Engineering

Abstract: Autonomous inspection of ballast water tanks requires three-dimensional (3D) LiDAR-based simultaneous localization and mapping (SLAM) in Global Positioning System (GPS)-denied, geometrically repetitive interiors, where sensing, mapping, and control modules share a limited onboard memory budget. Graph SLAM backends that rely on sparse factorization can incur fill-in, increasing peak memory and limiting deployment on edge computers. The proposed architecture couples a robust hierarchical bundle adjustment frontend with a factorization-free Kaczmarz backend. The frontend combines residual-adaptive weighting, damped and bounded pose updates, soft fallback, local-map compression, and memory-aware keyframe control. The backend stores the whitened Jacobian in compressed sparse row (CSR) format and performs row-wise projections without explicitly forming the normal equations, a Cholesky factor, or a transpose cache. Evaluation used Norwegian University of Science and Technology (NTNU) Ballast Water Tank missions 1--3, containing 851, 1202, and 1084 LiDAR frames. Following robust local bundle adjustment and verified similarity alignment, translational root-mean-square errors were 0.080, 0.110, and 0.127m, corresponding to 0.137%, 0.144%, and 0.122% of the reference path lengths; archived baseline ratios ranged from 0.281% to 0.372%. The results support a numerical architecture that combines frontend stabilization, row-wise optimization, and memory-aware policies for resource-constrained marine inspection robots.

Abstract: Ship recycling is expanding in Nigeria with no dedicated statutory framework to govern it, creating a mismatch between a high-hazard industry and an underdeveloped regulatory environment. Although Nigeria ranks among the top ten emerging ship-recycling countries, the sector remains informally governed, with overlapping mandates, thin enforcement capacity, and fragmented risk-assessment practices. This study evaluates Nigeria's approach through a robustness lens, drawing on Rasmussen's socio-technical levels, Renn's integrative risk-governance model, and Hale's robustness criteria, with Hollnagel's Safety-II as a complementary, proactive perspective. The analysis combines documentary review, field observations across four recycling hubs (Lagos, Warri, Onne/Port Harcourt, and Calabar), and semi-structured interviews with fifteen stakeholders, including yard managers, regulators, workers and safety officers, and maritime experts, followed by a multi-stakeholder validation workshop. Of nineteen sub-criteria, only six are formally present, four are partially addressed, and nine are absent altogether, and even the elements that exist on paper are generic and weakly enforced. Field visits revealed ad hoc practices, hazardous working conditions, weak supervision, and little institutional learning. On this basis we propose a Nigeria-fit Robustness-Focused Risk-Governance Framework built on three pillars: multi-level safety oversight, an integrative risk-governance process, and enhanced mitigation strategies. It emphasises legal recognition, clearer accountabilities, interdisciplinary assessment, transparent risk-acceptance criteria, continuous monitoring, and feedback loops, with a phased implementation pathway. The study shows why robustness, not resilience, should be the organising principle for governing emergent high-hazard sectors in developing economies, and offers practical steps for strengthening institutional capacity in Nigeria's maritime domain.

Abstract: Liquefied natural gas (LNG) is increasingly used in maritime propulsion systems as a means to reduce atmospheric emissions. However, methane slip from dual-fuel engines remains a critical limitation due to the high global warming potential of methane. This study presents a comprehensive experimental assessment of greenhouse gas (GHG) emissions from a new-generation four-stroke dual-fuel engine installed on a cruise vessel and operating on both LNG and marine gas oil (MGO). Measurements were carried out during full-scale sea trials under real navigation conditions. Results show that methane slip remains strongly dependent on engine load, with low and stable values at medium-to-high loads (1.95 g/kWh average over the 60–90% range) and a significant increase at low load. Compared with a previous engine generation (46DF), the 46TS-DF engine exhibits an approximate 18% reduction in methane slip above 60% load. On a well-to-wake basis, this results in an overall CO₂-equivalent emission reduction of about 6%, of which 42% is attributable to methane slip reduction and the remainder to improved energy efficiency. In contrast, switching from LNG to MGO operation leads to a 23.5% increase in CO₂-equivalent emissions. Black carbon (BC) emissions were measured and as expected despite the limited number of available studies, they were found to be significantly lower in LNG mode, with reductions exceeding 90% compared with MGO operation. Finally, an Engine Load Monitoring (ELM) analysis based on one year of operational data highlights the strong influence of vessel operating profiles on methane slip. The application of both IMO and FuelEU methodologies yields consistent methane slip coefficients (1.34% and 1.36%, respectively), significantly lower than current default values, noting that these estimates do not include crankcase emissions. These results demonstrate the importance of integrating real operational conditions into emission assessment frameworks for LNG-fuelled vessels.

