[
Adeyeri, O.E., Laux, P., Arnault, J., Lawin, A. E., Kunstmann, H., 2020. Conceptual hydrological model calibration using multi-objective optimization techniques over the trans-boundary Komadugu-Yobe basin, Lake Chad Area, West Africa. Journal of Hydrology: Regional Studies, 27, 100655. DOI: 10.1016/j.ejrh.2019.100655
]Open DOISearch in Google Scholar
[
Ardia, D., Mullen, K., 2010. DEoptim: Differential Evolution Optimization in R. R package version 2.0-4, URL http://CRAN.R-project.org/package=DEoptim
]Search in Google Scholar
[
Beck, H.E., Pan, M., Miralles, D.G., Reichle, R.H., Dorigo, W. A., Hahn, S. et al., 2021. Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors. Hydrol. Earth Syst. Sci., 25, 1, 17–40. DOI: 10.5194/hess-25-17-2021
]Open DOISearch in Google Scholar
[
Bergstrom, S., 1992. The HBV model - its structure and applications. Report No. 4. Swedish Meteorological and Hydro-logical Institute.
]Search in Google Scholar
[
Bomhof, R.H., Gärtner, F.R., Stiggelbout, A.M., et al., 2019. Key components of shared decision making models: a systematic review. BMJ Open, 9 12, e031763. DOI: 10.1136/bmjopen-2019-031763693710131852700
]Open DOISearch in Google Scholar
[
Brocca, L., Melone, F., Moramarco, T., Morbidelli, R., 2009. Antecedent wetness conditions based on ERS scatterometer data. J. Hydrol., 2009, 364, 73–87.10.1016/j.jhydrol.2008.10.007
]Search in Google Scholar
[
Brocca, L., Ciabatta, L., Massari, C., Camici, S., Tarpanelli, A., 2017. Soil moisture for hydrological applications: Open questions and new opportunities. Water, 9, 2, 140. DOI: 10.3390/w9020140
]Open DOISearch in Google Scholar
[
Ciupak, M., Ozga-Zielinski, B., Adamowski, J., Deo, R. C., Kochanek, K., 2019. Correcting satellite precipitation data and assimilating satellite-derived soil moisture data to generate ensemble hydrological forecasts within the HBV Rainfall-Runoff Model. Water, 11, 10, 2138. DOI: 10.3390/w11102138
]Open DOISearch in Google Scholar
[
Demirel, M.C., Özen, A., Orta, S., Toker, E., Demir, H.K., Ekmekcioğlu, Ö. et al., 2019. Additional value of using satellite-based soil moisture and two sources of groundwater data for hydrological model calibration. Water, 11, 10, 2083. https://doi.org/10.3390/w11102083
]Search in Google Scholar
[
De Santis, D., Biondi, D., Crow, W.T., Camici, S., Modanesi, S., Brocca, L., Massari, C., 2021. Assimilation of satellite soil moisture products for river flow prediction: An extensive experiment in over 700 catchments throughout Europe. Water Resources Research, 57, e2021WR029643. https://doi.org/10.1029/2021WR029643
]Search in Google Scholar
[
Efstratiadis, A., Koutsoyiannis, D., 2010. One decade of multi-objective calibration approaches in hydrological modelling: a review. Hydrological Sciences Journal, 55, 1, 58–78. DOI: 10.1080/02626660903526292
]Open DOISearch in Google Scholar
[
EODC, 2021. Product User Manual ASCAT DIREX SWI 0.5 km, v1.0.
