Articles | Volume 24, issue 6
https://doi.org/10.5194/nhess-24-1929-2024
https://doi.org/10.5194/nhess-24-1929-2024
Brief communication
 | 
11 Jun 2024
Brief communication |  | 11 Jun 2024

Brief communication: SWM – stochastic weather model for precipitation-related hazard assessments using ERA5-Land data

Melody Gwyneth Whitehead and Mark Stephen Bebbington

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Cited articles

Arnaud, P., Bouvier, C., Cisneros, L., and Dominguez, R.: Influence of rainfall spatial variability on flood prediction, J. Hydrol., 260, 216–230, https://doi.org/10.1016/S0022-1694(01)00611-4, 2002. 
Burton, A., Kilsby, C., Fowler, H., Cowpertwait, P., and O'Connell, P.: RainSim: A spatial–temporal stochastic rainfall modelling system, Environ. Modell. Softw., 23, 1356–1369, https://doi.org/10.1016/j.envsoft.2008.04.003, 2008. 
Chappell, P. R.: The climate and weather of Bay of Plenty, 3rd edn., NIWA Science and Technology Series, Number 62, https://niwa.co.nz/static/BOP ClimateWEB.pdf (last access: 23 August 2023), 2013. 
DiCiccio, T. and Efron, B.: Bootstrap Confidence Intervals, Stat. Sci., 11, 189–212, https://www.jstor.org/stable/2246110 (last access: 5 June 2024), 1996. 
Fox, J. and Weisberg, S.: An R Companion to Applied Regression, 3rd edn., Sage Publications, Thousand Oaks CA, USA, 576 pp., https://socialsciences.mcmaster.ca/jfox/Books/Companion/ (last access: 5 June 2024), 2019. 
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Short summary
Precipitation-driven hazards including floods, landslides, and lahars can be catastrophic and difficult to forecast due to high uncertainty around future weather patterns. This work presents a stochastic weather model that produces statistically similar (realistic) rainfall over long time periods at minimal computational cost. These data provide much-needed inputs for hazard simulations to support long-term, time and spatially varying risk assessments.
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