Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-4825-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-4825-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Sep 2025
Research article |
| 30 Sep 2025
| 30 Sep 2025
Linking woody plants, climate, and evapotranspiration in a temperate savanna
Ecology and Conservation Biology, Texas A&M University, College Station, TX, 77843, USA
Bradford P. Wilcox
Ecology and Conservation Biology, Texas A&M University, College Station, TX, 77843, USA
Sorin C. Popescu
Ecology and Conservation Biology, Texas A&M University, College Station, TX, 77843, USA
Cited articles
Abbott, B. W., Bishop, K., Zarnetske, J. P., Minaudo, C., Chapin, F. S., Krause, S., Hannah, D. M., Conner, L., Ellison, D., Godsey, S. E., Plont, S., Marçais, J., Kolbe, T., Huebner, A., Frei, R. J., Hampton, T., Gu, S., Buhman, M., Sara Sayedi, S., Ursache, O., Chapin, M., Henderson, K. D., and Pinay, G.: Human domination of the global water cycle absent from depictions and perceptions, Nat. Geosci., 12, 533–540, https://doi.org/10.1038/s41561-019-0374-y, 2019.
Acharya, B. S., Kharel, G., Zou, C. B., Wilcox, B. P., and Halihan, T.: Woody Plant Encroachment Impacts on Groundwater Recharge: A Review, Water, 10, 1466, https://doi.org/10.3390/W10101466, 2018.
Afinowicz, J. D., Munster, C. L., and Wilcox, B. P.: Modeling Effects of Brush Management on the Rangeland Water Budget: Edwards Plateau, Texas, JAWRA J. Am. Water Resour. Assoc., 41, 181–193, https://doi.org/10.1111/J.1752-1688.2005.TB03727.X, 2005.
Aguilar, A. L., Flores, H., Crespo, G., Marín, M. I., Campos, I., and Calera, A.: Performance Assessment of MOD16 in Evapotranspiration Evaluation in Northwestern Mexico, Water, 10, 901, https://doi.org/10.3390/W10070901, 2018.
Akinsanola, A. A., Chen, Z., Kooperman, G. J., and Bobde, V.: Robust future intensification of winter precipitation over the United States, npj clim. Atmos. Sci., 7, 212, https://doi.org/10.1038/s41612-024-00761-8, 2024.
Alessi, M. J., Herrera, D. A., Evans, C. P., DeGaetano, A. T., and Ault, T. R.: Soil Moisture Conditions Determine Land-Atmosphere Coupling and Drought Risk in the Northeastern United States, J. Geophys. Res.-Atmos., 127, e2021JD034740, https://doi.org/10.1029/2021JD034740, 2022.
Ali, Y. and Rahman, M. M.: Quantifying forest stocking changes in Sundarbans mangrove using remote sensing data, Sci. Remote Sens., 11, 100181, https://doi.org/10.1016/J.SRS.2024.100181, 2025.
Allred, B. W., Jones, M. O., Naugle, D. E., and Twidwell, D.: Rangeland Analysis Platform (RAP) Canopy Cover Product [data set], https://rangelands.app/rap (last access: 25 January 2025), 2020.
Allred, B. W., Bestelmeyer, B. T., Boyd, C. S., Brown, C., Davies, K. W., Duniway, M. C., Ellsworth, L. M., Erickson, T. A., Fuhlendorf, S. D., Griffiths, T. V., Jansen, V., Jones, M. O., Karl, J., Knight, A., Maestas, J. D., Maynard, J. J., McCord, S. E., Naugle, D. E., Starns, H. D., Twidwell, D., and Uden, D. R.: Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty, Method. Ecol. Evol., 12, 841–849, https://doi.org/10.1111/2041-210X.13564, 2021.
Althoff, D. and Destouni, G.: The global freshwater system: Patterns and predictability of green-blue water flux partitioning, arXiv, https://doi.org/10.48550/arXiv.2302.11245, 2023.
Anav, A., Proietti, C., Menut, L., Carnicelli, S., De Marco, A., and Paoletti, E.: Sensitivity of stomatal conductance to soil moisture: Implications for tropospheric ozone, Atmos. Chem. Phys., 18, 5747–5763, https://doi.org/10.5194/ACP-18-5747-2018, 2018.
Anchang, J. Y., Prihodko, L., Ji, W., Kumar, S. S., Ross, C. W., Yu, Q., Lind, B., Sarr, M. A., Diouf, A. A., and Hanan, N. P.: Toward operational mapping of woody canopy cover in tropical savannas using google earth engine, Front. Environ. Sci., 8, 4, https://doi.org/10.3389/fenvs.2020.00004, 2020.
Anderson, M. C., Kustas, W. P., Norman, J. M., Diak, G. T., Hain, C. R., Gao, F., Yang, Y., Knipper, K. R., Xue, J., Yang, Y., Crow, W. T., Holmes, T. R. H., Nieto, H., Guzinski, R., Otkin, J. A., Mecikalski, J. R., Cammalleri, C., Torres-Rua, A. T., Zhan, X., Fang,L., and Agam, N.: A brief history of the thermal IR-based Two-Source Energy Balance (TSEB) model – diagnosing evapotranspiration from plant to global scales, Agr. Forest Meteorol., 350, 109951. https://doi.org/10.1016/j.agrformet.2024.109951, 2024.
