Abstract
The Qinghai–Tibet Plateau has complex geomorphic features, which makes it difficult to interpolate surface air temperature precisely because of sparse samples and intensive spatial variations. Different interpolation methods have been developed, and they perform differently under various situations. The statistical errors of interpolation methods are determined by the population properties, the condition of the samples, and the adequacy of covariates. However, few studies have focused on optimal interpolation strategies for Qinghai–Tibet Plateau. In this study, seven typical interpolation models were used and compared. The model-based methods (e.g., ordinary kriging), design-based methods (inverse distance weight (IDW), thin plate splines (TPS), and combined methods (e.g., spatiotemporal regression kriging) were considered. Using auxiliary information, spatiotemporal ordinary kriging, and spatiotemporal stratified kriging models were built. Methods were evaluated by cross validation with mean absolute error (MAE) and root-mean-square error (RMSE). Results showed that for both of the index (RMSE, MAE), spatiotemporal kriging stratified by seasons (1.016 °C RMSE, 0.767 °C MAE) < spatiotemporal kriging stratified by climate regions (1.018 °C, 0.767 °C) < spatiotemporal ordinary kriging (1.022 °C, 0.774 °C) < spatiotemporal regression kriging (1.058 °C, 0.806 °C) < TPS (1.551 °C, 1.143 °C) < ordinary kriging (2.674 °C, 2.044 °C) < IDW (2.917 °C, 2.296 °C). In conclusion, under the condition of sparsely distributed stations and complex geomorphic features in the study area, taking advantages of time dimensional information, spatiotemporal heterogeneity and covariates (i.e., elevation) can improve interpolation precision effectively.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Daily surface air temperature dataset was downloaded from the China Meteorological Data Service Center (http://data.cma.cn/). DEM data from DEMSRE3 was obtained from WorldGrids.org portal (http://worldgrids.org/doku.php/wiki:demsre3).
References
Alsafadi K, Mohammed S, Mokhtar A, Sharaf M, He H (2021) Fine-resolution precipitation mapping over Syria using local regression and spatial interpolation. Atmos Res 256:105524. https://doi.org/10.1016/j.atmosres.2021.105524
Berndt C, Haberlandt U (2018) Spatial interpolation of climate variables in Northern Germany-Influence of temporal resolution and network density. J Hydrol-Reg Stud 15:184–202. https://doi.org/10.1016/j.ejrh.2018.02.002
Bogaert P (1996) Comparison of kriging techniques in a space-time context. Math Geol 28:73–86. https://doi.org/10.1007/bf02273524
Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Oxford University Press, Oxford
Cai DL, You QL, Fraedrich K, Guan YN (2017) Spatiotemporal temperature variability over the Tibetan Plateau: altitudinal dependence associated with the global warming hiatus. J Clim 30:969–984. https://doi.org/10.1175/jcli-d-16-0343.1
Che ML, Chen BZ, Innes JL, Wang GY, Dou XM, Zhou TM, Zhang HF, Yan JW, Xu G, Zhao HW (2014) Spatial and temporal variations in the end date of the vegetation growing season throughout the Qinghai–Tibetan Plateau from 1982 to 2011. Agric for Meteorol 189:81–90. https://doi.org/10.1016/j.agrformet.2014.01.004
Daya AA, Bejari H (2015) A comparative study between simple kriging and ordinary kriging for estimating and modeling the Cu concentration in Chehlkureh deposit, SE Iran. Arab J Geosci 8:6003–6020. https://doi.org/10.1007/s12517-014-1618-1
Fick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302–4315. https://doi.org/10.1002/joc.5086
Goovaerts P (1998) Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Biol Fertil Soils 27:315–334. https://doi.org/10.1007/s003740050439
Graler B, Pebesma E, Heuvelink G (2016) Spatio-temporal Interpolation using gstat. R J 8:204–218. https://doi.org/10.32614/RJ-2016-014
Guo B, Yang F, Wu HW, Zhang R, Zang WQ, Wei CX, Jiang GM, Meng C, Zhao HH, Zhen XY, Zhang DF, Zhang HL (2021) How the variations of terrain factors affect the optimal interpolation methods for multiple types of climatic elements? Earth Sci Inform 14:1021–1032. https://doi.org/10.1007/s12145-021-00609-2
Hancock PA, Hutchinson MF (2006) Spatial interpolation of large climate data sets using bivariate thin plate smoothing splines. Environ Modell Softw 21:1684–1694. https://doi.org/10.1016/j.envsoft.2005.08.005
