# Presentation: 916286

## Britta Bierwagen
Global Change Research Program
NCEA/ORD

*Global Change Research Program*
*NCEA/ORD*

**Effects of Climate Change on Aquatic Ecosystems and Biological Indicators**

- The views expressed in this presentation are those of the author and they do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency

## Warming Intensifies Hydrologic Cycle

## Climatic changes

- Effects in aquatic ecosystems

- Biological and ecological responses

- Increased water temperature

- Reduced ice cover

- Increased salinity / altered water chemistry

- Increased CO2 in waters

- Increased snowmelt

- Altered stratification regime

- Increased sea levels

- Altered flow

- Altered evapo-transpiration

- Increased air temperature

- Altered precipitation regimes

- Increased CO2 in atmosphere

*Ecosystem*
- Altered energy flow and cycling

*Community*
- Altered species tolerances & interactions

*Population*
- Altered demographic rates

*Individual*
- Altered vital rates

*Responses can be measured using indicators*

- Assessment of responses

## Observed Temp Trends Link to other Indicators

**Observed Temp Trends Link to other Indicators**

- Cht. 14, IPCC 2007

**Notes:**
Figure 14.1. Observed trends in some biophysical and socio-economic indicators. Background: change in annual mean temperature from 1955 to 2005 (based on the GISS2001 analysis for land from Hansen et al., 2001; and on the Hadley/Reyn_V2 analysis for sea surface from Reynolds et al., 2002). Insets: (a) trend in April 1 snow water equivalent (SWE) across western North America from 1925 to 2002, with a linear fit from 1950 to 2002 (data from Mote, 2003), (b) Spring bud-burst dates for trembling aspen in Edmonton since 1900 (data from Beaubien and Freeland, 2000), (c) anomaly in 5-year mean area burned annually in wildfires in Canada since 1930, plus observed mean summer air temperature anomaly, weighted for fire areas, relative to 1920 to 1999 (data from Gillett et al., 2004) (d) relative sea-level rise from 1850 to 2000 for Churchill, MB, Pointe-au-Père, QB, New York, NY, and Galveston, TX, (POL, 2006) (e) hurricane energy (power dissipation index (PDI) based on method of Emanuel, 2005), economic damages, million U.S. dollars (adjusted to constant 2005 US dollars and normalized accounting for changes in personal wealth and coastal population to 2004), and deaths from Atlantic hurricanes since 1900 (data from Pielke Jr. and Landsea, 1998 updated through 2005), and, (f) trend North American Net Primary Production (NPP) from 1981 to 1998 (data from Hicke et al., 2002).

## Outline

- Climate change consequences for aquatic systems
- Effects on biological indicators
- Current and on-going research
-  Examples from Maine
- Adaptation – what can be done?

## Climate Change Effects on Aquatic Ecosystems

- Changes in air temperature influence changes in ***water temperature***
- Changes in precipitation timing and amount affect ***water quantity*** and ***quality***, and ***timing of flows***
- Thermal expansion and polar melting cause ***sea level rise***
- Increasing atmospheric CO2 decreases ***pH***
- Effects ***vary regionally and seasonally***
- Alterations have consequences throughout ecosystem

## Observed Temperature Changes 1901 - 2004

- Source: Koblinsky 2006 ICLEI

***Observed Temperature Changes 1901 - 2004***

**Notes:**
Fig. 2 Projected changes in annual, winter (DJF) and summer
(JJA) temperature (C) and precipitation (%) under the SRES
B1 (low), A2 (mid-high) and A1FI (higher) emissions scenarios
for the periods 2035–2064 and 2070–2099 relative to the 1961–
1990 average over the US Northeast. Solid bars indicate the
ensemble average value from 9 AOGCMs (B1, A2) and 3
AOGCMs (A1FI), while the whiskers indicate the range of
projections from individual simulations. All models consistently
indicate increases in temperature over all seasons and in annual
and winter precipitation that become greater over time. They
also indicate a larger temperature change under higher relative
to lower emissions scenarios

## December 2001 Precipitation

- Source:  National Climatic Data Center/NESDIS/NOAA

**Percent of 1961 – 1990 Normal**

## Evidence of Stream and Lake Warming

- Strong evidence of changes in length of seasonFreeze dates are later,thaw dates are earlier

