《Water Resources Research》刊登“气候变化条件下运用统计学框架量化水文模型的不确定性”

作者：Scott Steinschneider, Austin Polebitski1, Casey Brown, Benjamin H. Letcher

刊物：Water Resources Research，2012年，48卷，第11期，页码：1483–1502

关键词：贝叶斯统计；气候变化影响；计算水文学；水文尺度；模型模拟；不确定性评价

摘要：强调区域水文气候影响评估的连级不确定性削弱了他们对长期水资源规划与管理的价值。本研究提出了一种在气候变化条件下水文模型对未来径流变化响应的不确定性进行量化的统计学框架。本文运用贝叶斯模型来考虑水文模型中的各种不确定性。模型残差的分布特征用来量化预测技巧，运用马尔科夫链蒙特卡洛方法来推测水文模型和误差参数的后验分布。参数和残差错误的不确定性经过整合之后用来确定径流序列的预测置信区间。随后扩展贝叶斯水文模型框架来评价气候变化影响。运用全球环流模式降尺度数据来整合基线年和未来年的气候模式，并通过这些数据来驱动径流序列的模拟。通过基线年与未来预测的数据来计算径流时间序列的统计学特征。水文模型的不确定性响应、采样错误、未来气候预测范围，经过整合后能够帮助确定基线年与未来气候预测的可信度。将该方法应用于美国佛蒙特州的白河流域。研究结果表明该方法能够得到水文模型的可靠预测范围。

Toward a statistical framework to quantify the uncertainties of hydrologic response under climate change

Authors: Scott Steinschneider, Austin Polebitski1, Casey Brown, Benjamin H. Letcher

Journal: Water Resources Research, Volume 48, Issue 11, November 2012

Keywords: Bayesian statistics; climate impacts; computational hydrology; hydrologic scaling; modeling; uncertainty assessment

Abstracts: The cascade of uncertainty that underscores climate impact assessments of regional hydrology undermines their value for long-term water resources planning and management. This study presents a statistical framework that quantifies and propagates the uncertainties of hydrologic model response through projections of future streamflow under climate change. Different sources of hydrologic model uncertainty are accounted for using Bayesian modeling. The distribution of model residuals is formally characterized to quantify predictive skill, and Markov chain Monte Carlo sampling is used to infer the posterior distributions of both hydrologic and error model parameters. Parameter and residual error uncertainties are integrated to develop reliable prediction intervals for streamflow estimates. The Bayesian hydrologic modeling framework is then extended to a climate change impact assessment. Ensembles of baseline and future climate are downscaled from global circulation models and are used to drive simulations of streamflow over parameters drawn from the posterior space. Time series of streamflow statistics are calculated from baseline and future ensembles of simulated flows. Uncertainties in hydrologic model response, sampling error, and the range of future climate projections are integrated to help determine the level of confidence associated with hydrologic alteration between baseline and future climate regimes. A case study is conducted on the White River in Vermont, USA. Results indicate that the framework can be used to present a reliable depiction of the range of hydrologic alterations that may occur in the future.

原文链接：http://onlinelibrary.wiley.com/doi/10.1029/2011WR011318/abstract