Abstract: The deployment of Autonomous Underwater Vehicle (AUV) swarms has become pivotal for oceanographic exploration and monitoring. However, the efficacy of swarm formation control is heavily constrained by the harsh characteristics of underwater acoustic communication networks (UACNs), particularly in non-fully connected networks where topology changes dynamically. To tackle high packet collision rates and excessive signaling overhead in existing Medium Access Control (MAC) protocols, this paper proposes a novel Cluster-based Mobile MAC (CM-MAC) protocol tailored for hierarchical clustered AUV networks. The CM-MAC protocol operates under a distributed two-tier architecture. The first-level cluster head initiates scheduling, followed by the second-level cluster heads, which coordinate transmissions within their sub-clusters by exploiting locally known state information and scheduling decisions. We establish the transmission constraints that prevent packet collisions among mobile nodes amid topology changes. Building upon the transmission constraints, genetic algorithms are applied across all layers’ transmission scheduling to optimize the sending sequence and timing, reducing overall latency. Simulation results indicate that the CM-MAC protocol significantly improves network throughput and decreases information-sharing update intervals compared to traditional TDMA, pure Aloha, and random-access CM-MAC. This study provides a robust communication framework for large-scale AUV swarm coordination in complex underwater environments.

Abstract: Accurate significant wave height prediction is essential for fuel-efficient ship operation and weather routing, as wave-induced resistance directly affects propulsion demand and fuel consumption. This study proposes a Residual U-Net-based deep learning correction model to improve long-range SWH forecasts from WAVEWATCH III (WW3). WW3 global forecast fields were corrected using the proposed model, with CMEMS reanalysis data used as the ground-truth reference. The corrected outputs, denoted as WW3_UNET, were evaluated against 10-minute-resolution main engine fuel oil consumption (ME1_FOC) records and onboard wave observations from a commercial vessel traversing the South Atlantic in 2025. WW3_UNET showed markedly improved agreement with ship observations compared with the raw WW3 forecast across all lead times from 0 to 288 h. When a 24-hour moving average was applied, WW3_UNET achieved a correlation of 0.720 with ME1_FOC at the 168–180 h lead time, closely approaching the 0.736 obtained from onboard wave measurements. These results indicate that AI-corrected forecasts can provide observation-consistent wave information up to 7–8 days in advance. The proposed approach can support fuel-aware weather routing and voyage planning, thereby contributing to improved maritime energy efficiency and decarbonization.

Abstract: This comprehensive review investigates hybrid propulsion technologies as a pathway to decarbonization and improved energy efficiency in the maritime sector. Through a review of recent literature, this study synthesizes current knowledge on energy management strategies and capacity sizing approaches for hybrid ship propulsion systems. Reported results indicate that optimized energy management can reduce fuel consumption and greenhouse gas emissions while minimizing total operational costs. Among real-time strategies, the equivalent consumption minimization strategy emerges as particularly suitable for maritime use due to its low computational demand and independence from full voyage profile knowledge, yet its maritime application remains far less developed than in the automotive domain. Capacity sizing and energy management are usually treated as separate optimization problems, limiting the achievability of truly optimal solutions. Only a few studies adopt integrated co-optimization frameworks, and these are typically built around simplified or fixed operational profiles. Moreover, the coupling between energy management parameters, such as the ECMS equivalence factor, and hardware sizing remains insufficiently explored. The findings suggest that future research should prioritize adaptive energy management formulations calibrated for stochastic maritime duty cycles, the incorporation of battery degradation models into co-optimization, and validation against stochastic, real-world operating conditions.

