Spennemann P C, Fernández-Long M E, Gattinoni N N, Cammalleri C, Naumann G
Consejo Nacional de Investigaciones Ciencia y Tecnología (CONICET)-Servicio Meteorológico Nacional (SMN), Buenos Aires, Argentina.
Universidad Nacional de Tres de Febrero (UNTREF), Buenos Aires, Argentina.
J Hydrol Reg Stud. 2020 Oct;31:100723. doi: 10.1016/j.ejrh.2020.100723.
The Pampas region is located in the central-east part of Argentina, and is one of the most productive agricultural regions of the world under rainfed conditions.
This study aims at examining how different Land Surface Models (LSMs) and satellite estimations reproduce daily surface and root zone soil moisture variability over 8 in-situ observation sites. The ability of the LSMs to detect dry and wet events is also evaluated.
The surface and root zone soil moisture of the LSMs and the surface soil moisture of the ESA CCI (European Space Agency Climate Change Initiative, hereafter ESA-SM) show in general a good performance against the in-situ measurements. In particular, the BHOA (Balance Hidrológico Operativo para el Agro) shows the best representation of the soil moisture dynamic range and variability, and the GLDAS (Global Land Data Assimilation System)-Noah, ERA-Interim TESSEL (Tiled ECMWF's Scheme for Surface Exchanges over Land) and Global Drought Observatory (GDO)-LISFLOOD are able to adequately represent the soil moisture anomalies over the Pampas region. In addition to the LSM results, also the ESA-SM satellite estimated anomalies proved to be valuable. However, the LSMs and the ESA-SM have difficulties in reproducing the soil moisture frequency distributions. Based on this study, it is clear that accurate forcing data and soil parameters are critical to substantially improve the ability of LSMs to detect dry and wet events.
潘帕斯地区位于阿根廷中东部，是世界上雨养条件下最具生产力的农业地区之一。
本研究旨在考察不同的陆面模式（LSM）和卫星估算如何再现8个实地观测站点的每日地表和根区土壤湿度变异性。同时还评估了LSM检测干湿事件的能力。
LSM的地表和根区土壤湿度以及欧洲航天局气候变化倡议（以下简称ESA-SM）的地表土壤湿度总体上与实地测量结果表现良好。特别是，BHOA（农业水文运行平衡模型）对土壤湿度动态范围和变异性的表现最佳，而GLDAS（全球陆地数据同化系统）-Noah、ERA-Interim TESSEL（欧洲中期天气预报中心陆地表面交换平铺方案）和全球干旱观测站（GDO）-LISFLOOD能够充分代表潘帕斯地区的土壤湿度异常。除了LSM的结果外，ESA-SM卫星估算的异常也被证明是有价值的。然而，LSM和ESA-SM在再现土壤湿度频率分布方面存在困难。基于本研究，很明显，准确的强迫数据和土壤参数对于大幅提高LSM检测干湿事件的能力至关重要。
