Journal of Geographical Sciences ›› 2021, Vol. 31 ›› Issue (3): 389-402.doi: 10.1007/s11442-021-1849-5
Previous Articles Next Articles
QIN Yun1(), REN Guoyu1,2,*(
), HUANG Yunxin3, ZHANG Panfeng1, WEN Kangmin1
Received:
2020-05-16
Accepted:
2020-09-11
Online:
2021-03-25
Published:
2021-05-25
Contact:
REN Guoyu
E-mail:shuyunchenyun@163.com;guoyoo@cma.gov.cn
About author:
Qin Yun (1990‒), PhD Candidate, specialized in regional climatology and climate change. E-mail: shuyunchenyun@163.com
Supported by:
QIN Yun, REN Guoyu, HUANG Yunxin, ZHANG Panfeng, WEN Kangmin. Application of geographically weighted regression model in the estimation of surface air temperature lapse rate[J].Journal of Geographical Sciences, 2021, 31(3): 389-402.
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
Table 1
Moran's Index of residuals returned by the MWR and the GWR model, respectively"
Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
MWR | 0.0354** | 0.0518** | 0.0661** | 0.0497** | 0.0449** | 0.0415** | 0.0419** | 0.0486** | 0.0345** | 0.0318** | 0.0471** | 0.0377** |
GWR | 0.0038 | 0.0102** | 0.0082* | ‒0.0055 | ‒0.0119** | ‒0.0175** | ‒0.0194** | ‒0.0186** | ‒0.0224** | ‒0.0224** | ‒0.0072 | ‒0.0001 |
Figure 4
Boxplot of the differences between the predicted and the observed monthly temperatures in the 10,924 automatic stations (The differences above-mentioned were the values subtracted the observed values from the predicted values. The low and the high edge of the boxes represented the position of the lower quartile of the 25th percentile and the upper quartile of the 75th percentile, respectively. The white lines across the boxes represented the medians. The whiskers extending from the boxes represented the 2.5th percentile and the 97.5th percentile, and the red “+” represented the outliers.)"
Appendix Figure 1
AICc varied with the increase of nearest neighbor stations Notes: The number of nearest neighbor stations was 17 for the year, and 17, 14, 13, 17, 17, 17, 19, 19, 19, 19, 17 and 17 for the 12 months from January to December, respectively. The x-value of the vertical dashed line was 17. The x-value of the gray band off the vertical dashed line ranged from 13 to 19."
Appendix Figure 2
Standard deviations of βi1 when n varied from 13 to 19 Notes: The x-value of a black dot represented the average local R2 when n varied from 13 to 19 at the fit station. The blue vertical line represented the critical value of local R2=0.7, and the red horizontal lines represented the standard deviation of βi1 equal to 1°C/km. The meanings of the parameters above-mentioned were described in Section 2."
[1] | Akaike H, 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6):716-723. |
[2] | Atabay D, 2016. Pyrenn: First Release (Version v0.1) [CP]. Zenodo. doi: 10.5281/zenodo.45022. |
[3] | Barry R G, Chorley R J, 2003. Atmosphere, Weather, and Climate. London and New York: Routledge. |
[4] | Bengio Y, Gingras F, 1996. Recurrent neural networks for missing or asynchronous data. In: Advances in Neural Information Processing Systems. Cambridge, Massachusetts: MIT Press, 395-401. |
[5] | Blandford T R, Humes K S, Harshburger B J et al., 2008. Seasonal and synoptic variations in near-surface air temperature lapse rates in a mountainous basin. Journal of Applied Meteorology and Climatology, 47(1):249-261. |
[6] | Brunsdon C, Fotheringham A S, Charlton M E, 1996. Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4):281-298. |
[7] | Cao L, Zhu Y, Tang G et al., 2016. Climatic warming in China according to a homogenized data set from 2419 stations. International Journal of Climatology, 36(13):4384-4392. |
[8] | Cliff A D, Ord J K, 1969. The problem of spatial autocorrelation. In: Studies in Regional Science. London: Pion, 25-55. |
[9] | Cliff A D, Ord J K, 1972. Testing for spatial autocorrelation among regression residuals. Geographical Analysis, 4(3):267-284. |
[10] | Dodson R, Marks D, 1997. Daily air temperature interpolated at high spatial resolution over a large mountainous region. Climate Research, 8(1):1-20. |
[11] | Du M, Zhang M, Wang S et al., 2018. Near-surface air temperature lapse rates in Xinjiang, northwestern China. Theoretical and Applied Climatology, 131(3/4):1221-1234. |
