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Multiple linear regression models for the estimation of water flows for forest management and planning in Türkiye
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Multiple linear regression models for the estimation of water flows for forest management and planning in Türkiye
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Journal Article
Multiple linear regression models for the estimation of water flows for forest management and planning in Türkiye
2023
Overview
While there are many factors, including climatology, geography, topography, vegetation and soil, that afect hydrologic processes, understanding the role of forests seems most essential, due to their manageable nature. In this study, a holistic approach was taken, and possible factors afecting streamflow, including tree, sapling, shrub, herb and soil strata, were measured for 29 small catchments/stream basins located in Turkey. Linear regression models were developed in order to estimate water flow (m³ ·ha−1). Several models were suggested for use in practice. These models were based on the data on hand and displayed a suficient level of explained variance in the dependent variable. Model 5, based on the variables of catchment area (ha), drainage density, ratio of coniferous stand areas in the catchment (%), tree volume (m³·ha−1), leaf area index, number of short saplings (number·ha−1), and topsoil sand rate (%), was recommended for flow estimation, achieving a 0.73 adjR² value for test data. These variables can be obtained as part of a survey and water managers can use them to estimate water flow of the catchment. The generated models can be used in multiple-use planning of forests, e.g. in adjusting the volume of stands to get optimum benefit from wood and water production. One of the most interesting results and one that was opposite to that documented in the general literature, was the positive correlation between tree volume and flow per hectare, which suggests a strategy of growing older tree stands to enable greater water production.
