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Impacts of Climate Extremes on Water Quality Management and Regulations

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Impacts of Climate Extremes on Water Quality Management and Regulations Balaji Rajagopalan Department of Civil, Environmental and Architectural Engineering & CIRES – PowerPoint PPT presentation

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Title: Impacts of Climate Extremes on Water Quality Management and Regulations


1
Impacts of Climate Extremes on Water Quality
Management and Regulations
Balaji Rajagopalan Department of Civil,
Environmental and Architectural Engineering
CIRES University of Colorado, Boulder, CO US
CLIVAR/NCAR ASP Research Colloqium Statistical
Assessment of Extreme Weather Phenomena under
Climate Change June 13-17, 2011 Boulder, CO
2
Acknowledgments
  • Erin Towler (NCAR)
  • Scott Summers (CU)
  • JoAnn Silverstein (CU)
  • Rick Katz (NCAR)
  • Eric Gilleland (NCAR)
  • Scott Weirich (CU)

3
Outline
  • Extreme climate aspects relevant for
  • Drinking water quality
  • Waste water treatment
  • Utility planning management
  • Regulation
  • Translation of climate information to water
    quality extremes
  • GEV with covariates
  • Portland water utility
  • Waste water treatment
  • Regulatory violation model using GLM
  • Parting thoughts

4
Contributors to Source Water Quality
  • Source waters
  • Flowing streams and lakes/ reservoirs
  • Natural system runoff
  • Nonpoint source
  • Agricultural input
  • Urban and storm water discharge
  • Point source
  • Combined sewer overflow (CSO) discharge
    diluted, but untreated wastewater
  • Treated wastewater discharge
  • All can impact drinking water quality

5
Typical Drinking Water Quality / Waste Water
Treatment Framework
6
Impact on Drinking Water Quality
fromTemperature, Precipitation and Flow Extremes
Potential Climate Change Impact Water Quality Variable Relevance to Drinking Water Treatment
Increased source water temperature Temperature Affects reaction rates and process efficiency Influences source water algal blooms
Increased source water temperature Dissolved oxygen (DO) Causes decreased DO solubility Affects algal blooms and increases natural organic matter (NOM) - a disinfection byproduct (DBP) precursor
Sea level rise, drought, wastewater reuse Bromide, salinity, Increase in Br causes increase in brominated DBPs Increase in salinity causes taste issues
7
Contd..
Potential Climate Change Impact Water Quality Variable Relevance to Drinking Water Treatment
Increased influence of wastewater discharge, increased algal blooms Natural Organic Matter (NOM) NOM serves as the organic precursor for disinfection by-product (DBP) formation
Increased influence of wastewater discharge during low flows Specific contaminants anthropogenic and naturally occurring Regulated and non-regulated compounds can cause long- and short-term negative health effects
Increased influence of wastewater discharge, decrease in-stream attenuation Nutrients Promote algal blooms and cause an increase in total DBPs and in problematic DBPs (e.g., nitrogenous DBPs)
Increased stream flows Turbidity Regulated as microbial pathogen treatment indicator
8
Impact of extreme climate events on wastewater
treatment and water quality
  • Wastewater discharges from 22,000 permitted
    facilities in the US (130 million m3/yr) are the
    6th-ranked source of contamination of rivers and
    streams and 7th-ranked source for lakes and
    reservoirs.
  • Contaminant removal during treatment and
    receiving water quality after discharge are
    susceptible to climate-related factors
  • Influent flow rates and hydraulic loading affect
    treatment
  • Water temperature variability (high and low
    extremes) affects oxygen solubility, biological
    reaction rates, pathogen survival
  • Flooding, including from sea level rise in
    coastal facilities, submerges outfalls or results
    in uncontrolled contaminant releases
  • Drought, low stream flows mean decreased
    contaminant assimilation capacity and
    unattainable discharge standards
  • Combinations of two or more climate events have
    synergistic effects. For example reduced average
    precipitation and intense storms and flooding.
  • Combined sewer (sanitary and storm drain)
    overflows are highly vulnerable to intense
    storms, releasing an additional 3.2 billion m3/yr
    of mostly untreated wastewater/storm drainage.

