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Advances in Water and Stormwater Networks: Modelling and Pollutant Degradation, 2nd Edition

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".

Deadline for manuscript submissions: 23 February 2026 | Viewed by 2681

Special Issue Editors

Prof. Dr. Cong Li

E-Mail Website
Guest Editor
School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200433, China
Interests: water treatment; endocrine disruption estrogens; water purification technologies; water analysis; drinking water quality; water chemistry; disinfection byproducts; water purification; water quality research in water distribution systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yuqiong Gao

E-Mail Website
Guest Editor
School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: water treatment; advanced oxidation processes; advanced reduction processes; sonochemistry in environmental remediation; disinfection byproducts (DBPs)
Special Issues, Collections and Topics in MDPI journals
Dr. Yunshu Zhang

E-Mail Website
Guest Editor
School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: bioelectrochemistry; electrochemistry; molecular biology; advanced oxidation processes; sewage and sludge treatment and resource recovery

Special Issue Information

Dear Colleagues,

We invite you to submit a manuscript to this Special Issue, entitled “Advances in Water and Stormwater Networks: Modelling and Pollutant Degradation, 2nd Edition”.

Water is an international, cross-disciplinary, peer-reviewed, open access journal that features innovative research papers and visionary perspectives. The journal welcomes articles that address all aspects of the science and technology of water reuse, water quality sensing, and water management.

Research articles must provide full methodical and/or experimental details, and we encourage scientists to publish their research in as much detail as possible. Computed data or files regarding the full details of the experimental procedure or model set-up, if unable to be published as part of the main manuscript, can be deposited as supplementary materials.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Mathematical modeling, systems analysis and machine learning related to urban water networks and urban hydrological evaluation and prediction;
  • Remediation processes for pollutants in water and the degradation transfer process;
  • Contaminants in water (anthropogenic pollutants such as nanomaterials, microplastics, disinfection by-products, PPCPs, etc.) and water quality evaluation.

This Special Issue belongs to the section “Urban Water Management”.

Prof. Dr. Cong Li
Dr. Yuqiong Gao
Dr. Yunshu Zhang
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 250 words) can be sent to the Editorial Office for assessment.

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. Water is an international peer-reviewed open access semimonthly 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 2600 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 water contamination
  • disinfection byproducts
  • advanced oxidation
  • bioremediation
  • network leakage simulation
  • urban hydrology and modeling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

