Article, 2024

Possibility of using the STORAGE rainfall generator model in the flood analyses in urban areas

Water Research, ISSN 1879-2448, 0043-1354, Volume 251, Page 121135, 10.1016/j.watres.2024.121135

Contributors

Wałęga, Andrzej S 0000-0001-6839-4745 [1] Młyński, Dariusz Piotr 0000-0002-8925-2591 (Corresponding author) [1] Petroselli, Andrea 0000-0003-4943-0928 [2] De Luca, Davide Luciano 0000-0001-5255-7343 [3] Apollonio, Ciro 0000-0003-3576-7052 [2] Pancewicz, Michał [4]

Affiliations

  1. [1] University of Agriculture in Krakow
    2. [NORA names: Poland; Europe, EU; OECD];
  2. [2] Tuscia University
    3. [NORA names: Italy; Europe, EU; OECD];
  3. [3] University of Calabria
    4. [NORA names: Italy; Europe, EU; OECD];
  4. [4] SCALGO, Aabogade 40D, 8200 Aarhus N, Denmark.
    5. [NORA names: Denmark; Europe, EU; Nordic; OECD]

Abstract

In this investigation, we evaluated the applicability of the Stochastic Rainfall Generator (STORAGE) as a data source for deriving design hydrographs in urban catchments. This assessment involved a comparison with design rainfall calculated using Intensity-Duration-Frequency (IDF) curves derived from observed time-series data. The resulting design rainfall values from both methods were incorporated into a hydrodynamic model of the storm sewer network. To simulate peak discharge and flood areas, the Storm Water Management Model (SWMM) program was employed in conjunction with SCALGO. Our findings indicate that design rainfall values obtained from the STORAGE model exceeded those derived from the observed time-series, with a more pronounced difference for shorter rainfall durations. Simulations further revealed that peak runoff disparities between the two approaches were most evident at a 0.10 probability of exceedance compared to a 0.01 probability. Hydrodynamic simulations demonstrated that the flooding volume induced by design rainfall based on the STORAGE model surpassed that resulting from observed rainfall. Across all events, both the flooding volume and area from STORAGE were consistently greater than those derived from IDF curves. The integration of the SWMM model with the SCALGO application introduced a novel functionality for dynamic visualization of flooding, offering valuable insights for effective flood management in urban areas.

Keywords

Intensity–Duration–Frequency (IDF) curves, SWMM model, Storm Water Management Model, Water Management Model, analysis, applications, approach, area, assessment, catchment, comparison, conjunction, curves, data, data sources, design, design hydrographs, design rainfall, design rainfall values, discharge, disparities, duration, dynamic visualization, effective flood management, events, exceedance, findings, flood, flood analysis, flood management, flood volume, flooded areas, function, generation, generative model, hydrodynamic model, hydrodynamic simulations, hydrograph, integration, intensity-duration-frequency, intensity-duration-frequency curves, investigation, management, management model, method, model, network, observed rainfall, observed time series, observed time-series data, peak discharge, possibilities, probability, probability of exceedance, rainfall, rainfall duration, rainfall generation model, rainfall generator, rainfall values, sewer network, shorter rainfall durations, simulated peak discharge, simulation, source, stochastic rainfall generation model, stochastic rainfall generator, stochasticity, storm, storm sewer network, time series, time series data, urban areas, urban catchments, values, volume

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