Vol. 7 No. 1 (2026)
Stochastic Modelling of Road Network Disruption Due to Seasonal Flooding in South Sudan
Abstract
Seasonal flooding is the dominant cause of road network disruption in South Sudan, rendering large portions of the classified road network impassable for three to seven months annually and inflicting severe economic, humanitarian, and logistical losses. Despite the critical importance of quantifying flood-induced network disruption for infrastructure planning, maintenance programming, and humanitarian contingency management, no stochastic modelling framework calibrated to the South Sudan context has previously been published. This paper develops and applies a comprehensive stochastic model integrating three complementary probabilistic approaches: (i) a Gamma-distributed flood duration model fitted to 14 years of MODIS-derived inundation data for 12 road segments; (ii) a four-state continuous-time Markov chain (CTMC) model representing transitions between Accessible, Partially Flooded, Fully Impassable, and Under Rehabilitation road states, with seasonally varying transition rate matrices; and (iii) a Monte Carlo simulation framework generating 10,000 realisations of annual network-wide disruption to construct probability distributions of aggregate disruption metrics and Network Reliability Index (NRI). Model parameters are estimated by maximum likelihood estimation from remotely-sensed inundation records (2010–2023) and field condition survey data. The fitted Gamma flood duration model (shape α = 2.85, rate β = 18.4 days) provides a significantly better fit than exponential or log-normal alternatives (Anderson-Darling p = 0.312 vs. p = 0.038 and p = 0.071). The CTMC model predicts that the N-8 Juba–Bor corridor operates in the Accessible state for only 28% of days in August at its current condition, declining to a projected 19% by 2034 under the no-intervention scenario
Read the Full Article
The HTML galley is loaded below for inline reading and better discovery.
How to Cite
Keywords
Research Snapshot
Desktop reading viewReferences
- Adey, B.T., Hackl, J., Lam, J.C., van Gelder, P., van Erp, N. and Heitzler, M. (2016) "Ensuring acceptable levels of infrastructure related risks due to natural hazards with emphasis on stress tests." International Journal of Disaster Risk Reduction, 18, pp. 196–207.
- Aksoy, H. (2000) "Use of gamma distribution in hydrological analysis." Turkish Journal of Engineering and Environmental Sciences, 24, pp. 419–428.
- Anhiem, A.M. (2025) "Flood-resilient road design standards for the Sudd Wetland Region of South Sudan." African Journal of Climate Adaptation and Vulnerability Assessment, 6(2), pp. 112–138.
- AASHTO (2008) Mechanistic-Empirical Pavement Design Guide. Washington DC: AASHTO.
- Aven, T. and Jensen, U. (1999) Stochastic Models in Reliability. New York: Springer.
- Bell, M.G.H. and Iida, Y. (1997) Transportation Network Analysis. Chichester: Wiley.
- Butt, A.A., Shahin, M.Y., Feighan, K.J. and Carpenter, S.H. (1987) "Pavement performance prediction model using the Markov process." Transportation Research Record, 1123, pp. 12–19.
- Colbourn, C.J. (1987) The Combinatorics of Network Reliability. Oxford: Oxford University Press.
- Hall, J.W., Dawson, R.J., Barr, S.L. and Bates, P.D. (2013) "Sensitivity analysis of a stochastic flood risk model." Reliability Engineering and System Safety, 62(1), pp. 87–97.
- Hong, F. and Prozzi, J.A. (2006) "Estimation of pavement performance deterioration using Bayesian approach." Journal of Infrastructure Systems, 12(2), pp. 77–86.
- Kalbfleisch, J.D. and Lawless, J.F. (1985) "The analysis of panel data under a Markov assumption." Journal of the American Statistical Association, 80(392), pp. 863–871.
- Kroese, D.P., Brereton, T., Taimre, T. and Botev, Z.I. (2014) "Why the Monte Carlo method is so important today." Wiley Interdisciplinary Reviews: Computational Statistics, 6(6), pp. 386–392.
- Lemnitzer, A., Briaud, J.L. and Bonab, M.H. (2010) "Stochastic assessment of bridge scour: A Monte Carlo simulation." Journal of Geotechnical and Geoenvironmental Engineering, 136(9), pp. 1289–1301.
- MoRB — Ministry of Roads and Bridges, South Sudan (2023) National Transport Master Plan 2023–2035. Juba: MoRB.
- Morcous, G. and Lounis, Z. (2005) "Maintenance optimisation of infrastructure networks using genetic algorithms." Automation in Construction, 14(1), pp. 129–142.
- OCHA (2023) South Sudan Humanitarian Overview 2023. Geneva: OCHA.
- Ouyang, M., Dueñas-Osorio, L. and Min, X. (2012) "A three-stage resilience analysis framework for urban infrastructure systems." Structural Safety, 36, pp. 23–31.
- Pregnolato, M., Ford, A., Wilkinson, S.M. and Dawson, R.J. (2017) "The impact of flooding on road transport: A depth-disruption function." Transportation Research Part D, 55, pp. 67–81.
- Reggiani, A., Nijkamp, P. and Lanzi, D. (2015) "Transport resilience and vulnerability: The role of connectivity." Transportation Research Part A, 81, pp. 4–15.
- Rubinstein, R.Y. and Kroese, D.P. (2016) Simulation and the Monte Carlo Method. 3rd edn. Hoboken: Wiley.
- Svensson, C., Hannaford, J., Kundzewicz, Z.W. and Marsh, T.J. (2005) "Trends in river floods: Why is there no clear signal in observations?" In: Huber, U. et al. (eds.) Global Change and Mountain Regions. Advances in Global Change Research, Vol. 23. Springer.
- Wakabayashi, H. and Iida, Y. (1992) "Upper and lower bounds of terminal reliability of road networks: An efficient method with Boolean algebra." Journal of Natural Disaster Science, 14(1), pp. 29–44.
- World Bank (2022) South Sudan Infrastructure Sector Assessment 2022. Washington DC: World Bank Group.
- © 2025 African Journal of Mathematical Statistics and Engineering Systems. All rights reserved. DOI: 10.XXXXX/ajmses.2025.0718
- The CTMC model predicts the N-8 Juba–Bor corridor operates in the Accessible state for only 28% of days in August, declining to 19% by 2034 under no intervention.
- Conversion notesMessage(type='warning', message='An unrecognised element was ignored: {http://schemas.openxmlformats.org/officeDocument/2006/math}oMathPara')