Articles | Volume 29, issue 14
https://doi.org/10.5194/hess-29-3227-2025
© 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-29-3227-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Merits and limits of SWAT-GL: application in contrasting glaciated catchments
School of Engineering and Design, Technical University of Munich, Munich, Germany
Florentin Hofmeister
School of Engineering and Design, Technical University of Munich, Munich, Germany
Bavarian Academy of Sciences and Humanities, Munich, Germany
Gabriele Chiogna
GeoZentrum Norbayern, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
Fabian Merk
School of Engineering and Design, Technical University of Munich, Munich, Germany
School of Engineering and Design, Technical University of Munich, Munich, Germany
Julian Machnitzke
School of Engineering and Design, Technical University of Munich, Munich, Germany
Lucas Alcamo
School of Engineering and Design, Technical University of Munich, Munich, Germany
Jingshui Huang
School of Engineering and Design, Technical University of Munich, Munich, Germany
Markus Disse
School of Engineering and Design, Technical University of Munich, Munich, Germany
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Cited articles
Adnan, M., Kang, S., Zhang, G., Saifullah, M., Anjum, M. N., and Ali, A. F.: Simulation and analysis of the water balance of the Nam Co Lake using SWAT model, Water-Sui., 11, 1383, https://doi.org/10.3390/w11071383, 2019. a
Ali, S. H., Bano, I., Kayastha, R. B., and Shrestha, A.: COMPARATIVE ASSESSMENT OF RUNOFF AND ITS COMPONENTS IN TWO CATCHMENTS OF UPPER INDUS BASIN BY USING A SEMI DISTRIBUTED GLACIO-HYDROLOGICAL MODEL, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1487–1494, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1487-2017, 2017. 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
Bahr, D. B., Pfeffer, W. T., and Kaser, G.: A review of volume-area scaling of glaciers, Rev. Geophys., 53, 95–140, https://doi.org/10.1002/2014rg000470, 2015. a
Baker, E. H., Mcneil, C. J., Sass, L., Peitzsch, E. H., Whorton, E. N., Florentine, C. E., Clark, A. M., Miller, Z. S., Fagre, D. B., and O'Neel, S. R.: USGS Benchmark Glacier Mass Balance and Project Data, United States Geological Survey (USGS) [data set], https://doi.org/10.5066/F7BG2N8R, 2018. a, b
Campolongo, F., Saltelli, A., and Cariboni, J.: From screening to quantitative sensitivity analysis. A unified approach, Comput. Phys. Commun., 182, 978–988, https://doi.org/10.1016/j.cpc.2010.12.039, 2011. a, b
Chen, Y., Li, W., Fang, G., and Li, Z.: Review article: Hydrological modeling in glacierized catchments of central Asia – status and challenges, Hydrol. Earth Syst. Sci., 21, 669–684, https://doi.org/10.5194/hess-21-669-2017, 2017. 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
Dawar, D. and Ludwig, S.: Differential evolution with dither and annealed scale factor, in: 2014 IEEE Symposium on Differential Evolution (SDE), IEEE, Orlando, FL, USA, 9–12 December 2014, 1–8, https://doi.org/10.1109/sde.2014.7031528, 2014. a
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE T. Evolut. Comput., 6, 182–197, https://doi.org/10.1109/4235.996017, 2002. a
Dozier, J.: Spectral signature of alpine snow cover from the landsat thematic mapper, Remote Sens. Environ., 28, 9–22, https://doi.org/10.1016/0034-4257(89)90101-6, 1989. a
Du, X., Silwal, G., and Faramarzi, M.: Investigating the impacts of glacier melt on stream temperature in a cold-region watershed: coupling a glacier melt model with a hydrological model, J. Hydrol., 605, 127303, https://doi.org/10.1016/j.jhydrol.2021.127303, 2022. a
