Carbon flux responses to seasonal and annual hydroclimatic variability in a tropical dry forest in South Ecuador

Murkute, Charuta; Pucha-Cofrep, Franz; Carrillo-Rojas, Galo; Homeier, Jürgen; Limberger, Oliver; Fries, Andreas; Bendix, Jörg; Trachte, Katja

doi:10.5194/egusphere-2026-222

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https://doi.org/10.5194/egusphere-2026-222

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22 Jan 2026

| 22 Jan 2026

Carbon flux responses to seasonal and annual hydroclimatic variability in a tropical dry forest in South Ecuador

Charuta Murkute, Franz Pucha-Cofrep, Galo Carrillo-Rojas, Jürgen Homeier, Oliver Limberger, Andreas Fries, Jörg Bendix, and Katja Trachte

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⁻²year⁻¹. The strongest carbon uptake occurred during the wet period (Feb–May) with 173.86 ± 66 gCm⁻²month⁻¹, while it was reduced to 39.80 ± 8.12 gCm⁻²month⁻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.

Received: 15 Jan 2026 – Discussion started: 22 Jan 2026

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Charuta Murkute, Franz Pucha-Cofrep, Galo Carrillo-Rojas, Jürgen Homeier, Oliver Limberger, Andreas Fries, Jörg Bendix, and Katja Trachte

Status: final response (author comments only)

RC1: 'Comment on egusphere-2026-222', Anonymous Referee #1, 15 Feb 2026

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.

Citation: https://doi.org/10.5194/egusphere-2026-222-RC1
RC2:
'Comment on egusphere-2026-222', Anonymous Referee #2, 02 Mar 2026
I hereby provide my review of the manuscript entitled ‘Carbon flux responses to seasonal and annual hydroclimatic variability in a tropical dry forest in South Ecuador’ by Murkute and others. In this work, the authors report three years of eddy covariance flux measurements at a tropical dry forest in the Southern Hemisphere. This dataset is unique, and the study is important. However, the manuscript in its present form has several lacunae. The language needs significant improvement. The figures are inappropriate. The referencing is done poorly. The results are interesting, but not explained. Neither are they compared against previous and relevant studies. The data processing lacks critical details, casting doubt on the validity of its findings. Keeping these serious limitations in mind, I recommend a ‘Rejection’ of this manuscript. My detailed comments are provided below.
Major comments:
The language used in this manuscript needs severe improvement. In its present form, it is difficult to understand at several places what the authors want to convey. There are multiple instances of incorrect sentence formation, dangling sentences, typos, etc., which make the reading incomprehensible.

All the references are incorrectly formatted. For example, Siyum (2020), instead of (Siyum, 2020), Miles et al. (2006) instead of (Miles et al. 2020), Foken et al., etc.

Lines 37-38: How can the dry seasons be related to reduced microbial activity? Usually, water stress accelerates respiration.

Figure 1: It is required to show the flux footprint at the site, preferably overlaid on the site picture.

Lines 103-104: Any EC instrument must be installed at a sufficiently higher height to ensure it is above the surface roughness height. This is done to ensure that the EC measurement principles are met and that they are carried in the well-mixed boundary layer. As a rule of thumb, a measurement height of at least 1.5 times the average height of the surrounding canopy is recommended. Not meeting this condition can result in a gradient-driven exchange, which will not accurately represent the ecosystem-atmosphere exchanges of carbon and water.

Line 103: Site latitude and longitude are not appropriately written.

Section 2.3: The high-frequency EC measurements are susceptible to random noise, which is evident in the time series as spikes. How were these handled? Also, were the trends in the data removed?

Lines 127-128: It is an acceptable practice to remove the outliers in any dataset that fall beyond (mu plus-minus 3 sigma) if the dataset is randomly distributed. It is not clear how the authors arrived at the strategy of removing flux values that are less than or equal to 2 times the dataset's sigma. This raises serious questions about the further processing of the dataset and the conclusions drawn.

Equation 3: The components in the definition of TKE are wrong.

Line 139: It is not clear to me what is meant by "... magnitude of turbulent ... and energy.".

Lines 147-149: It is unclear what is intended to be stated in these lines. Two references are mentioned without explaining the context. Table 1 does not report these parameters as claimed in the text. What are b0, b1, and b2?

Lines 151-154: The process described here is unclear. Please write in simple sentences and in an elaborate manner. What are the dimensionless filters? How are they inversely proportional to the effectiveness of turbulent transport?

Lies 155-157: It is not appropriate to say that the boundary layer is weak or absent. What is the isothermal stratification? In fact, at nighttime, stable stratification in the atmospheric boundary layer is observed due to a cooler Earth surface in the absence of thermal heating.

Line 163: Please elaborate on the moving point method and its significance for the present study.

