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Accurate runoff forecasting is crucial for reservoir operators as it allowsoptimized water management, flood control and hydropower generation. Landsurface models in mountainous regions depend on climatic inputs such asprecipitation, temperature and solar radiation to model the water and energydynamics and produce runoff as output. With the rapid development of cheapelectronics applied in various systems, such as Wireless Sensor Networks(WSNs), satellite and airborne technologies, the prospect of practicallymeasuring spatial Snow Water Equivalent in a dense temporal scale isincreasing. We present a framework for updating the Precipitation RunoffModeling System (PRMS) with Snow Water Equivalent (SWE) maps and runoffmeasurements on a daily timescale based on the Ensemble Kalman Filter (ENKF).Results show that by assimilating SWE daily, the modeled SWE gets updatedaccordingly, however no improvement is observed at the runoff model output.Instead, a deterioration consistently occurs. Augmenting the state space withmodel parameters and runoff model output allows for filter update with previousday measured runoff using the joint state-parameter method, and showed aconsiderable improvement in the daily runoff output of up to 60% reduction inRMSE for the wet water year 2011 relative to the no assimilation scenario, andimprovement of up to 28% compared to a naive autoregressive AR(1) filter.Additional simulation years showed consistent improvement compared to noassimilation, but varied relative to the previous day autoregressive forecastduring the dry year 2014.

Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter