| 应用层次贝斯模型研究气候变化对威塔流域流量的影响(英文) |
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| 引用本文： | 吴蔚,James S.CLARK,James M.VOSE.应用层次贝斯模型研究气候变化对威塔流域流量的影响(英文)[J].资源与生态学报（英文版）,2012,3(2):118-128. |
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| 作者姓名： | 吴蔚 James S.CLARK James M.VOSE |
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| 作者单位： | 1. 美国杜克大学环境学院,达勒姆,NC 27708,美国
2. 美国农业部林务局威塔水文实验室,奥托,NC 28763,美国 |
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| 摘 要： | 我们应用层次贝叶斯模型模拟大气二氧化碳浓度加倍可能对美国北卡罗来纳州西部威塔(Coweeta)流域水文的影响。这个模型整合了多重数据来源并且同时考虑了数据,参数和模型结构的不确定性。贝叶斯分析的预测分布显示流量和土壤含水量在秋季和夏季将明显下降,这将造成这两个季节更严重的干旱。同时我们用通用极值分布(Generalized Extreme Value distribution)和通用普拉托分布(Generalized Pareto distribution)分析预测流量,结果显示洪水频率也会增减。层次贝叶斯模型,和许多只能得到最佳参数估计的水文模型相比,能提供更丰富的信息,包括预测的不确定性。这将有助于可持续水资源管理的大前提下发展应对气候变化的措施。
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| 关 键 词： | 层次贝叶斯模型 水文模型 气候变化 不确定性 水文极值 |
| 收稿时间： | 2012-02-17 |
Application of a Full Hierarchical Bayesian Model in Assessing Streamflow Response to a Climate Change Scenario at the Coweeta Basin, NC, USA |
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| WU Wei
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James S.CLARK
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James M.VOSE.Application of a Full Hierarchical Bayesian Model in Assessing Streamflow Response to a Climate Change Scenario at the Coweeta Basin, NC, USA[J].Journal of Resources and Ecology,2012,3(2):118-128. |
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| Authors: | WU Wei James SCLARK James MVOSE |
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| Institution: | 1.Nicholas School of the Environment, Duke University, Durham, NC 27708, USA;2.USDA-Forest Service, Coweeta Hydrologic Laboratory, Otto, NC 28763, USA |
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| Abstract: | We have applied a full hierarchical Baysian （HB） model to simulate streamffow at the Coweeta Basin that drains western North Carolina, USA under a doubled CO2 climate scenario. The full HB model coherently assimilated multiple data sources and accounted for uncertainties from data, parameters and model structures. Full predictive distributions for streamflow from the Bayesian analysis indicate not only increasing drought, with substantial decrease in fall and summer flows, and soil moisture content, but also increase in the frequency of flood events when they were fit with Generalized Extreme Value （GEV） distribution and Generalized Pareto Distribution （GPD） under this doubled CO2 climate scenario compared to the current climate scenario. Full predictive distributions based on the hierarchical Bayesian model, compared to deterministic point estimates, is capable of providing richer information to facilitate development of adaptation strategy to changing climate for a sustainable water resource management. |
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| Keywords: | hierarchical Bayes hydrological modeling climate change uncertainty hydrological extremes |
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