GEOtop 2008

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It contains a description of the model GEOtop till version 0.9375

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GEOtop 2008

  1. 1. The rational behind GEOtop: through its historical development and applications R. Rigon Dipartimento di Ingegneria Civile ed Ambientale - CUDAM Università di Trento
  2. 2. Four or Five problems we wanted to investigate
  3. 3. Rainfall–Runoff spatial patterns Problem : We cannot currently predict the spatial pattern of watershed response to precipitation and cannot quantitatively describe the surface and subsurface contributions to streamflow with enough accuracy and consistency to be operationally useful. Critical issues: Watershed runoff and streamflow are affected by heterogeneity in soil hydraulic properties, landscape structural properties, soil moisture profile, surface–subsurface interaction, interception by plants, snowpack, and storm properties. (Committee of hydrological Sciences NRC, 2003)
  4. 4. Snow mantle evolution and ablation Problem: We would like to predict the spatial pattern of snow cover, its volumes and its effects on runoff with enough accuracy and consistency to be operationally useful. Critical Issue: Also in this case we know enough of the snow physics “in a point” but we do not have many tools to understand the snow cover effects on larger, catchment scales. Related problem: snow avalanches
  5. 5. Soil freezing and permafrost Problem: We would like to predict the spatial pattern of soil temperatures even in complex terrain and in presence of phase transitions Critical Issue: Soil freezing introduces high non linearities at low temperatures. Related problem: snow avalanches
  6. 6. Landslide and debris flow initiation Problem: We cannot currently predict, the triggering of shallow landslides which eventually turns into a debris or a mudflow. Critical Issue: Initial and boundary conditions. Landslide initiation is affected by heterogeneity in soil hydraulic and geotechnical properties, landscape structural and geological properties, soil moisture profile, surface– subsurface interactions.
  7. 7. We did not forget Ecohydrology ... but we will not discuss it here Problem: We wouldlike to better understand the interactions between soils, hydrology and plants. Critical Issues: the biological system are highly non linear. The basic physiological laws are not really known (or hydrologist ignore many of them)
  8. 8. Committee of hydrological Sciences NRC, 2003: “Although our understanding of individual processes is improving, the integration of that body of knowledge in spatially distributed predictive models has not been approached systematically”.
  9. 9. This talk is not about new equations or new paradigms: is mostly abot consistency of the modeling approach. It show a tool we use to lear about the hydrological processes at the small scales.
  10. 10. A small tribute to Stephan Gruber We present here the evolution of the GEOTOP models and discuss their limitation
  11. 11. The GEOtop Project
  12. 12. GEOtop 0.5 was the ancestor (1997) Paolo Verardo and Marco Pegoretti coded it
  13. 13. GEOtop 0.5 was the ancestor (1997) GIUH + Kinematic wave+ Bucket model Paolo Verardo and Marco Pegoretti coded it
  14. 14. GEOtop 0.5 was the ancestor (1997) Radiation Physics Penman-Monteith GIUH + Kinematic wave+ + Bucket model Paolo Verardo and Marco Pegoretti coded it
  15. 15. GEOtop 0.5 (1997) P Qsup ∂Qsup ∂Qsup + c(x) = c(x) qL I ∂t ∂s c ∝ y(x)m Qsub Qsub = T ∇z b Z Z x W (x, τ) x − u(t − τ)2 t L Qc(t) = exp − dτ dx 4DL(t − τ) 4πDL(t − τ) 0 o Eagleson, 1971; Beven and Kirkby, 1979; Rodriguez-Iturbe and Valdes, 1979; Rinaldo et al., 1991
  16. 16. GEOtop 0.5 (1997) ET Rn Δ/λ(Rn − G) + ρ/ra δqa ET = 1 + Δ/γ + rg /ra Rn = [sh R ↓SW + V R ↓SW D ] (1 −V α) + P V εs R ↓LW −V εs σTs4 G Brutsaert, 1975; Iqbal, 1983; Garrat, 1992, Enthekabi, 1997 and many others
  17. 17. GEOtop 0.5 (1997) ET Rn Δ/λ(Rn − G) + ρ/ra δqa ET = 1 + Δ/γ + rg /ra Rn = [sh R ↓SW + V R ↓SW D ] (1 −V α) + P V εs R ↓LW −V εs σTs4 G Brutsaert, 1975; Iqbal, 1983; Garrat, 1992, Enthekabi, 1997 and many others
  18. 18. Calculating ET in highly complex terrain needs a proper treatment of radiation physics (including the effect of the vie angle and the shadowing). Below you see how much this is a limit for radiation to arrive to the surface.