Abstract: Autonomous underwater glider fleets are increasingly deployed to observe mesoscale eddies, yet a methodology for evaluating observation quality under uncertainty remains lacking. This paper presents SCOPE, a framework integrating uncertainty propagation, multi-dimensional assessment, and objective metric selection. A 27-metric evaluation system spanning seven quality categories is constructed; an Adaptive Core Metric Selection (ACMS) algorithm compresses these to a compact core subset. Two-stage sensitivity analysis identifies the velocity ratio as the dominant parameter with a non-monotonic effect. The optimal velocity ratio decreases with eddy intensity in both an analytical model (0.58 to 0.47) and four real HYCOM eddies (2.35 to 0.35). ACMS converges on all four real eddies, and parameter rankings are consistent across environments. An ablation experiment in the analytical model validates the effectiveness of the two sensitivity-derived design rules (25% and 45.8%) gap closure and reveals their non-additive interaction. SCOPE offers a framework and diagnostic workflow for uncertainty-aware evaluation of autonomous ocean observation systems.

Abstract: The pronounced aero-hydrodynamic coupling effects of modern Wind-Assisted Propulsion System (WAPS) ships challenge the applicability of traditional stability frameworks, which are predicated on hydrostatic energy balance, in satisfying the dynamic constraints of the Second Generation Intact Stability Criteria (SGISC). This paper systematically reviews the methodological evolution of dynamic stability assessments for WAPS ships under extreme and damaged conditions. By introducing a "Hierarchy of Evidence" evaluation framework, this study delineates the applicability boundaries of aerodynamic Reduced-Order Models (ROM), extended 3/4-DOF maneuvering equations, and 6-DOF time-domain hybrid architectures, defining the role of high-fidelity CFD-VPP in establishing calibration benchmarks. The review also discusses the damping distortion mechanisms induced by multiphase flow sloshing under damaged conditions. Synthesized findings indicate that transitioning towards a 6-DOF time-domain coupled architecture provides clear advantages for capturing unsteady aerodynamic hysteresis and nonlinear interference. Meanwhile, surrogate models, such as Physics-Informed Neural Networks (PINNs), offer a potential pathway to mitigate the computational demands associated with long-term extreme value extrapolations. Ultimately, this review provides a methodological reference for the high-fidelity assessment of WAPS and the development of Digital Twin systems.

