Skip to main content

Advertisement

SpringerLink
Log in

A framework to identify homogeneous drought characterization regions

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

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.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • 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:1–13

  • 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 2017:1–9

    Article  Google Scholar 

  • Atta-Ur Rahman (2016) Disaster risk reduction approaches in Pakistan. Springer Verlag, Tokyo

    Google Scholar 

  • 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):1949–1963

    Article  Google Scholar 

  • 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), 87–101

    Article  Google Scholar 

  • Bharath R, Srinivas VV, Basu B (2016) Delineation of homogeneous temperature regions: a two-stage clustering approach. Int J Climatol 36(1):165–187

    Article  Google Scholar 

  • Brebbia CA (2011) The sustainable world (vol. 142). WIT Press

  • 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):159–170

    Article  Google Scholar 

  • Conrads PA, Darby LS (2017) Development of a coastal drought index using salinity data. Bull Am Meteorol Soc 98(4):753–766

    Article  Google Scholar 

  • Crommelin DT, Vanden-Eijnden E (2006) Fitting time series by continuous-time Markov chains: a quadratic programming approach. J Comput Phys 217(2):782–805

    Article  Google Scholar 

  • 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)

  • Dikbas F, Firat M, Koc AC, Gungor M (2012) Classification of precipitation series using fuzzy cluster method. Int J Climatol 32(10):1596–1603

    Article  Google Scholar 

  • Dimtriadou E (2009) Cclust: convex clustering methods and clustering indexes. R package version 0.6–16, URL https://CRAN.R-project.org/package=cclust. Accessed Sept 2017

  • Goddard S, Harms SK, Reichenbach SE, Tadesse T, Waltman WJ (2003) Geospatial decision support for drought risk management. Commun ACM 46(1):35–37

    Article  Google Scholar 

  • 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 81–85

  • Güneralp B, Güneralp İ, Liu Y (2015) Changing global patterns of urban exposure to flood and drought hazards. Glob Environ Chang 31:217–225

    Article  Google Scholar 

  • 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 49:771–798

    Article  Google Scholar 

  • Hosking, J. R. M., & Hosking, M. J. (2017). Package ‘lmom’. Retrieved from https://www.r-project.org

  • 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 48–67

  • Hussain I, Pilz J, Spoeck G (2011) Homogeneous climate regions in Pakistan. Int J Global Warm 3(1–2):55–66

    Article  Google Scholar 

  • 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):319–328

    Article  Google Scholar 

  • 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 154:1169–1175

    Article  Google Scholar 

  • Lee HY, Chen SL (2006) Why use Markov-switching models in exchange rate prediction? Econ Model 23(4):662–668

    Article  Google Scholar 

  • 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):5645–5661

    Article  Google Scholar 

  • 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 179–183

  • Mishra AK, Desai VR (2005) Drought forecasting using stochastic models. Stoch Env Res Risk A 19(5):326–339

    Article  Google Scholar 

  • Mishra AK, Singh VP, Desai VR (2009) Drought characterization: a probabilistic approach. Stoch Env Res Risk A 23(1):41–55

    Article  Google Scholar 

  • 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):45–65

    Article  Google Scholar 

  • Pallis G, Angelis L, Vakali A (2007) Validation and interpretation of web users’ sessions clusters. Inf Process Manag 43(5):1348–1367

    Article  Google Scholar 

  • Palmer WC (1965) Meteorological drought (Vol. 30). US Department of Commerce, Weather Bureau, Washington, DC

    Google Scholar 

  • Paulo AA, Pereira LS (2007) Prediction of SPI drought class transitions using Markov chains. Water Resour Manag 21(10):1813–1827

    Article  Google Scholar 

  • R. Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, p 2014

    Google Scholar 

  • Rahmat SN, Jayasuriya N, Bhuiyan MA (2017) Short-term droughts forecast using Markov chain model in Victoria, Australia. Theor Appl Climatol 129(1–2):445–457

    Article  Google Scholar 

  • Rajsekhar D, Singh VP, Mishra AK (2015) Multivariate drought index: an information theory based approach for integrated drought assessment. J Hydrol 526:164–182

    Article  Google Scholar 

  • 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):287

    Article  Google Scholar 

  • 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):1523–1539

    Article  Google Scholar 

  • Schittkowski K (2002) EASY-FIT: a software system for data fitting in dynamical systems. Struct Multidiscip Optim 23(2):153–169

    Article  Google Scholar 

  • Scholz M (2016) R package clickstream: analyzing clickstream data with Markov chains. J Stat Softw 74(4)

  • 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 164–175

  • Shatanawi K, Rahbeh M, Shatanawi M (2013) Characterizing, monitoring and forecasting of drought in Jordan River basin. J Water Resour Prot 5(12):1192–1202

    Article  Google Scholar 

  • Svoboda M, Fuchs B (2016) Handbook of drought indicators and indices. lincoln, national drought mitigation center. Google Scholar https://scholar.google.com/scholar_lookup?title=Handbook+of+Drought+Indicators+and+Indices&author=Svoboda,+M.&author=Fuchs,+B.&publication_year=2016

  • 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):416–425

    Article  Google Scholar 

  • Tigkas D, Vangelis H, Tsakiris G (2017) An enhanced effective reconnaissance drought index for the characterisation of agricultural drought. Environmental Processes 4(1):137–148

  • 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):1696–1718

    Article  Google Scholar 

  • 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 3–29

  • 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: http://www.finance.gov.pk/survey_0708.html

  • 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):1137–1160

    Article  Google Scholar 

  • 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):3697–3717

    Article  Google Scholar 

Download references

Acknowledgements

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

Funding

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.

Author information

Authors and Affiliations

  1. Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan

    Zulfiqar Ali & Ijaz Hussain

  2. Faculty of Health Studies, University of Bradford, Bradford, BD7 1DP, UK

    Muhammad Faisal

  3. Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK

    Muhammad Faisal

  4. Arriyadh Community College, King Saud University, Riyadh, Saudi Arabia

    Alaa Mohamd Shoukry

  5. KSA workers University, Cairo, Egypt

    Alaa Mohamd Shoukry

  6. College of Business Administration, King Saud University Muzahimiyah, Riyadh, Saudi Arabia

    Showkat Gani

  7. Department of Mathematics and Statistics, Faculty of Basic Sciences, International Islamic University, Islamabad, 44000, Pakistan

    Ishfaq Ahmad

  8. Department of Mathematics, College of Science, King Khalid University, Abha, 61413, Kingdom of Saudi Arabia

    Ishfaq Ahmad

Authors
  1. Zulfiqar Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ijaz Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Faisal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alaa Mohamd Shoukry
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Showkat Gani
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ishfaq Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ijaz Hussain.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ali, Z., Hussain, I., Faisal, M. et al. A framework to identify homogeneous drought characterization regions. Theor Appl Climatol 137, 3161–3172 (2019). https://doi.org/10.1007/s00704-019-02797-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-019-02797-w

Search

Navigation