  19. 19. GEOtop 0.5 (1997) ET Rn Δ/λ(Rn − G) + ρ/ra δqa ET = 1 + Δ/γ + rg /ra Rn = [sh R ↓SW + V R ↓SW D ] (1 −V α) + P V εs R ↓LW −V εs σTs4 G
  20. 20. Albedo is a key factor too. It can be easily detected from Earth Observation (EO) products and simple modelling of the canopy evolution during the seasons (actually still not included in GEOTOP)
  21. 21. GEOtop 0.5 Real ET After Feddes et al, 1988
  22. 22. Real ET is obtained cutting the potential ET in dependence of water availability. In complex terrain water tend to accumulate in lowland concave - convergent sites. Many large scale hydrological models pretends to give ET estimation by neglecting this fact ;-)
  23. 23. GEOtop 0.5 worked well for flood predictions and weekly ET (after a proper parameter calibration). It also showed some dynamics on the soil moisture storage (dS/dt = 0 in some models!) and redistribution at catchment scale, HOWEVER .... It could not describe properly the vertical distribution of soil moisture in soils (essential to landslide forecasting and emissivity estimations). Moreover, using air T for soil T (Ts) was a huge limitation.
  24. 24. GEOtop 0.75 (2000) Radiation Physics Energy budget GIUH + integration Kinematic wave+ Bucket model Code integrations by Giacomo Bertoldi
  25. 25. GEOtop 0.75 is conceived to integrate the full energy balance. As a consequence Ts becomes a variable of the model (this obviuosly complicates GEOtop) but increases at the same the possibility to check its behavior (Ts or its radiative effect is a measurable quantity): we add complexity but at the same time we add observables. Ts is strongly affected by water content.
  26. 26. GEOtop 0.75 Energy Balance (2000) Rn Qp ET H dE/dt Qm G dE dTs = Cp = Rn − H − ET + Q p − G − Qm dt dt H = ρ c pCH u (T s − Ta) ˆ ET = λρ Ce u (q∗(Ta) − q∗(Ta) Ua) ˆ
  27. 27. GEOtop 0.75 (2000): Turbulent fluxes appear! Ts>Ta CH, CE ↑ CH, CE ↓ Ts<Ta Aerodynamic roughness length Similarity theory by Louis (1979)
  28. 28. Pointwise calibration of fluxes Little Washita (OK) SGP 97 data set Key parameters: roughness length, initial soil moisture
  29. 29. We did also simulation of the soil moisture content in the Washita basin. However the soil moisture content given by the model has no vertical distribution and any comparison with the SGP97 dataset CANNOT be very reliable. Below you see results of the model for other cases studies that show the opportunities that a model like GEOtop offers.
