...
首页> 外文期刊>Technological forecasting and social change >Towards harmonizing competing models: Russian forests' net primary production case study
【24h】

Towards harmonizing competing models: Russian forests' net primary production case study

机译:努力使竞争模型协调一致:俄罗斯森林的净初级生产案例研究

获取原文
获取原文并翻译 | 示例

摘要

This paper deals with the issue of reconciling gaps between stochastic estimates (probability distributions) provided by alternative statistically inaccurate observation/estimation techniques. We employ a posterior reconciliation (integration) method based on selection of mutually compatible test outcomes. Unlike other methods used in this context, the posterior integration method employed does not include assessment of the credibility of the original (prior) estimation sources, which is usually based on analysis of their past performance. The quality of the resulting posterior integrated distribution is evaluated in terms of change in the variance. The method is illustrated by integration of stochastic estimates of the annual net primary production (NPP) of forest ecosystems in seven bioclimatic zones of Russia. The estimates result from the use of two alternative NPP estimation techniques - the landscape-ecosystem approach based on empirical knowledge, and an ensemble of dynamic global vegetation models. The estimates differ by up to 23%. Elimination of these gaps could help better quantify the terrestrial ecosystems' input to the global carbon cycle. The paper suggests a set of candidates for credible integrated NPP estimates for Russia, which harmonize those provided by two alternative sources. (C) 2015 Elsevier Inc. All rights reserved.
机译:本文讨论了调和由统计上不准确的替代观测/估计技术提供的随机估计(概率分布)之间的差距的问题。我们基于相互兼容的测试结果的选择采用后验和解(集成)方法。与在此情况下使用的其他方法不同,所采用的后合并方法不包括评估原始(先前)估计来源的可信度,这通常是基于对它们过去的表现进行分析。根据方差的变化评估所得后验综合分布的质量。该方法通过对俄罗斯七个生物气候区的森林生态系统的年度净初级生产力(NPP)的随机估计值进行积分来说明。估算是通过使用两种替代的NPP估算技术得出的-基于经验知识的景观生态系统方法和动态全球植被模型的集成。估计差异最大为23%。消除这些差距有助于更好地量化陆地生态系统对全球碳循环的投入。该文件提出了一组可靠的俄罗斯国家核电厂综合估计数的候选人,这使两个替代来源提供的估计数相协调。 (C)2015 Elsevier Inc.保留所有权利。

著录项

  • 来源
    《Technological forecasting and social change 》 |2015年第9期| 245-254| 共10页
  • 作者单位

    Int Inst Appl Syst Anal, A-2361 Laxenburg, Austria|Moscow MV Lomonosov State Univ, Fac Computat Math & Cybernet, Moscow 119991, Russia|VA Steklov Math Inst, Moscow 119991, Russia;

    Int Inst Appl Syst Anal, A-2361 Laxenburg, Austria|Moscow MV Lomonosov State Univ, Fac Computat Math & Cybernet, Moscow 119991, Russia;

    Int Inst Appl Syst Anal, A-2361 Laxenburg, Austria|RAS, Siberian Branch, Sukachev Inst Forest, Krasnoyarsk 660036, Russia;

    Int Inst Appl Syst Anal, A-2361 Laxenburg, Austria|Lviv Polytech Natl Univ, Dept Int Informat, UA-79013 Lvov, Ukraine;

    Int Inst Appl Syst Anal, A-2361 Laxenburg, Austria|Moscow State Forest Univ, Mytishchi 141005, Moscow Region, Russia;

    Int Inst Appl Syst Anal, A-2361 Laxenburg, Austria;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Net primary production of forest; Multi-model ensembles; Integration of models; Bayesian approach;

    机译:森林净初级生产力;多模型合奏;模型集成;贝叶斯方法;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号