Abstract
A key aspect of the land surface response to the atmosphere is how quickly it dries after a rainfall event. It is key because it will determine the intensity and speed of the propagation of drought and also affects the atmospheric state through changes in the surface heat exchanges. Here, we test the theory that this response can be studied as an inherent property of the land surface that is unchanging over time unless the above- and below-ground structures change. This is important as a drydown metric can be used to evaluate a landscape and its response to atmospheric drivers in models used in coupled land–atmosphere mode when the forcing is often not commensurate with the actual atmosphere. We explore whether the speed of drying of a land unit can be quantified and how this can be used to evaluate models. We use the most direct observation of drying: the rate of change of evapotranspiration after a rainfall event using eddy-covariance observations, or commonly referred to as flux tower data. We analyse the data and find that the drydown timescale is characteristic of different land cover types, then we use that to evaluate a suite of global hydrological and land surface models. We show that, at the site level, the data suggest that evapotranspiration decay timescales are longer for trees than for grasslands. The studied model’s accuracy to capture the site drydown timescales depends on the specific model, the site, and the vegetation cover representation. A more robust metric is obtained by grouping the modeled data by vegetation type and, using this, we find that land surface models capture the characteristic timescale difference between trees and grasslands, found using flux data, better than large-scale hydrological models. We thus conclude that the drydown metric has value in understanding land–atmosphere interactions and model evaluation.
Highlights
Soil moisture state and dynamics are key for climate and water resources assessments, in water-limited periods and regions [1]
No calibration to better represent local conditions was performed by the models for this study, as we aim to evaluate the drydown model skill on large-scale applications such as WRR1
In this work, using flux tower to predict drought responses, and provide useful information to both model developers that data, we look for a process based evaluation that helps characterise the models in their capability to are constantly working to improve the tools and water managers or other potential users of WRR1 predict drought responses, and provide useful information to both model developers that are that might be interested in drought response over particular regions
Summary
Soil moisture state and dynamics are key for climate and water resources assessments, in water-limited periods and regions [1]. The memory of soil moisture, understood as the persistence of soil moisture anomalies, can be of the order of weeks to months [8] and, longer than the memory of atmospheric anomalies (hours to days). This difference in residence times gives soil moisture a buffering or intensifying impact on climate extremes at the surface such as droughts, floods, or heat waves [9], as well as a role in the development of atmospheric processes at shorter timescales [10]. GHMs and LSMs can work in stand-alone mode, driven by meteorological data, or coupled to the atmosphere in general circulation and earth system models
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