- From J.J. Magnuson and IPCC reports

## Projected changes in temperature & precipitation for New England

- Hayhoe et al. 2007

## Hayhoe et al. 2007

**Notes:**
Fig. 6 Frequency of short (1–3 month), medium (3–6 months) and long-term (6 + months) droughts and the maximum drought duration (in months) for the historic (1961–1990) and future (2070–2099) periods. Droughts are defined as deficits of 10% or more in monthly soil moisture relative to the climatological mean. Values are the average of the HadCM3 and PCM-forced VIC simulations for the A1FI and B1 scenarios. Increases are greatest for the shorter drought events and under the A1FI scenario as compared with B1

## Projected change in probability of low flows

- Hayhoe et al. 2007

**Projected change in probability of low flows**

- Hydrologic modeling using projected climate scenarios indicates **decreased** probability of **low flows**

**Notes:**
Fig. 8 Projected change in the probability of (a) low (10%) and (b) high (90%) flows from the historic (1961–1990) to the future (2070–2099) periods for winter (DJF) for selected unmanaged basins. AOGCM-driven VIC simulations indicate a decreased probability of low-flow events and increased probability of high flows, with significantly higher flows across much of the northern part of the NE under the A1FI scenario as compared with B1

## Projected change in probability of high flows

**Projected change in probability of high flows**

- Hydrologic modeling using projected climate scenarios indicates **increased** probability of **high flows**, particularly in northern New England

- Hayhoe et al. 2007

**Notes:**
Fig. 8 Projected change in the probability of (a) low (10%) and (b) high (90%) flows from the historic (1961–1990) to the future (2070–2099) periods for winter (DJF) for selected unmanaged basins. AOGCM-driven VIC simulations indicate a decreased probability of low-flow events and increased probability of high flows, with significantly higher flows across much of the northern part of the NE under the A1FI scenario as compared with B1

## Number of snow-covered days per month

- Hayhoe et al. 2007

**Number of snow-covered days per month**

- Decreasing over time throughout New England region

**Notes:**
Fig. 13 Number of snowcovered days per month for winter (DJF) averaged over 30-year historic and future periods. Also shown are the differences in  number of snow-covered days between the future and historic period. Values are the average of the HadCM3 and PCM-forced VIC simulations

## Snowmelt-driven high spring flow

**Snowmelt-driven high spring flow**

- Projections indicate earlier spring snowmelt

- Hayhoe et al. 2007

**Notes:**
Fig. 10 Projections of timing of snowmelt-driven high spring flow, as represented by shifts in the center of volume date for 51 unregulated rivers in the NE, corresponding to VIC simulations driven by observed temperature and precipitation and by the A1FI and B1 scenarios as simulated by HadCM3 and PCM. By end-of-century, changes under the A1FI scenario are approximately twice those projected under the B1 scenario

## Projected changes in average daily flows

- Projections indicate longer periods of summer low flows

- Hayhoe et al. 2007

**Notes:**
Fig. 12 Projected changes in average daily flows during the
period 21 May through 31 October, in units of cubic meters per
second per square kilometer of drainage area, such that the
overall average reflects the mean of the 51 stream values
irrespective of the area of their drainage basins. 1961–1990
(mod) is based on VIC-simulated hydrology driven by HadCM3/
PCM simulations based on estimated natural and human forcing,
while 1961–1990 (obs) is based on VIC-simulated hydrology
driven by observed temperature and precipitation over that time.
Future projections correspond to VIC model simulations driven
by the A1FI and B1 scenarios as simulated by HadCM3 and
PCM. The US Fish and Wildlife Service (USFWS) low flow
threshold of 0.037 m3/s/km2 (USFWS 1981) is shown for
reference. Compared with the black line (historical observations),
the scenarios tend to drop below the threshold earlier and
remain below it longer by ~3 weeks under the A1FI scenarios
and a few days to a week under B1

