Spatio-Temporal Prediction of Tree Water Deficit from a Sparse Network of Dendrometers

Music City Center 

Dendrometers are small devices attached to trees which measure stem radius with high precision. These can capture long-term growth, diurnal cycles, and more importantly sustained stem shrinkage due to lack of water. Thanks to a unique network of such dendrometers in Switzerland (TreeNet), which operates continuously, we can model tree water deficit (TWD) in space-time. Crucial for drought monitoring efforts is then the creation of TWD maps at the national scale. However, dendrometers are installed on trees only at a few sites, with a low spatial coverage, which makes spatial prediction a challenging extrapolation task. But at each site a high temporal frequency is available to model TWD as a response to climatic and environmental variables. We leverage this and design a recurrent neural network architecture for joint modeling of multiple TWD series. This allows to not only forecast well in time but also to predict/extrapolate in space.

spatial extrapolation

time series forecasting

deep neural network

joint modeling

regularization 

Section on Statistics and the Environment