research on water quality modelling

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• Review Article
• Published: 12 March 2024

Deep learning for water quality

• Wei Zhi 1 , 2 ,
• Alison P. Appling   ORCID: orcid.org/0000-0003-3638-8572 3 ,
• Heather E. Golden   ORCID: orcid.org/0000-0001-5501-9444 4 ,
• Joel Podgorski   ORCID: orcid.org/0000-0003-2522-1021 5 &
• Li Li   ORCID: orcid.org/0000-0002-1641-3710 2  

Nature Water volume  2 ,  pages 228–241 ( 2024 ) Cite this article

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Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.

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Streamflow data (Fig. 1a ) from the Global Streamflow Indices and Metadata Archive (GSIM) were compiled from repositories at https://doi.org/10.1594/PANGAEA.887477 and https://doi.org/10.1594/PANGAEA.887470 . Water-quality data (Fig. 1b ) from the Global River Water Quality Archive (GRQA) were downloaded from https://doi.org/10.5281/zenodo.7056647 .

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Acknowledgements

W.Z. was supported by the National Natural Science Foundation of China (52121006) and by the Barry and Shirley Isett Professorship (to L.L.) at Penn State University. L.L. was supported by the US National Science Foundation via the Critical Zone Collaborative Network (EAR-2012123 and EAR-2012669), Frontier Research in Earth Sciences (EAR-2121621), Signals in Soils (EAR-2034214), and US Department of Energy Environmental System Science (DE-SC0020146). J.P. was supported by Swiss Agency for Development and Cooperation (SDC) (WABES project, 7F-09963.02.01). This paper has been reviewed in accordance with the US Environmental Protection Agency’s peer and administrative review policies and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement or recommendation for use by the US Government. Statements in this publication reflect the authors’ professional views and opinions and should not be construed to represent any determination or policy of the US Environmental Protection Agency.

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Modeling water quality in watersheds: From here to the next generation

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In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in‐stream water quality and process interactions, soil health and land management, and (peri‐)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

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Surface Water Quality Modeling

Surface water quality models are critically important tools for managing our nation's surface waters. Quantitative models help local communities and environmental managers better understand how surface waters change in response to pollution and how to protect them.

Water quality specialists use models for many purposes:

• Assessing water quality conditions and causes of impairment
• Predicting how surface waters will respond to changes in their watersheds and the environment (e.g., future growth, climate change)
• Developing Total Maximum Daily Loads (TMDLs) and National Pollutant Discharge Elimination Systems (NPDES) permits
• Forecasting quantitative benefits of new surface water protection policies

EPA's Water Modeling Workgroup (WMW) works to facilitate the use of surface water quality models through collaboration, information-sharing, and training on models and modeling resources.

• Assessment of Surface Water Model Maintenance and Support Status
• Surface Water Quality Modeling Training
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This project:

• Presents a review and summary of approximately 130 runoff, hydrodynamic, receiving water, and/or groundwater models available in either the public or private domain.
• Provides guidance on selecting the appropriate model for screening-level versus detailed planning-level applications.
• Describes the use of a Model Selection Tool, developed as part of this project. The Model Selection Tool guides the user to identify the appropriate model(s) available suitable for addressing the environmental problem being considered.

As of February 2020, the web tool is no longer available.

Originally funded as WERF project 99-WSM-5.

Roadmap Workshop on Prioritizing Permitting and Linkages Research in Water Quality

This project held a roadmap workshop to identify and foster research priorities exploring the linkages between receiving waterbody water quality and inputs such as wastewater discharges, nonpoint sources...

Modeling Guidance for Developing Site-Specific Nutrient Goals

This research report presents guidance and tools for the use of models to set waterbody-specific nutrient goals, including Numeric Nutrient Criteria (NNC) and allowable nutrient loadings. The developed...

Technical Approaches for Setting Site Specific Nutrient Criteria

This project developed a methodology for deriving site-specific nutrient criteria (SSNC) for surface waters, including streams and rivers, lakes and reservoirs, and coastal estuaries. The methodology was developed...

Linking Nutrient Reductions to Receiving Water Responses

While the water sector has made significant investments in nutrient control, there is a need for increased availability of data and information on effective practices, expanded engagement with...

Navigating One Water Planning through Municipal Water Programs: Meeting Multiple Objectives and Regulatory Challenges

Today’s water utilities are facing unprecedented challenges to their primary mission—providing reliable and accessible water service, protecting human health and the environment, and making wise infrastructure investments—all while...

Satellite and Drone Remote Sensing Models and Tools for Water Quality Monitoring and Ecological Assessment of Fresh Water Resources

Lakes, reservoirs, and rivers are invaluable inland freshwater resources. Freshwater resources are increasingly experiencing widespread degradation and deterioration, manifested as poor water clarity and high turbidity, eutrophication, and...

Water Quality Modeling (WQM)

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Yoshihide Wada

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Water quality modeling for water availability/scarcity assessment, water-energy-land-environment nexus analysis and identification of cost-effective solutions under long-term changes.

Water quality issues pose increasing risks to human health, water security and ecosystem functioning worldwide. Water quality is an important consideration in both water supply and environmental quality. 

In this context, water quality modeling is added as a new component into IIASA’s Water Program to assess the long-term impacts of future changing socio-economic and climatic conditions on water quality and water resources, and to identify potential solution options. 

• The nutrient export model MARINA is soft-linked to other IIASA models to explore basin-scale nexus solutions.
• A global gridded water quality model is being developed (currently for nutrients, next steps including sediment transport and salinity).
• The model is intended to be open source, modular and will be coupled with existing IIASA models, including CWATM and ECHO

Two main lines of ongoing activities:

1. Water quality modeling using the  MARINA  model (Strokal et al., 2016), mainly in collaboration with the  Water Systems and Global Change  Group, Wageningen University & Research, Netherlands.

