10th ANNUAL CLEAN RIVERS, CLEAN LAKES CONFERENCE May 1, 2014
#217803
NOAA Sectoral Applied Research Program Grant
2
Study to Assess Potential Mid-Century Climate Change Effects
NOAA SARP Project Team
Sandra McLellan, Ph.D.: Professor, University of Wisconsin-Milwaukee, School of Freshwater Sciences
Hector Bravo, Ph.D.: Professor, University of Wisconsin-Milwaukee, Department of Civil and Environmental Engineering
Daniel Talarczyk, P.E., RLS: Ph.D. Candidate, University of Wisconsin-Milwaukee, Department of Civil and Environmental Engineering
David Lorenz, Ph.D., Assistant Scientist, University of Wisconsin-Madison, Center for Climatic Research
Jonathan Butcher, P.H., Ph.D.: Director of Modeling, Tetra Tech
Kevin Kratt: Director, Great Lakes Water Resources Projects, Tetra Tech
Michael Hahn, P.E., P.H.: Chief Environmental Engineer, Southeastern Wisconsin Regional Planning Commission
Study Report: Climate Change Risks and Impacts on Urban Coastal Water Resources in the Great Lakes, October 29, 2013
http://www.sewrpc.org/SEWRPCFiles/Environment/Rainfall/1-24-14-SARP-FinalReport.pdf
Applied watershed water quality models developed under the 2007 MMSD/WDNR/SEWRPC Water Quality Initiative
SEWRPC Water Quality Model
Used for facility planning
Predicts
Fecal coliforms (FC)
TSS
Nutrients
Cu
BOD
Lake Pathogen Model
Funded by NOAA Oceans
and Human Health Initiative
Shows fate and transport of
bacteria in the nearshore; i.e.,
to beaches and water intakes
Today’s Presentation How climate change effects
were represented in the models
Possible climate change effects on instream and Lake Michigan water quality
Value of modeling in representing complex interactions in natural systems
Current limitations on ability to represent climate change in models
Areas for future study
Downscaling Climate Data Begin with Global Climate Model (GCM) "forced" with projected future
concentrations of atmospheric greenhouse gases.
GCM's solve fluid dynamical, chemical, and/or biological equations that are either derived directly from physical laws (e.g. Newton's law) or constructed by more empirical means.
Resolution of GCM's quite coarse (100-400km)
(Wikipedia)
Downscaling Climate Data Downscaling: estimate local-scale (or small-scale) surface weather from
regional-scale (or large-scale) atmospheric variables that are provided by GCMs.
Types of downscaling: 1. Dynamical downscaling: “imbed” higher resolution regional climate
model in GCM 1. Statistical downscaling: use observed relationships between regional-
scale and local-scale weather to predict local weather from regional scale output from GCM
Differences in precipitation at different scales:
Unresolved boundary conditions (i.e. topography and lakes) Leads to systematic bias in regions of strong topography or in the vicinity of the
Great Lakes. Unresolved physical processes (i.e. small-scale thunderstorms/clouds)
Source of systematic bias But significant portion is "noise" (i.e. GCM may simulate a coarse-scale version
of the observations very well, it simply cannot simulate how coarse-scale is distributed locally)
For local impact studies, nature of local distribution important.
Regional-scale precipitation distributed evenly in space => relatively low variance & weak extremes
Regional-scale precipitation concentrated in a few locations => relatively high variance & strong extremes
(all else being equal of course)
Downscaling Climate Data Need to characterize the
signal AND the “noise”
Signal: y = a1x + a0
Downscaling Climate Data Need to characterize the
signal AND the “noise”
Signal: y = a1x + a0
Noise: Normal distribution with constant variance (assumption of least squares linear regression) and mean: a1x + a0
If one neglects the noise, the variance is underestimated by a factor of 1-r2, where r is the correlation of your fit.
Our statistical downscaling methodology Predict Probability Density Function (PDF) of precipitation, temperature,
etc. given the regional-scale predicted by GCM.
