Modeling the effects of climate change forecasts on streamflow

in the Nooksack River basin

Thesis Proposal for the Master of Science Degree, Department of Geology,

Western Washington University, Bellingham, Washington

Susan E. Dickerson

January 2009

Approved by Advisory Committee Members:

Dr. Robert Mitchell, Thesis Committee Chair

Dr. Doug Clark, Thesis Committee Advisor

Dr. Andrew Bunn, Thesis Committee Advisor

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Table of Contents

1.0 Problem Statement ................................................................................................................... 3

2.0 Introduction .............................................................................................................................. 3

3.0 Background .............................................................................................................................. 4

3.1 Nooksack River Basin ......................................................................................................... 4

3.1.1 Physical characteristics of the Nooksack basin ............................................................ 4

3.1.2 Use and allocation of the Nooksack .............................................................................. 5

3.1.3 Regional Climate .......................................................................................................... 6

3.1.4 Streamflow in the Nooksack River ............................................................................... 7

3.2 Climate Change .................................................................................................................... 8

3.2.1 Climate System .............................................................................................................. 8

3.2.2 Global Climate Change ................................................................................................. 9

3.3.2 Climate change and water resources ............................................................................. 9

3.3.3 Climate Change Technical Committee ....................................................................... 11

3.3 Hydrologic Modeling ......................................................................................................... 11

4.0 Proposed Research ................................................................................................................. 13

5.0 Methods.................................................................................................................................. 13

5.1 Scope of Work ................................................................................................................... 13

5.2 DHSVM ............................................................................................................................. 14

5.2.1 DHSVM Setup ............................................................................................................ 14

5.2.3 DHSVM Algorithms ................................................................................................... 15

5.2.4 DHSVM Calibration ................................................................................................... 16

5.3 Climate Change Forecasts ................................................................................................. 16

5.3.1 General Circulation Models ........................................................................................ 16

5.3.2 GCM – Emissions scenario couples ........................................................................... 17

5.3.3 GCM downscaling ...................................................................................................... 18

5.4 Modeling ............................................................................................................................ 19

5.5 Timeline ............................................................................................................................. 19

5.6 Expected Outcomes ........................................................................................................... 19

6.0 Significance of Research ........................................................................................................ 20

7.0 References .............................................................................................................................. 21

8.0 Tables ..................................................................................................................................... 25

9.0 Figures.................................................................................................................................... 26

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1.0 Problem Statement

The goal of my research is to predict the timing and magnitude of streamflow in the

Nooksack River under changing climate conditions. Streamflow in a high relief drainage basin

has a complex relationship to changes in temperature and precipitation; an understanding of the

timing and magnitude of future streamflow and extreme events under a range of possible climate

conditions is critically important for effective water resources planning in Whatcom County,

Washington. General Circulation Models (GCMs) provide predictions of the large-scale climate

trends of the future, but forecasts of regional climate, including local-scale weather patterns and

topographic effects, are required to characterize future streamflow. I propose to use statistical

methods that utilize the record of historical weather variability in order to downscale GCM

forecasts from a global scale to a local scale. I will use the downscaled data set as

meteorological input and present-day basin characteristics as spatial inputs into the Distributed

Hydrology-Soil-Vegetation model (DHSVM; Wigmosta et al., 1994) in order to predict the

effects of climate change on the timing and magnitude of streamflow in the Nooksack River.

2.0 Introduction

The Nooksack River has its headwaters in the North Cascade Mountains and a watershed

that incorporates approximately 2000 square kilometers (Figure 1). Municipalities and industries

in Whatcom County, WA, and the Nooksack Tribe and Lummi Nation depend on the Nooksack

River for water use and for fish habitat. An understanding of the probable response of the

Nooksack River to climate change is of vital importance to these agencies for water resource

planning purposes. Global climate change may include changes to both temperature and

precipitation patterns, which influence the timing and magnitude of streamflow in a snowmelt-

dominated basin such as the Nooksack River basin.

Prediction of future streamflow depends on prediction of future climate, rather than on

historical observations of the variability of streamflow. GCMs have been developed by

researchers at institutions worldwide to predict changes in global climate under a range of future

emissions scenarios. In order to use data produced by selected GCMs as the input for a regional-

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scale hydrologic model I will use a statistical downscaling process described by Salathe, et al.

(2007) to convert the GCM data from coarse resolution to fine resolution. I will then use the

downscaled data as meteorological inputs to the Distributed Hydrology-Soil-Vegetation model

(DHSVM; Wigmosta et al., 1994) to predict the impacts of climate change forecasts on

streamflow in the Nooksack River basin.

This project is a regional case study application of an important technique for

incorporating climate change forecasts into hydrologic modeling. I will use the methodology of

the Climate Change Technical Committee (CCTC), associated with the Climate Impacts Group

at the University of Washington. The CCTC investigated hydrologic impacts of climate change

on five river basins in Pierce, King, and Snohomish Counties; the research was funded by a

variety of groups interested in the long-term management of water resources (Palmer, 2007b).

3.0 Background

3.1 Nooksack River Basin

3.1.1 Physical characteristics of the Nooksack basin

The Nooksack River basin is an approximately 2000 km2 watershed, located primarily in

Whatcom County, Washington, that provides freshwater for domestic and commercial use,

agriculture, salmon and shellfish habitat, and a variety of recreational opportunities (Figure 1).

The headwaters are in the North Cascade Mountains, and the North, Middle, and South forks

flow approximately west to their convergence near Deming, WA; the main stem of the river

meanders through the lowland until it discharges into Bellingham Bay.