Hengl T, Heuvelink GBM, Rossiter DG (2007) About regression-kriging: from equations to case studies. Comput Geosci 33:1301–1315. https://doi.org/10.1016/j.cageo.2007.05.001
Hengl T, Heuvelink GBM, Tadic MP, Pebesma EJ (2012) Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Theor Appl Clim 107:265–277. https://doi.org/10.1007/s00704-011-0464-2
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. https://doi.org/10.1002/joc.1276
Hofstra N, Haylock M, New M, Jones P, Frei C (2008) Comparison of six methods for the interpolation of daily, European climate data. J Geophys Res Atmos. https://doi.org/10.1029/2008jd010100
Hubbard KG, You JS (2005) Sensitivity analysis of quality assurance using the spatial regression approach—a case study of the maximum/minimum air temperature. J Atmos Ocean Technol 22:1520–1530. https://doi.org/10.1175/jtech1790.1
Hudson G, Wackernagel H (1994) mapping temperature using kriging with external drift—theory and an example from Scotland. Int J Climatol 14:77–91. https://doi.org/10.1002/joc.3370140107
Hutchinson MF (1995) Interpolating mean rainfall using thin plate smoothing splines. Int J GIS 9:385–403. https://doi.org/10.1080/02693799508902045
Isaaks EH, Srivastava RM (1989) Applied geostatistics. Oxford University Press, New York
Jeffrey SJ, Carter JO, Moodie KB, Beswick AR (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ Model Softw 16:309–330. https://doi.org/10.1016/s1364-8152(01)00008-1
Kang SC, Xu YW, You QL, Flugel WA, Pepin N, Yao TD (2010) Review of climate and cryospheric change in the Tibetan Plateau. Environ Res Lett 5:015101. https://doi.org/10.1088/1748-9326/5/1/015101
Kilibarda M, Hengl T, Heuvelink GBM, Graler B, Pebesma E, Tadic MP, Bajat B (2014) Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. J Geophys Res-Atmos 119:2294–2313. https://doi.org/10.1002/2013jd020803
Kurtzman D, Kadmon R (1999) Mapping of temperature variables in Israel: a comparison of different interpolation methods. Clim Res 13:33–43. https://doi.org/10.3354/cr013033
Li J, Heap AD (2011) A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecol Inform 6:228–241. https://doi.org/10.1016/j.ecoinf.2010.12.003
Li X, Cheng GD, Lu L (2005) Spatial analysis of air temperature in the Qinghai–Tibet Plateau. Arct Antarct Alp Res 37:246–252. https://doi.org/10.1657/1523-0430(2005)037[0246:saoati]2.0.co;2
Li T, Zheng XG, Dai YJ, Yang C, Chen ZQ, Zhang SP, Wu GC, Wang ZL, Huang CC, Shen Y, Liao RW (2014) Mapping near-surface air temperature, pressure, relative humidity and wind speed over mainland China with high spatiotemporal resolution. Adv Atmos Sci 31:1127–1135. https://doi.org/10.1007/s00376-014-3190-8
Liu TJ, Wang JF, Xu C, Ma JQ, Zhang HY, Xu CD (2018) Sandwich mapping of rodent density in Jilin Province, China. J Geogr Sci 28:445–458. https://doi.org/10.1007/s11442-018-1483-z
Matheron G (1963) Principles of geostatistics. Econ Geol 58:1246–1266. https://doi.org/10.2113/gsecongeo.58.8.1246
Minasny B, McBratney AB (2007) Spatial prediction of soil properties using EBLUP with the Matern covariance function. Geoderma 140:324–336. https://doi.org/10.1016/j.geoderma.2007.04.028
Pingale SM, Khare D, Jat MK, Adamowski J (2014) Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centers of the arid and semi-arid state of Rajasthan, India. Atmos Res 138:73–90. https://doi.org/10.1016/j.atmosres.2013.10.024
Qiu J (2008) The third pole. Nature 454:393–396. https://doi.org/10.1038/454393a
Rosenfeld A, Dorman M, Schwartz J, Novack V, Just AC, Kloog I (2017) Estimating daily minimum, maximum, and mean near surface air temperature using hybrid satellite models across Israel. Environ Res 159:297–312. https://doi.org/10.1016/j.envres.2017.08.017
Sekulic A, Kilibarda M, Protic D, Tadic MP, Bajat B (2020) Spatio-temporal regression kriging model of mean daily temperature for Croatia. Theor Appl Clim 140:101–114. https://doi.org/10.1007/s00704-019-03077-3
Tang PC, Xu B, Tian DL, Ren J, Li ZK (2021) Temporal and spatial variations of meteorological elements and reference crop evapotranspiration in Alpine regions of Tibet, China. Environ Sci Pollut Res 28:36076–36091. https://doi.org/10.1007/s11356-021-12771-7