  30. 30. Mean seasonal ET at Serraia (TN) Spring Winter 84 96 108 120 W/m2 0 12 24 36 48 60 72 Fall Summer
  31. 31. Hydrological Balance 1998-2000 Serraia (TN) Grafico bilancio del bacino del Lago della Serraia (1998 - 2000) 1.200 363.4 1.100 333.1 1.000 302.9 Portata media mensile (mc/s) 0.900 272.6 Intensita (mm/mese) 0.800 242.3 0.700 212.0 0.600 181.7 0.500 151.4 0.400 121.1 0.300 90.9 0.200 60.6 0.100 30.3 0.000 0.0 -0.100 -30.3 -0.200 -60.6 ma 98 ma 99 ma 00 no 98 no 99 no 00 lug 8 ag 98 ott 8 lug 9 ag 99 ott 9 lug 0 ag 00 ott 0 ap 98 ap 99 ap 00 ma -98 ma -99 ma -00 giu 8 giu 9 giu 0 ge 98 ge 99 -0 0 feb 8 se 8 feb 9 se 9 feb 00 se 0 dic 8 dic 9 dic 0 -9 t-9 -9 t-9 -0 t-0 g-9 g-9 g-0 n-9 o-9 n-9 o-9 o-0 v-9 v-9 v-0 - - - - - - - - - r- r- r- - - n- r r r ge P - precipitazione ET - evapotraspirazione Tempo (mese-anno) Inv - volume invasato (accumulo) R - rilascio
  32. 32. One interesting thing to check would be the sensitivity of the hydrological balance partition to the parameter set.
  33. 33. Serraia
  34. 34. There is a strong spatial variability of vertical surface fluxes: do they induce feedback effects ? Are those processes negligible at larger scales ? A more accurate ABL model would be necessary to try an answer. We cannot compare our model result with ESTAR because we do not have vertical distribution of soil moisture. What happens when no topographic gradient is present ?
  35. 35. GEOtop 0.875 (2003) Radiation Physics Energy budget GIUH + integration Kinematic wave+ Richards + Soil freezing & Snow Cover Code integrations by Davide Tamanini
  36. 36. Richards’ equation is solved ∂ψ = ∇ · (K(ψ)∇(ψ + ηz)) + qs σ(ψ) ∂t 1/n 1/m θr − θs 1 ψ= −1 θs − θr α 2 m 1/m ν θr − θs θr − θs K(θ) = KS 1− 1− θs − θr θs − θr Horton Overland Flow Dunne Saturation Overland Flow Surface Layer Unsaturated Layer Saturated Layer: Modified from Abbot et al., 1986 Richards, 1931; van Genuchten, 1980; Mualem, 1976; Veerecken, 1990; Sposito, 1997, Putti et al, 2004
  37. 37. We acknowledge the SHE model however GEOtop REALLY integrate the energy balance. Still GEOtop is 1D for energy fluxes but it is a complete 3D system for mass fluxes. As one can notice we used van Genuchten and Mualem parametrizations of Richards equation. Under this hypothesis Sposito 1997 shows that the equation is almost scale invariant (at the price to introduce a suitable factor in block effective hydraulic conductivities). Parameters are obtained by the Veerecken pedotransfer function.
  38. 38. Snow cover is modeled (single layer) Tarboton and Luce, 1996; Zanotti et al, 2004
  39. 39. Snow cover and soil freezing cannot be neglected in mountain areas and if we want to model the hydrological cycle during the whole year. Because water viscosity strongly depends on temperature we added it to the model as a first step to have a consistent thermodynamic system. As you find below, parametrization of subgrid variability is still needed also at these scales. Finally we could realistically compare GEOtop and ESTAR.
  40. 40. - Rilling is parametrized - Conductivity is made dependent on Ts
  41. 41. ESTAR vs GEOtop soil moisture evolution Jackson T.J., http://hydrolab.arsusda.gov/sgp97; Jackson et al, 1995
  42. 42. Rigon et al., JHM, 2006
  43. 43. Rigon et al., JHM, 2006
  44. 44. Some other case studies
  45. 45. Saturation overland flow in a headwater catchment: application to Solstice Basin (CA) in collaboration with Bill Dietrich and Norman Miller (Berkeley University)
  46. 46. The Solstice Basin (CA) Headwater catchment located in Marin County, CA, area 16’000 m2; Colluvial soil: maximum thickness from 2 to 5.5 meters in the hollows, from 0.2 to 0.7 m on sideslopes Monitored during years 1986-’87 (C. Wilson, W.E. Dietrich) Basin and bedrock topography 120 piezometers on sideslopes and hollows Saturated source area measurement.
