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Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France)

机译:高时空分辨率地球观测对农田中模拟水文气象变量的影响(法国西南部)

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Agricultural landscapes are often constituted by a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes and must be taken into account in simulations of land surface and distributed hydrological models. The Sentinel-2 mission allows for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20?m and 5?days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy fluxes via the Interactions between the Surface Biosphere Atmosphere?(ISBA) land surface model included in the EXternalized SURface?(SURFEX) modeling platform. The study focuses on the effect of the leaf area index?(LAI) spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8?m). The study area is an agricultural zone in southwestern France covering 576?kmsup2/sup (24?km??×??24?km). An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux?(LE). Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops. The comparison with in situ measurements shows an improvement of roughly?0.3 in the correlation coefficient and a decrease of around 30?% of the root mean square error (RMSE) in the simulated evapotranspiration. This finding is attributable to a better description of LAI evolution processes with Formosat-2 data, which further modify soil water content and drainage of soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4?% of annual precipitation levels with simulations using Formosat-2 data in comparison to the reference simulation values. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.
机译:农业景观通常是由一些农田的拼凑而成的,其季节演变取决于特定的作物轮作模式和物候。这种时间和空间异质性影响地表水文气象过程,在模拟地表和分布式水文模型时必须考虑到这一点。 Sentinel-2任务允许以前所未有的空间分辨率监视土地覆盖和植被动态,并重新访问与此类异质农业景观完全兼容的频率(分别为20?m和5?days)。在这里,我们通过外部化SURface?(SURFEX)建模平台中包括的表面生物圈大气?(ISBA)地表模型之间的相互作用,评估了Sentinel-2类遥感数据对地表水和能量通量模拟的影响。 。该研究集中于叶面积指数(LAI)的时空变化对这些通量的影响。我们比较了ECOCLIMAP-II中默认使用于SURFEX-ISBA中的LAI气候学的使用以及从高分辨率Formosat-2卫星数据(8?m)中得出的LAI的时间序列。研究区域是法国西南部的一个农业区,占地576?km 2 (24?km ??×?? 24?km)。使用了一种创新的地块比例方法,其中每个计算单元都具有同质植被类型。通过将模型输出与潜热通量(LE)的原位涡动协方差测量值进行比较,可以评估仿真质量。我们的结果表明,相对于ECOCLIMAP-II,使用源自高分辨率遥感的LAI可以显着改善模拟的蒸散量,尤其是在夏季作物覆盖地面的情况下。与原位测量的比较表明,在模拟的蒸散过程中,相关系数提高了约0.3%,而均方根误差(RMSE)降低了约30%。这一发现归因于用Formosat-2数据更好地描述了LAI演化过程,该过程进一步修改了土壤含水量和土壤储层的排水。对年排水模式的影响仍然很小但很显着,即与参考模拟值相比,使用Formosat-2数据进行的模拟增加了大约相当于年降水量4%的增长。这项研究说明了Sentinel-2任务有可能更好地代表作物管理对大型人工流域水预算的影响。

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