the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Drought thresholds shape divergent responses in ecosystem water use efficiency
Abstract. Ecosystem water use efficiency (WUE), the ratio of gross primary productivity (GPP) to evapotranspiration (ET), couples the land carbon and water cycles and is a key indicator of water–carbon coupling and ecosystem resilience. Yet how WUE responds to intensifying drought has remained unresolved, with studies reporting both increases and decreases. Here, combining multi-source global satellite, reanalysis, and model-fusion datasets, we show that across 57.5 % of vegetated land WUE first rises and then falls as drought deepens, reversing only once drought reaches extreme severity. This reconciles the apparent contradiction in earlier work, whose reported increases and decreases are the rising and falling limbs of one threshold response rather than competing outcomes. The reversal arises from an ecohydrological asymmetry, in which the early rise is driven by ET declining faster than GPP while continued GPP loss under severe drought overturns it. The depth of the threshold is not uniform but set by aridity, radiation, leaf area, and atmospheric dryness overlaid on vegetation type, so that denser, more productive ecosystems tolerate more severe drought before reversing. Because these thresholds cluster in the semi-arid and transitional biomes that dominate the variability of the land carbon sink, a rising WUE signals only transient coping rather than durable resilience, and the threshold marks an early indicator of where intensifying drought begins to erode ecosystem carbon uptake.
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Status: open (until 26 Nov 2026)
- RC1: 'Comment on egusphere-2026-5551', Anonymous Referee #1, 27 Oct 2026 reply
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- 1
The manuscript presents a global analysis of WUE responses to drought and proposes a two-stage threshold framework. The topic is relevant and the analysis is extensive. However, the following issues should be resolved before publication.Â
1. Logarithmic decomposition (Section 2.6, Fig. 7). The identity ln(1+WUEA) = ln(1+GPPA) − ln(1+ETA) is exact only if mean(WUE) = mean(GPP)/mean(ET), which generally does not hold. The reported means do not close: GPPA = −2.6% and ETA = −9.8% at the threshold imply WUEA ≈ 8.0%, not the reported 14.8%. In addition, Fig. 7d appears to show the reversal driven mainly by the ET term rather than by GPP loss. Please report the closure residual and check that the mechanistic statements (e.g., L16, L276–278) are consistent with Fig. 7d.
2. FLUXCOM coverage (L73; L504–506). The cited FLUXCOM GPP product covers 1980–2013, whereas the study period is 1982–2016. Please clarify how 2014–2016 were handled.
3. Threshold robustness (Section 2.4). Values of DSI below −1.67 represent only about 5% of months, so Stage II rests on few observations. Please report the minimum sample size per stage and provide an uncertainty estimate for the threshold (e.g., bootstrap).
4. Machine-learning attribution (Sections 2.5 and 3.3). Two-stage cells make up 60.0% of valid cells, so the 72.6% accuracy should be compared against this baseline; please add balanced accuracy or kappa. Please also define the aridity index (range 0–200 in Fig. 6a) and its direction.
Overall, the manuscript has improved substantially through the previous rounds of review, and I consider it suitable for publication once the authors have addressed the points above. I leave it to the editor to verify that the revisions have been made, and I do not need to see the manuscript again.