MARINA was originally developed for China to quantify nutrient export to seas (Strokal et al., 2016) and recently up-scaled to the world for multiple pollutants in rivers from point sources. As part of on-going IIASA projects, the MARINA model is used to quantify current and future nutrient export to coastal waters for selected large river basins (e.g., Zambezi, Indus, Yangtze) under different socioeconomic development and climate change pathways. To this end, the model is soft-linked to other IIASA models ( CWATM ,  ECHO ,  GLOBIOM ,  EPIC , etc.) to explore basin-scale nexus solutions. The model linkages ( Figure 1 ) and some example outputs ( Figure 2 ) for the Zambezi river basin are shown below. 

2. Development of a global gridded water quality model (currently for nutrients, next steps including sediment transport and salinity) The model is intended to be open source and will be coupled with  CWATM  and  ECHO . To facilitate the coupling with these models, it is designed as a flexible modular process-based parsimonious model with a mixture of empirical or mechanistic process descriptions. This is part of our efforts to develop a next-generation global hydro-economic modeling framework that can explore the economic trade-offs among different water management options, encompassing both water infrastructure and management of water demand and water resources.

Figure 1 The MARINA model is soft-linked to CWATM and ECHO at IIASA’s Water Program to explore economically-optimal water management solutions for selected river basins.

Figure 2 Illustrative example of the MARINA output for annual river export of total dissolved nitrogen by source (TDN, kg/km2/yr) for the Zambezi river basin. The figure illustrates the increase in river export of TDN to sea between 2010 and 2050.

Relevant publications:

Strokal M, Kroeze C, Wang M, Bai Z, Ma L, (2016). The MARINA model (Model to Assess River Inputs of Nutrients to seAs): Model description and results for China.  Sci. Total Environ . 562, 869–888. DOI:  10.1016/j.scitotenv.2016.04.071

Tang T , Strokal M, van Vliet MTH, Seuntjens P,  Burek P , Kroeze C,  Langan S , &  Wada Y  (2019).  Bridging global, basin and local-scale water quality modeling towards enhancing water quality management worldwide.   Current Opinion in Environmental Sustainability  36: 39-48. DOI: 10.1016/j.cosust.2018.10.004 .

Tang T , Strokal M,  Wada Y ,  Burek P ,  Kroeze C ,  van Vliet M , &  Langan S  (2018). Sources and export of nutrients in the Zambezi River basin: status and future trend.  In:  International Conference Water Science for Impact , 16-18 October 2018, Wageningen, Netherlands.

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The U.S. Geological Survey (USGS) provided technical assistance to the National Park Service (NPS) as part of the USGS-NPS Water-Quality Partnership, by gathering references related to water-quality research conducted in the three units of Gateway National Recreation Area (GATE): Jamaica Bay and Staten Island in New York, and Sandy Hook in New Jersey. As part of this effort, a literature search was performed to compile previous water-quality research conducted within the boundaries of GATE. The resulting bibliography is meant to assist GATE resource managers in understanding the extent of available data and developing plans to close data gaps.

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Enhancing the reliability of hydrological simulations through global weather data assimilation in watersheds with limited data

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• Mahalingam Jayaprathiga 1 ,
• A. N. Rohith 2 ,
• Raj Cibin 2 , 5 &
• K. P. Sudheer 1 , 3 , 4  

Hydrological models are critical for water resources planning and management. The precision and reliability of the simulations hinge greatly on the accessibility and quality of available input data. Particularly in developing nations, the major challenge in modeling is the scarcity of fine-scale spatiotemporal input data, specifically precipitation. Remotely sensed weather data has been increasingly used in recent years. However, they possess bias compared to ground observations due to the nature of indirect measurement and may affect the simulated water balance. To address these limitations, we explored data assimilation techniques to improve the Global Precipitation Measurement product (IMERG) precipitation using limited ground observations. Multiple assimilation methods are applied by incorporating Linear scaling Correction Factor (CF) and Power Transformation Function methods (PF). The assimilated IMERG precipitation from the most effective method identified, is utilized in an eco-hydrological model, and the resulting stream flow simulations are validated against observed flow data. The findings indicate that assimilated precipitation enhances the monthly flow statistics in both CF and PF methods and also in conditional merged precipitation. An ensemble of hydrological simulations, outperformed those based on raw IMERG precipitation. Additionally, the hydrological simulations are compared with observed gauge precipitation data and the widely used Climate Forecast System Reanalysis (CFSR) dataset in data-limited watersheds. The simulations utilizing the assimilated IMERG dataset (NSE = 0.52) are comparable to gauge precipitation-based simulations (NSE = 0.61) and significantly superior to CFSR-based simulations (NSE=-0.2). These results highlight the potential of utilizing assimilated remote sensing data for hydrological modeling in data-limited watersheds, leading to improved simulation accuracy and reliability.

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Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India

Mahalingam Jayaprathiga & K. P. Sudheer

Department of Agricultural and Biological Engineering, The Pennsylvania State University, University Park, PA, USA

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All authors contributed to the study conceptualization and design. Material preparation, data collection were performed by JM. Analysis was performed by JM and RAN. The first draft of the manuscript was prepared by JM. Supervision, reviewing and editing of the manuscript were performed by RAN, CR, and KPS. All authors have read and approved the final manuscript.

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Jayaprathiga, M., Rohith, A., Cibin, R. et al. Enhancing the reliability of hydrological simulations through global weather data assimilation in watersheds with limited data. Stoch Environ Res Risk Assess (2024). https://doi.org/10.1007/s00477-024-02758-4

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Accepted : 11 June 2024

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DOI : https://doi.org/10.1007/s00477-024-02758-4

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