]Search in Google Scholar
[
Fang, B., Lakshmi, V., 2014. Soil moisture at watershed scale: Remote sensing techniques. J. Hydrol., 516, 258–272.10.1016/j.jhydrol.2013.12.008
]Search in Google Scholar
[
Hahn, S., Wagner, W., Steele-Dunne, S., Vreugdenhil, M., Melzer, T., 2021. Improving ASCAT soil moisture retrievals with an enhanced spatially variable vegetation parameterization. IEEE Transactions on Geoscience and Remote Sensing, 59, 10, 8241–8256. https://doi.org/10.34726/1622
]Search in Google Scholar
[
Hiebl, J., Frei, C., 2016. Daily temperature grids for Austria since 1961 – Concept, creation and applicability. Theor. Appl. Clim., 124, 161–178.10.1007/s00704-015-1411-4
]Search in Google Scholar
[
Hiebl, J., Frei, C., 2017. Daily precipitation grids for Austria since 1961 – Development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling. Theor. Appl. Clim., 132, 327–345.10.1007/s00704-017-2093-x
]Search in Google Scholar
[
Jadidoleslam, N., Mantilla, R., Krajewski, W.F., Goska, R., 2019. Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region. Journal of Hydrology, 579, 124210. DOI: 10.1016/j.jhydrol.2019.124210
]Open DOISearch in Google Scholar
[
Jun, S., Park, J.H., Choi, HJ., Lee, Y.H., Lim, Y.J., Boo, K.O., Kang, H.S., 2021. Impact of soil moisture data assimilation on analysis and medium-range forecasts in an Operational Global Data Assimilation and Prediction System. Atmosphere, 12, 1089. https://doi.org/10.3390/atmos12091089
]Search in Google Scholar
[
Kim, H., Parinussa, R., Konings, A. G., Wagner, W., Cosh, M. H., Lakshmi, V. et al., 2018. Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products. Remote Sensing of Environment, 204, 260–275. DOI: 10.1016/j.rse.2017.10.026
]Open DOISearch in Google Scholar
[
Kim, S., Zhang, R., Pham, H., Sharma, A., 2019. A review of satellite-derived soil moisture and its usage for flood estimation. Remote Sens. Earth Syst. Sci., 2, 4, 225–246. DOI: 10.1007/s41976-019-00025-7
]Open DOISearch in Google Scholar
[
Kuban, M., Parajka, J., Tong, R., Pfeil, I., Vreugdenhil, M., Sleziak, P., Adam, B., Szolgay, J., Kohnová, S., Hlavcova, K., 2021. Incorporating advanced scatterometer surface and root zone soil moisture products into the calibration of a Conceptual Semi-Distributed Hydrological Model. Water, 13, 3366. https://doi.org/10.3390/w13233366
]Search in Google Scholar
[
Kundu, D., Vervoort, R.W., van Ogtrop, F.F., 2017. The value of remotely sensed surface soil moisture for model calibration using SWAT. Hydrol. Process., 31, 15, 2764–2780. DOI: 10.1002/hyp.11219
]Open DOISearch in Google Scholar
[
Kosugi, K., 1994. Three-parameter lognormal distribution model for soil water retention. AGU, https://doi.org/10.1029/93WR02931
]Search in Google Scholar
[
Kosugi, K., 1996. Lognormal distribution model for unsaturated soil hydraulic properties. Water Resources Research, 32, 2697–2703.10.1029/96WR01776
]Search in Google Scholar
[
Le, M.H., Nguyen, B.Q., Pham, H.T., Patil, A., Do, H.X., Ramsankaran, R.A.A.J., Bolten, J.D., Lakshmi, V., 2022. Assimilation of SMAP products for improving streamflow simulations over tropical climate region – Is spatial information more important than temporal information? Remote Sensing, 14, 1607. https://doi.org/10.3390/rs14071607
]Search in Google Scholar
[
Li, Y., Grimaldi, S., Pauwels, V., Walker, R.N., Jeffrey P., 2018. Hydrologic model calibration using remotely sensed soil moisture and discharge measurements: The impact on predictions at gauged and ungauged locations. Journal of Hydrology, 557, 897–909. DOI: 10.1016/j.jhydrol.2018.01.013
]Open DOISearch in Google Scholar
[
Lindstrom, G., Barbro J., Magnus, P., Marie, G., Sten, B., 1997. Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology, 201, 1–4, 272–288. https://doi.org/10.1016/S0022-1694(97)00041-3
