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1. Background and Purpose

Water quality is a leading challenge for water resource planners and managers worldwide. Scientific research is increasingly focused on the water quality issues created by extreme weather events associated with climate change [1,2]. At the same time, water resource planners are increasing their demand for climate information for developing vulnerability assessments and climate adaptation plans [3,4]. Vulnerability assessments and climate adaptation plans require integrating the available scientific knowledge on how climate change will affect water quality [5,6]. 


The simultaneous emergence of a supply of scientific research related to water quality and climate change, along with a demand for that knowledge among water resource planners, requires making scientific research actionable and responsive to decision-maker needs.


The challenge of adapting water quality management to climate change is burdened by a dilemma common in environmental planning: the potential disconnect between the production and use of science for environmental decision-making. This disconnect varies between:

  • a demand for more scientific information to meet user needs [7,8,9]
  • a supply of scientific information that users do not utilize for reasons of distrust or lack of organizational technical capacity [10,11,12,13]
  • an oversupply of scientific information that is not salient to users' needs [14]


This research seeks to evaluate whether water quality and extreme events science supply matches demand.




Acknowledgements

This work was supported by the Environmental Protection Agency. The authors thank Amanda L. Fencl for her assistance in developing the survey that informs this research. Additionally, the authors thank all of the participating drinking water utility managers and staff for sharing their experiences.





References

[1] Delpla, I., Jung, A.-V., Baures, E., Clement, M., Thomas, O., 2009. "Impacts of climate change on surface water quality in relation to drinking water production." Environ. Int. 35, 1225-1233.

[2] Kiem, A.S., Austin, E.K., 2013. "Drought and the future of rural communities: opportunities and challenges for climate change adaptation in regional Victoria, Australia." Glob. Environ. Chang. 23, 1307-1316.

[3] Changnon, S.A., Kunkel, K.E., 1999. "Rapidly Expanding Uses of Climate Data and Information in Agriculture and Water Resources: Causes and Characteristics of New Applications." Bull. Am. Meteorol. Soc. 80, 821-830.

[4] Kirchhoff, C.J., 2010. "Integrating science and policy: climate change assessments and water resources management." University of Michigan.

[5] Khan, S.J., Deere, D., Leusch, F.D.L., Humpage, A., Jenkins, M., Cunliffe, D., 2015. "Extreme weather events: should drinking water quality management systems adapt to changing risk profiles?" Water Res. 85, 124-136.

[6] Michalak, A.M., 2016. "Study role of climate change in extreme threats to water quality." Nature 535, 349-350.

[7] Nordgren, J., Stults, M., Meerow, S., 2016. "Supporting local climate change adaptation: where we are and where we need to go." Environ. Sci. Policy 66, 344-352.

[8] NRC, 1999. "Making climate forecasts matter." National Academy Press, Washington, DC.

[9] O'Brien, K., 2012. "Global environmental change III: closing the gap between knowledge and action." Prog. Hum. Geogr. 37, 587-596.

[10] Archie, K.M., Dilling, L, Milford, J.B., Pampel, F.C., 2014. "Unpacking the 'information barrier': Comparing perspectives on information as a barrier to climate change adaptation in the interior mountain West." J. Environ. Manage. 133, 397-410. doi:10.1016/j.jenvman.2013.12.015

[11] Foster, J., Winkelman, S., Lowe, A., 2011. "Lessons learned on local climate adaptation from the urban leaders adaptation initiative." Washington, DC.

[12] Lahsen, M., Nobre, C.A., 2007. "Challenges of connecting international science and local level sustainability efforts: the case of the large-scale biosphere-atmosphere experiment in Amazonia." Environ. Sci. Policy 10, 62-74.

[13] Measham, T.G., Preston, B.L., Smith, T.F., Brooke, C., Gorddard, R., Withycombe, G., Morrison, C., 2011. "Adapting to climate change through local municipal planning: barriers and challenges." Mitig. Adapt. Strateg. Glob. Chang. 16, 889-909.

[14] Stokes, D.E., 1997. "Pasteur's quadrant: basic science and technological innovation." Brookings Institution Press, Washington, DC.

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2. Supply - Demand Framework

Science supply: evaluated using a content analysis of published literature that addresses water quality and extreme events in California

Science demand: evaluated using water quality and extreme event threat perceptions from a survey of California water utilities

Science Supply-Demand Framework

**Framework developed based on previous work by a number of authors**

The Science Supply-Demand Framework maps the existing demand for scientific information onto the horizontal x-axis and the existing supply of scientific information onto the vertical y-axis. For example, if we find a high number of scientific articles documenting how extreme storm events degrade water quality, we would characterize the science information supply as high.

