Modeling the Effects of Forecasted Climate Change on Stream. Temperature in the Nooksack River Basin

Transcription

1 Modeling the Effects of Forecasted Climate Change on Stream Temperature in the Nooksack River Basin Thesis proposal for the Master of Science Degree, Department of Geology, Western Washington University, Bellingham, Washington Stephanie Truitt June 15, 2016 Approved by Advisory Committee Members: Dr. Robert Mitchell, Thesis Committee Chair Dr. Doug Clarke, Thesis Committee Advisor Dr. John Yearsley, Thesis Committee Advisor 1

2 Table of Contents 1.0 Problem Statement Introduction Background Nooksack River Basin Climate Change Stream Temperature Numerical Modeling Methods Scope of Work Stream Morphology and Discharge Data COllection DHSVM-RBM Calibration and Validation for the South Fork DHSVM-RBM Calibration and Validation for the North and Middle Forks Modeling Future Stream Temperature Results Analysis Project Timeline Expected Results and Significance of Research Simulation Results Significance of Research References Cited Tables Figures

3 1.0 Problem Statement My objective is to model the effects of climate change on stream temperature in the Nooksack River Basin in northwest Washington State (Figure 1). I will also quantify the timing and magnitude of stream temperature changes and identify stream reaches that are vulnerable to stream temperature change. The Nooksack River provides valuable habitat for endangered salmon species, as such it is critical to understand how stream temperatures will change due to future climate (Mantua 2009). I propose using the Distributed Hydrology Soil Vegetation Model (DHSVM; Wigmosta et al., 1994) in tandem with the stream temperature River Basin Model (RBM; Sun et. al., 2014) to model hydrology and stream temperature. I will focus my study on the North and Middle Forks of the river, both of which obtain a significant amount of baseflow from glaciers and snowmelt (Dickerson-Lange and Mitchell, 2013; Murphy, 2016). Hydrology modeling will employ the methods and results of Murphy (2016) who used the DHSVM and downscaled meteorological data from global climate models (Abatzoglou and Brown, 2012) to simulate future changes in streamflow. Stream temperature modeling will employ RBM using the hydrologic and meteorological forcings simulated with DHSVM. 2.0 Introduction Several factors affect stream temperature, including climate, topography, channel morphology, and vegetation (Sun et al., 2014). Stream temperature is also strongly influenced by hydrologic conditions (Story et al., 2003) which Murphy (2016) found are sensitive to climate change in the Nooksack basins. Previous studies have shown that linear regression models are able to simulate stream temperature using projected air temperatures, however these empirical models are unable to accurately predict stream temperature for air temperatures on the highest 3

4 and lowest ends of the spectrum (Mohseni et al., 1998, Arismendi et al., 2014).The DHSVM- RBM is a physically based model which is able to capture the environmental processes which affect hydrology and stream temperature including riparian shading (Sun et al., 2014). The Nooksack River is classified as a transient rain-snow basin due to a maritime climate, thus making it susceptible to temperature increases (Dickerson-Lange and Mitchell, 2014; Murphy, 2016). The South Fork of the Nooksack River is dominated by snowmelt and rain, while the North and Middle Forks rely on snowmelt and glaciers for baseflow in the late summer, low-flow months (Grah and Beaulieu, 2013). Glacial extent in the North Fork is projected to decrease by 66% to 88% and glacial extent in the Middle Fork is projected to decrease by 69% to 87% from present day until 2099, and the amount of glacial melt available to the stream will peak in 2050 (Murphy, 2016). Glacial melt as a percentage of stream flow will double in the Middle Fork and triple in the North Fork for the year 2025 (Murphy, 2016), which should decrease the stream temperature during periods of maximum glacial melt. However, after glacial melt peaks, subsequent years will continue to have less and less glacial input due to receding glaciers, which may in turn increase stream temperature from 2050 onward. Snow water equivalent (SWE) on the North Fork is projected to decrease by approximately half compared to historical means and similar decreases were predicted for the Middle Fork. Lower SWE will also affect the timing and magnitude of peak flows in the basin (Murphy, 2016) which will potentially affect stream temperatures (Figure 2). Models that include physical processes which influence hydrology and stream temperature provide a more comprehensive approach for examining the impacts of climate change in the upper reaches of the Nooksack River Basin. 4