]Search in Google Scholar
[
Liu, W., Wang, J., Xu, F., Li, C., Xian, T., 2022. Validation of four satellite-derived soil moisture products using ground-based in situ observations over Northern China. Remote Sensing, 14, 6, 1419. DOI: 10.3390/rs14061419
]Open DOISearch in Google Scholar
[
Loizu, J., Massari, C., Álvarez-Mozos, J., Tarpanelli, A., Brocca, L., Casalí, J., 2018. On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation. Advances in Water Resources, 111, 86–104. DOI: 10.1016/j.advwatres.2017.10.034
]Open DOISearch in Google Scholar
[
Mascaro, G., Ko, A., Vivoni, E.R., 2019. Closing the loop of satellite soil moisture estimation via scale invariance of hydrologic simulations. Scientific Reports, 9, 1, 16123. DOI: 10.1038/s41598-019-52650-3.683467431695120
]Open DOISearch in Google Scholar
[
Massari, C., Brocca, L., Tarpanelli, A., Moramarco, T., 2015. Data assimilation of satellite soil moisture into rainfall-runoff modelling: A complex recipe? Remote Sensing, 7, 9, 11403–11433. DOI: 10.3390/rs70911403
]Open DOISearch in Google Scholar
[
Meng, S., Xie, X., Liang, S., 2017. Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags. Journal of Hydrology, 550, 568–579. DOI: 10.1016/j.jhydrol.2017.05.024
]Open DOISearch in Google Scholar
[
Monteil, C., Zaoui, F., Le Moine, N., Hendrickx, F., 2020. Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm. Hydrol. Earth Syst. Sci., 24, 3189–3209. https://doi.org/10.5194/hess-24-3189-2020
]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. Comput. Geosci., 22, 3, 789–814. DOI: 10.1007/s10596-018-9726-8
]Open DOISearch in Google Scholar
[
Mullen, K.M., Ardia, D., Gil, D.L., Windover, D., Cline, J., 2011. DEoptim: An R Package for Global Optimization by Differential Evolution. Journal of Statistical Software, 40, 6, 1–26. https://doi.org/10.18637/jss.v040.i06
]Search in Google Scholar
[
Muñoz-Sabater, J., Al Bitar, A., Brocca, L., 2016. Soil moisture retrievals based on active and passive microwave data: State-of-the-art and operational applications. In: Petropoulos, G.P., Srivastava, P., Kerr, Y. (Eds.): Satellite Soil Moisture Retrievals: Techniques and Applications. Elsevier: Amsterdam, The Netherlands, 18, pp. 351–378.10.1016/B978-0-12-803388-3.00018-8
]Search in Google Scholar
[
Naeimi, V., Scipal, K., Bartalis, Z., Hasenauer, S., Wagner, W., 2009. An improved soil moisture retrieval algorithm for ERS and METOP scatterometer observations. IEEE Trans. Geosci. Remote Sensing, 47, 7, 1999–2013. DOI: 10.1109/TGRS.2008.2011617
]Open DOISearch 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. DOI: 10.1016/0022-1694(70)90255-6
]Open DOISearch in Google Scholar
[
Nijzink, R.C., Almeida, S., Pechlivanidis, I.G., Capell, R., Gustafssons, D., Arheimer, B. et al., 2018. Constraining conceptual hydrological models with multiple information sources. Water Resour. Res., 54, 10, 8332–8362. DOI: 10.1029/2017WR021895
]Open DOISearch in Google Scholar
[
Olofintoye, O., Ayanshola, A., Salami, A., Idrissiou, A., Iji, J., Adeleke, O., 2022. A study on the applicability of a Swat model in predicting the water yield and water balance of the Upper Ouémé catchment in the Republic of Benin. Slovak Journal of Civil Engineering, 30, 1, 57–66. https://doi.org/10.2478/sjce-2022-0007
]Search in Google Scholar
[
Parajka, J., Merz, R., Blöschl, G., 2003. Estimation of daily potential evapotranspiration for regional water balance modeling in Austria. In: 11th International Poster Day and Institute of Hydrology Open Day “Transport of Water, Chemicals and Energy in the Soil – Crop Canopy – Atmosphere System”. Slovak Academy of Sciences, Bratislava, pp. 299–306.
