AUC GEOGRAPHICA

The changes in land cover, particularly in vegetation, directly influence regional water systems through various processes and have potential to alter not only microclimates and local hydrological regimes, but also local ecosystems and downstream water resources. This article investigates the interplay between land cover change and hydrological processes over a 30-year period in the mid-latitude mountainous catchment. Key vegetation trends were identified by supervised classification of geometrically and radiometrically corrected Landsat satellite imagery. Specifically, the replacement of coniferous forests with transitional woodland-shrub and a gradual increase in mixed forests, influenced by disturbance events, such as windthrows and bark beetle outbreaks, were observed. The SWAT model was successfully calibrated and validated using long-term discharge data, allowing for the simulation of land cover scenarios. Results suggest that land cover changes exerted a limited influence on total water balance, indicating a degree of hydrological resilience of the catchment. However, they affected the partitioning of runoff components, such as direct flow, subsurface lateral flow, and groundwater recharge. The study demonstrates the value of integrating the satellite-based land cover analysis with process-based modelling to understand long-term land-hydrology interactions in complex terrain. The findings underscore the importance of improving spatial resolution, dynamic vegetation modelling, and soil-vegetation parameterization for future assessments under changing environmental conditions. This research contributes to a growing body of knowledge essential for sustainable water resource management in sensitive mountain regions.

1. Abbaspour, K. C. (2013): SWAT-CUP 2012: SWAT calibration and uncertainty programs - A User manual. Swiss Federal Institute of Aquatic Science and Technology, Eawag. Available online: https://swat.tamu.edu/media/114860/usermanual_swatcup.pdf (accessed on 11 October 2025).

2. Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., Kløve, B. (2015): A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology 524, 733-752. CrossRef

3. Alaoui, A., Lipiec, J., Gerke, H. H. (2011): A review of the changes in the soil pore system due to soil peformation: A hydrodynamic perspective. Soil and Tillage Research 115-116, 1-15. CrossRef

4. Archer, N. A. L., Bonell, M., Coles, N., MacDonald, A. M., Auton, C. A., Stevenson, R. (2013): Soil characteristics and landcover relationships on soil hydraulic conductivity at a hillslope scale: A view towards local flood management. Journal of Hydrology 497, 208-222. CrossRef

5. Arekhi, M., Yesil, A., Ozkan, U. Y. et al. (2018): Detecting treeline dynamics in response to climate warming using forest stand maps and Landsat data in a temperate forest. For Ecosyst 5: 23. CrossRef

6. Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., Santi, C., Harmel, R. D., Van Griensven, A., Van Liew, M. W., Kannan, N., Jha, M. K. (2012): SWAT: model use, calibration, and validation. Transactions of the ASABE 55(4), 1491-1508. CrossRef

7. Baig, M. H. A., Zhang, L., Shuai, T., Tong, Q. (2014): Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters 5(5), 423-431. CrossRef

8. Bernsteinová, J., Bässler, C., Zimmermann, L., Langhammer, J., Beudert, B. (2015): Changes in runoff in two neighbouring catchments in the Bohemian Forest related to climate and land cover changes. Journal of Hydrology and Hydromechanics 63(4), 342-352. CrossRef

9. Blackburn, D. A., Oliphant, A. J., Davis, J. D. (2021): Carbon and Water Exchanges in a Mountain Meadow Ecosystem, Sierra Nevada, California. Wetlands 41: 39. CrossRef

10. Blöschl, G., Ardoin-Bardin, S., Bonell, M. et al. (2007): At what scales do climate variability and land cover change impact on flooding and low flows? Hydrological Process 21(9), 1241-1247. CrossRef

11. Blöschl, G. (2022): Three hypotheses on changing river flood hazards. Hydrology and Earth System Sciences 26(19), 5015-5033. CrossRef

12. Breiman, L. (2001): Random Forests. Machine Learning 45, 5-32. CrossRef

13. Chaves, M. E. D., Picoli, M. C. A., Sanches, I. D. (2020): Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review. Remote Sensing 12(18): 3062. CrossRef

14. Chen, Z., Wang, W., Woods, R. A., Shao, Q. (2021): Hydrological effects of change in vegetation components across global catchments. Journal of Hydrology 595: 125775. CrossRef

15. Cognard-Plancq, A. L., Marc, V., Didon-Lescot, J. F., Normand, M. (2001): The role of forest cover on streamflow down sub-Mediterranean mountain watersheds: a modelling approach. Journal of Hydrology 254(1-4), 229-243. CrossRef

16. Crist, E. P., Cicone, R. C. (1984): A Physically-Based Transformation of Thematic Mapper Data - The TM Tasseled Cap. IEEE Transactions on Geoscience and Remote Sensing 22(3), 256-263. CrossRef

17. ČÚZK - Czech Office for Surveying, Mapping and Cadastre (2025): Geoprohlížeč [Online]. Available online: https://ags.cuzk.gov.cz/geoprohlizec/ (accessed on 11 October 2025).

