Meteorological Drought Monitor for two transboundary regions in Southern Africa

We address a need for hydrometeorological early warning and information systems (EWIS) in Southern Africa. In the project 'Co-Design of Hydrometeorological Information system for Sustainable Water Resource Management in Southern Africa' (Co-HYDIM-SA) we want to enhance water security in the two transboundary regions: Cuvelai-Cunene and Notwane (Namibia and Angola; Botswana and South Africa).

The Global Precipitation Climatology Centre (GPCC) has many years of experience in hosting an operational and publicly available global drought monitoring service with the drought index GPCC-DI. The main challenge of drought monitoring in the focus region is data scarcity. Only few parameters are available and gaps in time series from stations are often present. That’s why, we work on a flexible data input in the operational system, where we can decide which data source should be used. For precipitation we mainly rely on the gridded GPCC dataset based on station data, whereas for temperature the gridded dataset from the Climate Prediction Center (CPC) is used. Furthermore, we plan to include satellite products (GIRAFE, CHIRPS, GPCP) and reanalysis (ERA5-Land) datasets. For the data acquisition and the implementation of the product, the collaboration with stakeholders in the focus region is essential. Therefore, they are included in the decision making and informed about our progress. The ‘co-design’ approach is an essential part of the project and is achieved by a close partnership with local Universities and a regular contact to the stakeholders.

At the EMS26 we want to present the Co-HYDIM-SA project, our findings and challenges we have encountered. Until today, we have calculated time series for the two Drought Indices (SPI, SPEI) and compared them with specific drought events. In general, the indices are consistent with the described droughts. One disadvantage of the SPI is that it has limitations during the dry season, especially for short term data aggregation. Whereas, the SPEI is characterized by its all-year round usability, due to the integration of potential evapotranspiration in addition to the precipitation data. As a next step, we will compare the grid data to station time series and evaluate the results
by calculating skill scores.