Lake Champlain Basin SWAT Climate
Response Modeling
January 2015
Prepared for:
U.S. EPA Region 1 - New England
5 Post Office Square
Boston, MA 02109-3912
Prepared by:
Tetra Tech, Inc.
10306 Eaton Place, Suite 340
Fairfax, VA 22030
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Lake Champlain Basin Climate Response Modeling Report
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Lake Champlain Basin Climate Response Modeling Report
Contents
Climate Response Modeling 5
Climate Scenarios 5
Endpoints for Change Analysis 13
Climate Scenario Results 13
References 56
Tables
Table 1. Specific Climate Scenarios Evaluated 6
Table 2. Matrix of GCMs and Downscaling Approaches Evaluated 7
Table 3. Matrix of Available Climate Data for Scenarios 9
Table 4. SWAT Weather Generator Parameters and Adjustments Applied for Scenarios 12
Table 5. Poultney River - Changes in average annual flow and load across all NAARCAP Scenarios 15
Table 6. Otter Creek - Changes in average annual flow and load across all NAARCAP Scenarios 18
Table 7. Winooski River - Changes in average annual flow and load across all NAARCAP Scenarios 21
Table 8. Lamoille River - Changes in average annual flow and load across all NAARCAP Scenarios 24
Table 9. Missisquoi River - Changes in average annual flow and load across all NAARCAP Scenarios 27
Table 10. Mettawee River - Changes in average annual flow and load across all NAARCAP Scenarios 30
Table 11. Ausable - Changes in average annual flow and load across all NAARCAP Scenarios 33
Table 12. La Platte River, Lewis and Little Otter Creeks - Changes in average annual flow and load across all
NAARCAP Scenarios 37
Table 13. Saranac and Salmon Rivers - Changes in average annual flow and load across all NAARCAP Scenarios
41
Table 14. Boquet River - Changes in average annual flow and load across all NAARCAP Scenarios 45
Table 15. Rock and Pike Rivers - Changes in average annual flow and load across all NAARCAP Scenarios 48
Table 15. Chazy River - Changes in average annual flow and load across all NAARCAP Scenarios 52
Figures
Figure 1. Average monthly flow volume across all NAARCAP scenarios - Poultney River 15
Figure 2. Average annual flow volume across all NAARCAP scenarios - Poultney River 16
Figure 3. Average annual TSS load across all NAARCAP scenarios - Poultney River 16
Figure 4. Average annual TP load across all NAARCAP scenarios - Poultney River 17
Figure 5. Average monthly flow volume across all NAARCAP scenarios - Otter Creek 18
Figure 6. Average annual flow volume across all NAARCAP scenarios - Otter Creek 19
Figure 7. Average annual TSS load across all NAARCAP scenarios - Otter Creek 19
Figure 8. Average annual TP load across all NAARCAP scenarios - Otter Creek 20
Figure 9. Average monthly flow volume across all NAARCAP scenarios - Winooski River 21
Figure 10. Average annual flow volume across all NAARCAP scenarios - Winooski River 22
Figure 11. Average annual TSS load across all NAARCAP scenarios - Winooski River 22
Figure 12. Average annual TP load across all NAARCAP scenarios - Winooski River 23
Figure 13. Average monthly flow volume across all NAARCAP scenarios - Lamoille River 24
Figure 14. Average monthly flow volume across all NAARCAP scenarios - Lamoille River 25
Figure 15. Average monthly TSS load across all NAARCAP scenarios - Lamoille River 25
Figure 16. Average monthly TP load across all NAARCAP scenarios - Lamoille River 26
Figure 17. Average monthly flow volume across all NAARCAP scenarios - Missisquoi River 27
Figure 18. Average monthly flow volume across all NAARCAP scenarios - Missisquoi River 28
Figure 19. Average monthly TSS load across all NAARCAP scenarios - Missisquoi River 28
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Figure 20. Average monthly TP load across all NAARCAP scenarios - Missisquoi River 29
Figure 21. Average monthly flow volume across all NAARCAP scenarios - Mettawee River 30
Figure 22. Average monthly flow volume across all NAARCAP scenarios - Mettawee River 31
Figure 23. Average monthly TSS load across all NAARCAP scenarios - Mettawee River 31
Figure 24. Average monthly TP load across all NAARCAP scenarios - Mettawee River 32
Figure 25. Average monthly flow volume across all NAARCAP scenarios - Ausable 34
Figure 26. Average monthly flow volume across all NAARCAP scenarios - Ausable 35
Figure 27. Average monthly TSS load across all NAARCAP scenarios - Ausable 35
Figure 28. Average monthly TP load across all NAARCAP scenarios - Ausable 36
Figure 29. Average monthly flow volume across all NAARCAP scenarios - LaPlatte, Lewis, Little Otter 38
Figure 30. Average annual flow volume across all NAARCAP scenarios - LaPlatte, Lewis, Little Otter 39
Figure 31. Average annual TSS load across all NAARCAP scenarios - LaPlatte, Lewis, Little Otter 39
Figure 32. Average annual TP load across all NAARCAP scenarios - LaPlatte, Lewis, Little Otter 40
Figure 33. Average monthly flow volume across all NAARCAP scenarios - Saranac and Salmon Rivers 42
Figure 34. Average monthly flow volume across all NAARCAP scenarios - Saranac and Salmon Rivers 43
Figure 35. Average monthly TSS load across all NAARCAP scenarios - Saranac and Salmon Rivers 43
Figure 36. Average monthly TP load across all NAARCAP scenarios - Saranac and Salmon Rivers 44
Figure 37. Average monthly flow volume across all NAARCAP scenarios - Boquet River 45
Figure 38. Average monthly flow volume across all NAARCAP scenarios - Boquet River 46
Figure 39. Average monthly TSS load across all NAARCAP scenarios - Boquet River 46
Figure 40. Average monthly TP load across all NAARCAP scenarios - Boquet River 47
Figure 41. Average monthly flow volume across all NAARCAP scenarios - Rock and Pike Rivers 49
Figure 42. Average monthly flow volume across all NAARCAP scenarios - Rock and Pike Rivers 50
Figure 43. Average monthly TSS load across all NAARCAP scenarios - Rock and Pike Rivers 50
Figure 43. Average monthly TP load across all NAARCAP scenarios - Rock and Pike Rivers 51
Figure 41. Average monthly flow volume across all NAARCAP scenarios - Chazy River 53
Figure 42. Average monthly flow volume across all NAARCAP scenarios - Chazy River 54
Figure 43. Average monthly TSS load across all NAARCAP scenarios - Chazy River 54
Figure 43. Average monthly TP load across all NAARCAP scenarios - Chazy River 55
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Climate Response Modeling
This report was prepared for the U.S. Environmental Protection Agency (EPA) Region 1, in support of activities
pursuant to the revision of the Lake Champlain Phosphorus total maximum daily load (TMDL). Multiple
objectives were addressed under the scope of the overall project, including revising and recalibrating the lake
model used to develop the original TMDL and linking it to a Soil and Water Assessment Tool (SWAT) watershed
model to characterize loading conditions and sources in the watershed and estimate potential for loading
reductions in the Vermont portions of the basin. A specific objective of the SWAT watershed model was to
facilitate the analysis of the impacts of climate change to phosphorus loading in the watershed. This report
presents the results of that analysis. Response to future possible climates was evaluated for the Lake Champlain
basin for the 2040-2070 time horizon. A total of 6 climate scenarios were examined, as described below. Details
related to the calibrated SWAT model are available in the final model calibration report (Tetra Tech 2013).