There are two distinct provinces of the basin, delineated by topography, geology, and

land use: the upland headwaters in the Cascades, and the lowlands west of the confluence. The

majority of runoff into the Nooksack River comes from the upland province, whereas the

majority of water usage is in the lowland (Bach, 2002). Topography in the upland province is

rugged; elevation varies from 300 m to over 3000 m. The upland province includes Paleozoic

and Mesozoic metamorphic rocks of the North Cascade System crisscrossed by thrust faults and

strike-slip faults, late Cretaceous through Eocene sandstones, shales, and conglomerates of the

Chuckanut Formation, and Quaternary volcanic rocks. Landslide and lahar deposits overprint

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the bedrock geology and are an important influence in the geomorphology of the basin

(Dragovich & others, 1997). Upland land use primarily includes state and federally managed

land and conservation lands. The landscape is heavily forested and includes second growth

stands of coniferous trees (Douglas Fir (Pseudotsuga menziesii), Western Hemlock (Tsuga

heterophylla), Western Red Cedar (Thuja plicata), Red Alder (Alnus rubra), Maple (Acer), and

dense undergrowth (Salal (Gaultheria shallon), Devil‟s Club (Echinopanax horridum),

Huckleberry (Vaccinium), Oregon Grape (Berberis), ferns). Soils are formed from loess,

volcanic ash, colluvium, and slope alluvium derived from weathered bedrock and Quaternary

volcanic and glacial deposits, and range across the basin from shallow to very deep, and from

moderately well drained to well drained (Golden, 1992).

The lowland is characterized by low elevation (0-300 m) and low relief. The river

meanders through Quaternary glacial sediments including recessional outwashes of the Vashon

glaciation, Vashon till, and recent alluvial deposits. Lowland land use is dominated by

agricultural, commercial, and residential use; significant agricultural operations include fruit and

dairy farms. The lowland province of the basin will be excluded from this study because the

strong influence of agricultural, industrial, and municipal water usage create a challenge to

accurately modeling the hydrology of this portion of the basin.

The high elevations and abundant winter precipitation of the upland Nooksack basin

result in a significant snowmelt and/or glacial melt component to the streamflow of each fork.

The headwaters include Mount Shuksan, Mount Baker, and the Twin Sisters, with approximately

16 to 40 percent of streamflow derived from snowmelt (Bach, 2002). The North Fork originates

from the East Nooksack glacier on Mount Shuksan and the headwaters of the Middle Fork

include the Deming glacier; the South Fork currently contains no glaciers. During the

Pleistocene glacial maximum of the Fraser glaciation the Cordilleran Ice Sheet covered the

Nooksack basin, except for Mt. Baker, Mt. Shuksan, and a few other peaks.

3.1.2 Use and allocation of the Nooksack

The Nooksack River flows past several municipalities and tribal reservation lands in

Whatcom County, including Deming, Everson, Lynden, Ferndale, the Nooksack reservation, and

the Lummi reservation. Streamflow is consumed for drinking water, irrigation, and industrial

processes. Streamflow is also used for recreation and for providing habitat to salmon and

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shellfish. A 1500 kilowatt hydroelectric plant at Nooksack Falls on the North Fork has been

operated by Puget Sound Hydro LLC since 2003 (FERC, 2004). Two salmon hatcheries are

operated in the Nooksack basin: one operated on Skookum Creek (South Fork) by the Lummi

Tribe, and the other operated on Kendall Creek (North Fork) by Washington Department of Fish

and Wildlife.

The Washington Watershed Management Act of 1998 provided the framework for local

control of watershed planning. As a result, Water Resource Inventory Area No. 1 (WRIA 1),

which encompasses the surface and ground water in the Nooksack River basin, was established;

stakeholders include the Nooksack Tribe and Lummi Nation and Whatcom County

municipalities, public utilities, industries, individuals, and farms that depend on the Nooksack

River for freshwater fish habitat and domestic, commercial, municipal, industrial, and irrigation

uses. The WRIA 1 Watershed Management Project includes assessment, planning, and action

related to water quantity, water quality, fish habitat, and instream flows in the Nooksack River

(WRIA 1, 2008).

The City of Bellingham operates a diversion pipeline from the Middle Fork of the Nooksack

River to Lake Whatcom in order to increase water quantity and quality. Approximately half of

the population of Whatcom County, about 65,000 people, rely on Lake Whatcom as a drinking

water source. Minimum instream flows for the MF Nooksack are regulated by the Washington

Department of Ecology; diversion to Lake Whatcom may occur only when the minimum

instream flow requirement is met (DOE, 1988).

3.1.3 Regional Climate

The climate of the Nooksack watershed is characterized by mild temperatures typical of a

maritime climate; fall and winter are characterized by frequent, low intensity precipitation,

whereas late spring and summer are relatively dry. Average annual precipitation (1971-2000)

ranges from 40 inches in the lowland to 140 inches at Mount Baker (PRISM, 2008). There is a

steep topographic gradient from west to east which creates a negative lapse rate for temperature

and a positive lapse rate for precipitation across the basin. As elevation generally increases from

west to east, the temperature decreases, allowing for snow to fall in the mountains while rain

falls in Bellingham. Conversely, the increase in elevation over the mountains causes an increase

in precipitation due to the orographic effect. As moisture-laden air is lifted over the mountains it

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experiences adiabatic cooling due to the decrease in pressure, causing more precipitation to fall

as the air is lifted to higher elevations (Figure 2).

3.1.4 Streamflow in the Nooksack River

Streamflow in the Nooksack River is characteristic of a snow-melt dominated basin that

lies within a mild, rainy climate at lower elevations (Figure 3). Streamflow increases and

remains high during the fall and winter as precipitation increases, and increasing soil moisture

content results in more and more water running off directly into the stream rather than infiltrating

into the soil and flowing slowly in the subsurface. Streamflow decreases in mid- to late spring as

precipitation decreases, followed by a peak in the hydrograph in late spring or early summer as

the snow melts. Streamflow decreases throughout the summer as snowmelt is depleted and

precipitation is low. Currently, low summer flows are buffered by snowmelt in glaciated basins

of the Nooksack River, but changes to the timing of melting and to the mass of glacial ice may

affect future streamflow in the drier summer months.

Streamflow in the Nooksack River is monitored by the USGS at five real-time stations:

Cascade Creek (North Fork), Wickersham (South Fork), Deming (Middle Fork), North

Cedarville (Nooksack River), and Ferndale (Nooksack River). The USDA National Resources

Conservation Center operates a SNOTEL sites on the Middle Fork of the Nooksack, at Wells

Creek, and at Elbow Lake that monitor precipitation, snow water equivalent (SWE), and

temperature in the watershed.