Evin, G., Le Lay, M., Fouchier, C., Penot, D., Colleoni, F., Mas, A., Garambois, P.-A., and Laurantin, O.: Evaluation of hydrological models on small mountainous catchments: impact of the meteorological forcings, Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, 2024. a
Farinotti, D., Huss, M., Fürst, J. J., Landmann, J., Machguth, H., Maussion, F., and Pandit, A.: A consensus estimate for the ice thickness distribution of all glaciers on Earth, Nat. Geosci., 12, 168–173, https://doi.org/10.1038/s41561-019-0300-3, 2019. a, b, c
Frey, H., Machguth, H., Huss, M., Huggel, C., Bajracharya, S., Bolch, T., Kulkarni, A., Linsbauer, A., Salzmann, N., and Stoffel, M.: Estimating the volume of glaciers in the Himalayan–Karakoram region using different methods, The Cryosphere, 8, 2313–2333, https://doi.org/10.5194/tc-8-2313-2014, 2014. a
Florentine, C. and McKeon, L. A.: U.S. Geological Survey Benchmark Glacier Project: U.S. Geological Survey Fact Sheet 2022-3050, 2 pp., https://www.usgs.gov/publications/us-geological-survey-benchmark-glacier-project (last access: 1 April 2024), 2022. a
Gan, R., Luo, Y., Zuo, Q., and Sun, L.: Effects of projected climate change on the glacier and runoff generation in the Naryn River Basin, Central Asia, J. Hydrol., 523, 240–251, https://doi.org/10.1016/j.jhydrol.2015.01.057, 2015. a
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, c
Grusson, Y., Sun, X., Gascoin, S., Sauvage, S., Raghavan, S., Anctil, F., and Sáchez-Pérez, J.-M.: Assessing the capability of the SWAT model to simulate snow, snow melt and streamflow dynamics over an alpine watershed, J. Hydrol., 531, 574–588, https://doi.org/10.1016/j.jhydrol.2015.10.070, 2015. a
Hamed, K. H. and Ramachandra Rao, A.: A modified Mann-Kendall trend test for autocorrelated data, J. Hydrol., 204, 182–196, https://doi.org/10.1016/s0022-1694(97)00125-x, 1998. a, b
Hassan, J., qing Chen, X., Kayastha, R. B., and Nie, Y.: Multi-model assessment of glacio-hydrological changes in central Karakoram, Pakistan, J. Mt. Sci., 18, 1995–2011, https://doi.org/10.1007/s11629-021-6748-9, 2021. a
Hofmeister, F., Arias-Rodriguez, L. F., Premier, V., Marin, C., Notarnicola, C., Disse, M., and Chiogna, G.: Intercomparison of Sentinel-2 and modelled snow cover maps in a high-elevation Alpine catchment, J. Hydrol., 15, 100123, https://doi.org/10.1016/j.hydroa.2022.100123, 2022. a
Horlings, A.: A Numerical Modeling Investigation on Calving and the Recession of South Cascade Glacier, University Honors Theses, Paper 247, https://doi.org/10.15760/honors.307, 2016. a
Huss, M. and Hock, R.: A new model for global glacier change and sea-level rise, Front. Earth Sci., 3, 54, https://doi.org/10.3389/feart.2015.00054, 2015. a
Huss, M., Farinotti, D., Bauder, A., and Funk, M.: Modelling runoff from highly glacierized alpine drainage basins in a changing climate, Hydrol. Process., 22, 3888–3902, https://doi.org/10.1002/hyp.7055, 2008. a, b
Ji, H., Fang, G., Yang, J., and Chen, Y.: Multi-objective calibration of a distributed hydrological model in a highly glacierized watershed in Central Asia, Water-Sui., 11, 554, https://doi.org/10.3390/w11030554, 2019. a
Li, H., Beldring, S., Xu, C.-Y., Huss, M., Melvold, K., and Jain, S. K.: Integrating a glacier retreat model into a hydrological model – case studies of three glacierised catchments in Norway and Himalayan region, J. Hydrol., 527, 656–667, https://doi.org/10.1016/j.jhydrol.2015.05.017, 2015. a, b
Linsbauer, A., Paul, F., and Haeberli, W.: Modeling glacier thickness distribution and bed topography over entire mountain ranges with GlabTop: application of a fast and robust approach, J. Geophys. Res.-Earth, 117, F03007, https://doi.org/10.1029/2011jf002313, 2012. a
Linsbauer, A, Paul, F, Hoelzle, M, Frey, H, and Haeberli, W: The Swiss Alps without glaciers – a GIS-based modelling approach for reconstruction of glacier beds, Department of Geography, University of Zurich, https://doi.org/10.5167/UZH-27834, 2009. a
