Selection of appropriate time scale with Boruta algorithm for regional drought monitoring using multi-scaler drought index

Zulfiqar Ali; Ijaz Hussain; Muhammad Faisal; Elsayed Elsherbini Elashkar; Showkat Gani; Muhammad Ahmed Shehzad

doi:10.1080/16000870.2019.1604057

Selection of appropriate time scale with Boruta algorithm for regional drought monitoring using multi-scaler drought index

Tellus

Volume 71 (2019): Issue 1

By: Zulfiqar Ali, Ijaz Hussain, Muhammad Faisal, Elsayed Elsherbini Elashkar, Showkat Gani and Muhammad Ahmed Shehzad

Open Access

|Jan 2019

Abstract

Spatial distribution of drought plays key role specifically in hydrological research. Drought is a complex stochastic natural hazard caused by prolonging shortage of rainfall and available water resources. The multi-scalar drought indices (based on probability distribution) are commonly used for making effective drought mitigation policies. In addition, the multi-scalar drought indices have great extent of the inaccurate determination of drought classes due to its probabilistic nature. However, the interpretation and applicability of various time scales are cumbersome for multi-scalar drought in various meteorological stations at a particular region. In this regards, accurate estimation and continuous monitoring of future drought at regional level requires a more appropriate and important time scale with respect to regions under study. In this study, we aimed to investigate the appropriate time scale for multi-scalar drought indices by using geo-reference points of meteorological stations. We used Boruta algorithm with two random forest adapters of machine learning algorithms for regionalized optimization of drought monitoring time scale. We explored the appropriate time scale for the Standardized Precipitation Temperature Index (SPTI) of 52 meteorological stations that are located across Pakistan. Results show that the significant importance of SPTI-1 (1-month time scale) for further spatial and temporal studies. That is, being high ranked and prominence, SPTI-1 has the ability to capture the characteristics of all other time scales that are in some circumstances applicable for drought characterization and classification.