## Climate Change Effects on Aquatic Ecosystems

- Changes in air temperature influence changes in ***water temperature***
- Changes in precipitation timing and amount affect ***water quantity*** and ***quality***, and ***timing of flows***
- Thermal expansion and polar melting cause ***sea level rise***
- Increasing atmospheric CO2 decreases ***pH***
- Effects ***vary regionally and seasonally***
- Alterations have consequences throughout ecosystem

## Aquatic Ecosystem Impacts

**Increasing water temperatures**
- Decreasing O2 concentration, P release from sediments, increasing thermal stability, altered mixing patterns in lakes
- Earlier ice cover break up, increasing ice free periods
- Species moving up in altitude and latitude
-  Changing species composition
-  Changing seasonality & productivity of plankton
-  Changing food web interactions

- Cht. 4, IPCC 2007

## Climate Change & Fish Habitat in US Streams

- Mohseni et al. (2003)

**Climate Change & Fish Habitat in US Streams**

**Notes:**
Air temperature increases under 2X CO2 predicted from Canadian climate model
Water temperature increases predicted from correlations with air temperature
Cold water fishes projected to experience a 36% habitat loss
Cool water fishes projected to experience a 15% habitat loss
Warm water fishes projected to experience a 31% habitat increase
Mohseni, O., Stefan, H. G. & Eaton, J. G. (2003) Global warming and potential changes in fish habitat in U.S. streams. Clim. Change 59, 389–409

## Increased Biological Production

- Benke 1993

- Elliott and Hurley 2000

## Land Use Effects

- Hydrologic alteration
- Sedimentation
- Nutrient enrichment
- Contaminant delivery
- Riparian clearing/canopy opening
- Loss of large woody debris

- Allan, J.D.  2004.  Landscape and riverscapes:  The influence of land use on river ecosystems.  *Annual Reviews of Ecology, Evolution and Systematics *35:257-284.

- _Pathways_
-  Impacts

**Notes:**
Fungible effects (impervious surface and more intense rains)
Be careful not to blame every ecosystem change on climate change.  At the local scale, you will see and be able to respond to many other stressors.

## Multiple factors influence flow

- Land-use change tends to increase flow variability
- Flow conveyances (urban, agriculture) increase flashiness
- Impoundments tend to reduce flow variability
- Water abstraction lowers seasonal base flows and accentuates effects of droughts

**Notes:**
OMIT?

## Climate Interacts with other stressors

- Warming interacts with impoundments, shade, and water abstraction
- Flow variability interacts with impoundments, land use, impervious surfaces, flow conveyances
- Species assemblages and food webs are affected by pollutants, habitat loss, invasive species

## Is climate change a new threat?

- Most of the impacts of climate change are similar to other existing stressors, which are being dealt with by current water resource decision-making.
- Higher high flows, lower low flows – similar to effect of urbanization
- Warmer water temperatures – similar to effect of thermal effluents
- Sea level rise effect on coastal community water supplies – similar to effect of extraction-driven salt water intrusion

- Asam, Freed, Scheraga, Allan, Loughran AWRA Conference Nov 12, 2007

## Climate and Land Use Change Interaction

- High flows
-  Land use likely to dominate signal
- Low flows
-  Climate likely to dominate signal

## The causal sequence from stressors and their sources through the five major water resource features to the biological responses, i.e., the biological endpoints. 

- TALU doc 08-31-05 Fig 1-2

**Climate change**
**Urban growth**
**Agriculture**
**Timber harvest**
**Mining**
**Atmospheric pollution**
**Water-born contaminants**

**Notes:**
P 9: FIGURE 1-2. The causal sequence from
stressors and their sources through the
five major water resource features to the
biological responses, i.e., the biological
endpoints.

## How do we continue to measure impairment due to existing stressors in a changing climate?

***How do we continue to measure impairment due to existing stressors in a changing climate?***

## Climate Change & Bioassessment Programs

- Additional stressor on ecosystem
- Affects both reference & non-reference sites
- Current indicators may be confounded by climate change effects on ecosystems
- Bioassessment program management goals
-  Difficult to establish goal if baseline is changing
-  Or goals may be impossible to meet

## Climate Change & Bioassessment Programs

- Affects both reference & non-reference sites
- Current indicators may be confounded by climate change effects on ecosystems
- Bioassessment program management goals
-  Difficult to establish goal if baseline is changing
-  Or goals may be impossible to meet

