Developing Regional Hydrological Drought Risk Models through Ordinary and Principal Component Regression Using Low-Flow Indexes in Susurluk Basin, Turkey

Abstract

Susurluk Basin is among the basins that may be most affected by drought risk due to its agricultural, economic, and natural resources. In this study, regional hydrological drought risk models were developed for water supply systems in the Susurluk Basin, Turkey. Twenty-four flow observation sites with 25 years or more of data showing natural flow characteristics as much as possible were converted into daily flow data with Q7, Q15, Q30, and Q60 low-flow indexes. Regionalization was carried out by two-stage multivariate cluster and principal component analysis using the basins’ physical and hydrological characteristics and low-flow statistics, and two homogeneous regions were obtained due to the discordancy, heterogeneity, and goodness of fit tests, which are L-moment approaches. Regional models were performed with ordinary and principal component regression techniques using the physical and hydrological characteristics of the watersheds and regional low-flow frequency analysis. The cross-validation procedure results for ungauged basins show that ordinary regression models are more effective in the lowland first region. In contrast, principal component regression models are more suitable for the mountainous second region. This study’s findings, which are a first for the Susurluk Basin, will have important results in terms of agricultural water management in the region and will help the water authority in water allocation. To investigate whether human impact and climate change impact the prediction of hydrological drought, we recommend seasonal non-stationary frequency analysis with the addition of useful empirical hydrological drought indexes.