Arumäe, T. and Lang, M.: Estimation of canopy cover in dense mixed-species forests using airborne lidar data, Eur. J. Remote Sens., 51, 132–141, https://doi.org/10.1080/22797254.2017.1411169, 2018.
Aschonitis, V. G., Papamichail, D., Demertzi, K., Colombani, N., Mastrocicco, M., Ghirardini, A., Castaldelli, G., and Fano, E. A.: High-resolution global grids of revised Priestley-Taylor and Hargreaves-Samani coefficients for assessing ASCE-standardized reference crop evapotranspiration and solar radiation, Earth Syst. Sci. Data, 9, 615–638, https://doi.org/10.5194/ESSD-9-615-2017, 2017.
Baldocchi, D. D.: Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: Past, present and future, Glob. Change Biol., 9, 479–492, https://doi.org/10.1046/J.1365-2486.2003.00629.X, 2003.
Basant, S., Wilcox, B. P., Wyatt, B. Newman, B. and Parada, C.: Thicketized oak woodlands reduce groundwater recharge, Sci. Total Environ., 862, 160811, https://doi.org/10.1016/j.scitotenv.2022.160811, 2023.
Bennemann, C., Labelle, E. R., and Lussier, J. M.: Influence of Tree, Stand, and Site Attributes on Hardwood Product Yield: Insights into the Acadian Forests, Forests, 14, 182, https://doi.org/10.3390/f14020182, 2023.
Berg, A. and Sheffield, J.: Soil Moisture–Evapotranspiration Coupling in CMIP5 Models: Relationship with Simulated Climate and Projections, J. Clim., 31, 4865–4878, https://doi.org/10.1175/JCLI-D-17-0757.1, 2018.
Bhat, S. A., Qadri, S. A. A., Dubbey, V., Sofi, I. B., and Huang, N. F.: Impact of crop management practices on maize yield: Insights from farming in tropical regions and predictive modeling using machine learning, J. Agric. Food Res., 18, 101392, https://doi.org/10.1016/J.JAFR.2024.101392, 2024.
Blan, L. and Butler, R.: Comparing Effects of Aggregation Methods on Statistical and Spatial Properties of Simulated Spatial Data, Photogramm. Eng. Remote Sens., 65, 73–84, 1999.
Bonan, G. B.: Forests and climate change: Forcings, feedbacks, and the climate benefits of forests, Science, 320, 1444–1449, 2008.
Brandsma, T. and Können, G. P.: Application of nearest-neighbor resampling for homogenizing temperature records on a daily to sub-daily level, Int. J. Climatol., 26, 75–89, https://doi.org/10.1002/JOC.1236, 2006.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Brudvig, L. A. and Asbjornsen, H.: The removal of woody encroachment restores biophysical gradients in Midwestern oak savannas, J. Appl. Ecol., 46, 231–240, https://doi.org/10.1111/j.1365-2664.2008.01590.x, 2009.
Brudvig, L. A.: Woody encroachment removal from Midwestern oak savannas alters understory diversity across space and time, Restor. Ecol., 18, 74–84, https://doi.org/10.1111/j.1526-100X.2008.00431.x, 2010.
Burchard-Levine, V., Nieto, H., Riaño, D., Kustas, W. P., Migliavacca, M., El-Madany, T. S., Nelson, J. A., Andreu, A., Carrara, A., Beringer, J., Baldocchi, D., and Martín, M. P.: A remote sensing-based three-source energy balance model to improve global estimations of evapotranspiration in semi-arid tree-grass ecosystems, Glob. Change Biol., 28, 1493–1515, https://doi.org/10.1111/gcb.16002, 2022
Campbell, E. G.: Plant relations in Brazos County, Texas with special reference to eastern and western types, Ecology, 6, 163–170, 1925.
Chen, W., Huang, C., and Yang, Z. L.: More severe drought detected by the assimilation of brightness temperature and terrestrial water storage anomalies in Texas during 2010–2013, J. Hydrol., 603, 126802, https://doi.org/10.1016/j.jhydrol.2021.126802, 2021.
Condon, L. E., Atchley, A. L., and Maxwell, R. M.: Evapotranspiration depletes groundwater under warming over the contiguous United States, Nat. Commun. 11, 1–8, https://doi.org/10.1038/s41467-020-14688-0, 2020.
Crous, K. Y., Uddling, J., and De Kauwe, M. G.: Temperature responses of photosynthesis and respiration in evergreen trees from boreal to tropical latitudes, New Phytol., 234, 353–374, https://doi.org/10.1111/NPH.17951, 2022.
Dai, A., Trenberth, K. E., and Karl, T. R.: Effects of Clouds, Soil Moisture, Precipitation, and Water Vapor on Diurnal Temperature Range, J. Clim., 12, 2451–2473, https://doi.org/10.1175/1520-0442(1999)012<2451:EOCSMP>2.0.CO;2, 1999.
Dai, A., Zhao, T., and Chen, J.: Climate Change and Drought: a Precipitation and Evaporation Perspective, Curr. Clim. Change Rep., 4, 301–312, 2018.