**Climate Change & Bioassessment Programs**

## Climate Change & Bioassessment Programs

- Affects both reference & non-reference sites
- Current indicators may be confounded by climate change effects on ecosystems
- Bioassessment program management goals
-  Difficult to establish goal if baseline is changing
-  Or goals may be impossible to meet

**Climate Change & Bioassessment Programs**

## Climate Change & Bioassessment Programs

- Affects both reference & non-reference sites
- Current indicators may be confounded by climate change effects on ecosystems
- Bioassessment program management goals
-  Difficult to establish goal if baseline is changing
-  Or goals may be impossible to meet

**Climate Change & Bioassessment Programs**

## How do existing biological indicators respond to climate change?

***How do existing biological indicators respond to climate change?***

## Project Overview – Phase 1

- Conducted literature review of biological indicators used in bioassesment programs
- Reviewed state biocriteria programs
- Developed framework to categorize biological indicators according to sensitivity to climate change
- Conducted case studies on effects on reference and non-reference sites and monitoring strategies
- Held workshops for biocriteria managers (Spring ’07 & ’08)
- Final report on preliminary analyses to be released April 2008

## Summary of Report Findings

- Biological indicators can be categorized into three broad areas
-  Sensitive to CC, sensitive to CC and other stressors, and insensitive to CC
- Mechanisms by which CC affects indicators can be used to define “sensitive to CC”
- Power to detect CC effects using monitoring data from bioassessments will vary depending on magnitude of CC variable, species loss rate, population variance, and degree of confidence specified
- CC may affect reference sites disproportionately, narrowing gap between reference and non-reference
- Potential increase in variability in condition among reference sites may impede a state’s ability to detect impairment

## Summary of Report Findings

- Biological indicators can be categorized into three broad areas
-  Sensitive to CC, sensitive to CC and other stressors, and insensitive to CC
- Mechanisms by which CC affects indicators can be used to define “sensitive to CC”
- Power to detect CC effects using monitoring data from bioassessments will vary depending on magnitude of CC variable, species loss rate, population variance, and degree of confidence specified
- CC may affect reference sites disproportionately, narrowing gap between reference and non-reference
- Potential increase in variability in condition among reference sites may impede a state’s ability to detect impairment

## Summary of Report Findings

- Biological indicators can be categorized into three broad areas
-  Sensitive to CC, sensitive to CC and other stressors, and insensitive to CC
- Mechanisms by which CC affects indicators can be used to define “sensitive to CC”
- Power to detect CC effects using monitoring data from bioassessments will vary depending on magnitude of CC variable, species loss rate, population variance, and degree of confidence specified
- CC may affect reference sites disproportionately, narrowing gap between reference and non-reference
- Potential increase in variability in condition among reference sites may impede a state’s ability to detect impairment

## Summary of Report Findings

- Biological indicators can be categorized into three broad areas
-  Sensitive to CC, sensitive to CC and other stressors, and insensitive to CC
- Mechanisms by which CC affects indicators can be used to define “sensitive to CC”
- Power to detect CC effects using monitoring data from bioassessments will vary depending on magnitude of CC variable, species loss rate, population variance, and degree of confidence specified
- CC may affect reference sites disproportionately, narrowing gap between reference and non-reference
- Potential increase in variability in condition among reference sites may impede a state’s ability to detect impairment

## Summary of Report Findings

- Biological indicators can be categorized into three broad areas
-  Sensitive to CC, sensitive to CC and other stressors, and insensitive to CC
- Mechanisms by which CC affects indicators can be used to define “sensitive to CC”
- Power to detect CC effects using monitoring data from bioassessments will vary depending on magnitude of CC variable, species loss rate, population variance, and degree of confidence specified
- CC may affect reference sites disproportionately, narrowing gap between reference and non-reference
- Potential increase in variability in condition among reference sites may impede a state’s ability to detect impairment

## Next Steps – Phase 2

- Evaluate and understand how current indicators and indices respond to climate change regionally through pilot studies
- Develop climate sensitive and insensitive indices
- Use historical data to examine trends at reference sites linked to climate variables
- Use data to project potential effects from CC
- Case studies on land use change interactions
-  Examine vulnerability of reference sites
-  Examine similarities and differences in responses of indicators to urbanization vs. climate change

