The Moderate Resolution Imaging Spectroradiometer (MODIS) is a multiband satellite sensor whose 500 m spatial resolution and daily temporal resolution provide near-ideal conditions for hydrologic applications. Our second approach will base regression

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Statistical Applications of Physical Hydrologic
Models and Satellite Snow Cover Observations to
Seasonal Water Supply Forecasts Eric
Rosenberg1, Qiuhong Tang1, Andrew W. Wood2, Anne
C. Steinemann1, and Dennis P. Lettenmaier1 1Depar
tment of Civil and Environmental Engineering,
University of Washington, Seattle, WA 98195
23Tier Incorporated, Seattle, WA
Satellite Snow Cover Observations
Project Overview
Regression Analysis
Despite research demonstrating the value of
physically based hydrologic models and satellite
observations of snow covered area (SCA) for water
supply forecasts, these tools remain
underutilized by operational agencies. Part of
the reason for this disparity lies in the
contrast between experimental forecasting
techniques, which tend to employ methods such as
ensemble streamflow prediction, and
operational
forecasting systems that rely on
simpler methods like statistical
regression to link surface
observations of snow water
equivalent (SWE) to seasonal runoff
volumes. We explore methods
that bridge this gap via a hybrid
approach which uses model-simulated
snow states and raw satellite data as
predictors in regression models that are

adapted to the operational
environment.

In addition to SWE, other predictors included in
DWRs runoff forecasts are precipitation and
prior water year runoff. Our model-based
regression forecasts were compared with DWRs
ground-based regression forecasts, both trained
on data from 1956-2005. The skill of each method
is compared by plotting the 10th and 90th
percentiles of the resulting residuals (black for
ground-based blue, green, or red for
model-based) in the so called funnel plots
below. Mean April-July flows at the yellow
forecast points to the left are provided in
parentheses.
The Moderate Resolution Imaging Spectroradiometer
(MODIS) is a multiband satellite sensor whose 500
m spatial resolution and daily temporal
resolution provide near-ideal conditions for
hydrologic applications. Our second approach will
base regression models on SWE and SCA output from
a VIC model that has been updated with snow cover
data from MODIS. Our third approach will employ
MODIS SCA data directly as predictors in the
regression models. Two challenges inherent in
using MODIS data involve its limited record (2000
to the present) and gaps in coverage caused by
cloud cover. To address the first problem, we
will attempt to extend the MODIS record with data
from the 1 km Advanced Very High Resolution
Radiometer, the 1 km SCA product from the
National Operational Hydrologic Remote Sensing
Center, and retrospective VIC simulations. The
second challenge will be addressed using the
MODSCAG product (Dozier et al. 2008), which also
provides advantages offered by fractional snow
cover data.
American (1240 taf)
U. Sacramento (2494 taf)
Yuba (1005 taf)
Feather (1781 taf)
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The 14 watersheds of the Sacramento (blue),
San Joaquin (green), and Tulare Lake (red)
hydrologic regions, which form our study areas
for the project. Together, the 3 regions are
responsible for roughly 60 of the states
runoff. Yellow circles represent runoff
forecast points for Californias Department of
Water Resources (DWR). Watersheds with both
light and dark colors are divided by DWR into
areas of high and low elevation for purposes of
snow measurement.
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Residual ( of mean annual flow)
Stanislaus (702 taf)
Cosumnes (126 taf)
Mokelumne (460 taf)
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MODIS image from April 7, 2006. Snow is
shown in white and clouds in cyan. MODSCAG
(Dozier et al. 2008) will be used to estimate
fractional snow cover and eliminate data gaps due
to clouds.
Merced (632 taf)
San Joaquin (1254 taf)
Tuolomne (1220 taf)
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The Terra satellite (top), the first to carry the
MODIS sensor, was launched in Dec 1999. The Aqua
satellite (middle), the second to carry the MODIS
sensor, was launched in May 2002.
Model-Simulated Snow Water Equivalent
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DWRs forecasts for April-July runoff rely on
manual measurements of SWE at various snow
courses throughout each watershed (left, for the
Feather). In our first approach, we develop new
regression equations based on SWE simulated by
the Variable Infiltration Capacity (VIC)
hydrologic model at points spaced
1/8 apart (right).
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Acknowledgements
Kaweah (286 taf)
Tule (64 taf)
Kern (461 taf)
Kings (1224 taf)
Residual ( of mean annual flow)
We are grateful to the Division of Flood
Management at DWR, and in particular, Adam
Schneider and David Rizzardo, for their
invaluable assistance. Funding has been provided
by NOAA and NASA.
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References
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Dozier J, Painter TH, Rittger K, and Frew JE.
2008. Time-space continuity of daily maps of
fractional snow cover and albedo from MODIS.
Advances in Water Resources. 31(11) 1515-1526.