Objective quantitative spatial verification of distributed snow cover simulations—an experiment for the whole of Switzerland / Vérification quantitative spatiale objective de simulations distribuées de la couche de neige—une étude pour l'ensemble de la Suisse

Abstract Skill measures based on 2 × 2 contingency tables were adopted for the quantitative internal verification of snow cover simulations with the distributed hydrological model PREVAH, which provided a high resolution simulation of the hydrological cycle for Switzerland for the 1981–2000 period. Simulated snow cover has been compared to data monitored at 103 stations. The skill measures provide valuable quantitative indications on the correspondence of the modelled and observed values. The analysis with objective scores reveals better model estimates of snow cover presence and distribution in locations above 1000 m a.s.l., relative to lower areas. For explicit spatial verification, 20 NOAA-AVHRR snow cover scenes were compared to the model results. The spatial and temporal differences in the agreement between observed and simulated snow cover patterns were assessed. PREVAH provides reliable snow cover simulations. The results also reveal that scores from 2 × 2 contingency tables provide objective methodological support in the quantitative estimation of the agreement between observed and simulated spatial patterns.

[1]  Massimiliano Zappa,et al.  Multiple-response verification of a distributed hydrologial model at different spatial scales , 2002 .

[2]  A. H. Murphy,et al.  Skill Scores and Correlation Coefficients in Model Verification , 1989 .

[3]  P. Bartelt,et al.  A snowdrift index based on SNOWPACK model calculations , 2000, Annals of Glaciology.

[4]  H. Storch,et al.  Statistical Analysis in Climate Research , 2000 .

[5]  P. Heidke,et al.  Berechnung Des Erfolges Und Der Güte Der Windstärkevorhersagen Im Sturmwarnungsdienst , 1926 .

[6]  K. Jasper,et al.  Coupled runoff simulations as validation tools for atmospheric models at the regional scale , 2003 .

[7]  Günter Blöschl,et al.  Spatial Patterns of Catchment Hydrology: Observations and Modelling , 2000 .

[8]  G. Blöschl,et al.  Distributed Snowmelt Simulations in an Alpine Catchment: 1. Model Evaluation on the Basis of Snow Cover Patterns , 1991 .

[9]  M. Rohrer,et al.  Long-Term Records of Snow Cover Water Equivalent in the Swiss Alps , 1994 .

[10]  M. Wigmosta,et al.  A distributed hydrology-vegetation model for complex terrain , 1994 .

[11]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[12]  M. Zappa,et al.  Extreme heat and runoff extremes in the Swiss Alps , 2007 .

[13]  Massimiliano Zappa,et al.  Verification of a coupled hydrometeorological modelling approach for alpine tributaries in the Rhine basin , 2006 .

[14]  R. Freeze,et al.  Blueprint for a physically-based, digitally-simulated hydrologic response model , 1969 .

[15]  M. Zappa,et al.  Simulation of soil moisture and evapotranspiration in a soil profile during the 1999 MAP-Riviera Campaign , 2003 .

[16]  Tomas Vitvar,et al.  A comparative study in modelling runoff and its components in two mountainous catchments , 2003 .

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

[18]  E. J. Klok,et al.  Distributed hydrological modelling of a heavily glaciated Alpine river basin , 2001 .

[19]  Günter Blöschl,et al.  Entering the Era of Distributed Snow Models , 1994 .

[20]  A. Baltensweiler,et al.  Spatially distributed hydrotope-based modelling of evapotranspiration and runoff in mountainous basins , 1999 .

[21]  C. Doswell,et al.  On Summary Measures of Skill in Rare Event Forecasting Based on Contingency Tables , 1990 .

[22]  Stefan Uhlenbrook,et al.  On the value of experimental data to reduce the prediction uncertainty of a process-oriented catchment model , 2005, Environ. Model. Softw..

[23]  M. Pfaundler,et al.  Die mittleren Abflüsse über die ganze Schweiz. Ein optimierter Datensatz im 500x500-m-Raster , 2006 .

[24]  Günter Blöschl,et al.  Regionalisation of catchment model parameters , 2004 .

[25]  Rodger B. Grayson,et al.  Quantitative comparison of spatial fields for hydrological model assessment––some promising approaches , 2005 .

[26]  Jeffrey J. McDonnell,et al.  On the dialog between experimentalist and modeler in catchment hydrology: Use of soft data for multicriteria model calibration , 2002 .

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

[28]  T. Casey,et al.  Verification of Categorical Probability Forecasts , 2000 .

[29]  M. Baumgartner,et al.  Towards an integrated geographic analysis system with remote sensing, GIS and consecutive modelling for snow cover monitoring , 1994 .

[30]  Günter Blöschl,et al.  Scaling issues in snow hydrology , 1999 .

[31]  Keith Beven,et al.  Dalton Medal Lecture: How far can we go in distributed hydrological modelling? , 2001 .

[32]  T. Schmugge,et al.  Remote sensing in hydrology , 2002 .

[33]  Fawwaz T. Ulaby,et al.  The active and passive microwave response to snow parameters: 1. Wetness , 1980 .

[34]  J. Schaefer.  The critical success index as an indicator of Warning skill , 1990 .

[35]  W. Mauser,et al.  Modelling the spatial and temporal variations of the water balance for the Weser catchment 1965–1994 , 2001 .

[36]  Günter Blöschl,et al.  Advances in the use of observed spatial patterns of catchment hydrological response , 2002 .

[37]  Jeff Dozier,et al.  Estimating the spatial distribution of snow in mountain basins using remote sensing and energy balance modeling , 1998 .

[38]  Massimiliano Zappa,et al.  Seasonal Water Balance of an Alpine Catchment as Evaluated by Different Methods for Spatially Distributed Snowmelt Modelling , 2003 .

[39]  Alex Hagen,et al.  Fuzzy set approach to assessing similarity of categorical maps , 2003, Int. J. Geogr. Inf. Sci..