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The present study investigated the effects of changes in landscape configuration on river water quality, which is calculated by chemical export coefficients, using spatial data onto 31 catchments in the southwestern part of the Caspian Sea basin by applying stepwise multivariate regression models. The water quality modeling has been carried out applying the chemical export coefficients of sulfate, bicarbonate, chlorine, calcium, magnesium, and sodium, and eight landscape metrics (including interspersion juxtaposition index, percentage of like adjacencies, aggregation index, clumpiness index, normalized landscape shape index, patch cohesion index, landscape division index, and splitting index), by which landscape configuration is analyzed. The results indicated that the sulfate (0.25 ± 0.33 gr ha−1yr−1), bicarbonate (0.61 ± 0.87 gr ha−1 yr−1), chlorine (0.17 ± 0.23 gr ha−1 yr−1), calcium (0.16 ± 0.21 gr ha−1 yr−1), magnesium (0.05 ± 0.07 gr ha−1 yr−1), and sodium (0.16 ± 0.21 gr ha−1 yr−1) are annually exported from the study catchments into the rivers. The change in landscape configuration has significantly explained the chemical export coefficients of sulfate, bicarbonate, chlorine, calcium, magnesium, and sodium. The findings showed the cohesion and coherence of the permanently irrigated land patches resulting in the discontinuity of the broad-leaved forest and grassland ecosystems degraded river water quality.

Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this study, we evaluated artificial neural network (ANN) training algorithms to predict the water quality index in a source catchment. Then, multiple linear regression (MLR) models were developed, using the predicted water quality index of the ANN training algorithms and water quality variables, as dependent and independent variables, respectively. The most appropriate MLR model has been selected on the basis of the Akaike information criterion, sensitivity and uncertainty analyses. The performance of the MLR model was then evaluated by a variable aggregation and disaggregation approach, for upscaling and downscaling proposes, using the data from four very large- and three large-sized catchments and from eight medium-, three small- and seven very small-sized catchments, where they are located in the southern basin of the Caspian Sea. The performance of seven artificial neural network training algorithms, including Quick Propagation, Conjugate Gradient Descent, Quasi-Newton, Limited Memory Quasi-Newton, Levenberg–Marquardt, Online Back Propagation, and Batch Back Propagation, has been evaluated to predict the water quality index. The results show that the highest mean absolute error was observed in the WQI, as predicted by the ANN LM training algorithm; the lowest error values were for the ANN LMQN and CGD training algorithms. Our findings also indicate that for upscaling, the aggregated MLR model could provide reliable performance to predict the water quality index, since the r2 coefficient of the models varies from 0.73 ± 0.2 for large catchments, to 0.85 ± 0.15 for very large catchments, and for downscaling, the r2 coefficient of the disaggregated MLR model ranges from 0.93 ± 0.05 for very large catchments, to 0.97 ± 0.02 for medium catchments. Therefore, scaled models could be applied to catchments that lack sufficient data to perform a rapid assessment of the water quality index in the study area.

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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.