The model runs for the climate change analysis were completed in 2013 using baseline P, sediment, and flow
estimates generated by SWAT for the Lake Champlain basin in 2013. The baseline flow and load estimates have
subsequently been revised based on 2014 updates to the TMDL SWAT modeling analysis. The climate change
analyses described in this report do not take into account the updated baseline estimates. However, the main
purpose of the climate change analysis was to understand the percent change projected for these parameters by
mid-century. The percent change values are expected to be almost identical for both sets of baseline data, so the
climate change projections have not been re-run with the new baseline data due to resource limitations.
Climate Scenarios
The scientific uncertainties related to our understanding of the physical climate system are large, and they will
continue to be large for the foreseeable future. It is beyond our current capabilities to predict with accuracy
decadal (and longer) climate changes at the regional spatial scales of relevance for watershed processes (e.g., see
Cox and Stephenson, 2007; Stainforth et al., 2007; Raisanen, 2007; Hawkins and Sutton, 2009; among many
others). The uncertainties associated with socioeconomic trajectories, technological advances, and regulatory
changes that will drive greenhouse gas emissions changes (and land use changes) are even larger and less
potentially tractable.
Faced with this uncertainty, an appropriate strategy is to take a scenario-based approach to the problem of
understanding climate change impacts on water quality. A scenario is a coherent, internally consistent, plausible
description of a possible future state of the world. Scenarios are used in assessments to provide alternative views
of future conditions considered likely to influence a given system or activity. By systematically exploring the
implications of a wide range of plausible alternative futures, or scenarios, we can reveal where the greatest
vulnerabilities lie. This information can be used by decision makers to help understand and guide the
development of response strategies for managing climate risk. A critical step in this approach is to create a
number of plausible future states that span the key uncertainties in the problem. The goal is not to estimate a
single, "most likely" future trajectory for each study watershed, but instead to understand, to the extent feasible,
how big an envelope of potential future impacts we are unable to discount and must therefore incorporate into
future planning.
A fundamental issue in interpreting Global Climate Models (GCMs) to watershed impacts is that the global
models predict climate at a large spatial scale (approximately 200x200 km) and ignore many details of local
topography that influence rainfall and temperature. This scale is too coarse for analyzing watershed response;
therefore it is necessary to develop refined local estimates of future climate through the process of "downscaling."
This can be done in several ways, including dynamical downscaling, in which regional climate models (RCMs)
are forced with the GCM climate output scenario, and through a variety of statistical methods, both of which are
intended to develop more reliable local climate forecasts.
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Lake Champlain Basin Climate Response Modeling Report
In practice, when relying on models to develop climate scenarios, the ensemble approach means sampling across
multiple GCMs, multiple methodologies for downscaling the model output to finer scales, and, depending on the
time horizon considered, multiple greenhouse gas pathways. Use of a single model run is not considered
scientifically rigorous for climate impacts studies. This is because, while the leading GCMs often produce very
different results for future climate change in a given region, there is no consensus in the climate sciences
communities that any of these are across-the-board better or more accurate than the others (e.g., see Gleckler et al.
2008).
NARCCAP (2040-2070)
EPA provided data for six regionally downscaled climate change scenarios (based on four underlying GCMs)
acquired from the National Center for Atmospheric Research (NCAR) North American Regional Climate Change
Assessment Program (NARCCAP) project (representative of the future period 2040-2070) (Mearns 2009;
http://www.narccap.ucar.edu). The NARCCAP program uses a variety of different RCMs to downscale the
output from a few of the Intergovernmental Panel on Climate Change (IPCC) GCMs to higher resolution over the
conterminous United States and most of the rest of North America. NARCCAP's purpose is to provide detailed
scenarios of regional climate change over the continent, while, by employing the RCMs and GCMs in different
combinations, systematically investigating the effect of modeling uncertainties on the scenario results (i.e.,
uncertainties associated with using different GCMs, RCMs, model physical parameterizations and
configurations). The downscaled output is archived for the periods 1970-2000 and 2040-2070 at a temporal
resolution of three hours.
NARCCAP uses the IPCC's A2 greenhouse gas storyline, which is a relatively "pessimistic" future greenhouse
gas trajectory, represents a continuously increasing global population, regionally oriented economic development,
and relatively moderate and fragmented per capita economic growth and technological change. Use of a single
greenhouse gas storyline is reasonable in this case where the focus is on a mid-century future period, as the
different IPCC greenhouse gas storylines have not yet diverged much by this point, and model uncertainty is
therefore correspondingly more important.
Summary of Climate Scenarios
Table 1 shows the specific climate scenarios evaluated. The table also contains a numbering key for shorthand
reference to climate scenarios. For example, climate scenario 2 may be seen to refer to the HadCM3 GCM,
downscaled with the HRM3 RCM. The matrix of available GCM-downscaling combinations evaluated is shown
in another way in Table 2.
