{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T04:59:20Z","timestamp":1784177960373,"version":"3.55.0"},"reference-count":75,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,11,3]],"date-time":"2019-11-03T00:00:00Z","timestamp":1572739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41890822"],"award-info":[{"award-number":["41890822"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51525902"],"award-info":[{"award-number":["51525902"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005416","name":"Research Council of Norway","doi-asserted-by":"publisher","award":["274310"],"award-info":[{"award-number":["274310"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Education \u201c111 Project\u201d Fund of China","award":["B18037"],"award-info":[{"award-number":["B18037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Integration of satellite-based data with hydrological modelling was generally conducted via data assimilation or model calibration, and both approaches can enhance streamflow predictions. In this study, we assessed the feasibility of another approach that uses satellite-based soil moisture data to directly estimate the parameter    \u03b2    to represent the degree of the spatial distribution of soil moisture storage capacity in the semi-distributed Hymod model. The impact of using historical root-zone soil moisture data from the Soil Moisture Active Passive (SMAP) mission on the prior estimation of the parameter    \u03b2    was explored. Two different ways to incorporate the root-zone soil moisture data to estimate the parameter    \u03b2    are proposed, i.e., one is to derive a priori distribution of    \u03b2   , and the other is to derive a fixed value for    \u03b2   . The simulations of the Hymod models employing the two ways to estimate    \u03b2    are compared with the results produced by the original model, i.e., the one without employing satellite-based data to estimate the parameter    \u03b2   , at three study catchments (the Upper Hanjiang River catchment, the Xiangjiang River catchment, and the Ganjiang River catchment). The results illustrate that the two ways to incorporate the SMAP root-zone soil moisture data in order to predetermine the parameter    \u03b2    of the semi-distributed Hymod model both perform well in simulating streamflow during the calibration period, and a slight improvement was found during the validation period. Notably, deriving a fixed    \u03b2    value from satellite soil moisture data can provide better performance for ungauged catchments despite reducing the model freedom degrees due to fixing the    \u03b2    value. It is concluded that the robustness of the Hymod model in predicting the streamflow can be improved when the spatial information of satellite-based soil moisture data is utilized to estimate the parameter    \u03b2   .<\/jats:p>","DOI":"10.3390\/rs11212580","type":"journal-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T04:13:08Z","timestamp":1572840788000},"page":"2580","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Prior Estimation of the Spatial Distribution Parameter of Soil Moisture Storage Capacity Using Satellite-Based Root-Zone Soil Moisture Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Yifei","family":"Tian","sequence":"first","affiliation":[{"name":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6990-2414","authenticated-orcid":false,"given":"Lihua","family":"Xiong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Xiong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruodan","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Department of European and Mediterranean Cultures: Architecture, Environment and Cultural Heritage (DiCEM), University of Basilicata, 75100 Matera, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7779","DOI":"10.1002\/2016WR019430","article-title":"Improving the realism of hydrologic model functioning through multivariate parameter estimation","volume":"52","author":"Rakovec","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3863","DOI":"10.5194\/hess-16-3863-2012","article-title":"Advancing data assimilation in operational hydrologic forecasting: Progresses, challenges, and emerging opportunities","volume":"16","author":"Liu","year":"2012","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yang, H., Xiong, L., Ma, Q., Xia, J., Chen, J., and Xu, C.-Y. (2019). Utilizing satellite surface soil moisture data in calibrating a distributed hydrological model applied in humid regions through a multi-objective Bayesian hierarchical framework. Remote Sens., 11.","DOI":"10.3390\/rs11111335"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8332","DOI":"10.1029\/2017WR021895","article-title":"Constraining conceptual hydrological models with multiple information sources","volume":"54","author":"Nijzink","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Brocca, L., Ciabatta, L., Massari, C., Camici, S., and Tarpanelli, A. (2017). Soil moisture for hydrological applications: Open questions and new opportunities. Water, 9.","DOI":"10.3390\/w9020140"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3161","DOI":"10.1007\/s11269-017-1722-6","article-title":"Satellite soil moisture: Review of theory and applications in water resources","volume":"31","author":"Srivastava","year":"2017","journal-title":"Water Resour. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1146\/annurev.earth.30.091201.140434","article-title":"Scaling of soil moisture: A hydrologic perspective","volume":"30","author":"Western","year":"2002","journal-title":"Annu. Rev. Earth Planetary. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6127","DOI":"10.5194\/hess-22-6127-2018","article-title":"How does initial soil moisture influence the hydrological response? A case study from southern France","volume":"22","author":"Uber","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_9","first-page":"1","article-title":"Modeling soil processes: Review, key challenges, and new perspectives","volume":"15","author":"Vereecken","year":"2016","journal-title":"Vadose Zone J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1175\/JHM-D-17-0240.1","article-title":"Identification of hydrologic models, optimized parameters, and rainfall inputs consistent with in situ streamflow and rainfall and remotely sensed soil moisture","volume":"19","author":"Wright","year":"2018","journal-title":"J. Hydrometeorol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mohanty, B.P., Cosh, M.H., Lakshmi, V., and Montzka, C. (2017). Soil moisture remote sensing: State-of-the-science. Vadose Zone J., 16.","DOI":"10.2136\/vzj2016.10.0105"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1029\/2018RG000618","article-title":"Ground, proximal, and satellite remote sensing of soil moisture","volume":"57","author":"Babaeian","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"F01002","DOI":"10.1029\/2007JF000769","article-title":"Multisensor historical climatology of satellite-derived global land surface moisture","volume":"113","author":"Owe","year":"2008","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"L20401","DOI":"10.1029\/2007GL031088","article-title":"Initial soil moisture retrievals from the METOP-A Advanced Scatterometer (ASCAT)","volume":"34","author":"Bartalis","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1109\/JPROC.2010.2043032","article-title":"The SMOS mission: New tool for monitoring key elements of the global water cycle","volume":"98","author":"Kerr","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/JPROC.2010.2043918","article-title":"The soil moisture active passive (SMAP) mission","volume":"98","author":"Entekhabi","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/2016RG000543","article-title":"A review of spatial downscaling of satellite remotely sensed soil moisture","volume":"55","author":"Peng","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1109\/TGRS.2018.2858004","article-title":"Toward global soil moisture monitoring with sentinel-1: Harnessing assets and overcoming obstacles","volume":"57","author":"Freeman","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.rse.2018.04.011","article-title":"The SMAP mission combined active-passive soil moisture product at 9 km and 3 km spatial resolutions","volume":"211","author":"Das","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3878","DOI":"10.1109\/TGRS.2016.2529659","article-title":"Investigation of SMAP fusion algorithms with airborne active and passive L-band microwave remote sensing","volume":"54","author":"Montzka","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2018","DOI":"10.1109\/TGRS.2013.2257605","article-title":"Tests of the SMAP combined radar and radiometer algorithm using airborne field campaign observations and simulated data","volume":"52","author":"Das","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1109\/JSTARS.2013.2272053","article-title":"Retrieving high-resolution surface soil moisture by downscaling AMSR-E brightness temperature using MODIS LST and NDVI data","volume":"7","author":"Song","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1007\/s11269-013-0337-9","article-title":"Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application","volume":"27","author":"Srivastava","year":"2013","journal-title":"Water Resour. Manag."},{"key":"ref_25","first-page":"315","article-title":"Fusion of active and passive microwave observations to create an essential climate variable data record on soil moisture","volume":"7","author":"Wagner","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. (ISPRS Ann.)"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Massari, C., Camici, S., Ciabatta, L., and Brocca, L. (2018). Exploiting satellite-based surface soil moisture for flood forecasting in the Mediterranean area: State update versus rainfall correction. Remote Sens., 10.","DOI":"10.3390\/rs10020292"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1016\/j.jhydrol.2014.07.041","article-title":"The impacts of assimilating satellite soil moisture into a rainfall\u2013runoff model in a semi-arid catchment","volume":"519","author":"Ryu","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.advwatres.2017.10.034","article-title":"On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation","volume":"111","author":"Loizu","year":"2018","journal-title":"Adv. Water Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Laiolo, P., Gabellani, S., Campo, L., Cenci, L., Silvestro, F., Delogu, F., Boni, G., Rudari, R., Puca, S., and Pisani, A.R. (2015, January 26\u201331). Assimilation of remote sensing observations into a continuous distributed hydrological model: Impacts on the hydrologic cycle. Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326015"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.5194\/hess-18-2343-2014","article-title":"The suitability of remotely sensed soil moisture for improving operational flood forecasting","volume":"18","author":"Wanders","year":"2014","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.advwatres.2011.01.011","article-title":"Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture","volume":"34","author":"Chen","year":"2011","journal-title":"Adv. Water Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2542","DOI":"10.1109\/TGRS.2011.2177468","article-title":"Assimilation of surface- and root-zone ASCAT soil moisture products into rainfall-runoff modeling","volume":"50","author":"Brocca","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2729","DOI":"10.5194\/hess-15-2729-2011","article-title":"Operational assimilation of ASCAT surface soil wetness at the Met Office","volume":"15","author":"Dharssi","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Baguis, P., and Roulin, E. (2017). Soil moisture data assimilation in a hydrological model: A case study in Belgium using large-scale satellite data. Remote Sens., 9.","DOI":"10.3390\/rs9080820"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1016\/j.jhydrol.2018.01.013","article-title":"Hydrologic model calibration using remotely sensed soil moisture and discharge measurements: The impact on predictions at gauged and ungauged locations","volume":"557","author":"Li","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2764","DOI":"10.1002\/hyp.11219","article-title":"The value of remotely sensed surface soil moisture for model calibration using SWAT","volume":"31","author":"Kundu","year":"2017","journal-title":"Hydrol. Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.5194\/hess-21-3125-2017","article-title":"Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products","volume":"21","author":"Sutanudjaja","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.5194\/hess-19-1727-2015","article-title":"Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data","volume":"19","author":"Silvestro","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.jhydrol.2016.02.018","article-title":"The efficacy of calibrating hydrologic model using remotely sensed evapotranspiration and soil moisture for streamflow prediction","volume":"535","author":"Ryu","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Vanderlinden, K., Vereecken, H., Hardelauf, H., Herbst, M., Mart\u00ednez, G., Cosh, M.H., and Pachepsky, Y.A. (2012). Temporal stability of soil water contents: A review of data and analyses. Vadose Zone J., 11.","DOI":"10.2136\/vzj2011.0178"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1007\/s11269-017-1640-7","article-title":"Assessing soil moisture patterns using a soil topographic index in a humid region","volume":"31","author":"Qiu","year":"2017","journal-title":"Water Resour. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3265","DOI":"10.1007\/s11269-015-0996-9","article-title":"Simple linear modeling approach for linking hydrological model parameters to the physical features of a river basin","volume":"29","author":"Huang","year":"2015","journal-title":"Water Resour. Manag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.5194\/hess-14-1881-2010","article-title":"Improving runoff prediction through the assimilation of the ASCAT soil moisture product","volume":"14","author":"Brocca","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Park, S.K., and Xu, L. (2017). Soil moisture data assimilation. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III), Springer International Publishing.","DOI":"10.1007\/978-3-319-43415-5"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.rse.2019.03.029","article-title":"Sampling depth of L-band radiometer measurements of soil moisture and freeze-thaw dynamics on the Tibetan Plateau","volume":"226","author":"Zheng","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1002\/hyp.10846","article-title":"Evaluation and hydrological application of precipitation estimates derived from PERSIANN-CDR, TRMM 3B42V7, and NCEP-CFSR over humid regions in China","volume":"30","author":"Zhu","year":"2016","journal-title":"Hydrol. Process."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ma, Q., Xiong, L., Liu, D., Xu, C.-Y., and Guo, S. (2018). Evaluating the temporal dynamics of uncertainty contribution from satellite precipitation input in rainfall-runoff modeling using the variance decomposition method. Remote Sens., 10.","DOI":"10.3390\/rs10121876"},{"key":"ref_49","unstructured":"Blaney, H.F., and Criddle, W.D. (1962). Determining Consumptive Use and Irrigation Water Requirements."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Luo, Y., Xu, Y.-P., Tian, Y., and Yang, T. (2019). Satellite soil moisture for agricultural drought monitoring: Assessment of SMAP-derived soil water deficit index in Xiang River Basin, China. Remote Sens., 11.","DOI":"10.3390\/rs11030362"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7010","DOI":"10.1029\/2019WR025245","article-title":"Diagnosing bias in modeled soil moisture\/runoff coefficient correlation using the SMAP level 4 soil moisture product","volume":"55","author":"Crow","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_52","unstructured":"Reichle, R.H., Ardizzone, J.V., Kim, G.-K., Lucchesi, R.A., Smith, E.B., and Weiss, B.H. (2018). Soil Moisture Active Passive (SMAP) Mission Level 4 Surface and Root Zone Soil Moisture (L4_SM) Product Specification Document, NASA Goddard Space Flight Center."