Vicente-Serrano SM, Saz-Sanchez MA, Cuadrat JM (2003) Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature. Clim Res 24:161–180. https://doi.org/10.3354/cr024161
Wang JF, Stein A, Gao BB, Ge Y (2012) A review of spatial sampling. Spat Stat 2:1–14. https://doi.org/10.1016/j.spasta.2012.08.001
Wang JF, Zhang TL, Fu BJ (2016) A measure of spatial stratified heterogeneity. Ecol Indic 67:250–256. https://doi.org/10.1016/j.ecolind.2016.02.052
Wang MM, He GJ, Zhang ZM, Wang GZ, Zhang ZJ, Cao XJ, Wu ZJ, Liu XG (2017) Comparison of spatial interpolation and regression analysis models for an estimation of monthly near surface air temperature in China. Remote Sens 9:16. https://doi.org/10.3390/rs9121278
Way RG, Lewkowicz AG, Bonnaventure PP (2017) Development of moderate-resolution gridded monthly air temperature and degree-day maps for the Labrador-Ungava region of northern Canada. Int J Climatol 37:493–508. https://doi.org/10.1002/joc.4721
Wu SH, Zheng D, Yin YH, Lin ED, Xu YL (2010) Northward-shift of temperature zones in China’s eco-geographical study under future climate scenario. J Geogr Sci 20:643–651. https://doi.org/10.1007/s11442-010-0801-x
Wu W, Xu AD, Liu HB (2015) High-resolution spatial databases of monthly climate variables (1961–2010) over a complex terrain region in southwestern China. Theor Appl Clim 119:353–362. https://doi.org/10.1007/s00704-014-1123-1
Xu H, Bechle MJ, Wang M, Szpiro AA, Vedal S, Bai YQ, Marshall JD (2019) National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging. Sci Total Environ 655:423–433. https://doi.org/10.1016/j.scitotenv.2018.11.125
Yang J, Hu MG (2018) Filling the missing data gaps of daily MODIS AOD using spatiotemporal interpolation. Sci Total Environ 633:677–683. https://doi.org/10.1016/j.scitotenv.2018.03.202
Yao TD, Xue YK, Chen DL, Chen FH, Thompson L, Cui P, Koike T, Lau WKM, Lettenmaier D, Mosbrugger V, Zhang RH, Xu BQ, Dozier J, Gillespie T, Gu Y, Kang SC, Piao SL, Sugimoto S, Ueno K, Wang L, Wang WC, Zhang F, Sheng YW, Guo WD, Ailikun YXX, Ma YM, Shen SSP, Su ZB, Chen F, Liang SL, Liu YM, Singh VP, Yang K, Yang DQ, Zhao XQ, Qian Y, Zhang Y, Li Q (2019) Recent third pole’s rapid warming accompanies cryospheric melt and water cycle intensification and interactions between monsoon and environment: multidisciplinary approach with observations, modeling, and analysis. Bull Am Meterol Soc 100:423–444. https://doi.org/10.1175/bams-d-17-0057.1
Yu WJ, Nan ZT, Wang ZW, Chen H, Wu TH, Zhao L (2015) An effective interpolation method for MODIS Land surface temperature on the Qinghai–Tibet Plateau. IEEE J STARS 8:4539–4550. https://doi.org/10.1109/jstars.2015.2464094
Zheng J, Bian J, Ge Q, Hao Z, Yin Y, Liao Y (2013) The climate regionalization in China for 1981–2010. Chin Sci Bull 58:3088–3099. https://doi.org/10.1360/972012-1491
Zhong L, Su ZB, Ma YM, Salama MS, Sobrino JA (2011) Accelerated changes of environmental conditions on the Tibetan Plateau caused by climate change. J Clim 24:6540–6550. https://doi.org/10.1175/jcli-d-10-05000.1
Zhu ML, Thompson LG, Zhao HB, Yao TD, Yang W, Jin SQ (2021) Influence of atmospheric circulation on glacier mass balance in Western Tibet: an analysis based on observations and modeling. J Clim 34:6743–6757. https://doi.org/10.1175/jcli-d-20-0988.1
Zimmerman D, Pavlik C, Ruggles A, Armstrong MP (1999) An experimental comparison of ordinary and universal kriging and inverse distance weighting. Math Geol 31:375–390. https://doi.org/10.1023/a:1007586507433
Acknowledgements
We thank contributions from all the reviewers in improving this paper. This study was supported by the National Science Foundation of China (nos. 41971357, 42130713)
Funding
This work was supported by the National Science Foundation of China (nos. 41971357, 42130713).
Author information
Authors and Affiliations
Contributions
FS and CX wrote the main manuscript text. FS completed figures and tables with the instructions from CX and MH. CX provided the meteorological observation data. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Conflict of interests
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shen, F., Xu, C. & Hu, M. Comparison of approaches to spatiotemporally interpolate land surface air temperature for the Qinghai–Tibet Plateau. Environ Earth Sci 82, 452 (2023). https://doi.org/10.1007/s12665-023-11151-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-023-11151-3