论文

1990 -2019年中国北方沙区太阳能资源评估

  • 刘淳 ,
  • 任立清 ,
  • 李学军 ,
  • 贾冰 ,
  • 鱼腾飞 ,
  • 张成琦 ,
  • 肖建华 ,
  • 赵春彦 ,
  • 朱猛
展开
  • <sup>1.</sup>国网甘肃省电力公司,甘肃 兰州 730000;<sup>2.</sup>新疆气象局遥感中心,新疆 乌鲁木齐 830011;<sup>3.</sup>乌鲁木齐气象卫星地面站,新疆 乌鲁木齐 830011;<sup>4.</sup>中国科学院西北生态环境资源研究院/内陆河流域生态水文重点实验室/ 阿拉善荒漠生态-水文试验研究站,甘肃 兰州 730000

收稿日期: 2020-11-24

  网络出版日期: 2021-10-28

基金资助

国家电网有限公司管理咨询项目(SGTYHT/19-WT-245);国家自然科学基金项目(41771252);甘肃省自然科学基金重大项目(18JR4RA002);甘肃省林业和草原科技创新计划项目(GYCX[2020]01);甘肃省科学技术协会智库平台建设项目(GSAST-ZKPT[2020]01);甘肃省重点研发计划项目(20YF8FA002)

Evaluation to the Solar Energy Resources in the Sandy Regions of Northern China from 1990 to 2019

  • Chun LIU ,
  • Liqing REN ,
  • Xuejun LI ,
  • Bing JIA ,
  • Tengfei YU ,
  • Chengqi ZHANG ,
  • Jianhua XIAO ,
  • Chunyan ZHAO ,
  • Meng ZHU
Expand
  • <sup>1.</sup>Gansu Electric Power Corporation,Lanzhou 730000,Gansu,China;<sup>2.</sup>Remote Sensing Center of Xinjiang Meteorological Bureau,Chinese Meteorological Administration,Urumqi 830011,Xinjiang,China;<sup>3.</sup>Urumqi Meteorological Satellite Ground Station,Urumqi 830011,Xinjiang,China;<sup>4.</sup>Alxa Desert Eco-Hydrological Experimental Research Station,Key Laboratory of Eco-hydrology of Inland River Basin,Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,Gansu,China

Received date: 2020-11-24

  Online published: 2021-10-28

摘要

沙漠地区太阳能资源丰富且地表植被稀疏, 非常适于太阳能资源开发。然而目前针对中国北方沙漠地区太阳能资源的评估仍然较少。本研究利用中国北方沙区(含沙漠、 沙地、 戈壁和盐碱地)46个太阳辐射站和189个常规气象站数据, 基于极端梯度提升算法(xgboost)估算了太阳总辐射量, 分析了总辐射量年、 季和月的时空分布并进行太阳能资源评估。结果表明, 中国北方沙区1990 -2019年平均日照时数和太阳总辐射量为2927.90 h和5888.39 MJ·m-2, 且以分别以22.48 h·(10a)-1和8.66 MJ·m-2·(10a)-1的速率减少, 但这种趋势并不显著(p>0.05)。日照时数和太阳总辐射量空间分布呈现出东西低中部高的特点, 青海和甘肃河西的沙区总辐射量最高, 在6300 MJ·m-2以上, 东北地区的沙地总辐射量最低, 不足5300 MJ·m-2。季节变化表现为夏季太阳总辐射量最大, 春季次之, 秋冬季最低。总体上看, 青海和河西走廊西部沙区太阳能资源丰富, 其他沙区较丰富, 但就太阳能资源稳定度而言, 仅青海、 河西走廊南部沙区为较稳定, 其他沙区为欠稳定或一般。因此, 青海和甘肃河西沙区太阳能资源开发潜力最大, 应加大对该区太阳能资源的开发投入, 提高区域经济发展水平, 改善沙区生态环境。

本文引用格式

刘淳 , 任立清 , 李学军 , 贾冰 , 鱼腾飞 , 张成琦 , 肖建华 , 赵春彦 , 朱猛 . 1990 -2019年中国北方沙区太阳能资源评估[J]. 高原气象, 2021 , 40(5) : 1213 -1223 . DOI: 10.7522/j.issn.1000-0534.2021.00058