5 3.0 Background The Nooksack River is an important cultural and economic fish resource for the Nooksack Indian Tribe and the Lummi Nation; and a valuable water resource for other Whatcom County stake holders such as industries, municipalities, and farmers. As such, it is important to understand how changes in local climate will influence the factors that control stream temperature. Previous studies predict that stream discharge, baseflow, and snow water equivalent will all change in the Nooksack River Basin under different climate change conditions (Dickerson- Lange and Mitchell, 2013; Murphy, 2016). I will employ the DHSVM-RBM to determine how stream temperature will change under different climate change scenarios over the twenty first century. This section will discuss characteristics of the Nooksack River Basin, potential effects of climate change on the basin, and the numerical modeling techniques I will employ to achieve my objectives. 3.1 Nooksack River Basin Located in the North Cascades, the Nooksack River drains an area of 2300 km 2 and has three main forks, the North, Middle, and South Forks. Elevation in the basin ranges from sea level to 3286 m at the peak of Mt. Baker. The focus of my study will be in the upper 1550 km 2 of the basin, 40% of which is above 1000 m and includes the three forks. The river discharges approximately 3,000-4,000 cfs annually into Bellingham Bay in the Salish Sea (Dickerson- Lange and Mitchell, 2014).The South Fork basin lacks glaciers and derives most if its baseflow from seasonal snowpack, precipitation, and groundwater. The North and Middle Forks rely on glacial melt during the late summer, low flow months as well as seasonal snowmelt during the 5

6 spring season. Both the North and Middle Forks contain mountainous terrain and their channels and stream forest buffers are minimally altered since much of their area is located in the Mount Baker National Forest. The South Fork basin is an agricultural area and is not protected by National Forest boundaries. Due to the highly varied terrain, the Nooksack River Basin experiences a wide range of climatic conditions. The western slopes of the North Cascades, where the upper reaches of the Nooksack are located, receive approximately 1250 mm of precipitation per year, primarily during the winter months (Grah and Beaulieu, 2013). Glaciers are also present at high elevations, covering approximately 3400 hectares while lower elevations maintain a mild climate (Murphy, 2016). 3.2 Climate Change Recent global climate change, specifically climate warming, is a well-documented phenomenon caused by emissions of greenhouse gases by fossil fuel combustion (Stocker et al., 2013). Researchers at the University of Idaho used the Multivariate Adaptive Constructed Analogues (MACA) method to statistically downscale climate data from the Coupled Model Intercomparison Project 5 (CMIP 5), which allows for fine scale, modeled temperature and precipitation data across the United Sates. In the Pacific Northwest, mean air temperature is projected to increase approximately 5 F from historic temperatures for the years 2070 through 2099 for RCP 4.5 and approximately 9 F for RCP 8.5. Precipitation is also projected to increase from 48 inches per year in the Pacific Northwest to approximately 50 inches for the same time period for both RCP 4.5 and RCP 8.5 ( Figure 4). Though projected precipitation increases are not very large, more of winter precipitation will fall as rain rather than snow due to warmer atmospheric temperatures (Murphy, 6

7 2016). This reduction in snow precipitation will cause retreat of snowpack and glacial area (Table 1), which will in turn have an effect on the amount of baseflow snow and ice contribute to the Nooksack River in the summer months (Table 2). In fact, by 2075 snow water equivalent may decrease by more than half; and glacial extent is projected to decrease by approximately 70% in the North and Middle Fork basins by 2099 for RCP 4.5 and by about 88% for RCP 8.5 (Murphy, 2016). All of these projected changes in climatic variables in the Nooksack River basin will cause significant changes in overall river discharge, baseflow, and stream temperature. Murphy (2016) found that winter stream flow is projected to double by the year 2075, while summer streamflow is projected to decrease by half by 2075 because of a decline in snowpack and decrease in glacial volume, which now support up to 20% of the Middle Fork s summer baseflow (Grah and Beaulieu, 2013). The effects of climate change in the Nooksack River Basin, and in the Pacific Northwest in general, will certainly have an effect on stream temperature throughout the next century. 3.3 Stream Temperature Stream temperature is an important indicator of the ecological health of a river system. Changes in stream temperature can affect metabolic rates, physiology, and lifecycles of aquatic organisms (Poole and Berman, 2001). Permanent changes in stream temperature may result in a permanent alteration of the biology of a river system and thus it is important to determine the magnitude and timing of stream temperature changes for at risk river systems. Climatic factors have the largest effect on stream temperatures which indicates that shifts in air temperature, precipitation, or any other number of important climate variables will also cause 7

