A combined index to characterize agricultural drought in Italy at municipality scale
Study region: Italy and in particular the provinces of Verona and Foggia. Study focus: The assessment of drought impacts at local scale requires adequately detailed spatio-temporal estimates of drought severity. Given the intrinsic uncertainty in drought severity estimates based on a single index, e...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581823000915 |
| _version_ | 1797801803285266432 |
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| author | Lauro Rossi Gustavo Naumann Simone Gabellani Carmelo Cammalleri |
| author_facet | Lauro Rossi Gustavo Naumann Simone Gabellani Carmelo Cammalleri |
| author_sort | Lauro Rossi |
| collection | DOAJ |
| description | Study region: Italy and in particular the provinces of Verona and Foggia. Study focus: The assessment of drought impacts at local scale requires adequately detailed spatio-temporal estimates of drought severity. Given the intrinsic uncertainty in drought severity estimates based on a single index, especially at high spatial resolution, the use of combined indices is preferable. However, the disagreement between the single indices needs to be addressed. We propose a methodology to combine the Standardized Precipitation-Evapotranspiration Index and the Soil Moisture Anomalies based on a double-entry matrix. The classification adopted to define five semi-quantitative severity classes is generalized by introducing an objective approach to assign values when the two base indices disagree. The methodology is tuned over two Italian provinces, Verona and Foggia, with focus on agricultural drought at the high spatial detail of single municipalities. New hydrological insights for the region: The methodology is proved to be skillful (Heidke Skill Score of 0.75) in capturing the spatio-temporal evolution of the major agricultural droughts observed in the two case study regions (2012, 2015 and 2017), which are benchmarked using data from drought impact databases. The temporal dynamics modeled by this index align with the timeline of the drought events, suggesting that the index is suitable for near-real time agricultural drought monitoring. The simplicity of the double-entry matrix approach allows for upscaling to the entire country. |
| first_indexed | 2024-03-13T04:56:03Z |
| format | Article |
| id | doaj.art-dfd7ccd2444944329e08d8fa7867dbed |
| institution | Directory Open Access Journal |
| issn | 2214-5818 |
| language | English |
| last_indexed | 2024-03-13T04:56:03Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj.art-dfd7ccd2444944329e08d8fa7867dbed2023-06-18T05:01:59ZengElsevierJournal of Hydrology: Regional Studies2214-58182023-06-0147101404A combined index to characterize agricultural drought in Italy at municipality scaleLauro Rossi0Gustavo Naumann1Simone Gabellani2Carmelo Cammalleri3CIMA Research Foundation, Via Magliotto 2, Savona, Italy; Corresponding author.CIMA Research Foundation, Via Magliotto 2, Savona, ItalyCIMA Research Foundation, Via Magliotto 2, Savona, ItalyPolitecnico di Milano, Piazza Leonardo da Vinci, 32, Milan, ItalyStudy region: Italy and in particular the provinces of Verona and Foggia. Study focus: The assessment of drought impacts at local scale requires adequately detailed spatio-temporal estimates of drought severity. Given the intrinsic uncertainty in drought severity estimates based on a single index, especially at high spatial resolution, the use of combined indices is preferable. However, the disagreement between the single indices needs to be addressed. We propose a methodology to combine the Standardized Precipitation-Evapotranspiration Index and the Soil Moisture Anomalies based on a double-entry matrix. The classification adopted to define five semi-quantitative severity classes is generalized by introducing an objective approach to assign values when the two base indices disagree. The methodology is tuned over two Italian provinces, Verona and Foggia, with focus on agricultural drought at the high spatial detail of single municipalities. New hydrological insights for the region: The methodology is proved to be skillful (Heidke Skill Score of 0.75) in capturing the spatio-temporal evolution of the major agricultural droughts observed in the two case study regions (2012, 2015 and 2017), which are benchmarked using data from drought impact databases. The temporal dynamics modeled by this index align with the timeline of the drought events, suggesting that the index is suitable for near-real time agricultural drought monitoring. The simplicity of the double-entry matrix approach allows for upscaling to the entire country.http://www.sciencedirect.com/science/article/pii/S2214581823000915Drought monitoringDrought impact assessmentCombined drought indicatorAgricultural drought |
| spellingShingle | Lauro Rossi Gustavo Naumann Simone Gabellani Carmelo Cammalleri A combined index to characterize agricultural drought in Italy at municipality scale Journal of Hydrology: Regional Studies Drought monitoring Drought impact assessment Combined drought indicator Agricultural drought |
| title | A combined index to characterize agricultural drought in Italy at municipality scale |
| title_full | A combined index to characterize agricultural drought in Italy at municipality scale |
| title_fullStr | A combined index to characterize agricultural drought in Italy at municipality scale |
| title_full_unstemmed | A combined index to characterize agricultural drought in Italy at municipality scale |
| title_short | A combined index to characterize agricultural drought in Italy at municipality scale |
| title_sort | combined index to characterize agricultural drought in italy at municipality scale |
| topic | Drought monitoring Drought impact assessment Combined drought indicator Agricultural drought |
| url | http://www.sciencedirect.com/science/article/pii/S2214581823000915 |
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