当前位置: X-MOL 学术J. Hydrol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Towards hyper-resolution land-surface modeling of surface and root zone soil moisture
Journal of Hydrology ( IF 6.4 ) Pub Date : 2021-01-05 , DOI: 10.1016/j.jhydrol.2020.125945
Tasnuva Rouf , Viviana Maggioni , Yiwen Mei , Paul Houser

The goal of this work is to estimate surface and root zone soil moisture at resolutions that are useful for decision making and water resources management. A 500-m atmospheric forcing dataset is developed from the 12.5-km NLDAS-2 (North America Land Data Assimilation System) products across Oklahoma, where high-quality observations are available for validation purposes. A land surface model is then forced with three combinations of input variables to simulate surface and root zone soil moisture: 1) NLDAS-2 atmospheric forcings at their original resolution; 2) downscaled NLDAS-2 atmospheric variables (i.e., near-surface air temperature and humidity, wind speed and direction, incident longwave and shortwave radiation, pressure) and original resolution NLDAS-2 precipitation; and 3) downscaled NLDAS-2 atmospheric variables and precipitation. Results show that the third simulation is able to bring modeled standard-normal deviates of both surface and root zone soil moisture closer to in-situ observations, whereas the second simulation only shows slight improvements with respect to one forced with original resolution NLDAS-2 data. This is particularly evident for negative values of standard-normal deviates, which correspond to drier than usual cases, due to the improved ability of the downscaled precipitation to detect missed events and no-rain cases. In summary, finer resolution forcings have the potential to improve simulations of soil moisture and the resolution of precipitation plays a critical role in improving time series of soil moisture standard-normal deviates.



中文翻译:

走向地表和根部土壤水分的超高分辨率陆面模拟

这项工作的目的是以有助于决策和水资源管理的分辨率估算表层和根区的土壤湿度。利用整个俄克拉荷马州12.5公里的NLDAS-2(北美土地数据同化系统)产品开发了一个500米的大气强迫数据集,可在其中使用高质量的观测值进行验证。然后使用三种输入变量组合来强制地表模型,以模拟地表和根区土壤湿度:1)NLDAS-2大气强迫处于其原始分辨率;2)降尺度的NLDAS-2大气变量(即近地表空气温度和湿度,风速和风向,入射的长波和短波辐射,压力)和原始分辨率NLDAS-2降水;3)降低了NLDAS-2的大气变量和降水量。结果表明,第三次模拟能够使模拟的表面和根部土壤水分的标准正态偏差更接近于原位观测,而第二次模拟仅显示了相对于原始分辨率为NLDAS-2数据的一个标准的轻微改进。 。这对于标准正态偏差的负值尤为明显,该负值比通常情况下更干燥,这是由于降尺度的降水检测漏失事件和无雨水情况的能力得到了提高。总之,更精细的分辨率强迫可能会改善对土壤水分的模拟,而降水的分辨率在改善土壤水分标准-正常偏差的时间序列中起着至关重要的作用。

更新日期:2021-01-20
down
wechat
bug