Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy

Submitted: 9 August 2023
Accepted: 27 October 2023
Published: 13 November 2023
Abstract Views: 816
PDF: 315
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


The occurrence of floods has the potential to escalate the transmission of infectious diseases. To enhance our comprehension of the health impacts of flooding and facilitate effective planning for mitigation strategies, it is necessary to explore the flood risk management. The variability present in hydrological records is an important and neglecting non-stationary patterns in flood data can lead to significant biases in estimating flood quantiles. Consequently, adopting a non-stationary flood frequency analysis appears to be a suitable approach to challenge the assumption of independent and identically distributed observations in the sample. This research employed the generalized extreme value (GEV) distribution to examine annual maximum flood series. To estimate non-stationary models in the flood data, several statistical tests, including the TL-moment method was utilized on the data from ten stream-flow stations in Johor, Malaysia, which revealed that two stations, namely Kahang and Lenggor, exhibited non-stationary behaviour in their annual maximum streamflow. Two non-stationary models efficiently described the data series from these two specific stations, the control of which could reduce outbreak of infectious diseases when used for controlling the development measures of the hydraulic structures. Thus, the application of these models may help prevent biased prediction of flood occurrences leading to lower number of cases infected by disease.



PlumX Metrics


Download data is not yet available.


Abaya SW, Mandere N, Ewald G, 2019. Floods and health in Gambella region, Ethiopia: a qualitative assessment of the strengths and weaknesses of coping mechanisms. Glob. Health Action 2:1-10. DOI:
Adikari Y, Yoshitani J, 2009. Global trends in waterrelated disasters: An insight for policy-makers. The United Nations world water assessment program. International centre for water hazard and risk management. Available from: Accessed: September 25, 2023.
Ahmad I, Tang D, Wang T, Wang M, Wagan B, 2015. Precipitation trends over time using Mann-Kendall and Spearman’s Rho Tests in Swat River Basin, Pakistan. Adv Meteorol, 2015: 1-15. DOI:
Akaike H, 1974. A new look at the statistical model identification. IEEE Trans Autom Control, 19:716–723. DOI:
Apisarnthanarak, A, Mundy, LM, Khawcharoenporn, T,Mayhall, CG, 2013. Hospital Infection Prevention and Control Issues Relevant to Extensive Floods. Infect Control Hosp Epidemiol 34:200–206. DOI:
Badyalina B, Mokhtar NA, Mat Jan NAM, Marsani MF, 2022. Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm. Sains Malays 51:2655-2668. DOI:
Badyalina B, Shabri A, Marsani MF, 2021. Streamflow estimation at ungauged basin using modified group method of data handling. Sains Malays 50:2765-2779. DOI:
Barteit S, Sié A, Zabré P, Traoré I,OuédraogoWA, BoudoV,Munga S,Khagayi S, Obor D, Muok E, Franke J, Schwarz M, Blass K, Su TT, Bärnighausen T, Sankoh O, Sauerborn R, 2023.Widening the lens of population-based health research to climate change impacts and adaptation: The climate change and health evaluation and response system (CHEERS).Front Public Health 11:1-19. DOI:
Bouza-Deano R, Ternero-Rodriguez M, Fernandez-Espinosa AJ, 2008. Trend study and assessment of surface water quality in the Ebro River (Spain). J Hydrol 361:227–239. DOI:
Brown L, Murray V, 2013. Examining the relationship between infectious diseases and flooding in Europe. Disaster Health 1: 117-127. DOI:
