Geostatistically based optimization of a rainfall monitoring network extension: case of the climatically heterogeneous Tunisia
| Autor: | Christophe Cudennec, Haifa Feki, Mohamed Slimani |
|---|---|
| Přispěvatelé: | Ecole Supérieure des Ingénieurs de l'Equipement Rural Medjez El-Bab, Partenaires INRAE, Université de Carthage - University of Carthage, Sol Agro et hydrosystème Spatialisation (SAS), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Laboratoire STE, Institut National de la Recherche Agronomique de Tunisie (INRAT), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Tunisia
Mean squared error Computer science 0208 environmental biotechnology [SDU.STU]Sciences of the Universe [physics]/Earth Sciences 02 engineering and technology [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology computer.software_genre Cross-validation kriging variance Kriging Statistics Network performance geostatistics Water Science and Technology rainfall network Simulation modeling 6. Clean water 020801 environmental engineering Data set 13. Climate action Variance reduction spatial variability Data mining computer optimization Interpolation |
| Zdroj: | Hydrology Research Hydrology Research, IWA Publishing, 2017, 48 (2), pp.514-541. ⟨10.2166/nh.2016.256⟩ Hydrology Research, IWA Publishing, 2016 |
| ISSN: | 0029-1277 |
| DOI: | 10.2166/nh.2016.256⟩ |
| Popis: | International audience; Rainfall data are an essential input for many simulation models. In fact, these latter have a decisive role in the development and application of rational water policies. Since the accuracy of the simulation depends strongly on the available data, the task of optimizing the monitoring network is of great importance. In this paper, an application is presented aiming at the evaluation of a precipitation monitoring network by predicting monthly, seasonal, and interannual average rainfall. The method given here is based on the theory of the regionalized variables using the well-known geostatistical variance reduction method. The procedure, which involves different analysis methods of the available data, such as estimation of the interpolation uncertainty and data cross validation, is applied to a case study data set in Tunisia in order to demonstrate the potential for improvement of the observation network quality. Root mean square error values are the criteria for evaluating rainfall estimation, and network performance is discussed based on kriging variance reduction. Based on this study, it was concluded that some sites should be dropped to eliminate redundancy and some others need to be added to the existing network, essentially in the center and the south, to have a more informative network. |
| Databáze: | OpenAIRE |
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