4>EPA
United States
Environmental Protection
Agency
Using Regional Monitoring Network (RMN) Data to
Track Climate Change Effects
The United States Environmental Protection Agency (U.S. EPA) is working with its regional offices, states, tribes and other entities to establish
Regional Monitoring Networks (RMNs) for freshwater wadeable streams (EPA 2016). The objective of the RMNs is to detect potentially small,
climate-related trends at a regional scale and in a decision-relevant timeframe. The RMN design calls for sampling at least 30 sites with similar
environmental and biological characteristics in each region on an annual basis for 10 or more years. Biological, thermal, hydrologic, physical
habitat and water chemistry data are being collected to document baseline conditions and detect long-term changes. Consistent methods are
being used to increase the comparability of data and minimize biases and variability. The intent is to pool the data at a regional scale, which will
enable more robust analyses and improve the ability to detect climate-related trends over shorter time periods.
One of the objectives of the RMNs is detection of climate
change effects in the context of biomonitoring. Climate change
requires managers to consider increasingly complex and
uncertain futures, often at longer time horizons than typically
considered in resource management. The RMN data are
important in the context of climate change, as managers can
use the monitoring data to help inform adaptive management
options. There are a number of climate change projections
that are relevant to aquatic life condition, including increasing
temperatures (Figure 1), increasing frequency and magnitude
of extreme precipitation events, and increasing frequency of
summer low flow events (Karl et al. 2009, Melillo et al. 2014).
The RMNs target high quality and least disturbed sites, since
there is a higher likelihood of detecting climate change-related
impacts in the absence of other non-clirnatic stressors.
Vulnerability Assessment: Many organizations are performing
climate change vulnerability assessments and developing
hypotheses about which organisms, community types, water-
sheds or stream classes are likely to be most vulnerable to
climate change effects. For example, the EPA and partners
are conducting a broad-scale climate change vulnerability
assessment on streams in the eastern US. This study assigns
vulnerability ratings to each watershed2 based on a scenario in
which stream temperatures warm and the frequency and dura-
tion of summer low flow events increases (Figure 2). The RMN
data can be used to help test these types of hypotheses and
predictive models related to climate change. If certain types of
streams show greater resilience to climate change effects than
others, this type of information could help inform adaptation
strategies and conservation planning.
Species Distribution: RMN data can also be used to monitor
changes in spatial distributions of biological indicators and to
evaluate whether these changes are associated with changing
thermal and hydrologic conditions. Based on preliminary
analyses, RMN sites in the Appalachians have relatively high
proportions of cold water macroinvertebrate taxa (EPA 2016).
Mean annual air temperature (°C)
Baseline (1961-1990)

Mid-century (2040-2069)

. .. 1	m
Figure t. Mean annual air temperature (°C) is projected to increase across the eastern
US by mid-century1.
Figure 2. EPA and partners are conducting a broad-scale ciimate change
vulnerability assessment on streams in the eastern US, based on a scenario in which
stream temperatures warm and the frequency and duration of summer low flow
events increases. Vulnerability ratings (least, moderate or most) are being assigned
to each watershed.
These data are based an the average of an ensemble of 15 GeneralCirculation ModelstSCMs) for the A2 (high) emissions scenario, and were obtained from .the Climate Wizard
website. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMOf) and the WGRP's Working Group on Coupled Modelling
(WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U-S. Department of Energy.
Watershed delineations are based on theSSbPlus v2 local catchment layer: http://www.horizon-systems.com/NHDPIus/NHDPIusV2_data.php
• Primary RMN sites (3/25/2014)
Scenario 1
Vulnerability rating
| least
moderate
| most
NA

