Diagnosing evapotranspiration responses to water deficit across biomes using deep learning
Diagnosing evapotranspiration responses to water deficit across biomes using deep learning
复制标题
使用深度学习诊断跨生物群落缺水的蒸散响应
DOI:
10.1111/nph.19197
复制
发表时间:
2023
期刊:
影响因子:
9.4
通讯作者:
Stocker, Benjamin D.
中科院分区:
文献类型:
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作者:
Giardina, Francesco;Gentine, Pierre;Konings, Alexandra G.;Seneviratne, Sonia I.;Stocker, Benjamin D.
Accounting for water limitation is key to determining vegetation sensitivity to drought. Quantifying water limitation effects on evapotranspiration (ET) is challenged by the heterogeneity of vegetation types, climate zones and vertically along the rooting zone.Here, we train deep neural networks using flux measurements to study ET responses to progressing drought conditions. We determine a water stress factor (fET) that isolates ET reductions from effects of atmospheric aridity and other covarying drivers. We regress fET against the cumulative water deficit, which reveals the control of whole‐column moisture availability.We find a variety of ET responses to water stress. Responses range from rapid declines of fET to 10% of its water‐unlimited rate at several savannah and grassland sites, to mild fET reductions in most forests, despite substantial water deficits. Most sensitive responses are found at the most arid and warm sites.A combination of regulation of stomatal and hydraulic conductance and access to belowground water reservoirs, whether in groundwater or deep soil moisture, could explain the different behaviors observed across sites. This variety of responses is not captured by a standard land surface model, likely reflecting simplifications in its representation of belowground water storage.