  47. 47. Experimental evidence: February 1986 storm 0 80 10 70 20 60 30 50 Rainfall mm/6h Streamflow l/s 40 40 50 30 60 20 70 10 80 0 11- 12- 13- 14- 15- 16- 17- 18- 19- 20- 21- 22- 23- 24- Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Solstice raingage mm/6h Pan Toll Raingage mm/6h Measured Streamflow l/s • measured rainfall each 6 h • measured stream flow each 6 h • measured saturated area: “squishy soil” • Total storm 400 mm in 10 days
  48. 48. Experimental evidence: hypothesis on the hydrologic behavior (Wilson and Dietrich, 1988)
  49. 49. Experimental evidence: hypothesis on the hydrologic behavior (Wilson and Dietrich, 1988) a) Cross - hollow direction - Deep water table in the sideslopes - Infiltration gradients in the sideslopes - Exfiltration gradients in the hollows
  50. 50. Experimental evidence: hypothesis on the hydrologic behavior (Wilson and Dietrich, 1988) a) Cross - hollow direction - Deep water table in the sideslopes - Infiltration gradients in the sideslopes - Exfiltration gradients in the hollows b) Long - hollow direction - Saturation overland flow - Shallow water table - Effects of local conductivity changes
  51. 51. Experimental evidence: hypothesis on the hydrologic behavior (Wilson and Dietrich, 1988) a) Cross - hollow direction - Deep water table in the sideslopes - Infiltration gradients in the sideslopes - Exfiltration gradients in the hollows b) Long - hollow direction - Saturation overland flow - Shallow water table - Effects of local conductivity changes c) Expansion of saturation saturated area - Expansion beginning from the nose of the hollows
  52. 52. GEOtop model settings Soil and bedrock properties: Sideslopes: shallow conductive bedrock Conductivities against depht: model with 8 layers Solstice1 Solstice2 Sideslope Model K decreasing with depth 0 Hollows: loamy-sand thick colluvium, deep impermeable bedrock, 200 some conductive points (Lehre et al. , 400 1986) depht cm 600 Soil parameters settings: 800 • 8 soil layers with 20 m thickness • Impermeable boundary condition 1000 • Spin-up of 3 years 1200 Bedrock shape variation: 1400 • With uniform soil profile 1.00E-01 1.00E-02 1.00E-03 1.00E-04 1.00E-05 1.00E-06 1.00E-07 Kv cm/s • With measured bedrock shape • Bedrock with different permeability
  53. 53. GEOtop model results • Surface conductivity 0.01 m/s • Subsurface conductivity in first layer 0.001 m/s Either slow turbulent surface flow or quick shallow subsurface strom flow: equifinallity or preferential flow evidence?
  54. 54. GEOtop model results Saturated area - water content first layer 5 cm 0 80 10 70 20 60 30 50 Rainfall mm/6h Streamflow l/s 40 40 50 30 60 20 70 10 80 0 11- 12- 13- 14- 15- 16- 17- 18- 19- 20- 21- 22- 23- 24- Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Solstice raingage mm/6h Pan Toll Raingage mm/6h Measured Streamflow l/s Hollows partially saturated at the beginning of the storm
  55. 55. GEOtop model results Saturated area - water content first layer 5 cm 0 80 10 70 20 60 30 50 Rainfall mm/6h Streamflow l/s 40 40 50 30 60 20 70 10 80 0 11- 12- 13- 14- 15- 16- 17- 18- 19- 20- 21- 22- 23- 24- Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Solstice raingage mm/6h Pan Toll Raingage mm/6h Measured Streamflow l/s Different behavior of the hollows and the side slopes at the peak
  56. 56. GEOtop model results Saturated area - water content first layer 5 cm 0 80 10 70 20 60 30 50 Rainfall mm/6h Streamflow l/s 40 40 50 30 60 20 70 10 80 0 11- 12- 13- 14- 15- 16- 17- 18- 19- 20- 21- 22- 23- 24- Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Solstice raingage mm/6h Pan Toll Raingage mm/6h Measured Streamflow l/s Measured discontinuous patterns at the end of the event