Table 1. Specific Climate Scenarios Evaluated
NAARCAP Scenario #
Climate Model(s)
1
CRCM CGCM3
2
HRM3_HadCM3
3
RCM3GFDL
4
GFDL high res GFDL
5
RCM3CGCM3
6
WRFPCCSM
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Table 2. Matrix of GCMs and Downscaling Approaches Evaluated
GCM
CGCM3
HadCM3
GFDL
CCSM
Downscaling
Method/RCM
CRCM (1)
HRM3 (2)
RCM3 (3)
WRFP (6)
RCM3 (5)
GFDL high res (4)
None (7)
None (8)
None (9)
None (10)
Statistical (11)
Statistical (12)
Statistical (13)
Statistical (14)
Data Processing for Climate Scenarios
The 50-km NARCCAP scale is still too coarse for watershed modeling. There are also known problems in the
ability of climate models to predict discrete rainfall events. Therefore, meteorological time series for input to the
watershed models were created using a "change factor" or "delta" method. As developed for the GCRP protocol
(Johnson et al., 2011), the general strategy for developing meteorological change scenarios appropriate for input
to the watershed models from the climate change scenarios is to take approximately 30-year time series of
observed local climate observations (to which the watershed models have been calibrated) and perturb these
observed data to reflect the change in climate as simulated by the global and regional climate models (and
downscaling approaches). The perturbations are based on statistical summaries of change for the different climate
scenarios, by month, as calculated from the differences between the 1971-2000 and 2040-2070 climate model
simulation periods. These change statistics were used to perturb the existing climate records of precipitation and
temperature using the Climate Assessment Tool (CAT), developed under another GCRP effort for EPA's
BASINS system1 (USEPA 2009c). Changes in additional meteorological variables (e.g., solar radiation, relative
humidity, wind) are represented by modifying the monthly parameters of the SWAT statistical weather generator.
The base weather data for simulation relies on the 2006 Meteorological Database in EPA's BASINS system,
which contains records for 16,000 stations for 1970-2006, set up on an hourly basis. Use of this system has the
advantage of providing a consistent set of parameters with missing records filled and daily records disaggregated
to an hourly time step. Whereas, a site-specific watershed project would typically assemble additional weather
data sources to address under-represented areas, use of the BASINS 2006 data is sufficient to produce reasonable
results of the relative magnitudes of potential future change at the broad spatial scales and wide geographic
coverage of this project.
1 http://www.epa.gov/waterscience/basins/
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The parameters requested from NARCCAP for the dynamically downscaled model runs were:
¦ Total precipitation change (mm/day and percent)
¦ Total accumulated precipitation data for five different percentile bins - 0-25, 25-50, 50-75, 75-90, and greater
than 90 percent.
¦ Surface air temperature, average, daily maximum, and daily minimum (°K and percent)
¦ Dew point temperature change (°K and percent)
¦ Relative humidity change
¦ Surface downwelling shortwave radiation change (W m 2 and percent)
¦ 10-meter wind speed change (m s_1 and percent)
The cited statistics were provided for locations corresponding to each of the BASINS meteorological stations and
SWAT weather generator stations used in the watershed models. The full suite of statistics is not available for the
statistically downscaled model runs or the raw GCM archives. Data availability is summarized in Table 3.
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Table 3. Matrix of Available Climate Data for Scenarios
Scenario
#
RCM
GCM
Temp.
Prec.
Dew
Point
Temp
Solar
Radiation
Wind
Speed
Min
Temp.
Max
Temp.
Prec. Bin
Data
NARCCAP RCM-downscaled scenarios
1
CRCM
CGCM3
X
X
X
X
X
X
X
X
2
HRM3
HadCM3
X
X
X
X
X
X
X
X
3
RCM3
GFDL
X
X
X
X
X
X
X
X
4
GFDL
high
res
GFDL
X
X
X
X
X
X
X
X
5
RCM3
CGCM3
X
X
X
n/a
X
X
X
X
6
WRFP
CCSM
X
X
X
X
X
X
X
X
Temperature Implementation
Implementation of future temperature is straightforward. Monthly variations (deltas) to the temperature time-
series throughout the entire time-period were applied using the CAT tool. Monthly adjustments based on each
scenario were used and a modified HSPF binary data (WDM) file was created. The time-series adjustment to the
temperature was adjusted based on an additive change using the deltas (deg K) provided from NARCCAP. An
automated script then creates the SWAT observed temperature files (daily maximum and daily minimum).
Precipitation Implementation
The downscaled climate model outputs do not directly provide usable time series of precipitation, as they tend to
overestimate the frequency of small events. Instead of using climate model output directly, changes to the
existing observed precipitation time series were made based on the relative difference between current and future
conditions predicted by the climate models.
A critical issue that was considered is that watershed response depends not only on the volume of precipitation,
but also on its timing and intensity. More intense precipitation may be expected to contribute a greater fraction to
direct runoff and may also cause a non-linear increase in sediment erosion and pollutant loading. It is anticipated
that future climate change will result in intensification of rainfall for some, if not many, regions and seasons
(Kundzewicz et al. 2007). The potential intensification (or, where appropriate, de-intensification) is accomplished
by partitioning the monthly change statistics into relative changes in the upper 30 percent and lower 70 percent of
rainfall event volumes, and modifying the existing time series while maintaining a mass balance as described
below. No modifications were made to the number of rainfall events in acknowledgment of the well-recognized
fact that climate models tend to predict too many trace rainfall events.
Comprehensive data from NARCCAP (all stations, all climate scenarios) consisting of total predicted
precipitation volume (over 30 years) and various percentiles of the 3-hr intensity distribution, by month (valid
data were provided for the 0-25, 25-50, 50-70, and 70-90, and >90 percentile bins relative to the existing intensity
distribution) were made available. These intensity percentiles yield information on where precipitation
intensification occurs, but they are not based on event volumes. The CAT tool (USEPA, 2009b) represents
intensification based on events in specific volume classes. It has the ability to specify a constant multiplier to
values within a user-specified event size class.
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The percentile bin-intensity data were available only for climate scenarios W1 though W6 (RCM-downscaled
scenarios). Bin data were not available for scenarios W7 through W14 (GCM and statistically downscaled
scenarios). Two approaches were developed to account for intensification of precipitation using the available
data: 1) precipitation bin data are available and 2) precipitation bin data are not available. Each approach is
discussed in more detail below.
Approach 1: Precipitation Bin Data are Available
This approach is applicable to scenarios 1 through 6 for which the total accumulated precipitation data for
different percentile bins (for each station by month) were provided by NARCCAP. Analysis of the
comprehensive (percentile, total volume) climate scenario data showed that for most weather stations, the change
in the lower percentiles of the intensity distribution appeared to be relatively small compared to the changes above
the 70th percentile.
First consider the representation of intensification of larger events. For this the change in the volume about the
70th percentile intensity can be taken as an index of the change in the top 30 percent of events. The change in the
top 30 percent was selected based on the information on the percentile values of the 3-hr events. At the same
time, it is necessary to honor the data on the relative change in total volume. This can be done as shown below.
Let the ratio of total volume in a climate scenario (Vj) relative to the baseline scenario volume (Vj) be given by r
(V2IV1). Further assume that the total event volume (V) can be decomposed into the top 30 percent (Vn) and
bottom 70 percent (Vl). These may be related by a ratio 5 = Vh/Vl. To conserve the total volume we must have
V2=rV1.
