**1. Introduction**

The sustainability of inland water resources worldwide is becoming increasingly endangered as climate change contributes to the human-induced problems of water supply scarcity and maldistribution. Environmental problems associated with water quality have been receiving some research attention; however, the litany of natural disasters that have accompanied changes faced by water-reliant ecosystems has created a current-day crisis. Multisectoral stressors imposed on water-related ecosystems exacerbate environmental problems. Environmental challenges associated with agriculture faced by the modern world include aquifer depletion [1–6], land subsidence [7–9], the seasonal drying of river flows [10,11], waterlogging [12–14], salinization of river water and aquifers [15,16], and human health impacts from excessive use of fertilizers and pesticides [17–19] as well as the use of a wide range of household chemicals. These problems have a water quality component that requires a radical re-thinking of resource management policy and new tools to help analysts and regulators craft novel solutions. Likewise, municipal and industrial sectors that rely on a high-quality drinking water supply are cognizant of the challenges associated with curtailing pollution, while minimizing the costs of treatment and pollutant disposal (e.g., References [20–22]). As a consequence, urban areas are increasingly looking to holistic [23] and nature-based pollution-abatement strategies [24,25].

While there is a general consensus among policy, scientific, practice, regulatory and management communities that science-based decision support is necessary to manage and mitigate the deleterious effects of water pollution under climate change (Figure 1), how these decision support tools (DSTs) are designed and implemented for different applications remains an open-ended question. Over the past four decades, with the advent and rapid progress in modeling capacity and computational technology, watershed models have increasingly become effective tools for tackling a wide range of issues regarding water resources and environmental management and supporting regulatory compliance. Statistical and machine learning methods are being used to support and even supplant more traditional simulation models to improve the estimation of temporal dynamics and patterns of variability in pollutant concentrations and loads. With the advancements in data-driven analyses and modeling approaches for water quality, there are also rapid developments in such model-based DSTs for water quality management.

These DSTs are playing central roles, in the following aspects: (i) driving socioeconomic decision making by helping multi-sectoral participants make better operational decisions; (ii) informing scientific policy and funding investments and guiding research by revealing data and knowledge gaps; (iii) allowing regulatory agencies to track progress towards achieving water quality goals and facilitating policy guidance; (iv) aiding managers and practitioners to make evidence-based water management decisions; and (v) serving as a conduit to the public, providing a means for leveraging citizen science initiatives (Figure 1).

**Citation:** Quinn, N.W.T.; Dinar, A.; Sridharan, V. Decision Support Tools for Water Quality Management. *Water* **2022**, *14*, 3644. https:// doi.org/10.3390/w14223644

Received: 2 November 2022 Accepted: 3 November 2022 Published: 11 November 2022

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**Figure 1.** Central role of decision support systems in water quality management. (All icons used in this figure are available freely for public use under creative commons licensing).

The objectives of this Special Issue are to demonstrate the usefulness of decision support tools applied to different types of water quality management issues and to showcase select examples of these issues where contemporary science and technology are used to overcome associated challenges. The aim of this Special Issue within the scientific community is to drive research on emerging tools in water quality management from large-scale, programmatic scopes to small-scale, localized applications. At the same time, it is crucial to highlight the critical role played by stakeholders in supporting programmatic and implementation initiatives, and the need for stakeholder buy-in to ensure the success of water quality management programs. Thus, this Special Issue also highlights how decision support tools can aid in stakeholder participation and engagement.
