Please login first

Seasonal and annual daily precipitation risk maps for the Andean region of Peru
Sergio Vicente-Serrano 1 , Juan Lopez-Moreno 1 , Kris Correa 2 , Grinia Avalos 2 , Cesar Azorin-Molina 3 , Ahmed El Kenawy 1 , Miquel Tomas-Burguera 1 , Francisco Navarro-Serrano 1 , Marina Peña-Gallardo 1 , Luis Gimeno 4 , Raquel Nieto 4
1  CSIC, Spain
2  SENAMHI
3  University of Gothenburg, Gothenburg, Sweden
4  University of Vigo, Spain

Published: 05 November 2017 by MDPI AG in Proceedings of First International Electronic Conference on the Hydrological Cycle in First International Electronic Conference on the Hydrological Cycle session Extreme Events
MDPI AG, 10.3390/CHyCle-2017-04836
Abstract:

We develop for the first time maps of annual and seasonal extreme precipitation risk in the Andean region of Peru. For this purpose, we used the complete daily precipitation records existing in Peru and after a careful quality control and homogeneity checking we selected 178 stations distributed across the mountainous chain. In each meteorological station, we obtained series of events of de-clustered daily intensity, total precipitation duration, total magnitude and dry-spell length. Using a peak-over-threshold approach we fitted the annual and seasonal series of these variables to a Generalized-Pareto distribution, obtained the distribution parameters and validated the performance of different thresholds to obtain reliable estimations of the precipitation probability. We found that a 90th percentile is in general the most suitable to develop the estimations for the different variables. The parameters obtained in the different meteorological stations were mapped using a universal krigging approach using the elevation and the distance to the ocean as co-variables. Maps of parameters were validated using a jack-knife approach and maximum expected precipitation intensity, magnitude, duration and dry-spell length estimated for a period of 25 and 50 years. The reliability of the spatial methodology was validated comparing observed precipitation and estimated by the spatial modelling in the different stations.

Keywords: Precipitation, Peru, modelling, Extreme, Maps, validated, Daily, Stations, Using, Annual and Seasonal
Related articles
Comments on this paper
Currently there are no comments available.




 
 
Top