This can be rewritten to account for intensification of the top 30 percent of events as
V2=rVL,+rVH
;n to account tor intensification ot tne top ju percent ot eve
1 + rqVH31 - rqVHA = [r VLA - rqVH^\+ [r (l + g)^ J,
where the first term in brackets at the right represents the total change in the volume of the lower 70 percent of
events and the right term represents the total change in the top 30 percent. Substituting for the first Vhj = s Vl,i
yields:
V2=(r- rqs)VL1 + (r + qr)VH1.
Here again the first term represents the change in the low range of events and the second term the change in the
high range. This provides multiplicative factors that can be applied to event ranges using CAT's built-in
capabilities on a month-by-month basis.
Next, q can be calculated by defining it relative to the lower 70 percentile values (i.e., from 0 to 70th percentile).
Specifically (r-rqs) which represents the events below the 70th percentile can be written as the ratio of the sum of
the volumes below the 70th percentile in a climate scenario relative to the sum of the volumes below the 70th
percentile for the current condition:
(>70>1 ©70>1
where (O70) 1 and (070)2 are the sum of the volumes reported up to the 70th percentile for a month for the current
condition and future condition respectively.
Again solving the above equation for q yields:
q = {\-Alr)ls
where A is defined as A = ^70^2
(070 )l
In sum, for each month at each station the following were calculated:
r = from the summary of the climate scenario output,
.s =' "/(y _ y ) from the existing observed precipitation data for the station, sorted into events and post-
processed to evaluate the top 30 percent (Vn) and bottom 70 percent (Vl) event volumes.
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The numerator is calculated as the difference between total volume and the top 30 percent
volume, rather than directly from Vl to correct for analyses in which some scattered
precipitation is not included within defined "events." The 5 value was calculated by
month and percentile (for every station, every month) using the observed precipitation
time-series data because this information cannot be reliably inferred from the percentile
bin output as it does not reflect the distribution of actual event volumes for the CAT
adjustments to preserve the relative volume changes predicted by the climate scenarios.
q = (l- AI r) / s from the summary of the percentile bin climate scenario output summary
The multiplicative adjustment factors can then be assembled as:
r (1 - c/s). for the events below the 70th percentile, and
r (1 + q). for the events above the 70th percentile.
This approach is also sufficient for the cases where there is a relative increase in the low-percentile. Here the
change in the 70th percentile intensity is relatively small and tends to be less than current conditions under the
future scenario, resulting in q being a small negative number. Then, application of the method results in a
decrease in the fraction of the total volume belonging to the larger events, with a shift to the smaller events - thus
approximating observed increases in intensity for smaller events.
In general, it is necessary to have -1 < q < 1 Is to prevent negative solutions to the multipliers. The condition that
q < 1 Is is guaranteed to be met by the definition of q (because Air is always positive); however, the lower bound
condition is not guaranteed to be met. Further, the calculation of q from the percentile bin data is at best an
approximation of the actual intensification pattern. To address this problem a further constraint is placed on q
requiring that some precipitation must remain in both the high and low ranges after adjustment: -0.8 < q < 0.8Is.
It should be noted that the cases in which negative solutions arose were rare and mainly occurred for stations
located in Arizona in the summer months.
Approach 2: Precipitation Bin Data not Available
This approach is applicable for scenarios 7 through 14, for which precipitation bin-intensity data were not
available. For all these scenarios it was assumed that all increases in precipitation occur in the top 30 percent of
events. In the cases where there was a decline in precipitation for a given month, the decreases were applied
across all events.
For the case when r = V-JVt > 1 (increasing precipitation), the future volume representing the climate scenario
(V2) can be defined as:
V2 = V\L + r ' V\H
?
*
where r is the change applied only to the upper range (>30%), Vh is the volume in the top 30 percent, and
Vl is the volume in the bottom 70 percent of events.
Setting r* =r + -(r — 1) • llI/ylH , the overall change is satisfied, as:
V2=VlL + r -Vm = VlL + r-Vw-VlL + r-VlL = r (Vm + VlL) = r-
Further, as r > 1, r* is always positive.
For the case of r <= 1 (decreasing precipitation), an across-the-board decrease in precipitation was applied as
follows:
V2 = r-VlL +r-Vm
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The adjustment factors can then be assembled as follows:
For the events above the 70th percentile, if
r > 1, then use r*.
r < =7, then use r.
For the events below the 70th percentile, if
r > 1, then use 1 (no change)
r <= 1, then use r.
PET Implementation
PET is simulated with the Penman-Monteith energy balance method. In addition to temperature and precipitation,
the Penman-Monteith method requires as input dew point (or relative humidity), solar radiation, and wind.
Because only a few stations have time series for all four additional variables that are complete over the entire
1970-2000 period, these variables are derived from the SWAT statistical weather generator (Neitsch et al. 2005).
The SWAT weather generator database (wgn) contains the statistical data needed to generate representative daily
climate data for the different stations. Adjustments to the wgn file parameters were made using monthly change
factors for the NARCCAP dynamically downscaled scenarios. Specifically solar radiation, dew point temperature
and wind speed were adjusted for each scenario (Table 4).
Table 4. SWAT Weather Generator Parameters and Adjustments Applied for Scenarios
SWAT wgn file
Parameter
Description
Adjustment Applied
SOLARAV1
Average daily solar radiation for month
(MJ/m2/day)
Adjusted based on surface downwelling
shortwave radiation change (%)
DEWPT1
Average daily dew point temperature in
month (°C)
Additive delta value provided for climate
scenario for each month
WNDAV1
Average daily wind speed in month (m/s)
Adjusted based on 10-meter wind speed
change (%)
The probability of a wet day following a dry day in the month (PRWl) and probability of a wet day following a
wet day in the month (PRW2) were kept the same as in the original wgn file. Based on discussion with EPA it
was noted that systematic biases in the climate models being introduced by scale mismatch (between a 50-km grid
and a station observation) for parameters in the weather generator like wet day/dry day timing and too many trace
precipitation events relative to reality prohibit use of the climate models to determine these parameters and hence
were kept the same as the original wgn file. Also an analysis of the dynamically downscaled raw 3-hourly time-
series for the CRCM downscaling of the CGCM3 GCM demonstrated that the probability that a rainy day is
followed by a rainy day (transition probability) did not change significantly at any of the five separate locations
that were evaluated.
For the statistically downscaled climate scenarios in the BOR repository information on these additional
meteorological variables is not provided. Many of these outputs are also unavailable from the archived raw GCM
output. For these scenarios it was assumed that the statistical parameters remained unchanged at current
conditions. While the lack of change is not physically realistic (e.g., changes in rainfall will cause changes in
cloud cover and thus solar radiation reaching the land surface), this reflects the way in which output from these
models is typically used.
