Comparing Methods for the Regionalization of Intensity−Duration−Frequency (IDF) Curve Parameters in Sparsely-Gauged and Ungauged Areas of Central Chile

Abstract

Estimating intensity−duration−frequency (IDF) curves requires local historical information of precipitation intensity. When such information is unavailable, as in areas without rain gauges, it is necessary to consider other methods to estimate curve parameters. In this study, three methods were explored to estimate IDF curves in ungauged areas: Kriging (KG), Inverse Distance Weighting (IDW), and Storm Index (SI). To test the viability of these methods, historical data collected from 31 rain gauges distributed in central Chile, 35° S to 38° S, are used. As a result of the reduced number of rain gauges to evaluate the performance of each method, we used LOOCV (Leaving One Out Cross Validation). The results indicate that KG was limited due to the sparse distribution of rain gauges in central Chile. SI (a linear scaling method) showed the smallest prediction error in all of the ungauged locations, and outperformed both KG and IDW. However, the SI method does not provide estimates of uncertainty, as is possible with KG. The simplicity of SI renders it a viable method for extrapolating IDF curves to locations without data in the central zone of Chile.

Review: Fractal Geometry in Precipitation

“…[105] estimated the precipitation intensities of pluviometric stations with the storm index. This technique transforms the precipitation intensity in 24 h, I known (24h, T), to a desired duration, I sim (t, T), using a K factor for each return period T. This factor is estimated from nearby rainfall observatories with subdaily data according to the following equation [109]:…”