As the horizontal grid spacing decreases, treatment of hydrologic processes in land surface models (LSMs), such as the lateral flow of surface and subsurface flow, need to be explicitly represented. Unlike previous studies that mainly focused on the mountainous regions, in this study, the offline Weather Research and Forecasting (WRF)-Hydro model is employed to study the impact of lateral flow on soil moisture and energy fluxes over the relatively flat southern Great Plains (SGP). The vast amount of measurements over the SGP provide a unique opportunity to assess the model behavior. In addition, newly developed land surface properties and input forcing are ingested into the model, in an attempt to reduce uncertainties associated with the initial and boundary forcing and help to identify model deficiencies. Our results show that the more realistic inputs (parameters, soil types, forcing) lead to larger underestimation of latent heat flux and dry bias, indicating the existence of model structural uncertainty (embedded errors) in WRF-Hydro that need to be characterized to inform future model development efforts. Including lateral flow processes partly mitigates the model deficiencies in representing hydrologic processes and alleviates the dry bias. In particular, both surface and subsurface lateral flow increase soil moisture mainly over the lower elevations, except that subsurface flow also affects soil moisture over steeper terrains. Additional simulations are performed to assess the effect of routing resolution on model results. When LSM resolution is high, noticeable differences in soil moisture are produced between different routing resolutions especially over steep terrain. Whereas when LSM resolution is coarse, differences between routing resolutions become negligible, especially over flat terrain.

中文翻译：

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侧向流对大平原南部地表水和能源收支的影响——模拟研究

随着水平网格间距的减小，需要明确表示地表模型 (LSM) 中的水文过程处理，例如地表侧向流和地下流。与以往主要针对山区的研究不同，本研究采用离线天气研究与预报（WRF）-Hydro模型研究侧向流对相对平坦的南部大平原土壤水分和能量通量的影响（ SGP）。SGP 上的大量测量为评估模型行为提供了独特的机会。此外，新开发的地表特性和输入强迫被纳入模型，以尝试减少与初始和边界强迫相关的不确定性，并帮助识别模型缺陷。我们的结果表明，更现实的输入（参数、土壤类型、强迫）导致对潜热通量和干偏差的更大低估，表明 WRF-Hydro 中存在模型结构不确定性（嵌入误差），需要对其进行表征以告知未来的模型开发工作。包括横向流动过程部分地减轻了模型在表示水文过程方面的缺陷并减轻了干偏差。特别是，地表和地下侧向流主要增加了低海拔地区的土壤水分，除了地下流也会影响陡峭地形上的土壤水分。执行额外的模拟以评估路由分辨率对模型结果的影响。当 LSM 分辨率高时，不同的布线分辨率之间会产生明显的土壤湿度差异，尤其是在陡峭的地形上。而当 LSM 分辨率粗糙时，路由分辨率之间的差异变得可以忽略不计，尤其是在平坦地形上。