Project title: Investigation of the Terrestrial HydrologicAl Cycle Acceleration (ITHACA)
Registration number: 22-33266M
Project schedule: 01/2022–12/2026
Recipient: Česká zemědělská univerzita v Praze
Provider: GA ČR
Programme: JUNIOR STAR
Project coordinator: Česká zemědělská univerzita v Praze
CZU Project leader: doc. Ioannis Markonis, Ph.D.
Contact person: Ing. Petr Bašta
Total project budget: 24 347 700 CZK
Abstrakt:
The hydrological cycle describes the continuous movement of water through Earth’s atmosphere, hydrosphere, lithosphere and biosphere. It is influenced by internal climate variability, as well as natural fluctuations of incoming solar radiation and volcanic eruptions. In the last decades, there has been a rising interest to the indirect effect of global warming to hydrological cycle changes. Both basic theory (Clausius-Clapeyron relationship), observational data and model simulations agree that as the atmosphere gets warmer the available atmospheric water increases. Numerical model simulations also showed that the water fluxes between the atmosphere and the surface increase too. On these findings, the acceleration or intensification hypothesis of the hydrological cycle was established, describing the behavior of water fluxes in a warmer world. Since water is necessary for the survival and growth of human societies, the implications of the changes in the terrestrial branch of the hydrological cycle can be enormous.
In this project, our aim is to understand how the acceleration of the terrestrial hydrological cycle (THC) will influence water availability. We will achieve this by analyzing and modelling the THC variability in a combination of multi-scale data-driven methods with process-based climate and hydrological modeling. Conceptually, our methodological approach has three discrete steps. Starting in the past, we will scrutinize paleoclimatic data to assess the range of THC variability and the transition times between dry and wet regimes. Then, moving to the present, we will use datasets from the observational period to quantify the on-going acceleration and its statistical significance. Finally, we will use process-based modeling to understand the drivers and impacts of future change.