Preprints
https://doi.org/10.5194/tc-2022-45
https://doi.org/10.5194/tc-2022-45
 
24 Mar 2022
24 Mar 2022
Status: a revised version of this preprint is currently under review for the journal TC.

Towards Large-Scale Daily Snow Density Mapping with Spatiotemporally Aware Model and Multi-Source Data

Huadong Wang1, Xueliang Zhang1, Pengfeng Xiao1, Tao Che2, Zhaojun Zheng3, Liyun Dai2, and Wenbo Luan1 Huadong Wang et al.
  • 1Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China
  • 2Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
  • 3The National Meteorological Satellite Meteorological Center, Beijing 100081, China

Abstract. Snow density plays a critical role in estimating water resources and predicting natural disasters such as floods, avalanches, and snowstorms. However, gridded products for snow density are lacking for understanding its spatiotemporal patterns. In this study, considering the multiple influencing variables and the strong spatiotemporal heterogeneity of snow density, the geographically and temporally weighted neural network (GTWNN) model is constructed for estimating daily snow density in China from 2013 to 2020, with the support of satellite, ground, and reanalysis data. The R2 and RMSE achieve 0.515 and 0.043 g/cm3, respectively. The constructed GTWNN model is able to improve the estimation of snow density by capturing the weak and nonlinear relationship between snow density and the meteorological, snow, topographic, and vegetation variables. The leaf area index of high vegetation, snow depth, and topographic variables make a relatively great contribution for estimating snow density among the 17 influencing variables. The importance of addressing the spatiotemporal heterogeneity for snow density estimation is further demonstrated by comparing the GTWNN model with other models. The performance of the GTWNN model is closely related to the state and amount of snow, in which more stable and plentiful snow would result in higher snow density estimation accuracy. With the benefit of the daily snow density map, we obtain knowledge of the spatiotemporal pattern and heterogeneity of snow density in different snow periods and snow cover regions in China. The proposed GTWNN model holds the potential for large-scale daily snow density mapping, which will be beneficial for snow parameter estimation and water resource management.

Huadong Wang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on tc-2022-45', Xiaofeng Li, 09 Apr 2022
    • AC1: 'Response to CC1', Xueliang Zhang, 03 May 2022
      • CC2: 'Reply on AC1', Xiaofeng Li, 04 May 2022
  • RC1: 'Comment on tc-2022-45', Anonymous Referee #1, 06 May 2022
    • AC2: 'Response to RC1', Xueliang Zhang, 21 Jun 2022
  • RC2: 'Comment on tc-2022-45', Anonymous Referee #2, 25 May 2022
    • AC3: 'Response to RC2', Xueliang Zhang, 21 Jun 2022

Huadong Wang et al.

Huadong Wang et al.

Viewed

Total article views: 639 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
489 124 26 639 12 8
  • HTML: 489
  • PDF: 124
  • XML: 26
  • Total: 639
  • BibTeX: 12
  • EndNote: 8
Views and downloads (calculated since 24 Mar 2022)
Cumulative views and downloads (calculated since 24 Mar 2022)

Viewed (geographical distribution)

Total article views: 613 (including HTML, PDF, and XML) Thereof 613 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2022
Download
Short summary
The geographically and temporally weighted neural network (GTWNN) model is constructed for estimating large-scale daily snow density by integrating satellite, ground, and reanalysis data, which addresses the importance of spatiotemporal heterogeneity and nonlinear relationship between snow density and impact variables, as well as allows us understanding the spatiotemporal pattern and heterogeneity of snow density in different snow periods and snow cover regions in China from 2013 to 2020.