Assimilating remotely sensed snow observations into a macroscale hydrologic model

1
Assimilating remotely sensed snow observations
into a macroscale hydrologic model Konstantinos
M. Andreadis1, Marketa McGuire2, and Dennis P.
Lettenmaier1 1. Department of Civil and
Environmental Engineering, Box 352700, University
of Washington, Seattle, WA 98195 2. Golder
Associates, Redmond WA Alfred T. C. Chang
Memorial Symposium NASA Goddard Space Flight
Center
Two additional experiments were conducted, to
assess the performance of a more sophisticated
data assimilation system in the prediction of
SWE. The first one involved the assimilation of
MODIS SCE data into VIC, using the enKF and a
snow depletion curve (SDC) model as the
observation operator H. The simulation period was
2000-2003, and the study area was the same as for
the direct insertion experiment. The enKF
successfully updated model-predicted SCE, and
also updated SWE in a consistent fashion with the
MODIS observations (Figure 6). Filter predicted
SWE values were validated against surface
observations from the SNOTEL station network (66
available stations). To account for scale
differences between areal estimates and point
measurements, SWE values from both SNOTEL and VIC
were expressed as percentiles of their respective
climatology (20 year dataset). The enKF improved
the RMSE between VIC and SNOTEL at about half of
the stations, but performed worse for the rest of
the stations. An interesting feature, was that
the overall performance of the enKF was better
during snowmelt and worse during accumulation
(Figure 7). This is happening because of
simplifications in the assumptions about the
error and SDC models, and the non continuity (0
to 1) of SCE, i.e. when both model-predicted and
observation values show full coverage the
assimilation has no effect, and some ensemble
members produce erroneous SWE estimates. The
second experimental option involved the
assimilation of AMSR-E SWE data for the winter of
2004. A cut-off SWE value of 240 mm was used to
The hydrologic model used in this study is the
Variable Infiltration Capacity (VIC) model (Liang
et al. 1994). Essentially the model solves a
water and energy balance over a grid mesh. VIC
accounts for subgrid variability in topography
and land cover by representing each grid cell as
a number of subgrid tiles of a certain land cover
type and elevation zone (Figure 2). SCE is
represented indirectly, by assuming that a tile
is fully covered if any snow is present. Thus,
SCE is just the area-weighted sum of all
snow-covered tiles.
Snowpack dynamics are modeled using a two-layer
energy and mass balance model (Figure 3). The
upper layer solves the energy balance between the
snowpack and the atmosphere, while the lower
layer acts as storage of excess snow and
simulates deeper snowpacks. Other processes
accounted for include snow densification and
interception (Cherkauer and Lettenmaier, 2003).
Snow plays a key role in the hydrologic cycle
over large areas of the mid latitudes, through
its effects on water storage and surface albedo.
In the western United States snowmelt accounts
for about 75 of the annual runoff. Consequently,
accurate estimation and monitoring of snow
properties, such as snow coverage and water
equivalent, have important implications for water
resources management. Surface measurements are
too sparse, in both time and space, for
observation of snow properties over large areas.
For this reason, large scale observation
strategies rely heavily on remote sensing.
Operational maps of both snow cover extent (SCE)
and snow water equivalent (SWE) have been
produced from various satellite instruments. SCE
is usually mapped using visible wavelength
sensors such as the AVHRR, while SWE can be
observed from the passive microwave brightness
temperature of the snowpack. However, both types
of sensors have limitations visible wavelength
sensors require cloud-free conditions and also
lack any information about snow water storage. On
the other hand, retrieval of snow parameters from
passive microwave sensors is hindered by snow
metamorphism, and presence of wet snow, among
others. Additional information about snow
properties can be obtained from land surface
hydrology models that are forced with
meteorologic variables, and represent the effects
of topography and land cover on snow
accumulation/ablation. Nonetheless, this
information is imperfect because of uncertainties
in forcing data, and model biases. Ideally, a
system that optimally combines snow information
from both remote sensing and modeling predictions
and at the same time accounts for the limitations
of each should provide estimates that are
superior to those derived from either models or
remote sensing alone. This method is commonly
known as data assimilation.
Retrospective streamflow forecasts were produced
from the VIC model in the Snake River basin,
utilizing MODIS SCE imagery to update model snow
cover. The updating occurred for all days (prior
to the forecast data) when MODIS images were
available and the cloud cover fraction was less
than 50. Direct insertion was used as the
updating procedure for SCE, while an arbitrary
addition of 5 mm of SWE was necessary for the
cases of VIC-MODIS disagreements. In addition to
the retrospective analysis, near-real time
forecasts were produced for four dates in winter
2004, using MODIS SCE updating.
account for the snowpack saturation effect on its
microwave emission. A normally distributed error
with zero mean and 20 standard deviation was
assumed for this study. The same procedure with
the MODIS assimilation was used for validation of
the simulated SWE values. The assimilation
actually improved the percentile RMSE for 32 (out
of 66 available) SNOTEL stations. although the
average RMSEs for both simulations were
comparable. Further insight can be obtained by
looking at the time series at a specific station
(Figure 8). The RMSEs were 0.145 and 0.236 for
VIC and the enKF respectively. We can see in the
figure, that AMSR-E SWE estimates have a large
error when compared to SNOTEL. In addition,
simplifications about the observation error,
hinder the enKF performance at a large extent. An
interesting aspect of the impact of assimilating
the AMSR-E SWE data can be seen in Figure 9,
which shows the percentile RMSE between the two
simulations and SNOTEL as a function of the peak
station SWE. While the enKF shows a slight
improvement for shallow snowpacks, it produces a
higher RMSE for deeper snowpacks. AMSR-E tends to
underestimate SWE for these, and when the
predicted value is larger than 240 mm (that is,
no updates are happening), SWE is simulated based
on model physics only and the initial value at
the last update. Nonetheless, this has been a
preliminary assessment of the value of AMSR-E
assimilation, and we believe that further
research, with a focus on better modeling of the
AMSR-E errors, is required.
In general, 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).
.Figure 4 shows the mean absolute error of
two-week lead-time streamflow forecasts, averaged
over 2000-2003, and the respective real-time
values, using unadjusted and MODIS adjusted
initial conditions. Forecasts of runoff volume
from the forecast date through July, indicate
smaller improvements in streamflow prediction
using MODIS for seasonal forecasts, than the
two-week forecasts.
Figure 5 shows the mean absolute errors of
streamflow forecast volume. Performance
improvements were more apparent in forecasts
produced for May. Accumulation of snow after the
forecast date could be the cause of poor results
in the early season forecasts. In general, MODIS
updated initial conditions appear to have a
greater impact on shorter lead time forecasts at
forecast dates within the snow ablation period.
Finally, regarding reservoir storage, 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 updates.
Forecast Step
Forecast Step
Analysis Step
Figure 8. SWE percentile time series for Jackson
Peak station (2121 m elevation).
Figure 9. VIC and enKF SWE percentile RMSE as a
function of peak SNOTEL SWE for the winter of
2004.
Time
Figure 1. Schematic representation of the
ensemble Kalman filter.