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One of the NARCCAP scenario archives (Scenario 5: CGCM3 downscaled with RCM3) does not include solar
radiation, which may be affected by changes in cloud cover. Current condition statistics for solar radiation
contained in the weather generator were used for this scenario. This does not appear to introduce a significant
bias as the resulting PET predictions fall within the range of those derived from the other NARCCAP scenarios.
Endpoints for Change Analysis
Climate and land use change both have the potential to introduce significant changes in the hydrologic cycle. The
mobilization and transport of pollutants will also be affected, both as a direct result of hydrologic changes and
through changes in land cover and plant growth.
Hydrologic Endpoints
At the larger scale, flow volumes and the seasonal timing of flow are of immediate and obvious concern. Flows
are analyzed in a variety of ways over the 30-year analysis period, including the minimum, median, mean, and
maximum change relative to existing conditions among the different scenarios. Because of the uncertainties
inherent in modeling at this scale, predictions of relative change are most relevant. In addition to basic flow
statistics, comparisons are made for 100-year flood peak (fit with Log Pearson type III distribution; USGS 1982),
average annual 7-day low flow, Richards-Baker flashiness index (a measure of the frequency and rapidity of short
term changes in streamflow; Baker et al. 2004), and days to the centroid of mass for the annual flow on a water-
year basis (i.e., days from previous October 1 at which half of the flow for the water year is achieved, an
important indicator of changes in the snow accumulation and melt cycle).
Water Quality Endpoints
The SWAT model (operating at a daily time step) is most reliable in predicting monthly pollutant loads, while
concentrations at shorter time steps may not be accurately simulated. Because the sediment load in-stream is
often dominated by channel adjustment processes, which are highly site-specific and occur at a fine spatial scale,
it is anticipated that precision in the prediction of sediment and sediment-associated pollutant loads will be
relatively low. Nutrient balances can also be strongly affected by biological processes in the channels, which can
only be roughly approximated at the scale of modeling proposed. Therefore, monthly and annual loads of
sediment, phosphorus, and nitrogen are likely the most useful and reliable measures of water quality produced by
the analysis and interpretation to finer time scales (e.g., concentration time series) is not recommended.
Accordingly, the focus of comparison among scenarios is on monthly and average annual loads for TSS, total
nitrogen, and total phosphorus.
As with the flow simulation, it is most appropriate to examine relative (rather than absolute) changes in simulated
pollutant loads when comparing scenarios to current conditions. Although calibrated and validated, the models
clearly have significant uncertainty in predicting current condition pollutant loads, and those loads are themselves
very imprecisely known due to limited monitoring data.
Climate Scenario Results
Results of applying the 6 climate scenarios in the Lake Champlain SWAT model (Tetra Tech 2013) are
summarized below in several ways. The different climate scenarios are in agreement on an increase in annual
flow volumes, peak flows, and pollutant loads - but do not all agree on the sign of change for some of the other
measures. Results are presented for all the major rivers and streams draining into Lake Champlain that are
monitored by the Lake Champlain Basin Program.
• Poultney River
• Otter Creek
• Winooski River
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Lake Champlain Basin Climate Response Modeling Report
• Lamoille River
• Missisquoi River
• Mettawee River - Barge Canal
• Ausable and Little Ausable Rivers
• La Platte River, Lewis Creek and Otter Creek
• Saranac and Salmon Rivers
• Boquet River
• Rock River and Pike River
• Great Chazy River and Little Chazy River
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Poultney River
Table 5. Poultney River - Changes in average annual flow and load across all NAARCAP Scenarios
Constituent
Min
Median
Mean
Max
Flow
2.14%
13.86%
14.12%
24.87%
TSS
-3.29%
17.04%
16.70%
33.25%
TP
16.71%
33.31%
35.91%
56.40%
Poultney River
20 -
o 10 -
6 7
Month
•Baseline
• CRCM_cgcm3
HRM3_hadcm3
-RCM3_gfdl
• GFDL_slice
RCM3_cgcm3
WRFP ccsm
Figure 1. Average monthly flow volume across all NAARCAP scenarios - Poultney River
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Annual Average Flow Volume
I Baseline ¦CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
I GFDL_slice ¦RCM3_cgcm3 BWRFP_ccsm
500
450
4-00
350
> 3 00
I 250
J 200
150
100
50
0
Poultney River
Figure 2. Average annual flow volume across all NAARCAP scenarios - Poultney River
TSS Loads
I Baseiine ¦ CRCM_cgcrn3 1 HRM3_hadcm3 1 RCM3_gfdl
I GFDL_slice ¦ RCM3_cgcm3 lWRFP_ccsm
12,000
10,000
8,000
e e.ooo
4,000
2,000
Poultney River
Figure 3. Average annual TSS load across all NAARCAP scenarios - Poultney River
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TP Loads
I Baseline ¦ CRCM_cgcm3 1 HRM3_hadcm3 1 RCM3_gfdl
GFDL_slice ¦ RCM3_cgcm3 lWRFP_ccsm
60
50
40
E 30
"O
2
20
10
Pouttney River
Figure 4. Average annual TP load across all NAARCAP scenarios - Poultney River
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Otter Creek
Table 6. Otter Creek - Changes in average annual flow and load across all NAARCAP Scenarios
Constituent
Min
Median
Mean
Max
Flow
1.83%
11.33%
11.70%
21.30%
TSS
-2.67%
15.30%
14.70%
27.74%
TP
13.31%
28.14%
29.00%
42.56%
Otter Creek
6 7
Month
9 10 11 12
Baseline
¦CRCM_cgcm3
HRM3_hadcm3
RCM3_gfdl
¦GFDL_slice
RCM3_cgcm3
WRFP ccsm
Figure 5. Average monthly flow volume across all NAARCAP scenarios - Otter Creek
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Lake Champlain Basin Climate Response Modeling Report
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Annual Average Flow Volume
I Baseline ¦CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
