BayCEER Workshop 2024

Handling the gaps in precipitation time series with remotely-sensed datasets: An instance of when gap filling is not worth it.

O 4.2 in Hydro- and biogeochemistry

11.10.2024, 09:50-10:05, H 36

The variability and changes noted in the climate over the past decades emphasize the importance of climate information such as precipitation datasets in the management of flood risks. The lack of extensive and functional ground observation networks, introduces satellite-based rainfall datasets as a better alternative which needs however to be evaluated beforehand. This study investigated the performance of four satellite and gauge-based rainfall products –Climate Hazards Group Infrared Precipitation with Station data version v2.0 (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN), Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT) and the Global Precipitation Climatology Centre full daily data (GPCC). The assessment was based on grid-to-point and hydrologic modelling approaches at different time scales over the Mono river basin in Benin and Togo. The four datasets showed poor performances at daily and annual scales while the seasonal cycles and the dekadal (10-days) precipitation were well reproduced. They all exhibited a high probability of rainfall detection (POD) and low false alarm ratio (FAR) at dekadal scale. Furthermore, by filling the gaps of gauge data with the satellite-based products, we noticed that filling the missing does not necessarily improve the quality of the data and that may not be needed in the case of the Mono basin if interpolation methods like kriging are applied.

Keywords: Gap filling; Remotely-sensed datasets; Precipitation

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