Abstract
Snow weight information is of interest to wide area of engineering and science. However, its direct measurement is costly in contrast to snow depth measurement where variety of methods from visual measure to advanced laser or remote sensing are available. Hence, snow weight information is usually sparse both in time and space compared to snow depth information. This paper proposes a hysteretic trilinear model to estimate snow weight from snow depth history. The model is verified by continuous monitoring data of snow depth and weight operated by National Research Institute for Earth Science and Disaster Resilience. The model is shown to match observed data well and selection procedure of parameters for the model is established. In addition, application to visual measure with sparse measurement in time is studied.
