Assessing seasonal meteorological and hydrological forecasts across South Korea

Due to the intensified impact of climate change, the intensity and severity of catastrophic droughts is increasing all over the world. South Korea had also suffered from extreme droughts, including a recent a drought that prolonged from 2013 to 2015 and caused nation-wide crop failures. As one of the measures to anticipate droughts and mitigate damages, past studies have evaluated the use of seasonal forecasts in other regions. However, few studies have focussed the assessment at catchment-scale, which is more suitable for practical water management, and no studies were found on the assessment of seasonal forecasts over South Korea.

Firstly, we assessed the skill of Seasonal Precipitation Forecasts (SPFs) over the 20 catchments in South Korea where the largest reservoirs are located, over the period 2011 to 2020. Ensemble SPFs from 4 weather forecasting centres (ECMWF, UK Met Office, Météo France and DWD) were evaluated, and the skill quantified using the Continuous Ranked Probability Skill Score (CRPSS). We analysed how the skill of the seasonal meteorological forecasts varies across the seasons and years, before and after bias correction, and if the skill can be linked to catchments characteristics. In doing so, we developed a methodology and a Python package to implement it, which is freely available for future applications to other regions (https://github.com/uobwatergroup/seaform.git). The results showed that amongst the four forecasting centres, SPFs by ECMWF were the most skilful in South Korea. In particular, they generally outperformed climatology for up to 2 months of lead time and during the Wet season of drier years for all the lead times. We also found that linear bias correction is useful to correct systematic seasonal biases and there is no significant correlation between the catchment characteristics and forecast skill. Additionally, we investigated the possibility of anticipating dry years from ENSO indices and the forecasts themselves, but we found no significant link.

Secondly, we looked at how skill in seasonal meteorological forecasts propagates into the skill of hydrological forecasts (SHFs). We used the lumped hydrological Tank model to generate ensembles of reservoir inflow from ECMWF’s seasonal forecasts data (precipitation, Evapotranspiration and temperature). Again, we quantified the skill (CRPSS) of SHFs at different lead times, seasons and in wet and dry year. The results showed that the skill of SHFs is highly dependent on the skill of SPFs, and it mimics the seasonal and annual (dry and wet years) features of precipitation forecasts. We also tested 4 different types of processing methods (raw, pre-processing, post-processing, pre/post-processing) found that pre-processing method which corrects bias of weather forcings is the most useful to improve forecast skill.