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.advwatres.2016.04.021","article-title":"Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation","volume":"94","author":"Pathiraja","year":"2016","journal-title":"Adv. Water Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.5194\/hess-19-1659-2015","article-title":"Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: Comparison between lumped and semi-distributed schemes","volume":"19","author":"Ryu","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4775","DOI":"10.5194\/hess-20-4775-2016","article-title":"The evolution of root-zone moisture capacities after deforestation: A step towards hydrological predictions under change?","volume":"20","author":"Nijzink","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1007\/s11269-013-0324-1","article-title":"A novel multi-objective shuffled complex differential evolution algorithm with application to hydrological model parameter optimization","volume":"27","author":"Guo","year":"2013","journal-title":"Water Resour. Manag."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/02626668509490989","article-title":"The probability-distributed principle and runoff production at point and basin scales","volume":"30","author":"Moore","year":"1985","journal-title":"Hydrol. Sci. J."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Duan, Q., Gupta, H., Sorooshian, S., Rousseau, A., and Turcotte, R. (2003). Multicriteria calibration of hydrologic models. Calibration of Watershed Models, AGU.","DOI":"10.1029\/WS006"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/0022-1694(92)90096-E","article-title":"The Xinanjiang model applied in China","volume":"135","year":"1992","journal-title":"J. Hydrol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2717","DOI":"10.1029\/91JD01786","article-title":"A land-surface hydrology parameterization with subgrid variability for general circulation models","volume":"97","author":"Wood","year":"1992","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"483","DOI":"10.5194\/hess-11-483-2007","article-title":"The PDM rainfall-runoff model","volume":"11","author":"Moore","year":"2007","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/0022-1694(78)90153-1","article-title":"Flood routing by the Muskingum method","volume":"36","author":"Gill","year":"1978","journal-title":"J. Hydrol."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Vrugt, J.A., ter Braak, C.J.F., Clark, M.P., Hyman, J.M., and Robinson, B.A. (2008). Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation. Water Resour. Res., 44.","DOI":"10.1029\/2007WR006720"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1007\/s00477-018-1528-y","article-title":"Remotely sensed ET for streamflow modelling in catchments with contrasting flow characteristics: An attempt to improve efficiency","volume":"32","author":"Ryu","year":"2018","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"8343","DOI":"10.1002\/2016WR018850","article-title":"Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods","volume":"52","author":"Broderick","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.1002\/2015WR018266","article-title":"Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty","volume":"52","author":"Moges","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1002\/hyp.3360060305","article-title":"The future of distributed models: Model calibration and uncertainty prediction","volume":"6","author":"Beven","year":"1992","journal-title":"Hydrol. Process."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1214\/ss\/1177011136","article-title":"Inference from iterative simulation using multiple sequences","volume":"7","author":"Gelman","year":"1992","journal-title":"Stat. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models part I\u2013A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.08.003","article-title":"Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling","volume":"377","author":"Gupta","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.5194\/hess-17-1893-2013","article-title":"A framework to assess the realism of model structures using hydrological signatures","volume":"17","author":"Euser","year":"2013","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.5194\/hess-20-1433-2016","article-title":"HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation","volume":"20","author":"Bahremand","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.jhydrol.2018.03.018","article-title":"Parameter transferability within homogeneous regions and comparisons with predictions from a priori parameters in the eastern United States","volume":"560","author":"Chouaib","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1080\/02626667.2013.803183","article-title":"A decade of predictions in ungauged basins (PUB)\u2014A review","volume":"58","author":"Hrachowitz","year":"2013","journal-title":"Hydrol. Sci. J."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"879","DOI":"10.5194\/hess-21-879-2017","article-title":"Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall\u2013runoff model","volume":"21","author":"Roy","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2580\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:31:36Z","timestamp":1760189496000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,3]]},"references-count":75,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11212580"],"URL":"https:\/\/doi.org\/10.3390\/rs11212580","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,3]]}}}