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An Integrated Approach to Simulate Stream Water Quality for Municipal Supply Under Changing Climate

Erin Towler: National Center for Atmospheric Research, Boulder, CO, USA

Balaji Rajagopalan: Department of Civil, Environmental and Architectural Engineering,

University of Colorado at Boulder, Boulder, CO, USA; Co-operative Institute for Research in

Environmental Sciences, University of Colorado, Boulder, CO, USA

David Yates: National Center for Atmospheric Research, Boulder, CO, USA

Alfredo Rodriguez: Aurora Water, Aurora CO, USA

R. Scott Summers: Department of Civil, Environmental and Architectural Engineering,

University of Colorado at Boulder, Boulder, CO, USA

Link to paper in the ASCE Civil Engineering Database:

http://cedb.asce.org/cgi/WWWdisplay.cgi?312536

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: (i) develop a

relationship between streamflow and water quality, (ii) simulate streamflow ensembles under

climate change scenarios, and (iii) 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

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concentrations with streamflow pose limits to its use. For 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.

Subject Headings: water quality; salinity; simulation; water supply; climate change; municipal

water

Introduction

For many municipalities, population and economic growth have spurred increased water

demands, requiring the development of new water sources. This can be challenging, as most

reliable and abundant supplies are often already allocated, and remaining options can be limited

due to issues ranging from investment costs to engineering limitations to public concerns. As

such, new development projects are often carefully weighed and go through extensive planning.

However, few consider the potential effects that a changing climate will have on these new

sources. This is critical as there is mounting evidence that climate change will continue to strain

water supplies, especially in the western United States (US) (Barnett et al. 2004). To date, most

efforts to understand changes have focused on water quantity and have largely overlooked

changes to water quality. This presents another level of complexity for drinking water managers,

but understanding changes to water quality characteristics is critical for comprehensive new

source water management and treatment planning.

The potential impacts of climate on water quality have been broadly identified (see

Murdoch et al. (2000) and Whitehead et al. (2009) and references therein), and are often built

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upon relationships established between water quantity and quality. For instance, pollutant

concentrations that impact water quality are often associated with streamflow fluctuations

(Johnson 1979; Manczak and Florczyk 1971; Stow and Borsuk 2003), and variability in low

flows can be of particular concern when balancing water quality protection and pollutant

discharges (Saunders and Lewis 2003; Saunders et al. 2004). These relationships have been

exploited to assess water quality, especially recently in predicting total maximum daily loads

(Borsuk et al. 2002), simulating the likelihood of exceeding a turbidity standard using seasonal

forecasts (Towler et al. 2010b) and climate change projections (Towler et al. 2010a), and for

estimating salinity in streamflows using parametric (Mueller et al. 1988) and nonparametric

statistical methodologies (Prairie et al. 2005; Prairie and Rajagopalan 2007). However, these

types of water quality evaluations are rarely incorporated with quantitative evaluations of climate

change. Clearly, there is a need to fully couple climate, streamflow, and water quality assessment

components to ensure that planning strategies are informed by the full range of potential impacts.

To this end, this paper puts forth an integrated stochastic framework that includes

simulation techniques that can be used to develop paired projections of streamflow and water

quality under a range of climate scenarios. This contribution is unique in that it combines

simulation techniques for water quantity and quality to create a new end-to-end approach for

comprehensive planning. The proposed integrated methodology has three steps: (i) develop a

relationship between streamflow and water quality, (ii) simulate streamflow ensembles under

climate change scenarios, and (iii) simulate water quality ensembles using the streamflow

ensembles from (ii) in conjunction with the relationship developed in (i). The approach is

demonstrated on a municipal water provider in Colorado, US, that is developing a new source of

water supply to meet its burgeoning demand – but salinity concentrations, as measured by total

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dissolved solids (TDS), vary with streamflow and pose limits to its use. Using climate scenarios,

streamflow and salinity simulations are generated that are relevant to the water provider’s

planning, and this application to water quality for municipal water supply is a distinctive aspect

of this study. Further, the potential for using this technique for other water quality variables is

also illustrated. In short, the goal of the study is to develop a general tool that can be used to

characterize water quality variability in current climate, as well as under climate change

scenarios.