Caldas-Alvarez A, Augenstein M, Ayzel G, Barfus K, Cherian R, Dillenardt L, Fauer F, Feldmann H, Heistermann M, Karwat A, Kaspar F, Kreibich H, Lucio-Eceiza EE, Meredith EP, Mohr S, Niermann D, Pfahl S, Ruff F, Rust HW, Schoppa L, Schwitalla T, Steidl S, Thieken AH, Tradowsky JS, Wulfmeyer V,Quaas J, 2022. Meteorological, impact and climate perspectives of the 29 June 2017 heavy precipitation event in the Berlin metropolitan area.Nat Hazard Earth Sys 22:3701–3724. DOI:
Chen M, Papadikis K, Jun C, 2021. An investigation on the non-stationarity of flood frequency across the UK. J Hydrol, 597:126309. DOI:
Chen X, Ye C, Zhang J, Xu C, Zhang L, 2019. Selection of an optimal distribution curve for non-stationary flood series.J Atmos 10:1–16. DOI:
Coles S, 2001.An introduction to statistical modeling of extreme value. Springer-Verlag, London. DOI:
Cunderlik JM, Burn DH, 2003. Non-stationary pooled flood frequency analysis. J Hydrol 276:210–23. DOI:
Díaz S, Settele J, Brondízio ES, Ngo HT, Guèze M, Agard J, Zayas C, 2019. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). IPBES secretariat, Bonn, Germany. 56 pages.
Dickey DA, Fuller WA, 1979. Distribution of the estimators for autoregressive time series with a unit root.J Am Stat Assoc,74:423–431. DOI:
El-Mousawi, F, Ariel, MO, Berkat,R.,Nasri, B, 2023. The Impact of Flood Adaptation Measures on Affected Population's Mental Health: A mixed method Scoping Review. medRxiv 2023:2023-04. DOI:
Elamir EAH, Seheult AH, 2003. Trimmed L-Moments. Comput Stat Data Anal 43:299–314. DOI:
Flood Management Guidelines (Health), 2008. Ministry of Health Malaysia. Accessed: September 22, 2023. Available from: 1 – FWBD UMU GP 001.pdf
French CE, Waite TD, Armstrong B, Rubin GJ, English National Study of Flooding and Health Study Group, Beck CR, Oliver I, 2019. Impact of repeat flooding on mental health and health- related quality of life: a cross- sectional analysis of the English National Study of Flooding and Health. BMJ Open 9:1-9. DOI:
Gado TA, Nguyen VTV, 2016b. An at-site flood estimation method in the context of nonstationarity II. Statistical analysis of floods in Quebec. J Hydrol 535: 722–736. DOI:
Gado TA, Nguyen VTV, 2016a. An at-site flood estimation method in the context of nonstationarity I. A simulation study. J Hydrol 535:710–721. DOI:
Gleneagles, 2022. Common Infectious Diseases in Malaysia During the Flood Season. Available from: Accessed: September 22, 2023.
Greenwood JA, Landwehr J M, Matalas NC, Wallis J R, 1979. Probability weighted moments: Definition and relation to parameters of several distributions expressable in inverse form. Water Resour Res 15:1049–1054. DOI:
Guru N, Jha R. 2014. A study on selection of probability distributions for at-site flood frequency analysis in Mahanadi River Basin, India. Taylor & Francis Group, London,1813-1819. DOI:
Haizan RYA, and Mamat, NS, 2023. Rivers exceed dangerous levels in several Malaysian states; Johor flood kills one. Channel News Asia.
Hipel KW, McLeod AI, 2005. Nonparametric tests for trend detection. In: Time series modelling of water resources and environmental systems. Elsevier, Amsterdam 853–938.
Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, Kanae S, 2013. Global food risk under climate change. Nat Clim Change 3:816–821. DOI:
Ho, J Y, Lavinya, A A, Dominic Shuen W K, Lee, C I SRazmi, A H, Claire L W, Michaela L G, and Jeyanthy E, Towards an integrated approach to improve the understanding of the relationships between water-borne infections and health outcomes: Using Malaysia as a detailed case study. Front Water 4:1-20. DOI:
