[
Armenise, E., Simmons, R.W., Ahn, S., Garbout, A., Doerr, S.H., Mooney, S.J., Sturrock, C.J., Ritz, K., 2018. Soil seal development under simulated rainfall: Structural, physical and hydrological dynamics. J. Hydrol., 556, 211–219. https://doi.org/10.1016/j.jhydrol.2017.10.073
]Search in Google Scholar
[
Baroni, G., Facchi, A., Gandolfi, C., Ortuani, B., Horeschi, D., van Dam, J.C., 2010. Uncertainty in the determination of soil hydraulic parameters and its influence on the performance of two hydrological models of different complexity. Hydrol. Earth Syst. Sci., 14, 251–270. https://doi.org/10.5194/hess-14-251-2010
]Search in Google Scholar
[
Bauer, T., Strauss, P., Grims, M., Kamptner, E., Mansberger, R., Spiegel, H., 2015. Long-term agricultural management effects on surface roughness and consolidation of soils. Soil Till. Res., 151, 28–38. https://doi.org/10.1016/j.still.2015.01.017
]Search in Google Scholar
[
Benke, K.K., Lowell, K.E., Hamilton, A.J., 2008. Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model. Math. Comput. Model., 47, 1134–1149. https://doi.org/10.1016/j.mcm.2007.05.017
]Search in Google Scholar
[
Beven, K., 2012. Rainfall‐Runoff Modelling. Wiley. https://doi.org/10.1002/9781119951001
]Search in Google Scholar
[
Beven, K., Binley, A., 2014. GLUE: 20 years on. Hydrol. Process., 28, 5897–5918. https://doi.org/10.1002/hyp.10082
]Search in Google Scholar
[
Beven, K., Binley, A., 1992. The future of distributed models: Model calibration and uncertainty prediction. Hydrol. Process., 6, 279–298. https://doi.org/10.1002/hyp.3360060305
]Search in Google Scholar
[
Blasone, R.S., Vrugt, J.A., Madsen, H., Rosbjerg, D., Robinson, B.A., Zyvoloski, G.A., 2008. Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling. Adv. Water Resour., 31, 630–648. https://doi.org/10.1016/j.advwatres.2007.12.003
]Search in Google Scholar
[
Boardman, J., Poesen, J., 2006. Soil Erosion in Europe. John Wiley & Sons, Ltd, Chichester, UK. https://doi.org/10.1002/0470859202
]Search in Google Scholar
[
Brunetti, G., Šimůnek, J., Bogena, H., Baatz, R., Huisman, J.A., Dahlke, H., Vereecken, H., 2019. On the information content of cosmic‐ray neutron data in the inverse estimation of soil hydraulic properties. Vadose Zone J., 18, 1–24. https://doi.org/10.2136/vzj2018.06.0123
]Search in Google Scholar
[
Carpenter, S.R., Caraco, N.F., Correll, D.L., Howarth, R.W., Sharpley, A.N., Smith, V.H., 1998. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl., 8, 559–568. https://doi.org/10.1890/1051-0761(1998)008[0559:NPOSWW]2.0.CO;2
]Search in Google Scholar
[
Cho, S.J., Wilcock, P., Gran, K., 2022. Implementing landscape connectivity with topographic filtering model: A simulation of suspended sediment delivery in an agricultural watershed. Sci. Total Environ., 836, 155701. https://doi.org/10.1016/j.scitotenv.2022.155701
]Search in Google Scholar
[
Cluckie, I.D., Xuan, Y., Wang, Y., 2006. Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction. Hydrol. Earth Syst. Sci. Discuss., 3, 3211–3237. https://doi.org/10.5194/hessd-3-3211-2006
]Search in Google Scholar
[
Devátý, J., Beitlerová, H., Lenz, J., 2020. An open rainfall-runoff measurement database. EGU General Assembly 2020. Online. https://doi.org/10.5194/egusphere-egu2020-9148
]Search in Google Scholar
[
Dohnal, M., Vogel, T., Šanda, M., Jelínková, V., 2012. Uncertainty analysis of a dual-continuum model used to simulate subsurface hillslope runoff involving oxygen-18 as natural tracer. J. Hydrol. Hydromech., 60, 194–205. https://doi.org/10.2478/v10098-012-0017-0
]Search in Google Scholar
[
Dostál, T., Váška, J., Vrána, K., 2000. SMODERP — A simulation model of overland flow and erosion processes. Soil Eros., 135–161. https://doi.org/10.1007/978-3-662-04295-3_8
]Search in Google Scholar
[
El Ghoul, I., Sellami, H., Khlifi, S., Vanclooster, M., 2023. Impact of land use land cover changes on flow uncertainty in Siliana watershed of northwestern Tunisia. Catena, 220, 106733. https://doi.org/10.1016/j.catena.2022.106733
]Search in Google Scholar
[
Esteves, M., Faucher, X., Galle, S., Vauclin, M., 2000. Overland flow and infiltration modelling for small plots during unsteady rain: Numerical results versus observed values. J. Hydrol., 228, 265–282. https://doi.org/10.1016/S0022-1694(00)00155-4
]Search in Google Scholar
[
Freer, J., Beven, K., 2000. Bayesian estimation of uncertainty in runoff prediction and the value of data: An application GLUE approach. Water Resour. Res., 32, 2161–2173.
