Synthetic generation of hydrologic time series based on nonparametric random generation
- Authors
- Kim, Tae-Woong; Valdes, Juan B.
- Issue Date
- Sep-2005
- Publisher
- American Society of Civil Engineers
- Keywords
- Hydrologic models; Precipitation; Random variables; Temperature; Time series analysis
- Citation
- Journal of Hydrologic Engineering - ASCE, v.10, no.5, pp.395 - 404
- Indexed
- SCIE
SCOPUS
- Journal Title
- Journal of Hydrologic Engineering - ASCE
- Volume
- 10
- Number
- 5
- Start Page
- 395
- End Page
- 404
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/45762
- DOI
- 10.1061/(ASCE)1084-0699(2005)10:5(395)
- ISSN
- 1084-0699
- Abstract
- Synthetic hydrologic time series can be used to quantify the uncertainty of a water resources system. Conventional parametric models, such as autoregressive moving average or Markovian models, assume that the variable under consideration is Gaussian. This assumption, however, is a shortcoming of parametric models and motivates the development of nonparametric approaches. Nonparametric models based on a kernel function have an innate low-order structure and are restricted to highly persistent variables. This study presented a seminonparametric (SNP) model that takes advantage of both parametric and nonparametric models to generate monthly precipitation and temperature in the Conchos River Basin in Mexico. By adopting a consistent and robust scheme from the Markovian model and a nonparametric mechanism to generate a distribution-free random, component, the SNP model reliably reproduced sample properties such as mean, variance, correlation, and multimodality in the probability density function.
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Collections - COLLEGE OF ENGINEERING SCIENCES > DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING > 1. Journal Articles
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