Bach (2002) quantified the amount of streamflow derived from a glaciated basin (the

North Fork Nooksack) as compared to a similar unglaciated basin (the South Fork Nooksack), by

comparing streamflow measured by the USGS at the Glacier station (North Fork) and the

Wickersham station (South Fork) to the total flow after the confluence of the forks. Bach

estimated that 26.9 percent of summer streamflow in the Nooksack River is attributable to high

elevation snow and glacier melt.

Donnell (2007) used DHSVM to quantify the glacial melt water component of

streamflow in the Middle Fork Nooksack. Estimated late summer glacial melt water contribution

based on 2002 glacier conditions and 2006 meteorological data was 8.4 – 26.1%. Donnell also

modeled the effect of glacier recession on streamflow using a linear rate of recession and modern

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climate data and predicted up to 8.6% decrease in streamflow in the Middle Fork during the next

fifty years s a result of glacier shrinkage.

3.2 Climate Change

3.2.1 Climate System

The Earth‟s climate is a complex system composed of the interaction of subsystems that

include the atmosphere, oceans, biosphere, land surface, and cryosphere. Global temperature

responds to a net change in the energy balance; an energy surplus leads to warming, whereas an

energy deficit leads to cooling. Energy inputs include short-wave radiation from the sun, long-

wave radiation reflected off the Earth and long-wave radiation re-emitted from the Earth.

Energy outputs include radiation that is reflected and re-emitted. The climate system is an

inertial system due to the high specific heat of water and the large percentage of the Earth

covered by oceans. Therefore it may take years for the climate system to reach an equilibrium

temperature after an energy imbalance occurs.

Climate is the statistical average of weather, commonly averaged on a 30 year cycle.

Within a climate, the daily or hourly weather can be extremely variable due to local weather

patterns and topography; additionally, the climate system, and average global temperature, varies

due to internal and external factors. Pseudo-periodic internal variations such as the El Nino

Southern Oscillation and the Pacific Decadal Oscillation result from global circulation of ocean

currents and air currents within the climate system. Global temperature also changes due to

variations in radiative forcings, external energy inputs or outputs, due to solar variations caused

the periodic Milankovitch cycles, as well as random changes such as volcanic events. A large

volcanic event that emits reflective sulfate aerosols into the stratosphere can create a negative

forcing on the climate system for up to tens of years (IPCC, 2007).

Feedbacks are complex mechanisms that add to climate variability. A positive feedback

amplifies the effect of a radiative forcing, whereas a negative feedback dampens the effect.

Some phenomena can act as both a positive and a negative feedback on the same or different

timescales. For example, the input of CO2 to the atmosphere increases the growth rate of

vegetation which causes an increase in surface albedo and a negative radiative forcing, while the

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increased level of CO2 in the atmosphere results in a positive radiative forcing due the increased

absorption and storage of energy.

3.2.2 Global Climate Change

Evidence indicates that the Earth‟s climate is warming. Average global temperature has

increased 0.74°C in the last 100 years (1906-2005), sea level has risen 1.8 mm/year from thermal

expansion and melting of continental ice sheets, and global ice cover has decreased (IPCC, 2007).

Proxy data that are sensitive to changes in climate such as tree rings, pollen, ice cores, and corals,

have been used to reconstruct global temperature into the last thousand or more years (National

Research Council, 2006). Studies of these different proxy data have produced climate

reconstructions that show that average global temperature has fluctuated over the last one

thousand years. The modern warming trend, however, departs sharply from the known upper

bounds of recent natural variability (National Research Council, 2006).

The warming of Earth‟s climate is attributed to the post-industrial increase of both

naturally occurring greenhouse gases (e.g., CO2, CH4) and purely anthropogenic greenhouse

gases (e.g., chlorofluorocarbons (CFCs)) to the atmosphere. The gases absorb and retain energy,

which increases the net storage of energy in the climate and increases global temperature; the

concentration of each greenhouse gas can be related directly to a radiative forcing that changes

the energy balance of the atmosphere. According to the 2007 Fourth Assessment Report (AR4)

from the Intergovernmental Panel on Climate Change (IPCC) there is a greater than 90%

probability that most of the observed warming is due to increases in anthropogenic greenhouse

gases, and predicted regional impacts including decreased snowpack, increased flooding and

decreased summer streamflow in western North America (IPCC, 2007).

3.3.2 Climate change and water resources

Three decades of studies on the effects of climate change on water resources indicate that

both temperature and precipitation have direct effects on the timing and magnitude of streamflow,

and that the response of a given stream is highly variable and non-linear based on basin

characteristics and local and regional climate (Alexander et al., 2007). Climate-driven effects on

streamflow include changes to the ratio of precipitation in the form of rain to snow, amount of

total precipitation, timing of snowmelt, and timing in seasonal changes in soil moisture content.

An average warming rate of 0.3°F/decade is projected for the Pacific Northwest (Mote et al.,

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2005; Alexander et al., 2007). The temperature change is expected to result in higher winter and

spring streamflow and an earlier melt season in snowmelt-dominated basins, resulting in

decreasing summer flows at the height of water usage demand. Projected changes to rainfall in

the Pacific Northwest are variable and modest; most models predict an increase in winter

precipitation and a decrease in summer precipitation (Mote et al., 2007).

Investigations on Western river basins, which are commonly snowmelt-dominated, have

focused on the impact of temperature on the type and amount of precipitation and on the timing

of snowmelt. A number of studies have documented the shift of the timing of spring snowmelt

to earlier in the year under warming climate conditions (e.g., Lettenmaier and Gan, 1990; Cayan

et al., 2001; Regonda et al., 2005; Stewart et al., 2004). Gleick and Chalecki (1999) pointed out

that despite a variety of different methods, assumptions, and models that had been used to

evaluate effects of increasing temperatures on the Sacramento River, that every study showed

changes to the timing and magnitude of runoff as a result of temperature-driven changes to the

snow dynamics in the river basin.