Dey, P., Roy, S., Bathi, J. R., and Mishra, A.: Evaluation of Uncertainty in Stream Flow Prediction Using Monte Carlo Simulation for Watershed-Scale Hydrological Modeling, J. Hydrol. Eng., 29, 05023026, https://doi.org/10.1061/JHYEFF.HEENG-6008, 2024.
Diggs, G. M., Lipscomb, B. L., and O'Kennon, R. J.: Illustrated flora of North Central Texas, Botanical Research Institute of Texas, Fort Worth, ISBN 978-1889878010, Texas, 1999.
Dixon, A. P., Faber-Langendoen, D., Josse, C., Morrison, J., and Loucks, C. J.: Distribution mapping of world grassland types, J. Biogeogr., 41, 2003–2019, https://doi.org/10.1111/JBI.12381, 2014.
Du, J. and Song, K.: Validation of Global Evapotranspiration Product (MOD16) Using Flux Tower Data from Panjin Coastal Wetland, Northeast China, Chin. Geogr. Sci., 28, 420–429, 2018.
Dugas, W. A., Hicks, R. A., and Wright, P.: Effect of removal of Juniperus ashei on evapotranspiration and runoff in the Seco Creek Watershed, Water Resour. Res., 34, 1499–1506, https://doi.org/10.1029/98WR00556, 1998.
Ershadi, A., McCabe, M. F., Evans, J. P., and Wood, E. F.: Impact of model structure and parameterization on Penman–Monteith type evaporation models, J. Hydrol., 525, 521–535, https://doi.org/10.1016/J.JHYDROL.2015.04.008, 2015.
Falkenmark, M., Lundqvist, J., and Widstrand, C.: Macro-scale water scarcity requires micro-scale approaches, Nat. Resour. Forum, 13, 258–267, https://doi.org/10.1111/J.1477-8947.1989.TB00348.X, 1989.
Feng, S. and Fu, Q.: Expansion of global drylands under a warming climate, Atmos. Chem. Phys., 13, 10081–10094, https://doi.org/10.5194/acp-13-10081-2013, 2013.
Fleischmann, A. S., Laipelt, L., Papa, F., Paiva, R. C. D. de, de Andrade, B. C., Collischonn, W., Biudes, M. S., Kayser, R., Prigent, C., Cosio, E., Machado, N. G., and Ruhoff, A.: Patterns and drivers of evapotranspiration in South American wetlands, Nat. Commun., 14, 6656, https://doi.org/10.1038/S41467-023-42467-0, 2023.
Fu, Z., Ciais, P., Feldman, A. F., Gentine, P., Makowski, D., Prentice, I. C., Stoy, P. C., Bastos, A., and Wigneron, J. P.: Critical soil moisture thresholds of plant water stress in terrestrial ecosystems, Sci. Adv., 8, 7827, https://doi.org/10.1126/sciadv.abq7827, 2022.
Garza, N. E. and Blackburn, W. H.: The Effect of Early Winter or Spring Burning on Runoff, Sediment, and Vegetation in the Post Oak Savannah of Texas, J. Range Manag., 38, 283–287, 1985.
González-Dugo, M. P., Chen, X., Andreu, A., Carpintero, E., Gómez-Giraldez, P. J., Carrara, A., and Su, Z.: Long-term water stress and drought assessment of Mediterranean oak savanna vegetation using thermal remote sensing, Hydrol. Earth Syst. Sci., 25, 755–768, https://doi.org/10.5194/hess-25-755-2021, 2021.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/J.RSE.2017.06.031, 2017.
Griffith, G. E., Bryce, S. B., and Omernik, J. M.: Ecoregions of Texas, U.S. Geological Survey, Austin, TX, https://dmap-prod-oms-edc.s3.us-east-1.amazonaws.com/ORD/Ecoregions/tx/TXeco_Jan08_v8_Cmprsd.pdf (last access: 29 September 2025), 2007.
Guenther, E., Magruder, L., Neuenschwander, A., Maze-England, D., and Dietrich, J.: Examining CNN terrain model for TanDEM-X DEMs using ICESat-2 data in Southeastern United States, Remote Sens. Environ., 311, 114293, https://doi.org/10.1016/j.rse.2024.114293, 2024.
Guo, L., Zhang, Y., Xu, M., Yan, J., Zhang, H., Zou, Y., and Gao, J.: A Novel Workflow for Mapping Forest Canopy Height by Synergizing ICESat-2 and Multi-Sensor Data, Forests, 15, 2139, https://doi.org/10.3390/f15122139, 2024.
Guzinski, R., Nieto, H., Sandholt, I., and Karamitilios, G.: Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion, Remote Sens., 12, 9, 1433, https://doi.org/10.3390/rs12091433, 2020
Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., and Neal, J.: A 30 m global map of elevation with forests and buildings removed, Environ. Res. Lett., 17, 024016, https://doi.org/10.1088/1748-9326/AC4D4F, 2022.
He, M., Anderson, J., Lynn, E., and Arnold, W.: Projected Changes in Water Year Types and Hydrological Drought in California's Central Valley in the 21st Century, Climate, 9, 26, https://doi.org/10.3390/CLI9020026, 2021.
