A 108-year rainfall dataset, Intensity-Duration-Frequency Curves under uncertainty, and design storm generation for Limassol, Cyprus through an automated Python workflow
Date Issued
December 2025
Author(s)
Abstract
Accurate rainfall records producing Intensity-Duration-Frequency (IDF) curves, relating rainfall intensity to its duration and return periods, and derived design storms, are cornerstones of resilient hydraulic and urban drainage design. By translating long-term rainfall statistics into specific storm profiles, engineers can reliably design and size sewers, culverts, basins, and flood defenses, and run hydrological models that predict runoff volumes and peak discharges under future extremes.
We merged two publicly available sources (national meteorological archives and the ECA&D homogenized series) to assemble a continuous 108-year (1916–2024) daily rainfall time‐series for Limassol, Cyprus. A Python‐based gap-filling tool (mean, linear interpolation, moving-average) ensures applicability to other sites. We generated IDF curves from two records lengths (1990–2024, 1970–2024) by fitting Gumbel, GEV, Log-Pearson III, and Weibull distributions, evaluating goodness-of-fit (using two different tests) and selecting the optimal model.
For each one, we produced 24-hour depth tables at multiple return periods and 95% confidence bounds via bootstrap resampling. Finally, we derived hyetographs for multiple return periods, using uniform, triangular, and Chicago methods.
This work provides the first publicly available detailed hydro-meteorological dataset, IDF analysis, and suite of design storms for Limassol, filling a critical gap in local resilience planning.
The process is discussed step by step in a Supplementary Material, guiding modelling, selection, and design decisions that an analyst may face in similar studies.
All steps are incorporated into user-friendly Python scripts, that are readily adaptable to other regions, and will be publicly released to support robust hydrological modelling and infrastructure design.
We merged two publicly available sources (national meteorological archives and the ECA&D homogenized series) to assemble a continuous 108-year (1916–2024) daily rainfall time‐series for Limassol, Cyprus. A Python‐based gap-filling tool (mean, linear interpolation, moving-average) ensures applicability to other sites. We generated IDF curves from two records lengths (1990–2024, 1970–2024) by fitting Gumbel, GEV, Log-Pearson III, and Weibull distributions, evaluating goodness-of-fit (using two different tests) and selecting the optimal model.
For each one, we produced 24-hour depth tables at multiple return periods and 95% confidence bounds via bootstrap resampling. Finally, we derived hyetographs for multiple return periods, using uniform, triangular, and Chicago methods.
This work provides the first publicly available detailed hydro-meteorological dataset, IDF analysis, and suite of design storms for Limassol, filling a critical gap in local resilience planning.
The process is discussed step by step in a Supplementary Material, guiding modelling, selection, and design decisions that an analyst may face in similar studies.
All steps are incorporated into user-friendly Python scripts, that are readily adaptable to other regions, and will be publicly released to support robust hydrological modelling and infrastructure design.