Are Global Drylands Self-Expanding?

Aridification threatens not only water availability but also adversely affects ecosystem health, and energy security. Using the (atmospheric) aridity index (AI) – defined as precipitation, (P) over potential evaporation (Ep) – several studies have shown that global drylands are either expanding or will expand in the future. Expansion is defined as the reduction of AI below 0.65, i.e., a change from a humid to a dry region may be owed to deficits in P and/or increases in Ep. However, the actual mechanisms and processes driving dryland expansion remain less explored. Here, we use an observationally-constrained Lagrangian transport model to test if expansion of drylands is self-fuelled: can reductions in moisture transport from existing drylands result in aridification of existing humid regions and thus lead to dryland expansion?

To estimate the spatial extent of drylands, we calculate AI using P from the Multi-Source Weighted-Ensemble Precipitation (MSWEP) (Beck et al 2019) and Ep from the hPET dataset (Singer et al. 2021). To quantify the changes in moisture and heat transport into newly expanded drylands, we use global simulations of the FLEXPART version 10.4, forced with the ERA-Interim reanalysis for a period of 38 years (1981–2018). The FLEXPART outputs include the properties of the air parcels at 3-hourly time steps, which are then post-processed using the Heat and Moisture Tracking Framework (HAMSTER v1.2.0) described by Keune et al. (2022) and bias-corrected using evaporation from the GLEAM-Hybrid dataset (Koppa et al. 2022).

Preliminary results show that between 1981 and 2018, ~5.5 million km2 of the terrestrial land surface underwent aridification (humid to dryland transition). Further, our results indicate that, on an average, ~45% of the reduction in AI can be attributed to reduction in P, out of which ~32% can be traced to reduction in moisture transport from existing drylands. Preliminary findings support our hypothesis that drylands are indeed self-expanding. 

References:

Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk, A. I. J. M., McVicar, T. R., & Adler, R. F. (2019). MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment, Bulletin of the American Meteorological Society, 100(3), 473-500.

Keune, J., Schumacher, D. L., & Miralles, D. G. (2022). A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models. Geoscientific Model Development, 15(5), 1875–1898. doi:10.5194/gmd-15-1875-2022.

Koppa, A., Rains, D., Hulsman, P., Poyatos, R., & Miralles, D. G. (2022). A deep learning-based hybrid model of global terrestrial evaporation. Nature Communications, 13(1), 1912. doi:10.1038/s41467-022-29543-7.

Singer, M. B., Asfaw, D. T., Rosolem, R., Cuthbert, M. O., Miralles, D. G., MacLeod, D., … Michaelides, K. (2021). Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981-present. Scientific Data, 8(1), 224. doi:10.1038/s41597-021-01003-9