8 changes in stream temperature. Physical characteristics of the stream also strongly influence stream temperature such as channel morphology, channel width and depth, as well as riparian canopy conditions (Sun et al., 2015). Humans have the ability to modify stream temperature both indirectly, by changing climatic factors through the emission of greenhouse gases, and directly by altering physical characteristics of the channel and the area surrounding the channel. Because many of the variables which control stream temperature are predicted to change, it follows that stream temperature will also change. What is unknown, however, is how much stream temperature will change, and will it change positively or negatively. Additionally, understanding the timing of stream temperature changes is important from a land and water resources management point of view. The Environmental Protection Agency (EPA) in cooperation with local stakeholders in the South Fork produced a Total Maximum Daily Load (TMDL) for water temperature of the South Fork in 2014 as required by Section 303(d) of the Federal Water Pollution Control Act of 1972 (FWPCA). This study modeled stream temperature for future climate change conditions using the QUAL2Kw stream temperature model (Figure 3). The results of this TMDL predict that by the year 2080, the maximum average stream temperature for the South Fork will increase by over 5 C, with the upper most reaches being most impaired (U.S. EPA, 2014). This study also found that the three variables which contribute most to stream temperature increase were an increase in air temperature, increase in tributary and groundwater temperature, and decreased flow. I expect temperature trends in the North and Middle Forks to differ from trends in the South Fork because the North and Middle Forks are at higher elevations and obtain more baseflow from glacial and snowmelt. 8

9 3.4 Numerical Modeling The DHSVM is a physically based hydrologic model which takes meteorological inputs and physical basin characteristics and outputs, among other quantities, stream discharge, snow water equivalent, and glacier melt. The scale of these outputs is dependent on the scale of the digital elevation model which produces the stream networks within the DHSVM (Wigmosta et al., 1994; 2002). In the case of this study, the resolution will be 50 m. Coarser resolution would degrade the spatial variability with the basin and processing at a finer resolution is less computationally efficient. The meteorological inputs for DHSVM-RBM include air temperature, wind speed, precipitation, shortwave radiation, and longwave radiation. Physical layers include land cover, soil type, soil depth, elevation, and shadow maps (Wigmosta et al., 2002). For the calibration and validation process, the publically available statistically derived 1/16 degree gridded historical surface data developed by Livneh et al. (2013; 2015) and modified by Murphy (2016) will be used to model stream temperature. The future meteorological conditions derived from Global Climate Models (GCMs) which were downscaled using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown, 2012) and modified for the Nooksack River basin by Murphy (2016) will be used for future climate change simulations. Select outputs from the DHSVM become inputs for the RBM stream temperature model. The RBM is a stream temperature model which is coupled with the DHSVM hydrologic model (DHSVM-RBM). The RBM is a one-dimensional model which solves time dependent equations for the conservation of thermal energy between the air-water interface using a semi-lagrangian method (Yearsley 2009). In order to gain solutions to these equations, the model tracks parcels of water through the river basin and determines results for points on the river basin grid. These 9

