Forecasting dryland vegetation condition months in advance through satellite data assimilation

Siyuan Tian (), Albert I. J. M. Van Dijk, Paul Tregoning and Luigi J. Renzullo
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Siyuan Tian: Australian National University
Albert I. J. M. Van Dijk: Australian National University
Paul Tregoning: Australian National University
Luigi J. Renzullo: Australian National University

Nature Communications, 2019, vol. 10, issue 1, 1-7

Abstract: Abstract Dryland ecosystems are characterised by rainfall variability and strong vegetation response to changes in water availability over a range of timescales. Forecasting dryland vegetation condition can be of great value in planning agricultural decisions, drought relief, land management and fire preparedness. At monthly to seasonal time scales, knowledge of water stored in the system contributes more to predictability than knowledge of the climate system state. However, realising forecast skill requires knowledge of the vertical distribution of moisture below the surface and the capacity of the vegetation to access this moisture. Here, we demonstrate that contrasting satellite observations of water presence over different vertical domains can be assimilated into an eco-hydrological model and combined with vegetation observations to infer an apparent vegetation-accessible water storage (hereafter called accessible storage). Provided this variable is considered explicitly, skilful forecasts of vegetation condition are achievable several months in advance for most of the world’s drylands.

Date: 2019
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DOI: 10.1038/s41467-019-08403-x

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