Tuninetti, M., Tamea, S., D'Odorico, P., Laio, F., & Ridolfi, L. (2015). Global sensitivity of high‐resolution estimates of crop water footprint. Water Resources Research, 51(10), 8257-8272.
Tuninetti, M., Tamea, S., Laio, F., & Ridolfi, L. (2017). A Fast Track approach to deal with the temporal dimension of crop water footprint. Environmental Research Letters, 12(7), 074010.
Tuninetti, M., Tamea, S., & Dalin, C. (2019). Water debt indicator reveals where agricultural water use exceeds sustainable levels. Water Resources Research, 55(3), 2464-2477.
Tamea, S., Tuninetti, M., Soligno, I., & Laio, F. (2020). Virtual water trade and water footprint of agricultural goods: the 1961–2016 CWASI database. Earth System Science Data Discussions, 2020, 1-23.
Falchetta, G., Semeria, F., Tuninetti, M., Giordano, V., Pachauri, S., & Byers, E. (2023). Solar irrigation in sub-Saharan Africa: economic feasibility and development potential. Environmental Research Letters, 18(9), 094044.
Wei, D., Castro, L. G., Chhatre, A., Tuninetti, M., & Davis, K. F. (2025). Swapping rice for alternative cereals can reduce climate-induced production losses and increase farmer incomes in India. Nature Communications, 16(1), 2108.
De Petrillo, E., Fahrländer, S.F., Tuninetti, M., Andersen, S. L, Monaco, M., Ridolfi, L., Laio, F. (2025). Reconciling tracked atmospheric water flows to close the global freshwater cycle. Commun. Earth & Environ. 6, 347.
De Petrillo, E., Monaco, L., Tuninetti, M., Staal, A., Laio, F. (2025). Cell-scale atmospheric moisture flows dataset reconciled with ERA5 reanalysis. Sci Data 12, 629.
Tuninetti, M., Ridolfi, L., & Laio, F. (2022). Compliance with EAT–Lancet dietary guidelines would reduce global water footprint but increase it for 40% of the world population. Nature Food, 3(2), 143-151.
Bertassello, L., Müller, M. F., Wiechman, A., Penny, G., Tuninetti, M., & Müller-Itten, M. C. (2023). Food demand displaced by global refugee migration influences water use in already water stressed countries. Nature Communications, 14(1), 2706.
De Petrillo, E., Tuninetti, M., Ridolfi, L. & Laio, F. (2023). International corporations trading Brazilian soy are keystone actors for water stewardship. Commun Earth Environ 4, 87
Giordano, V., Tuninetti, M., & Laio, F. (2026). High-income countries dietary trajectories diverge from the global nutrition transition. Environmental Research: Food Systems.
Tuninetti, M., Tamea, S., Laio, F., & Ridolfi, L. (2017). To trade or not to trade: Link prediction in the virtual water network. Advances in Water Resources, 110, 528-537.
Distefano, T., Tuninetti, M., Laio, F., & Ridolfi, L. (2020). Tools for reconstructing the bilateral trade network: a critical assessment. Economic Systems Research, 32(3), 378-394.
Semeria, F., Ridolfi, L., Tuninetti, M. (2024). A multi-level network tool to trace wasted water from farm to fork and backwards. Environ. Res. Lett. 19 074026.
CWASI database v1.4
Contains virtual water trade and water footprint of 236 agricultural products (1961-2023)
waterCROP model v2
Physically-based agro-hydrological model which solves soil water balance on a global grid, describing the main components of the soil-atmosphere-plant continuum as a function of soil, crop, and period during the growing season
RECON dataset
Reconciled cell-scale moisture connection data with ERA5 reanalysis for the average year 2008-2017. Based on the UTrack moisture flow connections dataset, RECON ensures the closure of the hydrological balance between any cell in the world at a spatial resolution of 0.5°.
Country-ocean-moisture-flows
Reconciled global atmospheric moisture flows between countries/oceans and subcontinents (tracked volumes of precipitation and evaporation reconciled with reanalysis data, closing the annual hydrological balance). The latest updated version is based on a 3D fractional mask.
Fractional 3D mask
3D fractional region mask to aggregate global gridded datasets (e.g., climate, hydrological, or environmental data) at a regional scale