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The results show that runoff predictions in ungauged catchments can benefit from a smart selection of donor catchments whose optimized parameter values are used to model runoff in the target ungauged catchment, output averaging of results from multiple\u2010donor catchments and incorporating leaf area index data into the rainfall\u2010runoff models. The biggest benefit comes from an educated selection of donor catchments (compared to a random selection of donor catchments) and output averaging of results from multiple\u2010donor catchments. The difference between the three commonly used approaches for selecting donor catchments is relatively small. The spatial proximity approach (where the geographically closest catchment is used as the donor catchment) performs slightly better than the physical similarity approach (where the catchment with the most similar attributes is used as the donor catchment), and the integrated similarity approach, which combines the spatial proximity and physical similarity approaches, performs only very marginally better than the spatial proximity approach. The incorporation of leaf area index data into the rainfall\u2010runoff models shows marginal improvements to the modeling results, although a more appropriate integration of vegetation and other remotely sensed data may further improve the results.<\/jats:p>","DOI":"10.1029\/2008wr007504","type":"journal-article","created":{"date-parts":[[2009,7,13]],"date-time":"2009-07-13T20:13:02Z","timestamp":1247515982000},"source":"Crossref","is-referenced-by-count":209,"title":["Relative merits of different methods for runoff predictions in ungauged catchments"],"prefix":"10.1029","volume":"45","author":[{"given":"Yongqiang","family":"Zhang","sequence":"first","affiliation":[{"name":"CSIRO Water for a Healthy Country National Research Flagship CSIRO Land and Water  Canberra, ACT Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francis H. 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