Assessing long-term groundwater level trends in Karakalpakstan using non-parametric statistical methods

Abstract

Climate change has significantly impacted global hydrometeorological variables, placing increasing stress on groundwater resources. This study investigates long-term groundwater level trends in the Republic of Karakalpakstan, Uzbekistan, using a combination of non-parametric statistical models. The Mann-Kendall test, Spearman’s rank correlation, and innovative polygon trend analysis (IPTA) were applied to assess spatiotemporal variations. To address the limitations of parametric methods, this study utilizes robust, assumption-free trend detection techniques. The results reveal statistically significant increasing trends in groundwater levels across most provinces, particularly in Muynak (Z=3.884, p<0.001) and Republic-wide (Z=3.603, p<0.001). In contrast, provinces such as Turtkul, Ellikkala, and Nukus exhibit no significant trends. The IPTA method highlights seasonal fluctuations, with notable decreases in specific months despite the overall upward trend. These findings emphasize the need for localized groundwater management strategies that consider both seasonal dynamics and long-term changes. By integrating multiple statistical techniques, this study provides a comprehensive evaluation of groundwater variability and offers valuable insights for policymakers and water resource managers in arid regions facing climate-induced water challenges.

Keywords

Groundwater trend analysis / Mann-Kendall test / Innovative polygon trend analysis / Climate change impact / Water resource management

Funding

None.

Conflict of interest

Upaka Rathnayake is an Editorial Board Member of this journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

References

[1] Intergovernmental Panel on Climate Change (IPCC). Working Group II Contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report Climate Change 2007: Climate Change Impacts, Adaptation and Vulnerability. Switzerland: IPCC; 2007. p. 9-10.

[2] Intergovernmental Panel on Climate Change (IPCC). Climate Change: Synthesis Report. An Assessment of Intergovernmental Panel on Climate Change. Geneva: IPCC; 2014.

[3] Intergovernmental Panel on Climate Change (IPCC). Summary for policymakers. In: Core Writing Team, Lee H, Romero J, editors. Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: IPCC; 2023. p. 1-34. doi: 10.59327/IPCC/AR6-9789291691647.001

[4] Dabanlı I, Şen Z, Yeleğen MÖ, Şişman E, Selek B, Güçlü YS. Trend assessment by the innovative-sen method. Water Resour Manage. 2016; 30:5193-5203. doi: 10.1007/s11269-016-1478-4

[5] Morsy M, Moursy FI, Sayad T, Shaban S. Climatological study of SPEI drought index using observed and CRU gridded dataset over Ethiopia. Pure Appl Geophys. 2022; 179(8):3055-3073. doi: 10.1007/s00024-022-03091-z

[6] Ayugi B, Eresanya EO, Onyango AO, et al. Review of meteorological drought in Africa: Historical trends, impacts, mitigation measures, and prospects. Pure Appl Geophys. 2022; 179(4):1365-1386. doi: 10.1007/s00024-022-02988-z

[7] Şen Z, Şişman E, Dabanlı I. Innovative polygon trend analysis (IPTA) and applications. J Hydrol. 2019; 575:202-210. doi: 10.1016/j.jhydrol.2019.05.028

[8] Wang Y, Xu Y, Tabari H, et al. Innovative trend analysis of annual and seasonal rainfall in the Yangtze River Delta, Eastern China. Atmos Res. 2020;231:104673. doi: 10.1016/j.atmosres.2019.104673

[9] Ozturk T, Ceber ZP, Turkes M, Kurnaz ML. Projections of climate change in the Mediterranean Basin by using downscaled global climate model outputs. Int J Climatol. 2015; 35(14):4276-4292. doi: 10.1002/joc.4285

[10] Cislaghi M, De Michele C, Ghezzi A, Rosso R. Statistical assessment of trends and oscillations in rainfall dynamics: Analysis of long daily Italian series. Atmos Res. 2005; 77:188-202. doi: 10.1016/j.atmosres.2004.12.014

[11] Cong Z, Zhao J, Yang D, Ni G. Understanding the hydrological trends of river basins in China. J Hydrol. 2010; 388:350-356. doi: 10.1016/j.jhydrol.2010.05.013

[12] Duhan D, Pandey A. Statistical analysis of long term spatial and temporal trends of precipitation during 1901-2002 at Madhya Pradesh, India. Atmos Res. 2013; 122:136-149. doi: 10.1016/j.atmosres.2012.10.010

[13] Fathian F, Morid S, Kahya E. Identification of trends in hydrological and climatic variables in Urmia Lake basin, Iran. Theor Appl Climatol. 2015; 119:443-464. doi: 10.1007/s00704-014-1120-4

