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Large-scale runoff routing with an aggregated network-response function

机译:具有聚合网络响应功能的大规模径流路由

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摘要

The accuracy of runoff routing for global water-balance models and land-surface schemes is limited by the low spatial resolution of flow networks. Many such networks have been developed for specific models at specific spatial resolutions. However, although low-resolution networks can be derived by up-scaling algorithms from high-resolution datasets, such low-resolution networks are inherently incoherent, and slight differences in their spatial resolution can cause significant deviations in routing dynamics. By neglecting convective delay, storage-based routing algorithms produce artificially early arriving peaks on large scales. A theoretical comparison between a diffusion-wave-routing algorithm and linear-reservoir-routing (LRR) algorithm on a 30-km cell demonstrated that the commonly used LRR method consistently underestimates the travel time through the cells. A new aggregated network-response-function (NRF) routing algorithm was proposed in this study and evaluated against a conventional flow-net-based cell-to-cell LRR algorithm. The evaluation was done for the 25,325 km~2 Dongjiang (East River) basin, a tributary to the Pearl River in southern China well equipped with hydrological and meteorological stations. The NRF method transfers high-resolution delay dynamics, instead of networks, to any lower spatial resolution where runoff is generated. It preserves, over all scales, the spatially distributed time-delay information in the 1-km HYDRO1k flow network in the form of simple cell-response functions for any low-resolution grid. The NRF routing was shown to be scale independent for latitude-longitude resolutions ranging from 5' to 1°. This scale independency allowed a study of input heterogeneity on modelled discharge modelled with a daily version of the WASMOD-M water-balance model. The model efficiency of WASMOD-M-generated daily discharge at the Boluo gauging station in the Dongjiang basin in south China was constantly high (0.89) within the whole range of resolutions when routed by the NRF algorithm. The performance dropped sharply for decreasing resolution when runoff was routed with the LRR method. The three WASMOD-M parameters were scale independent in combination with NRF, but not with LRR, and the same parameter values gave equally good results at all spatial resolutions. The effect of spatial resolution on the routing delay was much more important than the spatial variability of the climate-input field for scales ranging from 5' to 1°. The extra information in a distributed versus a uniform climate input could only be used when the NRF method was used to route the runoff. NRF requires more labour than LRR to set up but the model performance is very much higher than the LRR's once this is done. The NRF method, therefore, provides a significant potential to improve global-scale discharge predictions.
机译:全球水量平衡模型和地表方案径流路径的准确性受到流动网络空间分辨率低的限制。已经针对特定模型以特定空间分辨率开发了许多这样的网络。但是,尽管可以通过按比例缩放算法从高分辨率数据集中获得低分辨率网络,但此类低分辨率网络本质上是不连贯的,其空间分辨率的细微差异可能会导致路由动态发生重大偏差。通过忽略对流延迟,基于存储的路由算法可人为地大规模产生早到达的峰值。在30 km的小区上,扩散波路由算法和线性储层路由(LRR)算法之间的理论比较表明,常用的LRR方法始终低估了通过这些单元的旅行时间。这项研究中提出了一种新的聚合网络响应功能(NRF)路由算法,并针对基于传统流网的单元到单元LRR算法进行了评估。对25,325 km〜2东江流域(东江流域)进行了评估,东江流域是中国南部珠江的支流,该流域水文气象站十分完善。 NRF方法将高分辨率延迟动力学(而不是网络)转移到生成径流的任何较低空间分辨率。它以简单的单元响应功能的形式,在所有低分辨率网格中,在所有尺度上保留了1 km HYDRO1k流网络中空间分布的时延信息。 NRF路由显示为与尺度无关,其纬度-经度分辨率范围为5'至1°。这种规模的独立性允许对以日版WASMOD-M水平衡模型为模型的排水模型进行输入异质性研究。当使用NRF算法进行路由时,在整个分辨率范围内,华南东江盆地博罗计量站的WASMOD-M产生的日排放模型效率一直很高(0.89)。当使用LRR方法路由径流时,性能会急剧下降以降低分辨率。三个WASMOD-M参数与NRF结合使用时,与比例无关,但与LRR结合使用时不相同,并且在所有空间分辨率下,相同的参数值都给出了同样好的结果。对于从5'到1°的尺度,空间分辨率对路由延迟的影响比气候输入场的空间变异性重要得多。仅当使用NRF方法确定径流时,才可以使用分布式气候输入和均匀气候输入中的额外信息。建立NRF所需的劳动比LRR还要多,但是一旦完成,模型的性能将比LRR高得多。因此,NRF方法具有巨大的潜力,可以改善全球范围的排放预测。

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