Modelling the Linkage Between Landscape Metrics and Water Quality Indices of Hydrological Units in Sihu Basin, Hubei Province, China: An Allometric Model

Abstract

Studying quantitative relationships between landscape pattern and water quality is a fundamental step to assess the impacts of non-point source pollution. Many hydrological models with multi-functionality have been developed as useful tools to study several key mechanisms in non-point source pollution. In landscape ecological studies, however, the empirical modelling approaches have been dominated with emphasis on the relationships between the landscape metrics and water quality indices. The main techniques for developing those models of landscape-water quality are statistical regression analysis based on linear models. In this article, Allometric models and the traditional multiple linear regression models for estimating the linkage between landscape metrics and water quality were tested in Sihu Basin, Hubei Province, China. The models at patch class level were established in 24 hydrological units of the basin, which took nine water quality indices (EC, pH, SS, DO, COD, TN, TP, NO3--N, NH4+-N) as the dependent variables and eighteen landscape metrics calculated in FRAGSTATS 3.3 as independent variables. The results suggested that, compared with the traditional multiple linear regression models, Allometric models were more suitable for SS, DO, TP, TN, NH4+-N, in which landscape pattern metrics could explain the 80.5%, 77.7%, 58.2%, 43.9%, 67.6% of total variation, respectively. There had little difference between multiple linear regression models and Allometric models for EC and NO3--N. The coefficients of determination in Allometric models were not as strong as that obtained in the multiple linear regression models for pH and COD. The above results indicated that using Allometric model may potentially provide a new way to study the linkage between landscape metrics and water quality indices, which will help protect our regional water resources.