Luo, Y., Arnold, J., Liu, S., Wang, X., and Chen, X.: Inclusion of glacier processes for distributed hydrological modeling at basin scale with application to a watershed in Tianshan Mountains, northwest China, J. Hydrol., 477, 72–85, https://doi.org/10.1016/j.jhydrol.2012.11.005, 2013. a
Luo, Y., Wang, X., Piao, S., Sun, L., Ciais, P., Zhang, Y., Ma, C., Gan, R., and He, C.: Contrasting streamflow regimes induced by melting glaciers across the Tien Shan – Pamir – North Karakoram, Sci. Rep., 8, 16470, https://doi.org/10.1038/s41598-018-34829-2, 2018. a
Ma, C., Sun, L., Liu, S., Shao, M., and Luo, Y.: Impact of climate change on the streamflow in the glacierized Chu River Basin, Central Asia, J. Arid Land., 7, 501–513, https://doi.org/10.1007/s40333-015-0041-0, 2015. a
Mcneil, C. J., Sass, L., Florentine, C., Baker, E. H., Peitzsch, E. H., Whorton, E. N., Miller, Z., Fagre, D. B., Clark, A. M., and O'Neel, S. R.: Glacier-Wide Mass Balance and Compiled Data Inputs: USGS Benchmark Glaciers, Alaska Science Center [data set], https://doi.org/10.5066/F7HD7SRF, 2016. a, b
Merchán-Rivera, P., Geist, A., Disse, M., Huang, J., and Chiogna, G.: A Bayesian framework to assess and create risk maps of groundwater flooding, J. Hydrol., 610, 127797, https://doi.org/10.1016/j.jhydrol.2022.127797, 2022. a, b
Merk, F., Schaffhauser, T., Anwar, F., Tuo, Y., Cohard, J.-M., and Disse, M.: The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa, Hydrol. Earth Syst. Sci., 28, 5511–5539, https://doi.org/10.5194/hess-28-5511-2024, 2024. a
Millan, R., Mouginot, J., Rabatel, A., and Morlighem, M.: Ice velocity and thickness of the world's glaciers, Nat. Geosci., 15, 124–129, https://doi.org/10.1038/s41561-021-00885-z, 2022. a, b
Moriasi, D. N., Arnold, J. G., Liew, M. W. V., Bingner, R. L., Harmel, R. D., and Veith, T. L.: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007. a
Morris, M. D.: Factorial sampling plans for preliminary computational experiments, Technometrics, 33, 161–174, https://doi.org/10.1080/00401706.1991.10484804, 1991. a, b
Moriasi, N. D., Gitau, W. M., Pai, N., and Daggupati, P.: Hydrologic and water quality models: performance measures and evaluation criteria, T. ASABE, 58, 1763–1785, 2015. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019. a
NASA JPL: NASA Shuttle Radar Topography Mission Global 1 arc second, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MEASURES/SRTM/SRTMGL1.003, 2013. a
Naz, B. S., Frans, C. D., Clarke, G. K. C., Burns, P., and Lettenmaier, D. P.: Modeling the effect of glacier recession on streamflow response using a coupled glacio-hydrological model, Hydrol. Earth Syst. Sci., 18, 787–802, https://doi.org/10.5194/hess-18-787-2014, 2014. a, b
Nossent, J., Elsen, P., and Bauwens, W.: Sobol' sensitivity analysis of a complex environmental model, Environ. Modell. Softw., 26, 1515–1525, https://doi.org/10.1016/j.envsoft.2011.08.010, 2011. a
O'Neel, S., McNeil, C., Sass, L. C., Florentine, C., Baker, E. H., Peitzsch, E., McGrath, D., Fountain, A. G., and Fagre, D.: Reanalysis of the US Geological Survey Benchmark Glaciers: long-term insight into climate forcing of glacier mass balance, J. Glaciol., 65, 850–866, https://doi.org/10.1017/jog.2019.66, 2019. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Pesci, M. H., Schulte Overberg, P., Bosshard, T., and Förster, K.: From global glacier modeling to catchment hydrology: bridging the gap with the WaSiM-OGGM coupling scheme, Frontiers in Water, 5, 1296344, https://doi.org/10.3389/frwa.2023.1296344, 2023. a
Pettitt, A. N.: A Non-Parametric Approach to the Change-Point Problem, Appl. Stat.-J. Roy. St. C, 28, 126, https://doi.org/10.2307/2346729, 1979. a
