{"title":"两级多站点多变量天气发生器","authors":"Z. Li, J. Li, X. Shi","doi":"10.3808/jei.201900424","DOIUrl":null,"url":null,"abstract":"The spatial structure of climatic variables synthesized by a weather generator has considerable impact on the modeling of hydrological variability; however, in most cases, it needs computationally intensive work to reproduce multisite and/or multivariate correlations. This work proposed a two-stage weather generator (TSWG) to preserve intersite and intervariable correlations of daily precipitation, maximum and minimum temperatures. The first stage generates daily precipitation and temperature for each site and for each variable with, but not limited to, the Richardson-type approach. The second stage rebuilds the multisite multivariate correlation using a distribution-free shuffle procedure. The TSWG was applied to a network of 15 stations in the Jing River catchment (Northwest China). It reproduced the statistical parameters and multisite and multivariate correlations well. Furthermore, indirect validation by hydrological modeling showed TSWG outputs could be used satisfactorily for simulating streamflow variability. As a distribution-free method, the correlation reconstruction method can be applied to variables with different probability distributions. The TSWG can efficiently reconstruct the correlation with one optimization for all stations and all variables, which is superior to most current methods operated once for one station pair and one variable. The TSWG provides an option for improved multisite and multivariate weather generation.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2019-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"A Two-Stage Multisite and Multivariate Weather Generator\",\"authors\":\"Z. Li, J. Li, X. Shi\",\"doi\":\"10.3808/jei.201900424\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The spatial structure of climatic variables synthesized by a weather generator has considerable impact on the modeling of hydrological variability; however, in most cases, it needs computationally intensive work to reproduce multisite and/or multivariate correlations. This work proposed a two-stage weather generator (TSWG) to preserve intersite and intervariable correlations of daily precipitation, maximum and minimum temperatures. The first stage generates daily precipitation and temperature for each site and for each variable with, but not limited to, the Richardson-type approach. The second stage rebuilds the multisite multivariate correlation using a distribution-free shuffle procedure. The TSWG was applied to a network of 15 stations in the Jing River catchment (Northwest China). It reproduced the statistical parameters and multisite and multivariate correlations well. Furthermore, indirect validation by hydrological modeling showed TSWG outputs could be used satisfactorily for simulating streamflow variability. As a distribution-free method, the correlation reconstruction method can be applied to variables with different probability distributions. The TSWG can efficiently reconstruct the correlation with one optimization for all stations and all variables, which is superior to most current methods operated once for one station pair and one variable. The TSWG provides an option for improved multisite and multivariate weather generation.\",\"PeriodicalId\":54840,\"journal\":{\"name\":\"Journal of Environmental Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2019-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Environmental Informatics\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.3808/jei.201900424\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Environmental Informatics","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.3808/jei.201900424","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
A Two-Stage Multisite and Multivariate Weather Generator
The spatial structure of climatic variables synthesized by a weather generator has considerable impact on the modeling of hydrological variability; however, in most cases, it needs computationally intensive work to reproduce multisite and/or multivariate correlations. This work proposed a two-stage weather generator (TSWG) to preserve intersite and intervariable correlations of daily precipitation, maximum and minimum temperatures. The first stage generates daily precipitation and temperature for each site and for each variable with, but not limited to, the Richardson-type approach. The second stage rebuilds the multisite multivariate correlation using a distribution-free shuffle procedure. The TSWG was applied to a network of 15 stations in the Jing River catchment (Northwest China). It reproduced the statistical parameters and multisite and multivariate correlations well. Furthermore, indirect validation by hydrological modeling showed TSWG outputs could be used satisfactorily for simulating streamflow variability. As a distribution-free method, the correlation reconstruction method can be applied to variables with different probability distributions. The TSWG can efficiently reconstruct the correlation with one optimization for all stations and all variables, which is superior to most current methods operated once for one station pair and one variable. The TSWG provides an option for improved multisite and multivariate weather generation.
期刊介绍:
Journal of Environmental Informatics (JEI) is an international, peer-reviewed, and interdisciplinary publication designed to foster research innovation and discovery on basic science and information technology for addressing various environmental problems. The journal aims to motivate and enhance the integration of science and technology to help develop sustainable solutions that are consensus-oriented, risk-informed, scientifically-based and cost-effective. JEI serves researchers, educators and practitioners who are interested in theoretical and/or applied aspects of environmental science, regardless of disciplinary boundaries. The topics addressed by the journal include:
- Planning of energy, environmental and ecological management systems
- Simulation, optimization and Environmental decision support
- Environmental geomatics - GIS, RS and other spatial information technologies
- Informatics for environmental chemistry and biochemistry
- Environmental applications of functional materials
- Environmental phenomena at atomic, molecular and macromolecular scales
- Modeling of chemical, biological and environmental processes
- Modeling of biotechnological systems for enhanced pollution mitigation
- Computer graphics and visualization for environmental decision support
- Artificial intelligence and expert systems for environmental applications
- Environmental statistics and risk analysis
- Climate modeling, downscaling, impact assessment, and adaptation planning
- Other areas of environmental systems science and information technology.