[12] | Du Z, Wang Z, Wu S et al., 2020. Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. International Journal of Geographical Information Science, 34(7):1353-1377. |
[13] | ESRI, 2018. How GWR works [OL]. https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-statistics-toolbox/how-gwr-regression-works.htm |
[14] | Fischer M M, Getis A, 2010. Handbook of Applied Spatial Analysis. Heidelberg: Springer. |
[15] | Fotheringham A S, Brunsdon C, Charlton M, 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: John Wiley & Sons. |
[16] | Gardner A S, Sharp M, 2009. Sensitivity of net mass-balance estimates to near-surface temperature lapse rates when employing the degree-day method to estimate glacier melt. Annals of Glaciology, 50(50):80-86. |
[17] | Gardner A S, Sharp M J, Koerner R M et al., 2009. Near-surface temperature lapse rates over Arctic glaciers and their implications for temperature downscaling. Journal of Climate, 22(16):4281-4298. |
[18] | Guo X, Wang L, Tian L, 2016. Spatio-temporal variability of vertical gradients of major meteorological observations around the Tibetan Plateau. International Journal of Climatology, 36(4):1901-1916. |
[19] | Harlow R C, Burke E J, Scott R L et al., 2004. Research note: Derivation of temperature lapse rates in semi-arid south-eastern Arizona. Hydrology and Earth System Sciences, 8(6):1179-1185. |
[20] | Harrell Jr F E, 2015. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. New York: Springer. |
[21] |
He Y, Wang K, 2020. Contrast patterns and trends of lapse rates calculated from near-surface air and land surface temperatures in China from 1961 to 2014. Science Bulletin, 65(14):1217-1224.
doi: 10.1016/j.scib.2020.04.001 |
[22] | Holden J, Rose R, 2011. Temperature and surface lapse rate change: A study of the UK’s longest upland instrumental record. International Journal of Climatology, 31(6):907-919. |
[23] | Hurvich C M, Simonoff J S, Tsai C-L, 1998. Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60(2):271-293. |
[24] | Jóhannesson T, Sigurdsson O, Laumann T et al., 1995. Degree-day glacier mass-balance modelling with applications to glaciers in Iceland, Norway and Greenland. Journal of Glaciology, 41(138):345-358. |
[25] | Kattel D B, Yao T, Panday P K, 2018. Near-surface air temperature lapse rate in a humid mountainous terrain on the southern slopes of the eastern Himalayas. Theoretical and Applied Climatology, 132(3/4):1129-1141. |
[26] | Kattel D B, Yao T, Yang K et al., 2013. Temperature lapse rate in complex mountain terrain on the southern slope of the central Himalayas. Theoretical and Applied Climatology, 113(3/4):671-682. |
[27] | Kattel D B, Yao T, Yang W et al., 2015. Comparison of temperature lapse rates from the northern to the southern slopes of the Himalayas. International Journal of Climatology, 35(15):4431-4443. |
[28] | Kim P, 2017. MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence. New York: Apress. |
[29] | Leung Y, Mei C, Zhang W, 2000. Testing for spatial autocorrelation among the residuals of the geographically weighted regression. Environment and Planning A: Economy and Space, 32(5):871-890. |
[30] | Li X, Wang L, Chen D et al., 2013. Near-surface air temperature lapse rates in the mainland of China during 1962-2011. Journal of Geophysical Research: Atmospheres, 118(14):7505-7515. |
[31] | Li Y, Zeng Z, Zhao L et al., 2015. Spatial patterns of climatological temperature lapse rate in mainland of China: A multi-time scale investigation. Journal of Geophysical Research: Atmospheres, 120(7):2661-2675. |
[32] | Lin C, Chang X, 2018. Spatio-temporal variations of surface temperature lapse rate on Qilian Mountains. Advances in Geosciences, 8(3):691-698. (in Chinese) |
[33] | Lloyd C D, 2006. Local Models for Spatial Analysis. Boca Raton: CRC Press. |
[34] | Lu B, Brunsdon C, Charlton M et al., 2017. Geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science, 31(5):982-998. |
[35] | Marshall S J, Sharp M J, Burgess D O et al., 2007. Near-surface-temperature lapse rates on the Prince of Wales Icefield, Ellesmere Island, Canada: Implications for regional downscaling of temperature. International Journal of Climatology, 27(3):385-398. |