21 pages, 2916 KB  
Article
Bridging Uncertainty in SWMM Model Calibration: A Bayesian Analysis of Optimal Rainfall Selection
by Zhiyu Shao, Jinsong Wang, Xiaoyuan Zhang, Jiale Du and Scott Yost
Water 2025, 17(23), 3435; https://doi.org/10.3390/w17233435 - 3 Dec 2025
Viewed by 303
Abstract
SWMM (Stormwater Management Model) is one of the most widely used computation tools in urban water resources management. Traditionally, the choice of rainfall data for calibrating the SWMM model has been arbitrary, lacking clarity on the most suitable rainfall types. In addition, the [...] Read more.
SWMM (Stormwater Management Model) is one of the most widely used computation tools in urban water resources management. Traditionally, the choice of rainfall data for calibrating the SWMM model has been arbitrary, lacking clarity on the most suitable rainfall types. In addition, the simplification in the SWMM hydrological module of the rainfall–runoff process, coupled with measurement errors, introduces a high level of uncertainty in the calibration. This study investigates the influences of rainfall types on the highly uncertain SWMM model calibration by implementing the Bayesian inference theory. A Bayesian SWMM calibration framework was established, in which an advanced DREAM(zs) (Differential Evolution Adaptive Metropolis, Version ZS) sampling method was used. The investigation focused on eight key hydrological parameters of SWMM. The impact of rainfall types was analyzed using nine rainfall intensities and three rainfall patterns. Results show that rainfall events equivalent to a one-year return period (R5, 42.70 mm total depth) or higher generally yield the most accurate parameters, with posterior distribution standard deviations reduced by 40–60% compared to low-intensity rainfalls. Notably, three parameters (impervious area percentage [Imperv], storage depth of impervious area [S-imperv], and Manning’s coefficient of impervious area [N-imperv]) demonstrated consistent accuracy irrespective of rainfall intensity, with a coefficient of variation below 0.05 for Imperv and S-imperv across all rainfall intensities. Furthermore, it was found that rainfall events with double peaks resulted in more satisfactory calibration compared to single or triple peaks, reducing the standard deviation of the Width parameter from 168.647 (single-peak) to 110.789 (double-peak). The findings from this study could offer valuable insights for selecting appropriate rainfall events before SWMM model calibration for more accurate predictions when it comes to urban non-point pollution control strategies and watershed management. Full article
(This article belongs to the Special Issue Advances in Water and Stormwater Networks: Modelling and Pollutant Degradation, 2nd Edition)
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12 pages, 2471 KB  
Article
Sampling Urban Stormwater: Lessons Learned from a Field Campaign in a Little Town of Spain
by Pedro Luis Lopez-Julian, Alejandro Acero-Oliete, Diego Antolín Cañada, Carmelo Borque Horna, Mariarosaria Arvia and Beniamino Russo
Water 2025, 17(22), 3294; https://doi.org/10.3390/w17223294 - 18 Nov 2025
Viewed by 301
Abstract
The water quality characteristics of urban stormwater in a small town (La Almunia, 8000 inhabitants) in Northeast Spain with a combined sewer system have been studied. A specific device was designed to collect stormwater just before it enters the drainage network at five [...] Read more.
The water quality characteristics of urban stormwater in a small town (La Almunia, 8000 inhabitants) in Northeast Spain with a combined sewer system have been studied. A specific device was designed to collect stormwater just before it enters the drainage network at five different points in the urban area, thus obtaining an approximate calculation of the mean event concentration values for the surface runoff generated during eight rainfall episodes. The results obtained demonstrated a high variability in the average concentrations of the events. The highest measured values corresponded mainly to the periods of the greatest road traffic from agricultural machinery within the town (harvest and manure seasons), resulting in peaks mainly in electrical conductivity and dissolved oxygen demand. This finding has been confirmed by the spatial study of the results, since the maximum values of these parameters were located in those areas of preferential transit of agricultural machinery; in addition, a possible relationship has also been observed between the maximum values of nitrogen and phosphorus in stormwater and older urban areas, due to the washing of bird droppings accumulated on the roofs. In general, all obtained results indicate that the stormwater samples generated in La Almunia present a low contaminant load, with the mean concentration event values calculated for half of the events falling within the discharge limit values established by the European Union. This fact, combined with the spatial and temporal location of the highest levels of stormwater pollution, helps evaluate urban cleanup operations and the operational capacity of both the urban drainage network and the wastewater treatment plant. Full article
(This article belongs to the Special Issue Advances in Water and Stormwater Networks: Modelling and Pollutant Degradation, 2nd Edition)
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23 pages, 5960 KB  
Article
Comprehensive Evaluation of Urban Storm Flooding Resilience by Integrating AHP–Entropy Weight Method and Cloud Model
by Zhangao Huang and Cuimin Feng
Water 2025, 17(17), 2576; https://doi.org/10.3390/w17172576 - 31 Aug 2025
Viewed by 1846
Abstract
To address urban flooding challenges exacerbated by climate change and urbanization, this study develops an integrated assessment framework combining the analytic hierarchy process (AHP), entropy weight method, and cloud model to quantify urban flood resilience. Resilience is deconstructed into resistance, adaptability, and recovery [...] Read more.
To address urban flooding challenges exacerbated by climate change and urbanization, this study develops an integrated assessment framework combining the analytic hierarchy process (AHP), entropy weight method, and cloud model to quantify urban flood resilience. Resilience is deconstructed into resistance, adaptability, and recovery and evaluated through 24 indicators spanning water resources, socio-economic systems, and ecological systems. Subjective (AHP) and objective (entropy) weights are optimized via minimum information entropy, with the cloud model enabling qualitative–quantitative resilience mapping. Analyzing 2014–2024 data from 27 Chinese sponge city pilots, the results show resilience improved from “poor to average” to “good to average”, with a 2.89% annual growth rate. Megacities like Beijing and Shanghai excel in resistance and recovery due to infrastructure and economic strengths, while cities like Sanya enhance resilience via ecological restoration. Key drivers include water allocation (27.38%), economic system (18.41%), and social system (17.94%), with critical indicators being population density, secondary industry GDP ratio, and sewage treatment rate. Recommendations emphasize upgrading rainwater storage, intelligent monitoring networks, and resilience-oriented planning. The model offers a scientific foundation for urban disaster risk management, supporting sustainable development. This approach enables systematic improvements in adaptive capacity and recovery potential, providing actionable insights for global flood-resilient urban planning. Full article
(This article belongs to the Special Issue Advances in Water and Stormwater Networks: Modelling and Pollutant Degradation, 2nd Edition)
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