]Search in Google Scholar
[
Grayson, R.B., Western, A.W., Chiew, F.H.., 1997. Preferred states in spatial soil moisture patterns. Water Resour. Res., 33, 2897–2908.
]Search in Google Scholar
[
Gupta, A., Govindaraju, R.S., 2023. Uncertainty quantification in watershed hydrology: Which method to use? J. Hydrol., 616, 128749. https://doi.org/10.1016/j.jhydrol.2022.128749
]Search in Google Scholar
[
Hantush, M.M., Kalin, L., 2005. Uncertainty and sensitivity analysis of runoff and sediment yield in a small agricultural watershed with KINEROS2. Hydrol. Sci. J., 50. https://doi.org/10.1623/hysj.2005.50.6.1151
]Search in Google Scholar
[
Haruna, S.I., Anderson, S.H., Nkongolo, N. V., Zaibon, S., 2018. Soil hydraulic properties: influence of tillage and cover crops. Pedosphere, 28, 430–442. https://doi.org/10.1016/S1002-0160(17)60387-4
]Search in Google Scholar
[
Holý, M., 1984. Vztahy mezi povrchovým odtokem a transportem živin v povodí vodárenských nádrží (dílčí zpráva výzkumného ústavu VI-4-15/01-03) Prague. (In Czech.)
]Search in Google Scholar
[
Jeřábek, J., Zumr, D., Laburda, T., Krása, J., Dostál, T., 2022. Soil surface connectivity of tilled soil with wheel tracks and its development under simulated rainfall. J. Hydrol., 613, 128322. https://doi.org/10.1016/j.jhydrol.2022.128322
]Search in Google Scholar
[
Kavka, P., Jeřábek, J., Landa, M., 2022. SMODERP2D – Sheet and rill runoff routine validation at three scale levels. Water (Switzerland), 14, 327. https://doi.org/10.3390/w14030327
]Search in Google Scholar
[
Kavka, P., Jeřábek, J., Landa, M., Pesek, O., 2024. SMODERP2D - reference manual and user guide [WWW Document]. https://doi.org/https://github.com/storm-fsvcvut/smoderp2d-manual
]Search in Google Scholar
[
Kavka, P., Jeřábek, J., Landa, M., Pešek, O., 2023. SMODERP2D - Distributed event-based model for surface and subsurface runoff and erosion [WWW Document]. https://github.com/storm-fsv-cvut/smoderp2d. URL https://github.com/storm-fsv-cvut/smoderp2d
]Search in Google Scholar
[
Kavka, P., Strouhal, L., Jáchymová, B., Krása, J., Báčová, M., Laburda, T., Dostál, T., Devátý, J., Bauer, M., 2018. Double size fulljet field rainfall simulator for complex interrill and rill erosion studies. Stavební obzor - Civ. Eng. J., 27, 183–194. https://doi.org/10.14311/cej.2018.02.0015
]Search in Google Scholar
[
Kubát, J.-F., Strouhal, L., Kavka, P., 2024. Estimation of infiltration parameters: The role of pedotransfer functions and initial moisture conditions. J. Hydrol., 633, 130954. https://doi.org/10.1016/j.jhydrol.2024.130954
]Search in Google Scholar
[
Li, T., Jerabek, J., Zumr, D., Noreika, N., Dostal, T., 2021. Assessing spatial soil moisture patterns at a small agricultural catchment. In: 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). IEEE, pp. 279–284. https://doi.org/10.1109/MetroAgri-For52389.2021.9628588
]Search in Google Scholar
[
Loosvelt, L., Pauwels, V.R.N., Cornelis, W.M., De Lannoy, G.J.M., Verhoest, N.E.C., 2011. Impact of soil hydraulic parameter uncertainty on soil moisture modeling. Water Resour. Res., 47, 1–16. https://doi.org/10.1029/2010WR009204
]Search in Google Scholar
[
Madsen, H., 2000. Automatic calibration of a conceptual rainfall- runoff model using multiple objectives. J. Hydrol., 235, 276–288. https://doi.org/10.1016/S0022-1694(00)00279-1
]Search in Google Scholar
[
Manning, R., 1891. On the flow of water in open channels and pipes. Trans. Inst. Civ. Eng. Irel., 20, 161–207.
