the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Linking Woody Plants, Climate, and Evapotranspiration in a Temperate Savanna
Abstract. Evapotranspiration is the dominant pathway by which water returns from land surfaces and vegetation to the atmosphere in many semiarid and subhumid regions. In this study, we integrated satellite-based estimates of evapotranspiration with climate, runoff, and woody-vegetation data to evaluate how changes in precipitation, temperature, and canopy cover jointly influence water loss in a temperate savanna that spans both semiarid and subhumid climates. Our validation at the sub-basin scale showed that modeled evapotranspiration agreed moderately well with water-balance estimates (coefficient of determination ≈ 0.65, bias −7 millimeters per water year, and root mean square error 103 millimeters per water year). Across the region, annual evapotranspiration totals generally reached about 90 percent of precipitation, indicating an ecosystem strongly driven by atmospheric water demand. In dry years, water loss occasionally exceeded rainfall, highlighting a heightened sensitivity to soil moisture shortages and extreme heat. Areas with high woody-canopy cover consistently exhibited higher evapotranspiration and lower net water surplus. Notably, where canopy cover exceeded 80 percent in the driest portions of the study area, the soil water surplus turned negative over multiple years. These findings underscore the potential for expanding woody cover to limit groundwater recharge and reduce overall water availability, especially under warming and more variable precipitation regimes. Future work could explore fine-scale, long-term impacts of woody plant density and targeted management strategies that optimize trade-offs among vegetation growth, ecosystem health, and water resources.
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Status: open (until 11 Jun 2025)
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RC1: 'Comment on egusphere-2025-1594', Anonymous Referee #1, 13 May 2025
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This work analyses evapotranspiration using satellite estimates and examines the effects of climate and vegetation across different landscapes and climatic regions in Texas. The interactions between climate and vegetation landscapes are an interesting topic, and their analysis can contribute to improving our understanding of their effects. The satellite-derived evapotranspiration showed good validation against field runoff data at the sub-basin scale. The results presented are as expected in terms of the differences observed between climatic regions and vegetation cover in the study area, which supports the credibility of the satellite estimates, beyond the validation against field data. I provide some suggestions to improve the manuscript in my specific comments below.
Q1. In Section 3.1, the comparison of the MOD16 product with WBET estimates showed good overall accuracy, but performance was very low between 2009–2011. Even 2018 and 2022 can be considered as years of poor performance, since the R² did not reach 0.5. In addition, sub-basins 1–3 showed low accuracy. It might be better not to include these years and sub-basins in the subsequent analyses, as the evapotranspiration estimates are not reliable and could introduce bias into the interpretation of results.
Moreover, a more detailed explanation should be provided in the discussion (Section 4.1) about why satellite estimates performed poorly in these years and sub-basins. You mention the effects of Hurricane Ike and that performance is worse in dry years (2011 and 2022), but 2009 and 2010 also show low performance despite precipitation being closer to the average. The MOD16 product performs better in drier regions than in wetter ones. Therefore, why does accuracy decrease in dry years if the product tends to perform better in dry conditions? It would be helpful to elaborate on why performance was poor in those years as well. Additionally, although you mention that performance is lower in HUC8s 1–4, possible reasons are not discussed.
Q2. Consider displaying Figure 7 as a 2 × 2 panel to increase the size of the scatterplots.
Q3. In the discussion section, all figures are referenced as "Figure 4" (e.g., Figure 4–5, Figure 4–6, etc.). I assume this is a mistake, as Figure 4 is only relevant to the accuracy of the validation.
Q4. In Section 4.3, you explain that there is a negative relationship between temperature and ET, and that the landscape includes a mix of deciduous and evergreen vegetation. Usually, evergreen vegetation can reduce their transpiration in summer (water saver) but deciduous vegetation increases it due to higher water demand (water spender). Therefore, under higher temperatures, ET would be expected to increase in deciduous vegetation. You might consider better explaining the differences between vegetation types (evergreen vs. deciduous) across the region and their role in ET.
Also, the relationship between temperature and ET is usually non-linear. Higher temperatures increase ET up to a threshold, after which ET decreases due to stomatal closure (as you explain in the section). It might be useful to include a non-linear analysis, such as a Generalized Additive Model (GAM), to test whether there is a positive relationship up to a certain threshold. Therefore, temperature does not have a strictly negative effect on ET, as its impact depends on the temperature range.