I GFDL_sIice ¦RCM3_cgcm3 BWRFP_ccsm
1800
1600
1400
1200
L_
S*
m" 1000
E
—
"g 800
-S
LL
600
400
200
0
Otter Creek
Figure 6. Average annual flow volume across all NAARCAP scenarios - Otter Creek
I Baseline
. GFDL slice
TSS Loads
I CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfdl
l RCM 3_cgcm3 ¦ WRFP_ccsm
Otter Creek
Figure 7. Average annual TSS load across all NAARCAP scenarios - Otter Creek
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TP Loads
I Baseline ¦ CRCM _cgc m 3 ¦ HRM3_hadcm3 1 RCM3_gfdl
IGFDL_slice BRCMSjigcnnS lWRFP_cc5m
Otter Creek
Figure 8. Average annual TP load across all NAARCAP scenarios - Otter Creek
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Lake Champlain Basin Climate Response Modeling Report
Winooski River
Table 7. Winooski River - Changes in average annual flow and load across all NAARCAP Scenarios
"It
Constituent
Min
Median
Mean
Max
Flow
1.31%
8.72%
8.77%
17.31%
TSS
-11.43%
6.90%
6.41%
19.03%
TP
8.81%
24.45%
26.11%
46.34%
Winooski River
i
"
J
M
I
M
fn
A
A
A
/ V
— A*
T "J
i
| A
i
1
2
3
4
5
6 7 8
Month
1
9 10
11
12
Baseline
•CRCM_cgem3
HRM3_hadcm3
RCM3_gfdl
GFDL_slice
RCM3_cgcm3
WRFP ccsm
Figure 9. Average monthly flow volume across all NAARCAP scenarios - Winooski River
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Annual Average Flow Volume
I Baseline ¦CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
I GFDL_slice ¦RCM3_cgcm3 BWRFP_ccsm
Winooski River
Figure 10. Average annual flow volume across ail NAARCAP scenarios - Winooski River
TSS Loads
I Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
I GFDL_slice ¦ RCM3_cgcm3 BWRFP_ccsm
120.000
100,000
30,000
60,000
"O
a
3
40,000
20,000
Winooski River
Figure 11. Average annual TSS load across all NAARCAP scenarios - Winooski River
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TP Loads
250
200
150
5 100
I Baseline ¦ CRCM_cgcnn3 ¦ HRM3_hadcm3 ¦ RCM3_gfdl
IGFDL_slice ¦ RCM3_cgcm3 lWRFP_cc5nn
Winooski River
Figure 12. Average annual TP load across all NAARCAP scenarios - Winooski River
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Lamoille River
Table 8. Lamoille River - Changes in average annual flow and load across all NAARCAP Scenarios
Constituent
Min
Median
Mean
Max
Flow
4.44%
14.85%
15.13%
26.45%
TSS
-3.96%
15.62%
15.57%
32.01%
TP
30.06%
43.06%
44.81%
63.02%
Lamoille River
Baseline
¦CRCM_cgcm3
HRM3_hadcm3
¦RCM3_gfdl
GFDL_slice
RCM3_cgcm3
WRFP ccsm
Figure 13. Average monthly flow volume across all NAARCAP scenarios - Lamoille River
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Lake Champlain Basin Climate Response Modeling Report
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Annual Average Flow Volume
I Baseline ¦CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
I GFDL_sIice ¦RCM3_cgcm3 BWRFP_ccsm
1800
1600
1400
1200
L_
S*
m" 1000
E
—
"g 800
-S
LL
600
400
200
0
Lamoille River
Figure 14. Average monthly flow volume across all NAARCAP scenarios - Lamoille River
TSS Loads
I Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfdl
I GFDL_slice ¦ RCM3_cgcm3 lWRFP_ccsm
35,000
30,000
25,000
20,000
J 15,000
10,000
5,000
Lamoille River
Figure 15. Average monthly TSS load across ail NAARCAP scenarios - Lamoille River
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TP Loads
¦ Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 1 RCM3_gfdl
GFDL_slice ¦ RCM3_cgcm3 lWRFP_ccsm
Lamoille River
Figure 16. Average monthly TP load across all NAARCAP scenarios - Lamoille River
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Missisquoi River
Table 9. Missisquoi River - Changes in average annual flow and load across all NAARCAP Scenarios
Constituent
Min
Median
Mean
Max
Flow
0.63%
11.43%
10.24%
15.40%
TSS
-22.83%
-2.53%
-3.18%
12.46%
TP
13.36%
31.46%
30.44%
42.10%
140
120
100
\n'
E 80
o
| 60
Ll_
40
20
0
* Baseline
¦ CRCM_cgcm3
-*-HRM3_hadcm3
— RCM3_gfdl
K GFDL_slice
• RCM3_cgcm3
i WRFP ccsm
Missisquoi River
123456789 10 11 12
Month
Figure 17. Average monthly flow volume across all NAARCAP scenarios - Missisquoi River
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Annual Average Flow Volume
I Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfdl
I GFDL_s!ice ¦ RCM3_cgcm3 lWRFP_ccsm
1650
1600
1550
1500
I 1450
•fi
1400
1350
1300
Missisquoi River
Figure 18. Average monthly flow volume across all NAARCAP scenarios - Missisquoi River
TSS Loads
I Baseline ¦ CRCM_cgcm3 1 HRM3_hadcm3 ¦ RCM3_gfdl
I GFDL_slice ¦ RCM3_cgcm3 !WRFP_ccsm
70,000
60.000
50,000
40,000
] 30,000
20,000
10,000
Missisquoi River
Figure 19. Average monthly TSS load across ail NAARCAP scenarios - Missisquoi River
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TP Loads
¦ Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 1 RCM3_gfdl
GFDL_slice ¦ RCM3_cgcm3 lWRFP_ccsm
Missisquoi River
Figure 20. Average monthly TP load across all NAARCAP scenarios - Missisquoi River
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Mettawee River
Table 10. Mettawee River - Changes in average annual flow and load across all NAARCAP Scenarios
Constituent
Min
Median
Mean
Max
Flow
3.12%
14.19%
13.57%
20.69%
TSS
-4.84%
18.74%
16.62%
27.61%
TP
24.24%
39.80%
40.69%
57.75%
Mettawee River - Barge Canal
6 7
Month
10 11 12
¦Baseline
• CRCM_cgcm3
HRM3_hadcm3
¦ RCM3_gfdl
¦ GFDL_slice
RCM3_cgcm3
-WRFP ccsm
Figure 21. Average monthly flow volume across all NAARCAP scenarios - Mettawee River
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Annual Average Flow Volume
¦ Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
¦ GFDL_sIice ¦RCM3_cgcm3 CWRFP_ccsm
Mettawee River - Barge Canal
Figure 22. Average monthly flow volume across all NAARCAP scenarios - Mettawee River
I Baseline
IGFDL slice
TSS Loads
I CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfdl
I RCM3_cgcm3 CWRFP_ccsm
Mettawee River - Barge Canal
Figure 23. Average monthly TSS load across all NAARCAP scenarios - Mettawee River
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TP Loads
¦ Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfdl
¦ G F DL_slice ¦RCM3_cgcm3 lWRFP_ccsnn
Mettawee River - Barge Canal
Figure 24. Average monthly TP load across all NAARCAP scenarios - Mettawee River