Background

Study Area

The approach is demonstrated for the city of Aurora, Colorado, US, a growing suburb of

Denver, which is served by the municipal water provider, Aurora Water. To help meet rising

demand and to reduce drought vulnerability (Pielke et al. 2005), the utility has pursued demand

management options (Kenney et al. 2008) and new source development. The recent Prairie

Waters Project (PWP) is an example of the latter, and is an effort that utilizes water rights that

Aurora Water already owns to increase their supply by 20% (American Water Works

Association (AWWA) 2010). The PWP pumps water out of the South Platte River near the US

Geological Survey (USGS) Henderson streamflow gage (Figure 1 and see upcoming Data

section), which is downstream of a treated wastewater discharge location and is influenced by

return flows from agricultural sources, causing it to have a higher concentration of contaminants

and solutes. As such, the water undergoes multiple processes to ensure public health and

compliance with all Environmental Protection Agency (EPA) primary standards. The treatment

process for the new water source has been designed with multiple barriers, and includes two

main purification steps: first, the water flows through a riverbank filtration system and is pumped

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into a protected aquifer basin for recharge and recovery at the PWP North Campus, and second,

it undergoes advanced treatment at the Peter Binney Water Purification Facility. The advanced

treatment includes softening, ultraviolet oxidation, filtration, and activated carbon adsorption

(AWWA 2010). However, none of the processes are specifically designed to remove TDS,

which have been observed to range from 200 to 900 mg/L (milligrams per liter; Figure 2). The

secondary EPA standard for salinity (as measured by TDS) is 500 mg/L. The TDS at the intake

location varies throughout the year, and regularly exceeds the EPA standard (Figure 2). To reach

the desired TDS level, the PWP treated water will be blended with water from the nearby Aurora

Reservoir, which is a water supply that has a more constant TDS level of about 250 mg/L.

Clearly, the ability to simulate the TDS in the South Platte under climate change conditions will

be extremely useful to Aurora Water, as increased TDS values will constrain the use of this new

water source and affect their blending planning.

Climate Change Scenarios

Aurora Water draws from surface water supplies primarily from three river basins: the

Arkansas, Platte, and Colorado. Understanding the potential changes to runoff in these basins is

of great interest for Aurora Water, as well as for the many other users who rely on them, and has

been a subject of extensive study (e.g., see Ray et al. 2008 and references therein). Further, the

recent Joint Front Range Climate Change Vulnerability Study (JFRCCVS) quantified streamflow

sensitivity to climate change specifically for drinking water suppliers along the Front Range of

Colorado, including Aurora Water (Woodbury et al. 2011). The study examined several General

Circulation Model-based climate change scenarios in conjunction with two hydrologic models,

the Water Evaluation and Planning (WEAP) model from Stockholm Environment Institute

(Yates et al. 2005a; Yates et al. 2005b), and the Sacramento model used in the National Weather

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Service River Forecast System. An outcome of this effort was a dataset of simulated streamflow

at specific gage locations throughout the study area, including the South Platte River. For this

study, the simulations for the South Platte River gage at Henderson, which is an approximate

location for the PWP intakes (see upcoming Data section), were examined. Results showed that

except for one climate scenario examined, the majority of scenarios and both hydrologic models

projected annual streamflow reductions in the range of 5 to 36% (Woodbury et al. 2011 or see

Table 1 in Towler et al. 2012). Given these findings and concerns over decreased runoff, it was

determined that four flow volume reduction scenarios in the range of 0% to 30% would be

selected for the approach presented in this paper, which represent likely trajectories under a

changing climate. The first flow scenario, CC0, represents baseline conditions or natural

variability, i.e., the flow is reduced by 0%. Three additional scenarios, CC10, CC20, and CC30,

are subjected to 10, 20, and 30% annual flow volume reductions, respectively. The streamflow

data used to create these scenarios are detailed in the next section.

Data

The two datasets used in this analysis are described below. All years indicated refer to

water years, where the water year spans from October 1 to September 30.

(i) Undepleted streamflow data for the USGS South Platte River at Henderson gage

06720500, which is an approximate location for the PWP intakes, were obtained from Denver

Water, a neighboring municipal water provider, for the period 1947 – 1991. “Undepleted” flows

are calculated to represent what the gage flow would have been if the effects of management –

such as diversions, reservoirs, and return flows – were removed. While an estimate of natural

flow, the term undepleted flow recognizes the fact that certain changes to flow that are unknown

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or unquantifiable are not accounted for. Nonetheless, undepleted flows are valuable in that they

can provide a baseline for assessing the implications of climate change (Woodbury et al. 2011).

