Integrated Approach to Simulate Stream Water Quality for Municipal Supply under a Changing Climate
Publication: Journal of Environmental Engineering
Volume 139, Issue 12
Abstract
To better plan for potential changes to stream water quality under climate change, an integrated approach to simulate paired streamflow and water quality under a range of climate scenarios is developed. Several stochastic nonparametric simulation techniques are integrated to create an end-to-end approach for comprehensive planning, with three steps: (1) develop a relationship between streamflow and water quality, (2) simulate streamflow ensembles under climate change scenarios, and (3) simulate water quality ensembles using the streamflow ensembles in conjunction with the developed relationship. The framework is demonstrated on a municipal water provider developing a new water supply source—but variations of salinity concentrations with streamflow pose limits to its use. For the current climate, the simulations accurately reproduce all of the relevant distributional and threshold statistics of flow and water quality, providing confidence in their use in long-term planning. Under climate change, reduced streamflow scenarios result in ensembles with higher salinity concentrations, which can be used in risk management and impact assessments. The approach is general and extends to other water quality variables associated with hydroclimate.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work is based on Ph.D. research at the University of Colorado. The authors would like to acknowledge Water Research Foundation project 3132, Incorporating climate change information in water utility planning: A collaborative, decision analytic approach, the National Water Research Institute (NWRI) through a NWRI fellowship to the senior author, and the U.S. EPA through a STAR fellowship to the senior author for partial financial support on this research effort. This publication was developed under a STAR Research Assistance Agreement No. F08C20433 awarded by the U.S. Environmental Protection Agency. It has not been formally reviewed by the EPA. The views expressed in this document are solely those of the authors and the EPA does not endorse any products or commercial services mentioned in this publication. NCAR is sponsored by the National Science Foundation.
References
American Water Works Association (AWWA). (2010). “Industry news—Colorado’s Prairie Waters project dedicated.” J. AWWA, 102(11), 88.
Barnett, T., Malone, R., Pennell, W., Stammer, D., Semtner, B., and Washington, W. (2004). “The effects of climate change on water resources in the West: Introduction and overview.” Clim. Change, 62(1–3), 1–11.
Barnett, T. P., and Pierce, D. W. (2008). “When will Lake Mead go dry?” Water Resour. Res., 44(3), W03201.
Block, P., and Rajagopalan, B. (2009). “Statistical-dynamical approach for streamflow modeling at Malakal, Sudan, on the White Nile River.” J. Hydrol. Eng., 185–196.
Borsuk, M. E., Stow, C. A., and Reckhow, K. H. (2002). “Predicting the frequency of water quality standard violations: A probabilistic approach for TMDL development.” Environ. Sci. Technol., 36(10), 2109–2115.
Buishand, T. A., and Brandsma, T. (2001). “Multisite simulation of daily precipitation and temperature in the Rhine basin by nearest- neighbor resampling.” Water Resour. Res., 37(11), 2761–2776.
Chatfield, C. (2004). The analysis of time series: An introduction, 6th Ed., Chapman and Hall/CRC, Boca Raton, FL.
Chau, K. W., Wu, C. L., and Li, Y. S. (2005). “Comparison of several flood forecasting models in Yangtze river.” J. Hydrol. Eng., 485–491.
Dawson, C. W., and Wilby, R. (1998). “An artificial neural network approach to rainfall- runoff modelling.” Hydrolog. Sci. J., 43(1), 47–66.
Efron, B., and Tibshirani, R. (1993). An introduction to the bootstrap, Chapman and Hall, New York.
Ficklin, D. L., Luo, Y., Luedeling, E., and Zhang, M. (2009). “Climate change sensitivity assessment of a highly agricultural watershed using SWAT.” J. Hydrol., 374(1–2), 16–29.
Fukunaga, K. (1972). Introduction to statistical pattern recognition, Academic Press, New York.
Gibson, G., et al. (2000). “Nutrient criteria technical guidance manual: Lakes and reservoirs, first edition.” EPA-822-B00-001, Environmental Protection Agency, Washington, DC.
Grantz, K., Rajagopalan, B., Clark, M., and Zagona, E. (2005). “A technique for incorporating large-scale climate information in basin-scale ensemble streamflow forecasts.” Water Resour. Res., 41(10), W10410.
Grantz, K., Rajagopalan, B., Zagona, E., and Clark, M. (2007). “Water management applications of climate-based hydrologic forecasts: Case study of the Truckee-Carson River basin.” J. Water Resour. Plann. Manage., 339–350.
Grygier, J. C., and Stedinger, J. R. (1988). “Condensed disaggregation procedures and conservation corrections for stochastic hydrology.” Water Resour. Res., 24(10), 1574–1584.