10 thermal energy inputs are a result of the DHSVM hydrologic model. The RBM model is used to determine how future climate conditions may affect stream temperature and has been used in the Pacific Northwest (Sun et al., 2015, Yearsley, 2012, Yearsley et al., 2009), though not yet in Whatcom County, WA. 4.0 Methods To achieve my objective to model the effects of climate change on stream temperature in the Nooksack River Basin using the DHSVM-RBM, I will accomplish the following scope of work. 4.1 Scope of Work 1. Collect stream morphology and discharge data from several tributaries to each of the three forks. 2. Calibrate and validate the DHSVM-RBM to the South Fork of the Nooksack River using historical stream temperature data and compare the results to the 2014 TMDL on the South Fork. 3. Calibrate and validate the DHSVM-RBM to the North and Middle Forks of the Nooksack River using historical stream temperature data. 4. Perform numerical simulations on the North and Middle Forks of the Nooksack River using MACA downscaled meteorological data to assess the impact of climate change scenarios on stream temperature. 5. Analyze the results of the model to determine timing and magnitude of stream temperature change as well as at-risk stream reaches. 4.2 Stream Morphology and Discharge Data Collection 10

11 The RBM stream temperature model requires stream width, depth and discharge for each stream segment within the basin. The United States Geological Survey (USGS) maintains gauges on each of the three main forks of the Nooksack River Basin which provide morphological and discharge data. Some of the larger tributaries such as Cascade Creek and Skookum Creek are monitored by the USGS, however no information on discharge exists on the more minor tributaries. In order to collect morphological and discharge data, I will travel to 27 sites across the three sub-basins to measure velocity and morphology using a Marsh-McBirney Flo-Mate portable velocity flow meter and a top setting wading rod. I will then use the morphological data and the velocity measurements to derive discharge. The first set of 27 measurements will take place in late July, and the second set of measurements will take place in late September. This six to eight week gap in time will allow me to measure mid-summer flows as well as late summer flows when glaciers are more likely to be contributing a larger percentage of baseflow for the North and Middle Forks. These measurements will allow me to derive the parameters for estimating stream speed and depth using the method of Leopold and Maddock (1953). 4.3 DHSVM-RBM Calibration and Validation for the South Fork The RBM stream temperature model has not yet been implemented in the Nooksack River basin and as such my first task after data collection will be to calibrate the RBM using historical stream temperature and meteorological data to determine if the model is predicting similar values to observed data. Historical stream temperature data will come from 124 stream temperature monitoring stations maintained by the Nooksack Indian Tribe throughout the three sub basins (Figure 1). Stream temperature data exists for some sites as far back as 1999, though the time series is not consistent in many of the sites. In recent years, there is a more complete record of stream temperature measured in 30-minute time increments throughout all three sub basins. 11

12 Historical meteorological inputs will employ the modified gridded Linveh et al., (2013) data ( ) employed by Murphy (2016). The RBM will be calibrated to a series of historical stream temperature data and meteorological conditions. The goal of the calibration process is to maximize the Nash-Sutcliffe model efficiency coefficient by varying the Leopold and Mohseni parameters until the modeled time series reasonably matches the historical observed time series. An efficiency coefficient near 1 indicates that the RBM is closely modeling the physical processes of the basin and should be able to predict future stream temperature without bias (Nash and Sutcliffe, 1970). When this comparison of observed and modeled data achieves a Nash-Sutcliffe Efficiency value of 0.7 or higher, the model will be considered calibrated. This validation process ensures that the adjusted variables are reasonable and the model is accurately capturing processes and characteristics within the basin. Because there is already modeled stream temperature data available for the South Fork, I will also be able to determine how well RBM performs for future simulations by comparing my outputs to the QUAL2Kw outputs. This comparison will be qualitative in nature as the QUAL2Kw model is used to model water quality in general (Pelletier et al., 2005) while RBM is used specifically to model stream temperature. QUAL2Kw, therefore, is not well suited for the scope of this project and as such, results for the QUAL2Kw study on the South Fork may differ from those produced using RBM, though general trends in stream temperature should be similar for both models. 4.4 DHSVM-RBM Calibration and Validation for the North and Middle Forks 12