References

Abramowitz, M. and Stegun, I. A. 1965. Handbook of Mathematical Functions: With Formulas, Graphs, and Mathematical Tables, Vol. 55. New York, NY: Courier Corporation.
Search in Google Scholar Back to article
Adnan, S., Ullah, K., Shuanglin, L., Gao, S., Khan, A. H. and co-authors. 2018. Comparison of various drought indices to monitor drought status in Pakistan. Clim. Dyn. 51(5–6), 1885–1899.
Search in Google Scholar Back to article
Ali, Z., Hussain, I., Faisal, M., Nazir, H. M., Abd-el Moemen, M. and co-authors. 2017. A novel multi-scalar drought index for monitoring drought: the standardized precipitation temperature index. Water Resour. Manage., 31(15), 4957–4969.
Search in Google Scholar Back to article
Archer, K. J. and Kimes, R. V. 2008. Empirical characterization of random forest variable importance measures. Comput. Stat. Data Anal. 52, 2249–2260. doi:10.1016/j.csda.2007.08.015
Open DOI Search in Google Scholar Back to article
Aswathanarayana, U. 2001. Water Resources Management and the Environment. Rotterdam, Netherlands: CRC Press.
Search in Google Scholar Back to article
Bazrafshan, J., Hejabi, S. and Rahimi, J. 2014. Drought monitoring using the multivariate standardized precipitation index (MSPI). Water Resour. Manage. 28, 1045–1060. doi:10.1007/s11269-014-0533-2
Open DOI Search in Google Scholar Back to article
Blain, G. C. and Meschiatti, M. C. 2015. Inadequacy of the gamma distribution to calculate the Standardized Precipitation Index. Rev. Bras. Eng. Agríc. Ambient. 19, 1129–1135. doi:10.1590/1807-1929/agriambi.v19n12p1129-1135
Open DOI Search in Google Scholar Back to article
Dawadi, S. and Ahmad, S. 2013. Evaluating the impact of demand-side management on water resources under changing climatic conditions and increasing population. J. Environ. Manage. 114, 261–275. doi:10.1016/j.jenvman.2012.10.015
Open DOI Search in Google Scholar Back to article
Dogan, S., Berktay, A. and Singh, V. P. 2012. Comparison of multi-monthly rainfall-based drought severity indices, with application to semi-arid konya closed basin, Turkey. J. Hydrol. 470, 255–268.
Search in Google Scholar Back to article
Dracup, J. A., Lee, K. S. and Paulson, E. G. 1980. On the definition of droughts. Water Resour. Res. 16, 297–302. doi:10.1029/WR016i002p00297
Open DOI Search in Google Scholar Back to article
Cunnane, C. 1978. Unbiased plotting positions—a review. J. Hydrol. 37, 205–222. doi:10.1016/0022-1694(78)90017-3
Open DOI Search in Google Scholar Back to article
Farahmand, A. and AghaKouchak, A. 2015. A generalized framework for deriving nonparametric standardized drought indicators. Adv. Water Resour. 76, 140–145. doi:10.1016/j.advwatres.2014.11.012
Open DOI Search in Google Scholar Back to article
Ghamghami, M., Hejabi, S., Rahimi, J., Bazrafshan, J. and Olya, H. 2017. Modeling a drought index using a nonparametric approach. Global Nest J. 19, 58–68.
Search in Google Scholar Back to article
Gringorten, I. I. 1963. A plotting rule for extreme probability paper. J. Geophys. Res. 68, 813–814. doi:10.1029/JZ068i003p00813
Open DOI Search in Google Scholar Back to article
Guttman, N. B. 1998. Comparing the palmer drought index and the standardized precipitation index. J. Am. Water Resources Assoc. 34, 113–121. doi:10.1111/j.1752-1688.1998.tb05964.x
Open DOI Search in Google Scholar Back to article
Fiorillo, F. and Guadagno, F. M. 2010. Karst spring discharges analysis in relation to drought periods, using the SPI. Water Resour. Manage. 24, 1867–1884. doi:10.1007/s11269-009-9528-9
Open DOI Search in Google Scholar Back to article
Hao, Z. and AghaKouchak, A. 2013. Multivariate standardized drought index: a parametric multi-index model. Adv. Water Resour. 57, 12–18. doi:10.1016/j.advwatres.2013.03.009
Open DOI Search in Google Scholar Back to article
Hao, Z. and Singh, V. P. 2015. Drought characterization from a multivariate perspective: a review. J. Hydrol. 527, 668–678. doi:10.1016/j.jhydrol.2015.05.031
Open DOI Search in Google Scholar Back to article
Hao, Z., Yuan, X., Xia, Y., Hao, F. and Singh, V. P. 2017. An overview of drought monitoring and prediction systems at regional and global scales. Bull. Amer. Meteor. Soc. 98, 1879–1896. doi:10.1175/BAMS-D-15-00149.1
Open DOI Search in Google Scholar Back to article
Hayes, M. J., Wilhelmi, O. V. and Knutson, C. L. 2004. Reducing drought risk: bridging theory and practice. Nat. Hazards Rev. 5, 106–113. doi:10.1061/(ASCE)1527-6988(2004)5:2(106)
Open DOI Search in Google Scholar Back to article
Keyantash, J. and Dracup, J. A. 2002. The quantification of drought: An evaluation of drought indices. Bull. Am. Meteorol. Soc. 83, 1167–1180.
Search in Google Scholar Back to article
Kogan, F. N. 2000. Contribution of remote sensing to drought early warning. In: Early Warning Systems for Drought Preparedness and Drought Management. World Meteorological Organization, Geneva, 75–87.
Search in Google Scholar Back to article
Kursa, M. B. and Rudnicki, W. R. 2010. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13.
Search in Google Scholar Back to article
Liaw, A., and Wiener, M. 2002. Classification and regression by random forest. R. News 2, 18–22.
Search in Google Scholar Back to article
McKee, T. B., Doesken, N. J., Kleist, J. 1993. The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society, Boston, MA, USA, vol. 17, pp. 179–183.
Search in Google Scholar Back to article
Mishra, A. K. and Desai, V. R. 2005. Drought forecasting using stochastic models. Stoch. Environ. Res. Ris. Assess. 19, 326–339. doi:10.1007/s00477-005-0238-4
