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Sea Level Rise Vulnerability and Coastal Management
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Sea Level Rise Vulnerability and Coastal Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Oceans and Coastal Zones".

Deadline for manuscript submissions: 10 October 2026 | Viewed by 855

Editor

Prof. Dr. Claudia Cherubini

E-Mail Website
Guest Editor
Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy
Interests: coastal aquifers; saltwater intrusion; sea level rise; numerical modelling; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sea level rise (SLR) is a critical, accelerating impact of climate change, with global mean levels rising at roughly 0.44 cm per year, driving increased coastal erosion, flooding, and saltwater intrusion. Effective management requires transitioning from reactive disaster responses to proactive, integrated frameworks that combine "hard" engineering (seawalls, tidal gates) with "soft" natural solutions (restoring mangroves, wetlands, and dunes) to enhance resilience. As a countermeasure against sea level rise and saltwater intrusion, Managed Aquifer Recharge (MAR), a hybrid approach sitting between soft and hard engineering, is also recommended.

Moreover, numerical simulations and Artificial Intelligence (AI) are essential, complementary tools for understanding and managing the impact of sea level rise (SLR) on coastal aquifers, specifically addressing saltwater intrusion (SWI) and groundwater flooding.

Numerical simulations and Artificial Intelligence (AI) are rapidly transforming sea level rise (SLR) prediction and coastal management, moving from slow, traditional, physics-based modeling to rapid, data-driven, hybrid approaches. While numerical models provide physics-based, spatial, and temporal insights, AI/Machine Learning (ML) excels at handling complex, non-linear relationships; managing large datasets; and providing rapid, real-time predictions. 

AI, and particularly deep learning, allows for real-time flood forecasting and high-resolution mapping, while numerical models provide the foundational, physics-based understanding of hydrodynamics. 

This Special Issue examines coastal water management under sea level rise (SLR) though integrated approaches combining traditional engineering, nature-based solutions, advanced numerical modeling, and Artificial Intelligence (AI) to enhance resilience and mitigate flooding and erosion. 

The future of coastal management relies on combining these approaches within Integrated Coastal Zone Management (ICZM) frameworks. This includes using AI to identify vulnerabilities, numerical models to test solutions, and soft/hard engineering to implement these solutions. 

Prof. Dr. Claudia Cherubini
Guest Editor

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-anonymized 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

  • sea level rise
  • flooding
  • saltwater intrusion
  • artificial intelligence
  • numerical simulations
  • managed aquifer recharge

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Published Papers (1 paper)

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Research

34 pages, 5849 KB  
Article
WaveDroughtNet: A Multi-Modal Wavelet-Enhanced Temporal Convolutional Network for Multi-Horizon Drought Forecasting and Onset Analysis
by K. Venkatachalam, Claudia Cherubini and Alphonse Anushya
Water 2026, 18(12), 1415; https://doi.org/10.3390/w18121415 - 10 Jun 2026
Viewed by 494
Abstract
Drought is a slowly evolving, multi-driver hydro-meteorological hazard whose accurate early prediction is a cornerstone of climate-smart agriculture and water-resource planning. Existing data-driven drought forecasting frameworks suffer from three persistent limitations: (i) most models concatenate heterogeneous climate variables into a single flat feature [...] Read more.
Drought is a slowly evolving, multi-driver hydro-meteorological hazard whose accurate early prediction is a cornerstone of climate-smart agriculture and water-resource planning. Existing data-driven drought forecasting frameworks suffer from three persistent limitations: (i) most models concatenate heterogeneous climate variables into a single flat feature vector, implicitly assuming a single dominant driver such as precipitation, even though atmospheric moisture demand, radiation and wind-mediated evapotranspiration co-determine drought onset; (ii) wavelet preprocessing is typically applied to the full series, introducing future-information leakage that violates the operational causality requirement of forecasting; and (iii) most architectures predict a single horizon and provide no causal attribution explaining when, where and which climatic variables initiated the event. This study proposes WaveDroughtNet, a multi-modal, multi-horizon deep-learning framework that addresses these limitations through five integrated components: (a) a strictly causal Daubechies-4 wavelet decomposition computed in a rolling fashion; (b) six modality-specific encoders with stochastic modality dropout (p = 0.15); (c) cross-modal multi-head attention with four heads; (d) a four-layer temporal convolutional network (TCN) backbone with dilation factors yielding a 240-step receptive field; and (e) a post hoc DroughtOriginTracer that combines temporal attention, modal-attribution and inter-district propagation scans. The Standardised Precipitation Evapotranspiration Index (SPEI), used as the supervisory target, is computed following the canonical Vicente-Serrano formulation. water balance D=P−PET (Hargreaves PET) at a 4-week (≈1-month) timescale, fitted with a three-parameter log-logistic distribution via L-moments, validated by Kolmogorov–Smirnov goodness-of-fit testing (α=0.05) per district, and standardised through the inverse-normal cumulative distribution function. Trained on 18,304 weekly district records from NASA POWER reanalysis (2014–2025) covering all 32 districts of Tamil Nadu, India, WaveDroughtNet uses only 256,869 parameters and produces, in a single forward pass, four forecasts (1 week, 1 month, 3 months, 1 year). On the held-out 2024 test partition (N=1728), the model attains weighted F1=0.9221 and R2=0.8512 at the 1-week horizon, and weighted F1=0.8498 and R2=0.6812 at the 1-year horizon. Diebold–Mariano tests confirm that WaveDroughtNet significantly outperforms naive persistence, seasonal naive, LSTM, ConvLSTM and a vanilla Transformer at the 3-month and 1-year horizons (p < 0.001). The DroughtOriginTracer successfully back-projects 15 Coimbatore events to causal origins 29–41 weeks prior to onset. We explicitly acknowledge three limitations that constrain operational deployment in its current form—zero severe events in the 2024 test partition (F1severe = 0.000), static inter-district modelling, and absence of vegetation-index supervision—and propose concrete mitigation pathways in the Discussion. Full article
(This article belongs to the Special Issue Sea Level Rise Vulnerability and Coastal Management)
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