Johnson, A. H. (1979). “Estimating solute transport in streams from grab samples.” Water Resour. Res., 15(5), 1224–1228.
Kendall, D. R., and Dracup, J. A. (1991). “A comparison of index-sequential and Ar(1) generated hydrologic sequences.” J. Hydrol., 122(1–4), 335–352.
Kenney, D. S., Goemans, C., Klein, R., Lowrey, J., and Reidy, K. (2008). “Residential water demand management: Lessons from Aurora, Colorado.” J. Am. Water Resour. Assoc., 44(1), 192–207.
Lall, U., and Sharma, A. (1996). “A nearest neighbor bootstrap for resampling hydrologic time series.” Water Resour. Res., 32(3), 679–693.
Loader, C. (1999). Local regression and likelihood, Springer, New York.
Manczak, H., and Florczyk, H. (1971). “Interpretation of results from the studies of pollution of surface flowing waters.” Water Res., 5(8), 575–584.
Mueller, D. K., and Osen, L. L. (1988). “Estimation of natural dissolved-solids discharge in the upper Colorado River basin, Western United States.” Water Resources Investigation Rep. No. 87–4069, United States Geological Survey, Denver, CO.
Murdoch, P. S., Baron, J. S., and Miller, T. L. (2000). “Potential effects of climate change on surface-water quality in North America.” J. Am. Water Resour. Assoc., 36(2), 347–366.
Muttil, N., and Chau, K. W. (2006). “Neural network and genetic programming for modelling coastal algal blooms.” Int. J. Environ. Pollut., 28(3–4), 223–238.
Nowak, K., Prairie, J., Rajagopalan, B., and Lall, U. (2010). “A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow.” Water Resour. Res., 46(8), W08529.
Pielke, R. A., et al. (2005). “Drought 2002 in Colorado: An unprecedented drought or a routine drought?” Pure Appl. Geophys., 162(8–9), 1455–1479.
Prairie, J. R., and Rajagopalan, B. (2007). “A basin wide stochastic salinity model.” J. Hydrol., 344(1–2), 43–54.
Prairie, J. R., Rajagopalan, B., Fulp, T. J., and Zagona, E. A. (2005). “Statistical nonparametric model for natural salt estimation.” J. Environ. Eng., 130–138.
Prairie, J. R., Rajagopalan, B., Fulp, T. J., and Zagona, E. A. (2006). “Modified K-NN model for stochastic streamflow simulation.” J. Hydrol. Eng., 371–378.
Prairie, J., Nowak, K., Rajagopalan, B., Lall, U., and Fulp, T. (2008). “A stochastic nonparametric approach for streamflow generation combining observational and paleoreconstructed data.” Water Resour. Res., 44(6), W06423.
Prairie, J., Rajagopalan, B., Lall, U., and Fulp, T. (2007). “A stochastic nonparametric technique for space-time disaggregation of streamflows.” Water Resour. Res., 43(3), W03432.
Rajagopalan, B., Grantz, K., Regonda, S. K., Clark, M., and Zagona, E. (2005). “Ensemble streamflow forecasting: Methods and applications.” Advances in Water Science Methodologies, Taylor and Francis, Netherlands.
Rajagopalan, B., and Lall, U. (1999). “A K-nearest-neighbor simulator for daily precipitation and other weather variables.” Water Resour. Res., 35(10), 3089–3101.
Rajagopalan, B., Nowak, K., Prairie, J., Hoerling, M., Harding, B., Barsugli, J., Ray, A., and Udall, B. (2009). “Water supply risk on the Colorado River: Can management mitigate?” Water Resour. Res., 45(8), W08201.
Rajagopalan, B., Salas, J. D., and Lall, U. (2010). “Stochastic methods for modeling precipitation and streamflow.” Advances in data-based approaches for hydrologic modeling and forecasting, World Scientific, Singapore.
Ray, A. J., Barsugli, J. J., and Averyt, K. B. (2008). Climate change in Colorado, Colorado Water Conservation Board, Denver, CO.
Regonda, S. K., Rajagopalan, B., Clark, M., and Zagona, E. (2006). “A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin.” Water Resour. Res., 42(9), W09404.
Rode, M., Arhonditsis, G., Balin, D., Kebede, T., Krysanova, V., van Griensven, A., and van der Zee, S. (2010). “New challenges in integrated water quality modelling.” Hydrol. Process., 24(24), 3447–3461.
Salas, J. D. (1985). “Analysis and modeling of hydrologic time series.” Handbook of hydrology, McGraw-Hill, New York, 19.1–19.72.
Saunders, J. F., and Lewis, W. M. (2003). “Implications of climatic variability for regulatory low flows in the South Platte River Basin, Colorado.” J. Am. Water Resour. Assoc., 39(1), 33–45.