Using the above heuristic, we propose three categories of disconnect: 

  • undersupply, an insufficient amount of scientific information to meet user needs
  • potentially good fit, the supply meets the relative amount of demand
  • oversupply, more scientific information exists than users demand



[1] Archie, K.M., Dilling, L, Milford, J.B., Pampel, F.C., 2014. "Unpacking the 'information barrier': Comparing perspectives on information as a barrier to climate change adaptation in the interior mountain West." J. Environ. Manage. 133, 397-410. doi:10.1016/j.jenvman.2013.12.015

[2] Cash, D.W., Clark, W.C., Alcock, F., Dickson, N.M., Eckley, N., Guston, D.H., Jager, J., Mitchell, R.B., 2003. "Knowledge systems for sustainable development." Proc. Natl. Acad. Sci. 100, 8086–8091.

[3] Jasanoff, S., 2010. "A New Climate for Society." Cult. Soc. 27, 2–3.

[4] Kirchhoff, C.J., 2013. "Understanding and enhancing climate information use in water management." Clim. Change 119, 495–509.

[5] Lemos, M.C., Morehouse, B.J., 2005. "The co-production of science and policy in integrated climate assessments." Glob. Environ. Chang. 15, 57–68.

[6] McNie, E.C., 2007. "Reconciling the supply of scientific information with user demands: an analysis of the problem and review of the literature." Environ. Sci. Policy 10, 17– 38.

[7] Sarewitz, D., Pielke, R.A., 2007. "The neglected heart of science policy: reconciling supply of and demand for science." Environ. Sci. Policy 5–16.

[8] Treml, E.A., Fidelman, P.I.J., Kininmonth, S., Ekstrom, J.A., Bodin, Ö., 2015. "Analyzing the (mis)fit between the institutional and ecological networks of the Indo-West Pacific." Glob. Environ. Chang. 31, 263–271.

[9] van Kerkhoff, L., Pilbeam, V., 2017. "Understanding socio-cultural dimensions of environmental decision-making: A knowledge governance approach." Environ. Sci. 26 Policy 73, 29–37.


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3. Evaluating Science Supply and Demand

Hypotheses

1. There is a higher level of fit for surface water than groundwater.

2. There are topical differences in the degree of fit among pairings of extreme events and water quality issues, specifically an undersupply of drought information and oversupply of extreme storm information.

3. There are regional differences in the degree of fit within California.


Evaluating Science Demand

Data used to represent science demand came from an online survey distributed in 2015 to 756 California water utility planners that asked questions about water quality, perceived threats of climate change, and information needs. A total of 259 (34%) partial and complete surveys were received. Survey results were categorized into their respective California Climate Impact Regions to evaluate science demand across sub-regions.

The below map shows the survey respondents within each sub-region (map by Amanda Fencl).

To evaluate science demand, we developed a metric using responses from two survey questions about current water quality and extreme event issues that water planners encounter.

Science Demand

Using data from these questions, we calculated a Science Demand Metric as the proportion of respondents that reported each extreme event as triggering each water quality issue.

Evaluating Science Supply

To evaluate science supply, we used formal research reported in scientific literature. We used this representation because of its accessibility and repeatability, however we recognize it is only one type of information produced and available for environmental decision-making. Using Scopus©, we generated a compilation of literature published between the years of 2006-2016 that had titles or abstracts containing terms related to our study's focal topics of water quality, water quantity, extreme events, and climate change.

By searching for different combinations of water characteristics, system shocks, and geographies, we were able to capture a high level view of water quality vs. water quantity literature that is available both globally and within California specifically. We found that at both global and California scales:

  • Overall, there is more literature containing "water quality" than "water quantity", however
  • When "climate change" or "extreme events" terms are included, there is less literature containing "water quality" than "water quantity"

Using these results, we next manually reviewed the subset of unique literature containing "water quality" and "extreme events" or "climate change" (N = 115) and coded them using the water quality issues and extreme events described in the Science Demand Metric. Using these data, we calculated our Science Supply Metric as the proportion of the manually reviewed publications that discussed specific water quality issues and extreme event triggers.

Comparing Science Supply and Demand

We then compared the science supply scores with the science demand scores to begin to evaluate fit.

Each fit score was calculated by subtracting the appropriate supply score from its demand score counterpart. For example:

  • demand score for groundwater-agriculture is 0.21
  • supply score for groundwater-agriculture is 0.00
  • 0.21 - 0.00 = 0.21 (demand - supply = fit)
In this example, the fit score for groundwater- agricultural contaminants is 0.21. The fit scores range from -1 (oversupply) to 1 (undersupply), where a fit score of 0 means the relative supply matches the relative demand (potentially good fit). Fit scores were plotted onto heat maps for easy visual interpretation (See Step 4).


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4. Comparing Science Supply and Demand

To compare science supply and demand we conducted three analyses:

  • Plotted science supply-demand fit scores onto heat maps
  • Calculated Quadratic Assignment Procedure (QAP) scores
  • Plotted science supply and demand onto our Science Supply-Demand Framework

Science Supply-Demand Fit Heat Maps

As explained in Step 3, science supply-demand fit scores were plotted onto heat maps for ease of visual interpretation.