To develop a longer time series, the daily undepleted flow record was reconstructed for

the period of 1992-2008. This was done by utilizing the observed daily streamflow data from the

Henderson gage, or the depleted flows (also referred as ‘gage flows’), which were available from

1926-2008, in conjunction with a relationship identified between the gage flow and undepleted

flow for the overlap period of 1976-1991. The flows for the overlap period exhibit a strong

positive correlation (ρ=0.92, figure not shown). The reader is referred to Towler (2010) for

procedural details, but in general, the functional relationship was applied to the unpaired gage

flows from 1992-2008 to reconstruct the corresponding undepleted flows at the Henderson gage.

This resulted in a final time series of undepleted flows for sixty-two years, 1947 - 2008, where

the daily value matrix, w, is of size 62x365, and the annual value matrix, W, is of size 62x1.

Several measures were tested to evaluate the predictive skill of the model, which indicated that

the model does well in a predictive mode (figures not shown, see Towler (2010)).

(ii) TDS concentrations were calculated from specific conductance data obtained from

the SPCURE (South Platte Coalition for Urban River Evaluation) monitoring network

(http://spcure.org/). The station is identified as METRO SP-124 (South Platte River at 124th

Avenue), which is the approximate location of the Henderson gage, and is available through

EPA’s STORNET database (http://www.epa.gov/storet/). Data was available at a sub-monthly

frequency from 1991-2008, with a sample size of 257. Specific conductance was multiplied by a

factor of 0.64 to obtain TDS values (Snoeyink and Jenkins 1980).

Integrated Framework

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The integrated methodology involves simulating the streamflow first and subsequently

the stream water quality. The three main steps of the proposed approach are: (1) Develop flow

and TDS relationship, (2) Simulate undepleted flows, and (3) Simulate stream water quality

(TDS). These steps are detailed below.

Step 1: Develop Flow and TDS Relationship

A functional relationship based on local polynomials is developed between the daily TDS

and undepleted flows available for the 1991-2008 period. The scatterplot is shown in Figure 3

and a strong inverse relationship (ρ=-0.68), characteristic of a dilution curve, can be seen. The

relationship is smoothed using a local polynomial technique (Loader 1999), which is a

nonparametric regression technique that ‘‘locally’’ evaluates the function at each desired point

(grey line in the figure). Two parameters need to be estimated for this approach: first the degree

of the polynomial (1 or 2), as well as a smoothing parameter, alpha, which indicates the fraction

of data points included in each estimation (0≤alpha≤1). The parameter combination is selected

by minimizing an objective criteria, the generalized cross validation function (see Loader 1999).

Here, the function was estimated at each point using a second order polynomial (i.e., degree =2)

and all of the data points (i.e., alpha = 1). To do this, the Locfit library (http://cran.r-

project.org/web/packages/locfit/index.html) in the statistical package R (http://www.r-

project.org/) was used. Though the relationship between streamflow and salinity may be

qualitatively known a priori, it is important to fit a model that has the ability to effectively

simulate quantitative estimates. The dynamic nature of nonparametric models provides valuable

flexibility in capturing any arbitrary underlying feature (i.e., linear or nonlinear). A step-by-step

overview of this technique is provided by Prairie et al. (2005).

Step 2: Simulate Undepleted Flows

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The second part in the framework is to stochastically simulate undepleted flows based on

natural variability (i.e., CC0) and plausible climate change scenarios (i.e., CC10, CC20, CC30).

This is accomplished in two-parts: (1) annual streamflow values are simulated and (2) they are

disaggregated to daily flow values such that they sum to the aggregate annual flow. Below

descriptions of each part are provided.

(1) Annual Streamflow Simulation

A well-known key to water resource management is a strong understanding of

streamflow variability, and there is a rich history of stochastic simulation efforts that offer ways

to develop additional synthetic sequences of hydrology. Linear time series modeling is a well-

developed and widely applied approach (Chatfield 2004; Salas 1985), though it suffers from

several drawbacks, including the assumption of a normal distribution of data and errors, as well

as only being able to model underlying linear features (Rajagopalan et al. 2005). As such,

nonparametric techniques have been explored as a means of providing a more flexible and

general approach.

