Using the SAPFLUXNET database to understand transpiration regulation of trees and forests

R. Poyatos, V. Flo, V. Granda, K. Steppe, M. Mencuccini, J. Martínez-Vilalta
Understanding the environmental sensitivity and the spatiotemporal patterns of transpiration of trees and forests globally is paramount to predicting vegetation drought responses and hydrological changes in a warmer and drier world. Sap flow measurements provide whole-plant estimates of transpiration rates at sub-daily to multiannual scales, but the lack of global data sets have hampered our progress on understanding the enormous diversity of water regulation strategies and drought responses in trees. Within the SAPFLUXNET initiative we have compiled the first global database of tree-level sap flow measurements. The SAPFLUXNET database contains 202 sub-daily data sets, distributed across the bioclimatic space and representative of growing-season conditions for GROTERDAN2700 trees belonging to 175 species. Sap flow time series are complemented with associated tree-, stand- and site-level metadata and with time series of the main hydro-meteorological drivers of transpiration. Here we show two cases studies of how SAPFLUXNET can be used to better understand the global patterns of tree and forest water use. First, we show how the application of neural network models can help disentangle the roles of evaporative demand and soil moisture in controlling tree transpiration. Secondly, we show how SAPFLUXNET data sets can be upscaled to the stand level, to quantify the spatiotemporal variation of forest transpiration. The application of similar analyses to the entire SAPFLUXNET database will provide an unprecedented understanding of the large-scale patterns in the environmental sensitivity of tree water use and of the biogeographical patterns of forest transpiration.
Poyatos, R., Flo, V., Granda, V., Steppe, K., Mencuccini, M. and Martínez-Vilalta, J. (2020). Using the SAPFLUXNET database to understand transpiration regulation of trees and forests. Acta Hortic. 1300, 179-186
DOI: 10.17660/ActaHortic.2020.1300.23
drought, forests, machine learning, sap flow, transpiration, upscaling

Acta Horticulturae