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Ausable River and Little Ausable River
Table 11. Ausable - Changes in average annual flow and load across all NAARCAP Scenarios
Constituent
Min
Median
Mean
Max
Ausable River
Flow
-1.74%
7.79%
6.87%
16.66%
TSS
-16.78%
3.86%
1.72%
19.37%
TP
-6.58%
5.23%
4.33%
18.34%
Little Ausable River
Flow
9.32%
25.51%
26.62%
46.47%
TSS
13.25%
32.72%
40.46%
87.84%
TP
16.81%
37.63%
44.28%
89.01%
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Ausable River
6 7
Month
^Baseline
• CRCM_cgcm3
HRM3_hadcm3
•RCM3_gfdl
*GFDL_slice
RCM3_cgcm3
WRFP ccsm
Figure 25. Average monthly flow volume across all NAARCAP scenarios - Ausable
i Baseline
¦ CRCM_cgcm3
* HRM3_hadcm3
—*-RCM3_gfdl
.!<( GFDL_slice
> RCM3_cgcm3
i WRFP ccsm
Little Ausable River
4 5 6 7 8 9 10 11 12
Month
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Annual Average Flow Volume
I Baseline ¦ CRCM _cgcm3 ¦ HRM3_hadcnn3 1 RCM3_gfdl
I GFDL_sJice ¦RCM3_cgcm3 ¦ WRFP_ccsm
900
Ausable River
Little Ausable River
"It
Figure 26. Average monthly flow volume across all NAARCAP scenarios - Ausable
I Baseline
I GFOL slice
TSS Loads
I CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfdl
: RCM3_cgcnn3 CWRFP_ccsm
14,000
12,000
Figure 27. Average monthly TSS load across all NAARCAP scenarios - Ausable
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TP Loads
I Baseline ¦ CRCM_cgcm3 1 HRM3_hadcm3 1 RCM3_gfdl
I GFDL_slice ¦ RCM3_cgcm3 ¦ WRFP_ccsm
Ausable River
LittleAusable River
Figure 28. Average monthly TP load across all NAARCAP scenarios - Ausable
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LaPlatte River, Lewis Creek and Little Otter Creek
Table 12. La Platte River, Lewis and Little Otter Creeks - Changes in average annual flow and load across
all NAARCAP Scenarios
Constituent
Min
Median
Mean
Max
LaPlatte River
Flow
-12.46%
-2.38%
-2.91%
2.92%
TSS
-12.46%
1.94%
0.96%
12.99%
TP
32.67%
50.68%
49.91%
63.23%
Lewis Creek
Flow
-14.99%
-6.32%
-6.52%
0.07%
TSS
-39.38%
-32.28%
-29.51%
-14.67%
TP
14.10%
30.54%
31.35%
44.09%
Little Otter Creek
Flow
-13.14%
-4.42%
-4.74%
-0.02%
TSS
-27.69%
-13.30%
-13.73%
-3.87%
TP
26.16%
42.87%
43.11%
55.02%
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LaPlatte River
4 5 6 7
Month
-Baseline
-CRCM_cgcm3
-HRM3_hadcm3
-RCM3_gfdl
-GFDL_slice
RCM3_cgcm3
- WRFR_ccsm
Lewis Creek
6 7
Month
-Baseline
-CRCM_cgcm3
-HRM3_hadcm3
-RCM3_gfdl
-GFDL_slice
RCM3_cgcm3
•WRFP ccsm
Little Otter Creek
4 5 6 7
Month
10 11 12
-Baseline
-CRCM_cgcm3
-HRM3_hadcm3
-RCM3_gfdl
-GFDL_slice
RCM3_cgcm3
WRFP ccsm
Figure 29. Average monthly flow volume across all NAARCAP scenarios - LaPlatte, Lewis, Little Otter
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Lake Champlain Basin Climate Response Modeling Report
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Annual Average Flow Volume
¦ Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
¦ GFDL_sIice ¦RCM3_cgcm3 CWRFP_ccsm
LaPlatte River Lewis Creek Little Otter Creek
Figure 30. Average annual flow volume across ail NAARCAP scenarios - LaPlatte, Lewis, Little Otter
TSS Loads
I Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfdl
I GFDL_slice ¦ RCM3_cgcm3 BWRFP_ccsm
6,000
5,000
4,000
1,000
LaPlatte River
Lewis Creek
Little Otter Creek
Figure 31. Average annual TSS load across all NAARCAP scenarios - LaPlatte, Lewis, Little Otter
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TP Loads
¦ Baseline BCRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfdl
¦ GFDL_slice BRCM3_cgcm3 lWRFP_ccsm
LaPlatte River Lewis Creek Little Otter Creek
Figure 32. Average annual TP load across all NAARCAP scenarios - LaPlatte, Lewis, Little Otter
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Saranac River and Salmon River
Table 13. Saranac and Salmon Rivers - Changes in average annual flow and load across all NAARCAP
Scenarios
Constituent
Min
Median
Mean
Max
Saranac River
Flow
-0.67%
11.11%
9.66%
22.64%
TSS
-2.91%
14.70%
13.02%
32.69%
TP
-8.55%
2.26%
1.54%
11.89%
Salmon River
Flow
4.51%
18.05%
17.29%
27.22%
TSS
0.33%
22.10%
21.64%
43.51%
TP
18.93%
33.33%
33.05%
46.69%
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Saranac River
6 7
Month
10 11 12
Baseline
¦CRCM_egcm3
HRM3_hadcm3
RCM3_gfdl
GFDL_slice
RCM3_cgcm3
WRFP ccsm
Figure 33. Average monthly flow volume across all NAARCAP scenarios - Saranac and Salmon Rivers
i Baseline
¦ CRCM_cgcm3
* HRM3_hadcm3
—*-RCM3_gfdl
n< GFDL_slice
> RCM3_cgcm3
i WRFP ccsm
Salmon River
6 7 8 9 10 11 12
Month
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Lake Champlain Basin Climate Response Modeling Report
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Annual Average Flow Volume
I Baseline ¦CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
I GFDL_sIice ¦RCM3_cgcm3 BWRFP_ccsm
1200
1000
800
600
400
200
Saranac River
Salmon River
Figure 34. Average monthly flow volume across all NAARCAP scenarios - Saranac and Salmon Rivers
TSS Loads
IBaseiine ¦CRCM_cgcm3 1 HRM3_hadcm3 1 RCM3_gfdl
I GFDL_s!ice ¦RCM3_cgcm3 lWRFP_ccsrin
4,000
3,500
Saranac River
Salmon River
Figure 35. Average monthly TSS load across all NAARCAP scenarios - Saranac and Salmon Rivers
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TP Loads
¦ Baseline ¦CRCM_cgcnn3 ¦ HRM3_hadcm3 1 RCM3_gfdl
¦ GFDL_slice ¦ RCM3_cgcnn3 ¦ WRFP_ccsm
Saranac River Salmon River
Figure 36. Average monthly TP load across all NAARCAP scenarios - Saranac and Salmon Rivers
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Boquet River
Table 14. Boquet River - Changes in average annual flow and load across all NAARCAP Scenarios
Constituent
Min
Median
Mean
Max
Flow
1.29%
11.39%
10.23%
13.68%
TSS
-9.88%
14.90%
12.92%
24.18%
TP
14.20%
30.34%
28.21%
34.26%
Figure 37. Average monthly flow volume across all NAARCAP scenarios - Boquet River
Boquet River
25
to
E 20
o
| 15
UL
10
5
» Baseline
¦ CRCM_cgem3
* HRM3_hadcrn3
—«—RCM3_gfdl
M GFDL_slice
• RC M 3_cg cm3
l WRFP ccsm
6 7 8 9
Month
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Annual Average Flow Volume
I Baseline ¦CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
I GFDL_slice ¦RCM3_cgcm3 BWRFP_ccsm
390
380
370
360
L.