Abstract: Electric vehicle (EV) transport by ship is expanding beyond industrial logistics centered on automobile production, trade, and pure car and truck carriers (PCTCs) into daily transportation for island tourism, commuting, and essential mobility. According to Korea Maritime Transportation Safety Authority (KOMSA) vessel status data as of March 2026, 104 of 146 domestic passenger ships were car-ferry passenger ships, accounting for 71.2% of the fleet and operating on 75 of 99 designated routes nationwide. Korea Shipping Association (KSA) operational records show that the EV transport rate on these routes increased from 0.76% in 2024 to 1.21% in 2025, with some routes exceeding 2.0–4.7%. Unlike enclosed multi-deck PCTC vehicle spaces, Korean coastal car-ferry passenger ships generally have single-tier open vehicle decks and bow ramp gates. Crosswinds on open decks may reduce smoke detector activation probability by 60–75%. Although Article 97 of the Standard for Ship Fire-Fighting Appliance newly requires dedicated EV fire-fighting equipment for car-ferry ships, it remains primarily equipment-prescriptive and does not yet provide open-deck-specific performance requirements for wind-resistant detection, fixed EV-zone cooling, EV-designated stowage arrangements, or passenger-operator safety management obligations. This study applies the five-step International Maritime Organization (IMO) Formal Safety Assessment (FSA) procedure to support improvements to EV fire-fighting equipment standards for coastal car-ferry passenger ships. Hazard Identification (HAZID) was conducted with a 15-member advisory panel, and probability elicitation was performed through a Delphi survey with 10 core experts, showing strong consensus (Kendall’s W = 0.74, p < 0.01). Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) probabilities were derived from the Delphi results and international literature. H-07, representing wind-induced smoke dilution, was identified as the only first-order minimal cut set. Monte Carlo-based FTA–ETA analysis (n = 10,000) estimated annual fire frequencies of 5.9 × 10⁻², 1.8 × 10⁻¹, and 2.9 × 10⁻¹ yr⁻¹ at EV loading ratios of 10%, 30%, and 50%, respectively, with 2.47 expected fatalities per fire. Risk entered the IMO ALARP band above a 30% EV loading ratio and exceeded the maximum tolerable crew risk above 50%. The combined application of Risk Control Option (RCO) 2, 3, and 4 reduced annual expected fatalities by 85.6%. Based on these results, six RCOs and institutional recommendations are proposed, including strengthened safety management obligations for passenger ship operators.

Abstract: To ensure the operational safety of the OCTABUOY platform used for offshore wind turbine installation in shallow waters, an eight-point symmetric mooring system was designed based on its octagonal structural configuration. The system provides high horizontal stiffness and balanced load distribution, enhancing stability under complex environmental conditions.Physical model tests were conducted under combined wind, wave, and current loading, considering multiple wave directions, environmental cases, and five draft conditions. The mooring tensions and six-degree-of-freedom motions were systematically analyzed to evaluate system performance and safety.Results show that the proposed mooring system effectively limits platform motions and maintains stable load-sharing characteristics. The minimum safety factor under the most unfavorable condition exceeds the design requirement. In addition, the system demonstrates good redundancy: after single-line failure, remaining mooring lines redistribute loads without progressive collapse. Draft and wave incident angle significantly influence peak tensions and motion responses, with smaller drafts and oblique wave directions producing relatively higher loads.The experimental results confirm the reliability and safety margin of the eight-point mooring system and provide practical guidance for the engineering application and operational assessment of the OCTABUOY platform in shallow-water wind installation projects..

Abstract: A systematic experimental investigation was conducted on the motion response (RAO) and mooring performance of a novel disk-shaped buoy (geometric scale 1:10) subjected to combined wind, wave, and current actions. A hybrid experimental strategy was employed, integrating a large-scale wave flume (for long-period waves and currents) with a harbor basin (for short-period waves and wind), aiming to mitigate the scale effects inherent in Froude-scaled models, particularly with regard to drag force measurements. The test matrix included free decay in calm water, RAOs under regular waves, motion and mooring line tension under irregular waves, and measurements of wind and current drag coefficients. Key results indicate a natural roll period of approximately 3.0 s with a notably high dimensionless damping ratio (ζ ≈ 0.14–0.15), which is conducive to rapid motion attenuation. A pronounced resonance peak in the roll RAO (26.6°/m) was observed near the 3 s period. Under an extreme sea state (Hₛ = 13.8 m, Tₚ = 16.1 s), the maximum roll angle and dynamic mooring line tension reached 21.30° and 61.56 kN, respectively, the latter being about 3.0 times the static pretension. The mean wind drag coefficient and current drag coefficient were determined as 0.76 and 0.44. This research provides a validated dataset and critical insights for the design, mooring system optimization, and operational safety assessment of such disk-shaped buoys. The effectiveness of the hybrid testing approach is confirmed, and the favorable damping characteristic of this buoy form is highlighted.

