Use of satellite data for streamflow and reservoir storage forecasts in the
Snake River Basin, ID
Marketa Mcguire1 and Dennis P. Lettenmaier1
ABSTRACT
We describe an approach to seasonal streamflow forecasting for the Snake River that uses
the Variable Infiltration Capacity (VIC) macroscale hydrology model in conjunction with updates
of the model’s initial snow state using Moderate Resolution Imaging Spectroradiometer (MODIS)
imagery for winters 2000-2004. We evaluated seasonal streamflow forecasts made on March 1,
April 1, and May 1 through the end of July, as well as short lead forecasts for two-week durations
beginning on April 15 and May 15 retrospectively for 2000-2003. We also evaluated short lead
streamflow forecasts in real-time for a subset of the above forecast dates in 2004. In general,
reduction in mean absolute error was greater for the spring two-week forecasts than for the longer
seasonal forecasts, and there was some indication that error reduction for the seasonal forecasts
was higher for forecasts made later in the spring. Inclusion of the MODIS data resulted in
forecast error reduction (or no change in forecasts) in 63% of the seasonal forecasts (71% of the
two-week forecasts), while mean absolute error increased in only 37% of the seasonal forecasts
(29% of the two-week forecasts). We also evaluated the effect of MODIS updating on reservoir
storage volume forecasts using the SnakeSim monthly reservoir operation model. For reservoirs
where the reservoir model performed well in retrospective simulations, storage forecast errors
were reduced (or unchanged) in 74% of the seasonal forecasts as a result of MODIS updated
streamflow forecasts. Results for the retrospective and real-time 2004 streamflow and reservoir
storage forecasts all indicated general improvement in accuracy using MODIS, even though 2004
experienced anomalous weather conditions.
1
Department of Civil and Environmental Engineering Box 352700, University of Washington,
Seattle, WA 98195
2
INTRODUCTION
In mountainous regions like the western U.S., where spring and summer
streamflow is dominated by snowmelt, quantifying the extent of snow and its liquid water
content is important for producing accurate forecasts of spring and summer streamflow.
The purpose of this paper is to show that improvements in streamflow forecast accuracy
can be made through updating with Moderate Resolution Imaging Spectroradiometer
(MODIS) remotely sensed snow cover products and to outline the implications of these
improved forecasts for water management.
More accurate forecasts of streamflow allow water managers to operate reservoirs
and groundwater resources more efficiently. Hamlet et al (2002) and Maurer et al (2004)
demonstrated that accurate prediction of seasonal streamflow and use of climate
information can benefit the hydropower industry and can benefit management of
reservoir storage. Yao and Georgakakos (2001) showed that increased predictability of
inflows to Folsom Lake on the American River in California through use of ensemble
streamflow forecasts and dynamic reservoir operating policies could reduce flood
damages and increase hydropower revenues.
Streamflow forecasting methods based on snowpack estimates date to the early
1900s, and under many conditions they demonstrate good skill (Garen 1992).
Hydrologists have used both model-based methods and regression-based methods but the
latter still dominate in practice. The Natural Resources Conservation Service (NRCS)
and the National Oceanic and Atmospheric Administration (NOAA) National Weather
Service River Forecast Centers produce forecasts of seasonal streamflow volumes
approximately three times per month between January and May at a number of locations
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throughout the western U.S. These forecasts are based on multiple linear regression
techniques, typically using a combination of past snow observations, precipitation, and
streamflow from previous months (Garen, 1992). They provide most probable forecasts
(probability of flow volume exceedence 50 percent) as well as error bounds of 10%, 30%,
70%, and 90% exceedences.
Although regression-based forecasts often perform well under conditions within
the range of past observations, they can perform poorly in cases outside or near the limits
of the data used to “train” the regression models. For example, the 1999 April 1 forecast
for the April-July flow volume for the Animas River near Durango, CO had one of the
largest forecast errors in the historic record. That error was affected by an unusually low
snowpack on April 1, an extremely wet April, followed by normal conditions until midJuly, when flooding occurred. The most probable flow was within the 10% and 90%
confidence intervals, however the observed flow volume was more than triple the most
probable flow volume.
In 1977, the National Weather Service introduced an approach for seasonal
streamflow forecasting called Ensemble Streamflow Prediction (ESP; Twedt et al, 1977),
which uses conceptual streamflow simulation methods. In the ESP method, probabilistic
streamflow forecasts are produced using initial conditions on the forecast date and
resampling of historical observed climate sequences during the forecast period. Model
parameters remain constant for the simulation of each climatic sequence, thereby
suggesting that the only possible variables are the initial conditions and the model
forcings. However, the historic record constrains model forcings, and this method
assumes that future conditions during the forecast period will be similar to those observed
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in the historic past. And so, the forecast initial conditions are what set apart skillful
predictions from predictions based only in the range of historic climate. This method has
the advantage of capturing the full range of climatic variability in the historic record.
One weakness of this approach is that the uncertainties in the forecasts do not consider
data errors or model calibration errors. In addition, biases in the hydrologic model
propagate into the forecast ensembles. Because the skill of ESP forecasts depends on the
accuracy of the initial conditions, more accurate estimates of these initial conditions are
critical for improved forecasts. The work we describe here is a derivative of the ESP
method, which makes use of more modern models and data, in conjunction with data
assimilation methods, to provide improved initial conditions for ESP forecasts.
The test site for this application is the Snake River, the largest tributary of the
Columbia River. From its headwaters in Yellowstone National Park, the Snake River
drains an area of 280,000-km2 and covers approximately 87% of Idaho, as well as parts
of Wyoming, Utah, Nevada, and Oregon (Figure 1). Most of the Snake River’s runoff
originates as snowpack in the Teton and Sawtooth mountain ranges. The Snake River
basin is highly regulated by a series of dams that have a total storage capacity of over 11
BCM. Like many western U.S. rivers, the Snake is overallocated. Irrigated agriculture
accounts for almost 99% of the annual total 24.7 billion cubic meters (BCM) of out-ofstream water diversions and groundwater pumping. This has forced competition among
irrigators, municipalities, in-stream aquatic uses (such as endangered and threatened
species), and recreationalists. Listing of the Snake River Sockeye as endangered on
November 20, 1991 (NOAA 1991), has lead to further competition and stress on the
already over-allocated waters of the basin.
