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对哥伦比亚条件的实证土壤湿度模型进行比较,验证和改进
Alejandro Rincón1,2, Fredy E Hoyos3, John E Candelo-Becerra3
1Grupo de Investigación en Desarrollos Tecnológicos y Ambientales (GIDTA), Facultad de Ingeniería y Arquitectura, Universidad Católica de Manizales, Carrera 23 N. 60-63, Manizales 170002, Colombia.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
使用气象数据准确地估计土壤湿度对农业至关重要. 这项研究开发并测试了新的实证模型,发现最好的模型达到0.8以上的纳什-萨特克利夫效率,但因位置而异.
科学领域:
- 农业科学 农业科学
- 水文学的水文学
- 环境建模环境建模
背景情况:
- 准确的土壤湿度估计对于灌计划等农业应用至关重要.
- 气象数据,包括降水和温度,是土壤湿度模型的关键输入.
- 经验模型为估计土壤水分动态提供了一种实际方法.
研究的目的:
- 评估经验水平衡和区间模型,以估计哥伦比亚的土壤水分.
- 提出和评估三个新的经验模型,整合最近的文献和修改.
- 分析参数对模型性能的影响,并比较使用AIC,NS和RMSE的模型.
主要方法:
- 作为输入变量,利用了每日降水和平均,最大和最小的空气温度.
- 开发了经验水平衡模型,采用哈格里夫斯式的蒸发透气和降水/土壤水分依赖的排水和透.
- 使用现场数据校准模型,并使用Akaike信息标准 (AIC),纳什-萨特克利夫 (NS) 系数和根平均平方误差 (RMSE) 进行性能比较.
主要成果:
- 性能最好的模型显示了纳什-萨特克利夫 (NS) 效率超过0.8的土壤湿度估计.
- 参数分析确定了影响模型装配质量的关键因素.
- 没有一个单一的拟议模型在所有三个研究地点都实现了最佳性能.
结论:
- 经验模型可以有效地估计土壤水分,使用气象数据用于农业目的.
- 与现有方法相比,拟议的模型在准确性和参数化方面提供了改进.
- 模型的性能是特定于位置的,强调了在不同环境中需要量身定制的方法.
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