Abstract: Coastal zones are facing rising exposure to climate-related hazards alongside intensifying human pressures, which highlights the need for robust tools to assess vulnerability. This study uses a GIS-based Coastal Vulnerability Index (CVI) to quantify and map relative vulnerability along ~13 km of shoreline in Al Hoceima Bay (northern Morocco). The proposed CVI integrates eight geological and physical indicators, including geomorphology, shoreline erosion and accretion rates, coastal slope, elevation, natural habitats, relative sea-level rise, significant wave height, and tidal range. Spatial analyses were performed using remote sensing data, historical records, field measurements, and Geographic Information Systems (GIS). The analysis reveals that 37% of the shoreline is categorized as high vulnerability, 44% is moderate, and 19% is low. Highly vulnerable sectors are primarily associated with low elevations, gentle coastal slopes, sandy beach systems, limited natural habitat protection, and proximity to river mouths. These findings demonstrate that the applied CVI provides a rapid and cost-effective framework for identifying priority areas for coastal management and climate adaptation. The proposed approach offers valuable decision-support insights for sustainable coastal planning in Al Hoceima Bay and other Mediterranean coastal environments characterized by limited data availability.

Abstract: In extremely shallow water environments, the limited water depth is comparable to the maximum bubble radius. The pulsation of an underwater explosion bubble is strongly constrained by both the free surface and the rigid seabed, exhibiting complex nonlinear coupling effects, which are of great significance for the safety assessment and protection design of nearshore engineering. To address this issue, an axisymmetric two-dimensional numerical model based on the Eulerian finite element method (EFEM) with operator splitting technique and the Volume of fluid (VOF) interface-capturing approach is established. Under the assumptions of inviscid and incompressible flow, a systematic numerical investigation is carried out to examine the effects of the water depth parameter λ, position parameter γ)and buoyancy parameter δ on the bubble dynamics and the evolution of free surface structures. The results show that the maximum bubble radius, pulsation period and jet characteristics are all significantly regulated by the above three parameters. Moreover, under multi-period bubble pulsation, different parameter conditions lead to diverse evolution characteristics of free surface structures including the water spike, wrinkles and water skirt. The findings reveal the governing mechanisms of key dimensionless parameters on the nonlinear bubble-multi-boundary coupling dynamics in extremely shallow water explosions, providing an important numerical basis and theoretical reference for the theoretical analysis and safety design of related shallow water explosion engineering problems.

Abstract:

This study evaluates the techno-economic feasibility of LNG regasification alternatives, including offshore platform conversion, floating storage and regasification unit (FSRU) retrofit, and onshore LNG terminals, under conceptual design conditions at a capacity of 100 MMSCFD. The analysis integrates cost estimation, project schedule, and technical maturity within a multi-criteria decision-making framework based on the Analytic Hierarchy Process (AHP), combining quantitative techno-economic results with expert judgment to support structured comparison of alternatives. Cost estimation is conducted using two approaches, namely cost–capacity scaling and analogous estimation, to examine their influence on feasibility outcomes. The results indicate that the conventional scaling method, using an exponent of 0.6, produces inconsistent results across configurations, overestimating costs for offshore-based systems while underestimating costs for onshore LNG terminals. Back-calculation of effective scaling exponents yields values of approximately 0.43 for offshore platform conversion, 0.37 for FSRU retrofit, and 0.78 for onshore LNG terminals, demonstrating that cost–capacity relationships are configuration-dependent and cannot be represented using a single uniform exponent. The AHP evaluation, conducted under two scenarios based on the applied cost estimation methods, shows that offshore platform conversion consistently achieves the highest feasibility ranking, followed by FSRU retrofit and onshore LNG terminals. While the ranking remains unchanged, the choice of cost estimation method influences the magnitude of score differences, affecting the strength of preference among alternatives. These findings highlight the limitations of conventional scaling approaches and demonstrate that offshore platform conversion can serve as a cost-competitive and time-efficient alternative for LNG infrastructure development, particularly in regions with existing offshore assets.

