Detecting characteristics of extreme precipitation events using regional and satellite-based precipitation gridded datasets over a region in Central Europe

Perception of the spatio-temporal events of extreme precipitation and their variations is essential for diminishing the natural hazards linked with extreme events. In this research, a satellite-based precipitation dataset derived from remotely sensed soil moisture (SM2RAIN-ASCAT, obtained from ASCAT satellite soil moisture data through the Soil Moisture to Rain algorithm) was selected to evaluate the accuracy of daily precipitation and extreme events estimations against a regional gridded weather dataset by employing various performance indicators, and ETCCDI indicators (CDD, and CWD, SDII, R10mm, R20mm, R95p, R99p, Rx1day, and Rx5day). The study area includes entire Poland as well as small parts of Ukraine, Belarus, Slovakia, the Czech Republic, Russia, and Germany. According to PBIAS (similar to-3.9%) and coefficient of correlation (similar to 0.74), SM2RAIN-ASCAT has good accuracy in the study area. Assessments reveal that, in general, over southern, mountainous part SM2RAIN-ASCAT does not have accurate estimations. According to the reference dataset, during the 2007-2019 period, on average, the length of dry days was similar to 22 days, while SM2RAIN-ASCAT shows similar to 19.6 consecutive dry days. In contrast, SM2RAIN-ASCAT overestimated (16 days/year) the consecutive wet days compared to the reference dataset (similar to 8.7 days/year). SM2RAIN-ASCAT underestimated the number of heavy precipitation days index (R10mm) over the northern part of the region, close to the Baltic Sea), but the accuracy increased in the southern parts. SM2RAIN-ASCAT underestimated the maximum 1-day rainfall total and relative max 5-day precipitation amount indices. The total precipitation divided by the amount of wet days index shows that SM2RAIN-ASCAT has relatively acceptable accuracy in the center and south of the study area. Our results show that SM2RAIN-ASCAT should be improved for relatively higher extreme indicators.