Skip to main content
Log in

Quality control and homogenization of daily meteorological data in the trans-boundary region of the Jhelum River basin

  • Published:
Journal of Geographical Sciences Aims and scope Submit manuscript

Abstract

Many studies such as climate variability, climate change, trend analysis, hydrological designs, agriculture decision-making etc. require long-term homogeneous datasets. Since homogeneous climate data is not available for climate analysis in Pakistan and India, the present study emphases on an extensive quality control and homogenization of daily maximum temperature, minimum temperature and precipitation data in the Jhelum River basin, Pakistan and India. A combination of different quality control methods and relative homogeneity tests were applied to achieve the objective of the study. To check the improvement after homogenization, correlation coefficients between the test and reference series calculated before and after the homogenization process were compared with each other. It was found that about 0.59%, 0.78% and 0.023% of the total data values are detected as outliers in maximum temperature, minimum temperature and precipitation data, respectively. About 32% of maximum temperature, 50% of minimum temperature and 7% of precipitation time series were inhomogeneous, in the Jhelum River basin. After the quality control and homogenization, 1% to 11% improvement was observed in the infected climate variables. This study concludes that precipitation daily time series are fairly homogeneous, except two stations (Naran and Gulmarg), and of a good quality. However, maximum and minimum temperature datasets require an extensive quality control and homogeneity check before using them into climate analysis in the Jhelum River basin.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Aguilar E, Auer I, Brunet M et al., 2003. Guidelines on Climate Metadata and Homogenization WMO/TD No.1186. W. M. Organization: Geneva, 52 pp.

    Google Scholar 

  • Alexandersson H, 1986. A homogeneity test applied to precipitation data. Journal of Climatology, 6: 661–675. doi: 10.1002/joc.3370060607.

    Article  Google Scholar 

  • Archer D R, Fowler H J, 2008. Using meteorological data to forecast seasonal runoff on the River Jhelum, Pakistan. Journal of Hydrology, 361: 10–23. doi: http://dx.doi.org/10.1016/j.jhydrol.2008.07.017.

    Article  Google Scholar 

  • Atta Ur R, Shaw R, 2015. Disaster and climate change education in Pakistan. In: Rahman A U, Khan A N, Shaw R (eds.) Disaster Risk Reduction Approaches in Pakistan. Japan: Springer, 315–335.

    Google Scholar 

  • Buishand T A, 1982. Some methods for testing the homogeneity of rainfall records. Journal of Hydrology, 58: 11–27. doi: http://dx.doi.org/10.1016/0022-1694(82)90066-X.

    Article  Google Scholar 

  • Cao L J, Yan Z W, 2012. Progress in research on homogenization of climate data. Adv. Clim. Change Res., 3: 59–67. doi: 10.3724/SP.J.1248.2012.00059.

    Article  Google Scholar 

  • Costa A, Soares A, 2009. Homogenization of climate data: Review and new perspectives using geostatistics. Math. Geosci., 41: 291–305. doi: 10.1007/s11004-008-9203-3.

    Article  Google Scholar 

  • Easterling D R, Peterson T C, 1995. A new method for detecting undocumented discontinuities in climatological time series. International Journal of Climatology, 15: 369–377. doi: 10.1002/joc.3370150403.

    Article  Google Scholar 

  • Feng S, Hu Q, Qian W, 2004. Quality control of daily meteorological data in China, 1951–2000: A new dataset. International Journal of Climatology, 24: 853–870. doi: 10.1002/joc.1047.

    Article  Google Scholar 

  • González-Rouco J F, Jiménez J L, Quesada V et al., 2001. Quality control and homogeneity of precipitation data in the southwest of Europe. Journal of Climate, 14: 964–978. doi: 10.1175/1520-0442(2001)014<0964:QCAHOP>2.0.CO; 2.

    Article  Google Scholar 

  • Heitjan D, Little R, 1991. Multiple imputation for the fatal accident reporting system. Journal of the Royal Statistical Society. Series C (Applied Statistics), 40: 13–29. doi: 10.2307/2347902.

    Google Scholar 

  • Horton N J, Lipsitz S R, 2001. Multiple imputation in practice: Comparison of software packages for regression models with missing variables. The American Statistician, 55: 244–254. doi: 10.2307/2685809.

    Article  Google Scholar 

  • Kendall M G, 1975. Rank Correlation Methods (Charles Griffin). London: Oxford University Press.

    Google Scholar 

  • Kruskal W H, 1952. A nonparametric test for the several sample problem. The Annals of Mathematical Statistics, 23: 525–540. doi: 10.2307/2236578.