Hu, T., Cao, M., Zhao, X., Liu, Z., Liu, L., Huang, S., Tang, Z., Liu, M., Kelly, M., Fang, J., and Su, Y.: High-resolution mapping of grassland canopy cover in China through the integration of extensive drone imagery and satellite data, ISPRS J. Photogramm. Remote Sens., 218, 69– 83, https://doi.org/10.1016/j.isprsjprs.2024.09.004, 2024.
Hu, G., Jia, L., and Menenti, M.: Comparison of MOD16 and LSA-SAF MSG evapotranspiration products over Europe for 2011, Remote Sens. Environ., 156, 510–526, https://doi.org/10.1016/j.rse.2014.10.017, 2015.
Hudson, T. D., Reeves, M. C., Hall, S. A., Yorgey, G. G., and Neibergs, J. S.: Big landscapes meet big data: Informing grazing management in a variable and changing world, Rangelands, 43, 17–28, https://doi.org/10.1016/J.RALA.2020.10.006, 2021.
Irmak, S.: Estimating Crop Evapotranspiration from Reference Evapotranspiration and Crop Coefficients (NebGuide G1994), Nebraska Extension, University of Nebraska–Lincoln, Lincoln, NE, 9, https://extensionpubs.unl.edu/publication/g1994/na/html/view (last access: 29 September 2025), 2017.
Jackson, R. B., Pockman, W. T., Hoffmann, W. A., Bleby, T. M., and Armas, C.: Structure and Function of Root Systems, in: Functional Plant Ecology, 2nd Edn., CRC Press, Boca Raton, 24 pp., https://doi.org/10.1201/9781420007626-5, 2007.
Johnson, D. J., Magee, L., Pandit, K., Bourdon, J., Broadbent, E. N., Glenn, K., Kaddoura, Y., Machado, S., Nieves, J., Wilkinson, B. E., Almeyda Zambrano, A. M., and Bohlman, S. A.: Canopy tree density and species influence tree regeneration patterns and woody species diversity in a longleaf pine forest, Forest Ecol. Manag., 490, 119082, https://doi.org/10.1016/J.FORECO.2021.119082, 2021.
Jucker, T., Bouriaud, O., and Coomes, D. A.: Crown plasticity enables trees to optimize canopy packing in mixed-species forests, Funct. Ecol., 29, 1078–1086, https://doi.org/10.1111/1365-2435.12428, 2015.
Kim, H. W., Hwang, K., Mu, Q., Lee, S. O., and Choi, M.: Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia, KSCE J. Civil Eng., 16, 229–238, 2012.
Klockow, P. A., Vogel, J. G., Edgar, C. B., and Moore, G. W.: Lagged mortality among tree species four years after an exceptional drought in east Texas, Ecosphere, 9, e02455, https://doi.org/10.1002/ECS2.2455, 2018.
Koehler, T., Wankmüller, F. J. P., Sadok, W., and Carminati, A.: Transpiration response to soil drying versus increasing vapor pressure deficit in crops: physical and physiological mechanisms and key plant traits, J. Exp. Bot., 74, 4789, https://doi.org/10.1093/JXB/ERAD221, 2023.
Liu, C., Sun, G., McNulty, S. G., Noormets, A., and Fang, Y.: Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements, Hydrol. Earth Syst. Sci., 21, 311–322, https://doi.org/10.5194/hess-21-311-2017, 2017.
Loewensteiner, D. A., Bartolo, R. E., Whiteside, T. G., Esparon, A. J., and Humphrey, C. L.: Measuring savanna woody cover at scale to inform ecosystem restoration, Ecosphere, 12, e03437, https://doi.org/10.1002/ECS2.3437, 2021.
Majozi, N. P., Mannaerts, C. M., Ramoelo, A., Mathieu, R., Mudau, A. E., and Verhoef, W.: An intercomparison of satellite-based daily evapotranspiration estimates under different eco-climatic regions in South Africa, Remote Sens., 9, 307, https://doi.org/10.3390/rs9040307, 2017.
Malambo, L. and Popescu, S.: Mapping vegetation canopy height across the contiguous United States using ICESat-2 and ancillary datasets, Remote Sens. Environ., 309, 114226, https://doi.org/10.1016/J.RSE.2024.114226, 2024a.
Malambo, L. and Popescu, S.: ICESat-2 Canopy Height Products, Texas A&M University [data set], https://lasers.tamu.edu/ice-cloudand-land-elevation-satellite-icesat-2-applications/ (last access: 25 January 2025), 2024b.
McBride, J. B.: The vegetation and habitat factors of the Carrizo sands, Ecol. Monogr., 3, 247–297, 1933.
McDonald, J. E.: On the Ratio of Evaporation to Precipitation, Bull. Am. Meteorol. Soc., 42, 185–189, https://doi.org/10.1175/1520-0477-42.3.185, 1961.
McGowan, H. A., Lowry, A. L., and Gray, M. A.: Identification of Optimum Temperatures for Photosynthetic Production in Subtropical Coastal Ecosystems: Implications for CO2 Sequestration in a Warming World, J. Geophys. Res.-Biogeo., 125, e2020JG005678, https://doi.org/10.1029/2020JG005678, 2020.
