Mountain grasslands are important, also because one sixth of the world population lives inside watersheddominated by snowmelt. Also, grasslands provide food to both domestic and selvatic animals.The global warming will probably accelerate the hydrological cycle and increase the drought risk. Thecombination of measurements, modeling and remote sensing can furnish knowledge in such faraway areas (e.g.:Brocca et al., 2013). A better knowledge of water balance can also allow to optimize the irrigation (e.g.: Canoneet al., 2015).This work is meant to build a model of water balance in mountain grasslands, ranging between 1500 and2300 meters asl. The main input is the Digital Terrain Model, which is more reliable in grasslands than both inthe woods and in the built environment. It drives the spatial variability of shortwave solar radiation. The otheratmospheric forcings are more problematic to estimate, namely air temperature, wind and longwave radiation. Adhoc routines have been written, in order to interpolate in space the meteorological hourly time variability.The soil hydraulic properties are less variable than in the plains, but the soil depth estimation is still anopen issue. The soil vertical variability has been modeled taking into account the main processes: soil evaporation,root uptake, and fractured bedrock percolation.The time variability latent heat flux and soil moisture results have been compared with the data measuredin an eddy covariance station. The results are very good, given the fact that the model has no fitting parameters.The space variability results have been compared with the results of a model based on Landsat 7 and 8 data, applied over an area of about 200 square kilometers. The spatial correlation is quite in agreement betweenthe two models.

Evapotranspiration measurement and modeling without fittingparameters in high-altitude grasslands

Stefano Ferraris;Maurizio Previati;Davide Canone;Stefano Bechis
2016-01-01

Abstract

Mountain grasslands are important, also because one sixth of the world population lives inside watersheddominated by snowmelt. Also, grasslands provide food to both domestic and selvatic animals.The global warming will probably accelerate the hydrological cycle and increase the drought risk. Thecombination of measurements, modeling and remote sensing can furnish knowledge in such faraway areas (e.g.:Brocca et al., 2013). A better knowledge of water balance can also allow to optimize the irrigation (e.g.: Canoneet al., 2015).This work is meant to build a model of water balance in mountain grasslands, ranging between 1500 and2300 meters asl. The main input is the Digital Terrain Model, which is more reliable in grasslands than both inthe woods and in the built environment. It drives the spatial variability of shortwave solar radiation. The otheratmospheric forcings are more problematic to estimate, namely air temperature, wind and longwave radiation. Adhoc routines have been written, in order to interpolate in space the meteorological hourly time variability.The soil hydraulic properties are less variable than in the plains, but the soil depth estimation is still anopen issue. The soil vertical variability has been modeled taking into account the main processes: soil evaporation,root uptake, and fractured bedrock percolation.The time variability latent heat flux and soil moisture results have been compared with the data measuredin an eddy covariance station. The results are very good, given the fact that the model has no fitting parameters.The space variability results have been compared with the results of a model based on Landsat 7 and 8 data, applied over an area of about 200 square kilometers. The spatial correlation is quite in agreement betweenthe two models.
EGU General Assembly 2016
Vienna
17-22 Aprile 2015
Geophysical Research Abstracts
European Geosciences Union
18
15730
15730
https://meetingorganizer.copernicus.org/EGU2016/EGU2016-15730.pdf
Stefano Ferraris, Maurizio Previati, Davide Canone, Niccolò Dematteis, Marco Boetti, Jacopo Balocco, Stefano Bechis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1725547
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