Nonparametric methods are appealing in that they do not make any prior assumptions

about the underlying structure of the time series. Methods have evolved from the simple index-

sequential method (Kendall and Dracup 1991) to nearest neighbor bootstrap resampling methods

(Efron and Tibshirani 1993; Lall and Sharma 1996) and kernel methods (Sharma et al. 1997), as

well as to more advanced local polynomial models with residual resampling approaches (Prairie

et al. 2006) and incorporation of paleoreconstruction information (Prairie et al. 2008). For a

detailed overview of parametric and nonparametric methods of precipitation and streamflow

simulation, the reader is referred to the review by Rajagopalan et al. (2010).

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Here, a lag-1 nearest neighbor bootstrapping approach is employed, similar to what is

used by Lall and Sharma (1996), for its simplicity and ease of implementation. Further, this

nonparametric approach has the ability to capture all the relevant statistics, and has been

successfully applied to generate ensembles of daily weather (Rajagopalan and Lall 1999; Yates

et al. 2003; Buishand and Brandsma 2001), streamflows (Lall and Sharma 1996, Grantz et al.

2005; Prairie et al. 2006), and water quality (Prairie et al. 2005; Towler et al. 2009). Though the

reader is referred to Lall and Sharma (1996) for details, an overview of the main steps of the

algorithm is given here:

(i) Randomly resample one of the reconstructed annual flow values, Wi.

(ii) Calculate the scalar distance between this and all the annual flows in the record.

(iii) Sort the distances calculated in (ii) and select K-nearest neighbors. There are

several methods for selecting K , but the heuristic rule, where K is calculated as the square root

of the sample size, or in this case 62=K , with its theoretical justifications (Fukunaga 1972;

Lall and Sharma 1996) has worked well in generating streamflow ensembles (Lall and Sharma

1996, Grantz et al. 2005; Prairie et al. 2006).

(iv) A probability metric is used to assign weights to each of the K-nearest neighbors

given as:

∑=

= K

ii

jjp

1

1

1

for all j = 1,2,…,K.

This results in the closest neighbor receiving the highest weight and the furthest (i.e., the Kth

neighbor) receiving the lowest weight. The cumulative sum of these weights provides a

cumulative distribution function as:

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∑=

=i

jji pcp

1

for all i = 1,2,….,K.

Other weight functions (e.g., bisquare function) can also be used, though it has been shown that

simulations are robust to the choice of weight function (Lall and Sharma 1996).

(v) One of the K neighbors (i.e., one of the historical years) is resampled, say year t,

using the cumulative weight function described in (iv), and the annual flow corresponding to

year t+1 is the simulated value.

(vi) Steps (ii) through (v) are repeated to generate ensembles (i.e., 250 in this case), each

of 61-years in length to simulate the time horizon of 2010-2070.

The simulated annual values based on the historical streamflows provide a robust

characterization of the natural variability, i.e., CC0. As previously mentioned, three additional

streamflow reduction scenarios are examined in this study: CC10, CC20, and CC30, which

represent annual streamflow reductions of 10, 20, and 30 percent, respectively. Simulations

were developed for a time horizon that spanned from 2010 to 2070 (sixty-one years). For each

scenario, the appropriate linear trend was gradually imposed on each 61-year CC0 flow

simulation. This methodology has also been used to study the water supply risk in the Colorado

River Basin due to climate change (Barnett and Pierce 2008; Rajagopalan et al. 2009).

(2) Disaggregation to Daily Streamflows

The simulated annual streamflows from (1) need to be disaggregated to daily values.

Stochastic disaggregation techniques have been developed by hydrologists and widely used in

basin-wide flow simulation, traditionally using linear approaches (Grygier and Stedinger 1988;

Stedinger and Vogel 1984; Valencia and Schaake 1973) and improved nonparametric techniques

(Prairie et al. 2007; Tarboton et al. 1998). However, disaggregation to finer time scales (i.e.,

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daily) is computationally challenging, especially when using traditional methods. Recently,

Nowak et al. (2010) developed a simple method based on resampling historical proportion

vectors, which we adopted for this study. The reader is referred to Nowak et al. (2010) for

procedural details and method validation, but here the main steps are provided in brief:

(i) For each year of the historic record, the observed daily streamflow values, w, are

converted to a proportion of the year’s total annual flow. The resulting matrix P, will have

dimensions 62 x 365, where 62 is the number of years of observed data.

(ii) For each simulated annual flow, Z, K-nearest neighbors are identified and one of

them (i.e., one of the historical years, say year y) is selected using the approach outlined in (1)

above. The corresponding proportion vector (py) is applied to the simulated value to obtain the

daily flow vector (zy), such that:

Zpz yy =

(iii) Repeat step (ii) for all the annual flows. Thus, ensembles of daily streamflow

sequences are generated.

