Decision Support Tools for Water Quality Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 36054

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors


E-Mail Website
Guest Editor
1. Berkeley National Laboratory, Berkeley, CA, USA
2. Department of Civil Engineering, University of California, Merced, Merced, CA, USA
Interests: agricultural systems; hydro-ecology; irrigation and drainage management; sensor networks; TMDL modelling; water quality; decision support
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Public Policy, University of California, Riverside, CA, USA
Interests: environmental and resource economics

E-Mail Website
Guest Editor
Hebrew University of Jerusalem, Rehovot, Israel
Interests: economics; environment; agriculture

E-Mail Website
Guest Editor
1. University of California, Santa Cruz, CA 95060, USA
2. Affiliated with Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanographic and Atmospheric Administration, Santa Cruz, CA 95060, USA
Interests: hydrodynamic modelling; nonlinear system dynamics; signal processing; TMDL analysis; decision support
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The sustainability of water resources worldwide is increasingly imperiled as climate change contributes to the human-induced problems of water-supply scarcity and maldistribution. The environmental problems associated with water quality have been slower to receive attention; however, the litany of natural disasters that have accompanied these ecosystem changes have created a present-day crisis. The environmental problems associated with agriculture such as aquifer depletion, land subsidence, the seasonal drying of river flows, waterlogging, the salinization of river water and aquifers, and human health problems from the excessive use of fertilizers and pesticides all 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 potable water supply are cognizant of the challenges of curtailing the pollution of the environment while minimizing the costs of treatment and pollutant disposal.

Over the past several decades, with the advent and rapid progress of computational technology, watershed models have increasingly become important and effective tools for tackling a wide range of water resource and environmental management issues and for 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 the temporal dynamics of and patterns of variability in pollutant concentrations and loads. With the advancements in modelling approaches for water quality, there have also been developments in decision-support tools for water-quality management.

This Special Issue on “Decision Support Tools for Water Quality Management” seeks contributions that describe innovative decision-support approaches from around the world and across sectors that can be applied by stakeholders, government entities and regulators to reduce environmental pollution and result in cost-effective and sustainable water management strategies. Examples from agriculture, the municipal and industrial sectors and environmental ecosystems are encouraged.

Submitted papers may address one or more of the following aspects of environmental-decision support:

  • Provide an overview of water-quality sustainability challenges and opportunities.
  • Describe novel or successful techniques for measuring and monitoring water quantity and quality as a basis for progressing towards sustainable management.
  • Explore computer-based-simulation modeling and other analytical techniques that enhance understanding of the water-quality problem or issue and help to formulate solution strategies.
  • Describe sensor and remote-sensing technologies that can be integrated with other more-traditional approaches to develop sustainable water-quality-enhancement strategies.
  • Use benefit–cost analysis to demonstrate the economic benefits and costs associated with the development and application of decision-support tools.
  • Demonstrate the use of various decision-support systems for the optimal management of water quality within a region and for considering the welfare consequences of water-quality regulations.

Dr. Nigel W.T. Quinn
Prof. Dr. Ariel Dinar
Prof. Dr. Iddo Kan
Dr. Vamsi Krishna Sridharan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • climate change
  • water pollution
  • water quality
  • decision support
  • simulation
  • stakeholder
  • remote sensing
  • sensors
  • political processes
  • regulatory framework

Published Papers (12 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review

8 pages, 447 KiB  
Editorial
Decision Support Tools for Water Quality Management
by Nigel W. T. Quinn, Ariel Dinar and Vamsi Sridharan
Water 2022, 14(22), 3644; https://doi.org/10.3390/w14223644 - 11 Nov 2022
Cited by 3 | Viewed by 2154
Abstract
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 [...] Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