18. Danáčová, M., Földes, G., Labat, M. M., Kohnová, S., Hlavčová, K. (2020): Estimating the effect of deforestation on runoff in small mountainous basins in Slovakia. Water 12(11): 3113. CrossRef

19. Dingman, S. L. (2015): Physical hydrology. 3rd ed., Long Grove: Waveland Press.

20. Dubbert, M., Werner, C. (2018): Water fluxes mediated by vegetation: emerging isotopic insights at the soil and atmosphere interfaces. New Phytologist 221(4), 1754-1763. CrossRef

21. Fatichi, S., Vivoni, E. R., Ogden, F. L. et al. (2016): An overview of current applications, challenges, and future trends in distributed process-based models in hydrology. Journal of Hydrology 537, 45-60. CrossRef

22. Feranec, J., Solin, L., Kopecká, M., Oťahel, J., Kupková, L., Štych, P., Bičík, I., Kolář, J., Čerba, O., Soukup, T., Brodský, L. (2014): Analysis and expert assessment of the semantic similarity between land cover classes. Progress in Physical Geography: Earth and Environment 38(3), 301-327. CrossRef

23. Feranec, J., Soukup, T., Hazeu, G., Jaffrain, G. (2016): European Landscape Dynamics: CORINE Land Cover Data. 1st ed., CRC Press. CrossRef

24. Floriancic, M. G., Allen, S. T., Meier, R., Truniger, L., Kirchner, J. W., Molnar, P. (2023): Potential for significant precipitation cycling by forest-floor litter and deadwood, Ecohydrology 16(2), 1-16. CrossRef

25. Foody, G. M. (2009): Sample size determination for image classification accuracy assessment and comparison. International Journal of Remote Sensing 30(20), 5273-5291. CrossRef

26. Gao, L., Wang, X., Johnson, B. A., Tian, Q., Wang, Y., Verrelst, J., Mu, X., Gu, X. (2020): Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS Journal of Photogrammetry and Remote Sensing 159, 364-377. CrossRef

27. Gebhardt, T., Hesse, B. D., Hikino, K., Kolovrat, K., Hafner, B. D., Grams, T. E. E., Häberle, K. H. (2023): Repeated summer drought changes the radial xylem sap flow profile in mature Norway spruce but not in European beech. Agricultural and Forest Meteorology 329: 109285. CrossRef

28. Girons Lopez, M., Vis, M. J. P., Jenicek, M., Griessinger, N., Seibert, J. (2020): Assessing the degree of detail of temperature-based snow routinesfor runoff modelling in mountainous areas in central Europe. Hydrology and Earth System Sciences 24(9), 4441-4461. CrossRef

29. Griffiths, P., Kuemmerle, T., Baumann, M. et al. (2014): Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites. Remote Sensing of Environment 151, 72-88. CrossRef

30. Healey, S. P., Cohen, W. B., Zhiqiang, Y., Krankina, O. N. (2005): Comparison of tasseled cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment 97(3), 301-310. CrossRef

31. Huang, C., Goward, S. N., Masek, J. G., Thomas, N., Zhu, Z., Vogelmann, J. E. (2010): An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment 114(1), 183-198. CrossRef

32. Jasechko, S., Sharp, Z. D., Gibson, J. J., Birks, J., Yi, Y., Fawcett, P. J. (2013): Terrestrial water fluxes dominated by transpiration. Nature 496, 347-350. CrossRef

33. Ji, Z., Wang, Y., Wang, L. (2024): Threshold identification of evapotranspiration under different land-use types in the Loess Plateau, China. Journal of Hydrology: Regional Studies 53: 101780. CrossRef

34. Kofroňová, J., Šípek, V., Hnilica, J., Vlček, L., Tesař, M. (2021): Canopy interception estimates in a Norway spruce forest and their importance for the hydrological modelling. Hydrological Sciences Journal 66(7), 1233-1247. CrossRef

35. KRNAP - Krkonošský národní park (Krkonoše National Park Administration) (2025): Veřejný mapový portal. Available online: https://ags.krnap.cz/mapy/prohlizecka/ (accessed on 11 October 2025).

