评估大型数据集的适用性,以估计不同空间尺度上的负载和产量
Andrés Felipe Suárez-Castro1, Dale M Robertson2, Bernhard Lehner3
1Australian Rivers Institute, School of Environment and Science, Griffith University, 170 Kessels Rd, Nathan, Queensland 4111, Australia.
Water research
|October 13, 2024
概括
大规模数据可以预测淡水生态系统中的产量,但在监测不良的地区,本地数据通常会产生更好的结果. 这种方法有助于识别营养来源和知识差距,特别是在数据稀缺的情况下.
科学领域:
- 环境科学 环境科学
- 生态生态学 生态生态学
- 水文学的水文学
背景情况:
- 预测淡水生态系统中的营养负载和产量是具有挑战性的,因为在大空间尺度上信息不足.
- 决策者需要可靠的数据来有效管理水资源.
研究的目的:
- 评估大规模 (区域到全球) 数据集在较小规模 (捕获,流域) 预测产量的有效性.
- 为了比较在不同地区使用大规模与本地规模数据集的模型的性能.
- 评估大规模建模对产生假设和识别知识差距的有用性.
主要方法:
- 关于流域属性的空间参照回归 (SPARROW) 模型的应用.
- 从使用大规模和本地规模数据集构建的模型中比较三个不同的地区 (美国中西部,新西兰,巴西) 的气输送预测.
主要成果:
- 大规模模型在监测数据有限的地区通常表现不佳.
- 尽管性能差异很大,但大型模型有效地产生了对营养源影响的假设,并突出了数据缺陷.
- 具有代表性的水质监测网络对于提高大规模模型准确性至关重要.
结论:
- 大规模建模为营养管理提供了宝贵的见解,特别是在数据稀缺的地区.
- 将大规模建模与有针对性的监测相结合,可以改善地方范围的采水区管理决策.
- 需要进行进一步的研究,以优化大规模建模的应用,以便在实际决策中发挥作用.
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