Spatially distributed hydrology models have been used to predict the response and to

evaluate sensitivity of streamflow to different climate conditions, and to understand the causes of

the changes to observed streamflow. Leung and Wigmosta (1999) used DHSVM and regionally

downscaled GCM forecasts to predict the sensitivity of two Pacific Northwest basins, the

American River (coastal) and the Middle Fork Flathead River (continental), to changes in

climate under a doubling in atmospheric CO2 concentration. The American River responded

with a 60% decrease in basin SWE and an early spring melt, whereas the SWE in the MF

Flathead was reduced only by 18% and the timing of spring streamflow remained the same.

Their study demonstrated the regional variability of the magnitude of effects of climate change

on snowmelt-dominated basins. Mote and others (2005) documented a decreasing trend in SWE

in the Western U.S. from 1925-2000 based on observed data, and argued for a predominantly

climatic cause for the trend; the observed data agreed with simulations using the Variable

Infiltration Capacity (VIC) hydrologic model, and with the spatial pattern of climatic data from

the same time frame. Largest relative decreases were in the Pacific Northwest, which pointed to

the increased sensitivity of SWE to elevation, and mean winter temperature. Mote and others

(2008) more recently investigated the relationship of spring snowpack in the Cascades to

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temperature and precipitation, and determined that temperature was the dominant long-term

influence. The study quantified the decline of April 1 SWE at 15-35% since the 1950s.

The understanding of the first-order effects of changing climate of water resources is

necessary precursor to the understudied area of second-, third- and fourth- order effects, such as

hydropower, electricity prices, and national security, respectively (Chalecki and Gleick, 1999).

Many studies are focused on the effects of climate change on streamflow in order to plan for

future water resource availability and needs. Milly et al. (2008) asserted that water resource

planners can no longer rely on historical records to characterize an unchanging range of weather

variability; rather, planners need to consider future water resources based on changing

probabilities of future weather frequency.

3.3.3 Climate Change Technical Committee

The Climate Change Technical Committee (CCTC) was formed as part of the Regional

Water Supply Planning Process, a collaborative effort between the Climate Impacts Group (CIG)

at the University of Washington, the WA Department of Ecology, public utilities, tribes, and

other community groups. The goal of the group was to gather data and tools to assess the impact

of climate change on local water resources, and thus to assist in water resource management and

planning (Alexander et al., 2007). The CCTC used DHSVM and downscaled GCM predictions

to model the effects of climate change forecasts on five river basins in King, Pierce, and

Snohomish Counties: the Cedar, Green, White, Sultan, and Tolt Rivers. The CCTC outlined

background, methods, and results of their research in a series of eight technical memos and made

their results available in a variety of formats to interested groups and individuals (e.g., Polebitski,

2007a; http://www.climate.tag.washington.edu/).

Results of the simulations included an overall shift in the hydrographs of each river to an

earlier spring melt off (Figure 4). Each river showed different changes in the magnitude of

streamflow under a range of future climate conditions; the average change in all five rivers was a

positive net increase in annual flow, with increasingly negative changes to summer flow and

increasingly positive changes to winter flow in the next 75 years (Table 1).

3.3 Hydrologic Modeling

Hydrologic modeling to predict streamflow began in the 1950s and 1960s with spatially

lumped models that used a water balance approach and meteorological data averaged over an

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entire watershed to forecast streamflow (Storck et al., 1998). However, the availability of digital

spatial data, such as digital elevation models and soil maps, combined with advancements in

computing power, led to the development of spatially distributed hydrology models that simulate

rainfall and runoff in individual pixels of a spatially heterogeneous watershed (Storck et al.,

1998). The Distributed Hydrology-Soil-Vegetation Model (DHSVM) is a physically based,

spatially distributed hydrology model that was developed at the University of Washington; the

model was originally tested and validated in the Middle Fork Flathead River basin in Montana

(Wigmosta et al., 1994).

The model‟s explicit spatial representation of watershed characteristics has allowed

applications to understanding hydrologic impacts of land use changes. Storck et al. (1995)

investigated the effect of forestry impacts on peak flows in the Snoqualmie River basin and

modified the model to simulate flood events in maritime mountainous watersheds. An accurate

representation of the Pacific Northwest basin required a variable time step, with an hourly time

step to model flood events and a daily time step during periods of consistent base flow, a

precipitation lapse rate, and a two-layer snowpack component. DHSVM has been used to model

the effects of timber harvesting, including change in vegetation cover and addition of roads, on

the magnitude of flood events (e.g., Storck et al., 1998; Wigmosta and Perkins, 2001). DHSVM

has recently been applied to partially urbanized watersheds, with representations of impervious

surfaces and retention ponds in the model (Cuo et al., 2008).

The DHSVM combines spatially variable watershed characteristics, including elevation,

soil type, soil thickness, and vegetation with temporally variable meteorological information

such as temperature and precipitation to predict the magnitude and timing of streamflow in the

watershed. The DHSVM utilizes the physical relationships in the hydrologic cycle, such as the

relationship between temperature and evaporation, to calculate the flux of water and energy in

and out of each grid box, or pixel, in a digital elevation model (DEM). Water and energy can be

stored in a pixel or can move between adjacent pixels, with direction and rate dependent on

topography, soil type, and other factors; water flows across the surface and through the

subsurface, and collects in stream valleys, which translates to the simulated discharge of the

stream. Thus, the DHSVM provides a tool for understanding the surface water hydrology in a

mountainous watershed given information about past, present, or future spatial characteristics

and climate.

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The DHSVM is calibrated and validated against historical records of meteorology and

measured streamflow in order to use the model as a predictive tool. The small size of each pixel

(30 m - 150 m on a side), allows the spatial heterogeneity of the watershed to be represented in

the model; small variations in elevation and topography have an important effect on regional

hydrology, as illustrated in the temperature and precipitation lapse rates across the Nooksack

basin.

