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Data-driven worldwide quantification of large-scale hydroclimatic co-variation patterns and comparison with reanalysis and Earth System modeling
  • Navid Ghajarnia,
  • Zahra Kalantari,
  • Georgia Destouni
Navid Ghajarnia
Stockholm University

Corresponding Author:navid.ghajarnia@natgeo.su.se

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Zahra Kalantari
Department of Physical Geography, Stockholm University
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Georgia Destouni
Stockholm University
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Large-scale co-variations of freshwater fluxes and storages on land can critically regulate green (vegetation) and blue (hydrosphere) water balances, land-atmosphere interactions, and hydroclimatic hazards. Such essential co-variation patterns still remain largely unknown over large scales and in different climates around the world. To contribute to bridging this large-scale knowledge gap, we synthesize and decipher different data time series over the period 1980-2010 for 6405 hydrological catchments around the world. From observation-based data, we identify dominant large-scale co-variation patterns between main freshwater fluxes and soil moisture (SM) for different world parts and climates. These co-variation patterns are also compared with those obtained from reanalysis products and Earth System Models (ESMs). The observation-based datasets robustly show the strongest large-scale hydrological co-variation relationship to be that between SM and runoff (R), consistently across the study catchments and their different climate characteristics. The predominantly strongest large-scale SM-R co-variation relationship, however, is also the most misrepresented by ESMs and reanalysis products, followed by that between precipitation and R. Comparison between corresponding observation-based and ESM results also shows that an ESM may perform well for individual hydrological variables, but still fail in representing the patterns of large-scale co-variations between variables.
Oct 2021Published in Water Resources Research volume 57 issue 10. 10.1029/2020WR029377