S*
m" 350
E
—
"g 340
-S
tL
330
320
310
300
Boquet River
Figure 38. Average monthly flow volume across all NAARCAP scenarios - Boquet River
TSS Loads
I Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
I GFDL_slice ¦ RCM3_cgcm3 BWRFP_ccsm
14,000
12,000
10,000
8,000
J 6,000
4,000
2,000
Boquet River
Figure 39. Average monthly TSS load across all NAARCAP scenarios - Boquet River
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TP Loads
¦ Baseline ¦ CRCM_cgcm3 1 HRM3_hadcm3 1 RCM3_gfdl
¦ GFDL_slice BRCM3_cgcm3 lWRFP_ccsrri
30
25
20
>•
I 15
¦o
IB
3
10
5
0
Boquet River
Figure 40. Average monthly TP load across all NAARCAP scenarios - Boquet River
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Rock River and Pike River
Table 15. Rock and Pike Rivers - Changes in average annual flow and load across all NAARCAP
Scenarios
Constituent
Min
Median
Mean
Max
Rock River
Flow
18.70%
38.55%
37.24%
48.11%
TSS
26.19%
55.06%
53.81%
73.14%
TP
24.58%
40.04%
38.96%
52.50%
Pike River
Flow
10.22%
27.72%
26.23%
35.27%
TSS
9.06%
39.22%
35.30%
52.70%
TP
0.55%
15.24%
14.85%
26.72%
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Rock River
^Baseline
• CRCM_cgcm3
HRM3_hadcm3
•RCM3_gfdl
*GFDL_slice
RCM3_cgcm3
WRFP ccsm
Pike River
2 3 4 5 6 7
Month
10 11 12
i Baseline
¦ CRCM_cgcm3
* HRM3_hadcm3
—*-RCM3_gfdl
.!<( GFDL_slice
> RCM3_cgcm3
i WRFP ccsm
Figure 41. Average monthly flow volume across all NAARCAP scenarios - Rock and Pike Rivers
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Annual Average Flow Volume
I Baseline ¦CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
I GFDL_slice ¦RCM3_cgcm3 BWRFP_ccsm
400
350
300
T 250
>
J, 200
I
"¦ 150
100
Rock River
Pike River
Figure 42. Average monthly flow volume across all NAARCAP scenarios - Rock and Pike Rivers
TSS Loads
I Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfdI
I GFDL_s!ice ¦RCM3_cgcm3 lWRFP_ccsrin
30,000
25,000
20,000
15,000
-o
2
10,000
5,000
Rock River
Pike River
Figure 43. Average monthly TSS load across all NAARCAP scenarios - Rock and Pike Rivers
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Lake Champlain Basin Climate Response Modeling Report
"It
I Baseline
GFDL slice
TP Loads
I CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfdl
I RCM3_cgcm3 lWRFP_ccsm
50
50
40
£ 30
¦o
<¦3
3
20
Rock River
Pike River
Figure 44. Average monthly TP load across all NAARCAP scenarios - Rock and Pike Rivers
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Lake Champlain Basin Climate Response Modeling Report
Chazy River
Table 16. Chazy River - Changes in average annual flow and load across all NAARCAP Scenarios
Constituent
Min
Median
Mean
Max
Great Chazy River
Flow
0.83%
14.19%
14.50%
26.87%
TSS
-9.86%
11.87%
11.02%
25.51%
TP
-1.80%
19.48%
18.78%
40.05%
Little Chazy River
Flow
-0.76%
15.68%
14.89%
26.10%
TSS
-17.19%
15.27%
14.51%
38.35%
TP
-3.42%
17.50%
19.65%
46.73%
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Great Chazy River
6 7
Month
10 11 12
Baseline
¦CRCM_egcm3
HRM3_hadcm3
RCM3_gfdl
GFDL_slice
RCM3_cgcm3
WRFP ccsm
Little Chazy River
• Baseline
•CRCM_cgcm3
HRM3_hadcm3
• RCM3_gfdl
•GFDL_slice
RCM3_cgcm3
•WRFP ccsm
10 11 12
Figure 45. Average monthly flow volume across all NAARCAP scenarios - Chazy River
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Lake Champlain Basin Climate Response Modeling Report
Annual Average Flow Volume
I Baseline ¦CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfd1
I GFDL_slice ¦RCM3_cgcm3 BWRFP_ccsm
400
Great Chazy River
Little Chazy River
Figure 46. Average monthly flow volume across all NAARCAP scenarios - Chazy River
TSS Loads
I Baseline ¦ CRCM_cgcm3 ¦ HRM3_hadcm3 ¦ RCM3_gfdl
I GFDL_slice ¦ RCM3_cgcm3 BWRFP_ccsm
7,000
6,000
5,000
4,000
J 3,000
2,000
1,000
Great Chazy River
Little Chazy River
Figure 47. Average monthly TSS load across all NAARCAP scenarios - Chazy River
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25
I Baseline
IGFDL slice
TP Loads
I CRCM_cgcm3 ¦ HRM3_hadcm3 1 RCM3_gfd
RCM 3_cgcm3 lWRFP_ccsm
Great Chazy River
Little Chazy River
Figure 48. Average monthly TP load across all NAARCAP scenarios - Chazy River
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Lake Champlain Basin Climate Response Modeling Report
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