Research

Jump to: Editorial, Review

21 pages, 2454 KiB  
Article
Developing a Decision Support System for Regional Agricultural Nonpoint Salinity Pollution Management: Application to the San Joaquin River, California
by Ariel Dinar and Nigel W. T. Quinn
Water 2022, 14(15), 2384; https://doi.org/10.3390/w14152384 - 1 Aug 2022
Cited by 4 | Viewed by 2199
Abstract
Environmental problems and production losses associated with irrigated agriculture, such as salinity, degradation of receiving waters, such as rivers, and deep percolation of saline water to aquifers, highlight water-quality concerns that require a paradigm shift in resource-management policy. New tools are needed to [...] Read more.
Environmental problems and production losses associated with irrigated agriculture, such as salinity, degradation of receiving waters, such as rivers, and deep percolation of saline water to aquifers, highlight water-quality concerns that require a paradigm shift in resource-management policy. New tools are needed to assist environmental managers in developing sustainable solutions to these problems, given the nonpoint source nature of salt loads to surface water and groundwater from irrigated agriculture. Equity issues arise in distributing responsibility and costs to the generators of this source of pollution. This paper describes an alternative approach to salt regulation and control using the concept of “Real-Time Water Quality management”. The approach relies on a continually updateable WARMF (Watershed Analysis Risk Management Framework) forecasting model to provide daily estimates of salt load assimilative capacity in the San Joaquin River and assessments of compliance with salinity concentration objectives at key monitoring sites on the river. The results of the study showed that the policy combination of well-crafted river salinity objectives by the regulator and the application of an easy-to use and maintain decision support tool by stakeholders have succeeded in minimizing water quality (salinity) exceedances over a 20-year study period. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

16 pages, 3559 KiB  
Article
Can Remote Sensing Fill the United States’ Monitoring Gap for Watershed Management?
by Vamsi Krishna Sridharan, Saurav Kumar and Swetha Madhur Kumar
Water 2022, 14(13), 1985; https://doi.org/10.3390/w14131985 - 21 Jun 2022
Cited by 3 | Viewed by 2103
Abstract
Remote sensing has been heralded as the silver bullet in water quality modeling and watershed management, and yet a quantitative mapping of where its applicability is likely and most useful has not been undertaken so far. Here, we combine geospatial models of cloud [...] Read more.
Remote sensing has been heralded as the silver bullet in water quality modeling and watershed management, and yet a quantitative mapping of where its applicability is likely and most useful has not been undertaken so far. Here, we combine geospatial models of cloud cover as a proxy for the likelihood of acquiring remote scenes and the shortest time of travel to population centers as a proxy for accessibility to ground-truth remote sensing data for water quality monitoring and produce maps of the potential of remote sensing in watershed management in the United States. We generate several maps with different cost-payoff relationships to help stakeholders plan and incentivize remote sensing-based monitoring campaigns. Additionally, we combine these remote sensing potential maps with spatial indices of population, water demand, ecosystem services, pollution risk, and monitoring coverage deficits to identify where remote sensing likely has the greatest role to play. We find that the Southwestern United States and the Central plains regions are generally suitable for remote sensing for watershed management even under the most stringent costing projections, but that the potential for using remote sensing can extend further North and East as constraints are relaxed. We also find large areas in the Southern United States and sporadic watersheds in the Northeast and Northwest seaboards and the Midwest would likely benefit most from using remote sensing for watershed monitoring. Although developed herein for watershed decision support in the United States, our approach is readily generalizable to other environmental domains and across the world. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

14 pages, 2666 KiB  
Article
Development and Demonstration of an Endocrine-Disrupting Compound Footprint Calculator
by Rachel Taylor, Kathryn Hayden, Marc Gluberman, Laura Garcia, Serap Gorucu, Bryan Swistock and Heather Preisendanz
Water 2022, 14(10), 1587; https://doi.org/10.3390/w14101587 - 16 May 2022
Cited by 1 | Viewed by 2533
Abstract
Chemicals in personal care products used in everyday lives become part of the wastewater stream. Wastewater treatment plants were not designed to remove these chemicals; therefore, these products and their metabolites persist in the effluent. Many of these chemicals are known, or suspected [...] Read more.
Chemicals in personal care products used in everyday lives become part of the wastewater stream. Wastewater treatment plants were not designed to remove these chemicals; therefore, these products and their metabolites persist in the effluent. Many of these chemicals are known, or suspected to be, endocrine-disrupting compounds (EDCs) and can cause adverse impacts to aquatic organisms at trace concentrations. Here, we developed a publicly available EDC footprint calculator to estimate a household’s EDC footprint. The calculator prompts users to input the number of products they own in each of three categories: health and beauty, laundry, and cleaning. The calculator, which is programmed with average values of EDCs in each product, outputs an estimate of the user’s EDC footprint (mass) and ranks the contribution of each product to the footprint. When used by a group of 39 citizen scientists across the Susquehanna River Basin in the northeastern United States, the average household EDC footprint was ~150 g. Results of this tool aid in decision making by providing users with the information necessary to reduce the household’s footprint through product selection that avoids specific ingredients or by replacing the top-ranking products with greener alternatives. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

