{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T16:29:11Z","timestamp":1775233751118,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T00:00:00Z","timestamp":1583193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["641811"],"award-info":[{"award-number":["641811"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["730005"],"award-info":[{"award-number":["730005"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["870497"],"award-info":[{"award-number":["870497"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["820852"],"award-info":[{"award-number":["820852"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The assimilation of different satellite and in situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we used the European-scale Hydrological Predictions for the Environment hydrological model in the Ume\u00e4lven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimised model performance when the ensemble Kalman filter method is used. We further assessed the effect of assimilating different satellite products; namely, snow water equivalent, fractional snow cover, and actual and potential evapotranspiration, as well as in situ measurements of river discharge and local reservoir inflows. We finally investigated the combinations of those products that improved model predictions of the target variables and how the model performance varied through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without evapotranspiration products improved the model performance.<\/jats:p>","DOI":"10.3390\/rs12050811","type":"journal-article","created":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T13:06:23Z","timestamp":1583240783000},"page":"811","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions"],"prefix":"10.3390","volume":"12","author":[{"given":"Jude Lubega","family":"Musuuza","sequence":"first","affiliation":[{"name":"Swedish Meteorological and Hydrological Institute, 60176 Norrk\u00f6ping, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Gustafsson","sequence":"additional","affiliation":[{"name":"Swedish Meteorological and Hydrological Institute, 60176 Norrk\u00f6ping, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6990-4874","authenticated-orcid":false,"given":"Rafael","family":"Pimentel","sequence":"additional","affiliation":[{"name":"Fluvial Dynamics and Hydrology Research Group, Andalusian Institute for Earth System Research, University of C\u00f3rdoba, 14071 C\u00f3rdoba, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5776-6275","authenticated-orcid":false,"given":"Louise","family":"Crochemore","sequence":"additional","affiliation":[{"name":"Swedish Meteorological and Hydrological Institute, 60176 Norrk\u00f6ping, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilias","family":"Pechlivanidis","sequence":"additional","affiliation":[{"name":"Swedish Meteorological and Hydrological Institute, 60176 Norrk\u00f6ping, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lahoz, W., Khattatov, B., and M\u00e9nard, R. (2010). Data Assimilation and Information. Data Assimilation: Making Sense of Observations, Springer.","DOI":"10.1007\/978-3-540-74703-1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5337","DOI":"10.1002\/hyp.10005","article-title":"Application and evaluation of a snowmelt runoff model in the Tamor River basin, Eastern Himalaya using a Markov Chain Monte Carlo (MCMC) data assimilation approach","volume":"28","author":"Panday","year":"2014","journal-title":"Hydrol. Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3895","DOI":"10.5194\/hess-20-3895-2016","article-title":"Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments","volume":"20","author":"Griessinger","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"247","DOI":"10.5194\/tc-12-247-2018","article-title":"Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites","volume":"12","author":"Aalstad","year":"2018","journal-title":"Cryosphere"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mishra, A.K., and Coulibaly, P. (2009). Developments in hydrometric network design: A review. Rev. Geophys., 47.","DOI":"10.1029\/2007RG000243"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"751","DOI":"10.5194\/hess-21-751-2017","article-title":"Application of CryoSat-2 altimetry data for river analysis and modelling","volume":"21","author":"Schneider","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Crochemore, L., Isberg, K., Pimentel, R., Pineda, L., Hasan, A., and Arheimer, B. (2019). Lessons learnt from checking the quality of openly accessible river flow data worldwide. Hydrol. Sci. J., 1\u201313.","DOI":"10.1080\/02626667.2019.1659509"},{"key":"ref_8","unstructured":"Tiefenbacher, J. (2012). Hydrologic Data Assimilation. Approaches to Managing Disaster, IntechOpen. Chapter 3."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1175\/JHM-D-14-0106.1","article-title":"Precipitation Seasonality over the Indian Subcontinent: An Evaluation of Gauge, Reanalyses, and Satellite Retrievals","volume":"16","author":"Rana","year":"2015","journal-title":"J. Hydrometeorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1080\/02626667.2013.837578","article-title":"Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin","volume":"58","author":"Ruhoff","year":"2013","journal-title":"Hydrol. Sci. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"338","DOI":"10.2166\/nh.2011.156","article-title":"Predictions in a data-sparse region using a regionalized grid-based hydrologic model driven by remotely sensed data","volume":"42","author":"Samaniego","year":"2011","journal-title":"Hydrol. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7816","DOI":"10.1002\/2014WR015302","article-title":"Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods","volume":"50","author":"Magnusson","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4831","DOI":"10.5194\/hess-19-4831-2015","article-title":"The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales","volume":"19","author":"Draper","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., and Burlando, P. (2011). The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model. Water Resour. Res., 47.","DOI":"10.1029\/2010WR009824"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.5194\/hess-22-1299-2018","article-title":"Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model","volume":"22","author":"Demirel","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1002\/hyp.10789","article-title":"Error distribution modelling of satellite soil moisture measurements for hydrological applications","volume":"30","author":"Zhuo","year":"2016","journal-title":"Hydrol. Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1016\/j.advwatres.2005.08.004","article-title":"Assimilating remotely sensed snow observations into a macroscale hydrology model","volume":"29","author":"Andreadis","year":"2006","journal-title":"Adv. Water Resour."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.advwatres.2017.09.010","article-title":"Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons","volume":"109","author":"Karthikeyan","year":"2017","journal-title":"Adv. Water Resour."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4559","DOI":"10.5194\/hess-19-4559-2015","article-title":"Large-scale hydrological modelling by using modified PUB recommendations: The India-HYPE case","volume":"19","author":"Pechlivanidis","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15702","DOI":"10.3390\/rs71115702","article-title":"On the Use of Global Flood Forecasts and Satellite-Derived Inundation Maps for Flood Monitoring in Data-Sparse Regions","volume":"7","author":"Hirpa","year":"2015","journal-title":"Remote Sens."},{"key":"ref_22","unstructured":"Daley, R. (1991). Atmospheric Data Analysis, Cambridge University Press."},{"key":"ref_23","unstructured":"Bennett, A.F. (1992). Inverse Methods in Physical Oceanography, Cambridge University Press. Cambridge Monographs on Mechanics."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Moore, J.C. (2015). Data Assimilation. Mathematical and Physical Fundamentals of Climate Change, Elsevier.","DOI":"10.1016\/B978-0-12-800066-3.00009-7"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"271","DOI":"10.5194\/asr-14-271-2017","article-title":"Comparison between 3D-Var and 4D-Var data assimilation methods for the simulation of a heavy rainfall case in central Italy","volume":"14","author":"Mazzarella","year":"2017","journal-title":"Adv. Sci. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1002\/qj.3211","article-title":"Comparison of 3D-Var and 4D-Var data assimilation in an NWP-based system for precipitation nowcasting at the Met Office","volume":"144","author":"Li","year":"2018","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1175\/JHM505.1","article-title":"Snow Data Assimilation via an Ensemble Kalman Filter","volume":"7","author":"Slater","year":"2006","journal-title":"J. Hydrometeorol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"16","DOI":"10.3389\/fenvs.2014.00016","article-title":"Data assimilation: Making sense of Earth Observation","volume":"2","author":"Lahoz","year":"2014","journal-title":"Front. Environ. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.5194\/hess-19-2999-2015","article-title":"Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: Evaluating the effect of ensemble size and localization on filter performance","volume":"19","author":"Rasmussen","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Nichols, N.K. (2010). Mathematical Concepts of Data Assimilation. Data Assimilation: Making Sense of Observations, Springer.","DOI":"10.1007\/978-3-540-74703-1_2"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.advwatres.2008.06.005","article-title":"Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model","volume":"31","author":"Clark","year":"2008","journal-title":"Adv. Water Resour."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1016\/j.jhydrol.2014.06.052","article-title":"Comparative evaluation of maximum likelihood ensemble filter and ensemble Kalman filter for real-time assimilation of streamflow data into operational hydrologic models","volume":"519","author":"Rafieeinasab","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ravela, S., and Sandu, A. (2015). Ensemble Adjustment Kalman Filter Data Assimilation for a Global Atmospheric Model. Dynamic Data-Driven Environmental Systems Science, Springer International Publishing.","DOI":"10.1007\/978-3-319-25138-7"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1111\/j.1751-5823.2003.tb00194.x","article-title":"Sequential Data Assimilation Techniques in Oceanography","volume":"71","author":"Bertino","year":"2003","journal-title":"Int. Stat. Rev."},{"key":"ref_35","unstructured":"Lahoz, W., Khattatov, B., and M\u00e9nard, R. (2010). Ensemble Kalman Filter: Current Status and Potential. Data Assimilation: Making Sense of Observations, Springer."