PhD opportunity at University of Torino, Italy

[Stefano Ferraris]

Please write to stefano....@unito.it if interested to the following PhD position

in the frame of the program in: 

Modeling and data analysis at Università degli Studi di Torino.

Applicants must hold a master in Engineering, Physics, Mathematics, Statistics, Informanatics, Agricultural or Forest Sciences, or similar curricula.

“Environmental monitoring data and probabilistic evaluation of
irrigation water resources in Piemonte”

The doctoral project regards the use of environmental monitoring
data for the evaluation of the irrigated areas and the water discharges
actually utilised in Piemonte.
This project aims to quantify areas and discharges in order to
prevent agricultural lack of water. Some drought has occurred in the last
years, but today, an impressive amount of data is available and a variety
of statistical and computer science tools can be applied for the study of
this big data. Hundreds of meteorological stations in the Piemonte Region
are operated in real time by ARPA every day. Also, the river level
monitoring allows us to quantify the amount of water available for diversion in
irrigation canals. These data cover more than twenty years and can be
compared with satellite data.
This project will join specific mathematical models and a machine
learning approach to combine ground and satellite data in an effective and
unprecedented way. Furthermore, the use of such results will suggest
prevention actions in order to limit the drought related risks due also to
climate change.

Stefano Ferraris

Full professor

DIST Interuniversity Dept. of Regional and Urban Studies and Planning

Politecnico and Università of Turin, Italy

Our papers since 2022:

 

1) aquifer recharge, in HESS https://hess.copernicus.org/articles/26/407/2022/ Impact Factor: 6.617; 5-Year Impact Factor: 7.062, Q1

2) cosmic ray soil moisture, in ESSD, https://essd.copernicus.org/articles/14/1125/2022/ Impact Factor: 11.815; 5-Year Impact Factor: 12.880, Q1

3) grassland water balance, in J. Hydrology, https://doi.org/10.1016/j.jhydrol.2022.127948 Impact Factor: 6.708; 5-Year Impact Factor: 6.731, Q1

4) shrubs water balance https://www.mdpi.com/2073-4433/13/6/977/pdf Impact Factor: 3.110; 5-Year Impact Factor: 3.222, Q2

5) rainfall statistics https://www.mdpi.com/2504-3110/6/9/509/pdf Impact Factor: 3.577; 5-Year Impact Factor: 3.396, Q2

6) mountain age of water,  in HESS, https://hess.copernicus.org/articles/27/2301/2023/ Impact Factor: 6.617; 5-Year Impact Factor: 7.062, Q1

7) snow cover area model, in STOTEM, https://www.sciencedirect.com/science/article/pii/S0048969722062945, Impact Factor: 10.753; 5-Year Impact Factor: 10.237, Q1

8) wet and dry spells, in ASMCO https://ascmo.copernicus.org/articles/10/51/2024/ SJR: 1 Q1

9) African agriculture management, in Sensors, https://doi.org/10.3390/s23177632, Impact Factor: 3.9, Q1

10) transit time in catchments, in HESS https://hess.copernicus.org/articles/28/1915/2024/ HESS Impact Factor: 6.617; 5-Year Impact Factor: 7.062, Q1

11) mountain evapotranspiration, in J. Hydrology, https://doi.org/10.1016/j.jhydrol.2024.131223 Impact Factor: 6.708; 5-Year Impact Factor: 6.731, Q1