Hosking JRM, Wallis JR, 1997.Regional frequency analysis: an approach based on L-Moments. Cambridge University Press, United Kingdom. DOI:
Ishak E, Rahman A, 2019. Examination of Changes in Flood Data in Australia. Water.11:1734. DOI:
Ishak EH, Rahman A, Westra S, Sharma A, Kuczera G, 2013. Evaluating the non-stationarity of australian annual maximum flood. J Hydrol 494:134–45. DOI:
Kendall MG, 1975. Rank correlation methods. Griffin, London.
Khaliq MN, Ouarda TBMJ, Ondo JC, Gachon P, Bobée B, 2006. Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: A review. J Hydrol 329:534–552. DOI:
Kuriqi A, Ardiçlioglu M, 2018. Investigation of Hydraulic regime at middle part of the Loire River in context of foods and low fow events. Pollack Period 13:145–156. DOI:
López J, Francés F, 2013. Non-stationary flood frequency analysis in Continental Spanish Rivers, using climate and reservoir indices as external covariates. Hydrol Earth Syst Sci 17:3189–3203. DOI:
Ludwig P, Ehmele F, Franca, MJ, Mohr, S, Caldas-Alvarez, A, Daniell, JE, Ehret, U, Feldmann, H, Hundhausen, M, Knippertz, P, Küpfer, K, Kunz, M, Mühr, B, Pinto, JG, Quinting, J, Schäfer, AM, Seidel, F,Wisotzky, C, 2023. A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 2: Historical context and relation to climate change, Nat Hazards Earth Syst Sci 23:1287–1311. DOI:
Malaymail, 2023.Two states fully recover, nearly 55,000 flood victims still at relief centres in three states. Available from:
Mann HB, 1945. Nonparametric tests against trend. J Econom 13:245–259. DOI:
Mat Jan NA, Shabri A, Samsudin R, 2020. Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter. J Water Clim Chang 11:966–979. DOI:
Mehmood A, Jia S, Mahmood R, Yan J, Ahsan M, 2019. Non-stationary bayesian modeling of annual maximum floods in a changing environment and implications for flood management in the Kabul River Basin, Pakistan. Water 11:1-30. DOI:
Milly PCD,Betancourt J, Falkenmark M, Hirsch R M, Kundzewicz Z W, Lettenmaier D P, Stouffer RJ, 2008. Stationarity is dead: Whither water management? Science 319:573–574. DOI:
Mohr S, Ehret U, Kunz M, Ludwig P, Caldas-Alvarez A, Daniell J E, Ehmele F, Feldmann H, Franca M J, Gattke C, Hundhausen M, Knippertz P, Küpfer K, Mühr B, Pinto J G, Quinting J, Schäfer A M, Scheibel M, Seidel F,Wisotzky C, 2023.A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 1: Event description and analysis, Nat Hazards Earth Syst Sci,23:525–551. DOI:
Mondal A, Roy R, Kalai, C, 2023. Regionalization for Flood Frequency Analysis: Sensitivity to Choice of Clustering Algorithm and Distance Metric. In World Environmental and Water Resources Congress 2023353-366 pp.). DOI:
Ochani S, Aaqil SI, Nazir A, Athar FB, Ochani K, Ullah K, 2022. Various health-related challenges amidst recent floods in Pakistan; strategies for future prevention and control. Ann Surg 82:1-2. DOI:
Okaka FO, Odhiambo BDO, 2018. Relationship between flooding and out break of infectious diseases in Kenya: A review of the literature. J Environ Public Health 2018:1-8. DOI:
Okaka FO, Odhiambo BDO, 2019. Households’ perception of flood risk and health impact of exposure to flooding in flood-prone informal settlements in the coastal city of Mombasa. Int J Clim Chang Strateg Manag 11:592-606. DOI:
Ouarda T B M J, Charron C, 2019. Changes in the distribution of hydro-climatic extremes in a non-stationary framework. Sci Rep 9:1-8. DOI:
Pan X, Rahman A, Haddad K, Ouarda, TBMJ, 2002. Peaks-over-threshold model in flood frequency analysis: a scoping review. Stoch Environ Res Risk Assess 36:2419–2435. DOI:
Panagoulia D, Economou P, Caroni, C, 2014. Stationary and nonstationary generalized extreme value modelling of extreme precipitation over a mountainous area under climate change. Environmetrics 25:29–43. DOI:
