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Fu B, Horsburgh JS, Jakeman AJ, Gualtieri C, Arnold T, Marshall L, Green TR, Quinn NWT, Volk M, Hunt RJ, Vezzaro L, Croke BFW, Jakeman JD, Snow V, Rashleigh B. Modeling Water Quality in Watersheds: From Here to the Next Generation. Water Resour Res 2020; 56:10.1029/2020wr027721. [PMID: 33627891 PMCID: PMC7898158 DOI: 10.1029/2020wr027721] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 10/21/2020] [Indexed: 05/19/2023]
Abstract
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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Affiliation(s)
- B. Fu
- Fenner School of Environment and Society and Institute for Water Futures, Australian National University, Canberra, ACT, Australia
| | - J. S. Horsburgh
- Department of Civil and Environmental Engineering and Utah Water Research Laboratory, Utah State University, Logan, UT, USA
| | - A. J. Jakeman
- Fenner School of Environment and Society and Institute for Water Futures, Australian National University, Canberra, ACT, Australia
| | - C. Gualtieri
- Department of Civil, Architectural and Environmental Engineering, University of Napoli Federico II, Naples, Italy
| | - T. Arnold
- Grey Bruce Centre for Agroecology, Allenford, Ontario, Canada
| | - L. Marshall
- Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, New South Wales, Australia
| | - T. R. Green
- Agricultural Research Service, U.S. Department of Agriculture, Fort Collins, CO, USA
| | - N. W. T. Quinn
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - M. Volk
- Helmholtz Centre for Environmental Research—UFZ, Department of Computational Landscape Ecology, Leipzig, Germany
| | - R. J. Hunt
- Upper Midwest Water Science Center, United States Geological Survey, Middleton, WI, USA
| | - L. Vezzaro
- Department of Environmental Engineering (DTU Environment), Technical University of Denmark, Kongens Lyngby, Denmark
| | - B. F. W. Croke
- Fenner School of Environment and Society and Institute for Water Futures, Australian National University, Canberra, ACT, Australia
- Mathematical Sciences Institute, Australian National University, Canberra, ACT, Australia
| | - J. D. Jakeman
- Optimization and Uncertainty Quantification, Sandia National Laboratories, Albuquerque, NM, USA
| | - V. Snow
- AgResearch—Lincoln Research Centre, Christchurch, New Zealand
| | - B. Rashleigh
- Office of Research and Development, United States Environmental Protection Agency, Narragansett, RI, USA
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Taylor JA, Simpson RW, Jakeman AJ. Statistical modeling of restricted pollutant data sets to assess compliance with air quality criteria. Environ Monit Assess 1987; 9:29-46. [PMID: 24254099 DOI: 10.1007/bf00394214] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/1985] [Indexed: 06/02/2023]
Abstract
Three statistical models are used to predict the upper percentiles of the distribution of air pollutant concentrations from restricted data sets recorded over yearly time intervals. The first is an empirical quantile-quantile model. It requires firstly that a more complete date set be available from a base site within the same airshed, and secondly that the base and restricted data sets are drawn from the same distributional form. A two-sided Kolmogorov-Smirnov two-sample test is applied to test the validity of the latter assumption, a test not requiring the assumption of a particular distributional form. The second model represents the a priori selection of a distributional model for the air quality data. To demonstrate this approach the two-parameter lognormal, gamma and Weibull models and the one-parameter exponential model were separately applied to all the restricted data sets. A third model employs a model identification procedure on each data set. It selects the 'best fit' model.
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Affiliation(s)
- J A Taylor
- Centre for Resource and Environmental Studies, Australian National University, Australia
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