[14] Rahman MA, Yunsheng L, Sultana N. Analysis and prediction of rainfall trends over Bangladesh using Mann-Kendall, Spearman’s rho tests and ARIMA model. Meteorol Atmos Phys. 2017; 129:409-424. doi: 10.1007/s00703-016-0479-4

[15] Hırca T, Eryılmaz Türkkan G, Niazkar M. Applications of innovative polygonal trend analyses to precipitation series of Eastern Black Sea Basin, Turkey. Theor Appl Climatol. 2022; 147:651-667. doi: 10.1007/s00704-021-03837-0

[16] Spearman C. The proof and measurement of association between two things. In: SharifM, ArcherD, HamidA, editors. Trends in Streamflow Magnitude and Timings in Satluj River Basin. World Environmental and Water Resources Congress; 2012. p. 1904-2021.

[17] Anderson RL. Distribution of serial correlation coefficient. Ann Math Stat. 1942; 13(1):1-13. doi: 10.1214/aoms/1177731638

[18] Mann HB. Nonparametric tests against trend. Econometrica. 1945; 13:245-259. doi: 10.2307/1907187

[19] Cox DR, Miller HD. The Theory of Stochastic Process. London: Methuen Publishing; 1965.

[20] Kendall MG. Rank Correlation Methods. 4th ed. London: Charles Griffin; 1975.

[21] Hirsch RM, Slack JR, Smith RA. Techniques of trend analysis for monthly water quality data. Water Resour Res. 1982; 18(1):107-121. doi: 10.1029/WR018i001p00107

[22] Hirsch RM, Slack JR. A nonparametric trend test for seasonal data with serial dependence. Water Resour Res. 1984; 20(6):727-732. doi: 10.1029/WR020i006p00727

[23] Daniel WW. Spearman rank correlation coefficient. In: Applied Nonparametric Statistics. 2nd ed. Boston: PWS-Kent; 1990. p. 358-365.

[24] Wilcox RR. Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy. New York: Springer; 2001.

[25] Şen Z. Innovative trend significance test and applications. Theor Appl Climatol. 2017; 127:939-947. doi: 10.1007/s00704-015-1681-x

[26] Gaddikeri V, Sarangi A, Singh DK, Jatav MS, Rajput J, Kushwaha NL. Trend and change-point analyses of meteorological variables using Mann-Kendall family tests and innovative trend assessment techniques in New Bhupania command (India). J Water Clim Change. 2024; 15(5):2033-2058. doi: 10.2166/wcc.2024.462

[27] Çelebioğlu T, Tayanç M. A study on precipitation trends in Türkiye via linear regression analysis and non-parametric Mann-Kendall test. Sürdürülebilir Çevre Derg. 2024; 4(1):19-28. doi: 10.62816/cevder.1489546

[28] Qadem Z, Tayfur G. In-depth exploration of temperature trends in Morocco: Combining traditional methods of Mann Kendall with innovative ITA and IPTA approaches. Pure Appl Geophys. 2024; 181(8):2717-2739. doi: 10.1007/s00024-024-03535-8

[29] Alashan S. Non-monotonic trend analysis using Mann- Kendall with self-quantiles. Theor Appl Climatol. 2024; 155(2):901-910. doi: 10.22541/essoar.167751622.23605443/v1

[30] San M. Combined innovative trend analysis methods for seasonal trend testing. J Hydrol. 2024;649:132418. doi: 10.1016/j.jhydrol.2024.132418

[31] Kessabi R, Hanchane M, Ait Brahim Y, El Khazzan B, Addou R, Belmahi M. Characterization of annual and seasonal rainfall trend using innovative trend analysis (ITA) and classical methods: The case of Wadi Sebou basin (WSB) Morocco. Eur Mediterr J Environ Integr. 2024; 10:555-573. doi: 10.1007/s41207-024-00507-1

[32] Likinaw A, Alemayehu A, Bewket W. Trends in extreme precipitation indices in Northwest Ethiopia: Comparative analysis using the Mann-Kendall and innovative trend analysis methods. Climate. 2023; 11(8):164. doi: 10.3390/cli11080164

[33] Agbo EP, Nkajoe U, Edet CO. Comparison of Mann- Kendall and Şen’s innovative trend method for climatic parameters over Nigeria’s climatic zones. Clim Dyn. 2023; 60(11):3385-3401. doi: 10.21203/rs.3.rs-1731818/v1

[34] Sanogo A, Kabange RS, Owusu PA, Djire BI, Donkoh RF, Dia N. Investigation into recent temperature and rainfall trends in Mali using Mann-Kendall trend test: Case study of Bamako. J Geosci Environ Protect. 2023; 11(3):155-172. doi: 10.4236/gep.2023.113011

[35] Gul S, Ren J. Application of non-parametric innovative trend analysis of different time scale precipitation during (1951-2016) in Khyber Pakhtunkhwa, Pakistan. Acta Geophys. 2022; 70(1):485-503. doi: 10.1007/s11600-021-00703-5

[36] Nguyen HM, Ouillon S, Vu VD. Sea level variation and trend analysis by comparing Mann-Kendall test and innovative trend analysis in front of the Red River Delta, Vietnam (1961-2020). Water. 2022; 14(11): 1709. doi: 10.3390/w14111709

[37] Seenu PZ, Jayakumar KV. Comparative study of innovative trend analysis technique with Mann-Kendall tests for extreme rainfall. Arabian J Geosci. 2021;14:536.

