A newly developed model for estimating snow depth in ungauged areas

Abstract

The presence of snow significantly affects the hydrological cycle and soil moisture globally. Nowadays, with the expansion of science, different satellites can measure snow depth all over the world. By establishing a conditional relationship between mean temperature and the Proportion of Snowfall to Total Precipitation (PSTP), this study proposes a method for snow depth estimation for ungauged stations. The case study of this research is Iran. Most of Iran's climate is arid due to its location in the Middle East, making it one of the countries with the lowest rainfall worldwide. On the other hand, this country enormously depends on water resources. Consequently, an accurate and valid estimate of snow amounts in Iran is essential. To reach this goal, we analyzed daily data from 12 synoptic and climatological stations between 1970 and 2020, including rainfall, snow depth, and mean temperature. For each station, the PSTP at 0.5 °C air temperature intervals was determined and fitted to a double sigmoid model that allows snow depth estimation. The evaluation of the snow depth approximated by the double sigmoid model was done using R2, RRMSE, EF, and Bias statistics against the observational data. R2 values in the Era-5 dataset in all stations are lower than 0.5. Additionally, it should be noted that in certain stations, the bias values surpass 20 mm. Furthermore, in 75 percent of the stations, the RRMSE values exceed 0.6. By utilizing the DSBE model, it is possible to achieve a reduction of 122.4 and 0.7 in Bias and RRMSE values, respectively. The R2 between observed and estimated snow depth was estimated between 0.81 and 1 in ten of the studied stations and 0.65 and 0.8 in the other two stations. Moreover, the findings imply that the proposed model is a suitable technique for estimating snow depth in remote areas lacking snowfall measurement.