A framework to identify homogeneous drought characterization regions

  • Zulfiqar Ali
  • Ijaz HussainEmail author
  • Muhammad Faisal
  • Alaa Mohamd Shoukry
  • Showkat Gani
  • Ishfaq Ahmad
Original Paper


Drought monitoring is a complex phenomenon, as several climatic variables are required to accurately monitor and forecast drought. Furthermore, inappropriate existence of gauge stations scattered over the region without any comprehensive drought monitoring framework might end up to misleading conclusions. In this study, we aimed to develop a novel regionalized drought monitoring framework, which requires minimal drought monitoring stations. For this purpose, we considered K-means clustering algorithm based on the transient behavior to identify homogenous drought characterization regions. We applied our proposed framework on 52 meteorological stations across Pakistan in such way that each cluster consists of those meteorological stations that have a similar pattern with respect to the natural behavior of drought severity. We found nine meaningful clusters and the sum of square of the deviations in each cluster is very low. Further, the correlation within these clusters confirms the results of transition-based clustering method. The scatter plots and the Pearson correlation were used to assess the performance of the developed structure of homogenous drought characterization regions. Results show that instead of using individual observatory, any station located within the cluster can be considered for monitoring and forecasting drought of whole region. In summary, minimal and appropriate selection of optimal drought monitoring stations may incorporate to study overall regionalized behavior of drought. However, the choice of weather station depends on the existence of climatic parameters, its reliability, and historical availability of data on environmental variables.



The authors are grateful to the Pakistan Meteorological Department for providing data organized by the Karachi Data Processing Center.


The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no. RG-1437-027.