Groundwater Science Supply and Demand

  • Drought [undersupply]
  • Extreme storms & urban [oversupply]
  • Extreme storms & agriculture [oversupply]

Surface Water Science Supply and Demand

  • Drought [undersupply]
  • Extreme storms [oversupply]
  • High temps & eutrophication [undersupply]
  • Drought & salinity [undersupply]

QAP Scores

QAP scores give us an idea of correlation. In our research, they enable us to compare how similar the overall demand and supply scores are, thus aiding us in understanding the big picture (non)disconnects. Here we should the QAP results from comparing sub-regional demand and supply. 

We found:

  • Overall better fit between supply and demand for surface water (0.514**) than groundwater (0.287*) [Hypothesis 1]
  • Central Coast (0.602**) and Bay Area (0.584**) have strongest correlations with statewide surface water science supply [Hypothesis 3]
  • Desert (0.278) has the lowest correlation with statewide surface water science supply [Hypothesis 3]
  • North Sierra (0.469*) and Desert (0.336**) had the strongest correlation with statewide groundwater science supply [Hypothesis 3]

Science Supply-Demand Framework Plots

We then plotted the science supply and demand metrics presented in Step 3, onto the Science Supply-Demand Framework presented in Step 2, in order to identify potential disconnects. Overall we found:

  • Undersupply of science, particularly related to drought [Hypothesis 2]
  • Very little supply related to groundwater science
  • Oversupply of extreme storms science [Hypothesis 2]

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5. Identifying Priority Investments

As a final step, we wanted to contemplate moving beyond simply identifying disconnects. To do this, we added up the total disconnect for each water quality issue and extreme event by summing the absolute value of each fit score. This gives us an idea of the water quality issue and extreme event with the largest disconnect, regardless of whether that disconnect represents undersupply or oversupply. The investments scores ranged from 0 to 8 for water quality issues and 0 to 6 for extreme events, where a larger value represents a bigger disconnect.

For example, if we take the absolute value of each of the fit scores calculated for eutrophication (surface water) and sum them, we get an investment prioritization score of 2.11.


Groundwater Quality Issue Investments

  • Urban contaminants (1.47) has the highest investment score and the disconnect represents some oversupply and some undersupply of science
  • Overall, groundwater quality issues have lower investments than surface water quality issues

Surface Water Quality Issue Investments

  • Eutrophication (2.11) has the highest investment score; the disconnect is primarily comprised of undersupply of science
  • Overall, surface water quality issues had higher investment scores (larger disconnects) than groundwater quality issues

Extreme Events- Groundwater Investments

  • Drought (2.39) had the highest investment score among extreme events related to groundwater; the disconnect is completely made up of undersupply of science
  • Overall drought had the highest investment scores of all parameters, regardless of supply type

Extreme Events- Surface Water Investments

  • Drought (2.43) had the highest investment score among extreme events related to surface water; the disconnect is completely made up of undersupply of science
  • Extreme storms (2.30) had the second highest investment score among extreme events related to groundwater; the disconnect is completely made up of oversupply of science

Implications for decision-making and science production and use:

As the plots and findings above show, the disconnect between the production and use of science is much more nuanced than literature describes. The supply-demand disconnect instead differs based on the parameter being evaluated. In some cases, there appears to be not enough science supply to meet demand. In other cases, there appears to be more science supply than demand. Therefore, actions to bridge disconnects will need to take different forms. Additionally, further research is needed to explore each identified disconnect to determine the best and most efficient paths forward. Co-production is one option to help alleviate at least some of the identified disconnects.


For more information:

Meghan Klasic 

Email: [email protected]

Twitter: rogue_Phd

Website: klasicH20.com

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Abstract

The potential disconnect between the supply and demand for scientific knowledge is one of the central problems in environmental planning and decision-making. This paper presents a Science Supply – Demand framework that pulls on existing literature to help classify the types of disconnect. We apply this framework to a case study of California water management to begin understanding why water quality is not prioritized in climate adaptation planning. Science demand is evaluated using water quality and extreme event threat perceptions from a survey of California drinking water utilities and supply is evaluated using a content analysis of published literature that addresses water quality and extreme events in California. Overall, we found an undersupply of science with variation in the degree of fit both topically and regionally across California. This work benefits researchers and managers by i) proposing and applying a tractable method to evaluate the science-policy disconnect and ii) quantifying the extant water quality and extreme events concerns among California drinking water utility planners.

The potential disconnect between the supply and demand for scientific knowledge is one of the central problems in environmental planning and decision-making. This paper presents a Science Supply – Demand framework that pulls on existing literature to help classify the types of disconnect. We apply this framework to a case study of California water management to begin understanding why water quality is not prioritized in climate adaptation planning. Science demand is evaluated using water quality and extreme event threat perceptions from a survey of California drinking water utilities and supply is evaluated using a content analysis of published literature that addresses water quality and extreme events in California. Overall, we found an undersupply of science with variation in the degree of fit both topically and regionally across California. This work benefits researchers and managers by i) proposing and applying a tractable method to evaluate the science-policy disconnect and ii) quantifying the extant water quality and extreme events concerns among California drinking water utility planners.

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