13 Calibration and validation of the North and Middle Forks will mimic my process in the South Fork, and will involve using historical meteorological conditions and historical stream temperatures collected by the Nooksack Tribe to ensure that the model results continue to produce a Nash-Sutcliffe model efficiency value near 1 when compared with observed data. Historical meteorological inputs will employ the modified gridded Linveh et al., (2013) data ( ) employed by Murphy (2016). Input variables for RBM, such as the Mohseni parameter derived from historic stream temperature, and Leopold parameters derived from historic stream discharge, will be modified as necessary to account for the difference in physical characteristics from the South Fork. Stream temperature has not yet been modeled for the North and Middle Forks, thus there will be no additional model results to compare future simulations to as with the South Fork. 4.5 Modeling Future Stream Temperature After the DHSVM-RBM has been properly calibrated and validated to each of the three forks, I will model future stream temperature under different climate change conditions. The modified MACA gridded downscale meteorological data employed by Murphy (2016) will be used as inputs for the meteorological conditions. Physical basin characteristic inputs will remain the same for future stream temperature simulations as it will be assumed that drastic changes in topography, soil, and land cover will not take place in the next 80 years. The exception will be that the riparian shading variable within RBM will be changed to see how each basin might respond to changes in riparian shading. The model will be run for three periods into the future: 2025, 2050, 2080 in order to accurately capture climate trends over 30-year time periods. Additionally, running DHSVM-RBM at these time intervals allows me to compare my results to 13

14 the results of Murphy (2016) and determine the relationship between modeled stream temperature and modeled stream discharge. Glacial recession will also be incorporated into future modeling scenarios because glacial melt is established as an important component of baseflow in the North and Middle Forks. This will be accomplished by one of two approaches. Currently the DHSVM coupled with the dynamic glacier model (DHSVM-Glacier; Murphy, 2016) is not coupled with the RBM. Should the RBM be coupled with the DHSVM-Glacier model in a reasonable amount of time during this study, I will simulate glacier recession and stream temperature simultaneously using DHSVM- Glacier coupled with RBM. The second option is to use glacier extents produced by the DHSVM-Glacier model on a decade-by-decade time scale through the year These static glacier sizes will be imported into the DHSVM-RBM which simulates glacier melt, but the glacier remains static in size over the duration of the simulation (e.g. 10 year time intervals). 4.6 Results Analysis Statistical analyses will be performed on data using R, an open sourced statistical computing package. The primary focus of my analysis will be to determine the magnitude of stream temperature change from climate normal stream temperatures, and whether or not this change is due to natural variation or climate change using the Wilcoxon signed-rank test. The Wilcoxon signed-rank test is a non-parametric hypothesis test which is used to compare two sample sets to determine whether or not the two sets are statistically different. In this case, one sample set will be historical stream temperature and the second will be modeled stream temperature. The results of this test will indicate whether or not modeled future stream temperatures are statistically different from stream temperatures today. This analysis will allow me to identify stream reaches 14

15 which are most likely to be temperature impaired due to climate change. With this information, natural resource managers can determine how best to address stream temperature change and where mitigation efforts may be the most useful. Another focus of my analysis will be to determine the timing of stream temperature changes, both throughout the water year and throughout the next century. 5.0 Project Timeline Step Calibrate DHSVM-RBM historic stream temperature with climate forcings for the South Fork and validate to TMDL results from 2014 Calibrate DHSVM-RBM to North and Middle Forks using downscaled GCM climate forcings Simulate future stream temperature using DHSVM-RBM for North, Middle and South Forks Process DHSVM-RBM outputs to identify at risk stream reaches Planned Completion Summer 2016 Summer/Fall 2016 Fall 2016/Winter 2017 Winter/Spring Expected Results and Significance of Research 6.1 Simulation Results I expect to simulate stream temperature for all three forks of the Nooksack River through the year I will aggregate my results into 30-year climate normals surrounding the years 2025, 2050 and 2075 to provide an accurate account of climatic changes rather than changes in regional weather. From these future simulations, I expect trends in stream temperature to closely match trends in the variables which influence stream temperature the most. If stream temperature is most dependent on air temperature, stream temperature results should strongly correlate with 15