  57. 57. GEOtop model results total head gradient- first layer 5 cm Mostly topographically driven
  58. 58. GEOtop model results total head gradient- first layer 5 cm Not only topography drives down-slope water flow but also suction potential drives the up-slope expansion of the saturated area. Delay in basins response, increase of saturated area
  59. 59. Mass movements at Sauris (UD) basin Mostly worked out by S. Simoni and F. Zanotti
  60. 60. Geotop and trent-2d •DTM •Soil characterization •Meteo data (hydraulic parameters) •Rain data •land use •vegetation GEOtop 0.875 Geotop project 3D Mass and Energy budgets at catchment scale •Soil characterization GEOtop-FS (geotechnical parameters) •dynamic probability of landslide triggering •sediment availability •liquid and solid discharge 1 •detailed topography Run-out module trent-2d •closure relations (concentration & shear •run-out distance stress) •sediment and transport •erosion-deposit height •flow velocity •hazard maps
  61. 61. Simoni et al., Hydrol. Proc., 2008
  62. 62. model structure data exploited soil Cover •Soil characterization GEOtop 0.875 type and weight of soil (hydraulic parameters) Geotop project cover •land use 3D Mass and Energy •vegetation budgets at catchment Geomorphological scale analysis •DTM •Meteo data Geophysics •Rain data GEOtop-FS Soil Depth Water content •dynamic probability of •Soil characterization landslide triggering Geological info (geotechnical parameters) •sediment availability stratigraphy quaternary covers traditional photo •liquid and solid Run-out module trent-2d interpretation discharge •detailed topography rock presence •run-out distance •closure relations erosion signatures •erosion-deposit height human activities (concentration & shear •flow velocity stress) •hazard maps •sediment and transport Climate and Weather models Rainfall, wind... Earth Observations
  63. 63. A conceptual experiment
  64. 64. Effects of topography on hydrological balance Elevations 25% Elevations 40% Elevations 50% Elevations 100% Elevations 60% Elevations 125% Security Exit 2, 1 year simulation Serraia Basin, 15 km
  65. 65. Effects of topography on hydrological balance Security Exit Bertoldi et al.,JHM, 2006
  66. 66. Effects of topography on hydrological balance Bertoldi et al.,JHM, 2006
  67. 67. An application to the Valsugana valley Mostly worked out by S. Endrizzi x ● Pergine Borgo Valsugana ● Levico ● Caldonazzo
  68. 68. An application to the Valsugana valley GCMs Step1: Dynamical downscaling Mostly worked out by S. Endrizzi RCMs Step2: Bias-correction and data disaggregation From daily to hourly data Step3: Rainfall-runoff model calibrated Impacts of climate change
  69. 69. An application to the Valsugana valley Monthly mean discharge (m3/s) from HAD_P model, for historic (1961-1988) and simulated control and future scenario. Mostly worked out by L. Forlin Results indicate how the approximation is excellent from May to July, with overestimation during autumn and underestimation in winter.The flow is predicted substantially to increase from October to April and decrease during summer months.
  70. 70. An application to the Valsugana valley Monthly mean fluxes (mm/month) from HAD_P model, for control and future scenario. Mostly worked out by L. Forlin Results indicate a future seasonal variability with drier summers and wettest winters.
  71. 71. An application to the Valsugana valley Monthly mean snow cover (mm/month) and surface temperature (°C) from HAD_P model, for control and future scenario. Mostly worked out by L. Forlin Results indicate a substantial decrease in snow cover and increase in surface temperature.
