Linear Optimal Runoff Aggregate v1.0

No synthesized global gridded runoff product, derived from multiple sources, is available despite such a product being useful to meet the needs of many global water initiatives. We apply an optimal weighting approach to merge runoff estimates from hydrological models constrained with observational streamflow records. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products.To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance. We perform out-of-sample tests to examine the success of the dissimilarity approach and we confirm that the weighted product performs better than its 11 constituents products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly time scales, and includes time variant uncertainty, for the period 1980 – 2012 on a 0.5o grid. The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents well the seasonal runoff cycle for most of the globe. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and is freely available for download.

尽管合成全球网格化径流产品（synthesized global gridded runoff product）可有效支撑诸多全球水相关倡议的需求，但目前尚无公开可用的同类产品。本研究采用最优加权方法（optimal weighting approach），对经实测径流观测记录（observational streamflow records）约束的水文模型（hydrological models）径流预估结果进行融合。该加权方法以各模型匹配实测径流数据的能力为核心依据，同时考量参与融合的各产品间的误差协方差（error covariance）。针对多数区域缺乏实测径流观测资料的问题，本研究引入差异性度量方法（dissimilarity method）：以流域自然地理与气候特征的差异作为距离替代指标，将有观测流域（gauged basins）的产品权重迁移至距离最近的无观测流域（ungauged basins），以此实现权重在全域的覆盖。本研究通过样本外测试（out-of-sample tests）验证了该差异性权重迁移方法的有效性，并证实融合后的加权产品在多项评价指标上均优于其11个组成产品。本研究产出的合成全球网格化径流产品，时间分辨率为月尺度，空间分辨率为0.5°网格，时间跨度为1980年至2012年，且包含时变不确定性（time variant uncertainty）信息。该产品与诸多已发表的流域径流预估结果整体吻合度较高，且能较好地刻画全球多数区域的径流季节循环特征。这款被命名为线性最优径流聚合（Linear Optimal Runoff Aggregate，LORA）的新产品，是对现有径流产品的高质量融合产物，可免费下载获取。