4.0 Proposed Research

I propose to apply the methods of the CCTC and currently available climate change

forecasts to model the effects of climate change on streamflow in the Nooksack River. I will use

the methodology of the CCTC, outlined in eight technical memos (e.g., Polebitski, 2007a), for

three reasons: 1) The CCTC uses downscaling methods that have been validated as a way to use

coarse-scale climate predictions with the modeling of regional-scale hydrologic processes, and

used DHSVM as the runoff model, which is available for use at WWU; 2) support in learning the

methods is available, and has been offered, from contacts at the CCTC; and 3) outcomes for the

Nooksack will be valid for comparison to the other Western Washington river basins that the

CCTC studied; a direct regional comparison will be useful to this project and to the CCTC in

understanding regional hydrology and monitoring the forecasts into the future.

5.0 Methods

5.1 Scope of Work

This project will involve six main steps:

1) Setup DHSVM to the South Fork, Middle Fork, and North Fork basins of the Nooksack

River drainage.

2) Collect and process meteorological input data. The DHSVM requires a meteorological input

file that includes daily maximum temperature, precipitation, long wave radiation, short wave

radiation, wind speed, and relative humidity. Additionally, a 30 to 50 year historical time

series is required for the downscaling process.

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3) Collect and process historical streamflow data for use in model calibration and validation.

4) Calibrate DHSVM to the South Fork, Middle Fork, and North Fork basins of the Nooksack

drainage using data from 2003 to 2007.

5) Use statistical downscaling techniques to create a local climate change forecast dataset.

These data will be processed to obtain the remaining meteorological input required for

DHSVM (e.g., long wave radiation).

6) Perform hydrologic simulations using the downscaled climate change forecast data as the

meteorological input, and assess changes in snowpack, SWE, evapotranspiration, and

streamflow timing and magnitude.

5.2 DHSVM

5.2.1 DHSVM Setup

The DHSVM requires inputs of meteorological data and spatial data in order to simulate

the hydrology of the basin (Wigmosta et al., 2002). Meteorological inputs include temperature,

precipitation, wind speed, relative humidity, incoming shortwave radiation, and incoming long

wave radiation. Meteorological data are required for the time step at which the model is run,

ranging from hourly to daily.

The model requires six maps as spatial inputs, including elevation, watershed boundary,

land cover, soil type, soil depth, and stream network; the spatial inputs are managed in ArcGIS.

Elevation data are available as 10 m Digital Elevation Models (DEMs), and provide the base

layer of spatial information. Landcover data, including vegetation and glacial coverage, is based

on the 2001 National Oceanic and Atmospheric Administration (NOAA) land cover grid, and

soil type comes from the United States Department of Agriculture State Soil Geographic

(STATSGO) database. Watershed boundaries, soil depth, and stream networks are created from

elevation using ArcGIS tools. Flow routing, including the stream network and road network, is

also determined using ArcGIS tools (Wigmosta et al., 2002). For this study I will define the

lower extent of the basin just below the confluence at Nugent; west of Deming the streamflow is

impacted by removal of water for municipal, agricultural, and commercial use. I will collect

spatial data for the Nooksack River basin from USGS 7.5 minute, 10-meter DEM files, the

STATSGO database, and NOAA; I will use ArcGIS to set up and manage the basin information

as a series of layers.

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5.2.3 DHSVM Algorithms

At each time step (hourly to daily) and for each pixel, the model provides simultaneous

solutions to water and energy balance equations for seven hydrologic processes:

evapotranspiration, snowpack accumulation and melt, canopy snow interception and release,

unsaturated moisture movement, saturated subsurface flow, surface overland flow, and channel

flow (Wigmosta et al., 2002). Evapotranspiration is represented through a two layer canopy,

with each layer partitioned into a wet and dry percentage; the rate of evaporation and

transpiration is calculated based on meteorological factors, vegetation type, and soil type.

Snowpack is represented by a surface layer and a pack layer with energy and mass exchanged

between the layers. Snow that is intercepted by the canopy is represented by a single layer that

exchanges mass and energy with the air and ground below through interception, sublimation, and

melt. Vertical movement of water through the unsaturated zone is represented through three soil

layers in the model. Water that accumulates on the surface at a rate higher than the user-defined

infiltration rate is routed as excess overland flow; water that infiltrates moves into the layer

below at a rate described by Darcy‟s Law, or is removed through transpiration based on the type

of vegetation in the rooting zone. Water that reaches the water table is routed laterally as

subsurface flow. The direction and rate of saturated subsurface flow is determined via hydraulic

gradients. Overland flow in the model includes infiltration excess runoff, saturation excess

runoff when precipitation falls on a saturated soil surface, and return flow when the water table

rises to the ground surface. Channel flow includes flow in stream channels and in road drainage

ditches, and is routed through a linear storage routing algorithm in which outflow from the

channel is linearly related to storage in the channel (Wigmosta et al., 2002).

Version 2.4 of DHSVM includes a glacier movement component that may be useful to

incorporate in this study. In version 2.0 and 3.0 of DHSVM, glaciers are represented as

permanent snowpack, with all accumulation of snow remaining where it falls until it melts,

which leads to an unrealistic accumulation of ice in high altitude basins, such as the Nooksack

River basin. The new component characterizes the flow of accumulated ice into the ablation

zone based on basal shear stress and glacier velocity (University of Washington, 2008).

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5.2.4 DHSVM Calibration

The DHSVM model is calibrated to a watershed by comparing simulated streamflow and

SWE data to measured values for years in which there is recorded streamflow, SWE, and

meteorological data available. Historic streamflow data are available from USGS gauging

stations on the Nooksack and SWE data are available from SNOTEL monitoring stations.

Sensitive parameters in the model that were estimated as inputs such as soil thickness, lateral

hydraulic conductivity, and temperature and precipitation lapse rates are adjusted to fit the

simulated data to the historic data. Once the calibration is complete the model is then validated

by running simulations for a different time period for which there is historic data available.

Initial conditions for the simulations are created by running one year of data and using the

resulting soil moisture conditions as the initial conditions for subsequent simulations.

