Quantifying the impact of model inaccuracy in climate change impact assessment studies using an agro-hydrological model

Numerical simulation models are frequently ap- plied to assess the impact of climate change on hydrology and agriculture. A common hypothesis is that unavoidable model errors are reflected in the reference situation as well as in the climate change situation so that by comparing ref- erence to scenario model errors will level out. For a polder in The Netherlands an innovative procedure has been intro- duced, referred to as the Model-Scenario-Ratio (MSR), to express model inaccuracy on climate change impact assess- ment studies based on simulation models comparing a ref- erence situation to a climate change situation. The SWAP (Soil Water Atmosphere Plant) model was used for the case study and the reference situation was compared to two cli- mate change scenarios. MSR values close to 1, indicating that impact assessment is mainly a function of the scenario itself rather than of the quality of the model, were found for most indicators evaluated. A climate change scenario with enhanced drought conditions and indicators based on thresh- old values showed lower MSR values, indicating that model accuracy is an important component of the climate change impact assessment. It was concluded that the MSR approach can be applied easily and will lead to more robust impact as- sessment analyses.

[1]  J. Wolf,et al.  WOFOST: a simulation model of crop production. , 1989 .

[2]  Keith Beven,et al.  Communicating Uncertainty as Multiple Competing Hypotheses , 2005 .

[3]  양민선.  IPCC(Intergovernmental Panel on climate Change) 외 , 2008 .

[4]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[5]  P. Mantovan,et al.  Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology , 2006 .

[6]  Wim G.M. Bastiaanssen,et al.  Twenty-five years modeling irrigated and drained soils: State of the art , 2007 .

[7]  J. Aerts,et al.  Climate Change in Contrasting River Basins: Adaptation Strategies for Water, Food and Environment , 2004 .

[8]  Henrik Madsen,et al.  Sensitivity of soil parameters in unsaturated zone modelling and the relation between effective, laboratory and in situ estimates , 2005 .

[9]  M. Heinen.  Simplified denitrification models : Overview and properties , 2006 .

[10]  R. Romanowicz.  Data based mechanistic model for low flows: Implications for the effects of climate change , 2007 .

[11]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[12]  Wim G.M. Bastiaanssen,et al.  Distributed agro-hydrological modeling of an irrigation system in western Turkey , 2000 .

[13]  Richard H. McCuen,et al.  The role of sensitivity analysis in hydrologic modeling , 1973 .

[14]  Helge Bormann.  Evaluation of hydrological models for scenario analyses: signal-to-noise-ratio between scenario effects and model uncertainty , 2005 .

[15]  Philip D. Meyer,et al.  Information on Hydrologic Conceptual Models, Parameters, Uncertainty Analysis, and Data Sources for Dose Assessments at Decommissioning Sites , 2000 .

[16]  Peter Droogers,et al.  Adaptation to climate change to enhance food security and preserve environmental quality: example for southern Sri Lanka , 2004 .

[17]  Alain Dassargues,et al.  Climate change impacts on groundwater resources: modelled deficits in a chalky aquifer, Geer basin, Belgium , 2004 .

[18]  J. Dutoit.  The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) , 2007 .

[19]  Wesley W. Wallender,et al.  Performance evaluation of methods for the estimation of soil hydraulic parameters and their suitability in a hydrologic model , 2006 .

[20]  R. Feddes,et al.  Simulation of field water use and crop yield , 1978 .

[21]  K. H. Hartge,et al.  Feddes, R. A., Kowalik, P. I. und Zaradny, H.: simulation of field water use and crop yield. Pudoc (Centre for agricultural publishing and documentation) Wageningen, Niederlande, 195 Seiten, 13 Abbildungen, Paperback. Preis: hfl 30,– , 1980 .

[22]  Bart van den Hurk,et al.  Climate Change Scenarios 2006 for the Netherlands , 2006 .

[23]  J. C. van Dam,et al.  Field-scale water flow and solute transport: SWAP model concepts, parameter estimation and case studies. , 2000 .

[24]  Hubert H. G. Savenije,et al.  Comparison of two model approaches in the Zambezi river basin with regard to model reliability and identifiability , 2005 .

[25]  D. Dokken,et al.  Climate change 2001 , 2001 .

[26]  W. Immerzeel.  Historical trends and future predictions of climate variability in the Brahmaputra basin , 2008 .

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

[28]  Isabelle Braud,et al.  Development and assessment of an efficient vadose zone module solving the 1D Richards' equation and including root extraction by plants , 2006 .

[29]  Joop G Kroes,et al.  Reference Manual SWAP; version 3.0.3 , 2003 .

[30]  Breanndán Ó Nualláin,et al.  Parameter optimisation and uncertainty assessment for large-scale streamflow simulation with the LISFLOOD model , 2007 .

[31]  J. Houghton,et al.  Climate change 2001 : the scientific basis , 2001 .

[32]  Gail G. Wilkerson,et al.  Potential individual versus simultaneous climate change effects on soybean (C 3 ) and maize (C 4 ) crops: An agrotechnology model based study , 2006 .

[33]  Keith Beven,et al.  Multi-period and multi-criteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework , 2007 .