Articles | Volume 28, issue 24
https://doi.org/10.5194/hess-28-5511-2024
© Author(s) 2024. 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-28-5511-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Dec 2024
Research article |
| 19 Dec 2024
| 19 Dec 2024
The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa
Fabian Merk
CORRESPONDING AUTHOR
School of Engineering and Design, Technical University of Munich, Munich, Germany
Timo Schaffhauser
School of Engineering and Design, Technical University of Munich, Munich, Germany
Faizan Anwar
School of Engineering and Design, Technical University of Munich, Munich, Germany
School of Engineering and Design, Technical University of Munich, Munich, Germany
Jean-Martial Cohard
Institute of Engineering and Management, University of Grenoble Alpes, Grenoble, France
Markus Disse
School of Engineering and Design, Technical University of Munich, Munich, Germany
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Cited articles
Abbas, S. A., Bailey, R. T., White, J. T., Arnold, J. G., White, M. J., Čerkasova, N., and Gao, J.: A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+, Hydrol. Earth Syst. Sci., 28, 21–48, https://doi.org/10.5194/hess-28-21-2024, 2024. a
Abitew, T. A., Arnold, J., Jeong, J., Jones, A., and Srinivasan, R.: Innovative approach to prognostic plant growth modeling in SWAT+ for forest and perennial vegetation in tropical and Sub-Tropical climates, J. Hydrol. X, 20, 100156, https://doi.org/10.1016/j.hydroa.2023.100156, 2023. a
Ago, E. E., Agbossou, E. K., Galle, S., Cohard, J.-M., Heinesch, B., and Aubinet, M.: Long term observations of carbon dioxide exchange over cultivated savanna under a Sudanian climate in Benin (West Africa), Agr. Forest Meteorol., 197, 13–25, https://doi.org/10.1016/j.agrformet.2014.06.005, 2014. a, b, c, d
Ago, E. E., Agbossou, E. K., Cohard, J.-M., Galle, S., and Aubinet, M.: Response of CO2 fluxes and productivity to water availability in two contrasting ecosystems in northern Benin (West Africa), Ann. For. Sci., 73, 483–500, https://doi.org/10.1007/s13595-016-0542-9, 2016. a, b, c
Akoko, G., Le, T. H., Gomi, T., and Kato, T.: A Review of SWAT Model Application in Africa, Water, 13, 1313, https://doi.org/10.3390/w13091313, 2021. a
Archibald, J. A. and Walter, M. T.: Do Energy–Based PET Models Require More Input Data than Temperature–Based Models? – An Evaluation at Four Humid FluxNet Sites, J. Am. Water Resour. As., 50, 497–508, https://doi.org/10.1111/jawr.12137, 2014. a
Arnold, J. G. and Fohrer, N.: SWAT2000: current capabilities and research opportunities in applied watershed modelling, Hydrol. Process., 19, 563–572, https://doi.org/10.1002/hyp.5611, 2005. a
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT, J. Am. Water Resour. As., 34, 73–89, https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998. a, b, c, d
Atkinson, P. M., Jeganathan, C., Dash, J., and Atzberger, C.: Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology, Remote Sens. Environ., 123, 400–417, https://doi.org/10.1016/j.rse.2012.04.001, 2012. a, b
Bennour, A., Jia, L., Menenti, M., Zheng, C., Zeng, Y., Asenso Barnieh, B., and Jiang, M.: Calibration and Validation of SWAT Model by Using Hydrological Remote Sensing Observables in the Lake Chad Basin, Remote Sens., 14, 1511, https://doi.org/10.3390/rs14061511, 2022. a
Bliefernicht, J., Waongo, M., Salack, S., Seidel, J., Laux, P., and Kunstmann, H.: Quality and Value of Seasonal Precipitation Forecasts Issued by the West African Regional Climate Outlook Forum, J. Appl. Meteorol. Clim., 58, 621–642, https://doi.org/10.1175/JAMC-D-18-0066.1, 2019. a