    Article  Google Scholar 

  • Kruskal W H, Wallis W A, 1952. Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47: 583–621. doi: 10.2307/2280779.

    Article  Google Scholar 

  • Lo Presti R, Barca E, Passarella G, 2010. A methodology for treating missing data applied to daily rainfall data in the Candelaro River Basin (Italy). Environ. Monit. Assess., 160: 1–22. doi: 10.1007/s10661-008-0653-3.

    Article  Google Scholar 

  • Mahmood R, Babel M S, 2013. Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India. Theor. Appl. Climatol., 113: 27–44. doi: 10.1007/s00704-012-0765-0.

    Article  Google Scholar 

  • Mann H B, 1945. Nonparametric tests against trend. Econometrica, 13: 245–259. doi: 10.2307/1907187.

    Article  Google Scholar 

  • Maronna R, Yohai V J, 1978. A bivariate test for the detection of a systematic change in mean. Journal of the American Statistical Association, 73: 640–645. doi: 10.2307/2286616.

    Article  Google Scholar 

  • Peterson T C, Easterling D R, Karl T R et al., 1998. Homogeneity adjustments of in situ atmospheric climate data: A review. International Journal of Climatology, 18: 1493–1517. doi: 10.1002/(SICI)1097-0088(19981115) 18:13<1493::AID-JOC329>3.0.CO;2-T.

    Article  Google Scholar 

  • Pettitt A N, 1979. A non-parametric approach to the change-point problem. Applied Statistics, 28: 126–135. doi: 10.2307/2346729.

    Article  Google Scholar 

  • PMD, cited 2015: Extreme Events Reports. [Available online at http://www.pmd.gov.pk/journal/extreme-eventslist. htm.]

  • Potter K W, 1981. Illustration of a new test for detecting a shift in mean in precipitation series. Monthly Weather Review, 109: 2040–2045. doi: 10.1175/1520-0493(1981)109<2040:IOANTF>2.0.CO;2.

    Article  Google Scholar 

  • Seo S, 2006. A review and comparison of methods for detecting outliers in univariate data sets University of Pittsburgh 59 pp.

    Google Scholar 

  • Štepánek P, cited 2015: ProClimDB–Software for Processing Climatological Datasets. Available online at http://www.climahom.eu/ProcData.html.

  • Štěpánek P, Zahradníček P, Skalák P, 2009. Data quality control and homogenization of air temperature and precipitation series in the area of the Czech Republic in the period 1961–2007. Advances in Science and Research, 3: 23–26. doi: 10.5194/asr-3-23-2009.

    Article  Google Scholar 

  • Štěpánek P, Zahradníček P, Farda A, 2013. Experiences with data quality control and homogenization of daily records of various meteorological elements in the Czech Republic in the period 1961–2010. Quarterly Journal of the Hungarian Meteorological Service, 117: 1–158.

    Google Scholar 

  • Szentimrey T, 1999: Multiple analysis of series for homogenization (MASH). Proceedings of the Second Seminar for Homogenization of Surface Climatological Data, Budapest, Hungary, 27–46.

    Google Scholar 

  • Trewin B, 2013. A daily homogenized temperature data set for Australia. International Journal of Climatology, 33: 1510–1529. doi: 10.1002/joc.3530.

    Article  Google Scholar 

  • Tukey J W, 1977. Exploratory Data Analysis. Pearson.

    Google Scholar 

  • Vicente-Serrano S M, Beguería S, López-Moreno J I et al., 2010. A complete daily precipitation database for northeast Spain: Reconstruction, quality control, and homogeneity. International Journal of Climatology, 30: 1146–1163. doi:10.1002/joc.1850.

    Article  Google Scholar 

  • Vincent L A, 1998. A technique for the identification of inhomogeneities in Canadian temperature series. Journal of Climate, 11: 1094–1104. doi: 10.1175/1520-0442(1998)011<1094:ATFTIO>2.0.CO;2.

    Article  Google Scholar 

  • Zahradníček P, Rasol D, Cindric K et al., 2014. Homogenization of monthly precipitation time series in Croatia. International Journal of Climatology, 34: 3671–3682. doi: 10.1002/joc.3934.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rashid Mahmood.

Additional information

Foundation: National Natural Sciences Foundation of China, No.41471463; President’s International Fellowship Initiative CAS

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahmood, R., Jia, S. Quality control and homogenization of daily meteorological data in the trans-boundary region of the Jhelum River basin. J. Geogr. Sci. 26, 1661–1674 (2016). https://doi.org/10.1007/s11442-016-1351-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11442-016-1351-7

Keywords

Navigation