9
(No Transcript)
10
Water quality is a measure of regulatory
compliance
  • Maximum contaminant levels (MCLs) are identified
    which may compromise safety or limit particular
    treatment options

11
  • Application
  • (Portland Water Utility Turbidity)

Schoharie Reservoir, NY, after Hurricane Floyd in
1999 (Miller and Yates 2006)
Towler, E., B. Rajagopalan, E. Gilleland, R.
Summers, D. Yates and R. W. Katz, Modeling
hydrologic and water quality extremes in
changing climate, Water Resources Research, 46,
W11504, doi10.1029/2009WR008876, 2010
Towler, E., B. Rajagopalan, S. Summers and D.
Yates, A framework for probabilistic forecasting
of seasonal water quality threshold
exceedance, Water Resources Research, 46, W06511,
doi10.1029/2009WR00783, 2010
12
Seasonal turbidity ProjectionPortland Water
Bureau (PWB), OR
Forest to Faucet
- Rain
- Runoff
- 2 Storage Reservoirs
- Chemical Disinfection
- Distribution
- No physical filtration (unfiltered)
13
Watershed protection and high water quality allow
the utility to not filter
Compliance with Surface Water Treatment Rule
(SWTR) requires influent turbidity lt 5
nephelometric units (NTU)
14
High runoff events have caused source water
turbidity exceedances
Precipitation
Back-up groundwater source (Pumping )
High Flows
Turbidity SWTR Exceedances
Turbidity Exceedances Under Future Climate?
15
Identify relationship between hydroclimate and
water quality
Data from 1970 2007 Winter season (Nov-Mar)
Turbidity (PWB)
Flow (USGS)
Local Regression, Loader (1999)
16
Maximum Flow Turbidity Relationship
Probability of a turbidity spike given a certain
maximum flow
Local Logistic Regression, Loader (1999)
17
GEV
Maximum flow probability distribution
GEV Distribution is fit to historic maximum flow
values (S)
(Coles 2001)
18
Stationary GEV
Unconditional GEV
19
GEV with Covariates - nonstationarity
Apply to location parameter
Test covariate(s) to find best-fit Likelihood
Ratio Test
  • Precipitation (P)
  • Temperature (T)
  • Time
  • Palmer Drought Severity Index (PDSI)

PxT combination resulted in the best-fit
20
  GEV Model GEV Model GEV Model GEV Model GEV Model
  Uncond CondT CondR CondRT CondRT
Variable ß0 ß0ß1T ß0ß1R ß0ß1(RT) ß0ß1Rß2T
ß0 (sea) 1924 (120) 1930 (1000) 1739 (410) 611.4 (150) 1911 (880)
ß1 (se) - -0.8914 (27) 61.08 (32) 3.716 (0.36) 141.2 (14)
ß2 (se) - - - - -36.45 (24)
s (se) 1245 (84) 1220 (81) 1246 (160) 923.7 (69) 968.5 (74)
? (se) -0.02246 (0.065) -0.01286 (0.065) -0.06180 (0.084) 0.07009 (0.082) 0.01619 (0.075)
llh -1289 -1289 -1274 -1250 -1250
K 1 2 2 2 3
AIC 2580 2582 2552 2504 2506
M0b - Uncond Uncond Uncond CondR
D - 0 30 78 48
Sigc - No (0.635) Yes (0.000) Yes (0.000) Yes (0.000)
?d - - 0.5516 0.5989 0.5918
Generalized Extreme Value (GEV) Coefficients and
Goodness-of-Fit Statistics for Models of Winter
Monthly Maximum Streamflow, with Best Model
highlighted in red. a se standard error b
Nested model to which model is compared in
likelihood ratio test c Significance is tested at
a0.05 level, and ( ) indicates p-value. d
Correlation between the cross-validated z90
estimates and the observed maximum values
21
Conditional quantiles correspond well to observed
record (Cross validated)
Q99Uncond (100 Year Flood)
0 2000 4000 6000 8000
Maximum Streamflow (cfs)
1970 1980 1990
2000
(Towler et al., 2010)
Year
22
Conditional GEV shifts with climate covariates
(Towler et al., 2010)
23
Conditional GEV shifts with climate covariates
Q90
PSgtQ90Uncond ??
40
10
3
(Towler et al., 2010)
24
Climate change projections indicate that Pacific
Northwest will become wetter
Avg. Precip Response ()
(Solomon et al 2007)
Directly use projections to quantify extreme flow
and turbidity exceedances
25
Incorporate climate change projections as GEV
covariates
- A2 Emissions Path (worst-case)
General Circulation Model (GCM) Climate
Projections
- P T output from 36 Model runs
x
x x x x x
- Bias Correct P T to historic data for PWB
watershed
x
x
x
x
http//gdo-dcp.ucllnl.org/downscaled_cmip3_project
ions/Welcome
26
Results indicate increasing maximum streamflow
anomalies
Historic (USGS) 36 model runs 36 model average
Maximum Streamflow Anomaly ()
(Towler et al., 2010)
Year
27
Magnitude and variability of turbidity exceedance
probability
(Towler et al., 2010)
28
Magnitude and variability of risk exceedance
increase
Q9524
Q9513
Very wet 14
Q505.4
Q504.2
(Towler et al., 2010)
29
Turbidity exceedance projections are useful for
future planning
Expected Loss
( Change)
30
Small shifts in risk can result in large percent
increases
Change in risk is high, especially for the risk
averse (i.e., Q95)
Change in Expected Loss Relative to Historic
Period ()
31
Summary and conclusions
  • Developed a method to quantify risk of a
    turbidity event using climate information
  • Seasonal forecasts
  • Historic range of average flow and P(E)
  • Climate change projections
  • Future extreme flows and P(E)
  • Turbidity risk increased in terms of magnitude
    and variability relative to historic period

