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首页> 外文期刊>Journal of the American Water Resources Association >IMPROVED SWAT MODEL PERFORMANCE WITH TIME-DYNAMIC VORONOI TESSELLATION OF CLIMATIC INPUT DATA IN SOUTHERN AFRICA
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IMPROVED SWAT MODEL PERFORMANCE WITH TIME-DYNAMIC VORONOI TESSELLATION OF CLIMATIC INPUT DATA IN SOUTHERN AFRICA

机译：时空VORONI推算法改善了南部非洲气候输入数据的拍打模型性能

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摘要

In this study, we compared two approaches to obtain climatic time series for the Soil and Water Assessment Tool (SWAT), namely the conventional centroid method and time-dynamic Voronoi tessellation, and assessed the performance of SWAT in simulating discharge and smallholder maize yields in Southern Africa. Climatic time series were estimated with each method. The Voronoi method utilized all available precipitation and temperature data, but the centroid method used only 14.5 and 82.5%, respectively. After centroid processing, sub-basin time series were on average 42 and 63% incomplete, respectively. After Voronoi processing, all time series were complete. SWAT was fed with each climate dataset. Each model setup was independently calibrated and validated against discharge and maize yield. Similar model performance was obtained with both methods for yield. The root mean squared error during calibration was 0.26 and 0.27 t ha~(-1) for the centroid and Voronoi methods, respectively (p-value: 0.80). However, daily discharge simulations improved significantly with the Voronoi method. The coefficient of determination increased from 0.24 to 0.39 in the calibration period (p-value: 9.6 × 10~13) and from 0.41 to 0.48 in the validation period (p-value: 3.1 × 10~(-3)). The Voronoi method improved the simulation of the river flow regime. The largest improvements were obtained in data scarce situations, at high spatial and temporal resolution, and where the centroid method performed the worst.
机译：在这项研究中，我们比较了两种获取土壤和水评估工具（SWAT）的气候时间序列的方法，即常规质心方法和时间动态Voronoi细分，并评估了SWAT在模拟玉米中的排放和小农玉米产量方面的性能。非洲南部。用每种方法估算气候时间序列。 Voronoi方法利用了所有可用的降水和温度数据，但质心法分别仅使用了14.5和82.5％。经过质心处理后，子流域时间序列的平均不完整率分别为42％和63％。经过Voronoi处理后，所有时间序列均已完成。向SWAT提供了每个气候数据集。每个模型设置都经过独立校准，并针对排放量和玉米产量进行了验证。两种方法的产量都获得了相似的模型性能。对于质心法和Voronoi法，校准期间的均方根误差分别为0.26和0.27 t ha〜（-1）（p值：0.80）。但是，使用Voronoi方法可以大大改善日常排放模拟。测定系数在校准期间从0.24增加到0.39（p值：9.6×10〜13），在验证期间从0.41增加到0.48（p值：3.1×10〜（-3））。 Voronoi方法改进了河水流态的模拟。最大的改进是在数据稀缺情况下，在高空间和时间分辨率下以及质心方法表现最差的情况下获得的。

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