An integrated approach for prioritization of river water quality sampling points using modified Sanders, analytic network process, and hydrodynamic modeling.

Determination of the water quality monitoring network (WQMN) is a vital stage for surveying ecosystem health. Studies have been done in determining the optimal number and location of sampling points, but seasonality of water quality, especially for heavy metals, has been rarely studied. For the first time, this study proposes a framework to determine the optimal location of sampling points to monitor lead (Pb). This study was conducted for the Karoun River, located in southwestern Iran. First, hydraulic characteristics of the river were simulated by implementing of MIKE11 software as well as water quality(variation of Pb concentration). Nash­Sutcliffe coefficient were 0.91 and 0.91 for discharge calibration and validation, respectively. Second, 16 potential sampling points were proposed using modified Sanders' approach considering seasonality. For a better accuracy in the WQMN layout and a more efficient site selection of sampling points, a 1-km buffer is stretched along the river for determining non-point source pollution sources and prioritizing candidate points. This leads to considering different land uses in the study area, while GIS software has been employed. Seasonal changes and land use have a significant impact on the location of optimal sampling points. The presented framework can be used to improve water quality and support watershed protection efforts.

Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria.

Assessment of watershed health and prioritization of sub-watersheds are needed to allocate natural resources and efficiently manage watersheds. Characterization of health and spatial prioritization of sub-watersheds in data scarce regions helps better comprehend real watershed conditions and design and implement management strategies. Previous studies on the assessment of health and prioritization of sub-watersheds in ungauged regions have not considered environmental factors and their inter-relationship. In this regard, fuzzy logic theory can be employed to improve the assessment of watershed health. The present study considered a combination of climate vulnerability (Climate Water Balance), relative erosion rate of surficial rocks, slope weighted K-factor, topographic indices, thirteen morphometric characteristics (linear, areal, and relief aspects), and potential non-point source pollution to assess watershed health, using a new framework which considers the complex linkage between human activities and natural resources. The new framework, focusing on watershed health score (WHS), was employed for the spatial prioritization of 31 sub-watersheds in the Khoy watershed, West Azerbaijan Province, Iran. In this framework, an analytical network process (ANP) and fuzzy theory were used to investigate the inter-relationships between the above mentioned geo-environmental factors and to classify and rank the health of each sub-watershed in four classes. Results demonstrated that only one sub-watershed (C15) fell into the class that was defined as 'a potentially critical zone'. This article provides a new framework and practical recommendations for watershed management agencies with a high level of assurance when there is a lack of reliable hydrometric gauge data.

A cost-effective and efficient framework to determine water quality monitoring network locations.

A crucial part in designing a robust water quality monitoring network is the selection of appropriate water quality sampling locations. Due to cost and time constraints, it is essential to identify and select these locations in an accurate and efficient manner. The main contribution of the present article is the development of a practical methodology for allocating critical sampling points in present and future conditions of the non-point sources under a case study of the Khoy watershed in northwest Iran, where financial resources and water quality data are limited. To achieve this purpose, the river mixing length method (RML) was applied to propose potential sampling points. A new non-point source potential pollution score (NPPS) was then proposed by the analytic network process (ANP) to classify the importance of each sampling point prior to selecting the most appropriate locations for a river system. In addition, an integrated cellular automata-Markov chain model (CA-Markov) was applied to simulate future change in non-point sources during the period 2026-2036. Finally, by considering anthropogenic activities through land-use mapping, the hierarchy value, the non-point source potential pollution score values and budget deficiency in the study area, the seven sampling points were identified for the present and the future. It is not expected, however, that the present location of the proposed sampling points will change in the future due to the forthcoming changes in non-point sources. The current study provides important insights into the design of a reliable water quality monitoring network with a high level of assurance under certain changes in non-point sources. Furthermore, the results of this study should be valuable for water quality monitoring agencies looking for a cost-effective approach for selecting sampling locations.