Step 3: Simulate Stream Water Quality ( TDS )

The daily simulated flows from the previous step (i.e., Step 2) are used with the

developed functional relationship (i.e., Step 1) to simulate the daily TDS. To characterize the

variability, a nearest neighbor residual resampling technique is used, where residuals are

resampled (i.e., bootstrapped) within a neighborhood of the point estimate and then added to the

mean estimate from the local regression. This method for uncertainty quantification is detailed

in Prairie et al. (2005). This creates daily TDS sequences for each climate change scenario –

CC0, CC10, CC20, and CC30.

Results

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Streamflow and Salinity: Historic Validation

For streamflow validation, the simulation technique was used to generate ensembles of

daily streamflow of the same length as the historical data (i.e., 62 years) and a suite of

distributional and threshold exceedance statistics were computed at the annual and daily time

scales. The stochastic disaggregation approach to daily streamflow simulation has been well

tested (Nowak et al. 2010), but a sampling from the method validation to this data is provided

here.

The probability density function (PDF) of the historical and simulated annual streamflow

PDF is shown in Figure 4 (left), and it can be seen that the historical PDF is very well captured

by the simulations. This would indicate that all of the annual distributional statistics (such as

mean, variance, skew, lag-1 correlation etc.) are also well reproduced (figures not shown).

The historical PDF of daily streamflows for the month of May and those from the

simulations are shown in Figure 4 (right) – the highly skewed PDF is very well described. For

each month, it was also found that all the distributional properties of daily streamflow (e.g.,

mean, variance, skew, maximums, minimums) are faithfully simulated (figures not shown).

From both a water quantity and quality perspective, drought and threshold exceedance

statistics are of great importance. As such, the annual minimum 7-day-average flow was

computed, which is important for setting pollutant discharge permits, as well as the maximum

number of consecutive days below a flow threshold. Results are shown using the 33rd quantile of

the historical daily flows (Q33) as the threshold, though other thresholds were validated as well

(figures not shown). Box plots of these statistics from the simulations and the corresponding

historical values are shown in Figure 5. The threshold statistics are not all guaranteed to be

reproduced in the model, but the figures show that they are very well captured. It is quite

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remarkable that a single annual value disaggregated into 365 daily values is able to reproduce the

statistics at all time scales – from daily to annual.

The simulated daily streamflows were used to simulate TDS from the functional

relationship (i.e., Figure 3). However, it is not straightforward to provide a validation of the

TDS simulations similar to the streamflow described above. This is due to the fact that TDS

observations are “snapshots” in time, in that observations are not recorded daily like streamflow.

For instance, the month of June has only sixteen TDS observations for the entire record, whereas

flows are recorded every day. However, one of the key benefits of the proposed stochastic

technique is its ability to use a long record of streamflow data with limited water quality data to

simulate a rich variety of water quality. The box plots of TDS simulations alongside those of the

historical observations for two representative months, January (a relatively dry time of the year)

and June (a wet time of the year due to snowmelt runoff), are shown in Figure 6. As would be

expected, there is more variability from the simulations (black boxes), compared to the historical

variability (grey boxes). Similar results were found for the other months of the year (figures not

included).

The above results demonstrate that the coupled streamflow and TDS simulation

technique is useful, and provides a strong capability to generate rich variety of simulations and

reproduce the historical variability faithfully.

Streamflow and Salinity: Climate Change Simulation

Following the validation efforts, daily flow scenarios were simulated for the selected

climate change scenarios: CC10, CC20, and CC30. Annual streamflows were generated for the

future period of 2010 – 2070, and to this the appropriate linear reduction trend was applied (i.e.,

10%, 20% and 30%, respectively). Then, annual flows were disaggregated to generate daily

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streamflow sequences corresponding to these reduction trends. The average PDF of the

simulated daily streamflows for the year 2070 for CC0 and CC30 shows the shift towards lower

flows under the flow reduction scenario (Figure 7, left).