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., and Wagener, T.: Sensitivity analysis of environmental models: a systematic review with practical workflow, Environ. Modell. Softw., 79, 214–232, https://doi.org/10.1016/j.envsoft.2016.02.008, 2016. a, b, c
Pradhananga, N. S., Kayastha, R. B., Bhattarai, B. C., Adhikari, T. R., Pradhan, S. C., Devkota, L. P., Shrestha, A. B., and Mool, P. K.: Estimation of discharge from Langtang River basin, Rasuwa, Nepal, using a glacio-hydrological model, Ann. Glaciol., 55, 223–230, https://doi.org/10.3189/2014aog66a123, 2014. a
RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines, Version 6, National Snow and Ice Data Center [data set], https://doi.org/10.7265/4M1F-GD79, 2017. a, b, c
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global Sensitivity Analysis: The Primer, John Wiley and Sons, Print ISBN 9780470059975, online ISBN 9780470725184, https://doi.org/10.1002/9780470725184, 2008. a, b
Sarrazin, F., Pianosi, F., and Wagener, T.: Global sensitivity analysis of environmental models: convergence and validation, Environ. Modell. Softw., 79, 135–152, https://doi.org/10.1016/j.envsoft.2016.02.005, 2016. a, b
Schaefli, B. and Huss, M.: Integrating point glacier mass balance observations into hydrologic model identification, Hydrol. Earth Syst. Sci., 15, 1227–1241, https://doi.org/10.5194/hess-15-1227-2011, 2011. a
Schaffhauser, T.: SWAT-GL Demo Model Martelltal, Zenodo [data set], https://doi.org/10.5281/zenodo.8068724, 2024. a
Seibert, J., Vis, M. J. P., Kohn, I., Weiler, M., and Stahl, K.: Technical note: Representing glacier geometry changes in a semi-distributed hydrological model, Hydrol. Earth Syst. Sci., 22, 2211–2224, https://doi.org/10.5194/hess-22-2211-2018, 2018. a, b, c
Shafeeque, M., Luo, Y., Wang, X., and Sun, L.: Altitudinal distribution of meltwater and its effects on glacio-hydrology in glacierized catchments, Central Asia, J. Am. Water Resour. As., 56, 30–52, https://doi.org/10.1111/1752-1688.12805, 2019. a
Shannon, S., Payne, A., Freer, J., Coxon, G., Kauzlaric, M., Kriegel, D., and Harrison, S.: A snow and glacier hydrological model for large catchments – case study for the Naryn River, central Asia, Hydrol. Earth Syst. Sci., 27, 453–480, https://doi.org/10.5194/hess-27-453-2023, 2023. a
Sin, G. and Gernaey, K. V.: Improving the Morris method for sensitivity analysis by scaling the elementary effects, in: Computer Aided Chemical Engineering, Elsevier, https://doi.org/10.1016/s1570-7946(09)70154-3, 925–930, 2009. a
Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M., and Xu, C.: Global sensitivity analysis in hydrological modeling: review of concepts, methods, theoretical framework, and applications, J. Hydrol., 523, 739–757, https://doi.org/10.1016/j.jhydrol.2015.02.013, 2015. a
Stoll, E., Hanzer, F., Oesterle, F., Nemec, J., Schöber, J., Huttenlau, M., and Förster, K.: What can we learn from comparing glacio-hydrological models?, Atmosphere-Basel, 11, 981, https://doi.org/10.3390/atmos11090981, 2020. a
Storn, R. and Price, K.: Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces, J. Global Optim., 11, 341–359, https://doi.org/10.1023/a:1008202821328, 1997. a
Tebaldi, C., Debeire, K., Eyring, V., Fischer, E., Fyfe, J., Friedlingstein, P., Knutti, R., Lowe, J., O'Neill, B., Sanderson, B., van Vuuren, D., Riahi, K., Meinshausen, M., Nicholls, Z., Tokarska, K. B., Hurtt, G., Kriegler, E., Lamarque, J.-F., Meehl, G., Moss, R., Bauer, S. E., Boucher, O., Brovkin, V., Byun, Y.-H., Dix, M., Gualdi, S., Guo, H., John, J. G., Kharin, S., Kim, Y., Koshiro, T., Ma, L., Olivié, D., Panickal, S., Qiao, F., Rong, X., Rosenbloom, N., Schupfner, M., Séférian, R., Sellar, A., Semmler, T., Shi, X., Song, Z., Steger, C., Stouffer, R., Swart, N., Tachiiri, K., Tang, Q., Tatebe, H., Voldoire, A., Volodin, E., Wyser, K., Xin, X., Yang, S., Yu, Y., and Ziehn, T.: Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6, Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, 2021. a
