Preprints
https://doi.org/10.5194/egusphere-2024-889
https://doi.org/10.5194/egusphere-2024-889
05 Apr 2024
 | 05 Apr 2024
Status: this preprint is open for discussion.

Assessment of seasonal soil moisture forecasts over Central Mediterranean toward groundwater management

Lorenzo Silvestri, Miriam Saraceni, Giulia Passadore, and Paolina Bongioannini Cerlini

Abstract. It is highly likely that the Mediterranean region will experience increased aridity and hydrological droughts. Therefore, seasonal forecasts of soil moisture can be a valuable resource for groundwater management. However, their accuracy in this region has not been evaluated against observations. This paper presents an evaluation of soil moisture in the Central Mediterranean region during the period 2001–2021 using the seasonal forecast system SEAS5. Standardized anomalies of soil moisture are compared with observed values in ERA5 reanalysis. In terms of the average magnitude of the forecast error and the anomaly correlation coefficient, the forecasts demonstrate good performance only in certain regions of the domain for the deepest soil layer at 289 cm, the most interesting for groundwater management. No clear overlap with specific land features such as orography, land cover, or distance from the coast has been observed with respect to the forecast performance. Accordingly, seasonal forecasts can be used to detect wet and dry events for the deepest soil layer in certain regions, with lead-times of up to 6 months. In these regions, the area under the Relative Operating Characteristic (ROC) curve can reach values larger than 0.8. Dry events are generally better captured than wet events for all soil layers. We also analyzed the effectiveness of seasonal forecasts in predicting wet and dry events in Northern and Central Italy for the 2012–2013 period, with a lead-time of 6 months. We found that seasonal forecasting has great potential for groundwater management in certain areas of the Central Mediterranean. However, improvements are needed in observations, data assimilation methods, and the seasonal forecasting system to ensure reliable forecasts for upper soil layers and other regions.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Lorenzo Silvestri, Miriam Saraceni, Giulia Passadore, and Paolina Bongioannini Cerlini

Status: open (until 31 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2024-889', Giacomo Medici, 13 Apr 2024 reply
  • RC1: 'Comment on egusphere-2024-889', Anonymous Referee #1, 30 Apr 2024 reply
  • RC2: 'Comment on egusphere-2024-889', Anonymous Referee #2, 13 May 2024 reply
  • RC3: 'Comment on egusphere-2024-889', Anonymous Referee #3, 13 May 2024 reply
Lorenzo Silvestri, Miriam Saraceni, Giulia Passadore, and Paolina Bongioannini Cerlini
Lorenzo Silvestri, Miriam Saraceni, Giulia Passadore, and Paolina Bongioannini Cerlini

Viewed

Total article views: 262 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
197 46 19 262 10 9
  • HTML: 197
  • PDF: 46
  • XML: 19
  • Total: 262
  • BibTeX: 10
  • EndNote: 9
Views and downloads (calculated since 05 Apr 2024)
Cumulative views and downloads (calculated since 05 Apr 2024)

Viewed (geographical distribution)

Total article views: 262 (including HTML, PDF, and XML) Thereof 262 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 May 2024
Download
Short summary
This work demonstrates that seasonal forecasts of soil moisture are a valuable resource for groundwater management in certain areas of the Central Mediterranean. In particular, they show significant correlation coefficients and forecast skill for the deepest soil moisture at 289 cm depth. Wet and dry events can be predicted 6 months in advance and, in general, dry events are better captured than wet events.