Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019
Abstract Precipitation is a critical part of the global hydrological cycle that determines the distribution of water resources. It is also an essential meteorological variable used as input for hydroclimatic models and projections. However, precipitation data frequently lack complete series, especia...
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Wiley
2025-01-01
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| Series: | Geoscience Data Journal |
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| Online Access: | https://doi.org/10.1002/gdj3.267 |
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| author | Gamal AbdElNasser Allam Abouzied Guoqiang Tang Simon Michael Papalexiou Martyn P. Clark Eleonora Aruffo Piero Di Carlo |
| author_facet | Gamal AbdElNasser Allam Abouzied Guoqiang Tang Simon Michael Papalexiou Martyn P. Clark Eleonora Aruffo Piero Di Carlo |
| author_sort | Gamal AbdElNasser Allam Abouzied |
| collection | DOAJ |
| description | Abstract Precipitation is a critical part of the global hydrological cycle that determines the distribution of water resources. It is also an essential meteorological variable used as input for hydroclimatic models and projections. However, precipitation data frequently lack complete series, especially at daily and sub‐daily precipitation stations, which are usually large, bulky, and complex. To address this, gap filling is commonly used to produce complete hydrometeorological data series without missing values. Several gap‐filling methods have been developed and improved. This study seeks to fill the gaps of 201 daily precipitation time series in Central Italy by localizing the approach used to generate the Serially Complete dataset for the Planet Earth (SC‐Earth). This method combines the outcome of 15 strategies based on four various gap‐filling techniques (quantile mapping, spatial interpolation, machine learning, and multi‐strategy merging). These strategies employ the daily dataset of the neighbouring stations and the matched ERA5 data to estimate missing values at the target stations. Both raw data and the final serially complete station datasets (SCDs) underwent comprehensive quality control. Many accuracy indicators have been utilized to evaluate the performance of the strategies' estimations and the final SCD, such as Correlation Coefficient (CC), Root mean square error (RMSE), Relative bias (Bias %), and Kling‐Gupta efficiency (KGE″). Multi‐strategy merging strategy based on the Modified Kling‐Gupta efficiency (MS1) shows the highest performance as an individual precipitation gap‐filling strategy. However, the machine learning strategy using random forest (ML3) has the most outstanding share in the final estimates among all other strategies. In the end, the temporal–spatial performance of the final SCD is promising and depends on the pattern of the missing values (MV%). The mean values of KGE″, CC, variability (α), and bias term (β) are 0.9, 0.93, 1.064, and 4.98 × 10−7, respectively. |
| format | Article |
| id | doaj-art-cfbc3f700a13475fbe7ae9ada08060ea |
| institution | Kabale University |
| issn | 2049-6060 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geoscience Data Journal |
| spelling | doaj-art-cfbc3f700a13475fbe7ae9ada08060ea2025-01-27T08:26:33ZengWileyGeoscience Data Journal2049-60602025-01-01121n/an/a10.1002/gdj3.267Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019Gamal AbdElNasser Allam Abouzied0Guoqiang Tang1Simon Michael Papalexiou2Martyn P. Clark3Eleonora Aruffo4Piero Di Carlo5Department of Psychological Sciences, Health and Territory University of the Studies “G. d'Annunzio” Chieti ItalyClimate and Global Dynamics National Center for Atmospheric Research Boulder Colorado USADepartment of Civil Engineering University of Calgary Calgary Alberta CanadaColdwater Laboratory University of Saskatchewan Canmore Alberta CanadaDepartment of Advanced Technologies in Medicine & Dentistry University G. d'Annunzio Chieti‐Pescara ItalyDepartment of Advanced Technologies in Medicine & Dentistry University G. d'Annunzio Chieti‐Pescara ItalyAbstract Precipitation is a critical part of the global hydrological cycle that determines the distribution of water resources. It is also an essential meteorological variable used as input for hydroclimatic models and projections. However, precipitation data frequently lack complete series, especially at daily and sub‐daily precipitation stations, which are usually large, bulky, and complex. To address this, gap filling is commonly used to produce complete hydrometeorological data series without missing values. Several gap‐filling methods have been developed and improved. This study seeks to fill the gaps of 201 daily precipitation time series in Central Italy by localizing the approach used to generate the Serially Complete dataset for the Planet Earth (SC‐Earth). This method combines the outcome of 15 strategies based on four various gap‐filling techniques (quantile mapping, spatial interpolation, machine learning, and multi‐strategy merging). These strategies employ the daily dataset of the neighbouring stations and the matched ERA5 data to estimate missing values at the target stations. Both raw data and the final serially complete station datasets (SCDs) underwent comprehensive quality control. Many accuracy indicators have been utilized to evaluate the performance of the strategies' estimations and the final SCD, such as Correlation Coefficient (CC), Root mean square error (RMSE), Relative bias (Bias %), and Kling‐Gupta efficiency (KGE″). Multi‐strategy merging strategy based on the Modified Kling‐Gupta efficiency (MS1) shows the highest performance as an individual precipitation gap‐filling strategy. However, the machine learning strategy using random forest (ML3) has the most outstanding share in the final estimates among all other strategies. In the end, the temporal–spatial performance of the final SCD is promising and depends on the pattern of the missing values (MV%). The mean values of KGE″, CC, variability (α), and bias term (β) are 0.9, 0.93, 1.064, and 4.98 × 10−7, respectively.https://doi.org/10.1002/gdj3.267Central Italyclimate changeERA5gap fillingprecipitation |
| spellingShingle | Gamal AbdElNasser Allam Abouzied Guoqiang Tang Simon Michael Papalexiou Martyn P. Clark Eleonora Aruffo Piero Di Carlo Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019 Geoscience Data Journal Central Italy climate change ERA5 gap filling precipitation |
| title | Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019 |
| title_full | Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019 |
| title_fullStr | Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019 |
| title_full_unstemmed | Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019 |
| title_short | Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019 |
| title_sort | completion of the central italy daily precipitation instrumental data series from 1951 to 2019 |
| topic | Central Italy climate change ERA5 gap filling precipitation |
| url | https://doi.org/10.1002/gdj3.267 |
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