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首页> 外文期刊>Journal of hydrometeorology >Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill
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Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill

机译：量化流量预报技能对初始条件的弹性和气候预测技能

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Water resources management decisions commonly depend on monthly to seasonal streamflow forecasts, among other kinds of information. The skill of such predictions derives from the ability to estimate a watershed's initial moisture and energy conditions and to forecast future weather and climate. These sources of predictability are investigated in an idealized (i.e., perfect model) experiment using calibrated hydrologic simulation models for 424 watersheds that span the continental United States. Prior work in this area also followed an ensemble-based strategy for attributing streamflow forecast uncertainty, but focused only on two end points representing zero and perfect information about future forcings and initial conditions. This study extends the prior approach to characterize the influence of varying levels of uncertainty in each area on streamflow prediction uncertainty. The sensitivities enable the calculation of flow forecast skill elasticities (i.e., derivatives) relative to skill in either predictability source, which are used to characterize the regional, seasonal, and predictand variations in flow forecast skill dependencies. The resulting analysis provides insights on the relative benefits of investments toward improving watershed monitoring (through modeling and measurement) versus improved climate forecasting. Among other key findings, the results suggest that climate forecast skill improvements can be amplified in streamflow prediction skill, which means that climate forecasts may have greater benefit for monthly-to-seasonal flow forecasting than is apparent from climate forecast skill considerations alone. The results also underscore the importance of advancing hydrologic modeling, expanding watershed observations, and leveraging data assimilation, all of which help capture initial hydrologic conditions that are often the dominant influence on hydrologic predictions.
机译：水资源管理决策通常取决于每月到季节性的流量预测以及其他信息。这种预测的技巧源自估计流域的初始湿度和能量状况以及预测未来天气和气候的能力。使用可校准的水文模拟模型对横跨美国大陆的424个集水区进行了理想化（即完美模型）实验，研究了这些可预测性来源。在该领域的先前工作还遵循基于集合的策略来确定流量预测的不确定性，但只集中于两个端点，这些端点表示关于未来强迫和初始条件的零和理想信息。这项研究扩展了先前的方法，以表征每个区域中不同水平的不确定性对流量预测不确定性的影响。敏感性使得能够计算相对于任一可预测性源中的技能的流量预测技能弹性（即，导数），其用于表征流量预测技能依赖性的区域，季节，预测和变化。最终的分析提供了关于投资相对收益（通过建模和测量）与改进气候预测相比相对收益的见解。除其他主要发现外，结果还表明，可以将流量预报技能中的气候预报技能改进提高，这意味着与单独考虑气候预报技能时相比，气候预报对月至季节流量预报的好处更大。结果还强调了推进水文建模，扩展分水岭观测以及利用数据同化的重要性，所有这些都有助于捕获通常是水文预测主要影响因素的初始水文条件。

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