Midwood, A. J., Boutton, T. W., Archer, S. R., and Watts, S. E.: Water use by woody plants on contrasting soils in a savanna parkland: Assessment with δ2H and δ18O, Plant Soil, 205, 13–24, https://doi.org/10.1023/A:1004355423241, 1998.
Miralles, D. G., Jiménez, C., Jung, M., Michel, D., Ershadi, A., McCabe, M. F., Hirschi, M., Martens, B., Dolman, A. J., Fisher, J. B., Mu, Q., Seneviratne, S. I., Wood, E. F., and Fernández-Prieto, D.: The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets, Hydrol. Earth Syst. Sci., 20, 823–842, https://doi.org/10.5194/hess-20-823-2016, 2016.
Miranda, R. D. Q., Galvíncio, J. D., Moura, M. S. B. De, Jones, C. A., and Srinivasan, R.: Reliability of MODIS Evapotranspiration Products for Heterogeneous Dry Forest: A Study Case of Caatinga, Adv. Meteorol., 2017, 9314801, https://doi.org/10.1155/2017/9314801, 2017.
Mondal, S. and Mishra, A.: Quantifying the Precipitation, Evapotranspiration, and Soil Moisture Network's Interaction Over Global Land Surface Hydrological Cycle, Water Resour. Res., 60, e2023WR034861, https://doi.org/10.1029/2023WR034861, 2024.
Mondal, S. K., An, S. Il, Min, S. K., Jiang, T., and Su, B.: Enhanced soil moisture–temperature coupling could exacerbate drought under net-negative emissions, Clim. Atmos. Sci., 7, 1–12, https://doi.org/10.1038/s41612-024-00820-0, 2024.
Morford, S. L., Allred, B. W., Twidwell, D., Jones, M. O., Maestas, J. D., Roberts, C. P., and Naugle, D. E.: Herbaceous production lost to tree encroachment in United States rangelands, J. Appl. Ecol., 59, 2971–2982, https://doi.org/10.1111/1365-2664.14288, 2022.
Mu, Q., Heinsch, F. A., Zhao, M., and Running, S. W.: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sens. Environ., 111, 519–536, https://doi.org/10.1016/J.RSE.2007.04.015, 2007.
Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781–1800, https://doi.org/10.1016/J.RSE.2011.02.019, 2011.
Nadzri, M. I. and Hashim, M.: Validation of MODIS Data for localized spatio-temporal evapotranspiration mapping, in: IOP Conference Series: Earth and Environmental Science, 012183, https://doi.org/10.1088/1755-1315/18/1/012183, 2014.
Nagler, P. L., Glenn, E. P., Kim, H., Emmerich, W., Scott, R. L., Huxman, T. E., and Huete, A. R.: Relationship between evapotranspiration and precipitation pulses in a semiarid rangeland estimated by moisture flux towers and MODIS vegetation indices, J. Arid Environ., 70, 443–462, https://doi.org/10.1016/J.JARIDENV.2006.12.026, 2007.
Nielsen-Gammon, J. W.: The 2011 Texas Drought, Texas Water J., 3, 59–95, https://doi.org/10.21423/TWJ.V3I1.6463, 2012.
Nielsen-Gammon, J., Banner, J., Cook, B., Tremaine, D., Wong, C., Mace, R., Gao, H., Yang, Z., Gonzalez, M., Hoffpauir, R., Gooch, T., and Kloesel, K.: Unprecedented drought challenges for Texas water resources in a changing climate: What do researchers and stakeholders need to know?, Earths Future, 8, 001552, https://doi.org/10.1029/2020EF001552, 2020.
Norton, J. L. and Hart, S. C.: Hydraulic lift: A potentially important ecosystem process, Trends Ecol. Evol., 13, 232–235, https://doi.org/10.1016/S0169-5347(98)01328-7, 1998.
Novick, K. A., Ficklin, D. L., Stoy, P. C., Williams, C. A., Bohrer, G., Oishi, A. C., Papuga, S. A., Blanken, P. D., Noormets, A., Sulman, B. N., Scott, R. L., Wang, L., and Phillips, R. P.: The increasing importance of atmospheric demand for ecosystem water and carbon fluxes, Nat. Clim. Change, 6, 1023–1027, https://doi.org/10.1038/nclimate3114, 2016.
Null, S. E. and Viers, J. H.: In bad waters: Water year classification in nonstationary climates, Water Resour. Res., 49, 1137–1148, https://doi.org/10.1002/WRCR.20097, 2013.
Oki, T. and Kanae, S.: Global hydrological cycles and world water resources, Science, 313, 1068–1072, 2006.
Olariu, H. G.: TS34 Woody coverage code, Google Earth Engine [code], https://code.earthengine.google.com/08f4a2fdce7672cb261f48fc658850e2 (last access: 25 January 2025), 2025a.
Olariu, H. G.: Subbasin ET and Precipitation code, Google Earth Engine [code], https://code.earthengine.google.com/c77b2aeb8fc4687677b33c1c141d16bc (last access: 25 January 2025), 2025b.