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1029\/2016JG003753","article-title":"Connection Between Root Zone Soil Moisture and Surface Energy Flux Partitioning Using Modeling, Observations, and Data Assimilation for a Temperate Grassland Site in Germany","volume":"123","author":"Shrestha","year":"2018","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3238","DOI":"10.1002\/2014WR016667","article-title":"Assimilation of stream discharge for flood forecasting: Updating a semidistributed model with an integrated data assimilation scheme","volume":"51","author":"Li","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7564","DOI":"10.1029\/2018WR024618","article-title":"Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges","volume":"55","author":"Li","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, X., Xu, T., Bateni, S.M., Neale, C.M.U., Auligne, T., Liu, S., Wang, K., Mao, K., and Yao, Y. (2018). Evaluation of the Weak Constraint Data Assimilation Approach for Estimating Turbulent Heat Fluxes at Six Sites. Remote Sens., 10.","DOI":"10.3390\/rs10121994"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1175\/JHM-D-14-0046.1","article-title":"Study of Snow Dynamics at Subgrid Scale in Semiarid Environments Combining Terrestrial Photography and Data Assimilation Techniques","volume":"16","author":"Pimentel","year":"2015","journal-title":"J. Hydrometeorol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1175\/2009JHM1192.1","article-title":"Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model","volume":"11","author":"Reichle","year":"2010","journal-title":"J. Hydrometeorol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.advwatres.2013.02.005","article-title":"Assimilating satellite-based snow depth and snow cover products for improving snow predictions in Alaska","volume":"54","author":"Liu","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.5194\/hess-20-2103-2016","article-title":"Data assimilation in integrated hydrological modelling in the presence of observation bias","volume":"20","author":"Rasmussen","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.rse.2013.07.018","article-title":"Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction","volume":"138","author":"Ines","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Camporese, M., Paniconi, C., Putti, M., and Salandin, P. (2009). Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow. Water Resour. Res., 45.","DOI":"10.1029\/2008WR007031"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2136\/vzj2009.0018","article-title":"Comparison of Data Assimilation Techniques for a Coupled Model of Surface and Subsurface Flow","volume":"8","author":"Camporese","year":"2009","journal-title":"Vadose Zone J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"989","DOI":"10.5194\/hess-22-989-2018","article-title":"Near-real-time adjusted reanalysis forcing data for hydrology","volume":"22","author":"Berg","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.5194\/tc-9-1191-2015","article-title":"User requirements for the snow and land ice services - CryoLand","volume":"9","author":"Malnes","year":"2015","journal-title":"Cryosphere"},{"key":"ref_49","unstructured":"Hall, D.K., Riggs, G.A., and Salomonson, V.V. (2006). MODIS\/Terra Snow Cover 5-Min L2 Swath 500m, Version 5."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.1002\/hyp.6715","article-title":"Accuracy assessment of the MODIS snow products","volume":"21","author":"Hall","year":"2007","journal-title":"Hydrol. Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3517","DOI":"10.1016\/j.rse.2011.08.014","article-title":"Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements","volume":"115","author":"Takala","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.ejrh.2016.04.002","article-title":"A regional parameter estimation scheme for a pan-European multi-basin model","volume":"6","author":"Hundecha","year":"2016","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11222-006-8769-1","article-title":"A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: Easy Bayesian computing for real parameter spaces","volume":"16","year":"2006","journal-title":"Stat. Comput."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1175\/1525-7541(2003)004<1229:ATIOHE>2.0.CO;2","article-title":"Assessing the Impact of Horizontal Error Correlations in Background Fields on Soil Moisture Estimation","volume":"4","author":"Reichle","year":"2003","journal-title":"J. Hydrometeorol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1016\/j.rse.2007.02.042","article-title":"Ensemble member generation for sequential data assimilation","volume":"112","author":"Turner","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_57","unstructured":"Baghdadi, N., and Zribi, M. (2016). Data Assimilation of Satellite Observations. Microwave Remote Sensing of Land Surface, Elsevier."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/811\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:03:42Z","timestamp":1760173422000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/811"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,3]]},"references-count":57,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12050811"],"URL":"https:\/\/doi.org\/10.3390\/rs12050811","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,3]]}}}