The simulated daily streamflows from CC10, CC20, and CC30 were then translated to

climate change scenarios of daily TDS. As expected, the PDFs for CC0 and CC30 show that the

TDS shifts towards higher values with flow reductions (Figure 7, right), which is consistent with

the inverse relationship with streamflow. By computing the area under the curves from zero to

select thresholds, the PDFs can be used to calculated exceedance probabilities (Table 1). In terms

of the EPA secondary standard, the threshold of interest is 500 mg/L; for this there is a 72%

chance of exceedance in the 0% flow reduction case (i.e., natural climate variability). This

increases to 81% with a 30% flow reduction due to climate change. The higher threshold

exceedance probability indicates that in order to meet a constant finished TDS target, there will

be an elevated demand for the lower TDS source water. This is important for planning and

management, and has been found to significantly affect both utility treatment expenses and

residential costs (see Towler et al. 2012).

Extension to Other Water Quality Variables

Though the framework was developed and demonstrated for salinity, it should be pointed

out that the framework is portable to other water quality variables that are related to flow. For

this study site, additional water quality parameters are available through the SPCURE website,

and for illustrative purposes the strong, nonlinear relationship between streamflow and both

nitrogen (N) and phosphorus (P) can be seen (Figure 8). These nutrients play a critical role in

the primary productivity of water bodies, and algal blooms during the growing season are

predicted when total P levels are greater than 0.01 mg/L and/or total N level are greater than 0.15

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mg/L (Gibson et al. 2000). The utility currently plans for the PWP water to go directly into

treatment, but if raw water storage is ever considered, nutrient simulations would be useful. It is

straightforward to use the proposed integrated approach to simulate ensembles of N and P

(figures not shown), and consequently the probability of exceeding select levels (Table 2). From

the table, it is clear that both thresholds (i.e., 0.01 and 0.15 mg/L for P and N, respectively) are

consistently exceeded, more than 99% of the time for all scenarios. For P, the 1 mg/L level is

regularly exceeded (i.e., 66% in CC0 and 77% in CC30), which would require removal or

dilution of at least 90% to meet the threshold. Similarly for N, the 1.5 mg/L level is exceeded

83% and 88% of the time for CC0 and CC30, respectively, again requiring removal or dilution of

at least 90% to achieve the threshold.

Discussion

The availability of historic streamflow records and water quality observations makes

stochastic simulation an attractive tool for developing future projections. The approach offers an

efficient method to develop ensembles, which effectively characterize the accompanying

uncertainty, and have enormous potential for robust decision-making and impact assessments

(e.g., Grantz et al. 2007; Towler et al. 2012). One limitation is the stationarity assumption of the

functional relationships – namely between the daily flows and TDS values. While the

relationships can be updated with additional data, the assumption is that the historical

relationships remain valid for future climate. This assumption was tested for the historic period,

where an analysis that split the paired data into two halves indicated that the flow-TDS

relationship and the distributions of flow and TDS did not significantly change between the first

and second periods (figures not shown). One advantage of process-based models is that they can

explicitly account for disturbances – such as land use changes or reservoir construction – that

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might affect the historical relationship. Here, climate scenario selection was informed by two

conceptual hydrologic models (i.e., the aforementioned Sacramento and WEAP models).

Though other watershed-based models have shown promising results for estimating hydrology

and water quality (e.g., Ficklin et al. 2009; Tu 2009; Yoshimura et al. 2009), water quality

modeling remains a challenging task (Rode et al. 2010), and the stochastic approach presented

here provides a straightforward and informative alternative to quantifying water quality changes.

Moreover, the complexity of environmental systems suggests that there may not be a single

“best” modeling approach, and the benefits of combination dynamic-statistical models (Block

and Rajagopalan 2009) and multi-model ensembles (Regonda et al. 2006) have been found.

Further, we point out that other data-driven approaches, such as artificial neural networks, have

been applied successfully in a range of hydro-environmental case studies (e.g., Dawson and

Wilby 1998; Chau et al. 2005; Taormina et al. 2012; Muttil and Chau 2006). For future study, it

would be interesting to compare the results from this study using different data-driven

techniques, such as in Wu et al. (2009).

The method does not explicitly model changes in the timing of spring runoff from

melting snow, which is expected to occur earlier in Colorado due to climate change (Ray et al.

2008; Woodbury et al. 2011). This could prove to be an important attribute of climate change

with profound water quality and management implications. Though not demonstrated here, it

would be easy to shift the flow simulations in this framework to be consistent with projected

changes in peak timing by modifying the nearest neighbor scheme to consider both annual flow

and peak timing in the selection of the proportion vector to be used in the annual flow

disaggregation. The approach can also be modified to simulate multi-site streamflow (Nowak et

al. 2010) and water quality variables that capture the spatial correlations.

