the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Towards Large-Scale Daily Snow Density Mapping with Spatiotemporally Aware Model and Multi-Source Data
- 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
- 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)
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CC1: 'Comment on tc-2022-45', Xiaofeng Li, 09 Apr 2022
Snow density plays a critical role in estimating water resources and predicting natural disasters such as floods, avalanches, and snowstorms. A GTWNN model was constructed for snow density estimation and achieved daily snow density mapping from 2013 to 2020 in China with the support of remote sensing, ground observation, and reanalysis data. This study provides important spatiotemporal parameters for snow cover hydrology and other aspects. The main suggestions and opinions are as follows:
- L115, “Based on the SCA data, the snow cover duration (SCD) is calculated to account for the impact of gravity on snow density”, How to understand that snow density is affected by gravity, and what does it have to do with SCD?
- "Spatiotemporally Aware Model" in the title is not mentioned in the manuscript and should be explained.
- Whether the lack of observation data in 2019-2020 is related to the epidemic, making it impossible to conduct a large number of observations.
- The verification result in Fig.4 is that all the data as a whole is added to the training model, or is the training divided by region and month? Is it the 10-fold validation result of the trained model? Please explain further
- L200, Does the reason for the lower accuracies in Northeast China-Inner Mongolia consider the effect of different underlying surfaces on snow density? Forests and farmland in the Northeast, and grasslands in Inner Mongolia may have very different effects on snow density.
- The reasons for the slightly lower accuracy in the snow melting and accumulation periods are not only the rapid changes in the snow density itself, and insufficient sampling in observation time and space, but also because the snow accumulation in the early stage of snow accumulation is less, and the water content when the snow melts Therefore, the observation is more difficult, and the observation error is relatively large.
- The verification result of the snow density of ERA5 is worse than that of the model in this paper, but many parameters of ERA5 are used in the machine learning model of this paper, so the accuracy of these parameters, if there is also a large error, will not affect the final model accuracy?
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AC1: 'Response to CC1', Xueliang Zhang, 03 May 2022
We would like to thank the reviewers for the constructive comments, which helped to substantially improve the manuscript. We have addressed their comments in detail in the pdf supplement.
- CC2: 'Reply on AC1', Xiaofeng Li, 04 May 2022
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RC1: 'Comment on tc-2022-45', Anonymous Referee #1, 06 May 2022
This work employed geographically and temporally weighted neural network (GTWNN) model to construct the daily snow density grid products in China, with the support of satellite, ground, and reanalysis data, which is useful for estimating water resources and predicting natural disasters. However, some important issues need to be addressed. The details are as follows.
1.In terms of abstract, the content needs to be specified and well organized. For instance, in Line 6 of this section, the detailed results supporting that the GTWNN model can improve the estimation of snow density should be given; and in Line 10, the specific models should be listed.
2.In the introduction, it is not clear why satellite, ground, and reanalysis data are used.
3.In the end of Section 2.1, the snow season is divided into three periods. Considering the climate and environment show great spatial heterogeneity in snow cover areas in China, this division of snow season should be expounded.
4.In terms of Equation 2, not all the variables are explained in detail.
5.The variables in Figure 2 should be explained in or below this picture.
6.How each kinds of data are used specifically is not given in Section 2 or 3.
7.In Section 3.2, the title of this section is not appropriate for the content. In addition, how to evaluate the GTWNN model (such as the metrics to evaluate the performance) should be described.
8.In table 2, the details about each model are deficient.
9.The text and logic in the manuscript needs improved, particularly in the Results Section. For example, what’s the relationship between Section 4.1 (Descriptive Statistics of Ground Observations) and other results? The position of Section 4.2.3(Importance of the Influencing Variables for Snow Density Estimation) needs consideration.
10.Snow density and its CV in different snow cover regions vary apparently, as well as the monthly changes of snow density. While, the explications about these phenomena are limited in the present manuscript.- AC2: 'Response to RC1', Xueliang Zhang, 21 Jun 2022
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RC2: 'Comment on tc-2022-45', Anonymous Referee #2, 25 May 2022
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2022-45/tc-2022-45-RC2-supplement.pdf
- AC3: 'Response to RC2', Xueliang Zhang, 21 Jun 2022
Huadong Wang et al.
Huadong Wang et al.
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