## Pilot Studies

- In-depth studies focusing on needs of state programs, incorporating inputs from last workshop
- Focus on detailed evaluation of potential indicators
- Taxa traits associated with climate change responses
- Regional variation among indicators and traits
- Test for trends among state databases to test taxa and functional group hypotheses
- Categorize species according to sensitivities and responses
- Develop indices

## Evaluating bioassessment data from:

- Maine
- Utah
- Ohio
- North Carolina (and South)

- Length of record
- Repeat sampling (in addition to probabilistic)
- Corresponding environmental data
- Status of data (e.g. QC)
- Expectations for regional climate change effects

## Maine

- Montana

- Utah

- South Carolina

- Oregon

- Texas

**Notes:**
Dep Var: TEMP_MEAN   N: 3772   Multiple R: 0.168   Squared multiple R: 0.028

Adjusted squared multiple R: 0.028   Standard error of estimate: 2.816
Effect	Coefficient	Lower 95%	Upper 95%	
CONSTANT		-69.868		-86.818		-52.918	
YEAR		0.046		0.037		0.054	

## Dataset screening

- Locate data from reference sites
-  Maine ‘AA’ and ‘A’ rated sites
- Consistent collection methods
- Changes in taxonomy and level of identification
- Consistent sub-sampling methods
- Consistent sampling periods
- Locate sites at or near USGS gauges

## SITE 56817 – SHEEPSCOT RIVER - STATION 74

- 4th Order, Wadeable, Drainage Area 145 Sq. Mi.
- Reference Condition ‘AA’, but not ‘pristine; some non-point source pollution.
- In the Central Interior Biophysical Region and the Laurentian Plains and Hills Ecoregion
- Annual biological monitoring, 1984-2006 (=23 years).

**SITE 56817 – SHEEPSCOT RIVER - STATION 74**
- ABOVE RT. 126 BRIDGE AT USGS GAUGE

## Key Environmental Drivers

-   - Habitat structure
-       & dynamics
-   - Temperature
-   - Food resources

- _Species responses_
-  - What traits should vary
-    “mechanistically”?

## Categories of Indicators

|  | Insensitive to Climate Change
 | Sensitive to Climate Change | Sensitive to Climate Change and Other Stressors |
| --- | --- | --- | --- |
| Indicator | Warmwater fish | Fish community composition | Salmon egg to fry survival |
| Response | No change in majority of range
 | Cold- and coolwater fish species decline, warmwater fish species increase | Decreased survival due to increased turbidity from sediment input due to increased precipitation and/or land use change |

## What Defines Climate-Insensitive?

- Ecological events not cued to temperature
- Species is tolerant of broad temperature range
- Tolerant of wide range of hydrologic conditions
-  High flows or low flows
-  High variability in flow
-  Variation in salinity

## What Defines Climate-Sensitive?

- Ecological events cued to temperature
- Species exists in narrow temperature range
- Intolerant of certain hydrologic conditions
-  High flows or low flows
-  Saltwater intrusion

## Climate-Sensitive Traits

- Phenology (timing of emergence, reproduction, flowering, etc.)
- Longer growing season (number of reproductive periods)
- Life stage-specific
- Temperature sensitivity
- Hydrologic sensitivity

## Macroinvertebrate Traits and Trends

- Temperature preferences/sensitivities estimated from data, including weighted average and other modeling results from Oregon, California, Idaho, and current pilot studies
- Existing literature (e.g., EPA, USGS traits DB)
- Refine with testing of trends from these data bases
- Database will be made available

**Limitation:** there is a large amount of data on some species and virtually none or only scarce information for others

## Other traits may be useful explaining responses to climate change

- Dispersal Ability
- Distribution
- Voltinism
- Timing of Emergence

## Potential Cold-Water Indicator Taxa

**Parapsyche** (Trichoptera)
**Ameletus** (Ephemeroptera)
**Epeorus** (Ephemeroptera)
**Pteronarcys** (Plecoptera)
**Perlodidae** (Plecoptera)
**Micropsectra** (Chironomidae)
**Brillia** (Chironomidae)

**Potential Cold-Water Indicator Taxa**

**Notes:**
Based on these sources, we have compiled a preliminary list of potential ‘climate change indicator taxa.’  These are taxa that were most frequently cited in the aforementioned sources as having a preference for either cold or warm water, and that were considered to be sensitive to a fairly narrow range of temperatures.