13 pages, 3644 KiB  
Article
Meeting the Moment: Leveraging Temporal Inequality for Temporal Targeting to Achieve Water-Quality Load-Reduction Goals
by Nicole Opalinski, Daniel Schultz, Tamie L. Veith, Matt Royer and Heather E. Preisendanz
Water 2022, 14(7), 1003; https://doi.org/10.3390/w14071003 - 22 Mar 2022
Cited by 1 | Viewed by 2314
Abstract
Inequality is an emergent property of complex systems. In catchments, variation in hydroclimatic conditions and biogeochemistry cause streamflow and constituent loads to exhibit strong temporal inequality, with most loads exported during “hot moments”. Achieving water-quality-restoration goals in a cost-effective manner requires targeted implementation [...] Read more.
Inequality is an emergent property of complex systems. In catchments, variation in hydroclimatic conditions and biogeochemistry cause streamflow and constituent loads to exhibit strong temporal inequality, with most loads exported during “hot moments”. Achieving water-quality-restoration goals in a cost-effective manner requires targeted implementation of conservation practices in “hot spots” in the landscape and “hot moments” in time. While spatial targeting is commonly included in development of watershed management plans, the need for temporal targeting is often acknowledged, but no common way to address it has been established. Here, we implement a Lorenz Inequality decision-making framework that uses Lorenz Curves and Gini Coefficients to quantify the degree of temporal inequality exhibited by contaminant loads and demonstrate its utility for eight impaired catchments in the Chesapeake Bay watershed. The framework requires a load-reduction goal be set and then links the degree of temporal inequality in annual nutrient loads to the periods of time during which those loads could be targeted. These results are critical in guiding development of site-specific, cost-effective tools that facilitate load-reduction and water-quality goal attainment for individual catchments. The framework provides valuable insight into site-specific potentials for meeting load-reduction goals. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

15 pages, 2297 KiB  
Article
Blending Irrigation Water Sources with Different Salinities and the Economic Damage of Salinity: The Case of Israel
by Yehuda Slater, Ami Reznik, Israel Finkelshtain and Iddo Kan
Water 2022, 14(6), 917; https://doi.org/10.3390/w14060917 - 15 Mar 2022
Cited by 3 | Viewed by 2460
Abstract
Israel’s water and vegetative agriculture sectors are interdependent, as the latter constitutes the solution for wastewater disposal. We employ a dynamic mathematical programming model that captures this interdependence for evaluating the economic damage of irrigation water salinity under two strategies of blending water [...] Read more.
Israel’s water and vegetative agriculture sectors are interdependent, as the latter constitutes the solution for wastewater disposal. We employ a dynamic mathematical programming model that captures this interdependence for evaluating the economic damage of irrigation water salinity under two strategies of blending water sources with different salinities: field blending, which enables farmers to assign water with a specific salinity to each crop, and regional blending, under which all crops experience similar water salinity. Relative to field blending, the buildup rate of desalination under regional blending is slightly expedited; nevertheless, reallocations of water sources across sectors and crops increase the average irrigation water salinity, and the overall welfare decreases by USD 0.08 per cubic meter of irrigation water—about 20% of the water’s average value of marginal product. Salinity-sensitive crops will face the largest per hectare production reduction if regional blending replaces field blending; however, the combined variations in the prices of irrigation water and agricultural outputs may motivate farmers to move irrigation water to these crops. Under equilibrium conditions in the two sectors, a 1% increase in the average salinity of the irrigation water supplied to a region reduces the value of the marginal product of that water by 2.4% and 1.6% under field and regional blending, respectively. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