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More accurate forecasts of seasonal snowmelt runoff and reservoir storage have
the potential to help resolve some of the basin’s management problems. In this paper, we
evaluate the potential for satellite snow extent estimates to provide improved initial
conditions for streamflow forecasts. Specifically, we use the snow covered area (SCA)
product from the MODIS, flown on board the NASA Terra Earth Observing Satellite,
launched on December 18, 1999 to update the snow cover state in the Variable
Infiltration Capacity (VIC) macroscale hydrology model. We also use resulting
streamflow forecasts, for lead times from two weeks to six months, as input to a model of
the Snake River reservoir system, which produces forecasts of reservoir storage volumes.
MODIS SCA products are of particular interest because recent work (Maurer et al 2003,
Klein et al 2003) has shown that they are more accurate than operational satellite-based
products, which are based on coarser resolution imagery and fewer spectral bands.
We evaluated forecasts for seven locations within the Snake River basin. These
locations, noted in Figure 1, vary in basin size and elevation and correspond to inflows to
major storage reservoirs, which include American Falls (AMERI), Jackson Lake
(JLAKE), Palisades (PALIS), Island Park (IPARK), Ririe (RIRIE), Arrow Rock Ranch
(AROCK), Anderson Ranch (ANDRA), and Dworshak (DWORS). We also evaluated
storage forecasts in four locations: American Falls (AMERI), Island Park (IPARK),
Arrow Rock Ranch (AROCK), and Dworshak (DWORS). We evaluated retrospective
streamflow and reservoir storage forecast accuracy using the MODIS-based forecasts
over a range of lead times from two weeks to several months, for the four-year period
2000-2003. We also evaluated forecasts made in near real-time for winter and spring
2004.
6
SATELLITE SNOW PRODUCTS
Background
Since early in the satellite era, hydrologists have recognized the potential for
satellite-based estimates of snow properties – in particular SCA (Rango and Martinec
1979). Although SCA can be estimated from ground measurements, such as snow depth,
these estimates are inevitably limited by their use of point data rather than the spatial
characteristics they attempt to represent. SCA was one of the first practical uses of
remote sensing in hydrology due to the high reflectivity of snow at visible wavelengths
(Ostrem 1975). A number of operational satellites, which support weather prediction and
military operations, have been used to derive snow extent products. Since 1966, the
NOAA has produced weekly snow maps using visible wavelength satellite imagery (Frei
and Robinson 1999). The NOAA/NWS National Operational Hydrologic Remote
Sensing Center (NOHRSC) has produced a 1 km spatial resolution daily snow extent
product since the early 1990s, which utilizes a combination of operational satellite
information, including imagery from the polar orbiting Advanced Very High Resolution
Radiometer (AVHRR), from the visible bands from GOES geostationary satellites,
airborne surveys, and in situ observations from the Natural Resources Conservation
Service NRCS SNOTEL network (Ramsay 1998, Hall et al 2000).
The U.S. Geological Survey produces 30-m and 1-km SCA estimates every 16
days based on Landsat Thematic Mapper (TM) imagery. Hall et al (2000) and Armstrong
and Brodzik (2001) derived coarser resolution weekly snow maps from the polar orbiting
Spectral Sensor Microwave Imager (SSM/I) at 1/3-degree spatial resolution, or
approximately 30-km.
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Several research satellites are currently in orbit that are designed to provide high
resolution visible and near-infrared data that are appropriate for estimation of SCA.
NASA launched the Terra Earth Observing System (EOS) spacecraft on December 18,
1999. Among its various instruments is MODIS, which began producing usable data in
February 2000. NASA launched a second MODIS instrument on the EOS Aqua satellite
in May 2002, and it has produced data since June 2002. MODIS data are of particular
interest for both scientific and operational use due to their daily frequency and
intermediate spatial resolution of 500 m (higher resolution visible band instruments like
Landsat TM typically have much less frequent overpasses). The MODIS automated
snow-mapping algorithm calculates snow extent using Level 1B radiance data, cloud
masks, land/water masks, and thermal masks, as well as time of day. Continuous
improvements to the snow-mapping algorithm have been made since the beginning of the
data record (Hall 2002). Furthermore, Barton et al (2000) and Salomonson et al (2004)
have shown that fractional snow cover within MODIS 500 m pixels can be retrieved.
The EOS Aqua Satellite also supports the Advanced Microwave Scanning
Radiometer (AMSR-E). This instrument was designed to gather information about water
in the earth system, including snow water equivalent (SWE). A daily SWE product at 25
km spatial resolution derived from AMSR-E became available in February 2004. Given
the short length of the AMSR-E SWE product, we focus our attention here on the
MODIS SCA product.
Hydrologists have used satellite images of snow cover from the above-mentioned
sources to test and update simulated snow cover and snow water equivalent from
hydrologic models and, in some cases, have been used as input for hydrologic models.
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Turpin et al (1999) tested the transient snow line in an alpine glacier runoff model and
updated the SCA calculated by the HBV model (Bergstrom and Forsman 1973,
Bergstrom 1975) using Landsat TM imagery. For the periods reported, both models
tended to over-simulate observed SCA. In addition, SCA data based on AVHRR and
Landsat TM data forced the Snowmelt-Runoff Model (SRM) for use in real-time
operational streamflow forecasts by hydropower companies in Spain (Gomez-Landesa
and Rango 2002).
Due to their potential utility, the accuracy of MODIS snow cover products has
been researched since the data first became available. Maurer et al (2003) compared
MODIS snow covered area and operational NORHSC gridded snow maps over the
Columbia and Missouri River basins using several hundred surface stations, and
concluded that MODIS mis-classified fewer pixels overall and was able to discriminate
snow from clouds more accurately as compared with operational SCA products derived
from GOES and AVHRR. Klein et al 2003 compared MODIS daily snow cover,
NOHRSC operational snow maps, and in situ measurements over the Upper Rio Grande
Basin of Colorado and New Mexico for the 2000-2001 snow year and showed that
MODIS had an overall classification accuracy of 88% compared to SNOTEL
measurements, as opposed to 76% for the NOHRSC product. They also determined that
the majority of days for which snow cover was mis-classified by MODIS occurred where
snow depths were less than 4cm.
