Document Type

Presentation

Publication Date

8-1-2014

Abstract

Water resource planning requires the forecasting of precipitation at different time scales that are dependent on the planning horizon for decision making. In Mediterranean areas, the uncertainty associated to precipitation occurrence and amount is very much linked to alternate sequences of highly variable dry and wet pluriannual cycles, and the torrential character of some of the rainfall events within the wet season. Moreover, most of the precipitation is associated to cyclonic fronts passing over the region, and their interaction with the regional and local topography. This work presents a Weather Generator (WG) tool to simulate annual sequences of rainfall events in Mediterranean watersheds based on Monte Carlo simulation and Bayesian techniques. Ground measurements of precipitations and synoptic maps are used to define cyclonic-front rainfall events in terms of duration and rainfall amount on a local (basin) basis. The resulting event series are statistically studied to develop empirical probability functions for every step in the Bayesian hierarchy ranging from annual precipitation and number of events to every event occurrence and its amount and duration, to build N equally-probable samples of V years of precipitation in a given watershed, each year consisting of M precipitation events of given duration/amount at the watershed scale, distributed within each year. The Weather Generator was applied to the Guadalfeo River Basin (Southern Spain), a 1360-km2 coastal mountainous watershed with altitudes ranging from 3200 to 0 m.a.s.l. . The synthetic samples of V years obtained through the WG tool are used in this work to assess the current variability of climate in this area, the uncertainty of water resource availability in a 30-yr time horizon, and the uncertainty of extreme flood events in the main course of the river. These application examples are representative of the potential uses of the tool.

Comments

Session R67, Hydro-Climatological Data and Uncertainty

 
 

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