Saunders, J. F., Murphy, M., Clark, M., and Lewis, M. L. (2004). “The influence of climate variation on the estimation of low flows used to protect water quality: A nationwide assessment.” J. Am. Water Resour. Assoc., 40(5), 1339–1349.
Sharma, A., Tarboton, D. G., and Lall, U. (1997). “Streamflow simulation: A nonparametric approach.” Water Resour. Res., 33(2), 291–308.
Snoeyink, V. L., and Jenkins, D. (1980). Water chemistry, Wiley, New York.
Stedinger, J. R., and Vogel, R. M. (1984). “Disaggregation procedures for generating serially correlated flow vectors.” Water Resour. Res., 20(1), 47–56.
Stow, C. A., and Borsuk, M. E. (2003). “Assessing TMDL effectiveness using flow-adjusted concentrations: A case study of the Meuse River, North Carolina.” Environ. Sci. Technol., 37(10), 2043–2050.
Taormina, R., Chau, K. W., and Sethi, R. (2012). “Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon.” Eng. Appl. Artif. Intell., 25(8), 1670–1676.
Tarboton, D. G., Sharma, A., and Lall, U. (1998). “Disaggregation procedures for stochastic hydrology based on nonparametric density estimation.” Water Resour. Res., 34(1), 107–119.
Towler, E. (2010). “Understanding and modeling the impacts of climate change on source water quality and utility planning.” Ph.D. dissertation, Univ. of Colorado at Boulder, Boulder, CO.
Towler, E., Rajagopalan, B., Gilleland, E., Summers, R. S., Yates, D., and Katz, R. W. (2010a). “Modeling hydrologic and water quality extremes in a changing climate: A statistical approach based on extreme value theory.” Water Resour. Res., 46(11), W06511.
Towler, E., Rajagopalan, B., Seidel, C., and Summers, R. S. (2009). “Simulating ensembles of source water quality using a K-nearest neighbor resampling approach.” Environ. Sci. Technol., 43(5), 1407–1411.
Towler, E., Rajagopalan, B., Summers, R. S., and Yates, D. (2010b). “An approach for probabilistic forecasting of seasonal turbidity threshold exceedance.” Water Resour. Res., 46(6), W11504.
Towler, E., Raucher, B., Rajagopalan, B., Rodriguez, A., Yates, D., and Summers, R. S. (2012). “Incorporating climate uncertainty in a cost assessment for new municipal source water.” J. Water Resour. Plann. Manage., 396–402.
Tu, J. (2009). “Combined impact of climate and land use changes on streamflow and water quality in Eastern Massachusetts, USA.” J. Hydrol., 379(3–4), 268–283.
Valencia, D., and Schaake, J. C. (1973). “Disaggregation processes in stochastic hydrology.” Water Resour. Res., 9(3), 580–585.
Whitehead, P. G., Wilby, R. L., Battarbee, R. W., Kernan, M., and Wade, A. J. (2009). “A review of the potential impacts of climate change on surface water quality.” Hydrolog. Sci. J., 54(1), 101–123.
Woodbury, M., Yates, D., Baldo, M., and Kaatz, L. (2011). Front range climate change vulnerability group: A streamflow sensitivity study, Water Research Foundation, Denver, CO.
Wu, C. L., Chau, K. W., and Li, Y. S. (2009). “Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques.” Water Resour. Res., 45(8), W08432.
Yates, D., Gangopadhyay, S., Rajagopalan, B., and Strzepek, K. (2003). “A technique for generating regional climate scenarios using a nearest-neighbor algorithm.” Water Resour. Res., 39(7), 1199.
Yates, D., Purkey, D., Sieber, J., Huber-Lee, A., and Galbraith, H. (2005a). “WEAP21—A demand-, priority-, and preference-driven water planning model part 2: Aiding freshwater ecosystem service evaluation.” Water Int., 30(4), 501–512.
Yates, D., Sieber, J., Purkey, D., and Huber-Lee, A. (2005b). “WEAP21—A demand-, priority-, and preference-driven water planning model part 1: Model characteristics.” Water Int., 30(4), 487–500.
Yoshimura, C., Zhou, M., Kiem, A. S., Fukami, K., Prasantha, H. H. A., Ishidaira, H., and Takeuchi, K. (2009). “2020s Scenario analysis of nutrient load in the Mekong River Basin using a distributed hydrological model.” Sci. Total Environ., 407(20), 5356–5366.
Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 139 • Issue 12 • December 2013
Pages: 1432 - 1440
Copyright
© 2013 American Society of Civil Engineers.
History
Received: May 16, 2013
Accepted: Jul 15, 2013
Published online: Jul 17, 2013
Published in print: Dec 1, 2013
Discussion open until: Dec 17, 2013
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.