A comparative study of machine learning and statistical models in climate downscaling in the Shiyang River Basin

doi:10.7522/j.issn.1000-694X.2021.00210

Journal of Desert Research ›› 2022, Vol. 42 ›› Issue (1): 196-210.DOI: 10.7522/j.issn.1000-694X.2021.00210

A comparative study of machine learning and statistical models in climate downscaling in the Shiyang River Basin

Yulai Gong¹^,²(), Shaoxiu Ma¹(), Weiqi Liu¹^,²

^1.Key Laboratory of Desert and Desertification，Northwest Institute of Eco-Environment and Resources，Chinese Academy of Sciences，Lanzhou 730000，China
^2.University of Chinese Academy of Sciences，Beijing 100049，China

Received:2021-11-15 Revised:2021-12-23 Online:2022-01-20 Published:2022-01-28
Contact: Shaoxiu Ma

Abstract

Abstract:

High-resolution climate data are an important data source for studying the impacts of climate change on agriculture， ecology and hydrology. The Shiyang River Basin has diverse land use types and significant elevation changes， which is suitable for studying the applicability of downscaling models. In this study， two traditional statistical downscaling models and four machine learning models were selected and combined with two standardized methods to compare the downscaling of temperature and precipitation at four sites in the Shiyang River Basin to explore the optimal downscaling model for the region. The results show that the machine learning models have better downscaling capability than the traditional statistical models. The ensemble of multi-model gives stable downscaling results with high accuracy. The correlation coefficients of 0.98 （exceeding the 99% confidence level） for temperature results and 0.74 （exceeding the 99% confidence level） for precipitation results at each site indicate that the models screened in this study can achieve better downscaling and can be used to generate future climate scenario data for the region， providing reliable climate data for climate change related studies.

Key words: statistical downscaling, machine learning, climate change

CLC Number:

P468

Yulai Gong, Shaoxiu Ma, Weiqi Liu. A comparative study of machine learning and statistical models in climate downscaling in the Shiyang River Basin[J]. Journal of Desert Research, 2022, 42(1): 196-210.

Figures/Tables 10

References 40

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站点	主要土地类型	年平均气温/℃	年降水量/mm
武威	耕地、草地	8.26	169.93
乌鞘岭	冰川、积雪、林地	0.13	401.27
民勤	沙地、戈壁	8.26	115.60
永昌	沙地、草地	5.21	204.88

站点	主要土地类型	年平均气温/℃	年降水量/mm
武威	耕地、草地	8.26	169.93
乌鞘岭	冰川、积雪、林地	0.13	401.27
民勤	沙地、戈壁	8.26	115.60
永昌	沙地、草地	5.21	204.88

缩写	英文全名	中文全名	单位
T_as	Near Surface Air Temperature	近地面气温	K
P_s	Surface Air Pressure	地面气压	Pa
Z_g500	500hPa Geopotential Height	500 hPa高度	m
H_uss	Near Surface Relative Humidity	比湿
H_fls	Surface Upward LatentHeat Flux	向上潜热通量	W·m^-2
H_fss	Suface Upward SensibleHeat Flux	向上感热通量	W·m^-2
R_lds	Surface Downwelling Longwave Radiaion	向下长波辐射	W·m^-2
R_sds	Surface Downwelling Shortwave Radiation	向下短波辐射	W·m^-2

缩写	英文全名	中文全名	单位
T_as	Near Surface Air Temperature	近地面气温	K
P_s	Surface Air Pressure	地面气压	Pa
Z_g500	500hPa Geopotential Height	500 hPa高度	m
H_uss	Near Surface Relative Humidity	比湿
H_fls	Surface Upward LatentHeat Flux	向上潜热通量	W·m^-2
H_fss	Suface Upward SensibleHeat Flux	向上感热通量	W·m^-2
R_lds	Surface Downwelling Longwave Radiaion	向下长波辐射	W·m^-2
R_sds	Surface Downwelling Shortwave Radiation	向下短波辐射	W·m^-2

站点名称	站点编号	经度 /（°）	纬度 /（°）	气压传感器海拔/m	观测场海拔/m
武威	52679	102.4	37.55	1 532.7	1 531.5
乌鞘岭	52787	102.52	37.12	3 046.3	3 045.1
民勤	52681	103.05	38.38	1 368.7	1 367.5
永昌	52674	101.58	38.14	1 978.1	1 976.9

A comparative study of machine learning and statistical models in climate downscaling in the Shiyang River Basin

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序号	模型名称	英文名称及缩写
1	分位数映射法	Quantile Mapping (QM)
2	多元线性回归	Multiple linear regression (MLR)
3	支持向量回归+Z-score	Support Vector Regression (SVR-Z)
4	人工神经网络+Z-score	Artificial Neural Network (ANN-Z)
5	极限学习机+Z-score	Extreme Learning Machine (ELM-Z)
6	卷积长短时记忆网络+Z-score	Convolutional Long Short-term Memory (ConvLSTM-Z)
7	支持向量回归+Minmax	Support Vector Regression (SVR-M)
8	人工神经网络+Minmax	Artificial Neural Network (ANN-M)
9	极限学习机+Minmax	Extreme Learning Machine (ELM-M)
10	卷积长短时记忆网络+Minmax	Convolutional Long Short-term Memory (ConvLSTM-M)

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