Bossa, A., Diekkrüger, B., and Agbossou, E.: Scenario-Based Impacts of Land Use and Climate Change on Land and Water Degradation from the Meso to Regional Scale, Water, 6, 3152–3181, https://doi.org/10.3390/w6103152, 2014. a
Bossa, A. Y., Diekkrüger, B., Igué, A. M., and Gaiser, T.: Analyzing the effects of different soil databases on modeling of hydrological processes and sediment yield in Benin (West Africa), Geoderma, 173–174, 61–74, https://doi.org/10.1016/j.geoderma.2012.01.012, 2012. a
Bright, R. M., Miralles, D. G., Poyatos, R., and Eisner, S.: Simple Models Outperform More Complex Big-Leaf Models of Daily Transpiration in Forested Biomes, Geophys. Res. Lett., 49, e2022GL100100, https://doi.org/10.1029/2022GL100100, 2022. a
Buchhorn, M., Smets, B., Bertels, L., de Roo, B., Lesiv, M., Tsendbazar, N., Li, L., and Tarko, A.: Copernicus Global Land Service: Land Cover 100 m: version 3 Globe 2015-2019: Product User Manual, Zenodo [data set], https://doi.org/10.5281/zenodo.3938963, 2020. a
Campolongo, F., Cariboni, J., and Saltelli, A.: An effective screening design for sensitivity analysis of large models, Environ. Model. Softw., 22, 1509–1518, https://doi.org/10.1016/j.envsoft.2006.10.004, 2007. a, b, c
Chen, B., Black, T. A., Coops, N. C., Hilker, T., Trofymow, J. A., and Morgenstern, K.: Assessing Tower Flux Footprint Climatology and Scaling Between Remotely Sensed and Eddy Covariance Measurements, Bound.-Lay. Meteorol., 130, 137–167, https://doi.org/10.1007/s10546-008-9339-1, 2009. a
Chiogna, G., Marcolini, G., Engel, M., and Wohlmuth, B.: Sensitivity analysis in the wavelet domain: a comparison study, Stoch. Env. Res. Risk A., 38, 1669–1684, https://doi.org/10.1007/s00477-023-02654-3, 2024. a
Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haëntjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., and Zona, D.: Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites, Agr. Forest Meteorol., 301–302, 108350, https://doi.org/10.1016/j.agrformet.2021.108350, 2021. a, b, c, d, e, f
Danvi, A., Giertz, S., Zwart, S. J., and Diekkrüger, B.: Comparing water quantity and quality in three inland valley watersheds with different levels of agricultural development in central Benin, Agr. Water Manage., 192, 257–270, https://doi.org/10.1016/j.agwat.2017.07.017, 2017. a
Duan, Q., Sorooshian, S., and Gupta, V. K.: Optimal use of the SCE-UA global optimization method for calibrating watershed models, J. Hydrol., 158, 265–284, https://doi.org/10.1016/0022-1694(94)90057-4, 1994. a, b, c, d
Duku, C., Rathjens, H., Zwart, S. J., and Hein, L.: Towards ecosystem accounting: a comprehensive approach to modelling multiple hydrological ecosystem services, Hydrol. Earth Syst. Sci., 19, 4377–4396, https://doi.org/10.5194/hess-19-4377-2015, 2015. a
Duku, C., Zwart, S. J., and Hein, L.: Modelling the forest and woodland-irrigation nexus in tropical Africa: A case study in Benin, Agr. Ecosyst. Environ., 230, 105–115, https://doi.org/10.1016/j.agee.2016.06.001, 2016. a
Duku, C., Zwart, S. J., and Hein, L.: Impacts of climate change on cropping patterns in a tropical, sub-humid watershed, PLOS ONE, 13, e0192642, https://doi.org/10.1371/journal.pone.0192642, 2018. a
Fang, H., Baret, F., Plummer, S., and Schaepman-Strub, G.: An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications, Rev. Geophys., 57, 739–799, https://doi.org/10.1029/2018RG000608, 2019. a
Fernandez-Palomino, C. A., Hattermann, F. F., Krysanova, V., Vega-Jácome, F., and Bronstert, A.: Towards a more consistent eco-hydrological modelling through multi-objective calibration: a case study in the Andean Vilcanota River basin, Peru, Hydrolog. Sci. J., 66, 59–74, https://doi.org/10.1080/02626667.2020.1846740, 2021. a, b, c
