Print Email Facebook Twitter Remote Sensed and/or Global Datasets for Distributed Hydrological Modelling Title Remote Sensed and/or Global Datasets for Distributed Hydrological Modelling: A Review Author Ali, M.H. (TU Delft Water Resources; IHE Delft Institute for Water Education) Popescu, Ioana (IHE Delft Institute for Water Education) Jonoski, Andreja (IHE Delft Institute for Water Education) Solomatine, D.P. (TU Delft Water Resources; IHE Delft Institute for Water Education) Date 2023 Abstract This research paper presents a systematic literature review on the use of remotely sensed and/or global datasets in distributed hydrological modelling. The study aims to investigate the most commonly used datasets in hydrological models and their performance across different geographical scales of catchments, including the micro-scale (<10 km2), meso-scale (10 km2–1000 km2), and macro-scale (>1000 km2). The analysis included a search for the relation between the use of these datasets to different regions and the geographical scale at which they are most widely used. Additionally, co-authorship analysis was performed on the articles to identify the collaboration patterns among researchers. The study further categorized the analysis based on the type of datasets, including rainfall, digital elevation model, land use, soil distribution, leaf area index, snow-covered area, evapotranspiration, soil moisture and temperature. The research concluded by identifying knowledge gaps in the use of each data type at different scales and highlighted the varying performance of datasets across different locations. The findings underscore the importance of selecting the right datasets, which has a significant impact on the accuracy of hydrological models. This study provides valuable insights into the use of remote sensed and/or global datasets in hydrological modelling, and the identified knowledge gaps can inform future research directions. Subject distributed hydrological modellingglobal datasetsremote sensing To reference this document use: http://resolver.tudelft.nl/uuid:c53cf5a9-dc1a-4799-851e-456de6f9e1c3 DOI https://doi.org/10.3390/rs15061642 ISSN 2072-4292 Source Remote Sensing, 15 (6) Part of collection Institutional Repository Document type review Rights © 2023 M.H. Ali, Ioana Popescu, Andreja Jonoski, D.P. Solomatine Files PDF remotesensing_15_01642.pdf 1.35 MB Close viewer /islandora/object/uuid:c53cf5a9-dc1a-4799-851e-456de6f9e1c3/datastream/OBJ/view