Abstract

The solar radiation, as a kind of clean energy, is increasingly being developed in recent years under the background of energy crisis and global climate warming.An evaluation of the spatial patterns and storage of solar energy is of great importance for potential solar energy resources development.The deserts in northern China are characterized by abundant solar energy resources and sparse vegetation cover, which are very suitable for solar energy resources development.However, as the number of solar radiation observation stations in sandy regions of northern China is very limited, dedicated evaluation to solar energy resources in the sandy regions of northern China is still characterized by large uncertainties.In this study, data from 46 solar radiation stations and 189 common weather stations in the sandy regions (including deserts, sandy lands, Gobi, and salinity lands) of northern China were employed to estimate the global solar radiation based on a high-efficient machine learning model, i.e., the xgboost algorithm.The solar resources were assessed by analyzing the spatiotemporal patterns of annual, seasonal, and monthly radiation.The results showed that the average sunshine duration and global solar radiation from 1990 to 2019 are 2927.90 h and 5888.39 MJ·m-2, respectively.The average decreasing rates of sunshine hours and solar radiation over the past thirty years were 22.48 h·(10a)-1 and 8.66 MJ·m-2, respectively, which were not significant at the 0.05 level.The spatial patterns of sunshine duration and total solar radiation were characterized by higher values occurring in the middle parts while lower values in the east and west parts of northern China.The radiation was maximal in the deserts of Qinghai and Hexi Corridor with values higher than 6300 MJ·m-2, while minimum in the sandy lands of Northeast China with values less than 5300 MJ·m-2.The solar radiation was maximal in summer, followed by spring, and the lowest in autumn and winter.Generally, solar energy in deserts of the Qinghai and west Hexi Corridor were ranked as the most abundant level, while other sandy regions were very abundant level.However, in terms of the stability of solar energy, only deserts in the Qinghai and the southern Hexi Corridor were ranked as a relatively stable level, and other sandy regions are fewer stable levels.Therefore, the potential of solar energy resources in the sandy regions of Qinghai and Hexi Corridor was maximal, and should be given priority in terms of solar resources development, to promote the regional economic development and further improve the ecological environment of sandy regions.

