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Seasonal hydroclimatology of the continental United States: Forecasting and its relevance to water management.

机译：美国大陆的季节性水文气候学：预报及其与水资源管理的关系。

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摘要

Recent research in seasonal climate prediction has focused on combining multiple atmospheric General Circulation Models (GCMs) to develop multimodel ensembles. A new approach to combine multiple GCMs is proposed by analyzing the skill of candidate models contingent on the relevant predictor(s) state. To demonstrate this approach, we combine historical simulations of winter (December-February, DJF) precipitation and temperature from seven GCMs by evaluating their skill -- represented by Mean Square Error (MSE) -- over similar predictor (DJF Nino3.4) conditions. The MSE estimates are converted into weights for each GCM for developing multimodel tercile probabilities. A total of six multimodel schemes are considered that includes combinations based on pooling of ensembles as well as based on the long-term skill of the models. To ensure the improved skill exhibited by the multimodel scheme is statistically significant, we perform rigorous hypothesis tests comparing the skill of multimodels with individual models' skill. The multimodel combination contingent on Nino3.4 show improved skill particularly for regions whose winter precipitation and temperature exhibit significant correlation with Nino3.4.;Analyses of weights also show that the proposed multimodel combination methodology assigns higher weights for GCMs and lesser weights for climatology during El Nino and La Nina conditions. On the other hand, due to the limited skill of GCMs during neutral conditions over the tropical Pacific, the methodology assigns higher weights for climatology resulting in improved skill from the multimodel combinations. The proposed methodology is also evaluated within a forecasting context by combining real-time precipitation forecasts from five different coupled GCMs contingent on the forecasted Nino3.4. Thus, analyzing GCMs' skill contingent on the relevant predictor state provide an alternate approach for multimodel combination such that years with limited skill could be replaced with climatology.;The utility of the proposed multimodel combination methodology in the context of short-term (monthly to seasonal) water management is investigated by utilizing 3-month ahead probabilistic multimodel streamflow forecasts developed using climate information -- sea surface temperature conditions in the tropical Pacific, tropical Atlantic, and over the North Carolina coast -- to invoke restrictions for Falls Lake Reservoir in the Neuse River Basin, NC. Multimodel streamflow forecasts developed from two single models, a parametric regression approach and semiparametric resampling approach, are forced with a reservoir management model that takes ensembles to estimate the reliability of meeting the water quality and supply releases and the end of the season target storage. The study suggests that, by constraining the end of the season target storage conditions being met with high probability, the climate information based streamflow forecasts could be utilized for invoking restrictions during below-normal inflow years. Further, multimodel forecasts perform better in detecting the below-normal inflow conditions in comparison to single model forecasts by reducing false alarms and missed targets which could improve public confidence in utilizing climate forecasts for developing proactive water management strategies. This research also presents a systematic analysis for understanding the seasonal hydroclimatology of the continental United States. The relationship of seasonality in precipitation and temperature to mean monthly runoff are analyzed for 1373 watersheds across the U.S. using a physical model with no calibration.
机译：季节性气候预测的最新研究集中于结合多个大气总环流模型（GCM）来开发多模式集合体。通过分析取决于相关预测变量状态的候选模型的技巧，提出了一种组合多个GCM的新方法。为了证明这种方法，我们结合了类似预测因子（DJF Nino3.4）的条件，通过评估它们的技能（均方误差（MSE）表示），结合了七个GCM的冬季（12月至2月，DJF）降水和温度的历史模拟。 。 MSE估计值将转换为每个GCM的权重，以开发多模态可控概率。总共考虑了六种多模型方案，其中包括基于集合池以及基于模型的长期技能的组合。为了确保多模型方案展现出的提高的技能具有统计学意义，我们将多模型的技能与单个模型的技能进行了比较严格的假设检验。取决于Nino3.4的多模型组合显示出更高的技能，特别是在冬季降水和温度与Nino3.4显着相关的地区。权重分析还表明，所提出的多模型组合方法在气候变化过程中为GCM分配了较高的权重，为气候分配了较小的权重厄尔尼诺和拉尼娜条件。另一方面，由于在热带太平洋中性条件下GCM的技能有限，因此该方法为气候学分配了更高的权重，从而提高了多模式组合的技能。通过结合来自五个不同耦合GCM的实时降水预报（取决于Nino3.4预报），还可以在预报范围内评估所提出的方法。因此，根据相关预测因素状态分析GCM的技能为多模型组合提供了另一种方法，以便可以用气候学代替技能有限的年份。;在短期（每月至每月）的情况下，所提出的多模型组合方法的实用性季节性）水资源管理是通过利用气候信息（热带太平洋，热带大西洋以及北卡罗来纳州沿海地区的海面温度条件）开发的为期3个月的提前概率多模型流量预测来进行调查的，从而对美国的Falls Lake水库施加限制北卡罗来纳州Neuse流域。由两个单一模型（参数回归方法和半参数重采样方法）开发的多模型流量预测被强制采用水库管理模型，该模型采用集合模型来估计满足水质和供水量以及季节目标存储结束的可靠性。该研究表明，通过限制本季末目标存储条件的概率很高，可以将基于气候信息的流量预报用于在低于正常年份的年份进行限制。此外，与单一模型预测相比，多模型预测通过减少误报和错过的目标来更好地检测低于正常水平的流入状况，这可以提高公众对利用气候预测制定积极水管理策略的信心。这项研究还提供了系统的分析，以了解美国大陆的季节性水文气候学。使用未经校准的物理模型分析了美国1373个流域的降水量和温度季节性与平均月径流量之间的关系。

著录项

  • 作者

    Devineni, Naresh.;

  • 作者单位

    North Carolina State University.;

  • 授予单位 North Carolina State University.;
  • 学科 Hydrology.;Water Resource Management.;Engineering Civil.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 188 p.
  • 总页数 188
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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