Pohlert T, 2020. Trend: Non-parametric trend tests and change-point detection. R Package version 1.1.4. Accessed 18 February 2021. Available from:
Prasad AS, Francescutti LH, 2017. Natural disasters. In: Quah S. R. (ed) International encyclopedia of public health, 2nd edn. Elsevier 215-222 pp. DOI:
Pregnolato M, Ford A, Wilkinson SM, Dawson R J, 2017. The impact of flooding on road transport: A depth-disruption function. In: Button K (ed) Transportation research part D: Transport and environment 55:67-81. DOI:
Ren H, Hou ZJ, Wigmosta M, Liu Y, Leung LR, 2019. Impacts of spatial heterogeneity and temporal non-stationarity on intensity-duration-frequency estimates - A case study in a mountainous California-Nevada Watershed. Water 11:1–16. DOI:
Romali NS, Yusop Z, 2021. Flood damage and risk assessment for urban area in Malaysia. J Hydrol Res 52:142-159. DOI:
Sadri S, Kam J, Sheffield J, 2016. Nonstationarity of low flows and their timing in the Eastern United States. Hydrol Earth Syst Sci, 20:633–649. DOI:
Said SE, Dickey DA, 1984. Testing for unit roots in autoregressive moving-average models with unknown order. Biometrika 71:599–607. DOI:
Salas JD, Obeysekera J, 2014. Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. J Hydrol Eng 19:554–568. DOI:
Schwarz G, 1978. Estimating the dimension of a model. Annal of Statistics 6:461–464. DOI:
Shafii NZ, Mohd Saudi AS, Jyh CP, Abu, IF Norzahir Sapawe, Mohd Khairul AK, Mohamad Haiqal NM. Association of Flood Risk Patterns with Waterborne Bacterial Diseases in Malaysia. Water 15:2121. DOI:
Shokri A, Sabzevari S, Hashemi SA, 2020. Impacts of flood on health of Iranian population: Infectious diseases with an emphasis on parasitic infections. Parasite Epidemiol Control 9:1-11. DOI:
Sneyers R, 1990.On the statistical analysis of series of observations. Geneva, Switzerland.
Šraj M, Viglione A, Parajka J, Blöschl G, 2016. The Influence of Non-stationarity in Extreme Hydrological Events on Flood Frequency Estimation. J Hydrol Hydromech 64:426–437. DOI:
Tan X, Gan TY, 2015. Nonstationary analysis of annual maximum streamflow of Canada. J Clim 28:1788–1805. DOI:
Vasiliades L, Galiatsatou P, Loukas A, 2015. Nonstationary frequency analysis of annual maximum rainfall using climate covariates. Water Resour Manag 29:339–358. DOI:
Villarini G, Smith JA, Serinaldi F, Bales J, Bates PD, Krajewski WF, 2009. Flood frequency analysis for nonstationary annual peak records in an urban drainage basin. Adv Water Resour 32:1255-1266. DOI:
Weiskopf SR, Rubenstein MA, Crozier LG, Gaichas S, Griffis R, Halofsky JE, Hyde KJW, Morelli TL, Morisette JT, Muñoz RC, Pershing AJ, Peterson DL, Poudel R, Staudinger MD, Sutton-Grier AE, Thompson L, Vose J, Weltzin JF, Whyte KP. 2020. Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Sci. Total Environ 733:137782, 1-18. DOI:
Xiong L, Du T, Xu CY, Guo S, Jiang C, Gippel CJ, 2015. Non-stationary annual maximum flood frequency analysis using the norming constants method to consider non-stationarity in the annual daily flow series. Water Resour Manag 29:3615-3633. DOI:
Yao, BAF, Soro EG, 2021. Detection of Hydrologic Trends and Variability in Transboundary Cavally Basin (West Africa). Am J Water Resour 9:92-102. DOI:
Zalnezhad A, Rahman A, Vafakhah M, Samali B, Ahamed F, 2022. Regional Flood Frequency Analysis Using the FCM-ANFIS Algorithm: A Case Study in South-Eastern Australia. Water 14:1608. DOI:

How to Cite

Mat Jan, N. A., Marsani, M. F., Thiruchelvam, L. ., Zainal Abidin, N. B., Shabri, A., & Abdullah Sani, S. A. (2023). Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy. Geospatial Health, 18(2).