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Using Regional Monitoring Network (RMN) Data to Detect Climate Change Effects
The RMN data can be used to detect whether shifts in the distributions
of these taxa are occurring as temperatures warm. In some places,
species distribution models (SDMs) have been developed. For example,
Zheng et al.3 generated models to predict how species occurrence will
change by mid-century in the Northeast under conditions of rising air
temperatures and changing precipitation patterns4. Results suggest an
overall decline in species richness across much of the region (Figure 3).
Models have been generated for other regions as well, including SDMs by
Hawkins et al, (2013) that predict how the distributions of individual mac-
roinvertebrate taxa and entire assemblages of taxa will change across
the conterminous US by late century. These models could potentially be
applied to RMN sites, and the modeled data compared to observed data.
If the models perform well, they could serve as a valuable tool for water-
shed protection planning.
Change in species richness (mid-century minus baseline)
| Loss of 15 or more taxa	Loss of 1 to 4
| Loss of 10 to 14	-0.9 to 0.9
Loss of 5 to 9	Gain of 1 or more taxa
Figure 3. Modeling results by Zheng et al. predict declines in species
richness across much of the Northeast by mid-century (2040-2069)3.
Stream Temperature and Flow: Models also have been developed to
predict climate change effects on stream temperature and flow. For
example, at the national scale, Hill et al. (2014) developed an empirical
stream temperature model for the conterminous US and simulated the
effects of climate change on mean summer stream temperature. The
model predicts a mean warming of 2.2°C for stream temperatures by
late century (2090-2099) relative to a 2001-2010 baseline period, with
increases at individual sites ranging from 0°C to +6.2°C. in another study,
Dhungel (2014) developed statistical models to predict flow responses to
projected changes in precipitation and temperature. Results suggest that
changes in flow attributes will be most evident in rain-fed small perennial
streams and intermittent streams in the central and eastern US. These
models could potentially be applied to RMN sites, and the performance
of the models could be tracked over time.
Response to Extreme Events: RMN data also may provide insights into
how organisms respond to and recover from extreme events such as
droughts and floods, which are projected to occur with greater frequency
as the climate changes (Karl et al. 2009, Melillo et al. 2014). If an extreme
Zheng,JL,, Stamp, i and Hamilton, A. plflifj Species Distribution Modeling: Impaqt
of Climate Change and Species Vulnerability. Poster presented at the Joint Aquatic
Sciences Meeting, Portland, OR.
Mid-century (ZD,4fi"2069) projectionsfarair temperature, precipitation and moisture
surplus were based on average values from an ensemble of I S GCMs, using the a2 (high)
emissions scenario. Data were obtained from the Climate Wizard website and are based
an the WCRP CM1P3 multi-mo.dei daiaset.
event occurs at a RMN site, the data collected prior to the event can
be used to characterize "baseline" (pre-event) conditions, and the
continuous sensor data will capture the magnitude, frequency and
duration of the event. Impacts can be evaluated through compara-
tive analyses on the pre- and post-event data. Vermont Department
of Environmental Conservation (VT DEC) performed these types
of analyses on macroinvertebrate data collected before and after
flooding from Tropical Storm Irene, which occurred in August 2011.
Using data from 10 high-quality sentinel sites, VT DEC documented
immediate decreases in invertebrate densities of 69% on average,
but also found that most sites recovered to normal levels the
following year (Figure 4). The substantial decline in density and the
rapid recovery would have been missed if sampling had occurred at
longer intervals, such as on a traditional 5-year rotational sampling
schedule. Whether or not the RMN data can fully capture biological
responses to events like this will depend on the timing of the event
in relation to the RMN sampling period. It is possible that additional
sampling may be warranted.
~	P re-flood
¦ 1 month post-flood
~	1 year post-flood
ft 3000
j?
«2 2000
01
5
¦J1
I
Small Streams Medium Streams Large Streams
Figure 4. Comparison of macroinvertebrate density values at 10 stream
sites in Vermont before and after Tropical Storm Irene (provided by
Moore and Fiske, VT DEC, unpublished data).
More detailed information on how RMN data can be used to
detect climate change effects can be found in the RMN report
(U.S. EPA 2016: http://cfpub.epa.gov/ncea/risk/recordisplay.
cfm?deid=307973).
Literature cited:
Dhungel, S, 2014. Prediction of
climate .change effects on stream-
fiow regime important to stream
ecology. Utah State-University. Al
Graduate Theses and Dissertations.
Paper 3083. http://digitalcom-
mons.usu.edu/etd/3083
Hawkins, G.P, Tarboton, D.G. and
J. Jin. 2013. Consequences of
global climate change for stream
biodiversity and implications for
the application and interpretation
of biological indicators of aquatic
ecosystem condition. Final
Report, [EPA Agreement Number:
RD834186], Logan Utah: Utah State
University.
Hill, R. A., G. P. Hawkins and J.
Jin. 2014. Predicting thermal
vulnerability ofstream and river
ecosystems to climate change.
Climatic Change 125:399-412
Karl, T.R., J.M, Melillo, and T.C.
Peterson, (eds.). 2009. Global
Climate Change Impacts in the
United States. Cambridge University
Press, 189 pp.
Melillo, J. M., T. C. Richmond, and
G. W. Yohe (eds.). 2014. Climate
Change Impacts in the United
States: The Third National Climate
Assessment. U.S. Global Change
Research Program, Washington, DC.
841 pp.
U.S. Environmental Protection Agency
(EPA), 2016. Regional Monitoring
Networks (RMNs) to detect chang-
ing baselines, irifreshwater wadeable
Stream. (EPA/600/R 15/280).
Washington, DC: Office of Research
and Development, Washington.
Available online at https://cfpub.
epa.gov/ncea/risk/reoordisplay.
cfm?deid=307973.
For more information contact
Britta Bierwagen (bierwagen.britta@epa.gov)
Office of Research and Development, Air, Climate and Energy
Program, National Center for Environmental Assessment

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