  72. 72. GEOtop 0.9375 (2006-2008) GIUH + Radiation Physics Energy budget Kinematic wave+ Richards + Soil freezing & Snow Cover Mostly worked out by S. Endrizzi, E. Cordano, s, Simoni e M. Dall’Amico
  73. 73. GEOtop 0.9375 (2006-2008) Radiation Physics Mostly worked out by S. Endrizzi improved by accepting several parameterizations
  74. 74. GEOtop 0.9375 (2006-2008) Multilayer parameterization For each layer a system of 5 equations is solved Mostly worked out by S. Endrizzi 1  ∂W ∂Qw  θw = +   Liquid and solid water budget ρ w  ∂t ∂z  equations 1  ∂W ∂Qi  θi =  +  ρ i  ∂t ∂z  Energy € budget  ∂T  ∂W ∂  ∂T  ∂ (QwUw) ∂T  k  = − Rn + H + L C + Lf = k + equation  ∂z  ∂T ∂z  ∂z  ∂t ∂z € Continuity equation θ w +θ i +θ v = 1 € € W ≠ 0 if T = 273.15K ; W = 0 if T ≠ 273.15K Link phase change - temperature € €
  75. 75. GEOtop 0.9375 (2006-2008) MODIS GEOtop model mm SWE Mostly worked out by S. Endrizzi 24 OTTOBRE 2003
  76. 76. GEOtop 0.9375 (2006-2008) MODIS GEOtop model mm SWE Mostly worked out by S. Endrizzi 17 January 2004
  77. 77. Application of GEOtop to the Adamello-Mandrone Glacier Mostly worked out by S. Endrizzi (Trentino, Italy)
  78. 78. Distributed results mm w.eq. Mostly worked out by S. Endrizzi Mass balance 1 Oct 2004 - 30 Sep 2005 73
  79. 79. Comparison model - measurements Ice melting after snow disappearance • Problems in estimating the snow disapperance date (underestimation of snow precipitation measured with the classical rain gauge) • Good agreement, in particular for stakes 2 and 7 74
  80. 80. The Stubaital case by G. Bertoldi, P. Rastner, C. Notarnicola, and U. Tappeiner Data from G. Wolfhart, Institute of echology, Innsbruck
  81. 81. The Stubaital case by G. Bertoldi, P. Rastner, C. Notarnicola, and U. Tappeiner Data from G. Wolfhart, Institute of Echology, Innsbruck Initial assumption: time constant vegetation density Model - Observations comparison Snow - free season 2005 Model Obs Model-Obs 2 H [W/m ] 26 20 6 2 LE [W/m ] 88 85 3 G [W/m2] 4 4 0 2 H [W/m ] 80 76 4 Ts [K] 11 12 -1 SWC [ ] 0.48 0.37 0.11 • Can we perform a process based calibration ? • Can we avoid parameter equi-finality ? Yes if are considered … • overall consistency (different components of the water and energy balance) • different time and spatial scales.
  82. 82. The Stubaital case by G. Bertoldi, P. Rastner, C. Notarnicola, and U. Tappeiner Data from G. Wolfhart, Institute of Echology, Innsbruck Preliminary simulation with UNIFORM LAND COVER; meadow valley model calibration. LANDSAT LST GEOTOP LST ΔT=LSTGeoTop-LSTLandsat • The illuminations alone (sun incidence angle, shadows) explains 71% of the variability. • Best agreement for valley meadows and alpine pasture. • Major differences for high elevations regions (glaciers and south facing slopes) and for forests. What are the dominant processes ? What is the optimal model complexity level ?
  83. 83. The Stubaital case by G. Bertoldi, P. Rastner, C. Notarnicola, and U. Tappeiner Data from G. Wolfhart, Institute of Echology, Innsbruck
  84. 84. Some of the next steps
  85. 85. GEOtop Development 0.5 0.75 GEOTOP-SF 0.875 0.75 0.9375 GEOTOP-SF 0.875 0.9375- EO Automatic Calibration GEOTOP-SF 0.9375 Data GEOFRAME Assimilation
  86. 86. GEOtop will be splitted in components and the components managed by JGrass. The = components will be based on the OpenMI standard.
