Trend Analysis of Precipitation Extreme Indices in Iran Based on Quantile Regression Model

Abstract

The incidence of climate events such as droughts and floods in any given region is intricately tied to the temporal and spatial distribution of precipitation. In hydrological modeling, precise analyses of precipitation trends and extreme precipitation indices hold significant importance. This study aims to examine the trends in annual precipitation averages and extreme precipitation indices using a quantile regression (QR) model across 39 synoptic stations in Iran over a 50-year statistical period (1972-2021). Iran experienced its highest annual precipitation average in 1982, reaching 491.6 mm, while the lowest was recorded in 2021 at 218.3 mm. The quantile regression model analysis revealed a downward trend in Iran's annual precipitation averages across the 0.05, 0.5, and 0.95 quantiles, with significance levels of 0.1, 0.05, and 0.01, respectively. Extreme precipitation indices in the northern and western parts of Iran were notably higher than in other regions. The R10 and R20 indices also represent the number of days with at least 10 mm and 20 mm of precipitation, respectively. They show a decreasing trend in northern and northwestern Iran at significance levels of 0.1, 0.05, and 0.01. These trend analyses offer valuable insights into annual precipitation averages and extreme indices, aiding water resources.

Keywords

• extreme indices
• iran
• spatial variability
• quantile regression