Assessment of Water Stress Conditions in Central Italy by the Use of Ground and Remotely Sensed Weather Datasets

Aridity and drought, which are determined by climatic and temporary water scarcity, respectively, are important limiting factors for plant gross primary production. These phenomena are commonly assessed and/or monitored by means of weather indices, most of which are based on observations of precipitation and potential evapotranspiration. The estimation of such indices over large areas can be carried out using multiple datasets, i.e., those derived from weather stations, satellite images, and ground radars. The possibility of using interpolated or remotely sensed datasets in place of ground measurements was currently investigated for Tuscany, a region in Central Italy, showing complex and heterogeneous environmental features. The former weather datasets were first evaluated versus corresponding ground measurements. Next, the basic weather variables were combined and cumulated over 30-60 days to yield synthetic indicators of water deficit, which were assessed in the same way. Finally, these indicators were evaluated to predict the soil water conditions of a meadow and an olive grove during the 2021 summer period. The results obtained indicate that the use of the multi-source weather datasets induces only a minor deterioration of the water stress indicators and is therefore efficient to monitor the water status of different ecosystems with high spatial (200 m) and temporal (daily) details.

Automated summary

This study investigates the possibility of using interpolated or remotely sensed datasets instead of ground measurements for drought assessment in central Italy. The results show that this method is efficient and accurate for monitoring water status in different ecosystems at high spatial and temporal resolutions.