Comparative Trend Analysis of Precipitation...
URL: https://doi.org/10.3390/cli12120208
Giorgio Cannella, Alessandro Pezzoli and Maurizio Tiepolo.
Abstract
The increasingly frequent pluvial flood of West African urban settlements indicates the need to investigate the drivers of local rainfall changes. However, meteorological stations are few, unevenly distributed, and work irregularly. Daily satellite rainfall datasets can be used. Nevertheless, these products often need to be more accurate due to sensor errors and limitations in retrieval algorithms. The problem is, therefore, how to characterize rainfall where there is a need for ground-based rainfall records or incomplete series. This study aims to characterize urban rainfall using two satellite datasets. The analysis was carried out in the Sirba river catchment, Burkina Faso, using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and the Tropical Applications of Meteorology using SATellite and ground-based data (TAMSAT) datasets. Ten indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) of precipitation were calculated, and their statistical trends were evaluated from 1983 to 2023. The study introduces two key innovations: a comparative analysis of precipitation trends using two satellite datasets and applying this analysis to towns within a previously understudied 39,138 km2 catchment area that is frequently flooded. Both datasets agree on the increase of (i) annual cumulative rainfall over all towns, (ii) five-day maximum rainfall over the town of Manni, (iii) rainfall due to very wet days in Gayéri, (iv) days of heavy rainfall in Bogandé, Manni and Yalgho, and (v) days of very heavy rainfall in Yalgho. These findings suggest the need for targeted pluvial flood prevention measures in towns with increasing trends in heavy rainfall.
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Informations additionnelles
| Champ | Valeur |
|---|---|
| Dernière modification de la donnée | 5 décembre 2024 |
| Dernière modification de la métadonnée | 5 décembre 2024 |
| Créé le | 5 décembre 2024 |
| Format | |
| Licence | Creative Commons Attribution |
| Datastore active | False |
| Has views | False |
| Id | 58f5e172-01ad-451c-8494-40b68433e071 |
| Package id | c5af00bc-9e72-42f2-bd13-5f0296fbc05a |
| Position | 1 |
| State | active |