]Search in Google Scholar
[
Parajka, J., Merz, R., Blöschl, G., 2007. Uncertainty and multiple objective calibrations in regional water balance modeling: a case study in 320 Austrian catchments. Hydrol. Process., 21, 435–446. DOI: 10.1002/hyp.6253
]Open DOISearch 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. Hydrol. Earth Syst. Sci., 13, 259–271. https://doi.org/10.5194/hess-13-259-2009
]Search in Google Scholar
[
Paulik, C., Dorigo, W., Wagner, W., Kidd, R., 2014. Validation of the ASCAT Soil Water Index using in situ data from the International Soil Moisture Network. International Journal of Applied Earth Observation and Geoinformation, 30, 1–8. DOI: 10.1016/j.jag.2014.01.007
]Open DOISearch in Google Scholar
[
Pfeil, I., Vreugdenhil, M., Hahn, S., Wagner, W., Strauss, P., Blöschl, G., 2018. Improving the seasonal representation of ASCAT soil moisture and vegetation dynamics in a temperate climate. Remote Sensing, 10, 1788. https://doi.org/10.3390/rs10111788
]Search in Google Scholar
[
Rajib, M.A., Venkatesh, M., Zhiqiang, Y., 2016. Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture. Journal of Hydrology, 536, 2016, 192–207. ISSN 0022-1694. https://doi.org/10.1016/j.jhydrol.2016.02.037
]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, 341–359. https://doi.org/10.1023/A:1008202821328
]Search in Google Scholar
[
Sunwoo, W., Choi, M., 2017. Robust initial wetness condition framework of an event-based rainfall–runoff model using remotely sensed soil moisture. Water, 9, 2, 77. DOI: 10.3390/w9020077
]Open DOISearch in Google Scholar
[
Sleziak, P., Szolgay, J., Hlavčová, K., Danko, M., Parajka, J., 2020. The effect of the snow weighting on the temporal stability of hydrologic model efficiency and parameters. Journal of Hydrology, 583, 124639. ISSN 0022-1694. https://doi.org/10.1016/j.jhydrol.2020.124639
]Search in Google Scholar
[
Steele-Dunne, S.C., Hahn, S., Wagner, W., Vreugdenhil, M., 2021. Towards including dynamic vegetation parameters in the EUMETSAT H SAF ASCAT soil moisture products. Remote Sensing, 13, 1463. https://doi.org/10.3390/rs13081463
]Search in Google Scholar
[
SWI, 2022.https://land.copernicus.eu/global/products/swi
]Search in Google Scholar
[
Széles, B., Parajka, J., Hogan, P., Silasari, R., Pavlin, L., Strauss, P., Blöschl, G., 2020. The added value of different data types for calibrating and testing a hydrologic model in a small catchment. Water Resources Research, 56, 10, e2019WR026153. DOI: 10.1029/2019WR026153759444733149373
]Open DOISearch in Google Scholar
[
Tebbs, E., Gerard, F., Petrie, A., De Witte, E., 2016. Emerging and potential future applications of satellite-based soil moisture products. In: Petropoulos, G.P., Srivastava, P., Kerr, Y. (Eds.): Satellite Soil Moisture Retrievals: Techniques and Applications. Elsevier: Amsterdam, The Netherlands, 19, pp. 379–400.10.1016/B978-0-12-803388-3.00019-X
]Search in Google Scholar
[
Thornton, J.M., Mariethoz, G., Brauchli, T.J., Brunner, P., 2021. Efficient multi-objective calibration and uncertainty analysis of distributed snow simulations in rugged alpine terrain. Journal of Hydrology, 598, 126–241. DOI: 10.1016/j.jhydrol.2021.126241