The PDF is NOT constant but varies in space AND time (daily) conditioned on state of GCM
Generalize linear least squares regression (i.e. conditional Normal distribution) to arbitrary distributions (essential for precipitation)
We also characterize and simulate realistic co-variability in space, time and between variables (i.e. multi-dimensional PDF)
Variables: precipitation, maximum and minimum daily temperature, dew
point temperature (i.e. moisture), vector wind.
Our statistical downscaling methodology We predict PDF. How do we get a “normal” time series of values?
1. Draw random numbers from the PDFs to generate a possible realization
of the local scale that is consistent with the regional scale in the GCM.
2. Alternatively, use mean PDFs in present and future to map events in present climate to their analog under future climate change. In other words, map nth percentile in present to nth percentile in future.
Precipitation Example
Temperature Projections (2046-2065) Milwaukee, WI:
Winter: mean temperature increases by 3.8°C (6.9°F). From -4.8° to -1.0°C (23.4° to 30.3°F)
Summer: mean temperature increases by 2.8°C (5.1°F). From 21.0° to 23.9°C (69.8° to 74.9°F)
Future Analog:
Precipitation Projections (2046-2065)
Uncertainty in Model Projections Spread in model projections across 13 climate models:
Uncertainty in Model Projections Spread in model projections across 13 climate models:
Selection of Climate Scenarios Interested in Changes in Larger Precipitation depths associated with
combined and sanitary sewer overflows
For precipitation, the models are most consistent in Spring, therefore we focus on the effect of changes in larger precipitation events in Spring.
Model Selection Metric: Change in probability of precipitation greater than 3.0 cm (1.2 inches) in February-May. Use the 10th and 90th percentile.
Watershed Models Models originally developed for the
Water Quality Initiative (SEWRPC 2007 Regional Water Quality Management Plan Update and MMSD 2020 Facilities Plan)
Comprehensive models developed based on best available data
Rigorous calibration and validation and independent review by a modeling committee
Hourly output available for 14 parameters at 682 modeling subwatersheds for a 10 year period (10 billion+ data points)
Modeling Processes
Watershed Models (continued)
Comprehensive modeling system allowed for a regional watershed perspective to evaluate facility improvements and water quality management Key pollutant sources
Attainment of water quality standards
Response to management activities
1975
Rural-
Agricultural
Runoff
21%
CSO's
49%
Urban-Non-
Agricultural
Runoff
23%
WWTP
5%SSO's
2%
2000
Rural-
Agricultural
Runoff
21%
CSO's
7%
Urban-Non-
Agricultural
Runoff
68%
WWTP
2%SSO's
2%
Greater Milwaukee
Watersheds Fecal
Coliform Loadings
Industrial
Discharge0%
Industrial
Discharge0%
Estimated Pollutant Reduction over 25-Year Period About 50 Percent
CONCLUSION: Focus on abating stormwater runoff pollution
Climate Scenarios 2020 population and land use
Baseline Conditions 1988 through 1997 climate data
Future Conditions Selected based on 3.0 cm (1.2
inches) spring rainfall thresholds associated with CSO and SSO events
“Best” case had the least events of 3.0 cm or greater
“Worst” case had the most events of 3.0 cm or greater
Climate Variable Baseline (1988 – 1997)
Future – Best
Future - Worst
Precipitation (in/yr) 32.5 33.2 33.4
Average Temperature (˚F) 47.7 53.3 56.4
Potential Evapo-transpiration (in/yr)
30.4 37.5 42.1
Modeling Results Significant decreases in
annual flow are predicted
Most annual pollutant loads also predicted to decrease
Results for sediment vary Increased frequency of large
spring rainfall events results in more channel erosion which in some cases offsets reduced upland loading
Modeling Results (continued)
Modeling Results (continued)
Predicted future changes in annual mean and median concentrations of pollutants are small
Both best case and worst case scenarios can result in slight improvement or slight degradation
Result depends on the balance between changes in load and flow
Effects of Stomatal Closure Important effect of CO2
fertilization is increased stomatal closure
