Simulation of probabilistic precipitation fields using a geostatistical approach
| Autor: | Nardo Caseri, A., De Angelis, C.F., Leblois, E. |
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| Přispěvatelé: | RiverLy (UR Riverly), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA) |
| Jazyk: | portugalština |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Revista Gestão & Sustentabilidade Ambiental Revista Gestão & Sustentabilidade Ambiental, 2018, 7, pp.493-507. ⟨10.19177/rgsa.v7e02018493-507⟩ |
| ISSN: | 2238-8753 |
| DOI: | 10.19177/rgsa.v7e02018493-507⟩ |
| Popis: | International audience; Countless regions of the world have already been hit, at least once, by extreme flood events that have caused high socioeconomic and environmental losses, among others. Precipitation estimation data are essential to predict these events and generate alerts that can minimize the damage that can be caused. One of the main characteristics of these events is the high spatial and temporal variability. Due their complexity, the prediction has several sources of uncertainties, such as uncertainties from the observed rainfalls. These data, in turn, play an important role in the forecasting systems performances. This study has as main goal to develop a methodology, based on geostatistical method, able to generate possible scenarios of rainfall using meteorological radar and pluviometers data. The area of study is located in the region of Campinas, in the state of São Paulo, where numerous extreme events have already been detected. The obtained results show that the developed method in this study can be a solution to quantify the uncertainties that can be found in the precipitation data. |
| Databáze: | OpenAIRE |
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