[38] Güçlü YS. Improved visualization for trend analysis by comparing with classical Mann-Kendall test and ITA. J Hydrol. 2020;584:124674. doi: 10.1007/s12517-021-06906-w

[39] Caloiero T, Coscarelli R, Ferrari E. Assessment of seasonal and annual rainfall trend in Calabria (Southern Italy) with the ITA method. J Hydroinform. 2020; 22(4):738-748. doi: 10.2166/hydro.2019.138

[40] Hamed KH, Rao AR. A modified Mann-Kendall trend test for autocorrelated data. J Hydrol. 1998; 204(1-4):182-196. doi: 10.1016/S0022-1694(97)00125-X

[41] Yue S, Pilon P, Phinney B, Cavadias G. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process. 2002; 16(9):1807-1829. doi: 10.1002/hyp.1095

[42] Douglas EM, Vogel RM, Kroll CN. Trends in floods and low flows in the United States: Impact of spatial correlation. J Hydrol. 2000; 240(1-2):90-105. doi: 10.1016/S0022-1694(00)00336-X

[43] Tosunoglu F. Trend analysis of daily maximum rainfall series in Çoruh Basin, Turkey. J Inst Sci Technol. 2017; 7(1):195-205. doi: 10.21597/jist.2017127432

[44] Achite M, Ceribasi G, Ceyhunlu AI, Wałega A, Caloiero T. The innovative polygon trend analysis (IPTA) as a simple qualitative method to detect changes in environment-example detecting trends of the total monthly precipitation in semiarid area. Sustainability. 2021;13:1 2674. doi: 10.3390/su132212674

[45] Ceribasi G, Ceyhunlu AI, Ahmed N. Analysis of temperature data by using innovative polygon trend analysis and trend polygon star concept methods: A case study for Susurluk Basin, Turkey. Acta Geophys. 2021; 69(5):1949-1961. doi: 10.1007/s11600-021-00632-3

[46] Karacosta P, Pakalidou N, Douka M, Karacostas T. Innovative polygon trend analysis (IPTA): A case study for precipitation in Thessaloniki during the last 50 years (1971-2020). Environ Sci Proc. 2023; 26(1):161. doi: 10.3390/environsciproc2023026161

[47] Koycegiz C, Buyukyildiz M. Applications of innovative polygon trend analysis (IPTA) and trend polygon star concept (TPSC) methods for the variability of precipitation in Konya closed basin (turkey). Theor Appl Climatol. 2023; 155:2641-2656. doi: 10.1007/s00704-023-04765-x

[48] Yenice AC, Yaqub M. Trend analysis of temperature data using innovative polygon trend analysis and modeling by gene expression programming. Environ Monit Assess. 2022;194:543. doi: 10.1007/s10661-022-10156-y

[49] Koriyev MR, Fonseka PU, Umurzakova UN, et al. Soil salinity status in Namangan region, Uzbekistan. Suranaree J Sci Technol. 2024; 31(5):010335(1-15). doi: 10.55766/sujst-2024-05-e05652

[50] Chathuranika I, Khaniya B, Neupane K, Rustamjonovich KM, Rathnayake U. Implementation of water-saving agro-technologies and irrigation methods in agriculture of Uzbekistan on a large scale as an urgent issue. Sustain Water Resour Manage. 2022; 8(5):155. doi: 10.1007/s40899-022-00746-6

[51] Mukhamedjanov S, Mukhomedjanov A, Sagdullaev R, Khasanova N. Adaptation to climate change in irrigated agriculture in Uzbekistan. Irrig Drain. 2020; 70(1):169-176. doi: 10.1002/ird.2529

[52] Hamidov A, Khamidov M, Ishchanov J. Impact of climate change on groundwater management in the Northwestern part of Uzbekistan. Agronomy. 2020; 10(8):1173. doi: 10.3390/agronomy10081173

[53] Mitchell D, Williams RB, Hudson D, Johnson P. A Monte Carlo analysis on the impact of climate change on future crop choice and water use in Uzbekistan. Food Secur. 2017; 9(4):697-709. doi: 10.1007/s12571-017-0690-2