16 pages, 3117 KiB  
Article
Environmental Decision Support Systems as a Service: Demonstration on CE-QUAL-W2 Model
by Yoav Bornstein, Ben Dayan, Amir Cahn, Scott Wells and Mashor Housh
Water 2022, 14(6), 885; https://doi.org/10.3390/w14060885 - 11 Mar 2022
Cited by 3 | Viewed by 2366
Abstract
An environmental decision support system (EDSS) can be used as an important tool for the rehabilitation and preservation of ecosystems. Nonetheless, high assimilation costs (both money and time) are one of the main reasons these tools are not widely adopted in practice. This [...] Read more.
An environmental decision support system (EDSS) can be used as an important tool for the rehabilitation and preservation of ecosystems. Nonetheless, high assimilation costs (both money and time) are one of the main reasons these tools are not widely adopted in practice. This work presents a low-cost paradigm of “EDSS as a Service.” This paradigm is demonstrated for developing a water quality EDSS as a service that utilizes the well-known CE-QUAL-W2 model as a kernel for deriving optimized decisions. The paradigm is leveraging new open-source technologies in software development (e.g., Docker, Kubernetes, and Helm) with cloud computing to significantly reduce the assimilation costs of the EDSS for organizations and researchers working on the rehabilitation and preservation of water bodies. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

26 pages, 5276 KiB  
Article
A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory
by Junhao Wu and Zhaocai Wang
Water 2022, 14(4), 610; https://doi.org/10.3390/w14040610 - 17 Feb 2022
Cited by 92 | Viewed by 6110
Abstract
Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality conditions has a significant social and economic value. In this study, a model based [...] Read more.
Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality conditions has a significant social and economic value. In this study, a model based on an artificial neural network (ANN), discrete wavelet transform (DWT), and long short-term memory (LSTM) was constructed to predict the water quality of the Jinjiang River. Firstly, a multi-layer perceptron neural network was used to process the missing values based on the time series in the water quality dataset used in this research. Secondly, the Daubechies 5 (Db5) wavelet was used to divide the water quality data into low-frequency signals and high-frequency signals. Then, the signals were used as the input of LSTM, and LSTM was used for training, testing, and prediction. Finally, the prediction results were compared with the nonlinear auto regression (NAR) neural network model, the ANN-LSTM model, the ARIMA model, multi-layer perceptron neural networks, the LSTM model, and the CNN-LSTM model. The outcome indicated that the ANN-WT-LSTM model proposed in this study performed better than previous models in many evaluation indices. Therefore, the research methods of this study can provide technical support and practical reference for water quality monitoring and the management of the Jinjiang River and other basins. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

21 pages, 4814 KiB  
Article
Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA
by Abdul Hannan and Jagadeesh Anmala
Water 2021, 13(19), 2790; https://doi.org/10.3390/w13192790 - 8 Oct 2021
Cited by 7 | Viewed by 2981
Abstract
The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are [...] Read more.
The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are used routinely to solve the problem from a causal modeling perspective. However, a transparent process in the form of Decision Trees is used to shed more light on the structure of input variables such as climate and land use in predicting the stream water quality in the current paper. The Decision Tree algorithms such as classification and regression tree (CART), iterative dichotomiser (ID3), random forest (RF), and ensemble methods such as bagging and boosting are applied to predict and classify the unknown stream water quality behavior from the input variables. The variants of bagging and boosting have also been looked at for more effective modeling results. Although the Random Forest, Gradient Boosting, and Extremely Randomized Tree models have been found to yield consistent classification results, DTs with Adaptive Boosting and Bagging gave the best testing accuracies out of all the attempted modeling approaches for the classification of Fecal Coliforms in the Upper Green River watershed, Kentucky, USA. Separately, a discussion of the Decision Support System (DSS) that uses Decision Tree Classifier (DTC) is provided. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

32 pages, 15948 KiB  
Article
Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California
by Nigel W. T. Quinn, Michael K. Tansey and James Lu
Water 2021, 13(19), 2661; https://doi.org/10.3390/w13192661 - 27 Sep 2021
Cited by 5 | Viewed by 2547
Abstract
Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood [...] Read more.
Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

