Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km² agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

中文翻译：

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利用遥感大数据进行流域建模：MODIS叶面积指数可改善水文和水质预测

传统的分水岭建模常常忽略了植被动力学的作用。也几乎没有定量证据表明在基于过程的模型中增加的植被动态物理逼真度可以同时改善水文和水质预测。在这项研究中，我们应用了改良的土壤和水评估工具（SWAT）来量化同化遥感叶面积指数（LAI）可以在16,860 km的水流，土壤湿度和硝酸盐负荷模拟中传达的改进​​程度²美国中西部的农业分水岭。我们修改了SWAT源代码，以使用中等分辨率成像光谱仪（MODIS）的空间分布和时间连续估计自动覆盖模型的内置半经验LAI。与空间信息有限的“基本”传统模型相比，我们的LAI同化模型（i）在中低流量条件下显着改善了每日流量模拟；（ii）提供了生长期土壤水分的现实空间分布；以及（iii） ）基本上重现了长期观察到的每日硝酸盐负荷的变化。进一步的分析表明，LAI的高估或低估对模型如何分配水文通量和养分池具有一定的级联效应。因此，MODIS LAI数据的同化纠正了模型的LAI高估趋势，从而导致根区土壤水分成比例增加，植物氮吸收减少。有了这些新发现，我们的研究填补了流域建模中有关植被动态的现有知识空白，并证实了对MODIS LAI数据进行同化可以有效改善水文和水质预测。