Evaluation of gridded precipitation datasets over Madagascar

Abstract

Madagascar is among the countries whose agriculture is heavily dependent on rainfall. However, the country lacks accurate and reliable early warning systems for droughts and floods, partly due to insufficient station rainfall data. The purpose of this study is to identify rainfall datasets that can complement observation data by appraising 15 datasets (gauge-based, reanalysis, and satellite estimates). The study compares the temporal and spatial performance of datasets at annual and seasonal scales during 1983-2015. In all the analyses, CHIRPS presents lower biases, so it is chosen as the reference data in the Taylor diagram for the final evaluation analysis. Even though ranking datasets is neither possible nor appropriate since each dataset performs differently throughout each analysis, some datasets show reasonable consistency. This is the case with MSWEP, ERA5, and UDEL. On the other hand, MERRA2, CMAP, and TAMSAT are least preferred for use due to their considerable biases (specifically TAMSAT during the dry season). CRU, PRECL, ERAINT, CFSR, and JRA55 also present some degrees of deficiencies at either annual or seasonal scales. These findings are crucial for any future rainfall analysis over the country in order to minimize inaccuracy in monitoring rainfall.

Performance evaluation of CMIP6 HighResMIP models in simulating precipitation over Madagascar

“…It is perceived that more models are among the best-performing during the dry season than the annual and wet season. Ranking models without evaluating their seasonal performances is inappropriate and impossible (Randriatsara et al, 2022a), since each model performs differently during each scale. Thus, Tables 2 and 4 are given only for ranking the models based on each reference and each period scale.…”

“…The country's season is mainly triggered by the country's terrain and geographical position, the easterly trade winds and oceanic influences. The annual mean rainfall ranges between 350 and 4000 mm, decreasing from the eastern coast to the southwestern coast (Randriatsara et al, 2022a). The annual mean temperature varies from 23 to 27°C in coastal areas and from 14 to 22°C in the central highlands, with the highest mean value reaching above 36°C in November over the west coast and the lowest at about 5°C in July over the highlands (Randriamarolaza et al, 2021).…”

“…Therefore, an appraisal of the new generation CMIP6 HighResMIP experiments is presented in this study over Madagascar, which is a novel study that has never been done before over the study area. Moreover, the CHIRPS v2.0 satellite data is chosen as a reference throughout the whole analysis since the data have been evaluated and found to perform better in replicating Madagascar's rainfall compared to other gridded precipitation datasets during the recent study by Randriatsara et al (2022a). In addition, several studies have successfully used CHIRPS to compare with other precipitation datasets and to evaluate GCMs due to its high resolution and ability to capture local regions and topographic effects over different areas in the world (Almazroui et al, 2021;Ayugi et al, 2019Ayugi et al, , 2021Cattani et al, 2018;Dinku et al, 2018;Gebrechorkos et al, 2018;Kimani et al, 2017;Ngoma et al, 2021).…”

“…The data belongs to another type of gridded precipitation datasets apart from CHIRPS (a satellite data) and has a main advantage of horizontal grid resolution enhancement of about 9 km compared to the old products of the ECMWF (Muñoz-Sabater et al, 2021). Furthermore, the ERA5 has been found to be among the others precipitation products with lower biases following a robust and systematic study that evaluated the multiple precipitation products sourced from reanalysis, satellite and gauge-based over the study region (Randriatsara et al, 2022a). Besides, no study has assessed the performance of CMIP6 HighResMIP for its skilfulness in simulating precipitation features over Madagascar.…”

Evaluation of multiple bias correction methods with different satellite rainfall products in the Main Beles Watershed, Upper Blue Nile (Abbay) Basin, Ethiopia

“…This research looks at the capability and limitations of SRPs (CMORPH, CFSR, PERSIANN, and TMPA_3B42V7), as well as the performance of three bias correction methods (Delta, EQM, and QM) for the Main Beles basin with five metrological stations like Pawe, Dangla, Shahura, Durbete, and Yismala. By comparing statistical properties and Taylor diagrams (Randriatsara et al 2022), the performance of SRPs and bias correction approaches have been evaluated.…”