## Potential Warm-Water Indicator Taxa

**Pseudochironomus** (Chironomidae)
**Pentaneura** (Chironomidae)
**Cheumatopsyche** (Trichoptera)

**Potential Warm-Water Indicator Taxa**

**Notes:**
Note: this list was harder to come up with.  There is a long list of taxa that appear to have no strong temperature preference and are tolerant to a wide range of temperatures (0-30°C).  Few are documented to occur only in warm waters.  Here is a short list of genera that appear to favor warm water, and that have a fairly narrow tolerance range.

## Level of taxonomy

- Species within a genus with differing preferences/requirements
**Baetis** (Ephemeroptera)
- 	***Cold*** water preference
*Baetis bicaudatus*
*Baetis parvus*
***No strong temperature preference***
- Most Baetis fall into this category.  A few examples are:
*Baetis tricaudatus*
*Baetis brunneicolor*
- 	***Warm*** water preference
*Baetis australis*
*Baetis intermedius*

**Notes:**
Maine spp., but Jen will talk about traits of these spp.
Changes in taxonomy may make a difference, going from genus to species
Have collection methods and taxonomy changed? These may create trends that aren’t related to physical variables.

## Adaptation:

***Adaptation:***
***What can be done?***

## Key Vulnerability Identified by IPCC

- Decentralized system where adaptation tends to be reactive, unevenly distributed, and focused on coping with, not preventing problems
- Including **climate change considerations into decision making** is key for sustainability
- EPA starting on this track with OW Climate Change Strategy

## Adaptation

- Adaptation has several meanings
- Biological adaptation
- Adaptive management
- Strategic responses of managers (may include no response)

**Notes:**
Scientific Expert Group on Climate Change and Sustainable Development (SEG) was formed and is comprised of 18 distinguished international scientists. 

## Reducing Impacts to Aquatic Ecosystems

**Increase resilience of ecosystems**
- Reduce impacts from other stressors (*e.g.*, pollution, acidification, desertification, eutrophication, invasive species)
-  Likely to enhance resilience to climate change
- Maintain habitat connectivity, population sizes, genetic diversity
- Restore habitat
-  Activities need to account for new environmental conditions

## Some specific actions

- Accept moving target paradigm versus steady state model
- Establish sentinel sites for trend monitoring
- Mine historical data records to establish a basis for evaluating climate change
- Improve hydrological and temperature data collection
- Integrate the concept of climate change into planning
- Restore the natural capacity of rivers to buffer climate-change impacts

**Notes:**
Some important recommendations taken from last year’s workshop and Palmer et al SAP 4.4. Adaptation Options for Climate-Sensitive Ecosystems and Resources | Wild and Scenic Rivers
 Box 6.5. Adaptation Options for Resource Managers Wild and Scenic Rivers: Adaptation Options for Resource Managers 

## TALU doc 08-31-05 Fig 1-1

**TALU doc 08-31-05 Fig 1-1**

**Notes:**
DRAFT: Use of Biological Information to Better Define Designated Aquatic Life Uses in State
and Tribal Water Quality Standards: Tiered Aquatic Life Uses – Chapter 1 – August 10, 2005
Page 8

## What’s needed for more information?

- Monitoring of reference sites
-  Repeated sampling at same sites
- Baseline data
-  Biotic and abiotic variables (temperature, flow)
-  Species trait information and sensitivities
- Continuous and real time data acquisition
- Linkage between real-time data and near real-time modeling
- Improved year-round infrastructure

## Acknowledgements

- Susan Julius (EPA)
- Anna Hamilton, Michael Barbour, Jeroen Gerritsen, Jen Stamp, Mike Paul (Tetra Tech, Inc.)
- J. David Allan (U. Mich.), LeRoy Poff (Col. State)

## Thank You!Questions?bierwagen.britta@epa.gov703-347-8613