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)