Plants do not need to transpire as much water to obtain the CO2 they need for growth
Can potentially counterbalance predicted increases in temperature and potential evapotranspiration
Effects of Stomatal Closure (continued) Refined the Menomonee River
model to account for effects of stomatal closure
Adjusted model parameter that affects monthly plant transpiration
Results indicate a small increase in total flows
Total future flows remain less than under baseline
+2.6%
+2.8%
Effects of Stomatal Closure (continued) Flow or concentration changes relative to initially-modeled climate
change conditions without CO2 adjustment:
Average annual flow:
Fecal Coliform Bacteria:
Dissolved Oxygen: Essentially unchanged
Total Phosphorus:
Total Nitrogen:
Total Suspended Solids:
Total Copper: Unchanged
But the general conclusions regarding the direction of change between current and estimated future climate conditions was the same (e.g., an initially-modeled decrease in flow or concentration remained a decrease with CO2 adjustment)
• Development of hydrodynamic and transport model
• Field data and model validation
• Relation between tributary flows and bacteria concentration
• Analysis of climate change effects
Lake Michigan Modeling Component
Nested model domain
Station Map
Hydrodynamic and transport model The POM-based hydrodynamic model was expanded to
include a bacteria transport module.
Bacteria transport module simulates the processes of advection, dispersion or mixing, bacteria fall through the water column, light-dependent inactivation rate, and base mortality.
The model is online at: http://e320-lx01.ceas.uwm.edu/index.html
Measured (blue) and modeled (red) specific conductivity at stations GC, SG and HB for the 4/25 – 5/25/2008 period.
Field data and model validation
Field data and model validation
Measured (open circles) and modeled (continuous lines) fecal coliform (CFU/100mL) at stations MG, SG, NG and HB in June and July 2008.
Relation between tributary flows and bacteria concentration
Simultaneously-measured hourly streamflow (dashed line) and fecal coliform concentration (continuous line) at the Milwaukee River mouth between June 2009 and October 2011.
Measured (continuous line) and estimated (dotted line) logarithm of fecal coliform concentration.
• Important scientific questions :
• 1) the representation of physically correct climate change
scenarios to study the impacts on tributary flows and
bacteria loads, circulation and transport in Lake Michigan
coastal waters,
• 2) the selection of simulations periods, and
• 3) addressing uncertainty in climate change predictions.
Analysis of climate change effects
Climate change scenarios were developed using the arguments that bacteria loads to Lake Michigan are most sensitive to the spring season, and transport in coastal waters is most sensitive to changes in wind speed and direction.
Uncertainty in climate change predictions was dealt with by using the climate projections that yielded the 10th and 90th percentile changes in spring-season wind speed at the Milwaukee Airport station to define the worst-case and best-case climate change scenarios, respectively.
Analysis Assumptions
Location of 11 ASOS stations and NBDC buoys
March-May average wind speed for station KMKE, for the baseline period projected to 2046-2065 climate conditions by 13 models.
Baseline scenario Climate change scenario
March-May 2005 Worst case: model cccma_cgcm3_1 projection for 2005 yielded second highest (approximately 10th percentile) March-May average change in wind speed for station KMKE
March-May 2011 Best case: model mri_cgcm2_3_2a projection for 2011 yielded second lowest (approximately 90th percentile) March-May average change in wind speed for station KMKE
• The whole-lake model and the nested model were run for 1990 using
concurrent meteorological forcing over the watershed and the lake,
and both the baseline and projected watershed loads estimated by
SEWRPC/ Tetra Tech.
• No climate-change projection for meteorological forcing over the
lake could be developed for that year. The model results illustrate
the range of fecal coliform concentration that can exist at relevant
locations near Milwaukee.
