egu 2014 invited talk (mostly on simulation of ecohydrology) - by giacomo bertoldi et al
DESCRIPTION
This contains the description of the use of GEOtop 2.0 in simulating the ecohydrology of a mountain environmentTRANSCRIPT
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Process-based modelingin alpine catchments
Giacomo Bertoldi, Stefano Della Chiesa, Michael Engel, Georg Niedrist, Johannes G. Brenner , Stefano Endrizzi, Matteo Dall’Amico, Emanuele Cordano, Ulrike Tappeiner, Riccardo Rigon.
EGU 2014, Vienna, Austria, 28 April – 2 May 2014
Institute for Alpine Environment
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Aims and outlineMotivationIn mountain regions ecohydrological processes exhibit rapid changes within short distances due to the complex interplay of topography, soil, biological and atmospheric processes.
Are process‐based models able to deal with this complexity?OutlineAn (hopefully) useful instrument: the GEOtop 2 ‐ DV model.
Application of the model to mountain areas:1. Plot scaleModelling snow, soil moisture, ET, biomass along an elevation gradient:Model as a tool to investigate coupled eco‐hydrological processes.2. Catchment scaleRemote sensing land surface temperature in complex terrain:Model as tool to interpret processes behind observations
Discuss advantages and constraints of process based modelling in mountain areas
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Coupled process based modelling in mountain areas
LWatm V
D0VILWsurr
1V SWsurr
1V
sTs4
Shortwaveradiation (yellow)Longwave radiation(red)
SWrefl
Complex topography
Bertoldi et al., J of Hydromet, 2006.
sSnow module
Endrizzi et al., GMDD, 2014Zanotti et al., Hydrol Proc, 2004
Water budget
Rigon et al., J of Hydromet, 2006.
Figures adapted from VIC model (Liang et al., 1994)
Energy budget
Bertoldi al., Ecohydrol, 2010.
Vegetation dynamics
Della Chiesa et al., Ecohydrol., 2014
From SHE model (Abbot et al., 1986)
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The GEOtop 2.0 – DV model
Rigon et al., JHM, 2006; Endrizzi et al. GMDD, 2014.
Processes
Dynamic vegetationmodel (for grasslands)
From Montaldo et al., 2005;Della Chiesa et al., 2014
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Does it works?
Is it usable ? Or … too complex?
Is it useful ?
Two applications in mountain context
1. Plot scaleModelling snow, soil moisture, ET, biomass along an elevation gradient:Model as a tool to investigate coupled eco‐hydrological processes.
2. Catchment scaleRemote sensing land surface temperature in complex terrain:Model as tool to interpret processes behind observations.
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Application 1: modelling along an elevation gradient
Motivation
In dry inner‐alpine regions, managed grasslands are irrigated.Climate change raises issues about future water availability.
Which are the effects of the elevation gradient on SWE, SWC, ET,grassland productivity?
Della Chiesa et al., Modeling changes in grassland hydrological cycling along an elevational gradient in the Alps,Ecohydrology, 2014
.
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An experimental elevation transect
Elevation as a proxy of climate change: Mazia Valley, emerging LTER
Station B2000 mHs, SWC, Biomass, GAI
StationB1500 mHs, SWC, Biomass, GAI,ET
StationB1000 mHs, SWC, Biomass, GAI
T~ 3.5K
T~ 3.5K
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Elevation gradient: validation
Multiple variables validation: SWE, SWC, above ground biomass (Bag), ET
Two years of data: calibration in B1500, validation in B1000, B2000
B2000 m
B1500 m
B1000 m
Snow Height [cm] SWC 5cm [] ET [mm]
Not Measured
Not Measured
r2=0.66RMSE=7.1
r2=0.57RMSE=5.9
r2=0.55RMSE=2.9
r2=0.80
r2=0.78
r2=0.82
Bag [gDMm‐2]
RMSE=0.04
RMSE=0.05
RMSE=0.04
r2=0.93RMSE=58.39
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Elevation gradient: resultsB2
000 m
B1500 m
B1000 m
Simulation extension to 20 year
Coupling snow – veg – ET ‐ SWC Water limitation below 1500 m
SWC along the year
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Elevation gradient: resultsB2
000 m
B1500 m
B1000 m
Coupling snow – veg – ET ‐ SWC
SWC along the year
Irrigation below 1500 m
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Application 1: modelling along an elevation transect
Insight on process understanding
In the Vinschgau valley, water limits ET below an elevation threshold of 1500 m a.s.l. while, above, the temperature and vegetation period length act as limiting factor.
