DARWIN-OPD: Improving area-wide knowledge on Occult Precipitation and Drizzle with remote sensing-based hybrid modelling

• Applicant: Professor Dr. Jörg Bendix
• Subject Area: Physical Geography
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 455480427

The Galápagos Islands are best known for their unique, endemic biodiversity. They are listed as a UNESCO World Heritage Site, but also as a “World Heritage Site in Danger”, as climate change is threatening the islands. Due to their volcanic shape and lack of aquifers, rainfall is the main source of water supply for the ecosystems, the local population and the increasing number of tourists. How precipitation will change under climate change has been largely unknown until now, as operational measurement systems or spatio-temporally high-resolution data sources are lacking and climate projections are still very uncertain. In the first phase of DARWIN, we were able to provide the following data sources: (1) a network of automatic weather stations, (2) the model-based Galápagos Refined Analysis (GAR) and (3) parts of the remote sensing-based Galápagos Rainfall Retrieval (GRR). The retrospective analysis of precipitation has shown that El Niño and La Niña are responsible for the extreme anomalies. Overall, however, precipitation shows an increase in available precipitation water in warmer, El Niño-like years. However, it is still uncertain how possible changes in cloud water interception in the humid highlands would influence these results. Initial results suggest that this significant share of the water input could decrease in the cool season. Unfortunately, the corresponding retrievals could not yet be implemented in the GRR product, as training data of sufficient quality was not available. With the new ESA EarthCARE mission, however, such data is now available for the archipelago. The aims of the continuation proposal are therefore (1) to finalize the GRR as a hybrid product with a process-based modeling of convective precipitation and the derivation of cloud water interception (= occult precipitation) and the stratus-based drizzle with machine learning methods, and (2) to clarify how the available total precipitation changes in locally warmer (El Niño) or cooler (La Niña) situations with the help of the final processed data set. In a final conference, the overall results will be discussed with scientists and stakeholders and the hybrid GRR retrieval will then be implemented on site for continuous monitoring. In addition, an accompanying user workshop for GRR will be held with the relevant stakeholders.

• DFG Programme: Research Grants