  87. 87. GEOFRAME: JGrass 3.0 http://www.jgrass.org
  88. 88. JGrass (www.jgrass.org) is a full featured GIS system based on udig (www.refractions.net ). It allows = communication to databases, internet and provides an interfaces to components’ Input-Outputs
  89. 89. GEOFRAME: JGrass 3.0 www.jgrass.org
  90. 90. GEOFRAME external users web database Interfacce (Java/JGRASS) Tools di analisi Modelli (UNITN/R) (UNITN/OpenMI) Database (PostgresSQL/PostGIS/CUAHSI)
  91. 91. GEOFRAME: JGrass 3.0 structure JGrass Eclipse RCP uDig JGrass PostGIS J-Console engine Postgres GIS engine OpenMI Hydrolog. model Horton Machine Statistical anal. WEB UIBuilder services WMS HSQLDB WFS-T WPS GRASS GIS
  92. 92. udig itself lives upon the Rich Client Platform given by Eclipse = (www.eclipse.or). JGrass uses also HSQL as internal database, and a custom interfaces builder to give a GUI to any command.
  93. 93. GEOFRAME: OpenMI Serially linked models by file transfer. Feedback loops not represented. Interface Interface Model Interface Interface Model Data File Model Model File Data Data File Data 88
  94. 94. GEOFRAME: OpenMI Serially linked models by file transfer. Feedback loops not represented. Interface Interface Model Interface Interface Model Data File Model Model File Data Data File Data from HarmonIT Roger Moore’s, CEH, Wallingford, UK presentation 88
  95. 95. GEOFRAME: OpenMI Serially linked models by file transfer. Feedback loops not represented. Interface Interface Model Interface Interface Model Data File Model Model File Data Data File Data 88
  96. 96. OpenMI gives a set of standard interfaces to make model = components to communicate, even having feedbacks between components, and can link components programmed in C, FORTRAN or PASCAL
  97. 97. GEOFRAME: OpenMI tool Connection 1-D River 3-D Sea 3-D 2-D Estuary Graph tool 18 90
  98. 98. GEOFRAME: OpenMI tool Connection 1-D River 3-D Sea 3-D 2-D Estuary Graph tool 18 from HarmonIT Roger Moore’s, CEH, Wallingford, UK presentation 90
  99. 99. GEOFRAME: OpenMI tool Connection 1-D River 3-D Sea 3-D 2-D Estuary Graph tool 18 90
  100. 100. OpenMI provides methods to change at run-time the model = configuration. Different components doing the same job can be used in alternative seamlessly.
  101. 101. GEOFRAME: OpenMI Monitored Evapotranspiration data Weather Surfaces Surfaces Generator Interception Runoff Weather Subsurface Flow Forecast 18 http://www.openmi.org, http://www.openmi-life.org/, http://public.wldelft.nl/display/OPENMI/Home 92
  102. 102. GEOFRAME: OpenMI Channel Routing Evapotranspiration 1 Surfaces Channel Routing Runoff II Channel Routing Subsurface Flow III 18 http://www.openmi.org, http://www.openmi-life.org/, http://public.wldelft.nl/display/OPENMI/Home 93
  103. 103. GEOFRAME : J-Hydro
  104. 104. JGrass provides also the database to store and retrieve simulations =
  105. 105. after Dietrich et al., 2001
  106. 106. Still, as the painting by Rosseau, shows GEOtop is a mosaic of “realistic pieces” inside an improbable ecosystem (not to speak of other model). Things are however getting better ;-)
  107. 107. Core contributors DICA Dipartimento di Ingegneria Civile ed Ambientale CUDAM Centro Universitario per la Difesa dell’Ambiente Montano riccardo rigon GEOtop Developers Team (GDT) stefano endrizzi, emanuele cordano, christian tiso, giacomo bertoldi Mountain-eering: matteo dall’amico, silvia simoni, fabrizio zanotti. HYDROLOGIS: andrea antonello, silvia franceschi, www.hydrologis.com
  108. 108. Thank you for your attention Numerics Physics Dance, Henry Matisse, Hotel Biron early 1909 Analytics Hydrology Geography