36. Kupková, L., Potůčková, M., Lhotáková, Z., Albrechtová, J. (2018): Forest cover and disturbance changes, and their driving forces: A case study in the Ore Mountains, Czechia, heavily affected by anthropogenic acidic pollution in the second half of the 20th century. Environmental Research Letters 13: 095008. CrossRef

37. Kupková, L., Bičík, I., Jeleček, L. (2021): At the Crossroads of European Landscape Changes: Major Processes of Landscape Change in Czechia since the Middle of the 19th Century and Their Driving Forces. Land 10(1): 34. CrossRef

38. Kupková, L., Červená, L., Potůčková, M., Lysák, J., Roubalová, M., Hrázský, Z., Březina, S., Epstein, H. E., Müllerová, J. (2023): Towards reliable monitoring of grass species in nature conservation: Evaluation of the potential of UAV and PlanetScope multi-temporal data in the Central European tundra. Remote Sensing of Environment 294: 113645. CrossRef

39. Lachassagne, P., Wyns, R., Dewandel, B. (2011): The fracture permeability of Hard Rock Aquifers is due neither to tectonics, nor to unloading, but to weathering processes. Terra Nova 23(3), 145-161. CrossRef

40. Launiainen, S., Guan, M., Salmivaara, A., Kieloaho, A. J. (2019): Modeling boreal forest evapotranspiration and water balance at stand and catchment scales: a spatial approach. Hydrology and Earth System Sciences 23(8), 3457-3480. CrossRef

41. Mašek, J., Tumajer, J., Lange, J. et al. (2023): Shifting climatic responses of tree rings and NDVI along environmental gradients. Science of The Total Environment 908: 168275. CrossRef

42. Meier, R., Schwaab, J., Seneviratne, S. I. et al. (2021): Empirical estimate of forestation-induced precipitation changes in Europe. Nature Geoscience 14, 473-478. CrossRef

43. Mensah, J. K., Ofosu, E. A., Yidana, S. M., Akpoti, K., Kabo-bah, A. T. (2022): Integrated modeling of hydrological processes and groundwater recharge based on land use land cover, and climate changes: A systematic review. Environmental Advances 8: 100224. CrossRef

44. Moriasi, D. N., Gitau, M. W., Pai, N., Daggupati, P. (2015): Hydrologic and water quality models: performance measures and evaluation criteria. Transactions of the ASABE 58(6), 1763-1785. CrossRef

45. Němečková, S. (2008): The Map of soil subtypes of the Czech part of the Elbe river basin for the hydrological model SWIM. IH CAS, CD-ROM.

46. Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., Wulder, M. A. (2014): Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 148, 42-57. CrossRef

47. Pielke, R. A., Avissar, R., Raupach, M., Dolman, A. J., Zeng, X., Denning, A. S. (1998): Interactions between the atmosphere and terrestrial ecosystems: influence on weather and climate. Global Change Biology 4(5), 461-475. CrossRef

48. Potůčková, M., Kupková, L., Červená, L., Lysák, J., Krause, D., Hrázký, Z., Březina, S., Müllerová, J. (2021): Towards resolving conservation issues through historical aerial imagery: vegetation cover changes in the Central European tundra. Biodiversity and Conservation 30, 3433-3455. CrossRef

49. Priestley, C. H. B., Taylor, R. J. (1972): On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters. Monthly Weather Review 100(2), 81-92. CrossRef

50. Ramon, F. B., Millington, J. D. A., Moran, E. F., Batistella, M., Liu, J. (2020): Three decades of land-use and land-cover change in mountain regions of the Brazilian Atlantic Forest. Landscape and Urban Planning 204: 103948. CrossRef

51. Ritchie, J. T. (1972): A model for predicting evaporation from a row crop with incomplete cover. Water Resources Research 8(5), 1204-1213. CrossRef

52. Senf, C., Pflugmacher, D., Hostert, P., Seidl, R. (2017): Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe. ISPRS Journal of Photogrammetry and Remote Sensing 130, 453-463. CrossRef

53. Sevruk, B. (2005): Rainfall measurement: gauges. In Anderson, M. G. (Ed.), Encyclopedia of Hydrological Sciences. 3rd ed., New York: Wiley, 230-257. CrossRef