]Search in Google Scholar
[
Moges, E., Demissie, Y., Larsen, L., Yassin, F., 2021. Review: Sources of hydrological model uncertainties and advances in their analysis. Water (Switzerland), 13, 1, 28. https://doi.org/10.3390/w13010028
]Search in Google Scholar
[
Nanding, N., Rico-Ramirez, M.A., Han, D., Wu, H., Dai, Q., Zhang, J., 2021. Uncertainty assessment of radar-raingauge merged rainfall estimates in river discharge simulations. J. Hydrol., 603, 127093. https://doi.org/10.1016/j.jhydrol.2021.127093
]Search in Google Scholar
[
O’Callaghan, J.F., Mark, D.M., 1984. The extraction of drainage networks from digital elevation data. Comput. Vision, Graph. Image Process., 28, 323–344. https://doi.org/10.1016/S0734-189X(84)80011-0
]Search in Google Scholar
[
Onstad, C.A., Wolfe, M.L., Larson, C.L., Slack, D.C., 1984. Tilled soil subsidence during repeated wetting. Transactions of the ASAE, 27, 3, 0733-0736. https://doi.org/10.13031/2013.32862
]Search in Google Scholar
[
Penna, D., Tromp-van Meerveld, H.J., Gobbi, A., Borga, M., Dalla Fontana, G., 2011. The influence of soil moisture on threshold runoff generation processes in an alpine headwater catchment. Hydrol. Earth Syst. Sci., 15, 689–702. https://doi.org/10.5194/hess-15-689-2011
]Search in Google Scholar
[
PHILIP, J.R., 1957. The theory of infiltration. Soil Sci., 83, 345-358. https://doi.org/10.1097/00010694-195705000-00002
]Search in Google Scholar
[
Schübl, M., Brunetti, G., Fuchs, G., Stumpp, C., 2023. Estimating vadose zone water fluxes from soil water monitoring data: a comprehensive field study in Austria. Hydrol. Earth Syst. Sci., 27, 1431–1455. https://doi.org/10.5194/hess-27-1431-2023
]Search in Google Scholar
[
Schwen, A., Bodner, G., Loiskandl, W., 2011. Time-variable soil hydraulic properties in near-surface soil water simulations for different tillage methods. Agric. Water Manag., 99, 42–50. https://doi.org/10.1016/j.agwat.2011.07.020
]Search in Google Scholar
[
Shen, Z.Y., Chen, L., Chen, T., 2012. Analysis of parameter uncertainty in hydrological and sediment modeling using GLUE method: A case study of SWAT model applied to Three Gorges Reservoir Region, China. Hydrol. Earth Syst. Sci., 16, 121–132. https://doi.org/10.5194/hess-16-121-2012
]Search in Google Scholar
[
Šimůnek, J., Van Genuchten, M.T., 1996. Estimating unsaturated soil hydraulic properties from tension disc infiltrometer data by numerical inversion. Water Resour. Res., 32, 2683-2696. https://doi.org/10.1029/96WR01525
]Search in Google Scholar
[
Smith, M.W., 2014. Roughness in the Earth sciences. Earth-Science Rev., 136, 202–225. https://doi.org/10.1016/j.earscirev.2014.05.016
]Search in Google Scholar
[
Storn, R., Price, K., 1997. Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim., 11, 341–359. https://doi.org/10.1023/A:1008202821328
]Search in Google Scholar
[
Turunen, M., Gurarslan, G., Šimůnek, J., Koivusalo, H., 2020. What is the worth of drain discharge and surface runoff data in hydrological simulations? J. Hydrol., 587, 125030. https://doi.org/10.1016/j.jhydrol.2020.125030
]Search in Google Scholar
[
Vigiak, O., Sterk, G., Romanowicz, R.J., Beven, K.J., 2006. A semi-empirical model to assess uncertainty of spatial patterns of erosion. Catena, 66, 198–210. https://doi.org/10.1016/j.catena.2006.01.004
]Search in Google Scholar
[
Villarreal, R., Soracco, C.G., Lozano, L.A., Melani, E.M., Sarli, G.O., 2017. Temporal variation of soil sorptivity under conventional and no-till systems determined by a simple laboratory method. Soil Tillage Res., 168, 92–98. https://doi.org/10.1016/j.still.2016.12.013