MODIS Image and SCA Estimate Availability
The utility of streamflow and storage forecasts using MODIS updated initial
conditions is dependent upon the availability of MODIS images. Table 1 summarizes the
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number of days that images were not available during the update period, January 1 - June
1, in each year, 2000 - 2004. The first year MODIS images became available had 29
missing daily images, 17 of which occurred consecutively from March 17 through April
2. For this reason, and because the first images did not become available until the end of
February, there are fewer updatable days in 2000 than in the other years. In subsequent
years, there were fewer un-updateable days, with the exception of 2002, where 15 images
were missing, of which 10 occurred from March 18 to March 28.
Figure 2 shows monthly fractional snow cover, cloud cover, and no decision,
averaged over the entire Snake River basin and over the five years from 2000 - 2004.
The no decision classification corresponds to sites where the MODIS snow cover
algorithm could not discriminate among cloud, snow, or land. The no decision
classification is infrequent and does not have an apparent seasonal dependence. Cloud
cover is highest at the beginning of the winter season and generally decreases throughout
the winter and spring season, with greater declines occurring after March. Usually
fractional snow cover increases over the winter until March, after which temperatures and
snowmelt increase. However, between January and February the average monthly cloud
cover fraction decreases, while the average snow cover increases slightly.
MODELING APPROACH
Hydrological Model
We used the Variable Infiltration Capacity (VIC) hydrologic model, which is a gridbased macroscale hydrology model that solves the water and energy balances at the land
surface for each model grid cell (Figure 3a). The model produces runoff (for this study,
at a daily time step) from each grid cell as baseflow and surface runoff. Grid cell runoff
10
is routed to predefined points in the channel network as shown in Figure 3b, using
methods described by Lohmann et al 1998 a, b. The model simulates the spatial
distribution of soil moisture, snow water equivalent, reflected solar radiation, emitted
longwave radiation, surface temperature, and other fluxes and state variables associated
with land-atmosphere energy and moisture exchanges. For this study, we implemented
the model at one-eighth degree spatial resolution, which is the resolution used in the
North American Land Data Assimilation System (LDAS) (Mitchell et al 2004) and by a
long-term retrospective simulation for the continental U.S. performed by Maurer et al
(2002).
The snow accumulation and ablation model within VIC ran at a three-hour time step,
which resolves the diurnal cycle and helps to accurately estimate day-night variations in
snow-rain partitioning which often occur in transient rain-snow environments. Each VIC
one-eighth degree grid cell is further partitioned into vegetation classes and elevation
bands to represent dominant subgrid land surface characteristics. The model uses up to
five elevation bands within each grid cell in order to constrain the maximum elevation
range per grid cell to 500 m. We used the gridded vegetation data developed as part of
the LDAS project and based on the University of Maryland’s 1 km global land cover
product (Hansen et al 2004). Within the Snake River basin, the vegetation data contain a
total of 11 classes, ranging from cropland to evergreen needle leaf. This work used VIC
Version 4.0.4
(http://www.hydro.washington.edu/Lettenmaier/Models/VIC/Source_Code/Source_Code.
html). In Version 4.0.4, snow coverage is binary (no fractional cover), and is assumed to
be uniformly distributed over each elevation band and vegetation class in the manner
11
described by Wigmosta et al (1994).
Water Management Model
The water management model, SnakeSim, was developed by VanRheenen et al
(2003). It is a monthly water balance model that utilizes 21 inflow locations where either
naturalized streamflows or VIC modeled streamflows are prescribed (Figure 3b). The
model represents 18 reservoirs, with a total storage capacity of 16.4 BCM. Current
operations in the model adhere to rules for reservoir storage and releases determined by
the Idaho Department of Water Resources, U.S. Bureau of Reclamation, and U.S. Army
Corps of Engineers (IDWR 1997, USBR 1996a,b, USBR, 1997a,b, USBR 1998).
Instream flow targets exist for fish, water quality, and hydropower production. The
model groups surface water diversions by river reach rather than individually. Due to the
large degree of surface and groundwater interaction in the Snake River basin, a separate
groundwater model is used from which response curves are derived and incorporated into
the SnakeSim model. Groundwater response curves are non-linear and are based on an
analysis for a three-dimensional groundwater model performed by the University of
Idaho (Johnson et al 2003, Miller et al 2003). The SnakeSim model was calibrated using
data for 1951-1960 and was evaluated using data for 1966-1980, with exceptions for
reservoirs that came into operation during or after these periods. This study used mean
monthly water demands for 1982-1992. For further details of the reservoir model, the
reader is referred to Van Rheenan et al (2003).
Updating Approach
For the update period prior to each forecast date, we generated spatial plots to
determine whether there was spatial consistency in the updating. We hypothesized that
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the MODIS adjustment occurs more frequently in VIC model grid cells at the fringes of
snow covered areas, which can either translate to lower elevation transition zones that lie
between areas where snow rarely occurs and where snow rarely melts in mid-winter, or to
areas during the melt season that are nearly snow-free – in either case the areas generally
have a shallow snowpack.
We determined VIC model initial states (snow water storage, soil moisture) for
each grid cell and elevation band by running the model from several years prior to the
date of forecast up to the forecast date using gridded observations, similar to those
described by Maurer et al (2002). A key aspect of these “run-up” data is that archives of
the full set of NOAA Cooperative Observer stations are only available within 3-4 months
of the time of forecasts. For retrospective forecasts, all of the cooperative observer data
were available. For the 2004 near real-time forecasts, we used NCDC meteorological
station data as they became available (usually to within 2-4 months of the forecast date),
after which other available real-time meteorological forcings were used in an assimilation
and gridding procedure described by Wood et al (2004). Then we saved model states and
used them as the initial conditions for each ensemble in the streamflow forecast. We
updated the model states using MODIS snow cover images. In the retrospective analysis,
we updated the VIC model state each day that MODIS images were available, beginning
February 2000, and ending at the forecast date. In the near real-time analysis, we updated
the VIC model state each day within an 8-day update window.
The MODIS updating scheme reads the current VIC model snow states and three
preprocessed MODIS files, which contain fractional snow cover, fractional cloud cover,
and a fraction of no decision (not classified as snow, cloud, or land) in each grid cell
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elevation band (note that the fractional coverage was formed, for each elevation band, by
taking the ratio of the number of 500 m pixels classified as snow covered to the sum of
snow covered and snow free pixels in the elevation band). We used a 30-arcsecond
(approximately 1 km) resolution digital elevation model (DEM) of the basin to determine
the fractions of snow, cloud, and no decision within each elevation band and grid cell.