]Open DOISearch in Google Scholar
[
Tian, S., Renzullo, L.J., Pipunic, R.C., Lerat, J., Sharples, W., Donnelly, C., 2021. Satellite soil moisture data assimilation for improved operational continental water balance prediction. Hydrol. Earth Syst. Sci., 25, 4567–4584. https://doi.org/10.5194/hess-25-4567-2021, 202110.5194/hess-25-4567-2021
]Search in Google Scholar
[
Tong, R., Parajka, J., Salentinig, A., Pfeil, I., Komma, J., Széles, B. et al., 2021. The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model. Hydrol. Earth Syst. Sci., 25, 1389–1410. DOI: 10.5194/hess-25-1389-2021
]Open DOISearch in Google Scholar
[
Tramblay, Y., Bouaicha, R., Brocca, L., Dorigo, W., Bouvier, C., Camici, S., Servat, E., 2012. Estimation of antecedent wetness conditions for flood modelling in northern Morocco. Hydrol. Earth Syst. Sci., 16, 4375–4386.10.5194/hess-16-4375-2012
]Search in Google Scholar
[
Vachaud, G., Passerat De Silans, A., Balabanis, P., Vauclin, M., 1985. Temporal stability of spatially measured soil water probability density function. Soil Sci. Soc. Am. J., 49, 4, 822–828. DOI: 10.2136/sssaj1985.03615995004900040006x
]Open DOISearch in Google Scholar
[
Viglione, A., Parajka, J., 2020. Lumped/Semi-Distributed Hydrological Model for Education Purposes. http://CRAN.R-project.org/package=TUWmodel, published 2020-02-26, License: GPL-2 | GPL-3, accessed 07/04/2022.
]Search in Google Scholar
[
Viglione, A., Parajka, J., Rogger, M., Salinas, J.L., Laaha, G., Sivapalan, M., Blöschl, G., 2013. Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria. Hydrol. Earth Syst. Sci., 17, 2263–2279, https://doi.org/10.5194/hess-17-2263-2013
]Search in Google Scholar
[
Wagner, W., Lemoine, G., Rott, H., 1999. A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sens. Environ., 70, 2, 191–207.10.1016/S0034-4257(99)00036-X
]Search in Google Scholar
[
Wagner, W., Blöschl, G., Pampaloni, P., Calvet, J.C., Bizzarri, B., Wigneron, J.P., Kerr, Y., 2007. Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Hydrol. Res., 38, 1–20.10.2166/nh.2007.029
]Search in Google Scholar
[
Wagner, W., Pathe, C., Doubkova, M., Sabel, D., Bartsch, A., Hasenauer, S., Blöschl, G., Scipal, K., Martínez-Fernández, J., Löw, A., 2008. Temporal stability of soil moisture and radar backscatter observed by the advanced synthetic aperture radar (ASAR). Sensors, 8, 1174–1197. https://doi.org/10.3390/s80201174392750127879759
]Search in Google Scholar
[
Wanders, N., Bierkens, M.F.P., de Jong, S.M., de Roo, A., Karssenberg, D., 2014. The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models. Water Resour. Res., 50, 8, 6874–6891. DOI: 10.1002/2013WR014639
]Open DOISearch in Google Scholar
[
Xiong, L., Zeng, L., 2019. Impacts of introducing remote sensing soil moisture in calibrating a distributed hydrological model for streamflow simulation. Water, 11, 4, 666. DOI: 10.3390/w11040666
]Open DOISearch in Google Scholar
[
Zhang, Y., Schaap, M.G., Zha, Y., 2018. A high-resolution global map of soil hydraulic properties produced by a hierarchical parameterization of a physically based water retention model. Water Res., 54, 9774–9790.10.1029/2018WR023539
]Search in Google Scholar