Olariu, H. G.: ET, Precipitation, and excess water analysis code, Google Earth Engine [code], https://code.earthengine.google.com/80ef181f4002d7314a10ae391800189d (last access: 25 January 2025), 2025c.
Olariu, H. G.: Water year aggregation code, Google Earth Engine [code], https://code.earthengine.google.com/8b4ee77f99b3e067bae38c8386e150ff (last access: 25 January 2025), 2025d.
Olariu, H. G.: Pointwise sampling code, Google Earth Engine [code], https://code.earthengine.google.com/1957d01209128479a368e655b5b75064 (last access: 25 January 2025), 2025e.
Olariu, H. G.: Monthly MODIS ET code, Google Earth Engine [code], https://code.earthengine.google.com/2c21005c469551d5646b1ee86812cfe9 (last access: 25 January 2025), 2025f.
Olariu, H. G.: Monthly Precipitation and Temperature code, Google Earth Engine [code], https://code.earthengine.google.com/23bc61414ed99bb58892ea682a965b5e (last access: 25 January 2025), 2025g.
Olariu, H. G., Wilcox, B. P., and Popescu, S. C.: Examining changes in woody vegetation cover in a human-modified temperate savanna in Central Texas between 1996 and 2022 using remote sensing, Front. Forest. Glob. Change, 7, 1396999, https://doi.org/10.3389/ffgc.2024.1396999, 2024.
Omernik, J. M. and Griffith, G. E.: Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework, Environ. Manag., 54, 1249–1266, https://doi.org/10.1007/S00267-014-0364-1, 2014.
Oren, R., Sperry, J. S., Katul, G. G., Pataki, D. E., Ewers, B. E., Philips, N., and Shäfer, K. V. R.: Survey and synthesis of intra- and interspecific variation in stomatal sensitivity to vapour pressure deficit, Plant Cell Environ., 22, 1515–1526, https://doi.org/10.1046/j.1365-3040.1999.00513.x, 1999.
Papacharalampous, G. and Hristos, T.: Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability, J. Hydrol., 590, 125205, https://doi.org/10.1016/j.jhydrol.2020.125205, 2020.
Parmalee, P.: Some factors affecting nesting success of the Bob-white Quail in east-central Texas, Am. Midl. Nat., 53, 45–55, 1955.
Plappert, J. L., Veech, J. A., Martina, J. P., and Giocomo, J. J.: Canopy cover as the primary factor affecting habitat use by grassland-shrubland bird species in central Texas, USA, J. Field Ornithol., 95, art10, https://doi.org/10.5751/JFO-00518-950310, 2024.
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C. E., Armston, J., Dubayah, R., Blair, J. B., and Hofton, M.: Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sens. Environ., 253, 112165, https://doi.org/10.1016/J.RSE.2020.112165, 2021a.
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., and Kommareddy, I.: GEDI Canopy Height Dataset, University of Maryland [data set], https://glad.umd.edu/dataset/gedi (last access: 25 January 2025), 2021b.
Pourrahmati, M. R., Baghdadi, N., and Fayad, I.: Comparison of GEDI LiDAR Data Capability for Forest Canopy Height Es- 100 timation over Broadleaf and Needleleaf Forests, Remote Sens., 15, 1522, https://doi.org/10.3390/RS15061522, 2023.
Qiao, L., Zou, C. B., Will, R. E., and Stebler, E.: Calibration of SWAT model for woody plant encroachment using paired experimental watershed data, J. Hydrol., 523, 231–239, https://doi.org/10.1016/J.JHYDROL.2015.01.056, 2015.
Ramoelo, A., Majozi, N., Mathieu, R., Jovanovic, N., Nickless, A., and Dzikiti, S.: Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa, Remote Sens., 6, 7406–7423, https://doi.org/10.3390/RS6087406, 2014.
Reitz, M., Senay, G. B., and Sanford, W. E.: Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States, Remote Sens., 9, 1181, https://doi.org/10.3390/RS9121181, 2017.
Retallack, A., Finlayson, G., Ostendorf, B., Clarke, K., and Lewis, M.: Remote sensing for monitoring rangeland condition: Current status and development of methods, Environ. Sustain. Indic., 19, 100285, https://doi.org/10.1016/J.INDIC.2023.100285, 2023.
Roy, A. D., Mohan, M., Hendy, I., AlMealla, R., Watt, M. S., Burt, J. A., Torres-Florez, J. P., Almansoori, A., Alzahlawi, N., Abdullah, M., Ali, T., Nithyanandan, M., Aboobacker, V. A., and de-Miguel, A.: Optimizing mangrove afforestation site selection in gulf cooperation council nations using remote sensing and machine learning, Sci. Total Environ., 988, 179805, https://doi.org/10.1016/j.scitotenv.2025.179805, 2025.
Ruhoff, A. L., Paz, A. R., Aragao, L. E. O. C., Mu, Q., Malhi, Y., Collischonn, W., Rocha, H. R., and Running, S. W.: Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin, Hydrol. Sci. J., 58, 1658–1676, https://doi.org/10.1080/02626667.2013.837578, 2013.
Running, S. W., Mu, Q., Zhao, M., and Moreno, A.: MOD16A2GF MODIS/Terra Net Evapotranspiration Gap-Filled 8-Day L4 Global 500m SIN Grid, Version 6.1, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD16A2GF.061, 2021.