5.3 Climate Change Forecasts

5.3.1 General Circulation Models

General Circulation Models (GCMs) are coupled ocean-atmosphere 3-D models that

divide the surface into grid boxes that extend vertically into the atmosphere. At each time step,

within each box the model calculates the transfer of energy, mass and momentum based on

known physical relationships. GCMs have been developed utilizing the „first relationships‟

described by atmospheric physics. Computational efficiency of the models requires a coarse

spatial resolution (on the order of 100s of km per side) and thus requiring parameterization of

relationships that are below the resolution of the model (e.g., cloud formation). The models are

developed and run by large research institutions such as the Goddard Space Science Institute that

make their forecasts publicly available.

In order to understand the impact of anthropogenic changes on the global climate system,

the emissions of greenhouse gases are related directly to a positive radiative forcing due to the

net energy imbalance created by the increased storage of energy. The GCM is run using the

change in net energy in the climate system and the model characterizes changes in air

temperature, water temperature, precipitation, and other climatic factors (IPCC, 1997).

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5.3.2 GCM – Emissions scenario couples

Since emissions of greenhouse gases are directly related to a positive radiative forcing, it

is necessary to describe future emissions in order to model future global climate. Forty

emissions scenarios were developed by modeling teams associated with the IPCC in order to

model a range of future climate possibilities. Each scenario is a narrative that describes the

population, technology, and economy of the world into the future, and relates levels of emissions

to the fictitious future world. The scenarios are considered equally likely, and are a way of

forecasting future climate based on a range of possible future emissions. The A2 scenario

describes a future that includes continued population increase and an economy based on the

intensive use of fossil fuels. The B1 scenario characterizes a future in which world population

peaks and then declines, with a focus on alternative energies and economies (IPCC, 2000).

GCMs are run using a specific emission scenario and the associated radiative forcing

related to the emissions levels described in that scenario. Each model represents the climate

system differently, based on spatial resolution, and levels of parameterization of processes. Thus,

even utilizing the same emissions scenario, each GCM will provide a different forecast. For

average temperature change in the Pacific Northwest, ten GCMs predict warming of 0.5-2.5 °C

by the 2040s, with a range of possible warming provided by both the different GCMs and the

two emissions scenarios (Figure 5; Mote et al., 2005). The same group of GCMs disagree as to

whether precipitation in the PNW will increase or decrease into the 2040s.

With future climate being based in part on different conditions, and with the varying

levels of spatial resolution and complexity represented by the different GCMs, it is necessary to

use a suite of models and scenarios to predict a range of possible future climate conditions. I

will use the same three GCM-Emission scenario couples used by the CCTC, each representing a

group of GCM predictions for temperature and precipitation in the PNW by the 2040s (Figure 6).

These include the:

IPSL_CM4_A2 (GCM from the Institut Pierre Simon Laplace, with A2 emissions

scenario) which represents a group of couples that predict increase in temperature of 2-

5°C and 8-9% increase in precipitation by 2040.

Echam5_A2 (GCM from the Max Planck Institute for Meteorology, with A2 emissions

scenario), which represents a “middle of the road” scenario with 2% precipitation

increase and 1.7 ºC increase.

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GISS_ER_B1 (GCM from the Goddard Institute for Space Studies, with B1 emissions

scenario), which represents the group of couples that predicts a 0.5-4.0% decrease in

precipitation along with a 2-5°C increase in temperature (Mote et al., 2005; Polebitski,

2007b).

GCM data are publicly available from the research institutions developing the models.

5.3.3 GCM downscaling

GCM outputs are provided on a very coarse scale, on the order of 100 km per grid cell

side, and are inappropriate for use in a regional hydrologic study because the prediction does not

reflect the regional spatial variability of weather. The meteorological data predicted by a GCM

may be for just four to six locations for Washington State; therefore, the data must be converted

to a much finer scale in order to provide useful information about a watershed that is subject to

both local and regional meteorological influences such as topography and local weather patterns

(Polebitski, et al., 2007). Through statistical downscaling, I will use the GCM output combined

with historic weather data for the Nooksack basin to develop a local-scale climate prediction that

I can use as the meteorological forcings in the DHSVM.

Statistical downscaling relates the statistical properties of the predicted data to those of

the historic data in order to better represent local variability in the data. Quantile mapping is a

type of statistical downscaling that utilizes a long historic record of weather, and maps the

predicted future climate onto the historic record of climate. The historic record is used because it

reflects the variability between consecutive years, including extreme events. Cumulative

distribution functions are calculated for monthly temperature and precipitation data from the

historic record and from GCM simulations of the same period; transform functions are created

from the relationship between the two data sets and used to „correct‟ the GCM predictions to the

local scale. The goal of quantile mapping is to use the GCM forecast to provide the underlying

future climate trend while preserving the full range of spatial variability of weather seen on a

local level (Polebitski, et al., 2007; Wiley, et al., 2006).

I will collect historical meteorological data from weather stations and SNOTEL stations,

including Clearbrook weather station, Bellingham International Airport, and the Middle Fork

Nooksack SNOTEL station. Point measurements can be distributed through observed lapse rates,

interpolation methods, or through gridded climate estimate maps, such as the PRISM maps

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developed by NRCS National Water and Climate Center (NWCC) and Oregon State University

(OSU).

5.4 Modeling

Modeling will take place after the DHSVM is calibrated and validated to the Nooksack

basin and climate forecasts have been downscaled. DHSVM is written in ANSI-C and can be

run on a variety of platforms. I will use a Linux operating system on a Dell Precision with dual

2GHz processors in Dr. Robert Mitchell‟s hydrology modeling lab.

I will run the model using three meteorological data sets, each downscaled from a

different GCM, for three periods in the future, 2025, 2050, 2075. Each year of simulation will

represent a thirty-one year average of climate change forecasts, centered on the simulated year,

and the watershed variability including in the downscaling process by mapping the predictions to

fifty years of historical weather data.

