| Text | 461079 2021 10.1016/j.ejrh.2021.100880 Scopus 2 s2.0 85112784617 Machine Learning Random Forest Lake Bracciano Climate Change Water abstraction Water management Climate change and water abstraction impacts on the long term variability of water levels in Lake Bracciano Central Italy A Random Forest approach Guyennon N.; Salerno F.; Rossi D.; Rainaldi M.; Calizza E.; Romano E. National Research Council, Water Research Institute, IRSA CNR, Roma, National Research Council, Water Research Institute, IRSA CNR, Roma, Italy, , , Italy; National Research Council, Water Research Institute, IRSA CNR, Roma, National Research Council, Water Research Institute, IRSA CNR, Roma, Italy, , , Italy; National Research Council, Water Research Institute, IRSA CNR, Brugherio, National Research Council, Water Research Institute, IRSA CNR, Brugherio, Italy, , , Italy; National Research Council, Water Research Institute, IRSA CNR, Brugherio, National Research Council, Water Research Institute, IRSA CNR, Brugherio, Italy, , , Italy; Ministry of Education, University and Research Viale Trastevere, Roma, 76/a 00153, Ministry of Education, University and Research Viale Trastevere, 76/a 00153 Roma, , Italy; Department of Environmental Biology, Sapienza University of Rome, via dei Sardi 70, Rome, 00185, Department of Environmental Biology, Sapienza University of Rome, via dei Sardi 70, 00185 Rome, Italy, , Italy Study Region Lake Bracciano has been historically used as a strategic water reservoir for the city of Rome Italy since ancient times. However, following the severe water crisis of 2017, water abstraction has been completely stopped. Study Focus The relative impact of the various drivers of change climatological and management on fluctuations in lake water level is not yet clear. To quantify this impact, we applied the Random Forest RF machine learning approach, taking advantage of a century of observations. New Hydrological Insights for the Region Since the late 1990s the monthly variation in lake water levels has doubled, as has variation in monthly abstraction. Increased variation in annual cumulated precipitation and a rise in mean air temperature have also been observed. The RF machine learning approach made it possible to confirm the marginal role of temperature, the increasing role of abstraction during the last two decades from 24 % to 39 % , and the key role played by the increased precipitation variability. These results highlight the notable prediction and inference capabilities of RF in a complex and partially unknown hydrological context. We conclude by discussing the limits of this approach, which are mainly associated with its capacity to generates scenarios compared to physical based models. 37 Published version http //www.scopus.com/record/display.url eid=2 s2.0 85112784617 origin=inward Climate change and water abstraction impacts on the long term variability of water levels in Lake Bracciano Central Italy A Random Forest approach 2021 Guyennon et al Climate change and water abstraction impacts on the long term variability of water levels in Lake Bracciano.pdf Articolo in rivista Elsevier 2214 5818 Journal of Hydrology Regional Studies Journal of Hydrology Regional Studies franco.salerno SALERNO FRANCO emanuele.romano ROMANO EMANUELE david rossi ROSSI DAVID nicolasdominique.guyennon GUYENNON NICOLAS DOMINIQUE |