Friend, A. D., Arneth, A., Kiang, N. Y., Lomas, M., Ogée, J., Rödenbeck, C., Running, S. W., Santaren, J.-D., Sitch, S., Viovy, N., Woodward, I. F., and Zaehle, S.: FLUXNET and modelling the global carbon cycle, Glob. Change Biol., 13, 610–633, https://doi.org/10.1111/j.1365-2486.2006.01223.x, 2007. a
Galle, S., Grippa, M., Peugeot, C., Moussa, I. B., Cappelaere, B., Demarty, J., Mougin, E., Panthou, G., Adjomayi, P., Agbossou, E. K., Ba, A., Boucher, M., Cohard, J.-M., Descloitres, M., Descroix, L., Diawara, M., Dossou, M., Favreau, G., Gangneron, F., Gosset, M., Hector, B., Hiernaux, P., Issoufou, B.-A., Kergoat, L., Lawin, E., Lebel, T., Legchenko, A., Abdou, M. M., Malam-Issa, O., Mamadou, O., Nazoumou, Y., Pellarin, T., Quantin, G., Sambou, B., Seghieri, J., Séguis, L., Vandervaere, J.-P., Vischel, T., Vouillamoz, J.-M., Zannou, A., Afouda, S., Alhassane, A., Arjounin, M., Barral, H., Biron, R., Cazenave, F., Chaffard, V., Chazarin, J.-P., Guyard, H., Koné, A., Mainassara, I., Mamane, A., Oi, M., Ouani, T., Soumaguel, N., Wubda, M., Ago, E. E., Alle, I. C., Allies, A., Arpin-Pont, F., Awessou, B., Cassé, C., Charvet, G., Dardel, C., Depeyre, A., Diallo, F. B., Do, T., Fatras, C., Frappart, F., Gal, L., Gascon, T., Gibon, F., Guiro, I., Ingatan, A., Kempf, J., Kotchoni, D., Lawson, F., Leauthaud, C., Louvet, S., Mason, E., Nguyen, C. C., Perrimond, B., Pierre, C., Richard, A., Robert, E., Román-Cascón, C., Velluet, C., and Wilcox, C.: AMMA–CATCH, a Critical Zone Observatory in West Africa Monitoring a Region in Transition, Vadose Zone J., 17, 1–24, https://doi.org/10.2136/vzj2018.03.0062, 2018. a, b, c, d, e, f, g, h
Garcia Sanchez, D., Lacarrière, B., Musy, M., and Bourges, B.: Application of sensitivity analysis in building energy simulations: Combining first- and second-order elementary effects methods, Energ. Buildings, 68, 741–750, https://doi.org/10.1016/j.enbuild.2012.08.048, 2014. a, b
Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W., and Sitch, S.: Terrestrial vegetation and water balance–hydrological evaluation of a dynamic global vegetation model, J. Hydrol., 286, 249–270, https://doi.org/10.1016/j.jhydrol.2003.09.029, 2004. a
Giertz, S. and Diekkrüger, B.: Analysis of the hydrological processes in a small headwater catchment in Benin (West Africa), Phys. Chem. Earth Pts. A/B/C, 28, 1333–1341, https://doi.org/10.1016/j.pce.2003.09.009, 2003. a
Giertz, S., Diekkrüger, B., Jaeger, A., and Schopp, M.: An interdisciplinary scenario analysis to assess the water availability and water consumption in the Upper Ouémé catchment in Benin, Adv. Geosci., 9, 3–13, https://doi.org/10.5194/adgeo-9-3-2006, 2006. a
Good, S. P., Soderberg, K., Guan, K., King, E. G., Scanlon, T. M., and Caylor, K. K.: δ2H isotopic flux partitioning of evapotranspiration over a grass field following a water pulse and subsequent dry down, Water Resour. Res., 50, 1410–1432, https://doi.org/10.1002/2013WR014333, 2014. a
Ha, L., Bastiaanssen, W., van Griensven, A., van Dijk, A., and Senay, G.: Calibration of Spatially Distributed Hydrological Processes and Model Parameters in SWAT Using Remote Sensing Data and an Auto-Calibration Procedure: A Case Study in a Vietnamese River Basin, Water, 10, 212, https://doi.org/10.3390/w10020212, 2018. a
Hargreaves, G. H. and Samani, Z. A.: Reference Crop Evapotranspiration from Temperature, Appl. Eng. Agric., 1, 96–99, https://doi.org/10.13031/2013.26773, 1985. a
Hector, B., Cohard, J.-M., Séguis, L., Galle, S., and Peugeot, C.: Hydrological functioning of western African inland valleys explored with a critical zone model, Hydrol. Earth Syst. Sci., 22, 5867–5888, https://doi.org/10.5194/hess-22-5867-2018, 2018. a, b, c, d