5.5 Timeline

Step Planned Completion

Setup of DHSVM Winter 2009

Collect and process meteorological input data Winter 2009

Collect and process historical streamflow data Spring 2009

Calibrate DHSVM Summer 2009

Downscale climate change forecasts Winter/Spring 2009

Modeling Summer/Fall 2009

5.6 Expected Outcomes

The final product of my project will be a simulation of the watershed hydrology of the

Nooksack River basin during several points in the next century (e.g., 2025, 2050, 2075). Each

year of simulation will represent a thirty-one year average of climate change forecasts, centered

on the simulated year, and the watershed variability represented in fifty years of historical

20

weather data. Additionally, each simulation year will include three simulations based on the

three different GCM-Emission scenario couples, thus representing a range of possibilities. Both

the average prediction and the range of predictions are potentially useful in assessing the impacts

of climate change on future water resources in Whatcom County. This study will closely follow

the methods of the CCTC, and thus my final predictions will be comparable to their predictions

for the five rivers in Pierce, King, and Snohomish Counties. This direct comparison will be

useful for monitoring the accuracy of the predictions in comparison to other regional predictions.

The Nooksack River basin will be set-up and calibrated in DHSVM and available for

future use in understanding the hydrology of the basin and in predicting the effects on

streamflow from changes in climate, land-use, and vegetation. Additionally, a downscaled

climate forecast data set will be available for use in future simulations of local hydrologic

processes such as streamflow in the Lake Whatcom watershed.

The largest source of uncertainty in my study arises from the GCM forecasts, and GCMs

will become more sophisticated as the computing power to run the models efficiently at finer and

finer resolutions becomes possible, reducing the need to parameterize fine-scale processes. As

new GCM forecasts become available it will be possible to re-run streamflow simulations for the

Nooksack River since the basin will be set-up and calibrated in DHSVM, and historical weather

data will be available for the downscaling process. Simulations of future streamflow can be

updated as more detailed climate change predictions become available.

6.0 Significance of Research

Under changing climate conditions the range of weather variability will shift as the

underlying climate characteristics shift. A complete understanding of the sensitivity of the

Nooksack River to climatic changes is critical to future water resources in Whatcom County. In

order to plan for adaptation to future water availability we must anticipate the range of possible

change to the timing and magnitude of streamflow. Additionally, this project will lead to a

greater understanding of the complex hydrology of the Nooksack River basin, and a calibrated

and validated hydrology model available for future simulations.

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7.0 References

Alexander, D., R.N. Palmer, and A. Polebitski (2007), Technical Memorandum #1: Literature

review of research incorporating climate change into water resources planning, A report prepared

by the Climate Change Technical Subcommittee of the Regional Water Supply Planning Process,

Seattle, WA.

Bach, A. (2002), Snowshed contributions to the Nooksack River Watershed, North Cascades

Range, Washington, Geographical Review, 92(2), 192-212.

Cayan, D.R., S.A. Kammerdiener, M.D. Dettinger, J.M. Caprio, and D.H. Peterson (2001),

Changes in the onset of spring in the western United States, Bulletin of the American

Meteorological Society, 82, 399-415.

Chalecki, E.L., and P.H. Gleick (1999), A framework of ordered climate effects on water

resources: A comprehensive bibliography, Journal of the American Water Resources

Association, 35(6), 1657-1665.

Cuo, L., D.P. Lettenmaier, B.V. Mattheussen, P. Storck, and M. Wiley (2008), Hydrologic

prediction for urban watersheds with the Distributed Hydrology-Soil-Vegetation Model,

Hydrological Processes, 22, 4205-4213.

Donnell, C.A. (2007), Quantifying the glacial meltwater component of streamflow in the Middle

Fork Nooksack River, Whatcom County, WA, using a distributed hydrology model, M.S. Thesis,

Western Washington University.

Dragovich, J.D., D.K. Norman, R.A. Haugerud, and P.T. Pringle (1997), Geologic map and

interpreted geologic history of the Kendall and Deming 7.5 minute quadrangles, Western

Whatcom County, Washington, Open file report 97-2, U.S. Department of Natural Resources.

Federal Energy Regulatory Commission (FERC) (2004), Order on rehearing and dismissing

petition as moot, Docket No. JR02-1-00, http://www.ferc.gov/.

Gleick, P.H., and E.L. Chalecki (1999), The impacts of climatic changes for water resources of

the Colorado and Sacramento-San Joaquin River basins, Journal of the American Water

Resources Association, 35(6), 1429-1441.

Golden, A. (1992), Soil survey of Whatcom County area, Washington, United States Department

of Agriculture.

Intergovernmental Panel on Climate Change (IPCC) (1997), An introduction to simple climate

models used in the IPCC second assessment report, IPCC Technical Paper II, IPCC Working

Group I, Cambridge University Press.

IPCC (2000), IPCC Special Report: Emissions scenarios, Summary for policymakers,

Cambridge University Press.

22

IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group

I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon,

S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)],

Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.

Kovanen, D.J., and D.J. Easterbrook (2001), Late Pleistocene, post-Vashon alpine glaciations of

the Nooksack drainage, North Cascades, Washington, GSA Bulletin, 113(2), 274-288.

Lettenmaier, D.P. and T.Y. Gan (1990), Hydrologic sensitivities of the Sacramento-San Joaquin

River Basin, California, to global warming, Water Resources Research, 26(1), 69-86.

Lettenmaier, D.P., A.W. Wood, R.N. Palmer, E.F. Wood, and E.Z. Stakhiv (1999), Water

resources implications of global warming: A U.S. regional perspective, Climatic Change, 43,

537-579.

Leung, L.R., and M.S. Wigmosta (1999), Potential climate change impacts on mountain

watersheds in the Pacific Northwest, Journal of the American Water Resources Association,

35(6), 1463-1471.

Milly, P.C.D., J. Betancourt, M. Falkenmark, R.M. Hirsch, Z.W. Kundzewicz, D.P. Lettenmaier,

R.J. Stouffer (2008), Climate change - Stationarity is dead: Whither water management?

Science, 319, 573-574.