  109. 109. Comprehensive GEOtop Bibliography •Simoni, S., F. Zanotti, G. Bertoldi and R. Rigon, Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS, accepted for Hydrol. Proc., published on-line, Dec 2007 •Rigon R., Bertoldi G e T. M. Over, GEOtop: A distributed hydrological model with coupled water and energy budgets, Jour. of Hydromet., Vol. 7, No. 3, pages 371- 388., Vol. 7, No. 3, pages 371-388. •Bertoldi, G., R. Rigon & T. M. Over, Impact of watershed geomorphic characteristics on the energy and water budgets, Jour. of Hydromet., Vol. 7, No. 3, p. 371- 388. Vol. 7, No. 3, pages 389 - 394, 2006. B-
  110. 110. •Simoni S., Zanotti F., Rigon R., Squarzoni C., Approccio probabilistico alla determinazione dell'innesco di frane superficiali con in modello accoppiato idro-geotecnico: GEOTOP-SF, Atti del convegno quot;idra2006 : XXX Convegno di Idraulica e Costruzioni Idraulichequot;, Roma, 10-15 Settembre, 2006. •Bertoldi G., Dietrich W.E., Miller N. L., Rigon R.. Bedrock and soil contribution to the formation of sub-surface runoff by saturation in headwater catchments: observations and simulation using a distributed hydrological model, Atti del XXIX Convegno di Idraulica e Costruzioni Idrauliche, Trento, Settembre 2004. • Zanotti F, Endrizzi S, Bertoldi G, Rigon R. 2004. The GEOTOP snow module. Hydrological Processes 18: 3667–3679. DOI:10/1002/hyp.5794. B-
  111. 111. •Zanotti, F., Endrizzi S., Rigon R. Il modulo di accumulo e scioglimento della neve in Geotop. Atti del XXIX Convegno di Idraulica e Costruzioni Idrauliche, Trento, Settembre 2004. •Tiso, C., Bertoldi G. and R. Rigon. Il modello Geotop-SF per la determinazione dell'innesco di fenomeni di franamento e di colata. Atti del Convegno Interpraevent 2004, Riva del Garda, 24-28 Maggio 2004. •Bertoldi G., Rigon R. and Over T.M., Un'indagine sugli effetti della topografia sul ciclo idrologico con il modello GEOTOP, Atti del XXVIII Convegno di Idraulica e Costruzioni Idrauliche, Potenza, Italy, 2002.
  112. 112. A few relevant presentations
  113. 113. CAHMDA II - Princeton, October 25-27, 2004 GEOTOP: a distributed modeling of the hydrological cycle in the remote sensing era R. Rigon , G. Bertoldi, T.M. Over., D. Tamanini, Dipartimento di Ingegneria Civile ed Ambientale - CUDAM Università di Trento Geography Dept. Eastern Illinois University
  114. 114. San Francisco AGU Fall meeting - Dec 15 2006 The triggering of shallow landslides and channelized debris flows analyzed with the distributed model GEOtop - FS R. Rigon, S. Simoni, F. Zanotti & M. Dall’Amico DICA & CUDAM Università di Trento - ITALY
  115. 115. Beyond and side by side with Numerics Dance, Henry Matisse, Hotel Biron early 1909 A reflection on making applicable hydrology today Riccardo Rigon - Group of Hydrology - Trento University
  116. 116. The snow and glacier description in the GEOtop model Stefano Endrizzi Department of Civil and Environmental Engineering University of Trento, Italy Zürich, 18th March 2008
  117. 117. Application of a physically-based hydrological model to the Adamello-Mandrone Glacier Stefano Endrizzi, Riccardo Rigon Department of Civil and Environmental Engineering Università di Trento Italy Obergurgl (Austria), 28 August 2007
  118. 118. The water and energy balance in mountain catchments: a distributed modelling approach G.Bertoldi S. Endrizzi, F. Zanotti, T.M. Over, S. Simoni, R.Rigon, U. Tappeiner Institute for Alpine Environment EURAC, Bozen, Italy Innsbruck, 31th March 2008

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