Modelling lesson learning
Need of coupled modelling of energy and water fluxes, snow and vegetation dynamic.
Model validation against multiple variablesadds additional constrains to model consistency.
SWE → SWC↕
Bag ↔ LAI ↔ ET
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Application 2: land surface temperatureMotivationLST is a key variable of the surface energy budget.Improving its estimation in energy budget models can improve fluxes partitioning estimation.
Which are the factors controlling LST in mountain environments(i.e. elevation, solar radiation, land cover, soil moisture)?
Bertoldi et al., Topographical and ecohydrological controlson land surface temperature in an Alpine catchment, Ecohydrology, 3, 189 – 204, 2010.
tEwLSTETLSTHwLSTGLSTR ssn
),()(),()( 4
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Modeling land surface temperatureStubai Valley experimental area (Tyrol, Austria)
(Institute of Ecology Innsbruck University)
•257 km2, elevation 1000 ‐ 3500 m.
• Humid inner‐alpine climate.
• Comparison with 60 m LANDSAT LST TIR ETM+ map (13 September 1999, 10.50 AM).
• Parameters from field data (Hammerle et al. 2007) and literature for different land cover types (Findell et al. 2007).
• One year model spin‐up to reach equilibrium.
•Model validation against ground observations.
• Leaf Area Index (LAI); Roughness length (z0).
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LST: spatial patterns comparison
Aspect R2=0.63
Numerical experiment:add only one spatially varying factor at a time
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LST: spatial patterns comparison
Aspect R2=0.63
Elevation R2=0.74
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LST: spatial patterns comparison
Aspect R2=0.63
Elevation R2=0.74
Land cover R2=0.88
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LST: spatial patterns comparison
The model helps to identify factors controlling LST patterns
Aspect R2=0.63
Elevation R2=0.74
Land cover R2=0.88
Moisture R2=0.89
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Application 2: land surface temperatureInsight on process understanding
Most relevant factors controlling LST result radiation distribution and elevation.
Alpine vegetation and aspect strongly alter LST vertical distribution.
Modelling lesson learning
Need to have a model with LST as explicit prognostic variable.
Model helps to discriminate controlling factors.
(Complex) model allows to simplify complex patterns.
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Benefits (and issues) from process based modeling ?
Issues
“Distributed model are overparameterized”.
“ Such a models cannot be really calibrated”.
“They cannot be used for unequipped basins”.
“Reality is simpler than that (and we learn just from simple models)”.
From “analogic” ….
Possible solutions
Coupling processes introduces additional constrains.
Use multiple/ multi‐scale observations.
Tools to extend detailed experimental campaigns.
Numerical experiments allow to discriminate controlling factors.
Toward “digital”?
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GEOtop is an Open Source collaborative project and others are invited to bring into new components.
https://code.google.com/p/geotop/Main model developers:
Università di Trento; Zurich University (Now Quebec University);Mountain‐eering S.r.l; EURAC research; University of Augsburg KIT.
Creating a community. Try it!
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Acknowledgments
This study is supported by the projects “HiResAlp” and “HydroAlp” financed by Provincia Autonoma di Bolzano, Alto Adige, Ripartizione Diritto allo sudio, Università e ricerca scientifica.
The RADARSAT2 images were made available through the project ESA AO 6820 in the framework of the SOAR program.
• We hereby would like to thank:C. Notarnicola, EURAC, Institute for Alpine Environment.
Thank you for your attention!
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Application 3: remote sensing of soil moistureMotivation
Limited availability of reliable soil moisture high resolution products on mountain areas. Heterogeneity in soil type, land cover, topography limits distributed models parameterization.
How far can SAR remote sensing help for improving modelling surface soil moisture in mountain grassland areas?
Bertoldi, G., et al. Estimation of soil moisture patternsin mountain grasslands by means ofSAR RADARSAT2 images and hydrological modeling. J. Hydrol. (2014)
RADASAT2 SAR
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Soil moisture: observations
Fixed Stations
Field surveys
Mazia, South Tyrol, Italy ~ 100 km2
RADASAT2 SAR images 20m res
Surface SWC retrieval (SVR Pasolli
et el., 2011)
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Soil moisture: spatial patterns comparisonSWC SWC
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Soil moisture: spatial patterns comparison
Insight on process understandingModel suggest that soil type and land management are major controls on surface SWC.
Modelling lesson learningLimitation in model performance due coarse soil type / land cover information available.SAR remote sensing is able to provide higher spatial resolution information.Use RS information for model parameterization / data integration/ assimilation.
SWC SWC
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