAS initialisation run shows reduced discharge mean bias and increased correlation compared to the control SYS8 initialisation run, especially in Europe and South America. The LDAS initialisation generally leads to more accurate discharge forecasts for all seasons (spring-MAM, summer-JJA, autumn-SON and winter-DJF) in South American river basins. Similar conclusions apply to Europe, except for the spring season, where reduced performance
is observed in the Iberian Peninsula and basins in the Scandinavian Peninsula. North America only reports lower KGE for the winter forecast in basins on the western side, where the streamflow production is mainly driven by snow or drier conditions. Current results encourage ongoing efforts to enhance land initialisation through land data assimilation of other variables, particularly snow water equivalent and/or soil moisture.