• The transport of baseline and projected fecal coliform at relevant
sites showed negligible effect of using baseline or projected loads for
the same lake hydrodynamics.
Effect of climate change on watershed loads under the same
lake hydrodynamics
Calculated fecal coliform concentrations (CFU/100 mL) during
March-May 1990 at the Milwaukee River mouth (left), sites MG, SG
and NG right). a) Baseline loads and b) Projected loads.
Baseline loads
Projected loads
• The model was used to predict hydrodynamic conditions
and fecal coliform concentrations for the baseline and
projected worst case (2005) and best case (2011) climate
change conditions.
Effect of climate change on hydrodynamics and bacteria transport
Predicted number of hours with fecal coliform concentration larger than 1,000 CFU/100 mL at relevant locations, for 2005 baseline and worst-case scenario.
Worst-Case Scenario Station Baseline
condition Worst-case scenario
Main Gap (MG) 121 201
North Gap (NG) 156 223
South Gap (SG) 86 43
South Shore Beach (SSB)
129 58
Bradford Beach (BB)
35 74
Linnwood Intake (LI)
0 0
Howard Avenue Intake (HA)
0 0
Predicted number of hours with fecal coliform concentration larger than 1,000 CFU/100 mL at relevant locations, for 2011 baseline and best-case scenario.
Best-Case Scenario Station Baseline
condition Best-case scenario
Main Gap (MG) 334 321
North Gap (NG) 111 98
South Gap (SG) 206 227
South Shore Beach (SSB)
164 142
Bradford Beach (BB)
0 0
Linnwood Intake (LI)
0 0
Howard Avenue Intake (HA)
0 3
The changes in fecal coliform transport are explained by changes in current vector fields (time average, at each model cell, of the difference between projected current vectors minus baseline current vectors) under climate change conditions.
Model-predicted currents for baseline and worst-case (best-case) scenario showed that the change in average currents is mostly northward (southward), so the predictions indicate more days with concentration higher than the threshold at locations north (south) of the mouth of the Milwaukee River.
Conclusions
SEWRPC Water Quality Model
Many uses:
Facility planning
Predict water quality with land use
TMDL development
Mapping flood plains
Lake Pathogen Model
Deterministic model:
Pathogen delivery to beaches
and water intakes
Examine impacts of CSOs
and stormwater
Lake Pathogen Model
Deterministic model:
Pathogen delivery to beaches
and water intakes
Examine impacts of CSOs
and stormwater
Challenges in this effort
Climate models have uncertainty: which model to use?
worst and best case scenario adopted
Temperature and runoff intricately linked: how do we account variables not specifically addressed in climate projections?
plant response modeled
Time series for each effort not continuous, needed additional variables like wind direction
leverage other modeling efforts
Summary
Our ability to downscale GCMs has increased with more variables. Temperature, precipitation, dew point temperature (i.e. moisture) and wind vector. There is a proposal to downscale the new CMIP5
Increased rain does not necessarily equal increased FC loads. Continued research needed to evaluate impacts of C02 concentrations on plants and the water cycle
Wind is a major driver of lake currents; while loads may change, where it is distributed may also change.
What will we do with this information
Create more sophisticated tools to include a climate component in planning (water resource managers)
Evaluate hypothetical scenarios (risk vs. costs)
Increase our understanding of drivers of water quality
Set the bar: Our region is ahead of the curve for incorporating climate change predictions into planning
Milwaukee Working group
Wisconsin Initiative on Climate Change Impacts (WICCI) is a consortium of scientists, natural resource managers and stakeholders that look at adaptation strategies
Milwaukee as an urban area has unique challenges due to infrastructure, population density and location on Lake Michigan
Acknowledgements NOAA Sectoral Applied Research Program
MMSD for initial funding of SSO/CSO project
Collaborations and contributors:
Ron Printz (SEWRPC)
Joe Boxhorn (SEWRPC)
Elizabeth Sauer (GLWI)
Deb Dila (GLWI)
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