A software framework for construction of process-based stochastic spatio-temporal models and data assimilation |
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| Authors: | Derek Karssenberg Oliver Schmitz Peter Salamon Kor de Jong Marc F.P. Bierkens |
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| Affiliation: | 1. Department of Physical Geography, Faculty of Geosciences, Utrecht University, Heidelberglaan 2, PO Box 80115, 3508 TC, Utrecht, The Netherlands;2. Land Management and Natural Hazards Unit, Institute for Environment and Sustainability, DG Joint Research Centre, European Commission, Via Enrico Fermi 2749, TP 261, 21027 Ispra (Va), Italy;3. Deltares, Unit Soil and Groundwater, PO Box 3508 TA, Utrecht, The Netherlands;1. College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China;2. State Key Laboratory of Petroleum Resources and Prospecting, Beijing 102249, China;1. Department for Applied Mechanics and Automatic Control, Faculty of Engineering, University of Kragujevac, Sestre Janji? 6, 34000 Kragujevac, Serbia;2. Institute for Development of Water Resources “Jaroslav ?erni”, 80 Jaroslava ?ernog St., 11226 Beli Potok, Belgrade, Serbia;1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China;2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China;3. College of Resource and Environmental Sciences, Hebei Normal University, Shijiazhuang, 050024, China;4. Lancaster Environment Centre, Lancaster University, Bailrigg, Lancaster, LA1 4YQ, UK |
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| Abstract: | Process-based spatio-temporal models simulate changes over time using equations that represent real world processes. They are widely applied in geography and earth science. Software implementation of the model itself and integrating model results with observations through data assimilation are two important steps in the model development cycle. Unlike most software frameworks that provide tools for either implementation of the model or data assimilation, this paper describes a software framework that integrates both steps. The software framework includes generic operations on 2D map and 3D block data that can be combined in a Python script using a framework for time iterations and Monte Carlo simulation. In addition, the framework contains components for data assimilation with the Ensemble Kalman Filter and the Particle filter. Two case studies of distributed hydrological models show how the framework integrates model construction and data assimilation. |
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