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Article

Simulation of Daily Snow Depth Data in China Based on the NEX-GDDP

by 1,2,3,†, 1,*,†, 1,2,4,5, 1,3 and 1,3
1
State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730030, China
2
Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730030, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
5
China-Pakistan Joint Research Center on Earth Sciences, CAS-HEC, Islamabad 45320, Pakistan
*
Author to whom correspondence should be addressed.
The first two authors contributed equally to this paper.
Academic Editor: Hongyi Li
Water 2021, 13(24), 3599; https://doi.org/10.3390/w13243599
Received: 31 October 2021 / Revised: 9 December 2021 / Accepted: 10 December 2021 / Published: 15 December 2021
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)
In this study, a backpropagation artificial neural network snow simulation model (BPANNSIM) is built using data collected from the National Climate Reference Station to obtain simulation data of China’s future daily snow depth in terms of representative concentration pathways (RCP4.5 and RCP8.5). The input layer of the BPANNSIM comprises the current day’s maximum temperature, minimum temperature, snow depth, and precipitation data, and the target layer comprises snow depth data of the following day. The model is trained and validated based on data from the National Climate Reference Station over a baseline period of 1986–2005. Validation results show that the temporal correlations of the observed and the model iterative simulated values are 0.94 for monthly cumulative snow cover duration and 0.88 for monthly cumulative snow depth. Subsequently, future daily snow depth data (2016–2065) are retrieved from the NEX-GDPP dataset (Washington, DC/USA: the National Aeronautics and Space Administration(NASA)Earth Exchange/Global Daily Downscaled Projections data), revealing that the simulation data error is highly correlated with that of the input data; thus, a validation method for gridded meteorological data is proposed to verify the accuracy of gridded meteorological data within snowfall periods and the reasonability of hydrothermal coupling for gridded meteorological data. View Full-Text
Keywords: future daily snow depth; simulation; artificial neural network; snow cover future daily snow depth; simulation; artificial neural network; snow cover
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MDPI and ACS Style

Chen, H.; Yang, J.; Ding, Y.; He, Q.; Ji, Q. Simulation of Daily Snow Depth Data in China Based on the NEX-GDDP. Water 2021, 13, 3599. https://doi.org/10.3390/w13243599

AMA Style

Chen H, Yang J, Ding Y, He Q, Ji Q. Simulation of Daily Snow Depth Data in China Based on the NEX-GDDP. Water. 2021; 13(24):3599. https://doi.org/10.3390/w13243599

Chicago/Turabian Style

Chen, Hongju, Jianping Yang, Yongjian Ding, Qingshan He, and Qin Ji. 2021. "Simulation of Daily Snow Depth Data in China Based on the NEX-GDDP" Water 13, no. 24: 3599. https://doi.org/10.3390/w13243599

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