[36] | Minder J R, Mote P W, Lundquist J D, 2010. Surface temperature lapse rates over complex terrain: Lessons from the Cascade Mountains. Journal of Geophysical Research: Atmospheres, 115(D14):D14122. |
[37] | Mitchell A, 2005. The ESRI Guide to GIS Analysis. New York: ESRI Press. |
[38] | Mokhov I I, Akperov M G, 2006. Tropospheric lapse rate and its relation to surface temperature from reanalysis data. Izvestiya, Atmospheric and Oceanic Physics, 42(4):430-438. |
[39] | Moran P A P, 1948. The interpretation of statistical maps. Journal of the Royal Statistical Society: Series B (Methodological), 10(2):243-251. |
[40] | Ojha R, 2017. Assessing seasonal variation of near surface air temperature lapse rate across India. International Journal of Climatology, 37(8):3413-3426. |
[41] | Páez A, Uchida T, Miyamoto K, 2002. A general framework for estimation and inference of geographically weighted regression models: 2. Spatial association and model specification tests. Environment and Planning A: Economy and Space, 34(5):883-904. |
[42] | Pepin N, Benham D, Taylor K, 1999. Modeling lapse rates in the maritime uplands of Northern England: Implications for climate change. Arctic, Antarctic, and Alpine Research, 31(2):151-164. |
[43] |
Petersen L, Pellicciotti F, Juszak I et al., 2013. Suitability of a constant air temperature lapse rate over an Alpine glacier: Testing the Greuell and Böhm model as an alternative. Annals of Glaciology, 54(63):120-130.
doi: 10.3189/2013AoG63A477 |
[44] | Philip G M, Watson D F, 1982. A precise method for determining contoured surfaces. The APPEA Journal, 22(1):205-212. |
[45] | Qin Y, Ren G, Zhai T et al., 2018. A new methodology for estimating the surface temperature lapse rate based on grid data and its application in China. Remote Sensing, 10(10):1617. |
[46] | Qu R, Cui X, Yan H et al., 2013. Impacts of land cover change on the near-surface temperature in the North China Plain. Advances in Meteorology, 2013: 1-12. |
[47] | Rencher A C, Schaalje G B, 2008. Linear Models in Statistics. Hoboken, New Jersey: John Wiley & Sons. |
[48] |
Rolland C, 2003. Spatial and seasonal variations of air temperature lapse rates in Alpine regions. Journal of Climate, 16(7):1032-1046.
doi: 10.1175/1520-0442(2003)016<1032:SASVOA>2.0.CO;2 |
[49] | Shen Y J, Shen Y, Goetz J et al., 2016. Spatial-temporal variation of near-surface temperature lapse rates over the Tianshan Mountains, Central Asia. Journal of Geophysical Research: Atmospheres, 121(23):14006-14017. |
[50] | Sun M, Yao X, Li Z et al., 2015. Hydrological processes of glacier and snow melting and runoff in the Urumqi River source region, eastern Tianshan Mountains, China. Journal of Geographical Sciences, 25(2):149-164. |
[51] | Wang L, Sun L, Shrestha M et al., 2016a. Improving snow process modeling with satellite-based estimation of near-surface-air-temperature lapse rate. Journal of Geophysical Research: Atmospheres, 121(20):12005-12030. |
[52] |
Wang J, Zhang T, Fu B, 2016b. A measure of spatial stratified heterogeneity. Ecological Indicators, 67:250-256.
doi: 10.1016/j.ecolind.2016.02.052 |
[53] | Wang X, 2015. GIS-based study on temperature lapse rate in mountain areas in China [D]. Nanjing: Nanjing University of Information Science and Technology, (in Chinese) |
[54] | Wang Y, Wang L, Li X et al., 2018. Temporal and spatial changes in estimated near-surface air temperature lapse rates on Tibetan Plateau. International Journal of Climatology, 38(7):2907-2921. |
[55] | Watson D F, Philip G M, 1985. A refinement of inverse distance weighted interpolation. Geoprocessing, 2(4):315-327. |
[56] | Wheeler D, Tiefelsdorf M, 2005. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems, 7(2):161-187. |
[57] | Wilby R L, Dawson C W, Barrow E M, 2002. SDSM: A decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 17(2):147-159. |
[58] |
Xie Y, Zhang Y, Lan H et al., 2018. Investigating long-term trends of climate change and their spatial variations caused by regional and local environments through data mining. Journal of Geographical Sciences, 28(6):802-818.
