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首页> 外文期刊>Journal of the American Water Resources Association >A STREAMFLOW FORECASTING FRAMEWORK USING MULTIPLE CLIMATE AND HYDROLOGICAL MODELS
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A STREAMFLOW FORECASTING FRAMEWORK USING MULTIPLE CLIMATE AND HYDROLOGICAL MODELS

机译：使用多种气候和水文模型的连续流预报框架

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摘要

Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamfiow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamfiow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamfiow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974-1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi-model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast.
机译：水资源规划和管理的有效性取决于捕获来自气候和水文投入和模型的内在不确定性。在水库运行和配水决策中至关重要的溪流预报从根本上包含了由于假定的初始条件，模型结构和建模过程而产生的不确定性。解决这些不断增长的不确定性仍然是一个巨大的挑战。气候预报技能和水文建模方面的最新进展为进一步追求能够提供改进的水流预报的模型和模型组合提供了动力。但是，很少考虑包括将多种气候模型和多种水文模型耦合，增加流径预报集合成员库和考虑不确定性累积来源的方法。此处提出的框架提出了全球气候模型（GCM），多个区域气候模型以及许多水平衡模型的集成和离线耦合，以通过生成整体预报来改进流量预报。为了演示起见，将该框架强加于巴西东北部的Jaguaribe盆地，作为1974-1996年月流量的后向预报。 ECHAM 4.5和NCEP / MRF9 GCM和区域模型（包括动态模型和统计模型）与ABCD和“土壤水分核算程序”水平衡模型集成在一起。来自GCM的降水后预报通过区域模型进行了缩减，并输入到水平衡模型中，从而产生了水流后预报。多模型集成组合技术包括合并，线性回归加权和核密度估计器，以评估流后兆；与任何单个耦合模型合奏后播相比，后一种技术显示出更高的技巧。

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