Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions
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...
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2024-11-01
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| author | Ludovica De Gregorio Giovanni Cuozzo Riccardo Barella Francisco Corvalán Felix Greifeneder Peter Grosse Abraham Mejia-Aguilar Georg Niedrist Valentina Premier Paul Schattan Alessandro Zandonai Claudia Notarnicola |
| author_facet | Ludovica De Gregorio Giovanni Cuozzo Riccardo Barella Francisco Corvalán Felix Greifeneder Peter Grosse Abraham Mejia-Aguilar Georg Niedrist Valentina Premier Paul Schattan Alessandro Zandonai Claudia Notarnicola |
| author_sort | Ludovica De Gregorio |
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| description | 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<sup>3</sup>·m<sup>−3</sup> 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. |
| format | Article |
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| spelling | doaj-art-47e730f7ba104732ab57cf23d02c0cb32025-08-20T02:28:12ZengMDPI AGData2306-57292024-11-0191113610.3390/data9110136Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain RegionsLudovica De Gregorio0Giovanni Cuozzo1Riccardo Barella2Francisco Corvalán3Felix Greifeneder4Peter Grosse5Abraham Mejia-Aguilar6Georg Niedrist7Valentina Premier8Paul Schattan9Alessandro Zandonai10Claudia Notarnicola11Eurac Research, Viale Druso 1, 39100 Bolzano/Bozen, ItalyEurac Research, Viale Druso 1, 39100 Bolzano/Bozen, ItalyEurac Research, Viale Druso 1, 39100 Bolzano/Bozen, ItalyEdaphology Department, Facultad de Ciencias Agrarias, Universidad Nacional de Cuyo, Mendoza M5500, ArgentinaChloris Geospatial, 399 Boylston Street, Suite 600, Boston, MA 02116, USAInstitute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Straße 24/25, 14476 Potsdam, GermanyEurac Research, Viale Druso 1, 39100 Bolzano/Bozen, ItalyEurac Research, Viale Druso 1, 39100 Bolzano/Bozen, ItalyEurac Research, Viale Druso 1, 39100 Bolzano/Bozen, ItalyInstitute of Geography, University of Innsbruck, Innrain 52f, 6020 Innsbruck, AustriaEurac Research, Viale Druso 1, 39100 Bolzano/Bozen, ItalyEurac Research, Viale Druso 1, 39100 Bolzano/Bozen, ItalyIn 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<sup>3</sup>·m<sup>−3</sup> 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.https://www.mdpi.com/2306-5729/9/11/136remote sensinghydrologysnow cover fractionsoil moisture |
| spellingShingle | Ludovica De Gregorio Giovanni Cuozzo Riccardo Barella Francisco Corvalán Felix Greifeneder Peter Grosse Abraham Mejia-Aguilar Georg Niedrist Valentina Premier Paul Schattan Alessandro Zandonai Claudia Notarnicola Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions Data remote sensing hydrology snow cover fraction soil moisture |
| title | Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions |
| title_full | Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions |
| title_fullStr | Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions |
| title_full_unstemmed | Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions |
| title_short | Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions |
| title_sort | two datasets over south tyrol and tyrol areas to understand and characterize water resource dynamics in mountain regions |
| topic | remote sensing hydrology snow cover fraction soil moisture |
| url | https://www.mdpi.com/2306-5729/9/11/136 |
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