Assessment of the Water Cycle Acceleration in the Czech Republic

There is general agreement about the water cycle acceleration in the community, although its strength over land has been debated lately. While some common behavior is observed under similar climatic conditions across the globe, at the regional scale the water cycle's response to global warming is specific to its location's unique characteristics. Herein, we quantify the water cycle and characterize its climatology over the Czech Republic, which constitutes an essential headwaters area of the European continent, and in hydrological terms, it can be called the “roof of Europe”. The country's location involves three drainage catchments: the Elbe, Oder, and Danube rivers, which lead to the North Sea, the Baltic Sea, and the Black Sea respectively. Our analysis includes various data sets at  different spatiotemporal scales like: The Twentieth Century Reanalysis (20CR), CPC Merged Analysis of Precipitation (CMAP), CPC Global Unified Gauge-Based Analysis of Daily Precipitation (CPC), Climatic Research Unit gridded Time Series (CRU TS), Global Historical Climatology Network monthly (GHCN), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), Global Precipitation Climatology Centre (GPCC), The Global Precipitation Measurement Integrated Multi-satellite Retrievals (GPM IMERG), Global Runoff Data Centre (GRDC), Global Runoff Reconstruction (GRUN), Moderate Resolution Imaging Spectroradiometer Terra Net Evapotranspiration (MOD16A2), National Centers for Environmental Prediction DOE Reanalysis 2 (NCEP DOE), National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP NCAR), NOAA's Precipitation Reconstruction over Land (PRECL), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TRMM 3B43), and University of Delaware Precipitation (UDEL). To exploit the availability of the various data sets for each component of the water cycle we merged them via simple weighted averages, a multi-source data integration method that has proven to be effective and with low computational requirements. Subsequently, we linked the computed components constraining them by the water budget equation. Thereafter, the time series were analyzed to quantify trends and their statistical significance, as well as their uncertainty derived by the multiple datasets. In addition to the time series analysis and the statistics involved so far, a spatial analysis explored the water cycle climatology and its variability over the whole Czech Republic and then its behavior in subdomains defined by the watersheds within the borders of the country.