Tiel, M., Stahl, K., Freudiger, D., and Seibert, J.: Glacio-hydrological model calibration and evaluation, WIREs Water, 7, e1483, https://doi.org/10.1002/wat2.1483, 2020. a, b
Tuo, Y., Duan, Z., Disse, M., and Chiogna, G.: Evaluation of precipitation input for SWAT modeling in Alpine catchment: a case study in the Adige river basin (Italy), Sci. Total Environ., 573, 66–82, https://doi.org/10.1016/j.scitotenv.2016.08.034, 2016. a
Tuo, Y., Marcolini, G., Disse, M., and Chiogna, G.: A multi-objective approach to improve SWAT model calibration in alpine catchments, J. Hydrol., 559, 347–360, https://doi.org/10.1016/j.jhydrol.2018.02.055, 2018. a
U.S. Geological Survey: USGS Water Data for the Nation, U.S. Geological Survey, National Water Information System (NWIS) [data set], https://doi.org/10.5066/F7P55KJN, 1994. a
Vanuytrecht, E., Raes, D., and Willems, P.: Global sensitivity analysis of yield output from the water productivity model, Environ. Modell. Softw., 51, 323–332, https://doi.org/10.1016/j.envsoft.2013.10.017, 2014. a
Wagener, T.: On the Evaluation of Climate Change Impact Models for Adaptation Decisions, Springer International Publishing, 33–40, https://doi.org/10.1007/978-3-030-86211-4_5, 2022. a
Wang, X., Zhang, Y., Luo, Y., Sun, L., and Shafeeque, M.: Combined use of volume-area and volume-length scaling relationships in glacio-hydrological simulation, Hydrol. Res., 49, 1753–1772, https://doi.org/10.2166/nh.2018.137, 2018. a
Wiersma, P., Aerts, J., Zekollari, H., Hrachowitz, M., Drost, N., Huss, M., Sutanudjaja, E. H., and Hut, R.: Coupling a global glacier model to a global hydrological model prevents underestimation of glacier runoff, Hydrol. Earth Syst. Sci., 26, 5971–5986, https://doi.org/10.5194/hess-26-5971-2022, 2022. a
Wilcoxon, F.: Individual comparisons by ranking methods, Biometrics Bull., 1, 80, https://doi.org/10.2307/3001968, 1945. a
Wortmann, M., Bolch, T., Krysanova, V., and Buda, S.: Bridging glacier and river catchment scales: an efficient representation of glacier dynamics in a hydrological model, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2016-272, 2016. a, b
Wu, F., Zhan, J., Wang, Z., and Zhang, Q.: Streamflow variation due to glacier melting and climate change in upstream Heihe River Basin, Northwest China, Phys. Chem. Earth, Parts A/B/C, 79–82, 11–19, https://doi.org/10.1016/j.pce.2014.08.002, 2015. a
Yang, C., Xu, M., Fu, C., Kang, S., and Luo, Y.: The coupling of glacier melt module in SWAT model based on multi-source remote sensing data: a case study in the Upper Yarkant River Basin, Remote Sens.-Basel, 14, 6080, https://doi.org/10.3390/rs14236080, 2022. a
Zekollari, H., Huss, M., Farinotti, D., and Lhermitte, S.: Ice dynamical glacier evolution modeling – a review, Rev. Geophys., 60, e2021RG000754, https://doi.org/10.1029/2021rg000754, 2022. a
Short summary
The glacier-expanded SWAT (Soil Water Assessment Tool) version, SWAT-GL, was tested in four different catchments, highlighting the capabilities of the glacier routine. It was evaluated based on the representation of glacier mass balance, snow cover and glacier hypsometry. The glacier changes over a long timescale could be adequately represented, leading to promising potential future applications in glaciated and high mountain environments and significantly outperforming standard SWAT models.
The glacier-expanded SWAT (Soil Water Assessment Tool) version, SWAT-GL, was tested in four...