Open DOI Search in Google Scholar Back to article
Musuuza, J. L., Van Loon, A. F. and Teuling, A. J. 2016. Multiscale evaluation of the Standardized Precipitation Index as a groundwater drought indicator. Hydrol. Earth Syst.m Sci. 20, 1117.
Search in Google Scholar Back to article
Ntale, H. K. and Gan, T. Y. 2003. Drought indices and their application to East Africa. Int. J. Climatol. 23, 1335–1357. doi:10.1002/joc.931
Open DOI Search in Google Scholar Back to article
Patel, N. R., Chopra, P. and Dadhwal, V. K. 2007. Analyzing spatial patterns of meteorological drought using standardized precipitation index. Met. Apps. 14, 329–336. doi:10.1002/met.33
Open DOI Search in Google Scholar Back to article
Parker, W. 2014. Values and uncertainties in climate prediction, revisited. Stud. Hist. Philos. Sci. 46, 24–30. doi:10.1016/j.shpsa.2013.11.003
Open DOI Search in Google Scholar Back to article
Rehman, A., Jingdong, L., Shahzad, B., Chandio, A. A., Hussain, I. and co-authors. 2015. Economic perspectives of major field crops of Pakistan: an empirical study. Pacif. Sci. Rev. B Hum. Soc. Sci. 1, 145.
Search in Google Scholar Back to article
Schittkowski, K. 2002. Easy-fit: a software system for data fitting in dynamical systems. Struct. Multidisc. Optim. 23, 153–169. doi:10.1007/s00158-002-0174-6
Open DOI Search in Google Scholar Back to article
Schneider, S. H., Root, T. L. and Mastrandrea, M. D. 2011. Encyclopedia of Climate and Weather, Vol. 1. Oxford University Press, New York.
Search in Google Scholar Back to article
Spate, O. H. K. and Learmonth, A. T. A. 2017. India and Pakistan: A General and Regional Geography, Vol. 12. Routledge, UK.
Search in Google Scholar Back to article
Sheffield, J., Wood, E. F. and Roderick, M. L. 2012. Little change in global drought over the past 60 years. Nature 491, 435–438. doi:10.1038/nature11575
Open DOI Search in Google Scholar Back to article
Spiess, A. N. 2014. Propagate: Propagation of Uncertainty. R package version 1.0-4. Online at: https://CRAN.R-project.org/package=propagate.
Search in Google Scholar Back to article
Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., and Stahl, K. 2015. Candidate distributions for climatological drought indices (SPI and SPEI). Int. J. Climatol. 35, 4027–4040. doi:10.1002/joc.4267
Open DOI Search in Google Scholar Back to article
Svoboda, M. and Fuchs, B. and of Nebraska-Lincoln. National Drought Mitigation Center, U. 2016. Handbook of Drought Indicators and Indices. Integrated Drought Management Tools and Guidelines Series. World Meteorological Organization, Geneva, Switzerland.
Search in Google Scholar Back to article
Tigkas, D., Vangelis, H. and Tsakiris, G. 2016. Introducing a modified reconnaissance drought index (RDIe) incorporating effective precipitation. Proc. Eng. 162, 332–339. doi:10.1016/j.proeng.2016.11.072
Open DOI Search in Google Scholar Back to article
Tsakiris, G., Nalbantis, I., Vangelis, H., Verbeiren, B., Huysmans, M. and co-authors. 2013. A system-based paradigm of drought analysis for operational management. Water Resour. Manage. 27, 5281–5297. doi:10.1007/s11269-013-0471-4
Open DOI Search in Google Scholar Back to article
van Dorp, J. R. and Kotz, S. 2003. Generalized trapezoidal distributions. Metrika 58, 85–97. doi:10.1007/s001840200230
Open DOI Search in Google Scholar Back to article
Vicente-Serrano, S. M. 2006. Differences in spatial patterns of drought on different time scales: an analysis of the Iberian Peninsula. Water Resour. Manage. 20, 37–60. doi:10.1007/s11269-006-2974-8
Open DOI Search in Google Scholar Back to article
Vicente-Serrano, S. M., Beguería, S. and López-Moreno, J. I. 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Climate 23, 1696–1718. doi:10.1175/2009JCLI2909.1
Open DOI Search in Google Scholar Back to article
Vicente-Serrano, S. M., García-Herrera, R., Barriopedro, D., Azorin-Molina, C., López-Moreno, J. I., Martín-Hernández, N., … & Nieto, R. 2016. The Westerly Index as complementary indicator of the North Atlantic oscillation in explaining drought variability across Europe. Climate dynamics 47, 845–863. doi:10.1007/s00382-015-2875-8
Open DOI Search in Google Scholar Back to article
Wang, H., Pan, Y. and Chen, Y. 2017. Comparison of three drought indices and their evolutionary characteristics in the arid region of northwestern China. Atmos. Sci. Lett. 18, 132–139. doi:10.1002/asl.735
Open DOI Search in Google Scholar Back to article
Wilhite, D. A. 1994. Preparing for Drought: A Guidebook for Developing Countries. Diane Publishing, Darby, PA.
Search in Google Scholar Back to article
Wilhite, D. A. 2000. Drought as a natural hazard: concepts and definitions. Drought, a Global Assessment 1, 3–18.
Search in Google Scholar Back to article
Wright, M. N. and Ziegler, A. 2015. Ranger: A fast implementation of random forests for high dimensional data in C++ and R. arXiv preprint arXiv:1508.04409.
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.1080/16000870.2019.1604057 | Journal eISSN: 3035-9554

Journal RSS Feed

Language: English

Page range: 1604057 - 1604057

Published on: Jan 1, 2019

Published by: Stockholm University Press

In partnership with: Paradigm Publishing Services

Keywords:

multi-scalar drought index,

drought monitoring,

random forest

© 2019 Zulfiqar Ali, Ijaz Hussain, Muhammad Faisal, Elsayed Elsherbini Elashkar, Showkat Gani, Muhammad Ahmed Shehzad, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 71 (2019): Issue 1