]Search in Google Scholar
[
Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S.J., Brett, M., Wilson, J., Millman, K.J., Mayorov, N., Nelson, A.R.J., Jones, E., Kern, R., Larson, E., Carey, C.J., Polat, İ., Feng, Y., Moore, E.W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E.A., Harris, C.R., Archibald, A.M., Ribeiro, A.H., Pedregosa, F., van Mulbregt, P., Vijaykumar, A., Bardelli, A. Pietro, Rothberg, A., Hilboll, A., Kloeckner, A., Scopatz, A., Lee, A., Rokem, A., Woods, C.N., Fulton, C., Masson, C., Häggström, C., Fitzgerald, C., Nicholson, D.A., Hagen, D.R., Pasechnik, D. V., Olivetti, E., Martin, E., Wieser, E., Silva, F., Lenders, F., Wilhelm, F., Young, G., Price, G.A., Ingold, G.L., Allen, G.E., Lee, G.R., Audren, H., Probst, I., Dietrich, J.P., Silterra, J., Webber, J.T., Slavič, J., Nothman, J., Buchner, J., Kulick, J., Schönberger, J.L., de Miranda Cardoso, J.V., Reimer, J., Harrington, J., Rodríguez, J.L.C., Nunez-Iglesias, J., Kuczynski, J., Tritz, K., Thoma, M., Newville, M., Kümmerer, M., Bolingbroke, M., Tartre, M., Pak, M., Smith, N.J., Nowaczyk, N., Shebanov, N., Pavlyk, O., Brodtkorb, P.A., Lee, P., McGibbon, R.T., Feldbauer, R., Lewis, S., Tygier, S., Sievert, S., Vigna, S., Peterson, S., More, S., Pudlik, T., Oshima, T., Pingel, T.J., Robitaille, T.P., Spura, T., Jones, T.R., Cera, T., Leslie, T., Zito, T., Krauss, T., Upadhyay, U., Halchenko, Y.O., Vázquez-Baeza, Y., 2020. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261-272. https://doi.org/10.1038/s41592-019-0686-2
]Search in Google Scholar
[
Vrána, K., Váška, J., Dostál, T., 1996. Smoderp - uživatelský manuál. (in Czech).
]Search in Google Scholar
[
Vrugt, J.A., Bouten, W., 2002. Validity of first-order approximations to describe parameter uncertainty in soil hydrologic models. Soil Sci. Soc. Am. J., 66, 1740–1751. https://doi.org/10.2136/sssaj2002.1740
]Search in Google Scholar
[
Vrugt, J.A., ter Braak, C.J.F., Diks, C.G.H., Robinson, B.A., Hyman, J.M., Higdon, D., 2009. Accelerating Markov Chain Monte Carlo simulation by differential evolution with selfadaptive randomized subspace sampling. Int. J. Nonlinear Sci. Numer. Simul., 10, 273–290. https://doi.org/10.1515/IJNSNS.2009.10.3.273
]Search in Google Scholar
[
Wang, L., Zhang, Y., Jia, J., Zhen, Q., Zhang, X., 2021. Effect of vegetation on the flow pathways of steep hillslopes: Overland flow plot-scale experiments and their implications. Catena, 204, 105438. https://doi.org/10.1016/j.catena.2021.105438
]Search in Google Scholar
[
Zhang, D., Zhang, L., Guan, Y., Chen, Xi, Chen, Xinfang, 2012. Sensitivity analysis of Xinanjiang rainfall-runoff model parameters: A case study in Lianghui, Zhejiang province, China. Hydrol. Res., 43, 123–134. https://doi.org/10.2166/nh.2011.131
]Search in Google Scholar
[
Zhang, W., Cundy, T.W., 1989. Modeling of two-dimensional overland flow. Water Resour. Res., 25, 2019–2035.
]Search in Google Scholar
[
Zhou, R., Li, Y., Lu, D., Liu, H., Zhou, H., 2016. An optimization based sampling approach for multiple metrics uncertainty analysis using generalized likelihood uncertainty estimation. J. Hydrol., 540, 274–286. https://doi.org/10.1016/j.jhydrol.2016.06.030
]Search in Google Scholar
[
Zumr, D., Dostál, T., Devátý, J., 2015. Identification of prevailing storm runoff generation mechanisms in an intensively cultivated catchment. J. Hydrol. Hydromech., 63, 246–254. https://doi.org/10.1515/johh-2015-0022
]Search in Google Scholar