Preprints
https://doi.org/10.5194/nhess-2022-260
https://doi.org/10.5194/nhess-2022-260
21 Nov 2022
 | 21 Nov 2022
Status: this preprint has been withdrawn by the authors.

Multi-scale EO-based agricultural drought monitoring system for operative irrigation networks management

Chiara Corbari, Nicola Paciolla, Giada Restuccia, and Ahmad Al Bitar

Abstract. Drought prediction is crucial especially where the rainfall regime is irregular and agriculture is mainly based on irrigated crops, such as in Mediterranean countries. In this work, the main objective is to develop an EO-based agricultural drought monitoring system (ADMOS) for operative irrigation networks management at different spatial and temporal scales. Different levels of drought are identified based on an integrated indicator combining anomalies of rainfall, soil moisture, land surface temperature and vegetation indices, allowing to consider the different droughts types and their timing looking on the end-user’s perspective. Multiple remote sensing data, which differ on sensing techniques, spatial and temporal resolutions and electromagnetic frequencies, are used for each anomaly computation. The analyses have been performed over two Irrigation Consortia in Italy (the Chiese and Capitanata ones), which differ for climate, irrigation volumes and techniques, and crop types. The obtained results show a negative correlation between cumulated ADMOS and the irrigation volumes in the Capitanata area, while in the Chiese Consortium a zero correlation is obtained with an almost constant amount of irrigation volumes provided to the crops every year independently from the drought condition. In both areas, crop yields seem to be almost uncorrelated to the drought index, as production is highly sustained by irrigation. Moreover, discrepancies on the anomalies sign is observed, especially when soil moisture is considered. The results also clearly show that asynchronies may exist especially between soil moisture anomalies and vegetation or land surface temperature anomalies.

This preprint has been withdrawn.

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Chiara Corbari, Nicola Paciolla, Giada Restuccia, and Ahmad Al Bitar

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on nhess-2022-260', Qi Liu, 22 Dec 2022
  • RC1: 'Comment on nhess-2022-260', Anonymous Referee #1, 02 Jan 2023
  • RC2: 'Comment on nhess-2022-260', Anonymous Referee #2, 26 Jan 2023
  • RC3: 'Comment on nhess-2022-260', Anonymous Referee #3, 03 Feb 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on nhess-2022-260', Qi Liu, 22 Dec 2022
  • RC1: 'Comment on nhess-2022-260', Anonymous Referee #1, 02 Jan 2023
  • RC2: 'Comment on nhess-2022-260', Anonymous Referee #2, 26 Jan 2023
  • RC3: 'Comment on nhess-2022-260', Anonymous Referee #3, 03 Feb 2023
Chiara Corbari, Nicola Paciolla, Giada Restuccia, and Ahmad Al Bitar
Chiara Corbari, Nicola Paciolla, Giada Restuccia, and Ahmad Al Bitar

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This preprint has been withdrawn.

Short summary
We developed an EO-based agricultural drought index (ADMOS) for irrigation management. ADMOS identifies drought levels using rainfall, soil moisture, surface temperature and vegetation anomalies from multiple satellite data. ADMOS was tested in two Italian areas, diverse in climate, crop and irrigation. In one, ADMOS and irrigation volumes were negatively correlated; while in the other, no correlation was found, because the same irrigation is applied every year.
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