All vegetation classes were lumped for purposes of the updating, however future work
could entail determining these fractions within each elevation band and vegetation class.
Figure 4 shows basin average fractional snow cover and fractional cloud cover for the
update period, January 1 - June 1 averaged for 2000-2004. It shows that the entire basin
was never covered with snow, which was the case for the entire period (climatologically,
the lower elevations within the Snake River basin are rarely snow covered). In addition,
the figure shows that the snow cover fraction increased through February and then
subsequently decreased throughout the remainder of the update period. The cloud cover
plot indicates that, in general, there exists a downward trend in cloud cover, beginning in
March.
If the fraction of cloud cover and the fraction of no decision were both less than
50% for a given grid cell and elevation band, then updating was considered feasible. If
updating was feasible, and if the VIC modeled SWE was zero and the fraction of MODIS
snow cover was greater than or equal to 50%, then snow cover was reset to one (i.e.,
snow covered vs. bare) and 5 mm of snow water equivalent (SWE) was added uniformly
with a density of 250 kg/m3, which is between a typical new snow density of 100 kg/m3
and a basin average snow density on the order of 400 kg/m3. If SWE was greater than
zero and the fraction of MODIS snow cover was less that 50%, then snow cover, SWE,
14
and snow density were reset to zero. If SWE and fraction of MODIS snow cover were
both zero or SWE was greater than zero and fraction of snow cover was greater than or
equal to 50%, no updating occured. Table 2 provides a summary of the updating decision
tree.
Once the updating was complete, a revised model state map was produced. We
used these new states as the initial conditions for the next VIC model run, the duration of
which was one day. At the end of the one-day run, we saved the model state and the
updating procedure was repeated. We performed this sequence of VIC model run and
state file update on each day that MODIS images were available for the retrospective
analysis and each day in an eight-day window prior to the forecast date for the near realtime analysis.
Forecasting Procedure
We produced retrospective forecasts of streamflow and storage for the first day of
each month, for March through June, and for April 15 and May 15 of 2000 - 2003 using
the ESP approach following updating of the forecast initial conditions, where ensemble
members were resampled from the historic period, 1960-1999. We subsequently routed
each forecast ensemble to produce monthly streamflow. The routing model used its own
spin-up period of 2 months (2.5 months for mid-month forecasts), which provided
adequate time to obtain approximate soil moisture conditions on the forecast date. We
used a spin up of strictly unadjusted fluxes for routing of unadjusted forecast ensembles
and, likewise, a spin up of daily MODIS adjusted fluxes for routing of MODIS adjusted
forecast ensembles.
Streamflow forecasts were produced for 21 locations in the Snake River basin,
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seven of which were chosen for detailed analysis. Table 3 summarizes their
characteristics. We corrected the ensembles of monthly streamflow for systematic biases
in the VIC model using a technique described by Snover et al 2003. We corrected daily
streamflows using Eq. 1 below, where Qbc,monthly is bias corrected monthly flow,
Qraw,monthly is uncorrected monthly flow, and Qraw,daily is uncorrected daily flow.
 Qbc, monthly 

Qbc, daily  Qraw, daily * 
 Qraw, monthly 
(1)
We used monthly bias corrected streamflows as input for the SnakeSim water
management model, which, upon simulation, provided ensembles of reservoir storage.
We generated streamflow and reservoir storage forecasts using various lead times
ranging from two weeks to three months to assess the sustained impact of the MODIS
updating. We also generated two-week streamflow forecasts for each of the five forecast
dates (March 1, April 1, April 15, May 1, May 15) to determine the short-term impacts of
the updating. We also produced seasonal runoff volume forecasts, which are the average
monthly streamflow from forecast date through July, to determine whether MODIS
updated forecasts better predict overall seasonal runoff. Because the SnakeSim water
management model uses a monthly time step, we did not evaluate forecasts of storage
with a two-week lead-time (storage forecasts equivalent to the two-week streamflow
forecasts) in the context of reservoir storage, for which only forecasts of seasonal
volumes were produced.
We found that adjustments of forecast initial conditions using MODIS snow cover
during the snow accumulation period (typically November - February in the Snake River
basin) had little or no impact on subsequent seasonal streamflow or reservoir storage; and
16
therefore, we do not report forecasts for these dates. Likewise, adjustments of forecast
initial conditions using MODIS snow cover made after a majority of snowmelt has
occurred had inconclusive impacts on subsequent streamflow or reservoir storage, and for
this reason May 15 is the last forecast that we evaluated. MODIS updates impact
streamflow and storage predictions primarily during the melt season, when significant
depletion in the snowpack occurs and fewer snow events occur that could impact the
upcoming runoff season.
Assessment of Forecast Skill
We measured forecast accuracy using mean absolute error (MAE), which is one
common measure of forecast accuracy for continuous predictands (Wilks 1995). Mean
absolute errors (MAE) were calculated, with and without MODIS adjusted initial
conditions, for each streamflow forecast date and location as follows:
MAE 
1 n
 | Yk  Ok |
n k 1
(2)
where Yk is the predicted streamflow of each ensemble member and Ok is the observed
naturalized streamflow of each ensemble member. The Idaho Department of Water
Resources provided naturalized flows for the same period. We averaged mean absolute
errors for each forecast location for five forecast dates (March 1, April 1, April 15, May
1, and May 15) over the four years 2000-2003.
RESULTS AND DISCUSION
Historical VIC simulations of SWE (Figure 4a) at the seven locations chosen for
analysis show that for many of these locations, the month of maximum SWE is typically
March, with some locations showing rapid declines in SWE in subsequent months. In
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general, higher elevation areas experience higher SWE. Locations with the highest SWE
do not, however, experience the highest peak flows, as shown in Figure 4b. American
Falls, into which water at Jackson Lake and Palisades flow, experiences the highest
runoff of the eight locations. While the timing of peak runoff varies; the eight locations
typically experience peak flows in May or June, after SWE has begun to decrease.