Citation: https://doi.org/10.5194/egusphere-2025-1594-RC1 -
AC1: 'Reply on RC1', Horia Olariu, 15 May 2025
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We sincerely thank you for your thoughtful and constructive comments on our manuscript. We consider them fair, direct, and very helpful for strengthening the paper. Below, we address each point in turn.
Q1 - low-accuracy years and sub-basins:
We recognize that including years and sub-basins with weak validation can bias the analyses. Accordingly, we will recompute all temperature-, precipitation-, canopy-cover-, and canopy-height–ET relationships with 2009–2011, 2018, and 2022, as well as sub-basins 1–3, excluded, and will revise Figure 7 so that the updated results appear alongside the full data set for reference. The discussion will be expanded to explain the poor performance in 2009 and 2010: although basin-average precipitation was close to the long-term mean, rainfall was strongly concentrated in the northern catchments and deficient in the south, creating spatial inconsistencies between the water-balance validation and the MOD16 ET data. We will also clarify why model performance is lower in sub-basins 1–4; these catchments contain a higher density of small wetlands and ponds, and, at MODIS's 500 m resolution, many pixels remain mixed even after wetland and open-water masks are applied, leading to systematic overestimation of ET.Q2 - Figure 7 Modification:
We agree and will modify the figure to a 2 × 2 panel, incorporating the additional changes noted above.Q3 - Figure references in discussion:
Thank you for pointing this out. We will update the figure references accordinglyQ4 - Vegetation differences, inclusion of GAM analysis:
Regarding the non-linear analysis, we will implement the GAM as suggested. Would you find it more informative to incorporate the resulting analysis directly into Figure 7—perhaps as an additional panel overlaying the curve on the scatterplot—or would you prefer to keep Figure 7 in its current form and place the GAM diagnostics in the Supplement? Because our temperature–ET relationship is derived from annual aggregates, the frequent extreme summer temperatures in east-central Texas can dominate the annual mean and suppress total-year ET through moisture limitation and stomatal closure; we suspect this aggregation effect underlies the negative annual slope you noted. Would a brief clarification of this mechanism strengthen Section 4.3? Finally, we propose to add text outlining the contrasting thermal and stomatal strategies of the dominant evergreen (Pinus taeda) and deciduous oaks (Quercus stellata, Q. marilandica)—evergreens limiting transpiration above ~32 °C and deciduous species above ~35 °C—drawing on Novick et al., 2016 and Oren et al., 1999.
Thank you again for your helpful feedback.Citation: https://doi.org/10.5194/egusphere-2025-1594-AC1
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AC1: 'Reply on RC1', Horia Olariu, 15 May 2025
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Data sets
MODIS16 ET product USGS / NASA https://lpdaac.usgs.gov/products/mod16a2gfv061/
Daymet V4 Temperature and Precipitation product Oak Ridge National Laboratory / NASA https://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=32
Canopy Cover product Rangeland Analysis Platform / USDA https://rangelands.app/rap/?biomass_t=herbaceous&ll=36.5526,-101.3460&z=4&landcover_t=tre
2020 Canopy Height Product Malambo and Popescu, 2024 / Texas A&M University https://lasers.tamu.edu/ice-cloudand-land-elevation-satellite-icesat-2-applications/
2019 Canopy Height Product Potapov et al, 2022 / University of Maryland, College Park https://glad.umd.edu/dataset/gedi
Runoff Product USGS https://waterwatch.usgs.gov/index.php?id=romap3&sid=w__download
Model code and software
Woody Coverage code Horia G. Olariu https://code.earthengine.google.com/08f4a2fdce7672cb261f48fc658850e2
Sub-basin ET and P code Horia G. Olariu https://code.earthengine.google.com/c77b2aeb8fc4687677b33c1c141d16bc
ET/P and Excess water analysis code Horia G. Olariu https://code.earthengine.google.com/80ef181f4002d7314a10ae391800189d
Water Year aggregation code Horia G. Olariu https://code.earthengine.google.com/8b4ee77f99b3e067bae38c8386e150ff
Pointwise Sampling code Horia G. Olariu https://code.earthengine.google.com/1957d01209128479a368e655b5b75064
Monthly MODIS ET code Horia G. Olariu https://code.earthengine.google.com/2c21005c469551d5646b1ee86812cfe9
Monthly P and T code Horia G. Olariu https://code.earthengine.google.com/23bc61414ed99bb58892ea682a965b5e
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