Houska, T., Kraft, P., Chamorro-Chavez, A., and Breuer, L.: SPOTting Model Parameters Using a Ready-Made Python Package, PLOS ONE, 10, e0145180, https://doi.org/10.1371/journal.pone.0145180, 2015. a, b
Hoyos, N., Correa-Metrio, A., Jepsen, S. M., Wemple, B., Valencia, S., Marsik, M., Doria, R., Escobar, J., Restrepo, J. C., and Velez, M. I.: Modeling Streamflow Response to Persistent Drought in a Coastal Tropical Mountainous Watershed, Sierra Nevada De Santa Marta, Colombia, Water, 11, 94, https://doi.org/10.3390/w11010094, 2019. a
Jepsen, S. M., Harmon, T. C., and Guan, B.: Analyzing the Suitability of Remotely Sensed ET for Calibrating a Watershed Model of a Mediterranean Montane Forest, Remote Sens.-Basel, 13, 1258, https://doi.org/10.3390/rs13071258, 2021. a
Jolly, W. M. and Running, S. W.: Effects of precipitation and soil water potential on drought deciduous phenology in the Kalahari, Glob. Change Biol., 10, 303–308, https://doi.org/10.1046/j.1365-2486.2003.00701.x, 2004. a
Kim, J., Hwang, T., Schaaf, C. L., Kljun, N., and Munger, J. W.: Seasonal variation of source contributions to eddy-covariance CO2 measurements in a mixed hardwood-conifer forest, Agr. Forest Meteorol., 253–254, 71–83, https://doi.org/10.1016/j.agrformet.2018.02.004, 2018. a
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019. a, b, c
Koltsida, E. and Kallioras, A.: Multi-Variable SWAT Model Calibration Using Satellite-Based Evapotranspiration Data and Streamflow, Hydrology, 9, 112, https://doi.org/10.3390/hydrology9070112, 2022. a
Liang, S., Zhang, X., Xiao, Z., Cheng, J., Liu, Q., and Zhao, X.: Global LAnd Surface Satellite (GLASS) Products, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-319-02588-9, 2014. a, b, c
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan, W., Zhang, X., Zhao, X., and Zhou, J.: The Global Land Surface Satellite (GLASS) Product Suite, B. Am. Meteorol. Soc., 102, E323–E337, https://doi.org/10.1175/BAMS-D-18-0341.1, 2021. a, b, c
López-Ramírez, S. M., Mayer, A., Sáenz, L., Muñoz-Villers, L. E., Holwerda, F., Looker, N., Schürz, C., Berry, Z. C., Manson, R., Asbjornsen, H., Kolka, R., Geissert, D., and Lezama, C.: A comprehensive calibration and validation of SWAT-T using local datasets, evapotranspiration and streamflow in a tropical montane cloud forest area with permeable substrate in central Veracruz, Mexico, J. Hydrol., 603, 126781, https://doi.org/10.1016/j.jhydrol.2021.126781, 2021. a, b, c, d
Mamadou, O., Cohard, J. M., Galle, S., Awanou, C. N., Diedhiou, A., Kounouhewa, B., and Peugeot, C.: Energy fluxes and surface characteristics over a cultivated area in Benin: daily and seasonal dynamics, Hydrol. Earth Syst. Sci., 18, 893–914, https://doi.org/10.5194/hess-18-893-2014, 2014. a, b, c, d
Mamadou, O., Galle, S., Cohard, J.-M., Peugeot, C., Kounouhewa, B., Biron, R., Hector, B., and Zannou, A. B.: Dynamics of water vapor and energy exchanges above two contrasting Sudanian climate ecosystems in Northern Benin (West Africa), J. Geophys. Res.-Atmos., 121, 11269–11286, https://doi.org/10.1002/2016JD024749, 2016. a, b, c, d, e, f, g, h, i, j, k
Michel, D., Jiménez, C., Miralles, D. G., Jung, M., Hirschi, M., Ershadi, A., Martens, B., McCabe, M. F., Fisher, J. B., Mu, Q., Seneviratne, S. I., Wood, E. F., and Fernández-Prieto, D.: The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms, Hydrol. Earth Syst. Sci., 20, 803–822, https://doi.org/10.5194/hess-20-803-2016, 2016. a
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. a, b
Monteith, J. L.: Evaporation and the environment, Symposia of the Society for Experimental Biology, 205–234, https://repository.rothamsted.ac.uk/item/8v5v7 (last access: 17 December 2024) 1965. a
Nelder, J. A. and Mead, R.: A Simplex Method for Function Minimization, Computer J., 7, 308–313, https://doi.org/10.1093/COMJNL/7.4.308, 1965. a
