I° Riunione Gruppo di Lavoro SISEF Modellistica Forestale
Workshop, Bologna, 18 Dicembre 2009
The GEOtop model: eco-hydrological applications at plot and catchment scale.
G. Bertoldi, S. Della Chiesa, S. Endrizzi, M. Dall’Amico, E. Cordano, S. Simoni, U.Tappeiner, R. Rigon
1. The GEOtop model: eco-hydrological applications at plot and catchment scale Institute for Alpine Environment G. Bertoldi, S. Della Chiesa, S. Endrizzi, M. Dall’Amico, E. Cordano, S. Simoni, U.Tappeiner, R. Rigon Università di Trento Dip. Ing. Civile ed Ambientale I° Riunione Gruppo di Lavoro SISEF Modellistica Forestale Workshop, Bologna, 18 Dicembre 2009 Università di Bologna, Facoltà di Agraria
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3. Geo TOP model overview (I) Plot scale: Soil Vegetation Atmosphere Transfer (SVAT) Rigon R., Bertoldi G. and Over T.M., GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets, Journal of Hydrometeorology , 7, 371-388, 2006. Soil column water and energy budget Figure adapted from (Liang, X. et al. 1994)
4. GEOtop model overview (II) Land cover / Vegetation Soil moisture Runoff production Topography Evapotranspiration Climate Catchment scale: distributed hydrological model Figure adapted from (Abbott, M. B. et al. 1986) Snow
5. Landscape scale ecohydrological modeling Geo TOP hydrological model Landscape scale : remote sensing Plot scale : ground observations
6. On-going: dynamic vegetation Plot scale : Integration of a DVM model to simulate grassland dynamic in alpine meadows In collaboration with: Georg Wohlfahrt (Institute of Ecology Univ. of Innsbruck) John D. Albertson (CEE, Duke University, USA)
7. Modeling surface fluxes : LST Landscape scale : Land Surface Temperature in an alpine catchment In collaboration with: Riccardo Rigon and his team (CUDAM, Univ. of Trento) Stefano Endrizzi (University of Saskatchewan, Canada) Claudia Notarnicola (EURAC Remote Sensing Institute)
8. LST: spatial distribution comparison GEOtop LST [ o C] R 2 = 0.88 LANDSAT LST [ o C] Model spatial patterns validation
9. LST: land cover controls Topographic and land cover controls on surface energy budget
10. Water cycle modeling Landscape scale : Modeling water cycle in the Matsch Valley Financed by: Province of Bolzano Research Found
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14. Evapotranspiration ET June 2003 Effect of aspect (N/S) Effect of elevation 1. topography / energy forcing Effect of topographic convergence (hollow) Effect of topographic divergence (dry ridge) 3. water distribution Effect of land cover (forest/grassland) 2. land cover 2 w/m 2 Low High 286 w/m 2
15. GEOtop is an Open Source collaborative projects and other are invited to bring into new components www.geotop.org
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Notes de l'éditeur
Ok, non mettere GeoTOP
Try to turn temperature map to lastest view
Dynamic Vegetation Modeling (DVM) in an Alpine Meadow Geo TOP model validation and development: Land Surface Temperature in an alpine catchment Modeling the water cycle in the Match Valley Stubai Valley : model validation and process understanding. Plot scale Dynamic Vegetation Model (DVM) validation: grassland dynamic in an alpine meadow. 2. Catchment scale hydrological GEOtop model validation: Land Surface Temperature (LST) in an alpine catchment. Match Valley : experimental activities and climate change impacts evaluation. 3. Catchment scale hydrological cycle: modeling the 2003 water in the Match Valley.
Dynamic Vegetation Modeling (DVM) in an Alpine Meadow Geo TOP model validation and development: Land Surface Temperature in an alpine catchment Modeling the water cycle in the Match Valley Stubai Valley : model validation and process understanding. Plot scale Dynamic Vegetation Model (DVM) validation: grassland dynamic in an alpine meadow. 2. Catchment scale hydrological GEOtop model validation: Land Surface Temperature (LST) in an alpine catchment. Match Valley : experimental activities and climate change impacts evaluation. 3. Catchment scale hydrological cycle: modeling the 2003 water in the Match Valley.
Change the research questions. Update maps with latest results. Add assumptions: Distributed models need distributed data to be validated. only plot scale observations are not enough. 2. LST is a quantity easily measurable trough remote sensing. 3. LST is a key (and very) variable of the energy budget and if the model is able to correctly reproduce observed LST it is likey that it is able to give a good estimation of ET
negative skewness
Try to turn temperature map to latest view
Validation of the hydrological model at the plot scale <-> field activities Development of grassland growth model for different types of grasslands managements <-> field activities Retrospective simulation at catchment scale <-> streamflow, remote sensing data Future scenarios simulation <-> land management and Regional climate model scenarios Identification of critical variations of key parameters: evapo transpiration, soil moisture, groundwater recharge, runoff production, plant water stress, grassland productivity <-> local scale impacts