Abstract. Remotely sensed snow cover observations provide an
opportunity to improve operational snowmelt and streamflow forecasting in
remote regions. This is particularly true in Alaska, where remote basins and
a spatially and temporally sparse gaging network plague efforts to
understand and forecast the hydrology of subarctic boreal basins and where
climate change is leading to rapid shifts in basin function. In this study,
the operational framework employed by the United States (US) National
Weather Service, including the Alaska Pacific River Forecast Center, is
adapted to integrate Moderate Resolution Imaging Spectroradiometer (MODIS)
remotely sensed observations of fractional snow cover area (fSCA) to
determine if these data improve streamflow forecasts in interior Alaska
river basins. Two versions of MODIS fSCA are tested against a base case
extent of snow cover derived by aerial depletion curves: the MODIS 10A1
(MOD10A1) and the MODIS Snow Cover Area and Grain size (MODSCAG) product
over the period 2000–2010. Observed runoff is compared to simulated runoff
to calibrate both iterations of the model. MODIS-forced simulations have
improved snow depletion timing compared with snow telemetry sites in the
basins, with discernable increases in skill for the streamflow simulations.
The MODSCAG fSCA version provides moderate increases in skill but is similar
to the MOD10A1 results. The basins with the largest improvement in
streamflow simulations have the sparsest streamflow observations.
Considering the numerous low-quality gages (discontinuous, short, or
unreliable) and ungauged systems throughout the high-latitude regions of the
globe, this result is valuable and indicates the utility of the MODIS fSCA
data in these regions. Additionally, while improvements in predicted
discharge values are subtle, the snow model better represents the physical
conditions of the snowpack and therefore provides more robust simulations,
which are consistent with the US National Weather Service's move toward a
physically based National Water Model. Physically based models may also be
more capable of adapting to changing climates than statistical models
corrected to past regimes. This work provides direction for both the Alaska
Pacific River Forecast Center and other forecast centers across the US to
implement remote-sensing observations within their operational framework, to
refine the representation of snow, and to improve streamflow forecasting
skill in basins with few or poor-quality observations.