Basin-average MODIS fractional snow cover, over the period since images became
available, is greatest in February even though it reaches its peak SWE in March, which
implies that snow continues to accumulate in March in those areas that contribute most to
basin-average SWE (i.e., those with the deepest snowpacks).
Spatial Consistency of MODIS Updating
Figure 5 summarizes the spatial consistency of the MODIS updated snow cover
by reporting total monthly SWE added and removed for each month, March-May,
averaged over all years in the retrospective analysis period of 2000-2003. It shows that
the basin experiences greatest SWE in March, which corresponds to the high average
fractional snow cover shown in Figure 2. The update scheme added or subtracted snow
to a greater number of VIC grid cells in the month of March than in April or May. In
March, the western boundary of the basin, which is at lower elevation than the rest of the
basin, had more snow added than in subsequent months, when snow was generally
melting and the snow areal extent was decreasing. Likewise, along the western boundary
of the basin the update scheme removed more snow in March than in subsequent months.
Later in the season, the region where most updating occurred was confined to areas of
higher elevation. In April and May, the greatest amounts of snow were added to the
central and eastern part of the basin, whose melt contributes to streamflow at Island Park
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and American Falls. Likewise in April and May, the greatest amounts of snow were
removed from the northern and eastern-most part of the basin, whose melt contributes to
streamflows at Dworshak, Jackson Lake, Palisades, Anderson Ranch and Arrow Rock
Ranch. In general, much of the updating of snow cover occurred at the fringes of the
snow covered area, although this general pattern is confounded by the topographic
complexity of the basin.
The magnitudes of snow added and removed in Figure 5 indicate that not only the
regions of shallow snowpack are updated, but grid cells with deeper snowpack as well.
The maximum monthly SWE removed is 801 mm, although typcially only 10-20 mm is
removed monthly. The removal of great amounts of snow is likely attributable to
removal of higher elevation snowpack, which the VIC model associated with a particular
vegetation type. When snow is added to a VIC grid cell (i.e. when a MODIS image
designates an elevation band within a grid cell as more than 50% snow covered) the
updating procedure calls for the addition of 5 mm of SWE. Therefore, the amount of
snow added is constrained to this amount even though actual snow water equivalent may
be greater or less. While more sophisticated methods might be used (e.g. interpolation of
the surrounding SWE to determine the amount of snow to add, or use of in situ
measurements like SNOTEL to get a better estimate of the amount to be added) the
simple method we use suffices to evaluate the general nature of potential forecast
improvements that can be made through MODIS updating.
Retrospective Streamflow Forecast Analysis
March and April MODIS-adjusted short lead-time (i.e. two-week) forecasts,
although they showed improvement over unadjusted forecasts because MAEs were
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lower, resulted in less significant improvements than the mid-April through mid-May
forecasts. MODIS improved forecasts in 71% of the April 15 and May 1 two-week
forecasts, but only 65% of all two-week forecasts collectively. Figure 6 shows that twoweek forecast MAEs between predicted and naturalized streamflows at Island Park,
Palisades, American Falls, Anderson Ranch, and Arrow Rock Ranch for all forecast dates
without the use of MODIS adjusted initial conditions were greater than two-week
forecast MAEs between predicted and naturalized streamflows using MODIS, with the
exception of May 15 forecasts. Forecasts for Jackson Lake show mixed results. MODIS
forecasts made on March 1, April 1, and May 1 performed better than unadjusted
forecasts, while the April 15 and May 15 forecasts performed worse. Two-week
forecasts at Dworshak were not produced due to the unavailability of daily naturalized
flows to calculate mean absolute errors.
These results are consistent with the fact that for the earlier forecasts, the updating
occurred over January - March, when peak SWE had not yet been reached. These results
also indicate that greatest reduction in MAEs does not necessarily occur at locations
where the updated snowpack has the most impact on streamflow. In the Jackson Lake
sub-basin, for example, which typically has a deep winter snowpack and high correlation
between snowpack and streamflow, one would expect to see consistent improvements in
streamflow predictions using the MODIS update. However, MAEs in the MODIS
updated April 15 and May 15 forecasts were higher than in the un-updated forecast.
Forecasts of runoff volume from the forecast date through July, which were
produced to determine the impact of MODIS-adjusted initial conditions on predicted
seasonal streamflow volumes, indicate less definitive improvements in streamflow
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prediction using MODIS than the two-week forecasts. Figure 7 shows that adjusted
seasonal forecasts have lower MAEs in the May 1 and May 15 forecasts at Jackson Lake
and in the March 1, April 15, and May 1 forecasts at Palisades. Accumulation of snow
after the forecast date at these locations could be the cause of their poor performance in
the early season forecasts (April 1 primarily). MODIS-adjusted seasonal forecasts at
Ririe and Anderson Ranch did not produce any noticeable improvement, while MODIS
adjusted seasonal forecasts at Arrow Rock Ranch were worse than the unadjusted
forecasts. MODIS-adjusted seasonal forecasts had lower average MAEs than unadjusted
forecasts at Island Park and American Falls for all forecast dates, similar to results from
the two-week forecasts (Figure 6). Improved predictions at these locations could be due
to the fact that the Island Park region experienced frequent updating and its streamflow
contributes to flow at American Falls. Jackson Lake and Palisades, whose streamflow
also contribute to flow at American Falls, result in higher MAEs in the early season
adjusted forecasts. The improvements in other locations’ early season forecasts appear to
have a greater impact on predictions at American Falls, since we see reduced MAEs for
all forecast dates at American Falls. MODIS adjusted seasonal forecasts at Dworshak
exhibited lower MAEs than unadjusted forecasts for all but the March 1 forecast,
supporting the idea that snow accumulating after March 1 would affect the forecast.
Similar to results in the two-week streamflow forecasts, these results show that use of
MODIS updated initial conditions can achieve improved streamflow forecasts. However,
MODIS updated initial conditions appear to have a greater impact on shorter lead time
forecasts at forecast dates within the snow ablation period.