33 pages, 3939 KiB  
Article
Supporting Restoration Decisions through Integration of Tree-Ring and Modeling Data: Reconstructing Flow and Salinity in the San Francisco Estuary over the Past Millennium
by Paul H. Hutton, David M. Meko and Sujoy B. Roy
Water 2021, 13(15), 2139; https://doi.org/10.3390/w13152139 - 3 Aug 2021
Cited by 4 | Viewed by 2945
Abstract
This work presents updated reconstructions of watershed runoff to San Francisco Estuary from tree-ring data to AD 903, coupled with models relating runoff to freshwater flow to the estuary and salinity intrusion. We characterize pre-development freshwater flow and salinity conditions in the estuary [...] Read more.
This work presents updated reconstructions of watershed runoff to San Francisco Estuary from tree-ring data to AD 903, coupled with models relating runoff to freshwater flow to the estuary and salinity intrusion. We characterize pre-development freshwater flow and salinity conditions in the estuary over the past millennium and compare this characterization with contemporary conditions to better understand the magnitude and seasonality of changes over this time. This work shows that the instrumented flow record spans the range of runoff patterns over the past millennium (averaged over 5, 10, 20 and 100 years), and thus serves as a reasonable basis for planning-level evaluations of historical hydrologic conditions in the estuary. Over annual timescales we show that, although median freshwater flow to the estuary has not changed significantly, it has been more variable over the past century compared to pre-development flow conditions. We further show that the contemporary period is generally associated with greater spring salinity intrusion and lesser summer–fall salinity intrusion relative to the pre-development period. Thus, salinity intrusion in summer and fall months was a common occurrence under pre-development conditions and has been moderated in the contemporary period due to the operations of upstream reservoirs, which were designed to hold winter and spring runoff for release in summer and fall. This work also confirms a dramatic decadal-scale hydrologic shift in the watershed from very wet to very dry conditions during the late 19th and early 20th centuries; while not unprecedented, these shifts have been seen only a few times in the past millennium. This shift resulted in an increase in salinity intrusion in the first three decades of the 20th century, as documented through early records. Population growth and extensive watershed modification during this period exacerbated this underlying hydrologic shift. Putting this shift in the context of other anthropogenic drivers is important in understanding the historical response of the estuary and in setting salinity targets for estuarine restoration. By characterizing the long-term behavior of San Francisco Estuary, this work supports decision-making in the State of California related to flow and salinity management for restoration of the estuarine ecosystem. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

Review

Jump to: Editorial, Research

30 pages, 4137 KiB  
Review
The Municipal Water Quality Investigations Program: A Retrospective Overview of the Program’s First Three Decades
by Paul H. Hutton, Sujoy B. Roy, Stuart W. Krasner and Leslie Palencia
Water 2022, 14(21), 3426; https://doi.org/10.3390/w14213426 - 28 Oct 2022
Cited by 6 | Viewed by 2794
Abstract
This paper presents the history and evolution of the California Department of Water Resources’ Municipal Water Quality Investigations (MWQI) program. This program tracks source water quality in the Sacramento–San Joaquin Delta (Delta) for drinking water supply for nearly two-thirds of California. The program [...] Read more.
This paper presents the history and evolution of the California Department of Water Resources’ Municipal Water Quality Investigations (MWQI) program. This program tracks source water quality in the Sacramento–San Joaquin Delta (Delta) for drinking water supply for nearly two-thirds of California. The program provides early warning of changing conditions in source water quality, provides data and knowledge-based support for operational decision making, and provides scientific support to a variety of urban water users. This retrospective (i) documents program formation, (ii) describes its evolution in response to regulations and technological advances in water treatment and field monitoring, and (iii) notes how the development of federal drinking water quality regulations such as the Disinfection By-Products Rule impacted the program. The MWQI program is believed to be the first drinking water supply program in the United States to conduct continuous, real-time monitoring of organic carbon, bromide, and other anions and to report these data on the internet. In addition to its regular use for operational decision making, the data may be used for evaluating long-term trends and responses to specific changes in the Delta and its watershed. Future program directions will likely be guided by factors that may trigger changes in treatment plant processes and operations, such as emerging contaminants, changes in land and water management practices, permanent Delta island flooding, sea level rise, and climate change. While this retrospective focuses on one region, its multi-decade interplay of science, treatment and monitoring technology, and regulations (as well as practical aspects of managing such a large-scale program) are broadly relevant to professionals engaged in drinking water quality management in other urbanized and developed regions of the world. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
Show Figures

Figure 1

Back to TopTop