Scheff, J. and Frierson, D. M. W.: Scaling Potential Evapotranspiration with Greenhouse Warming, J. Clim., 27, 1539–1558, https://doi.org/10.1175/JCLI-D-13-00233.1, 2014.
Schmidly, D. J.: Texas natural history: a century of change, J. Mammal., 84, 329–331, https://doi.org/10.1644/1545-1542(2003)084<0329:R>2.0.CO;2, 2002.
Schwantes, A. M., Swenson, J. J., González-Roglich, M., Johnson, D. M., Domec, J. C., and Jackson, R. B.: Measuring canopy loss and climatic thresholds from an extreme drought along a fivefold precipitation gradient across Texas, Glob. Chang Biol., 23, 5120–5135, https://doi.org/10.1111/GCB.13775, 2017.
Scott, R. L., Huxman, T. E., Barron-Gafford, G. A., Darrel Jenerette, G., Young, J. M., and Hamerlynck, E. P.: When vegetation change alters ecosystem water availability, Glob. Chang Biol., 20, 2198–2210, https://doi.org/10.1111/GCB.12511, 2014.
Seaber, P. R., Kapinos, F. P., and Knapp, G. L.: Hydrologic Unit Maps, U.S. Geological Survey, 2294 pp., https://doi.org/10.3133/wsp2294, 1987.
Seager, R. and Vecchi, G. A.: Greenhouse warming and the 21st century hydroclimate of southwestern North America, Proc. Natl. Acad. Sci. U. S. A., 107, 21277–21282, https://doi.org/10.1073/pnas.0910856107, 2010.
Senay, G. B., Friedrichs, M., Singh, R. K., and Velpuri, N. M.: Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin, Remote Sens. Environ., 185, 171–185, https://doi.org/10.1016/J.RSE.2015.12.043, 2016.
Song, C., Katul, G., Oren, R., Band, L. E., Tague, C. L., Stoy, P. C., and McCarthy, H. R.: Energy, water, and carbon fluxes in a loblolly pine stand: Results from uniform and gappy canopy models with comparisons to eddy flux data, J. Geophys. Res.-Biogeo., 114, G04021, https://doi.org/10.1029/2009JG000951, 2009.
Stephens, S. L., Lydersen, J. M., Collins, B. M., Fry, D. L., and Meyer, M. D.: Historical and current landscape-scale ponderosa pine and mixed conifer forest structure in the southern Sierra Nevada, Ecosphere, 6, 1–63, https://doi.org/10.1890/ES14-00379.1, 2015.
Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.: Long-term climate change: projections, commitments and irreversibility, Cambridge, United Kingdom and New York, USA, Cambridge University Press, https://doi.org/10.1017/CBO9781107415324.024, 2013.
Stambaugh, M. C., Marschall, J. M., and Guyette, R. P.: Linking fire history to successional changes of xeric oak woodlands, For. Ecol. Manage., 320, 83–95, https://doi.org/10.1016/j.foreco.2014.02.022, 2014.
Subhashree, S. N., Igathinathane, C., Akyuz, A., Borhan, M., Hendrickson, J., Archer, D., Liebig, M., Toledo, D., Sedivec, K., Kronberg, S., and Halvorson, J.: Tools for Predicting Forage Growth in Rangelands and Economic Analyses – A Systematic Review, Agriculture, 13, 455, https://doi.org/10.3390/AGRICULTURE13020455, 2023.
Sun, S., Bi, Z., Xiao, J., Liu, Y., Sun, G., Ju, W., Liu, C., Mu, M., Li, J., Zhou, Y., Li, X., Liu, Y., and Chen, H.: A global 5km monthly potential evapotranspiration dataset (1982–2015) estimated by the Shuttleworth-Wallace model, Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, 2023.
Texas A&M Forest Service: 2011 Texas Wildfires: Common Denominators of Home Destruction, Texas A&M Forest Service [Technical report], https://tfsweb.tamu.edu/elibrary-item/2011-texas-wildfires-commondenominators- of-home-destruction (last access: 25 January 2025), 2011.
Tharp, B.: Structure of Texas vegetation east of the 98th meridian, University of Texas Bulletin, 2606, 45–54, 1926.
Thornton, M. M., Shrestha, R., Wei, Y., Thornton, P. E., and Kao, S. C.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 R1, https://doi.org/10.3334/ORNLDAAC/2129, 2022a.
Thornton, M. M., Shrestha, R., Wei, Y., Thornton, P. E., and Wilson, B. E.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4, Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) [data set], https://doi.org/10.3334/ORNLDAAC/1840, 2022b.
Trambauer, P., Dutra, E., Maskey, S., Werner, M., Pappenberger, F., Van Beek, L. P. H., and Uhlenbrook, S.: Comparison of different evaporation estimates over the African continent, Hydrol. Earth Syst. Sci., 18, 193–212, https://doi.org/10.5194/hess-18-193-2014, 2014.
U.S. Geological Survey (USGS): WaterWatch Runoff Product, USGS [data set], https://waterwatch.usgs.gov/index.php?id=romap3&sid= w_download, last access: 25 January 2025.
Velpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S., and Verdin, J. P.: A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET, Remote Sens. Environ., 139, 35–49, https://doi.org/10.1016/J.RSE.2013.07.013, 2013.
Wang, J., Xiao, X., Zhang, Y., Qin, Y., Doughty, R. B., Wu, X., Bajgain, R., and Du, L.: Enhanced gross primary production and evapotranspiration in juniper-encroached grasslands, Glob. Change Biol., 24, 5655–5667, https://doi.org/10.1111/GCB.14441, 2018.
Wasowski, S. and Wasowki, A.: Native Texas Plants: Landscaping Region by Region, Lone Star Books, Austin, Texas, 1–138, Taylor Trade Publishing, ISBN 978- 0891230779, 1988.
Whitehurst, A. S., Swatantran, A., Blair, J. B., Hofton, M. A., and Dubayah, R.: Characterization of Canopy Layering in Forested Ecosystems Using Full Waveform Lidar, Remote Sens., 5, 2014–2036, https://doi.org/10.3390/RS5042014, 2013.
Xi, X., Zhuang, Q., Kim, S., and Gentine, P.: Evaluating the Effects of Precipitation and Evapotranspiration on Soil Moisture Variability Within CMIP5 Using SMAP and ERA5 Data, Water Resour. Res., 59, e2022WR034225, https://doi.org/10.1029/2022WR034225, 2023.
Yamasaki, T., Yamakawa, T., Yamane, Y., Koike, H., Satoh, K., and Katoh, S.: Temperature Acclimation of Photosynthesis and Related Changes in Photosystem II Electron Transport in Winter Wheat, Plant Physiol., 128, 1087, https://doi.org/10.1104/PP.010919, 2002.
Yang, Z., Jiang, Y., Qiu, R., Gong, X., Agathokleous, E., Hu, W., and Clothier, B.: Heat stress decreased transpiration but increased evapotranspiration in gerbera, Front Plant Sci., 14, 1119076, https://doi.org/10.3389/fpls.2023.1119076, 2023.
Yu, Y., Zhou, Y., Xiao, W., Ruan, B., Lu, F., Hou, B., Wang, Y., and Cui, H.: Impacts of climate and vegetation on actual evapotranspiration in typical arid mountainous regions using a Budyko-based framework, Hydrol. Res., 52, 212–228, https://doi.org/10.2166/NH.2020.051, 2021.
Yuan, Q., Wang, G., Zhu, C., Lou, D., Hagan, D. F. T., Ma, X., and Zhan, M.: Coupling of Soil Moisture and Air Temperature from Multiyear Data During 1980–2013 over China, Atmosphere, 11, 25, https://doi.org/10.3390/ATMOS11010025, 2019.
Zahura, F. T., Bisht, G., Li, Z., McKnight, S., and Chen, X.: Impact of topography and climate on post-fire vegetation recovery across different burn severity and land cover types through random forest, Ecol. Inform., 82, 102757, https://doi.org/10.1016/J.ECOINF.2024.102757, 2024.
Zeide, B. and Stephens, J.: The densest loblolly pine stand and its silvicultural implications, edited by: Stanturf, J. A., Edn. 2010, in: Proceedings of the 14th biennial southern silvicultural research conference, Gen. Tech. Rep. SRS–121, Asheville, NC, U.S. Department of Agriculture, Forest Service, Southern Research Station, U.S. Department of Agriculture, 339–342, https://doi.org/10.2737/SRS-GTR-121, 2010.
Zeng, X., Barlage, M., Castro, C., and Fling, K.: Comparison of Land–Precipitation Coupling Strength Using Observations and Models, J. Hydrometeorol., 11, 979–994, https://doi.org/10.1175/2010JHM1226.1, 2010.
Zhang, K., Zhu, G., Ma, J., Yang, Y., Shang, S., and Gu, C.: Parameter Analysis and Estimates for the MODIS Evapotranspiration Algorithm and Multiscale Verification, Water Resour. Res., 55, 2211–2231, https://doi.org/10.1029/2018WR023485, 2019.
Zhao, T. and Dai, A.: The Magnitude and Causes of Global Drought Changes in the Twenty-First Century under a Low–Moderate Emissions Scenario, J. Clim., 28, 4490–4512, https://doi.org/10.1175/JCLI-D-14-00363.1, 2015.
Zirbel, C. R., Bassett, T., Grman, E., and Brudvig, L. A.: Plant Functional traits and environmental conditions shape community assembly and ecosystem functioning during restoration, J. Appl. Ecol., 54, 1070–1079, https://doi.org/10.1111/1365-2664.12885, 2017.
Short summary
Satellite data reveal that woody plants in Texas’s Post Oak Savannah now return almost all rainfall to the atmosphere. In drier regions, once trees and shrubs blanket more than 80 % of the land, yearly water loss to the atmosphere even surpasses rainfall, shifting the region from a water surplus to a deficit and shrinking groundwater recharge. Without brush control, warming and further canopy growth could leave soils drier, streams weaker, and local water supplies increasingly strained.
Satellite data reveal that woody plants in Texas’s Post Oak Savannah now return almost all...
