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A multivariate conditional model for streamflow prediction and spatial precipitation refinement
Liu, Zhiyong1; Zhou, Ping2; Chen, Xiuzhi3; Guan, Yinghui4,5,6
2015-10-16
Source PublicationJOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
ISSN2169-897X
Volume120Issue:19
AbstractThe effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile-copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine-based model is compared with that of traditional data-driven models such as the support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations.
SubtypeArticle
WOS HeadingsScience & Technology ; Physical Sciences
WOS Subject ExtendedMeteorology & Atmospheric Sciences
DOI10.1002/2015JD023787
WOS KeywordSUPPORT VECTOR REGRESSION ; PAIR-COPULA CONSTRUCTIONS ; NEURAL-NETWORKS ; SOIL-MOISTURE ; RAINFALL ; DEPENDENCE ; FORECASTS ; TIME ; SIMULATION ; VARIABLES
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Funds(4143000213) ; State Forestry Administration Public Benefit Research Foundation of China(201204104)
WOS SubjectMeteorology & Atmospheric Sciences
WOS IDWOS:000365432800018
PublisherAMER GEOPHYSICAL UNION
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/67850
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhou, Ping
Affiliation1.Heidelberg Univ, Inst Geog, Heidelberg, Germany
2.Guangdong Acad Forestry, Dept Forest Ecol, Guangzhou, Guangdong, Peoples R China
3.Chinese Acad Sci, South China Bot Garden, Guangzhou, Guangdong, Peoples R China
4.Northwest A&F Univ, Coll Resources & Environm, Yangling, Peoples R China
5.Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling, Peoples R China
6.Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, Yangling, Peoples R China
Recommended Citation
GB/T 7714
Liu, Zhiyong,Zhou, Ping,Chen, Xiuzhi,et al. A multivariate conditional model for streamflow prediction and spatial precipitation refinement[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2015,120(19).
APA Liu, Zhiyong,Zhou, Ping,Chen, Xiuzhi,&Guan, Yinghui.(2015).A multivariate conditional model for streamflow prediction and spatial precipitation refinement.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,120(19).
MLA Liu, Zhiyong,et al."A multivariate conditional model for streamflow prediction and spatial precipitation refinement".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 120.19(2015).
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