Novick, K. A., Biederman, J. A., Desai, A. R., Litvak, M. E., Moore, D., Scott, R. L., and Torn, M. S.: The AmeriFlux network: A coalition of the willing, Agr. Forest Meteorol., 249, 444–456, https://doi.org/10.1016/j.agrformet.2017.10.009, 2018. a
Odusanya, A. E., Mehdi, B., Schürz, C., Oke, A. O., Awokola, O. S., Awomeso, J. A., Adejuwon, J. O., and Schulz, K.: Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria, Hydrol. Earth Syst. Sci., 23, 1113–1144, https://doi.org/10.5194/hess-23-1113-2019, 2019. a, b
Odusanya, A. E., Schulz, K., Biao, E. I., Degan, B. A., and Mehdi-Schulz, B.: Evaluating the performance of streamflow simulated by an eco-hydrological model calibrated and validated with global land surface actual evapotranspiration from remote sensing at a catchment scale in West Africa, J. Hydrol.: Regional Studies, 37, 100893, https://doi.org/10.1016/j.ejrh.2021.100893, 2021. a
Park, J. Y., Ale, S., Teague, W. R., and Dowhower, S. L.: Simulating hydrologic responses to alternate grazing management practices at the ranch and watershed scales, J. Soil Water Conserv., 72, 102–121, https://doi.org/10.2489/jswc.72.2.102, 2017. a, b
Poméon, T., Diekkrüger, B., Springer, A., Kusche, J., and Eicker, A.: Multi-Objective Validation of SWAT for Sparsely-Gauged West African River Basins – A Remote Sensing Approach, Water, 10, 451, https://doi.org/10.3390/w10040451, 2018. a
Priestley, C. H. B. and Taylor, R. J.: On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters, Mon. Weather Rev., 100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972. a
Qiao, L., Will, R., Wagner, K., Zhang, T., and Zou, C.: Improvement of evapotranspiration estimates for grasslands in the southern Great Plains: Comparing a biophysical model (SWAT) and remote sensing (MODIS), Journal of Hydrology: Regional Studies, 44, 101275, https://doi.org/10.1016/j.ejrh.2022.101275, 2022. a, b
Rajib, A., Merwade, V., and Yu, Z.: Rationale and Efficacy of Assimilating Remotely Sensed Potential Evapotranspiration for Reduced Uncertainty of Hydrologic Models, Water Resour. Res., 54, 4615–4637, https://doi.org/10.1029/2017WR021147, 2018. a
Rodell, M., Beaudoing, H. K., L'Ecuyer, T. S., Olson, W. S., Famiglietti, J. S., Houser, P. R., Adler, R., Bosilovich, M. G., Clayson, C. A., Chambers, D., Clark, E., Fetzer, E. J., Gao, X., Gu, G., Hilburn, K., Huffman, G. J., Lettenmaier, D. P., Liu, W. T., Robertson, F. R., Schlosser, C. A., Sheffield, J., and Wood, E. F.: The Observed State of the Water Cycle in the Early Twenty-First Century, J. Climate, 28, 8289–8318, https://doi.org/10.1175/JCLI-D-14-00555.1, 2015. a
Rogelis, M. C., Werner, M., Obregón, N., and Wright, N.: Hydrological model assessment for flood early warning in a tropical high mountain basin, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2016-30, 2016. a
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global sensitivity analysis: The primer, Wiley, Chichester, West Sussex, ISBN 9780470059975, https://doi.org/10.1002/9780470725184, 2008. a
Schaefli, B. and Gupta, H. V.: Do Nash values have value?, Hydrol. Process., 21, 2075–2080, https://doi.org/10.1002/hyp.6825, 2007. a
Schlesinger, W. H. and Jasechko, S.: Transpiration in the global water cycle, Agr. Forest Meteorol., 189–190, 115–117, https://doi.org/10.1016/j.agrformet.2014.01.011, 2014. a, b
Schneider, K., Ketzer, B., Breuer, L., Vaché, K. B., Bernhofer, C., and Frede, H.-G.: Evaluation of evapotranspiration methods for model validation in a semi-arid watershed in northern China, Adv. Geosci., 11, 37–42, https://doi.org/10.5194/adgeo-11-37-2007, 2007. a
