Evaluation of GPM-IMERG rainfall estimates at multiple temporal and spatial scales over Greece

Hydrometeorological analysis of a flash flood event in an ungauged Mediterranean watershed under an operational forecasting and monitoring context

“…The latter played an important role in the production and distribution of excessive rainfall in the studied catchment during the examined flood episode (see Section 2.2). Further, the poor representation of the coastline in the GPM-IMERG retrieval algorithm increases the product's uncertainty in areas close to the sea (Caracciolo et al, 2018;Kazamias et al, 2022), such as the examined area in the present study.…”

“…Given this first indication of flooding provided by the WRF model, the GPM‐IMERG algorithm rainfall estimates could have been exploited in combination with the ground‐based observations to track the initiation of the event. The large errors of GPM‐IMERG algorithm concerning the amount of rainfall that affected the study area are associated with the incapability of the satellite sensors to represent adequately steep topography and coastlines (Kazamias et al, 2022). This fact highlights the need for further improving rainfall‐related satellite products (Brocca et al, 2019).…”

“…More specifically, the Early Run GPM-IMERG product used in the present analysis for monitoring rainfall in near real time is based only on satellite data (see Section 2.4). As reported by Kazamias et al (2022), who evaluated the GPM-IMERG rainfall estimates over Greece, satellite sensors are not capable of detecting the magnitude of rainfall produced due to orography. The latter played an important role in the production and distribution of excessive rainfall in the studied catchment during the examined flood episode (see Section 2.2).…”

“…As shown in Figure 2b, the rainfall produced by the GPM-IMERG algorithm over the north-eastern Chalkidiki was between 50 and 70 mm, far lower than the 140.2 mm observed in Stratoni (Table 1), while the maximum rainfall estimate did not exceed 90 mm over the greater study area. The inability of the GPM-IMERG algorithm to reproduce local-scale extreme phenomena, as the studied episode, can be mainly attributed to its coarse resolution and the limited data from passive microwave observations (Kazamias et al, 2022;Varlas et al, 2019). More specifically, the Early Run GPM-IMERG product used in the present analysis for monitoring rainfall in near real time is based only on satellite data (see Section 2.4).…”

Mapping Soil Erodibility over India

“…In India, approximately 45% of the total geographical area of the nation is susceptible to soil erosion (Bhattacharyya et al, 2015). Numerous physical and empirical models have been developed and implemented worldwide to estimate soil erosion coupled with remote sensing and geographic information system (GIS) systems covering a wide range of spatio-temporal scales (Flanagan et al, 2012;Jiang et al, 2019;Kazamias and Sapountzis, 2017;Kumar and Singh, 2021;Lobo and Bonilla, 2019;Nearing et al, 1989;Saghafian et al, 2015). Climate and soil properties also influence the erosion induced by water (Borrelli et al, 2020;Guo et al, 2019;Nearing et al, 2005;Senanayake and Pradhan, 2022).…”