Abstract: Accurate prediction of nitrogen oxide (NOx) emissions from marine medium-speed four-stroke diesel engines is crucial for meeting increasingly stringent environmental standards. This paper focuses on optimizing the first and most significant reaction of the extended Zeldovich mechanism for the formation of nitric oxide (NO). A numerical engine model was developed and validated against experimental measurements of combustion pressure, power, and emissions at 81.95% of the Maximum Continuous Rating (MCR). The research analyzes the influence of various chemical reaction rate constants (k_1,f) on the accuracy of NO concentration predictions. The results demonstrate that by carefully selecting the kinetic parameters, the deviation of the numerical model can be reduced to only -0.93%. Utilizing the optimized constant for the primary Zeldovich reaction k_1,f = 1.8*10¹⁴ *e^(-38300/T), significantly improves the reliability of combustion and emission formation simulations.

Abstract: Efficient path planning and trajectory tracking are central to the safe and autonomous navigation of autonomous underwater vehicles (AUVs) in complex and unknown environments. To address the inherent challenges of safety, smoothness, and exploration efficiency in such settings, this paper presents an integrated framework that synergistically couples three enhanced core modules with complementary innovations. First, improved I-LazyTheta* and A-IRRT* algorithms are developed to incorporate safety margin collision detection and dynamic obstacle avoidance weight regulation, which enable efficient generation of collision-free paths that proactively maintain safe clearance in cluttered 3D spaces. Second, a trajectory tracking module based on a finite-state machine is designed, integrating B-spline optimization and a curvature-adaptive speed control mechanism to ensure high-precision following with guaranteed path smoothness and trackability. Third, a novel 3D autonomous exploration strategy tailored to underwater sonar constraints is constructed, combining frontier point clustering, multi-dimensional information gain evaluation, and traveling salesman problem (TSP) path optimization to achieve efficient unknown environment traversal while significantly reducing redundant detection and energy consumption. The proposed framework supports modular decoupling for independent reuse as well as integrated collaborative operation, offering flexible adaptability to diverse underwater robotic platforms. Simulations demonstrate that the integrated approach achieves superior performance in path safety and tracking accuracy, along with an exploration coverage of 79.08%, validating its effectiveness for robust AUV autonomy in complex underwater scenarios.

Abstract: Dolphins are widely recognized as intelligent marine mammals with sophisticated communication and echolocation. Accurately classifying their whistles is essential for understanding how they communicate and for tracking population size, structure, and distribution. Here, we assemble a large, high-quality dataset of dolphin whistle signals collected at the Chimelong Ocean Kingdom, including a whistle type not previously available to researchers. We then explore Convolutional Neural Networks (CNNs) for classifying whistles of the Indo-Pacific bottlenose dolphin (Tursiops aduncus), testing 5 CNN architectures to analyse the signals. Model performance is reported using mean Average Precision (mAP), showing that CNN approaches can reliably separate different whistle classes. To probe robustness, we also introduce noise at defined SNR levels to increase testing complexity and assess the stability of the classifier. We use Bellhop for channel simulation to construct the channel impulse response. The simulated data can be used as augmented data to add to the original data training set. The results did indicate that this can enhance the robustness of the classification model. This work provides valuable tools for marine biologists and researchers specialising in animal acoustics, enhancing the understanding of dolphin communication. It also contributes to the conservation and management efforts of dolphin populations, offering significant insights into their behaviour and ecological needs.

