Hydrology and Earth System Sciences (Jan 2024)

Water cycle changes in Czechia: a multi-source water budget perspective

  • M. R. Vargas Godoy,
  • Y. Markonis,
  • O. Rakovec,
  • O. Rakovec,
  • M. Jenicek,
  • R. Dutta,
  • R. Dutta,
  • R. K. Pradhan,
  • Z. Bešťáková,
  • J. Kyselý,
  • J. Kyselý,
  • R. Juras,
  • S. M. Papalexiou,
  • S. M. Papalexiou,
  • S. M. Papalexiou,
  • M. Hanel

DOI
https://doi.org/10.5194/hess-28-1-2024
Journal volume & issue
Vol. 28
pp. 1 – 19

Abstract

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The water cycle in Czechia has been observed to be changing in recent years, with precipitation and evapotranspiration rates exhibiting a trend of acceleration. However, the spatial patterns of such changes remain poorly understood due to the heterogeneous network of ground observations. This study relied on multiple state-of-the-art reanalyses and hydrological modeling. Herein, we propose a novel method for benchmarking hydroclimatic data fusion based on water cycle budget closure. We ranked water cycle budget closure of 96 different combinations for precipitation, evapotranspiration, and runoff using CRU TS v4.06, E-OBS, ERA5-Land, mHM, NCEP/NCAR R1, PREC/L, and TerraClimate. Then, we used the best-ranked data to describe changes in the water cycle in Czechia over the last 60 years. We determined that Czechia is undergoing water cycle acceleration, evinced by increased atmospheric water fluxes. However, the increase in annual total precipitation is not as pronounced nor as consistent as evapotranspiration, resulting in an overall decrease in the runoff. Furthermore, non-parametric bootstrapping revealed that only evapotranspiration changes are statistically significant at the annual scale. At higher frequencies, we identified significant spatial heterogeneity when assessing the water cycle budget at a seasonal scale. Interestingly, the most significant temporal changes in Czechia occur during spring, while the spatial pattern of the change in median values stems from summer changes in the water cycle, which are the seasons within the months with statistically significant changes.