[
Ardia, D., Boudt, K., Carl, P., Mullen, K., Peterson, B.G., 2011. Differential evolution with DEoptim: an application to nonconvex portfolio optimization. The R Journal, 3, 1, 27–34. https://ssrn.com/abstract=1584905
]Search in Google Scholar
[
Bergström, S., 1992. The HBV model–its structure and applications. SMHI. https://www.smhi.se/polopoly_fs/1.83592!/Menu/general/extGroup/attachmentColHold/mainCol1/file/RH_4.pdf
]Search in Google Scholar
[
CORINE Land Cover, 2018. European Union, Copernicus Land Monitoring Service 2018. Available online: European Environment Agency (EEA). https://land.copernicus.eu/
]Search in Google Scholar
[
EODC, 2021. Product User Manual ASCAT DIREX SWI 0.5 km, v1.0.
]Search in Google Scholar
[
Holko, L., Parajka, J., Majerčáková, O., Faško, P., 2001. Hydrologická bilancia vybraných povodí Tatier v hydrologických rokoch 1989–1998. Journal of Hydrology and Hydromechanics, 49, 3–4, 200–222. (In Slovak.)
]Search in Google Scholar
[
Kubáň, M., Parajka, J., Tong, R., Greimeister-Pfeil, I., Vreugdenhil, M., Szolgay, J., Kohnova, S., Hlavcova, K., Sleziak, P., Brziak, A., 2022. The effects of satellite soil moisture data on the parametrization of topsoil and root zone soil moisture in a conceptual hydrological model. Journal of Hydrology and Hydromechanics, 70, 3, 295–307. https://doi.org/10.2478/johh-2022-0021
]Search in Google Scholar
[
Kubáň, M., Parajka, J., Tong, R., Pfeil, I., Vreugdenhil, M., Sleziak, P., Brziak, A., Szolgay, J., Kohnová, S., Hlavčová, K., 2021. Incorporating advanced scatterometer surface and root zone soil moisture products into the calibration of a conceptual semi-distributed hydrological model. Water, 13, 23, 3366. https://doi.org/10.3390/w13233366
]Search in Google Scholar
[
Laaha, G., Parajka, J., Viglione, A., Koffler, D., Haslinger, K., Schöner, W., Zehetgruber, J., Blöschl, G., 2016. A three-pillar approach to assessing climate impacts on low flows. Hydrology and Earth System Sciences, 20, 9, 3967–3985. https://doi.org/10.5194/hess-20-3967-2016
]Search in Google Scholar
[
Mostafaie, A., Forootan, E., Safari, A., Schumacher, M., 2018. Comparing multi-objective optimization techniques to calibrate a conceptual hydrological model using in situ runoff and daily GRACE data. Computational Geosciences, 22, 3, 789–814. https://doi.org/10.1007/s10596-018-9726-8
]Search in Google Scholar
[
Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I – A discussion of principles. Journal of Hydrology, 10, 3, 282–290. https://doi.org/10.1016/0022-1694(70)90255-6
]Search in Google Scholar
[
Parajka, J., Merz, R., Blöschl, G., 2003. Estimation of daily potential evapotranspiration for regional water balance modeling in Austria. In: Proc. 11th International Poster Day and Institute of Hydrology Open Day Transport of Water, Chemicals and Energy in the Soil – Crop Canopy – Atmosphere System, 20. November 2003, Bratislava, Slovakia. Published on CD-ROM, Slovak Academy of Sciences, ISBN 80 – 89139–02 – 7, pp. 299–306.
]Search in Google Scholar
[
Parajka, J., Merz, R., Blöschl, G., 2007. Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments. Hydrological Processes, 21, 4, 435–446. https://doi.org/10.1002/hyp.6253
]Search in Google Scholar
[
Parajka, J., Naeimi, V., Blöschl, G., Komma, J., 2009. Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria. Hydrology and Earth System Sciences, 13, 2, 259–271. https://doi.org/10.5194/hess-13-259-2009
]Search in Google Scholar
[
R Foundation for Statistical Computing: R: A Language and Environment for Statistical Computing. Version R version 4.3.1 (2023-06-16 ucrt): R Core Team. https://cran.rproject.org/bin/windows/base/
]Search in Google Scholar
[
Sleziak, P., Výleta, R., Hlavčová, K., Danáčová, M., Aleksić, M., Szolgay, J., Kohnová, S., 2021. A hydrological modeling approach for assessing the impacts of climate change on runoff regimes in Slovakia. Water, 13, 23, 3358. https://doi.org/10.3390/w13233358
]Search in Google Scholar
[
Storn, R., Price, K., 1997. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 4, 341–359. https://doi.org/10.1023/A:1008202821328
]Search in Google Scholar
[
Tong, R., Parajka, J., Salentinig, A., Pfeil, I., Komma, J., Széles, B., Kubáň, M., Valent, P., Vreugdenhil, M., Wagner, W., Blöschl, G., 2021. The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model. Hydrology and Earth System Sciences, 25, 3, 1389–1410. https://doi.org/10.5194/hess-25-1389-2021
]Search in Google Scholar
[
Tóth, B., Weynants, M., Pásztor, L., Hengl, T., 2017. 3D soil hydraulic database of Europe at 250 m resolution. Hydrological Processes, 31, 14, 2662–2666. https://doi.org/10.1002/hyp.11203
]Search in Google Scholar
[
Viglione, A., Parajka, J., 2020. TUWmodel: Lumped/Semi-Distributed Hydrological Model for Education Purposes, R package version 1.1-1.
]Search in Google Scholar
[
Zimmerman, D., Pavlik, C., Ruggles, A., Armstrong, M.P., 1999. An experimental comparison of ordinary and universal kriging and inverse distance weighting. Mathematical Geology, 31, 4, 375-390. https://doi.org/10.1023/A:1007586507433
]Search in Google Scholar