16 air temperatures from the downscaled GCMs. Should stream discharge have the largest effect on stream temperature, then I would expect stream temperature to decrease with increased stream volume and increase with decreased stream volume. The contribution that glacial meltwater makes to streamflow is projected to increase during the first half of this century as glacial area declines, then meltwater contributions will drop off after a large portion of glacial area is melted (Murphy, 2016). The glacial meltwater component of streamflow will contribute to cooler stream temperatures; however it is unknown if meltwater content or discharge will have a greater effect on stream temperature. Because the source of baseflow is different for the North and Middle Forks versus the South Fork, I anticipate that the magnitudes of stream temperature will differ between the forks. Riparian shading also strongly influences stream temperature, however, no major vegetation changes the North and Middle Forks are suspected to take place as they are located in a National Forest area. Any logging that takes place within the National Forest must maintain a buffer area so that they stream is not negatively influenced by the effects of logging (Forest Practices Act RCW 76.09). The South Fork, however, may undergo changes in riparian vegetation due to the agricultural nature of the land surrounding the river and thus is likely more susceptible to air temperature changes than the other basins. Likely, all of these variables will affect stream temperature, though some variables will be dominant depending on the basin, the time of year, and how climate variables differ over the rugged terrain of the basin. In addition to producing future stream temperatures at the headwater nodes of each stream reach and quantifying these changes, I also hope to identify stream reaches which are most likely to be impaired by stream temperature changes. In order to identify these reaches, I will compare simulated future stream temperature to historic stream temperatures to determine 16

17 which reaches have the largest difference between these two values. I anticipate that the stream reaches that will be most affected will be at lower elevations. 6.2 Significance of Research Global climate models predict that global air temperature will continue to increase well into the future, even if greenhouse gas emissions are mitigated (Mote and Salathe, 2010). Regional implications of climate change include reduced glacial volume, increased air temperature, reduced snowpack, and changes in the timing and magnitude of stream discharge. All of these changing variables will have an impact on stream temperature in the Pacific Northwest, though the magnitude of these changes is unknown. My study will quantify these potential changes in stream temperature for the Nooksack River Basin, which will give stake holders the information necessary to make management decisions on how to mitigate stream temperature changes. This study will also produce methodology on stream temperature modeling which can be implemented in other coastal, transient watersheds in the Pacific Northwest. 17

18 7.0 References Cited Abatzoglou, J.T., and Brown, T.J., 2012, A comparison of statistical downscaling methods suited for wildfire applications: International Journal of Climatology, v. 32, p , doi: /joc Arismendi, I., M. Safeeq, J. B. Dunham, and S. L. Johnson, 2014: Can air temperature be used to project influences of climate change on stream temperature? Environmental Research Letters, 9, , / /9/8/ Dickerson-Lange, S.E., and Mitchell, R., 2014, Modeling the effects of climate change projections on streamflow in the Nooksack River basin, Northwest Washington: EFFECTS OF CLIMATE CHANGE ON STREAMFLOW IN THE NOOKSACK RIVER BASIN: Hydrological Processes, v. 28, p , doi: /hyp Grah, O., and Beaulieu, J., 2013, The effect of climate change on glacier ablation and baseflow support in the Nooksack River basin and implications on Pacific salmonid species protection and recovery: Climatic Change, v. 120, p , doi: /s y. Leopold, L. B., and T. Maddock (1953), The hydraulic geometry of channels and some physiographic implications, U.S. Geol. Surv. Prof. Pap., 252. Livneh, B., Bohn, T.J., Pierce, D.W., Munoz-Arriola, F., Nijssen, B., Vose, R., Cayan, D.R., and Brekke, L., 2015, A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada : Scientific Data, v. 2, p , doi: /sdata Livneh, B., Rosenberg, E.A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K.M., Maurer, E.P., and Lettenmaier, D.P., 2013, A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions: Journal of Climate, v. 26. MACA -CMIP5 Future Climate Dataset, 2016, Boxplot Visualization of Future Projections: (accessed July 2016). Mohseni, O., Stefan, H.G., and Erickson, T.R., 1998, A nonlinear regression model for weekly stream temperatures: Water Resources Research, v. 34, p , doi: /98WR Mote, P.W., and Salathe, E.P.., 2010, Future climate in the Pacific Northwest: Climatic Change, v. 102, p , doi: /s z. Murphy, R., 2016, Modeling the Effects of Forecasted Climate Change and Glacier Recession on Late Summer Streamflow in the Upper Nooksack River Basin: WWU Masters Thesis Collection, 18