31
32
  • Application
  • (Waste water treatment)

City of Tacoma, Waste water treatment
plant (Miller and Yates 2006)
Weirich, S. R., J. Silvverstein and B.
Rajagopalan, Effect of average flow and capacity
utilization on effluent water quality from US
municipal wastewater treatment facilities, (in
press), Water Research, 2010
33
Effluent water quality from US Municipal Waste
Water Treatment Facilities
  • Decentralization of waste water treatment
  • Many small plants
  • Average flow, capacity utilization relate to BOD,
    TSS, Ammonia and fecal coliform
  • Used GLM to analyze data from 210 operating waste
    water facilities Weirich, et al., 2011, Water
    Research)
  • Relationship
  • Violations

34
Statistical analysis of waste treatment process
reliability and climate-related factors
  • Simulation of discharge permit violations for
    biochemical oxygen demand (BOD) as a function of
    average flow and utilization of plant hydraulic
    capacity
  • GLM Logit link function
  • Hydraulic overloading (could result from high
    infiltration into sewers during wet weather)
    particularly associated with frequent violations
    for smaller plants
  • Similar results for other contaminants (suspended
    solids and ammonia)(Weirich, et al., 2011, Water
    Research)

35
Capacity utilization is a function of extreme
climate events (high flow due to extreme
precipitation)
36
Adaptation Issues
  • Typical treatment plant design and construction
    life cycle may be 30 to 40 years collection
    system service life, 50 100 years.
  • Challenge is to adapt existing wastewater
    collection and treatment systems, including their
    location, to decadal scale climate extremes where
    new construction and relocation is very costly or
    not feasible.
  • Cost of adaptation for US wastewater
    infrastructure estimated to be in the range of
    123 billion to 252 billion, above normal
    maintenance and replacement costs.
  • Smaller treatment facilities (population served lt
    10,000) are 70 of permitted plants in the US and
    may be particularly vulnerable to climate
    effects, with fewer adaptation resources.

37
Parting Thoughts
  • Climate extremes have significant impact on
    drinking water and waste water treatment
  • GEV with covariates offers a good framework to
    translate large scale climate information to
    point scale water quantity and quality extremes
    for decision making
  • Combined climate extremes have the most impact
  • Multivariate Extreme Value Analysis
  • Consequently, major implications for planning,
    management, regulatory development and compliance
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