54. Sieker, F. (2000): Investigations of the Effects of On-Site Stormwater Management Measures in Urban and Agricultural Areas on Floods. In Marsalek, J., Watt, W. E., Zeman, E., Sieker, F. (Eds.), Flood Issues in Contemporary Water Management. NATO Science Series 71. Dordrecht: Springer. CrossRef

55. Siqueira, P. P., Oliveira, P. T. S., Bressiani, D., Neto, A. A. M., Rodrigues, D. B. B. (2021): Effects of climate and land cover changes on water availability in a Brazilian Cerrado basin. Journal of Hydrology: Regional Studies 37: 100931. CrossRef

56. Sonnenborg, T. O., Christiansen, J. R., Pang, B., Bruge, A., Stisen, S., Gundersen, P. (2017): Analyzing the hydrological impact of afforestation and tree species in two catchments with contrasting soil properties using the spatially distributed model MIKE SHE SWET. Agricultural and Forest Meteorology 239, 118-133. CrossRef

57. Sutanto, S. J., van den Hurk, B., Dirmeyer, P. A., Seneviratne, S. I., Röckmann, T., Trenberth, K. E., Blyth, E. M., Wenninger, J., Hoffmann, G. (2014): HESS Opinions "A perspective on isotope versus non-isotope approaches to determine the contribution of transpiration to total evaporation". Hydrology and Earth System Sciences 18(8), 2815-2827. CrossRef

58. Talukdar, S., Singha, P., Mahato, S., Shahfahad, P. S., Liou, Y. A., Rahman, A. (2020): Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review. Remote Sensing 12(7): 1135. CrossRef

59. Tolasz, R. (2007): Atlas podnebí Česka: Climate atlas of Czechia. Prague: Czech Hydrometeorological Institute.

60. Trinh, D. H., Chui, T. F. M. (2013): Assessing the hydrologic restoration of an urbanized area via an integrated distributed hydrological model. Hydrology and Earth System Sciences 17(12), 4789-4801. CrossRef

61. ÚHÚL (2022): Oblastní plán rozvoje lesů: Přírodní lesní oblast 22 - Krkonoše. Analýza stavu a vývoje. ÚHÚL, Brandýs nad Labem - Stará Boleslav.

62. Vacek, S., Bastl, M., Lepš, J. (1999): Vegetation changes in forest of the Krkonoše Mts over a period of air pollution stress (1980-1995). Plant Ecology 143, 1-11. CrossRef

63. Valencia, S., Villegas, J. C., Hoyos, N., Duque-Villegas, M., Salazar, J. F. (2024): Streamflow response to land use/land cover change in the tropical Andes using multiple SWAT model variants. Journal of Hydrology: Regional Studies 54: 101888. CrossRef

64. Wang, H., Tetzlaff, D., Soulsby, C. (2018): Modelling the effects of land cover and climate change on soil water partitioning in a boreal headwater catchment. Journal of Hydrology 558, 520-531. CrossRef

65. Winkler, K., Fuchs, R., Rounsevell, M., Herold, M. (2021): Global land use changes are four times greater than previously estimated. Nature Communications 12: 2501. CrossRef

66. Wojkowski, J., Walega, A., Radecki-Pawlik, A., Mlynski, D., Lepeška, T. (2022): The influence of land cover changes on landscape hydric potential and river flows: Upper Vistula, Western Carpathians. Catena 210: 105787. CrossRef

67. Yang, J. L., Zhang, G. L. (2011): Water infiltration in urban soils and its effects on the quantity and quality of runoff. Journal of Soils and Sediments 11, 751-761. CrossRef

68. Yu, X., Lamačová, A., Duffy, C., Krám, P., Hruška, J. (2016): Hydrological model uncertainty due to spatial evapotranspiration estimation methods. Computers and Geosciences 90(Part B), 90-101. CrossRef

69. Zagajewski, B., Kluczek, M., Raczko, E., Njegovec, A., Dabija, A., Kycko, M. (2021): Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve. Remote Sensing 13(13): 2581. CrossRef

70. Zelíková, N., Toušková, J., Kocum, J., Vlček, L., Tesař, M., Bouda, M., Šípek, V. (2025): Divergent water balance trajectories under two dominant tree species in montane forest catchment shifting from energy- to water-limitation. Hydrology and Earth System Sciences (Preprint, accepted).

71. Zhu, Z., Woodcock, C. E. (2014): Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment 152, 217-234. CrossRef