Abstract: Illegal, unreported, and unregulated (IUU) fishing threatens marine ecosystems in the Western Pacific. Traditional patrol strategies suffer from low efficiency due to insufficient utilization of multi-source surveillance data. This study proposes a maritime patrol framework integrating AIS fishing effort, Sentinel-1 SAR dark vessel detections, and vessel encounter records. An Adaptive Priority-Boosted Ant Colony Optimization (APB-ACO) algorithm with two-phase deadline-aware construction ensures high-priority coverage within 72 hours while minimizing total distance. Experiments on real satellite datasets demonstrate that APB-ACO achieves 7% shorter routes with 46× lower variance than conventional methods, with 100% high-priority task coverage. The framework provides an effective decision-support tool for maritime law enforcement. This framework can serve as a practical decision-support tool for maritime law enforcement and marine resource management.

Abstract: The rapid and accurate assessment of residual ultimate strength after ship damage is crucial for rescue decision-making and navigation safety, while traditional methods struggle to meet the demands of complex random damage scenarios in terms of efficiency or accuracy. This study proposes a hybrid framework that integrates high-fidelity nonlinear finite element simulation (FEM) and a Bayesian-regularized backpropagation neural network (BPNN). FEM is used to accurately simulate a large number of random damage scenarios, generating a physically credible benchmark dataset. BPNN serves as an efficient surrogate prediction model, with its key parameters—the number of hidden layers and the training algorithm—systematically optimized to enhance generalization capability. The results show that: 1) The FEM simulation results deviate by less than 5% compared to the Smith method, validating the reliability of the dataset. 2) The prediction performance of BPNN is highly dependent on the number of hidden layers and the training algorithm, exhibiting non-monotonic variation, with an optimal parameter combination identified as 8 hidden layers paired with the Bayesian algorithm, achieving a prediction regression value R of 0.91662. 3) Deep networks are prone to overfitting, while shallow networks suffer from insufficient feature capture. 4) The Bayesian algorithm performs best in terms of overfitting resistance and stability. This study not only provides a high-precision and efficient intelligent solution for residual strength assessment of damaged hulls, but its systematic neural network parameter optimization strategy, particularly the approach of identifying optimal depth and selecting anti-overfitting algorithms, also offers important reference for the design of intelligent damage assessment models for similar engineering structures.

Abstract: Coastal socio‑ecological systems are increasingly exposed to the combined pressures of climate change, land‑use intensification, hydrological alterations and expanding infrastructure networks. These pressures interact across the land–catchment–lagoon–sea continuum, generating complex feedbacks that challenge traditional planning instruments, which remain sectoral and fragmented. The Mar Menor (SE Spain), a semi‑enclosed Mediterranean lagoon affected by intensive agriculture, urbanisation, hydrological modifications and recurrent extreme climatic events, exemplifies this systemic vulnerability. Existing planning frameworks—local urban plans, regional territorial plans, river basin management plans, maritime spatial plans and lagoon‑specific strategies—operate independently, each addressing only a fragment of the system and none integrating climate change as a structuring axis. This article introduces Integrated Spatial Planning (ISP) as a novel territorial–climatic framework designed to overcome these limitations. ISP integrates climate forcing, land uses, catchment processes, lagoon dynamics, marine conditions, critical infrastructures, intermodal and energy corridors and multilevel governance into a single analytical structure. A central component of the methodology is a four‑zone multilevel zoning system that connects municipal, regional, basin, marine and EEZ planning domains within a unified territorial–climatic logic. The ISP matrix is applied to the Mar Menor to produce the first holistic diagnosis of the system. Results reveal strong land–sea–catchment interactions, high climatic exposure, vulnerable infrastructures and structural governance fragmentation. The matrix exposes systemic incompatibilities and vulnerabilities that remain invisible in sectoral planning instruments. The discussion demonstrates how ISP clarifies the roles and responsibilities of each governance level, supports multilevel coherence and integrates critical infrastructures and intermodal corridors into climate‑resilient planning. ISP reframes climate change as the organising principle of territorial planning and provides a replicable, scalable methodology for coastal socio‑ecological systems facing accelerating climate pressures. The Mar Menor case illustrates the urgent need for integrated territorial–climatic governance and positions ISP as a scientifically robust and operationally viable pathway for long‑term adaptation and resilience.