Excessive runoff and high soil erosion rate are the critical problems in the Himalayan terrain, mainly due to rugged topography and high intensity rains. Accurate quantification of runoff and erosion is thus of paramount importance for taking appropriate measures to sustain the soil productivity in the Himalayan watersheds. Distributed, process-based hydrological and erosion models are ideal for this purpose. However, model parameterization in the rugged, inaccessible and thus generally a data scarce Himalayan watershed is a major challenge. The present study primarily investigates the applicability of kinematic runoff and erosion model (KINEROS2) model in a Himalayan watershed besides exploring the potential of satellite remote sensing and GIS in spatially distributed runoff modeling. The KINEROS2 model, is an event-based, distributed, water and erosion process model. It discretizes the watershed into a mosaic of planes and channels based on topography. The runoff is estimated for each plane which eventually flows to adjacent channel and is then routed to estimate the total runoff at the watershed outlet. Remote sensing is primarily used for model parameterization, i.e., characterizing the individual planes and channels. Optimized digital elevation model and fine-scale land-use/land-cover information are generated using high-resolution panchromatic and multi-spectral optical and microwave satellite imagery. The resulting data on near-surface soil moisture from radar imagery (ENVISAT ASAR) calibrated the initial soil moisture in the model, whose performance is evaluated using root mean square error and Nash–Sutcliffe that reveals that KINEROS2 model works quite well in a small Himalayan watershed. The sensitivity analysis indicates that saturated soil hydraulic conductivity is the most sensitive parameter influencing the runoff compared to Manning’s coefficient and initial soil moisture. The model output is also used for validating the remote sensing and geographical information system (GIS) based hydrologic response units delineated in a previous research study. The study highlights that the coupling of remote sensing and GIS with process models, such as KINEROS2, can provide valuable information in planning sustainable watershed management practices in the Himalayan watersheds.

中文翻译：

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将遥感和 GIS 与 KINEROS2 模型结合用于喜马拉雅流域空间分布径流建模

径流过多和土壤侵蚀率高是喜马拉雅地区的关键问题，主要是由于崎岖的地形和高强度的降雨。因此，准确量化径流和侵蚀对于采取适当措施维持喜马拉雅流域的土壤生产力至关重要。分布式、基于过程的水文和侵蚀模型非常适合此目的。然而，在崎岖、难以接近、因此通常数据稀缺的喜马拉雅流域中进行模型参数化是一项重大挑战。除了探索卫星遥感和 GIS 在空间分布径流建模中的潜力外，本研究主要研究了运动学径流和侵蚀模型 (KINEROS2) 模型在喜马拉雅流域中的适用性。KINEROS2 模型，是一个基于事件的、分布式的、水和侵蚀过程模型。它根据地形将分水岭离散为平面和通道的马赛克。估算最终流向相邻河道的每个平面的径流，然后路由以估算流域出口处的总径流。遥感主要用于模型参数化，即表征单个平面和通道。使用高分辨率全色和多光谱光学和微波卫星图像生成优化的数字高程模型和精细尺度的土地利用/土地覆盖信息。来自雷达图像 (ENVISAT ASAR) 的近地表土壤湿度数据校准了模型中的初始土壤湿度，其性能使用均方根误差和 Nash-Sutcliffe 进行评估，结果表明 KINEROS2 模型在喜马拉雅小分水岭中运行良好。敏感性分析表明，与曼宁系数和初始土壤湿度相比，饱和土壤导水率是影响径流的最敏感参数。模型输出还用于验证先前研究中描述的基于遥感和地理信息系统 (GIS) 的水文响应单元。该研究强调，遥感和 GIS 与过程模型（如 KINEROS2）的耦合可以为规划喜马拉雅流域的可持续流域管理实践提供有价值的信息。敏感性分析表明，与曼宁系数和初始土壤湿度相比，饱和土壤导水率是影响径流的最敏感参数。模型输出还用于验证先前研究中描述的基于遥感和地理信息系统 (GIS) 的水文响应单元。该研究强调，遥感和 GIS 与过程模型（如 KINEROS2）的耦合可以为规划喜马拉雅流域的可持续流域管理实践提供有价值的信息。敏感性分析表明，与曼宁系数和初始土壤湿度相比，饱和土壤导水率是影响径流的最敏感参数。模型输出还用于验证先前研究中描述的基于遥感和地理信息系统 (GIS) 的水文响应单元。该研究强调，遥感和 GIS 与过程模型（如 KINEROS2）的耦合可以为规划喜马拉雅流域的可持续流域管理实践提供有价值的信息。模型输出还用于验证先前研究中描述的基于遥感和地理信息系统 (GIS) 的水文响应单元。该研究强调，遥感和 GIS 与过程模型（如 KINEROS2）的耦合可以为规划喜马拉雅流域的可持续流域管理实践提供有价值的信息。模型输出还用于验证先前研究中描述的基于遥感和地理信息系统 (GIS) 的水文响应单元。该研究强调，遥感和 GIS 与过程模型（如 KINEROS2）的耦合可以为规划喜马拉雅流域的可持续流域管理实践提供有价值的信息。