Meteorological characterization of floods in a mountainous poorly gauged catchment using principal component analysis

Abstract

Decision making and hydrologic design for coping with floods are complex tasks in poorly gauged high-relief basins. The response of such basins is driven by precipitation and temperature, which controls the freezing level elevation and size of the runoff contributing area. Moreover, early warning of floods based solely on real-time in situ monitoring is impractical. This study presents ameteorological characterization of daily flows based on off-site daily precipitation and temperature data in a high-relief catchment in central Chile. The results show that the variables that best explain daily discharges are the cumulative precipitation over the previous 3 days measured at a high elevation and the minimum temperature on the day of the maximum discharge measured at a lower elevation in the valley. These variables were used to build three multivariate regression models, based on principal component analysis, which are able to predict the occurrence of daily flows, particularly for low exceedance probabilities. Although developed for a particular catchment, and despite the specific meteorological threshold magnitudes identified for the catchment, the analysis is easily extendable to other similar high-relief locations.