21
Retrospective Storage Forecast Analysis
We produced reservoir forecasts for five forecast dates, consistent with the
streamflow forecasts (March 1, April 1, April 15, May 1, and May 15) for locations
where the SnakeSim water management model was able to reproduce historical storages
during the model validation period. Figure 8 shows model validation for four locations:
American Falls, Island Park, Arrow Rock Ranch - Lucky Peak, and Dworshak. The
calibration period for American Falls, Island Park, and Arrow Rock Ranch - Lucky Peak
was October 1951 - September 1960 and the validation period was October 1966 September 1980. The calibration and validation periods for Dworshak were October
1976 - September 1980 and October 1986 - September 1990 because Dworshak was not
operational until 1971. SnakeSim was able to reproduce the storage drawdown well in
the 1960s but had trouble, particularly during the late 1970s, when the region experienced
severe drought conditions. SnakeSim was also able to reproduce reasonably well the
drawdown cycles at Island Park, Arrow Rock Ranch - Lucky Peak, and Dworshak.
Figure 9 shows forecast mean absolute errors for forecasts of June 1 storage
volume, averaged over the four years of retrospective forecast simulations. We chose to
evaluate forecasts of June 1 volume because most of the system reservoirs fill in June.
For locations where the SnakeSim model validation was good and retrospective
streamflow forecasts using MODIS adjusted initial conditions showed improvement in
May 1 and May 15 forecasts at Arrow Rock Ranch-Lucky Peak and Dworshak, and no
significant change in the remaining forecast dates and locations over those using
unadjusted initial conditions. However, storage forecasts at Island Park using the
MODIS adjusted initial conditions had higher MAEs than the unadjusted forecasts. This
22
may be attributed to the fact that snow was added more than it was taken away in this
region of the basin and the uniform amount snow water equivalent (5 mm) that was
added was inappropriate. A more sophisticated technique of adding SWE might alleviate
this problem. In addition, although storage forecasts reflecting MODIS adjusted initial
conditions for the combined Arrow Rock Ranch - Lucky Peak system showed smaller
MAEs than the unadjusted forecasts, corresponding seasonal volume streamflow
predictions showed higher MAEs. The inclusion of Lucky Peak reservoir in the storage
forecast may have been a cause of this inconsistent result.
Near Real-Time Streamflow and Storage Forecast Analysis
We produced near real-time streamflow forecasts for two-week lead-time on four
forecast dates in 2004: March 1, April 1, April 15, and May 1. Results for these forecasts
were mixed. Results from the two-week near real-time forecasts in Figure 6, which show
MAE only for 2004 at each forecast date, indicate that March 1 MODIS adjusted
forecasts show no significant improvement over unadjusted forecasts. The April 1, 2004
MODIS adjusted forecasts resulted in smaller MAEs than the unadjusted forecasts at the
upper basin locations of Island Park, Palisades, and American Falls, while the forecast at
Jackson Lake resulted in no significant change. The April 15, 2004 forecast resulted in
smaller MAEs at all upper basin locations (Jackson Lake, Palisades, Island Park, and
American Falls) and Anderson Ranch, while showing little or no improvement at the
others. MODIS adjustments improved May 1 forecasts at all locations but Anderson
Ranch. Again, near real-time two-week forecasts for Dworshak were not produced
because daily naturalized flows were not available. Results from this near real-time
analysis are consistent with the retrospective analysis by showing greater improvements
23
in two-week forecasts (lower MAE) in mid-April through May, which is the basin snow
ablation period.
We also produced near real-time streamflow forecasts of seasonal volume at same
four dates in 2004. However, because observations for the forecast period were not yet
available at the time of writing, we do not report on MAEs for the near real-time seasonal
forecasts. Near real time storage forecasts of June 1 volume were produced for March 1,
2004; April 1, 2004; and April 15, 2004. These forecasts use as input streamflow
forecasts that use a combination of NCDC and interpolated meteorological station data.
Near real-time forecasts for the Arrow Rock Ranch-Lucky Peak system were not
produced because Arrow Rock reservoir was drained and underwent maintenance during
the winter and spring of 2004. Again, because observations for the June 1 storage
volumes were not available at the time of writing, we do not report MAEs from these
forecasts.
CONCLUSIONS
Our analysis confirms the potential to improve seasonal and short-term
streamflow forecasts through use of MODIS SCA products. The potential improvements
increase throughout the spring months, as the basin average fractional cloud cover
decrease, and the melt period progresses. In general, the greatest improvements in
forecast accuracy through use of the MODIS data were for April and May, following the
occurrence of peak SWE over much of the basin. This likely is the case because MODIS
images are decreasingly affected by cloud cover, and because snowmelt is most dynamic
during this period. The technique we use to update the VIC model snow water equivalent
using MODIS SCA is best suited for situations where snow is predominantly melting and
24
the model has overestimated SCA. Absent direct measurements of SWE, the method is
less successful when snow cover, and SWE, must be added due to model underestimation of SCA.
Streamflow forecasts at two-week lead-time using the VIC hydrologic model
were improved through use of MODIS updating in 71% of reported forecasts overall,
although the improvements in the real-time (2004) two-week forecasts were less
pronounced, primarily due to unusual conditions during the 2004 melt period. Seasonal
streamflow forecasts showed more mixed results (improvement in 63% of reported
forecasts overall), with improvements generally occurring for mid-April and May
forecasts for retrospective forecasts, but not before. Reductions in retrospective storage
forecast errors occured for reservoirs that the SnakeSim water management model
simulated successfully in retrospective evaluations. Overall, we found improvement in
74% of storage forecasts. Reservoir storage error reductions occurred for American Falls
and Dworshak, two of the largest storage reservoirs in the system.
Future studies could benefit from a more sophisticated handling of the addition of
SWE, using an interpolation technique, or more sophisticated data assimilation method.
Updating procedures for obtaining more accurate streamflow forecast initial conditions
could entail the conjunctive use of MODIS imagery and SNOTEL data when the
updating decision tree indicates the addition of SWE. In time, remote sensing products
may become available that supply SWE at a fine scale. Updating procedures could entail
the use of AMSR-E SWE for example, as opposed to MODIS SCA. More sophisticated
techniques, such as Ensemble Kalman Filters (Andreadis and Lettenmaier 2004), are also
likely to extract additional information from the MODIS data.
25
ACKNOWLEDGEMENTS
This publication was supported by the Pacific Northwest Regional Collaboratory
under funding from the Applications Division of the NASA Earth Sciences Enterprise.
The authors thank Pamela Pace of the Idaho Department of Water Resources for
providing the retrospective and near real-time streamflow and reservoir storage data.
