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排水和作物旋转增强农田数据集,以提高生态水文模型的空间准确性
1Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, Iowa, USA.
Scientific data
|January 30, 2026
概括
一个新的数据集,TREC,通过整合作物旋转和排水来改进生态水文建模. 这种增强的空间数据改善了美国玉米带的水平衡和流量估计.
科学领域:
- 生态水文学 生态水文学
- 地理空间数据分析.
- 农业科学 农业科学
背景情况:
- 准确的地理空间数据对于有效的生态水文建模至关重要,影响水平衡和流量估计.
- 美国玉米带的现有数据集缺乏详细的作物旋转和排水信息,限制了空间代表性和水质模拟.
- 瓦排水和作物轮换显著影响排列作物系统中的水质模拟.
研究的目的:
- 开发一个高分辨率 (30米) 的数据集,即排水和旋转增强农田 (TREC) 数据集,用于边界美国.
- 将连续作物旋转和排水数据整合到一个单一的,具有空间代表性的数据集中.
- 使用SWAT+模型,对TREC数据集与传统的农田数据层 (CDL) 的性能进行评估.
主要方法:
- 通过整合连续作物旋转和排水数据,开发了30m分辨率的TREC数据集.
- 利用SWAT+模型模拟流并评估中西部两个流域的水文性能.
- 将TREC数据集的性能与传统的农田数据层 (CDL) 和AgTile-US数据进行比较.
主要成果:
- 与CDL相比,TREC数据集提供了与CDL相比更好的排水的空间表示,更好地与AgTile-US映射模式保持一致.
- 未校准的模拟显示了TREC和CDL (147.6与147.4毫米) 之间的可比的流量.
- 与CDL (KGE = 0.25-0.59) 相比,TREC数据集在监测站 (KGE = 0.54-0.6) 中提升了水文性能,独立于校准.
结论:
- TREC数据集显著提高了农业地区生态水文模型的空间精度.
- 更高的精度支持更可靠的水平衡和溪流流动模拟,这对于流域管理至关重要.
- 这一数据集有助于为保护,营养管理和农业政策制定做出有针对性的决策.
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