Sections 2.5 and 2.6: Please provide equations relating Rnѱ, Rn, and Rgѱ.

At what height was PPFD measured? Mention the measurement heights of all the variables used in this study.

Please report SM in VWC (volumetric water content).

Lines 234-235: Could you elaborate more on the variability of VPD with rainfall? Usually, it is directly related to temperature and humidity.

Line 239: Before using it further in this section and thereon, could you explain more about the values of the Niño 1+2 index and its interpretation?

Figure 2: The units on the Y-axis are not written properly, for example, Wm-2 instead of W m-2.

Table 1: This table should be improved. It should be unambiguously stated whether the values reported in this table are based on half-hourly data or otherwise. The slope is actually the degree of EBC. The intercept has a unit and should be mentioned.

Line 253: The authors report higher closure of the surface energy budget during the wet season than the dry season. This is counterintuitive to several previous studies that reported a lower closure of the energy budget in wetter periods, for example, https://dx.doi.org/10.1127/0941-2948/2006/0167, https://dx.doi.org/10.1007/s12040-019-1158-x. Can the authors explain this? Perhaps, a comparison across sites will be more informative to the readers.

Figure 3: GPP and Reco are barely discernible from each other. Please use a better colour scheme.

Line 284: "Interestingly, the onset of the respective seasons starts later in 2022-2023". This is an interesting observation; however, it is not clear how the onsets of the seasons were identified. The authors should explain this explicitly with the formulation of any objective measures, if used.

Figure 6 seems to be redundant.

Figure 8: The unit of LUE is wrong. Markers, instead of MMarkers.

Section 3.6: It is not clear at what scale the PCA was implemented. It should ideally be done at a half-hourly scale for a more realistic result.

Lines 317-318: I do not understand this sentence.

Lines 319-325: I do not quite follow the argument here. PC1 during the wet season shows positive loading from VPD and LE. Despite this, it is marked as energy-driven. On the contrary, PC2 in the dry season has positive loadings from Ts. but marked as moisture-driven. Strictly speaking, the results of the PCA are not decisive here and need further investigation. more specific analyses to be doubly sure.

Line 326: ", but Reco contributed little to this axis (0.05)". Isn't Reco the target variable here? What am I missing?

Figure 9: The captions are wrong and indicate to wrong figures in a misleading manner. The biplots are congested, and the variable labels can not be read.

The Discussion section is written poorly and lacks depth. Mostly, the results are repeated and not explained sufficiently. The reasons behind observed variabilities are not objectively probed, but have somehow been attempted to justify by speculative means.

Lines 375-380: These discussions are mostly speculative and lack a concrete evidence-based connection to the observations. In fact, they seem disjoint to a large extent.

Lines 381-383: "Ta showed minimal ... changes in Rg". This is standard textbook information, which may be redundant to the audience of a scientific journal. However, it is more important to discuss the probable reasons governing the variabilities reported in this paper.

Please explain first the Birch Effect to the audience with appropriate reference(s). Moreover, this explanation again seems to be heuristic in nature.

A section in the Discussion section attempts to link El Niño with the observed features in this study. It is to be noted that ENSO is a planetary-scale event, and 3 years of record is too short to study its impact. Thus, it will probably result in wrong attribution and explanation.

Lines 430-431: "As conditions ... canopy light absorption". How? Why do leaf shedding and reduced light absorption by the canopy affect LUE but not WUE? The exchange of light, carbon, and water all depend on the leaf area and should be impacted equally by any change in the leaf area.

Minor comments:
Line 23: incomplete sentence.

Lines 44-45: Garcia et al. (2017) did not study any Indian ecosystem.

Line 105: ... at a frequency of …

Line 109: What is TDR?

Line 114: ... at a 15-minute interval …

Line 200: Version 2 of which product? Please include a link at which the readers can access this dataset.

Line 216: ".. mentioned above." Where?

Line 219: Replace "climatic factors" with "climate variables".

Line 273: "variables", instead of "factors".

Lines 302-303: "In the remaining .... lower values". Rephrase this sentence. The same with the next one.
Citation: https://doi.org/10.5194/egusphere-2026-222-RC2

Charuta Murkute, Franz Pucha-Cofrep, Galo Carrillo-Rojas, Jürgen Homeier, Oliver Limberger, Andreas Fries, Jörg Bendix, and Katja Trachte

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Short summary

We explored how a tropical dry forest in southern Ecuador absorbs and releases carbon over three years using field measurements. The forest acted as a carbon sink, taking up more carbon than it released, especially during the rainy season. Carbon uptake followed plant growth and light availability, while carbon release responded to heat and moisture. The results show these forests are sensitive to climate extremes and play an important role in climate regulation.


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