Abstrakt
Precipitation is one of the most difficult meteorological variables to measure due to its high spatiotemporal variability. It is applied in scientifically and technically oriented studies, including research of rainfall thresholds triggering landslides.
High quality of input precipitation data is important for reliable results, including early warning systems of the landslides. This paper presents a comprehensive review of the major sources of precipitation data as rainfall gauges, disdrometers, weather radars, weather satellites, reanalysis, or models with their pros and cons and the data sets created from them.
Rainfall gauges provide direct rainfall measurements at a single point, and their data has to be interpolated within a large and heterogeneous area. They cannot provide sufficient coverage, especially in sparsely populated areas, developing countries or oceanic areas.
Nevertheless, they are essential for the validation and verification of indirect rainfall measurements and models. The most recent technologies of indirect measurements (radar or satellites) offer a very fine spatiotemporal resolution on a large or global scale, but the rainfall data tends to be underestimated and show further inaccuracies.
Rainfall gauges remain the most common instrument used in the research of the landslide precipitation thresholds, even though an increase in the use of large-scale techniques is expected. The combination of more data sources can help improve the data quality and accuracy and the right choice of the data source for the specific purpose of the research.