Evolutionary assimilation of streamflow in distributed hydrologic modeling using in-situ soil moisture data |
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| Institution: | 1. School of Geography and Earth Sciences, and Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S4L8;2. Department of Civil Engineering, Monash University, Melbourne, Victoria 3800, Australia;1. Department of Hydrology and Hydraulic Engineering, Earth System Sciences Group, Vrije Universiteit Brussel (VUB), Brussels, Belgium;2. UNESCO-IHE Institute for Water Education, Core of Hydrology and Water Resources, The Netherlands;1. Department of Civil and Environmental Engineering, Colorado State University, Campus Delivery 1372, Fort Collins, CO, 80523-1372, USA;2. Department of Civil and Environmental Engineering, University of California Irvine, E4130 Engineering Gateway, Irvine, CA, 92697-2175, USA;3. USDA-ARS, Center for Agricultural Resources Research, 2150 Centre Ave, Fort Collins, CO, 80526-8119, USA;4. Department of Biological and Agricultural Engineering, North Carolina State University, Campus Box 7625, Raleigh, NC, 27695-7619, USA;1. State Key Joint Laboratory of Environmental Simulation and Pollution Control, China-Canada Center for Energy, Environment and Ecology Research, UR-BNU, School of Environment, Beijing Normal University, Beijing 100875, China;2. Environmental Systems Engineering Program, University of Regina, Regina, Saskatchewan S4S 0A2, Canada;3. Department of Civil and Environmental Engineering, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK;1. Centre d’applications et de recherches en télédétection (CARTEL), Département de géomatique appliquée, Université de Sherbrooke, Sherbrooke, Québec, Canada;2. Environment and Climate Change Canada, Saskatoon, Saskatchewan, Canada;3. Global Institute for Water Security, School of Environment and Sustainability, Saskatoon, Saskatchewan, Canada;1. LEN Technologies, Oak Hill, VA, USA;2. Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019-0308, USA;1. Computational Science Laboratory, Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060, USA;2. Department of Computer Science, Universidad del Norte, Barranquilla, Atlantico, Colombia |
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| Abstract: | This study has applied evolutionary algorithm to address the data assimilation problem in a distributed hydrological model. The evolutionary data assimilation (EDA) method uses multi-objective evolutionary strategy to continuously evolve ensemble of model states and parameter sets where it adaptively determines the model error and the penalty function for different assimilation time steps. The assimilation was determined by applying the penalty function to merge background information (i.e., model forecast) with perturbed observation data. The assimilation was based on updated estimates of the model state and its parameterizations, and was complemented by a continuous evolution of competitive solutions.The EDA was illustrated in an integrated assimilation approach to estimate model state using soil moisture, which in turn was incorporated into the soil and water assessment tool (SWAT) to assimilate streamflow. Soil moisture was independently assimilated to allow estimation of its model error, where the estimated model state was integrated into SWAT to determine background streamflow information before they are merged with perturbed observation data. Application of the EDA in Spencer Creek watershed in southern Ontario, Canada generates a time series of soil moisture and streamflow. Evaluation of soil moisture and streamflow assimilation results demonstrates the capability of the EDA to simultaneously estimate model state and parameterizations for real-time forecasting operations. The results show improvement in both streamflow and soil moisture estimates when compared to open-loop simulation, and a close matching between the background and the assimilation illustrates the forecasting performance of the EDA approach. |
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