Abstract
A time series of hydrologic data occurs in well defined groups possessing collective properties of the data. Such a collection of properties can be called a hydrologic pattern. The pattern indicates the inter-relationship between adjacent values within the groups i. e. the persistence. A method of streamflow synthesis based on the concept of pattern recognition was first described by Panu et al. The present paper aims at improving Panu's method using the ISODATA algorithm for pattern analysis to adapt to highly variable hydrologic data in Japan. The new model is applied to monthly precipitation data at Kanazawa, Fukui and Toyama in the Hokuriku region.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