Mote, P.W., A.F. Hamlet, M.P. Clark, D.P. Lettenmaier (2005), Declining mountain snowpack

in western North America, Bulletin of the Meteorological Society, 86(1), 39-51.

National Research Council (2006), Surface Temperature Reconstructions for the Last 2,000

Years, National Academy of Sciences, National Academy Press.

Palmer, R.N. (2007a), Technical Memorandum #6: Framework for incorporating climate change

into water resources planning, A reported prepared by the Climate Change Technical

Subcommittee of the Regional Water Supply Planning Process, Seattle, WA.

Palmer, R.N. (2007b), Final report of the Climate Change Technical Committee, A report

prepared by the Climate Change Technical Subcommittee of the Regional Water Supply

Planning Process, Seattle, WA.

Palmer, R.N., M.W. Wiley, A. Polebitski, B. Enfield, K. King, C. O‟Neil, and L. Traynham

(2006), Climate change building blocks, A report prepared by the Climate Change Technical

Subcommittee of the Regional Water Supply Planning Process, Seattle, WA.

Polebitski, A., M.W. Wiley, and R.N. Palmer (2007a), Technical Memorandum #2:

Methodology for downscaling meteorological data for evaluating climate change, A report

prepared by the Climate Change Technical Subcommittee of the Regional Water Supply

Planning Process, Seattle, WA.

23

Polebitski, A., L. Traynham, and R.N. Palmer (2007b), Technical Memorandum #4: Approach

for developing climate impacted meteorological data and its quality assurance/quality control, A

report prepared by the Climate Change Technical Subcommittee of the Regional Water Supply

Planning Process, Seattle, WA.

Polebitski, A., L. Traynham, and R.N. Palmer (2007c), Technical Memorandum #5: Approach

for developing climate impacted streamflow data and its quality assurance/quality control, A

report prepared by the Climate Change Technical Subcommittee of the Regional Water Supply

Planning Process, Seattle, WA.

PRISM Group (2008), Oregon State University, http://www.prism.oregonstate.edu, created 23

June 2008.

Regonda, S.K., B. Rajagopalan, M. Clark, and J. Pitlick (2005), Seasonal cycle shifts in

hydroclimatology over the western United States, Journal of Climate, 18, 372-384.

Stewart, I.T., D.R. Cayan, and M.D. Dettinger (2004), Changes in snowmelt runoff timing in

western North America under a „Business as Usual‟ climate change scenario, Climatic Change,

62, 217-232.

Storck, P., D.P. Lettenmaier, B.A. Connelly, T.W. Cundy (1995), Implications of forest practices

on downstream flooding: Phase II Final Report, Washington Forest Protection Association,

TFW-SH20-96-001, 100.

Storck, P., L. Bowling, P. Wetherbee, and D. Lettenmaier (1998), Application of a GIS-based

distributed hydrology model for prediction of forest harvest effects on peak stream flow in the

Pacific Northwest, Hydrologic Processes, 12, 899-904.

United States Geological Survey (USGS) (2008), USGS Water Science Center, Water Resources

Inventory Area 1 Watershed Management, http://wa.water.usgs.gov/projects/wria01/

University of Washington, Water Resources Management and Drought Planning Group (2008),

website description of new components included in Version 2.4 of DHSVM,

http://www.tag.washington.edu/research/dv24/dv24.htm#6.%20Glacier%20Movement.

Washington Department of Ecology (DOE) (1988), Instream Resources Protection Program –

Nooksack Water Resource Inventory Area (WRIA) 1, Chapter 173-501 WAC,

http://www.ecy.wa.gov/pubs/wac173501.pdf.

Washington State Department of Ecology (DOE) (2008), Watershed Planning, WRIA 1,

http://www.ecy.wa.gov/apps/watersheds/wriapages/01.html

Wigmosta, M.S., L.W. Vail, and D.P. Lettenmaier (1994), A distributed hydrology-vegetation

model for complex terrain. Water Resources Research, 30, 6, 1665-1679.

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Wigmosta, M.S. and W.A. Perkins (2001), Simulating the effect of forest roads on watershed

hydrology, in Land Use and Watersheds: Human Influence on Hydrology and Geomorphology in

Urban and Forest Areas, M.S. Wigmosta and S.J. Burges, eds., AGU Water Science and

Application, 2, 127-143.

Wigmosta, M.S., B. Nijssen, and P. Storck (2002), The distributed hydrology-soil-vegetation

model, In Mathematical Models of Small Watershed Hydrology and Applications, V.P. Singh

and D. Frevert, eds., Water Resources Publications, 7-42.

Wiley M.W., Palmer R.N., Salathe Jr., E.P. (2006), The development of GCM based climate

scenarios for use in water resource impact evaluations, Submitted to Journal of Water Resources

Planning and Management.

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http://wria1project.whatcomcounty.org.

25

8.0 Tables

Table 1. Seasonal averages for predicted streamflow for the Sultan, Tolt, Cedar, Green, and

White Rivers, Washington (Palmer, 2007b).

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9.0 Figures

Figure 1. Generalized map of the Nooksack River watershed (USGS, 2008).

Figure 2. Mean annual precipitation (1961-1990) in Water Resources Inventory Area (WRIA) 1,

which includes the Nooksack watershed, Lake Whatcom watershed and several small adjacent

watersheds; contours are in inches of precipitation (USGS, 2008).

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Figure 3. Discharge (cfs) in the Middle Fork of the Nooksack River, measured near Deming,

WA for WY2005 (USGS, 2008).

Figure 4. Temperature anomaly (°C) indicated by the instrumental record and a variety of

paleoclimate reconstructions; increasing uncertainty represented by darkening gray color

(National Research Council, 2006).

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Figure 5. Predicted 2050 hydrographs (colored lines) as compared to historic average (black)

for the Sultan River, WA (CCTC, http://www.climate.tag.washington.edu/, 2007).

Figure 6. 2040s change in temperature and precipitation for the Pacific Northwest, as predicted

by twenty GCM-emission scenario couples; three representative couples used by the CCTC, and

to be used in this study are in bold (Mote and others, 2005).