1. Introduction
Snow cover is an important component of the cryosphere and indicator of climate change [
1] as its properties change rapidly in response to changes in heat and water on the earth’s surface [
2,
3,
4]. Snowfall also has an important impact on socioeconomic factors of humanity; for example, insufficient snowfall in spring can lead to drought, and excessive snowfall can create disasters, such as snow-melt floods, and major property losses [
5,
6,
7,
8,
9]. Therefore, the effective prediction and detection of various snowfall parameters are crucial for alleviating or minimizing these effects. At present, the primary methods of acquiring snow parameter data include (1) on-site observations of meteorological stations [
10]; (2) optical remote sensing methods for identifying the extent of snow cover, based on the high reflectivity of snow in the visible band, and low reflectivity in the NIR band to define a normalized difference snow index (NDSI) [
11]; (3) passive microwave remote sensing for global/regional snow depth and snow water equivalent observations [
12,
13,
14]; and (4) fusion of optical and microwave remote sensing inversions [
15]. Thus, snow depth data acquisition mainly depends on two approaches: remote sensing observations and onsite observations.
Under changing global climate conditions, snow cover can serve as an important indicator. Accordingly, scientists have carried numerous studies examining the change in snow cover over historical periods [
3,
4,
6,
16], the relationship between snow cover change and climate, and the impact of snow cover change on human productivity. Some analyses have also studied future changes in snow cover based on the experimental data of the
Coupled Model Intercomparison
Project (CMIP) organized by the World Climate Research Programme (WCRP) [
17,
18]; however, there are few studies on the future daily snow depth, as snow accumulation and melting is a complex process affected by many factors, such as temperature, precipitation, wind speed, solar radiation, underlying surface type, and altitude [
11,
15,
19].
Here, using NASA Earth Exchange/Global Daily Downscaled Projections data, future daily depth was simulated based on a selection of factors affecting the snow accumulation process in accordance with Leathers and Luff who found that the duration of snow is highly correlated with snowfall and temperature [
20]. Here, snowfall (weight equivalent to solid precipitation in NEX-GDDP data) and temperature were selected as the input variables for simulating snow depth. A backpropagation neural network snow simulation model (BPNNSIM) was built using MATLAB, and data on the current daily snow depth, daily minimum air temperature, daily maximum air temperature, and daily precipitation were used to predict the next day’s snow depth. The neural network was trained, and model accuracy was verified with Climate Reference Station data. Based on the BPNNSI, the NEX-GDDP data were used as model input to simulate future snow depth in China. The NEX-GDDP data comprise the first multimodal high-resolution dataset based on Coupled Model Intercomparison Project Phase 5 (CMIP5) released by NASA in 2015 (
Table 1). A statistical downscaling method was used to convert the daily precipitation, maximum and minimum air temperature data from 21 CMIP5 models during the historical period from 1986 to 2005, and two future climate scenarios (RCP4.5 and RCP8.5) over the projection period from 2006 to 2100, at a spatial resolution of 0.25° × 0.25° [
21]. Comparatively, NEX-GDDP data have a higher and more uniform spatial resolution than CMIP5, and many studies have shown that the former can better reflect the characteristics of regional climate change in China than the direct use of CMIP5 data [
22,
23,
24]. CMIP6 data are in the release and preliminary application stage, and the resolution of each mode varies greatly [
25]; thus, the consistently higher and more uniform resolution of NEX-GDDP will continue to maintain high application value. Accordingly, the future daily snow depth dataset simulated using the NEX-GDDP can inform future research on snow cover and snow disaster risk assessment.
This paper is structured as follows: methods and data are presented in
Section 2;
Section 3 contains the results of the findings, including BPNNSIM construction, validation, and simulation of future daily snow depths in China; sources of errors in simulated snow depth data are discussed in
Section 4; conclusions are presented in
Section 5.
5. Conclusions
Based on previous findings of the most influential factors controlling snow accumulation, temperature and precipitation were selected here as the input variables for the backpropagation artificial neural network snow simulation model (BPANNSIM) created here using MATLAB. The model was trained and validated using the National Climate Reference Station data, and the results showed that the iterative simulation capability of the model was stronger for both spatiotemporal sequences, with temporal and regional correlations (R2) of monthly snow cover duration equal to 0.94 and 0.97, and 0.88 and 0.91 for monthly cumulative snow depth, respectively. The corresponding Nash coefficients between the observed and simulated values for the cumulative snow depth and duration were 0.91 and 0.87, respectively. Thus, the model’s temporal and snow depth, and iterative simulation capabilities were slightly weaker than its regional and snow cover duration abilities. The NEX-GDDP dataset was used as the input value for BPANNSIM to simulate the daily snow depth across China, and the corresponding snow depth data obtained from GFDL-ESM2G showed the highest level of accuracy. Finally, the causes of simulation errors in the GFDL-ESM2G model were also analyzed, revealing that the coupling of precipitation and temperature data from the GFDL-ESM2G model was relatively poor during snowfall periods. It was also found that DS and DST were highly correlated with DP and DT, and a new validation method for gridded meteorological data was proposed here based on this correlation. This method can verify the accuracy of gridded meteorological data within snowfall periods and verify whether the hydrothermal coupling of this data is reasonable. However, this method is applicable to the validation of meteorological data during the snowfall period. Meanwhile, the validation error of this method is causally related to the grid cell size of the meteorological data.