doi: 10.1007/s11442-018-1506-9 |
[59] | Yang X, Tang G, Xiao C et al., 2007. Terrain revised model for air temperature in mountainous area based on DEMs: A case study in Yaoxian county. Journal of Geographical Sciences, 17(4):399-408. |
[60] |
Yang X, Zhang Y, Liu L et al., 2009. Sensitivity of surface air temperature change to land use/cover types in China. Science in China Series D: Earth Sciences, 52(8):1207-1215.
doi: 10.1007/s11430-009-0085-0 |
[61] | Zhai D, Bai H, Qin J et al., 2016. Temporal and spatial variability of air temperature lapse rates in Mt. Taibai, Central Qinling Mountains. Acta Geographica Sinica, 71(9):1587-1595. (in Chinese) |
[62] | Zhang H, Zhang F, Zhang G et al., 2018. How accurately can the air temperature lapse rate over the Tibetan Plateau be estimated from MODIS LSTs? Journal of Geophysical Research: Atmospheres, 123(8):3943-3960. |
[63] | Zhang Z, Ren Z, Zhang Q et al., 2013. Analysis of quality control procedures for hourly air temperature data from automatic weather stations in China. Journal of Meteorology and Environment, 29(4):64-70. (in Chinese) |
[1] | LU Yao, GAO Yang, YANG Tiantian. A review of mass flux monitoring and estimation methods for biogeochemical interface processes in watersheds [J]. Journal of Geographical Sciences, 2020, 30(6): 881-907. |
[2] | DERDOURI Ahmed, MURAYAMA Yuji. A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan [J]. Journal of Geographical Sciences, 2020, 30(5): 794-822. |
[3] | Shaojian WANG, Yongyuan HUANG, Yuquan ZHOU. Spatial spillover effect and driving forces of carbon emission intensity at the city level in China [J]. Journal of Geographical Sciences, 2019, 29(2): 231-252. |
[4] | HU Yunfeng, ZHANG Yunzhi. Using 137Cs and 210Pbex to investigate the soil erosion and accumulation moduli on the southern margin of the Hunshandake Sandy Land in Inner Mongolia [J]. Journal of Geographical Sciences, 2019, 29(10): 1655-1669. |
[5] | Meijiao LI, Fanneng HE, Fan YANG, Shicheng LI. Reconstructing provincial cropland area in eastern China during the early Yuan Dynasty (AD1271-1294) [J]. Journal of Geographical Sciences, 2018, 28(12): 1994-2006. |
[6] | Yan YANG, Limao WANG, Zhi CAO, Chufu MOU, Lei SHEN, Jianan ZHAO, Yebing FANG. CO2 emissions from cement industry in China: A bottom-up estimation from factory to regional and national levels [J]. Journal of Geographical Sciences, 2017, 27(6): 711-730. |
[7] | Qingling SUN, Baolin LI, Chenghu ZHOU, Fei LI, Zhijun ZHANG, Lingling DING, Tao ZHANG, Lili XU. A systematic review of research studies on the estimation of net primary productivity in the Three-River Headwater Region, China [J]. Journal of Geographical Sciences, 2017, 27(2): 161-182. |
[8] | Mette V. ODGAARD, Peder K. BøCHER, Tommy DALGAARD, Jesper E. MOESLUND, Jens-Christian SVENNING. Human-driven topographic effects on the distribution of forest in a flat, lowland agricultural region [J]. , 2014, 24(1): 76-92. |
[9] | ZHUO Ga, LA Ba, PUBU Ciren, LUO Bu. Study on daily surface evapotranspiration with SEBS in Tibet Autonomous Region [J]. , 2014, 24(1): 113-128. |
[10] | YAO Yonghui, ZHANG Baiping. MODIS-based estimation of air temperature of the Tibetan Plateau [J]. , 2013, 23(4): 627-640. |
[11] | Sandeep KUMAR, Rattan LAL, Desheng LIU, Rashid RAFIQ. Estimating the spatial distribution of organic carbon density for the soils of Ohio,USA [J]. Journal of Geographical Sciences, 2013, 23(2): 280-296. |
[12] | YAO Yonghui, ZHANG Baiping. MODIS-based air temperature estimation in the southeastern Tibetan Plateau and neighboring areas [J]. Journal of Geographical Sciences, 2012, 22(1): 152-166 . |
[13] | HE Fanneng, GE Quansheng, ZHENG Jingyun. The urban land area Change in China from 1820 to 1999 [J]. Journal of Geographical Sciences, 2002, 12(4): 427-434. |
|