Improving the Representation of Vegetation in Global Hydrological Models using Earth Observation Data
Abstract
Vegetation structure and activity are the crucial link between the water, carbon and energy cycles. While the effects of water availability on vegetation activities have been studied extensively, the impact of vegetation on the global hydrological cycle has been given little attention. Additionally, the representation of vegetation remains a major source of uncertainty in large-scale models. Hydrological models vary in the way they include vegetation, and become less tangible when their complexity increases. This poses a challenge in validating these models, as shown by several comparisons of dynamic vegetation models.
In this context, the increasing availability and quality of Earth observation-based data provides a new avenue and valuable information to improve model simulations and gain insights into the role of vegetation within the global water cycle. In this study, we use diverse Earth observation-based data to constrain vegetation related parameters of a simple and highly transparent global hydrological model. We include GRACE terrestrial water storage anomalies, GlobSnow snow water equivalent, ESA CCI soil moisture as well as estimates of evapotranspiration from FLUXCOM and gridded runoff from GRUN in a multi-criteria calibration approach that considers the strengths and uncertainties of each data stream. The calibrated model is then used to investigate the impact of vegetation on hydrological processes as well as patterns of fluxes and storages. We conduct several factorial experiments to test alternative approaches for representing vegetation characteristics that influence processes like infiltration, root water uptake and transpiration. The approaches range from (1) the simple differentiation of vegetated and non-vegetated areas over (2) defining vegetation characteristics as a function of percentage of tree cover, to (3) applying plant functional type-specific parameters. Finally, we compare the experiments with each other and against observations to quantify their ability to reproduce observational patterns and to assess the effects of vegetation on simulated hydrological processes across spatio-temporal scales.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.H21A..07T
- Keywords:
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- 1804 Catchment;
- HYDROLOGY;
- 1805 Computational hydrology;
- HYDROLOGY;
- 1847 Modeling;
- HYDROLOGY;
- 1855 Remote sensing;
- HYDROLOGY