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Ensemble Forecasting through Evolutionary Computing and Data Assimilation: Application to Environmental Sciences pp. 135-160 $100.00
Authors:  (M. Kashif Gill, Mark Wigmosta, Andre Coleman, Lance Vail, WindLogics, Inc, St. Paul, Minnesota, and others)
Abstract:
A distributed modeling system for short-term to seasonal streamflow forecasts with
the ability to utilize daily remotely-sensed snow cover products and real-time streamflow
and meteorology measurements is presented herein. The modeling framework employs
the state-of-the-art data assimilation and evolutionary computing strategies to accurately
forecast environmental variables i.e., streamflow. Spatial variability in watershed
characteristics and meteorology is represented using a raster-based computational grid.
Snow accumulation and melt, simplified soil water movement, and evapotranspiration are
simulated in each computational unit. The model is run at a daily time-step with surface
runoff and subsurface flow aggregated at the watershed scale. The model is
parameterized using a multi-objective evolutionary computing scheme using Swarm
Intelligence. This approach allows the model to be updated with spatial snow water
equivalent from National Operational Hydrologic Remote Sensing Center’s (NOHRSC)
Snow Data Assimilation (SNODAS) and observed streamflow using an ensemble
Kalman-based data assimilation strategy that accounts for uncertainty in weather
forecasts, model parameters, and observations used for updating. The daily model inflow
forecasts for the Dworshak Reservoir in north-central Idaho are compared to
observations. The April-July volumetric forecasts issued by the U.S. Army Corps of
Engineers (USACE) for Water Years 2000 – 2007 are also compared with model
forecasts. October 1 and March 1 volumetric forecasts are comparable to those issued by
the USACE’s regression based method. An improvement in March 1 forecasts is shown
by pruning the initial ensemble set based on their similarity with the observed
meteorology. The short-term (one-, three-, and seven-day) forecasts using Kalmanassimilation
of streamflow show excellent agreement with observations. The scheme
shows great potential for the use of data assimilation in modeling streamflow and other
environmental variables. 


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Ensemble Forecasting through Evolutionary Computing and Data Assimilation: Application to Environmental Sciences pp. 135-160