19 Nash, J.E., and Sutcliffe, J.V., 1970, River flow forecasting through conceptual models part I A discussion of principles: Journal of Hydrology, v. 10, p , doi: / (70) Pelletier, G.J., Chapra, S.C., Tao, H., 2005, QUAL2Kw A framework for modeling water quality in streams and rivers using a genetic algorithm for calibration, (accessed June 2016). Pelto, M., and Brown, C., 2012, Mass balance loss of Mount Baker, Washington glaciers : MASS BALANCE LOSS OF MOUNT BAKER, WASHINGTON GLACIERS: Hydrological Processes, v. 26, p , doi: /hyp Poole, G.C., and Berman, C.H., 2001, An Ecological Perspective on In-Stream Temperature: Natural Heat Dynamics and Mechanisms of Human-CausedThermal Degradation: Environmental Management, v. 27, p , doi: /s Stocker, T.F., Qin, D., Plattner, G.-K., Alexander, L.V., Allen, S.K., Bindoff, N.L., Bréon, F.-M., Church, J.A., Cubasch, U., Emori, S., and others, 2013, Technical summary, in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, p , WG1AR5_TS_FINAL.pdf (accessed June 2016). Story, A., Moore, R.D., and Macdonald, J.S., 2003, Stream temperatures in two shaded reaches below cutblocks and logging roads: downstream cooling linked to subsurface hydrology: Canadian Journal of Forest Research, v. 33, p , doi: /x Sun, N., Yearsley, J., Voisin, N., and Lettenmaier, D.P., 2015, A spatially distributed model for the assessment of land use impacts on stream temperature in small urban watersheds: Hydrological Processes, v. 29, p , doi: /hyp U.S. EPA (Environmental Protection Agency). (2014) Quantitative Assessment of Temperature Sensitivity of the South Fork Nooksack River under Future Climates using QUAL2Kw. Wigmosta, M.S., Nijssen, B., Storck, P., and Lettenmaier, D.P., 2002, The distributed hydrology soil vegetation model: Mathematical models of small watershed hydrology and applications, p Wigmosta, M.S., Vail, L.W., and Lettenmaier, D.P., 1994, A distributed hydrology-vegetation model for complex terrain: Water resources research, v. 30, p Yearsley, J.R., 2012 A grid-based approach for simulating stream temperature: Water resources research., v. 48, p. n/a n/a, doi: /2011WR Yearsley, J.R., 2009, A semi-lagrangian water temperature model for advection-dominated river 19

20 systems: Water resources research., v. 45, p. n/a n/a, doi: /2008WR

21 8.0 Tables Table 1: Modeled changes in glacier melt magnitude for the North and Middle Fork basins of the Nooksack River for the years 2025, 2015, and Each percentage refers to a deviation from historical ( ) monthly median (Murphy, 2016) 21

22 Table 2: Modeled percent of monthly median streamflow component derived from glacier ice melt for the North and Middle Fork basins for historical conditions and two different GCM projections (Murphy, 2016). 22

23 8.0 Figures Figure 1: Stream temperature gauges maintained by the Nooksack Indian tribe within the Nooksack River Basin in Whatcom County, Northern Washington. 23

24 Figure 2: Modeled future daily mean streamflow at the North Cedarville gauge on the Nooksack River compared to historical stream follow (Murphy, 2016). 24

25 Figure 3: Predicted maximum hourly tributary temperatures for 2080s high-impact climate scenarios from the South Fork temperature TMDL (USGS, 2015). 25

26 Figure 4: Boxplots of annual maximum mean air temperature for historical and RCP 4.5 and 8.5 climate change scenarios ( 26