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Conclusions

This paper presents an integrated approach to jointly simulate streamflow and water

quality variables under climate scenarios. The methodology was demonstrated for a new source

water being developed in Aurora, Colorado, where this type of assessment is relevant to their

treatment and blending planning. The approach was validated for natural variability (current

climate), and results show that the simulations accurately reproduce all of the relevant

distributional and threshold statistics of the flow and water quality, providing confidence in their

use in long term planning. Climate change projections suggest reduced flow for this new source

water, and the streamflow reduction scenarios result in a shift of the salinity distribution towards

higher values. The salinity ensembles also provide the probability of exceeding different water

quality values, which can be used to manage risk. These have been used as inputs for a

companion study that examines cost impacts to the utility and residential customers from the

changing salinity (Towler et al. 2012). The potential for using this technique with other water

quality variables that are associated with streamflow is also illustrated.

The paper is distinctive in that it combines and builds on several techniques that

previously have only been used separately in water resources. Further, it extends the influence of

these techniques to understanding water quality variability for new source development for

municipal supply. In short, the integrated framework proposed here offers a simple and robust

planning tool for water utility managers to use and alter specific to their individual needs.

Acknowledgements

The authors would like to acknowledge Water Research Foundation project 3132,

“Incorporating climate change information in water utility planning: A collaborative, decision

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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. The first author acknowledges the National Center for

Atmospheric Research (NCAR); NCAR is sponsored by the National Science Foundation.

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30

Figures

Figure 1 Schematic of study area (not to scale).

31

Figure 2 Box plots show the observed total dissolved solids (TDS) values by season from 1991-

2008, where OND represents aggregated observations from October, November, and December.

Sample sizes are 51 (OND), 62 (JFM), 62 (AMJ), and 82 (JAS). Horizontal line is the EPA

secondary standard, grey triangles are seasonal averages.

32

Figure 3 Scatterplot between daily undepleted flow and total dissolved solids (TDS) from 1991-

2008. Grey line is local smoother.

Figure 4 Probability density function (PDF) of annual (left) and May daily (right) volumes in

thousand acre-feet (TAF), for all 250 simulations (box plots) and historic observed (grey line).

33

Figure 5 Average minimum 7-day flow (left) and longest consecutive flows below Q33 (33rd

quantile, right) for all 250 simulations (box plots) and observed (grey triangle).

34

Figure 6 Total dissolved solids (TDS) validation between observed (grey box plots) and

simulated (black box plots), and associated sample size (n).

35

Figure 7 Probability density function (PDF) of average daily undepleted flow (left) and total

dissolved solids (TDS; right) for 2070 time slice for natural variability (CC0) and the 30%

reduction (CC30) scenario.

Figure 8 Scatterplot of flow and nitrogen (left, sample size = 570) and phosphorus (right, sample

size = 650). Grey line is local smoother.

36

Tables

Table 1 Probability of Exceeding Select Total Dissolved Solids (TDS) Levels for the Year 2070 for Each Streamflow Reduction Scenario

Probability of Exceeding TDS Level Scenario Reduction (%)

TDS Level

(mg/L) 0 10 20 30 300 92 93 95 96 400 84 86 88 90 500 72 75 77 81 600 47 50 53 59 700 13 14 16 19 800 2.3 2.5 2.5 2.7

37

Table 2 Probability of Exceeding Select Phosphorus and Nitrogen Levels for the Year 2070 for Each Streamflow Reduction Scenario

Probability of Exceeding Level Scenario Reduction (%) Water

Quality Variable

Level (mg/L)

Threshold Reduction

Requirement (%) 0 10 20 30

0.01* - 99.7 99.7 99.8 99.9 0.1 90.0 99.3 99.3 99.5 99.7 1 99.0 65.8 69.5 71.8 76.6 2 99.5 17.0 18.4 19.7 21.9

Phosphorus** (mg/L)

3 99.7 3.70 4.00 4.20 4.50 0.15* - 99.7 99.7 99.8 99.9

1.5 90.0 82.8 83.8 85.8 88.2 3 95.0 58.0 60.6 63.1 67.9 5 97.0 23.4 25.1 26.9 30.4

Nitrogen*** (mg/L)

7 97.9 1.58 1.81 2.09 2.49 * Water quality threshold ** Measured orthophosphate and phosphorus as P *** Measured nitrite and nitrate as N