Sami Andrew Malek

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2019-101

June 14, 2019

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-101.pdf

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.

Advisors: Alexandre Bayen

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BibTeX citation:

@mastersthesis{Malek:EECS-2019-101,
    Author= {Malek, Sami Andrew},
    Title= {Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter},
    School= {EECS Department, University of California, Berkeley},
    Year= {2019},
    Month= {Jun},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-101.html},
    Number= {UCB/EECS-2019-101},
    Abstract= {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.},
}

EndNote citation:

%0 Thesis
%A Malek, Sami Andrew 
%T Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter
%I EECS Department, University of California, Berkeley
%D 2019
%8 June 14
%@ UCB/EECS-2019-101
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-101.html
%F Malek:EECS-2019-101