Ryan Hruska of the Idaho National Environmental and Engineering Laboratory provided
preprocessed MODIS images and fractional SCA data extracted from archives of the
National Snow and Ice Data Center.
26
APPENDIX I. REFERENCES
Andreadis, K., Lettenmaier, D.P. (2004). “Assimilating remotely sensed snow
observations into a macroscale hydrologic model.” Journal of Hydrology, in review.
Armstrong, R. L. and M. J. Brodzik. (2001). “Recent Northern Hemisphere snow extent:
a comparison of data derived from visible and microwave satellite sensors.”
Geophysical Research Letters, 28(19), 3673-3676.
Barton, J.S., Hall, D.K., Riggs, G.A. (2000). “Fractional snow cover from the MODIS
snow-mapping algorithm.” Proceedings of the 57th Eastern Snow Conference, 1719 May 2000, Syracuse, NY, 171-181.
Bergstrom, S. (1975). “The development of a snow routine for the HBV-2 model.”
Nordic Hydrology, 6, 73-92.
Bergstrom, S. Forsman, A. (1973). “Development of a conceptual deterministic rainfallrunoff model.” Nordic Hydrology, 4, 147-170.
Bureau of Reclamation. (1996a). “A Description of Bureau of Reclamation System
Operations Above Milner Dam.” January 1996 (Revised December 1997).
Bureau of Reclamation. (1996b). “A Description of Bureau of Reclamation System
Operation of the Boise and Payette Rivers.” November 1996 (Revised December
1997).
Bureau of Reclamation. (1997a). “A Description of Bureau of Reclamation System
Operation of Miscellaneous Tributaries of the Snake River.” April 1997 (Revised
December 1997).
27
Bureau of Reclamation. (1997b). “Cumulative Hydrologic Effects of Water Use -An
Estimate of the Hydrologic Impacts of Water Resources Development in the
Columbia River Basin.” Draft Report. October 1997
Bureau of Reclamation. (1998). “Biological Assessment. Bureau of Reclamation
Operations and Maintenance in the Snake River basin Above Lower Granite
Reservoir.” February 1998. Boise, Idaho.
Frei, A., Robinson, D.A. (1999). “Northern hemisphere snow extent: Regional variability
(1972-1994).” International Journal of Climatology, 19, 1535-1560.
Garen, D.C., (1992). “Improved techniques in regression-based streamflow volume
forecasting.” ASCE J. Water Resources Planning and Management, 118(6), 654 –
670.
Gomez-Landesa, E., Rango, A. (2002). “Operational snowmelt runoff forecasting in the
Spanish Pyrenees using snowmelt runoff model.” Hydrological Processes, 16,
1583-1591.
Hall, D.K., Riggs, G.A., Salomonson, V.V., DiGirolamo, N.E., Bayr, K.J. (2002).
“MODIS snow-cover products.” Remote Sensing of Environment, 83, 181-194.
Hall, D.K., Tait, A.B., Foster, J.L., Chang, A.T.C., and Allen, M. (2000).
"Intercomparison of satellite-derived snow cover maps." Annals of Glaciology, 31,
369-376.
Hamlet, A. F., Lettenmaier, D. P. (2002). “Economic value of long-lead streamflow
forecasts for Columbia River hydropower.” ASCE Journal of Water Resources
Planning and Management. 128(2) 91-101.
28
Hansen, M.C., DeFries, R.S., Townshend, J.R.G., and Sohlberg, R. (2004). “Global land
cover classification at 1km spatial resolution using a classification tree approach.”
International Journal of Remote Sensing (in press).
Idaho Department of Water Resources, (1997). “Upper Snake River basin study.” Idaho
Department of Water Resources, Boise, ID., 85.
Johnson, G.S., Sullivan, W.H., Cosgrove, D.M., Schmidt, R.D. (2003). “Recharge of the
Snake River Plain aquifer: transitioning from incidental to managed”. Journal of the
American Water Resources Association, 33(1), 23-132
Klein, A.G., Barnett, A.C. (2003). “Validation of daily MODIS snow cover maps of the
Upper Rio Grande river basin for the 2000-2001 snow year.” Remote Sensing of
Environment, 86, 162-176.
Liang, X., Lettenmaier, D.P. (1994). “A simple hydrologically based model of land
surface water and energy fluxes for general circulation models.” J. Geophysical
Research, 99(D7), 14,415-14,428.
Lohmann, D, Raschke, E., Nijssen, B., Lettenmaier, D.P. (1998a). “Regional scale
hydrology: I. Formulation of the VIC-2L model coupled to a routing model.”
Hydrological Sciences, 43(1), 131-141.
Lohmann, D, Raschke, E., Nijssen, B., Lettenmaier, D.P. (1998b). “Regional scale
hydrology: II. Application of the VIC-2L model to the West River, Germany.”
Hydrological Sciences, 43(1), 143-158.
Maurer, E.P., Lettenmaier, D.P. (2004). “Potential effects of long-lead hydrologic
predictability on Missouri River main-stem reservoirs.” Journal of Climate, 17(1),
29
174-186.
Maurer, E.P., Rhoads, J.D., Dubayah, R.O., Lettenmaier, D.P. (2003). “Evaluation of the
snow-covered area data product from MODIS.” Hydrological Processes, 17, 59-71.
Miller, S.A., Johnson, G.S., Cosgrove, D.M., Larson, R. (2003). ”Regional scale
modeling of surface and ground water interaction in the Snake River basin.”
Journal of the American Water Resources Association, 39(3), 517-528.
Mitchell, K. E., et al. (2004). “The multi-institution North American Land Data
Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a
continental distributed hydrological modeling system.” Journal of Geophysical
Research., 109.
National Oceanic and Atmospheric Administration. (2001). “Endangered and threatened
species; Endangered status for Snake River sockeye salmon.” Federal Register
November 20, 56(224). 58619–58624.
Ostrem, G. (1975). “ERTS data in glaciology – an effort to monitor glacier mass balance
from satellite imagery.” Journal of Glaciology, 15, 403-415.
Ramsay, B.H. (1998). “The interactive multisensor snow and ice mapping system.”
Hydrological Processes, 12, 1537-1546.
