  Accurate runoff forecasting is crucial for reservoir operators as it allows
optimized water management, flood control and hydropower generation. Land
surface models in mountainous regions depend on climatic inputs such as
precipitation, temperature and solar radiation to model the water and energy
dynamics and produce runoff as output. With the rapid development of cheap
electronics applied in various systems, such as Wireless Sensor Networks
(WSNs), satellite and airborne technologies, the prospect of practically
measuring spatial Snow Water Equivalent in a dense temporal scale is
increasing. We present a framework for updating the Precipitation Runoff
Modeling System (PRMS) with Snow Water Equivalent (SWE) maps and runoff
measurements on a daily timescale based on the Ensemble Kalman Filter (ENKF).
Results show that by assimilating SWE daily, the modeled SWE gets updated
accordingly, however no improvement is observed at the runoff model output.
Instead, a deterioration consistently occurs. Augmenting the state space with
model parameters and runoff model output allows for filter update with previous
day measured runoff using the joint state-parameter method, and showed a
considerable improvement in the daily runoff output of up to 60% reduction in
RMSE for the wet water year 2011 relative to the no assimilation scenario, and
improvement of up to 28% compared to a naive autoregressive AR(1) filter.
Additional simulation years showed consistent improvement compared to no
assimilation, but varied relative to the previous day autoregressive forecast
during the dry year 2014.
