Examining the Influence of Landscape Patch Shapes on River Water Quality

Mehdi Aalipour, Naicheng Wu, Nicola Fohrer, Yusef Kianpoor Kalkhajeh and Bahman Jabbarian Amiri ()
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Mehdi Aalipour: Department of Environmental Science, Faculty of Natural Resources, University of Tehran, Tehran 1417614411, Iran
Naicheng Wu: Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China
Nicola Fohrer: Institute of Natural Resource Conservation, Department of Hydrology and Water Resources Management, Christian Albrecht Universitaet zu Kiel, Olshausenstrasse 75, 24098 Kiel, Germany
Yusef Kianpoor Kalkhajeh: Department of Environmental Science, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou 325060, China
Bahman Jabbarian Amiri: Department of Regional Economics and the Environment, Faculty of Economics and Sociology, ul. POW nr 3/5, 90-255 Łódź, Poland

Land, 2023, vol. 12, issue 5, 1-15

Abstract: River water quality can be affected by a range of factors, including both point and non-point sources of pollution. Of these factors, changes in land use and land cover are particularly significant, as they can alter the structure of the landscape and consequently impact water quality in rivers. To investigate the relationship between patch shapes, a measure of landscape structure, and river water quality at the catchment scale, this study utilized spatial data from 39 catchments in the southern basin of the Caspian Sea. This study employed stepwise multivariate regression modeling to explore how changes in landscape structure, which can be measured by landscape metrics including the shape index, the contiguity index, the fractal dimension index, the perimeter–area ratio, and the related circumscribing circle, impact water quality variables. Four regression models—linear, exponential, logarithmic, and power models—were evaluated, and the most appropriate model for each water quality variable was determined using the Akaike information criterion. To validate the models, three groups of accuracy metrics were employed, and Monte Carlo simulation was utilized to analyze the models’ behavior. This study found that landscape structure metrics could explain up to 71% and 82% of the variations in the measures of TDS and Mg, respectively, and the shape index, the contiguity index, and fractal metric were particularly significant in predicting water quality. Moreover, this study verified the accuracy of the models and revealed that changes in landscape structure, such as a decline in patch continuity and an increase in patch complexity, can impact river water quality. The findings of this study suggest optimizing landscape structure metrics in land use planning to reduce river pollution and improve water quality.

Keywords: river water quality; landscape structure; patch shape; regression; uncertainty analysis (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:5:p:1011-:d:1139295

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