Rango, A., Martinec, J. (1979). ”Application of a snowmelt-runoff model using Landsat
data.” Nordic Hydrology, 10, 225-238.
Salomonson, V.V., Appel, I. (2004). Remote Sensing of Environment, v 89, n 3, 351-360.
Snover, A.K., Hamlet, A.F., Lettenmaier, D.P., (2003). “Climate Change Scenarios for
Water Planning Studies.” BAMS, 84(11), 1513-1518.
30
Turpin, O., Ferguson, R., Johansson, B. (1999). “Use of remote sensing to test and update
simulated snow cover in hydrological models.” Hydrological Processes, 13, 20672077.
Twedt, T. M., Schaake, J. C. Jr., and Peck, E. L. (1977). “National Weather Service
extended streamflow prediction.” Proc., Western Snow Conference, 52 – 57.
VanRheenen, N.T., Palmer, R.N., Hamlet, A.F., Lettenmaier, D.P. (2003). World Water
and Environmental Resources Congress, 2877-2884.
Wigmosta, M.S., Vail, L.W., Lettenmaier, D.P. (1994). “A distributed hydrologyvegetation model for complex terrain.” Water Resources Research, 30(6), 16651679.
Wilks, D.S. (1995). “Statistical Methods in the Atmospheric Sciences.” Academic Press.
Wood, A., S. Babu and Lettenmaier, D.P. (2004). “A retrospective assessment of realtime surface forcings derived from LDAS and from an index-station approach.”
ASCE J. of Water Resources Planning and Management, in preparation.
Yao, H., Georgakakos, A. (2001). “Assessment of Folsom Lake response to historical and
potential future climate scenarios.” Journal of Hydrology, 249(1-4), 176-196.
31
FIGURE TITLES
Figure 1: Snake River basin and reservoir inflow forecast locations
Figure 2: Basin average fractional snow cover, cloud cover, and no decision (neither
snow, cloud, nor land) by month, averaged over 2000-2004
Figure 3: Variable Infiltration Capacity model schematic and river routing scheme
Figure 4: a) Basin average snow water equivalent, averaged over 2000-2003 and b) basin
average naturalized flow, averaged over 2000-2003
Figure 5: Total monthly snow water equivalent in millimeters, averaged over 2000-2004,
added and removed as a result of MODIS updating
Figure 6: Mean absolute error of two-week lead-time streamflow forecasts, averaged over
2000-2003, using unadjusted and MODIS adjusted initial conditions. Open symbols are
for 2004 near real-time two-week forecasts
Figure 7: Mean absolute error of streamflow forecasts volume from forecast date through
July, averaged over 2000-2003, using unadjusted and MODIS adjusted initial conditions.
Figure 8: Validation of SnakeSim reservoir management model at select locations. Solid
line represents simulation of historic reservoir contents in million cubic meters (MCM);
32
dotted line represents historic reservoir contents in MCM.
Figure 9: Mean absolute error of June 1 reservoir storage forecasts averaged over 20002003, using unadjusted and MODIS adjusted initial conditions.
33
Table 1: Number of missing MODIS images in the context of total number of update
days: Feb 24 - Jun 1 for 2000, Jan 1 - Jun 1 for 2001 - 2003, January 1 - May 1 for 2004.
Year
2000
2001
2002
2003
2004
Number of missing images Total Number of Update Days
29
99
3
152
15
152
4
152
4
122
34
Table 2: Sequence of Actions for MODIS Updating.
MODIS
VIC
SF >= 0.50
SWE = 0
SF < 0.50
SWE > 0
SF >= 0.5
SF < 0.5
SWE > 0
SWE = 0
CF < 0.50 and NDF < 0.50
CF >=0.50 or NDF >=0.50
CF: Cloud Fraction within Elevation Band
NDF: No Decision Fraction within Elevation Band
SF: Snow Fraction within Elevation Band
Action
last_snow = 1
coverage = 1
swq = 0.005
density = 250
coverage = 0
swe = 0
density = 0
no update
no update
no update
35
Table 3: Characteristics of select forecast locations. Arrow Rock Ranch reservoir storage
includes Arrow Rock Ranch reservoir and Lucky Peak reservoir.
Location
Jackson Lake
Palisades
Island Park
Ririe
American Falls
Anderson Ranch
Arrow Rock Ranch
Dworshak
Forecast Location
Elevation (m)
2050
1632
1900
1506
1293
1167
881
296
Subbasin Area
(km2)
2090
13533
1246
1624
35224
2543
5750
24786
Mean Annual Flow
(m3/s)
41.9
185
18.4
4.12
215.4
27.8
65.4
140
Total Reservoir
Storage (BCM)
1.04
1.73
0.17
0.10
2.06
2.1
0.72
4.3
36
McGuire and Lettenmaier, Figure 1
37
McGuire and Lettenmaier, Figure 2
38
McGuire and Lettenmaier, Figure 3
00-03 Basin Average SWE (mm)
39
500
(a)
400
300
200
100
0
Oct
00-04 Avg. Naturalized Flow (cms)
700
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
(b)
600
500
400
300
200
100
0
Oct
AMERI
JLAKE
PALIS
IPARK
RIRIE
AROCK
ANDRA
DWORS
McGuire and Lettenmaier, Figure 4
40
McGuire and Lettenmaier, Figure 5
41
McGuire and Lettenmaier, Figure 6
42
McGuire and Lettenmaier, Figure 7
Oct-87
Jun-88
Jun-90
Feb-90
Oct-89
Jun-89
Feb-89
IPARK
Oct-88
Oct-78
Oct-76
Oct-74
Oct-72
Oct-70
AMERI
Feb-88
0
Oct-68
500
Jun-87
1000
Oct-66
1500
Storage (MCM)
2000
Feb-87
200
180
160
140
120
100
80
60
40
20
0
Storage (MCM)
Oct-78
Oct-76
Oct-74
Oct-72
Oct-70
Oct-68
Oct-66
Storage (MCM)
2500
Oct-86
Oct-78
Oct-76
Oct-74
Oct-72
Oct-70
Oct-68
Oct-66
Storage (MCM)
43
800
700
AROCK
600
500
400
300
200
100
0
4500
4000
3500
3000
2500
2000
1500
1000
500
0
DWORS
McGuire and Lettenmaier, Figure 8
44
McGuire and Lettenmaier, Figure 9