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Atmosphere
Special Issues
Climate Change Impacts on Urban Stormwater Management
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Climate Change Impacts on Urban Stormwater Management

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".

Deadline for manuscript submissions: closed (18 January 2023) | Viewed by 5540

Special Issue Editors

Dr. Stefania Anna Palermo

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Civile, Università della Calabria, Arcavacata, Italy
Interests: hydrology; urban stormwater management; urban flooding risk; water quality; nature-based solutions; low-impact development systems; modeling; numerical analysis; water resources management; water balance; soil sciences
Special Issues, Collections and Topics in MDPI journals
Dr. Michele Turco

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Calabria, Arcavacata, Italy
Interests: urban hydrology; nature-based solutions; low impact development; climate changes; urban drainage design and modeling; water quality; urban floods; rainfall-runoff modeling; soil sciences; hydrological modeling; water resources management; water flow modeling; contaminant transport hydrology
Special Issues, Collections and Topics in MDPI journals
Dr. Behrouz Pirouz

E-Mail Website
Guest Editor
Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy
Interests: sustainability of water and energy; water and wastewater treatmemt; NZEBs; PEDs
Special Issues, Collections and Topics in MDPI journals
Prof. Patrizia Piro

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Calabria, 87036 Rende, Italy
Interests: modeling; combined sewer overflows; water pollution; urban stormwater management; water treatment; urban drainage; low impact development; soil science; sustainability of water and energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change has led to an increase in extreme rainfall events, which, combined with urbanization, is one of the main causes of urban flooding risk and water pollution.

The analysis of the climate change impact on rainfall characteristics, the consequences of urban flooding risk and water quality deterioration, and the possible mitigation and adaptation strategies are key aspects to investigate for urban stormwater management.

This Special Issue aims to gather contributions that deal with advanced studies on climate change effects, specifically urban flood risk, and on new, smart, and sustainable urban stormwater management strategies. The main topics of interest for this publication are as follows:

  • Climate change impacts on rainfall events
  • Spatial and temporal rainfall variability
  • Urban stormwater management under climate change impacts
  • Numerical modeling of urban drainage systems
  • Criticalities analysis of urban drainage system
  • Tools, methods, and models for urban flood risk mapping and management
  • Stormwater qualitative-quantitative experimental analysis
  • Real-time control approach efficiency for urban stormwater management
  • Analysis of the effectiveness of sustainable solutions on urban runoff mitigation
  • Life cycle assessment analysis

Dr. Stefania Anna Palermo
Dr. Michele Turco
Dr. Behrouz Pirouz
Prof. Patrizia Piro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urban stormwater management
  • rainfall-runoff models
  • urban flooding
  • water quality
  • numerical modeling
  • experimental investigation
  • real-time control
  • low impact development systems
  • nature-based solutions
  • sustainable urban drainage system
  • LCA

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Published Papers (2 papers)

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Research

33 pages, 5623 KiB  
Article
Intercomparison and Assessment of Stand-Alone and Wavelet-Coupled Machine Learning Models for Simulating Rainfall-Runoff Process in Four Basins of Pothohar Region, Pakistan
by Muhammad Tariq Khan, Muhammad Shoaib, Raffaele Albano, Muhammad Azhar Inam, Hamza Salahudin, Muhammad Hammad, Shakil Ahmad, Muhammad Usman Ali, Sarfraz Hashim and Muhammad Kaleem Ullah
Atmosphere 2023, 14(3), 452; https://doi.org/10.3390/atmos14030452 - 24 Feb 2023
Cited by 1 | Viewed by 2309
Abstract
The science of hydrological modeling has continuously evolved under the influence of rapid advancements in software and hardware technologies. Starting from simple rational formulae for estimating peak discharge and developing into sophisticated univariate predictive models, accurate conversion of rainfall into runoff and the [...] Read more.
The science of hydrological modeling has continuously evolved under the influence of rapid advancements in software and hardware technologies. Starting from simple rational formulae for estimating peak discharge and developing into sophisticated univariate predictive models, accurate conversion of rainfall into runoff and the assessment of inherent uncertainty has been a prime focus for researchers. Therefore, alternative data-driven methods have gained widespread attention in hydrology. Moreover, scientists often couple conventional machine learning models with data pre-processing techniques, i.e., wavelet transformation (WT), to enhance modelling accuracy. In this context, this research work attempts to explore the latent linkage between rainfall and runoff in Pothohar region of Pakistan by developing a novel linkage of five streamline techniques of machine learning, including single decision tree (SDT), decision tree forest (DTF), tree boost (TB), multilayer perceptron (MLP), and gene expression modeling (GEP), with a more sophisticated variant of WT, i.e., maximal overlap discrete wavelet transformation (MODWT), for boundary correction of the transformed components of timeseries data. This study also implements these machine learning models in a stand-alone mode for a more comprehensive comparative analysis of performances. Furthermore, the study uses a combined-basin approach that divides Pothohar region into two basins to compensate for the complex topographic division of the study area. The results indicate that MODWT-based DTF outperformed other stand-alone and hybrid models in terms of modeling accuracy. In the first scenario, considering the Bunha-Kahan River basin, MODWT-DTF yielded the highest NSE (0.86) and the lowest RMSE (220.45 mm) and R2 (0.92 at lag order 3 (Lo3)) when transformed with daubechies4 (db4) at level three. While in the Soan-Haro River basin, MODWT-DTF produced the highest accuracy modeling at lag order 4 (Lo4) (NSE = 0.88, RMSE = 21.72 m3/s, and R2 = 0.91). The highly accurate performance of 3- and 4-days lagged models reflects the temporal consistency in hydrological response of the study area. The comparison of simple and hybrid model performance indicates up to a 55% increase in modeling accuracy due to data pre-processing with wavelet transformation. Full article
(This article belongs to the Special Issue Climate Change Impacts on Urban Stormwater Management)
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11 pages, 2310 KiB  
Article
General Method Based on Regressive Relationships to Parameterize the Three-Parameter Depth–Duration–Frequency Curve
by Amirabbas Mottahedin, Carlo Giudicianni, Giuseppe Barbero, Gabriella Petaccia and Enrico Creaco
Atmosphere 2023, 14(1), 190; https://doi.org/10.3390/atmos14010190 - 16 Jan 2023
Cited by 3 | Viewed by 1896
Abstract
This paper aims to present simple regressive equations to estimate the parameters of the three-parameter depth–duration–frequency (DDF) curve (3p-DDF), which accurately expresses, for a preassigned return period, the relationship between the rainfall depth and the rainfall duration over large duration ranges, from below [...] Read more.
This paper aims to present simple regressive equations to estimate the parameters of the three-parameter depth–duration–frequency (DDF) curve (3p-DDF), which accurately expresses, for a preassigned return period, the relationship between the rainfall depth and the rainfall duration over large duration ranges, from below 1 h (i.e., tens of minutes) to above 1 h (up to 24 h). These equations are developed to relate their parameters to those of the two-parameter DDF curve (2p-DDF), which can be estimated more easily being based on more readily available data related to rainfall durations above 1 h. In the applications, the regressive equations are first calibrated using recent pluviographic data in northern Italy, Germany, and Sweden. Two validation steps are then carried out to test the equations in terms of estimated rainfall depths using the same data as those used in the calibration step and data of stations from other geographic areas, i.e., Sicily in southern Italy, and from the past century, respectively. The results obtained prove this methodology capable of providing reliable estimation of short-duration rainfalls with various return periods in the absence of measurements with fine temporal resolution. Full article
(This article belongs to the Special Issue Climate Change Impacts on Urban Stormwater Management)
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