Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions

Ludovica De Gregorio (), Giovanni Cuozzo (), Riccardo Barella, Francisco Corvalán, Felix Greifeneder, Peter Grosse, Abraham Mejia-Aguilar, Georg Niedrist, Valentina Premier, Paul Schattan, Alessandro Zandonai and Claudia Notarnicola
Additional contact information
Ludovica De Gregorio: Eurac Research, Viale Druso 1, 39100 Bolzano/Bozen, Italy
Giovanni Cuozzo: Eurac Research, Viale Druso 1, 39100 Bolzano/Bozen, Italy
Riccardo Barella: Eurac Research, Viale Druso 1, 39100 Bolzano/Bozen, Italy
Francisco Corvalán: Edaphology Department, Facultad de Ciencias Agrarias, Universidad Nacional de Cuyo, Mendoza M5500, Argentina
Felix Greifeneder: Chloris Geospatial, 399 Boylston Street, Suite 600, Boston, MA 02116, USA
Peter Grosse: Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Straße 24/25, 14476 Potsdam, Germany
Abraham Mejia-Aguilar: Eurac Research, Viale Druso 1, 39100 Bolzano/Bozen, Italy
Georg Niedrist: Eurac Research, Viale Druso 1, 39100 Bolzano/Bozen, Italy
Valentina Premier: Eurac Research, Viale Druso 1, 39100 Bolzano/Bozen, Italy
Paul Schattan: Institute of Geography, University of Innsbruck, Innrain 52f, 6020 Innsbruck, Austria
Alessandro Zandonai: Eurac Research, Viale Druso 1, 39100 Bolzano/Bozen, Italy
Claudia Notarnicola: Eurac Research, Viale Druso 1, 39100 Bolzano/Bozen, Italy

Data, 2024, vol. 9, issue 11, 1-19

Abstract: In this work, we present two datasets for specific areas located on the Alpine arc that can be exploited to monitor and understand water resource dynamics in mountain regions. The idea is to provide the reader with information about the different sources of water supply over five defined test areas over the South Tyrol (Italy) and Tyrol (Austria) areas in alpine environments. The snow cover fraction (SCF) and Soil Moisture Content (SMC) datasets are derived from machine learning algorithms based on remote sensing data. Both SCF and SMC products are characterized by a spatial resolution of 20 m and are provided for the period from October 2020 to May 2023 (SCF) and from October 2019 to September 2022 (SMC), respectively, covering winter seasons for SCF and spring–summer seasons for SMC. For SCF maps, the validation with very high-resolution images shows high correlation coefficients of around 0.9. The SMC products were originally produced with an algorithm validated at a global scale, but here, to obtain more insights into the specific alpine mountain environment, the values estimated from the maps are compared with ground measurements of automatic stations located at different altitudes and characterized by different aspects in the Val Mazia catchment in South Tyrol (Italy). In this case, an MAE between 0.05 and 0.08 and an unbiased RMSE between 0.05 and 0.09 m 3 ·m −3 were achieved. The datasets presented can be used as input for hydrological models and to hydrologically characterize the study alpine area starting from different sources of information.

Keywords: remote sensing; hydrology; snow cover fraction; soil moisture (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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