| 22 Jan 2026
Carbon flux responses to seasonal and annual hydroclimatic variability in a tropical dry forest in South Ecuador
Abstract. Tropical dry forests play an important role in the global carbon cycle, but their responses to climate variability are still not well understood. Using a three-year period (April 2022 to March 2025) of eddy covariance measurements, we studied seasonal and annual controls on carbon balances in a Tumbesian dry forest in Southern Ecuador. During the study period, the forest functioned as a net carbon sink, with a net ecosystem exchange (NEE) of -285 gCm−2 year−1. The strongest carbon uptake occurred during the wet period (Feb–May) with 173.86 ± 66 gCm−2 month−1, while it was reduced to 39.80 ± 8.12 gCm−2 month−1 in the dry season (August–November).
Light use efficiency (LUE) and water use efficiency (WUE) were used to characterize the functional controls on carbon fluxes at both seasonal and annual scales. WUE showed relatively stable water–carbon exchange, whereas LUE displayed clear seasonal variation, reflecting the strong influence of seasonal vegetation growth and greenness. Principle component analysis (PCA) was conducted to further analyze controlling mechanisms in carbon fluxes. Seasonal results showed that gross primary productivity (GPP) was mainly controlled by energy-related factors, while ecosystem respiration (Reco) was primarily driven by a moisture–temperature gradient. Annually, GPP was predominantly influenced by variations in vapor pressure deficit (VPD), soil temperature (Ts), and incoming radiation (Rg), reflecting a strong coupling between surface energy balance and atmospheric moisture demand. These drivers were further modulated by ENSO related climate variability, as reflected by shifts in their PCA loadings across years. Overall, the results reveal a decoupling between photosynthesis and respiration and show that tropical dry forests are highly vulnerable to increasing climate extremes, highlighting the need for improved representation of these processes in Earth system models.
egusphere-2026-222
This study examined fluxes from a seasonal dry forest at altitude in Southern Ecuador. The site was a sink with sensitivity to moisture and radiation levels. This is a well implement and reported flux study, ideal for the modelling community for calibration of ecosystem models dealing with ecosystem trajectories and climate change. It is I a useful study in that it is data from under-represented ecosystem type (dry forest) and from an understudied region, and a forest at altitude.
The site appears to non-ideal and the energy balance closure reflects this at ~0.7 but this is openly reported and described. Unfortunately, it is only a 3 year study, hopefully the site can be supported at a decadal scale to develop a stronger data set capturing the significant inter- and intra-annual variability. PCA analysis was useful to determine the dominant environment drivers of C and water exchange, as well as separating the behaviours of WUE and LUE, key functional attributes to assess sensitivities to changes in rainfall distribution and increases in temperatures and atmospheric CO2.
So a somewhat stock-standard flux paper, but well acquitted and reported, a useful contribution. Worthy of publication.
Specific comments
L22 “… moderate annual carbon assimilation, but low mean respiration of this semi-arid ecosystems, dry forests are highly efficient carbon sequesters Poulter et al. (2014)”
Throughout the text the method to cite references is incorrect, looks like a ref software issue. Check and fix throughout.
This text sentence should be “… moderate annual carbon assimilation, but low mean respiration of this semi-arid ecosystems, dry forests are highly efficient carbon sequesters (Poulter et al., 2014).”
Also re-write as “… dry forests have a high carbon sequestration efficiency (Poulter et al., 2014).”
L54 “The authors highlighted that semi-arid ecosystems play an important role in driving trends and interannual variations in the global carbon cycle, primarily due to seasonal water availability, which was further supported by Biederman et al. (2016).”
A useful sentence is but is missing the fact that dry forest burns along with tropical savannas, especially high rainfall woody savannas, which behave similarly to these dry forests. A significant fraction of the interannual variation of global carbon cycle is driven by dry forest and savannas fire (see papers by van der Werf and colleagues) – especially large wet seasons that drive growth, followed by dry seasons that then burn this fuel with a major emission of CO2. So, the text needs to recognise the role of fire in dry forest carbon dynamics.
It would be also worth reporting the fire regime of the site and region. The word fire does not appear anywhere in the ms, odd for a paper on dry forests. The site is at altitude with perhaps low population density, with fire uncommon, but we need to know this. Is it possible to include a guesstimate of fire return interval? Think of fire as an herbivore, it consumes leaf area and primary productivity. No fire will turn a dry forest into a higher cover forest dry rainforest.
L90-91 Provide more detail on the typical extent of the seasonality - what is dry season rainfall/ less than 20-30 mm? The distribution of rainfall matters.
L101 Provide more detail on the forest structure, type and extent. Link to Figure 1a, is this a widely distributed forest type. Fig 1a provides a green coloured relief map, or forest distribution? The caption does not state what the green shading is.
Is there an herbaceous layer, what is the stem density, biomass and/or approximate seasonal range in LAI? I see you have used NDVI, but an LAI range would be useful as well.
L112 “the Li-COR Inc. smart chamber (LI-870 CO2/H2O gas analyser and 8200-01S Smart chamber) was used in field campaigns.” How many campaigns, in the wet and dry seasons?
L117 “… standard corrections Foken et al. to …”. Add year for this citation.
L167 “… account for the influence of slope and aspect on radiation measurements, corrections were applied to net radiation.” I have not seen a correction to Rnet for slope and aspect? The sensors are presumably level and are capturing the radiant load as a function of aspect. If you need to correct for slope, you should not be running an eddy covariance system on the site. And I don’t get why you need to correct for slope.
Figure 2 and caption add a space on the Y axis labels “mmmonth−1”, include a space “mm month-1). Easier to read, especially on a y axis label. Same for all units “Wm-2”, should be “W m-2” .
The term ‘water balance’ in the cation as P-ET maybe water deficit as the term here, water balance implies you quantified recharge and runoff.
Figure 4 panel g) what is this plot, its not described in the caption
Unit label “gCm−2”, change to “g C m-2” throughout.
Table 3 Useful data but maybe move out of the body text and to the Supplementary Information, keep the cites to the Table.
Figure 5 “Cumulative distribution of a) gross primary productivity …” This is a not a distribution, I would call this a cumulative time series plot. Plus the units are kind of odd, cumulative per hour? Why not per day given it’s a year i.e. 365 points.
I though the Discussion read well, an interesting study.