Abstract. Soil moisture is a key preconditioning factor influencing hillslope stability and the initiation of landslides. Direct measurements of soil moisture on a large scale are logistically complicated, expensive, and therefore sparse, resulting in large data gaps. In this study, we calibrate a numerical land surface model to improve our representation of post-monsoon soil drying in landslide-prone Nepal. We use a parameter perturbation experiment to identify optimal parameter sets at three field monitoring sites and evaluate the performance of those optimal parameter sets at each location. This process enables the calibration of key soil hydraulic parameters, in particular a higher hydraulic conductivity and a lower saturation moisture content relative to the default parameter setting. Runs with the calibrated model parameters provide a substantially more accurate (50 % or greater reduction in root mean squared error) soil moisture record than those with the default model parameters, even when calibrated from sites as much as 250 km apart. This process enables meaningful calculation of post-monsoon soil moisture decay at locations with no in situ monitoring, so as to inform a key component of landslide susceptibility mapping in Nepal and other regions where field measurements of soil moisture are limited.

中文翻译：

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尼泊尔季风后干燥模拟：对山体滑坡灾害的影响

摘要。土壤湿度是影响山坡稳定性和滑坡发生的关键预处理因素。大规模直接测量土壤湿度在逻辑上是复杂的、昂贵的，因此稀疏，导致巨大的数据差距。在这项研究中，我们校准了一个数值陆地表面模型，以改善对容易发生山体滑坡的尼泊尔季风后土壤干燥的表征。我们使用参数扰动实验来确定三个现场监测站点的最佳参数集，并评估这些最佳参数集在每个位置的性能。该过程能够校准关键土壤水力参数，特别是相对于默认参数设置更高的水力传导率和更低的饱和含水量。使用校准模型参数运行可提供比使用默认模型参数运行更准确的土壤湿度记录（均方根误差减少 50% 或更多），即使在相距 250 公里的地点进行校准也是如此。这一过程能够在没有现场监测的地方对季风后土壤湿度衰减进行有意义的计算，从而为尼泊尔和其他土壤湿度现场测量有限的地区滑坡敏感性绘图的关键组成部分提供信息。