Strauch, M. and Volk, M.: SWAT plant growth modification for improved modeling of perennial vegetation in the tropics, Ecol. Model., 269, 98–112, https://doi.org/10.1016/j.ecolmodel.2013.08.013, 2013. a, b, c, d
Tan, M. L., Gassman, P. W., Yang, X., and Haywood, J.: A review of SWAT applications, performance and future needs for simulation of hydro-climatic extremes, Adv. Water Resour., 143, 103662, https://doi.org/10.1016/j.advwatres.2020.103662, 2020. a
Togbévi, Q. F., Bossa, A. Y., Yira, Y., Preko, K., Sintondji, L. O., and van der Ploeg, M.: A multi-model approach for analysing water balance and water-related ecosystem services in the Ouriyori catchment (Benin), Hydrolog. Sci. J., 65, 2453–2465, https://doi.org/10.1080/02626667.2020.1811286, 2020. a
Viovy, N., Arino, O., and Belward, A. S.: The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series, Int. J. Remote Sens., 13, 1585–1590, https://doi.org/10.1080/01431169208904212, 1992. a, b
Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., and Gupta, H. V.: Towards reduced uncertainty in conceptual rainfall–runoff modelling: dynamic identifiability analysis, Hydrol. Process., 17, 455–476, https://doi.org/10.1002/hyp.1135, 2003. a
Wang, L., Good, S. P., and Caylor, K. K.: Global synthesis of vegetation control on evapotranspiration partitioning, Geophys. Res. Lett., 41, 6753–6757, https://doi.org/10.1002/2014GL061439, 2014. a
Weerasinghe, I., Bastiaanssen, W., Mul, M., Jia, L., and van Griensven, A.: Can we trust remote sensing evapotranspiration products over Africa?, Hydrol. Earth Syst. Sci., 24, 1565–1586, https://doi.org/10.5194/hess-24-1565-2020, 2020. a
Wei, Z., Yoshimura, K., Wang, L., Miralles, D. G., Jasechko, S., and Lee, X.: Revisiting the contribution of transpiration to global terrestrial evapotranspiration, Geophys. Res. Lett., 44, 2792–2801, https://doi.org/10.1002/2016GL072235, 2017. a, b
Weiss, M., Baret, F., Smith, G. J., Jonckheere, I., and Coppin, P.: Review of methods for in situ leaf area index (LAI) determination, Agr. Forest Meteorol., 121, 37–53, https://doi.org/10.1016/j.agrformet.2003.08.001, 2004. a
Xiang, X., Ao, T., Xiao, Q., Li, X., Zhou, L., Chen, Y., Bi, Y., and Guo, J.: Parameter Sensitivity Analysis of SWAT Modeling in the Upper Heihe River Basin Using Four Typical Approaches, Appl. Sci., 12, 9862, https://doi.org/10.3390/app12199862, 2022. a
Yang, Q. and Zhang, X.: Improving SWAT for simulating water and carbon fluxes of forest ecosystems, Sci. Total Environ., 569–570, 1478–1488, https://doi.org/10.1016/j.scitotenv.2016.06.238, 2016. a
Yang, Q., Almendinger, J. E., Zhang, X., Huang, M., Chen, X., Leng, G., Zhou, Y., Zhao, K., Asrar, G. R., Srinivasan, R., and Li, X.: Enhancing SWAT simulation of forest ecosystems for water resource assessment: A case study in the St. Croix River basin, Ecol. Eng., 120, 422–431, https://doi.org/10.1016/j.ecoleng.2018.06.020, 2018. a, b, c, d
Zhang, H., Wang, B., Liu, D. L., Zhang, M., Leslie, L. M., and Yu, Q.: Using an improved SWAT model to simulate hydrological responses to land use change: A case study of a catchment in tropical Australia, J. Hydrol., 585, 124822, https://doi.org/10.1016/j.jhydrol.2020.124822, 2020. a, b, c
Short summary
Evapotranspiration (ET) is computed from the vegetation (plant transpiration) and soil (soil evaporation). In western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented using the leaf area index (LAI). In this study, we evaluate the importance of the LAI for ET calculation. We take a close look at this interaction and highlight its relevance. Our work contributes to the understanding of terrestrial water cycle processes .
Evapotranspiration (ET) is computed from the vegetation (plant transpiration) and soil (soil...