  1. Ali Z, Hussain I, Faisal M, Nazir HM, Abd-el Moemen M, Hussain T, Shamsuddin S (2017a) A novel multi-scalar drought index for monitoring drought: the standardized precipitation temperature index. Water Resour Manag:1–13Google Scholar
  2. Ali Z, Hussain I, Faisal M, Nazir HM, Hussain T, Shad MY, Mohamd Shoukry A, Hussain Gani S (2017b) Forecasting drought using multilayer perceptron artificial neural network model. Adv Meteorol 2017:1–9CrossRefGoogle Scholar
  3. Atta-Ur Rahman (2016) Disaster risk reduction approaches in Pakistan. Springer Verlag, TokyoGoogle Scholar
  4. Avilés A, Célleri R, Paredes J, Solera A (2015) Evaluation of Markov chain based drought forecasts in an Andean regulated river basin using the skill scores RPS and GMSS. Water Resour Manag 29(6):1949–1963CrossRefGoogle Scholar
  5. Bazrafshan J, Hejabi S, Rahimi J (2014) Drought monitoring using the multivariate standardized precipitation index (MSPI). Water Resour Manag 28(4):1045–1060 methods. Sustainable Water Resources Management, 2(1), 87–101CrossRefGoogle Scholar
  6. Bharath R, Srinivas VV, Basu B (2016) Delineation of homogeneous temperature regions: a two-stage clustering approach. Int J Climatol 36(1):165–187CrossRefGoogle Scholar
  7. Brebbia CA (2011) The sustainable world (vol. 142). WIT PressGoogle Scholar
  8. Chen YD, Zhang Q, Xiao M, Singh VP (2017) Transition probability behaviors of drought events in the Pearl River basin, China. Stoch Env Res Risk A 31(1):159–170CrossRefGoogle Scholar
  9. Conrads PA, Darby LS (2017) Development of a coastal drought index using salinity data. Bull Am Meteorol Soc 98(4):753–766CrossRefGoogle Scholar
  10. Crommelin DT, Vanden-Eijnden E (2006) Fitting time series by continuous-time Markov chains: a quadratic programming approach. J Comput Phys 217(2):782–805CrossRefGoogle Scholar
  11. Desikan P, Srivastava J (2004) Mining temporally evolving graphs. In: Proceedings of the sixth WEBKDD workshop in conjunction with the 10th ACM SIGKDD conference (vol 22)Google Scholar
  12. Dikbas F, Firat M, Koc AC, Gungor M (2012) Classification of precipitation series using fuzzy cluster method. Int J Climatol 32(10):1596–1603CrossRefGoogle Scholar
  13. Dimtriadou E (2009) Cclust: convex clustering methods and clustering indexes. R package version 0.6–16, URL Accessed Sept 2017
  14. Goddard S, Harms SK, Reichenbach SE, Tadesse T, Waltman WJ (2003) Geospatial decision support for drought risk management. Commun ACM 46(1):35–37CrossRefGoogle Scholar
  15. Gui Y, Shao J (2017) Prediction of precipitation based on weighted Markov chain in Dangshan. In: Proceedings of the International Conference on High Performance Compilation, Computing and Communications. ACM, pp 81–85Google Scholar
  16. Güneralp B, Güneralp İ, Liu Y (2015) Changing global patterns of urban exposure to flood and drought hazards. Glob Environ Chang 31:217–225CrossRefGoogle Scholar
  17. Hanif U, Syed SH, Ahmad R, Malik KA, Nasir M (2010) Economic impact of climate change on the agricultural sector of Punjab [with comments]. Pak Dev Rev 49:771–798CrossRefGoogle Scholar
  18. Hosking, J. R. M., & Hosking, M. J. (2017). Package ‘lmom’. Retrieved from
  19. Huang JZ, Ng M, Ching WK, Ng J, Cheung D (2001) A cube model and cluster analysis for web access sessions. In: International workshop on mining web log data across all customers touch points. Springer, Berlin, pp 48–67Google Scholar
  20. Hussain I, Pilz J, Spoeck G (2011) Homogeneous climate regions in Pakistan. Int J Global Warm 3(1–2):55–66CrossRefGoogle Scholar
  21. Kim TW, Valdés JB (2003) Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. J Hydrol Eng 8(6):319–328CrossRefGoogle Scholar
  22. Le MH, Perez GC, Solomatine D, Nguyen LB (2016) Meteorological drought forecasting based on climate signals using artificial neural network—a case study in Khanhhoa Province Vietnam. Procedia Eng 154:1169–1175CrossRefGoogle Scholar
  23. Lee HY, Chen SL (2006) Why use Markov-switching models in exchange rate prediction? Econ Model 23(4):662–668CrossRefGoogle Scholar
  24. Ma M, Ren L, Yuan F, Jiang S, Liu Y, Kong H, Gong L (2014) A new standardized Palmer drought index for hydro-meteorological use. Hydrol Process 28(23):5645–5661CrossRefGoogle Scholar
  25. McKee TB, Doesken NJ, Kleist J (1993). The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology, vol 17, no. 22. American Meteorological Society, Boston, pp 179–183Google Scholar
  26. Mishra AK, Desai VR (2005) Drought forecasting using stochastic models. Stoch Env Res Risk A 19(5):326–339CrossRefGoogle Scholar
  27. Mishra AK, Singh VP, Desai VR (2009) Drought characterization: a probabilistic approach. Stoch Env Res Risk A 23(1):41–55CrossRefGoogle Scholar
