Multi-decadal change in global rainfall interception and its drivers

Rainfall interception loss (E_i) is one of the biggest unknowns in the global hydrological cycle. As a dynamic process, E_i depends on vegetation structure and canopy characteristics, but also on the precipitation and (micro)climatic conditions that determine the atmospheric demand for water. The spatial variability of these factors makes it difficult to reliably estimate E_i over large scales, and its sensitivity to non-stationary climate variability renders E_i trends highly uncertain. In this regard, process-based formulations accounting for key biophysical and climatic factors provide unique opportunities to examine the global patterns of E_i, as well as the driving mechanisms behind its variability.

Here we explore the estimates of E_i from the Global Land Evaporation Amsterdam Model (GLEAM v3; Martens et al., 2017), the Penman-Monteith-Leuning (PML v2; Zhang et al., 2019) model, and a recently proposed and validated global application constrained by a synthesis of global experimental data (Zhong et al., 2022). All three methods estimate long-term E_i based on Gash-type formulations (Valente et al., 1997; Van Dijk and Bruijnzeel, 2001). To reduce uncertainty, a multi-product approach is applied to examine the spatial-temporal trends in E_i. Moreover, we focus on a well-validated model (Zhong et al., 2022) to further isolate the relative contributions of precipitation, vegetation and evaporative demand to E_i variability. We find that E_i, described both in terms of the volume of evaporated water and as a percentage of precipitation, exhibits increasing trends globally. Contrasting regional changes are found, however, with a significant increase over Eurasia where the strongest vegetation greening occurs, and decreases over the Congo basin driven by a decline in precipitation. At decadal timescales, the increasing E_i is largely driven by global vegetation greening through an increase in the intercepting surface and storage capacity, while its inter-annual variations are mainly controlled by changes in precipitation. Moreover, the positive contribution of evaporative demand should not be overlooked, given the ubiquitous rise in global potential evaporation driven by atmospheric warming.

 

References

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Valente, F., David, J., and Gash, J.: Modelling interception loss for two sparse eucalypt and pine forests in central Portugal using reformulated Rutter and Gash analytical models, J. Hydrol., 190, 141–162, https://doi.org/10.1016/S0022-1694(96)03066-1, 1997.

Van Dijk, A. and Bruijnzeel, L.: Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 1. Model description, J. Hydrol., 247, 230–238, https://doi.org/10.1016/S0022-1694(01)00392-4, 2001.

Zhang, Y., Kong, D., Gan, R., Chiew, F. H. S., McVicar, T. R., Zhang, Q., and Yang, Y.: Coupled estimation of 500m and 8day resolution global evapotranspiration and gross primary production in 2002–2017, Remote Sens. Environ., 222, 165–182, https://doi.org/10.1016/j.rse.2018.12.031, 2019.

Zhong, F., Jiang, S., van Dijk, A. I., Ren, L., Schellekens, J., and Miralles, D. G.: Revisiting large-scale interception patterns constrained by a synthesis of global experimental data, Hydrol. Earth Syst. Sci., 26(21), 5647-5667, https://doi.org/10.5194/hess-26-5647-2022, 2022.