参考文献

[1]Chen T Q, He T, Benesty M, al et, 2020.Xgboost: Extreme gradient boosting.R package version 1.2.0.1[CP/OL].[2020-11-20]..
[2]Feng Q, Yang L S, Deo R C, al et, 2019.Domino effect of climate change over two millennia in ancient China's Hexi Corridor[J].Nature Sustainability, 2(10): 957-961.DOI: 10.1038/s41893-019-0397-9.
[3]Feng Y, Li Y T, 2018.Estimated spatiotemporal variability of total, direct and diffuse solar radiation across China during 1958-2016[J].International Journal of Climatology, 38(12): 4395-4404.DOI: 10.1002/joc.5676.
[4]He Y Y, Wang K C, 2020.Variability in direct and diffuse solar radiation across China from 1958 to 2017[J].Geophysical Research Letters, 47: e2019GL084570.DOI: 10.1029/2019GL084570.
[5]Tang W J, Yang K, Qin J, al et, 2011.Solar radiation trend across China in recent decades: a revisit with quality-controlled data[J].Atmospheric Chemistry and Physics, 11(1): 393-406.DOI: 10.5194/acp-11-393-2011.
[6]Tang W J, Yang K, Qin J, al et, 2018.First effort for constructing a direct solar radiation data set in China for solar energy applications[J].Journal of Geophysical Research-Atmospheres, 123(3): 1724-1734.DOI: 10.1002/2017jd028005.
[7]Wang L, Xu P Q, Chen W, al et, 2017.Interdecadal variations of the Silk Road pattern[J].Journal of Climate, 30(24), 9915-9932.DOI: 10.1175/JCLI-D-17-0340.1.
[8]Yang L S, Feng Q, Adamowskib J F, al et, 2020.Spatio-temporal variation of reference evapotranspiration in northwest China based on CORDEX-EA[J].Atmospheric Research, 238: 104868. DOI: 10.1016/j.atmosres.2020.104868.
[9]Yang L S, Feng Q, Yin Z L, al et, 2019.Application of multivariate recursive nesting bias correction, multiscale wavelet entropy and AI-based models to improve future precipitation projection in upstream of the Heihe River, Northwest China[J].Theoretical and Applied Climatology, 137: 323-339.DOI: 10.1007/s00704-018-2598-y.
[10]Yang S, Wang X L, Wild M, 2018.Homogenization and trend analysis of the 1958-2016 in situ surface solar radiation records in China[J].Journal of Climate, 31(11): 4529-4541.DOI: 10.1175/jcli-d-17-0891.1.
[11]杜东升, 张剑明, 张建军, 2015.湖南省太阳能资源时空分布特征及评估[J].中国农学通报, 31(36): 170-175.
[12]冯刚, 李卫华, 韩宇, 等, 2010.新疆太阳能资源及区划[J].可再生能源, 28(3): 133-139.DOI: 10.13941/j.cnki.21-1469/tk.2010. 03.025.
[13]冯晓莉, 申红艳, 李万志, 等, 2020.1961 -2017年青藏高原暖湿季节极端降水时空变化特征[J].高原气象, 39(4): 694-705.DOI: 10.7522/j.issn.1000-0534.2020.00029.
[14]胡琦, 潘学标, 李秋月, 等, 2016.气候变化背景下东北地区太阳能资源多时间尺度空间分布与变化特征[J].太阳能学报, 37(10): 2647-2652.
[15]胡亚男, 李兴华, 郝玉珠, 2019.内蒙古太阳能资源时空分布特征与评估研究[J].干旱区资源与环境, 33(12): 132-138.DOI: 10.13448/j.cnki.jalre.2019.357.
[16]李净, 王丹, 冯姣姣, 2017.基于MODIS遥感产品和神经网络模拟太阳辐射[J].地理科学, 37(6): 912-919.DOI: 10.13249/j.cnki.sgs.2017.06.013.
[17]李小军, 辛晓洲, 彭志晴, 2017.2003~2012年中国地表太阳辐射时空变化及其影响因子[J].太阳能学报, 38(11): 3057-3066.
[18]刘新伟, 黄武斌, 蒋盈沙, 等, 2021.基于LightGBM算法的强对流天气分类识别研究[J/OL].高原气象: 1-10..
[19]梁玉莲, 申彦波, 白龙, 等, 2017.华南地区太阳能资源评估与开发潜力[J].应用气象学报, 28(4): 481-492.DOI: 10.11898/1001-7313.20170409.
[20]卢毅敏, 岳天祥, 陈传法, 等, 2010.中国太阳总辐射的多元逐步回归模拟[J].遥感学报, 14(5): 852-864.
[21]孟宪红, 陈昊, 李照国, 等, 2020.三江源区气候变化及其环境影响研究综述[J].高原气象, 39(6): 1133-1143.DOI: 10.7522/j.issn.1000-0534.2019.00144.
[22]彭继达, 程兴宏, 孙治安, 等, 2014.两种不同初始场对太阳辐射模拟效果的影响[J].高原气象, 33(5): 1352-1362.DOI: 10. 7522/j.issn.1000-0534.2013.00098.
[23]申彦波, 2017.我国太阳能资源评估方法研究进展[J].气象科技进展, 7(1): 77-84.
[24]司建华, 冯起, 席海洋, 等, 2019.关于新时期中国西部发展沙产业的思考[J].中国沙漠, 39(1): 1-6.DOI: 10.7522/j.issn.1000-694X.2019.00014.
[25]陶苏林, 戚易明, 申双和, 等, 2016.中国1981~2014年太阳总辐射的时空变化[J].干旱区资源与环境, 30(11): 143-147.
[26]王涛, 2014.中国北方沙漠与沙漠化图集[M].北京: 科学出版社.
[27]吴林荣, 杜莉丽, 王娟敏, 等, 2013.陕北榆林地区太阳能资源空间分布特征及资源潜力评估[J].水土保持通报, 33(1): 238-242.DOI: 10.13961/j.cnki.stbctb.2013.01.006.
[28]熊燕琳, 周筠珺, 2020.四川地区地面太阳总辐射时空分布及气象影响因素研究[J].太阳能学报, 41(12): 162-171.
[29]许建伟, 高艳红, 彭保发, 等, 2020.1979 -2016年青藏高原降水的变化特征及成因分析[J].高原气象, 39(2): 234-244.DOI: 10.7522/j.issn.1000-0534.2019.00029.
[30]颜长珍, 王建华, 2019.中国1∶10万沙漠(沙地)分布数据集[DS/OL].国家冰川冻土沙漠科学数据中心.[2020-11-20]..
[31]曾雁, 曾万寿, 2019.酒泉市1958 -2016年太阳总辐射变化特征研究[J].中国农学通报, 35(11): 115-120.
[32]曾燕, 王珂清, 谢志清, 等, 2012.江苏省太阳能资源评估[J].大气科学学报, 35(6): 658-663.DOI: 10.13878/j.cnki.dqkxxb. 2012.06.001.
[33]张乾, 辛晓洲, 张海龙, 等, 2018.基于遥感数据和多因子评价的中国地区建设光伏电站的适宜性分析[J].地球信息科学学报, 20(1): 119-127.DOI: 10.12082/dqxxkx.2018.170393.
[34]中国气象局, 2019.太阳能资源评估方法[S].北京: 国家市场监督管理总局、 中国国家标准化管理委员会.
[35]周扬, 吴文祥, 胡莹, 等, 2010.西北地区太阳能资源空间分布特征及资源潜力评估[J].自然资源学报, 25(10): 1738-1749.
文章导航

/