  28. Nicholson SE, Dezfuli AK (2013) The relationship of rainfall variability in western equatorial Africa to the tropical oceans and atmospheric circulation. Part I: the boreal spring. J Clim 26(1):45–65CrossRefGoogle Scholar
  29. Pallis G, Angelis L, Vakali A (2007) Validation and interpretation of web users’ sessions clusters. Inf Process Manag 43(5):1348–1367CrossRefGoogle Scholar
  30. Palmer WC (1965) Meteorological drought (Vol. 30). US Department of Commerce, Weather Bureau, Washington, DCGoogle Scholar
  31. Paulo AA, Pereira LS (2007) Prediction of SPI drought class transitions using Markov chains. Water Resour Manag 21(10):1813–1827CrossRefGoogle Scholar
  32. R. Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, p 2014Google Scholar
  33. Rahmat SN, Jayasuriya N, Bhuiyan MA (2017) Short-term droughts forecast using Markov chain model in Victoria, Australia. Theor Appl Climatol 129(1–2):445–457CrossRefGoogle Scholar
  34. Rajsekhar D, Singh VP, Mishra AK (2015) Multivariate drought index: an information theory based approach for integrated drought assessment. J Hydrol 526:164–182CrossRefGoogle Scholar
  35. Sánchez N, González-Zamora Á, Piles M, Martínez-Fernández J (2016) A new soil moisture agricultural drought index (SMADI) integrating MODIS and SMOS products: a case of study over the iberian peninsula. Remote Sens 8(4):287CrossRefGoogle Scholar
  36. Sanusi W, Jemain AA, Zin WZW, Zahari M (2015) The drought characteristics using the first-order homogeneous Markov chain of monthly rainfall data in peninsular Malaysia. Water Resour Manag 29(5):1523–1539CrossRefGoogle Scholar
  37. Schittkowski K (2002) EASY-FIT: a software system for data fitting in dynamical systems. Struct Multidiscip Optim 23(2):153–169CrossRefGoogle Scholar
  38. Scholz M (2016) R package clickstream: analyzing clickstream data with Markov chains. J Stat Softw 74(4)Google Scholar
  39. Shafer BA, Dezman LE (1982) Development of a surface water supply index (SWSI) to assess the severity of drought conditions in snowpack runoff areas. In: Proceedings of the western snow conference, vol 50. Colorado State University Fort Collins, pp 164–175Google Scholar
  40. Shatanawi K, Rahbeh M, Shatanawi M (2013) Characterizing, monitoring and forecasting of drought in Jordan River basin. J Water Resour Prot 5(12):1192–1202CrossRefGoogle Scholar
  41. Svoboda M, Fuchs B (2016) Handbook of drought indicators and indices. lincoln, national drought mitigation center. Google Scholar,+M.&author=Fuchs,+B.&publication_year=2016
  42. Takahashi K, Morikawa K, Takeda D, Mizuno A (2007) Inventory control for a Markovian remanufacturing system with stochastic decomposition process. Int J Prod Econ 108(1):416–425CrossRefGoogle Scholar
  43. Tigkas D, Vangelis H, Tsakiris G (2017) An enhanced effective reconnaissance drought index for the characterisation of agricultural drought. Environmental Processes 4(1):137–148Google Scholar
  44. Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23(7):1696–1718CrossRefGoogle Scholar
  45. Wilhite DA, Buchanan-Smith M (2005) Drought as hazard: understanding the natural and social context. In: Wilhite DA (ed) Drought and water crises: science, technology, and management issues. CRC Press, Boca Raton, pp 3–29Google Scholar
  46. Wing EAS (2010) Finance division. Government of Pakistan, “Pakistan Economic Survey”, Varios números, with Pakistan. (2007). Pakistan Economic survey. Islamabad: Economic Advisor's Wing, Ministry of Finance. Available:
  47. Yang J, Wang Y, Chang J, Yao J, Huang Q (2016) Integrated assessment for hydrometeorological drought based on Markov chain model. Nat Hazards 84(2):1137–1160CrossRefGoogle Scholar
  48. Zhang Y, Moges S, Block P (2016) Optimal cluster analysis for objective regionalization of seasonal precipitation in regions of high spatial-temporal variability: application to Western Ethiopia. J Clim 29(10):3697–3717CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of StatisticsQuaid-i-Azam UniversityIslamabadPakistan
  2. 2.Faculty of Health StudiesUniversity of BradfordBradfordUK
  3. 3.Bradford Institute for Health ResearchBradford Teaching Hospitals NHS Foundation TrustBradfordUK
  4. 4.Arriyadh Community CollegeKing Saud UniversityRiyadhSaudi Arabia
  5. 5.KSA workers UniversityCairoEgypt
  6. 6.College of Business AdministrationKing Saud University MuzahimiyahRiyadhSaudi Arabia
  7. 7.Department of Mathematics and Statistics, Faculty of Basic SciencesInternational Islamic UniversityIslamabadPakistan
  8. 8.Department of Mathematics, College of ScienceKing Khalid UniversityAbhaKingdom of Saudi Arabia

Personalised recommendations