Data assimilation framework: Linking an open data assimilation library (OpenDA) to a widely adopted model interface (OpenMI)

Data assimilation optimally merges model forecasts with observations taking into account both model and observational uncertainty. This paper presents a new data assimilation framework that enables the many Open Model Interface (OpenMI) 2.0 .NET compliant hydrological models already available, access to a robust data assimilation library. OpenMI is an open standard that allows models to exchange data during runtime, thus transforming a complex numerical model to a ‘plug and play’ like component. OpenDA is an open interface standard for a set of tools, filters, and numerical techniques to quickly implement data assimilation. The OpenDA–OpenMI framework is presented and tested on a synthetic case that highlights the potential of this new framework. MIKE SHE, a distributed and integrated hydrological model is used to assimilate hydraulic head in a catchment in Denmark. The simulated head over the entire domain were significantly improved by using an ensemble based Kalman filter.

[1]  Jeffrey L. Anderson,et al.  The Data Assimilation Research Testbed: A Community Facility , 2009 .

[2]  H. Moradkhani.  Hydrologic Remote Sensing and Land Surface Data Assimilation , 2008, Sensors.

[3]  Joseph H. A. Guillaume,et al.  Characterising performance of environmental models , 2013, Environ. Model. Softw..

[4]  Harrie-Jan Hendricks Franssen,et al.  Operational real‐time modeling with ensemble Kalman filter of variably saturated subsurface flow including stream‐aquifer interaction and parameter updating , 2011 .

[5]  Matthew F. McCabe,et al.  Model parameter analysis using remotely sensed pattern information in a multi-constraint framework , 2011 .

[6]  K. Abbaspour,et al.  Modeling blue and green water availability in Africa , 2008 .

[7]  Mario Putti,et al.  Ensemble Kalman filter data assimilation for a process‐based catchment scale model of surface and subsurface flow , 2009 .

[8]  M. Verlaan,et al.  COSTA a problem solving environment for data assimilation applied for hydrodynamical modelling , 2007 .

[9]  Sander Janssen,et al.  Evaluating OpenMI as a model integration platform across disciplines , 2013, Environ. Model. Softw..

[10]  Henrik Madsen,et al.  Calibrating a soil–vegetation–atmosphere transfer model with remote sensing estimates of surface temperature and soil surface moisture in a semi arid environment , 2012 .

[11]  Kuolin Hsu,et al.  Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter , 2005 .

[12]  Jens Christian Refsgaard,et al.  Validation and Intercomparison of Different Updating Procedures for Real-Time Forecasting , 1997 .

[13]  Van Genuchten,et al.  A closed-form equation for predicting the hydraulic conductivity of unsaturated soils , 1980 .

[14]  Roger Moore,et al.  An overview of the open modelling interface and environment (the OpenMI) , 2005 .

[15]  G. Blöschl,et al.  Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting , 2008 .

[16]  Merete Styczen,et al.  Modelling of N-movements on catchment scale - a tool for analysis and decision making , 1993, Fertilizer research.

[17]  Henrik Madsen,et al.  Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling , 2008 .

[18]  J. Refsgaard,et al.  Large scale modelling of groundwater contamination from nitrate leaching , 1999 .

[19]  Pavel Sakov,et al.  Relation between two common localisation methods for the EnKF , 2011 .

[20]  P. Oke,et al.  Implications of the Form of the Ensemble Transformation in the Ensemble Square Root Filters , 2008 .

[21]  Henrik Madsen,et al.  Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives , 2003 .

[22]  Jeffrey L. Anderson.  An Ensemble Adjustment Kalman Filter for Data Assimilation , 2001 .

[23]  M. Butts,et al.  Flexible Integrated Watershed Modeling with MIKE SHE , 2005 .

[24]  Gennadii Donchyts,et al.  From OpenMI 1.4 to 2.0 , 2010 .

[25]  J. Whitaker,et al.  Ensemble Data Assimilation without Perturbed Observations , 2002 .

[26]  Merete Styczen,et al.  Modelling of N-movements on catchment scale - a tool for analysis and decision making , 2004, Fertilizer research.

[27]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[28]  J. Refsgaard.  Parameterisation, calibration and validation of distributed hydrological models , 1997 .

[29]  J. Whitaker,et al.  Ensemble Square Root Filters , 2003, Statistical Methods for Climate Scientists.

[30]  Arnold W. Heemink,et al.  Data Assimilation Algorithms for Numerical Models , 2009 .

[31]  Anthony M. Castronova,et al.  Integrated modeling within a Hydrologic Information System: An OpenMI based approach , 2013, Environ. Model. Softw..

[32]  K. Kristensen,et al.  A MODEL FOR ESTIMATING ACTUAL EVAPOTRANSPIRATION FROM POTENTIAL EVAPOTRANSPIRATION , 1975 .

[33]  Lars Nerger,et al.  Software for ensemble-based data assimilation systems - Implementation strategies and scalability , 2013, Comput. Geosci..

[34]  Peter Jan,et al.  Particle Filtering in Geophysical Systems , 2009 .

[35]  W. Kinzelbach,et al.  Real‐time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem , 2008 .

[36]  Herman Gerritsen,et al.  Application of generic data assimilation tools (DATools) for flood forecasting purposes , 2010, Comput. Geosci..

[37]  Seong Jin Noh,et al.  Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities , 2012 .

[38]  Soroosh Sorooshian,et al.  Dual state-parameter estimation of hydrological models using ensemble Kalman filter , 2005 .

[39]  Henrik Madsen,et al.  Adaptive state updating in real-time river flow forecasting—a combined filtering and error forecasting procedure , 2005 .

[40]  Nils van Velzen,et al.  A problem-solving environment for data assimilation in air quality modelling , 2010, Environ. Model. Softw..

[41]  J. Whitaker,et al.  Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter , 2001 .

[42]  Bruno De Fraine,et al.  OpenMI-based integrated sediment transport modelling of the river Zenne, Belgium , 2013, Environ. Model. Softw..

[43]  Michiel Blind,et al.  OpenMI: the essential concepts and their implications for legacy software , 2005 .

[44]  J. B. Gregersen,et al.  OpenMI: Open modelling interface , 2007 .