The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.

Unequivocal change in the climate system has put coastal regions around the world at increasing risk from climate-related hazards. Monitoring the coast is often difficult and expensive, resulting in sparse monitoring equipment lacking in sufficient temporal and spatial coverage. Thus, low-cost methods to monitor the coast at finer temporal and spatial resolution are imperative for climate resilience along the world’s coasts. Exploiting such low-cost methods for the development of early warning support could be invaluable to coastal settlements. This paper aims to provide the most up-to-date low-cost techniques developed and used in the last decade for monitoring coastal hazards and their forcing agents via systematic review of the peer-reviewed literature in three scientific databases: Scopus, Web of Science and ScienceDirect. A total of 60 papers retrieved from these databases through the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol were analysed in detail to yield different categories of low-cost sensors. These sensors span the entire domain for monitoring coastal hazards, as they focus on monitoring coastal zone characteristics (e.g., topography), forcing agents (e.g., water levels), and the hazards themselves (e.g., coastal flooding). It was found from the meta-analysis of the retrieved papers that terrestrial photogrammetry, followed by aerial photogrammetry, was the most widely used technique for monitoring different coastal hazards, mainly coastal erosion and shoreline change. Different monitoring techniques are available to monitor the same hazard/forcing agent, for instance, unmanned aerial vehicles (UAVs), time-lapse cameras, and wireless sensor networks (WSNs) for monitoring coastal morphological changes such as beach erosion, creating opportunities to not only select but also combine different techniques to meet specific monitoring objectives. The sensors considered in this paper are useful for monitoring the most pressing challenges in coastal zones due to the changing climate. Such a review could be extended to encompass more sensors and variables in the future due to the systematic approach of this review. This study is the first to systematically review a wide range of low-cost sensors available for the monitoring of coastal zones in the context of changing climate and is expected to benefit coastal researchers and managers to choose suitable low-cost sensors to meet their desired objectives for the regular monitoring of the coast to increase climate resilience.

Coastal climate services play a crucial role in developing customised climate information for diverse end-users and stakeholders. To build climate-resilient societies, decision-makers should be empowered through easy access to powerful tools that enable timely adaptation to future and ongoing hazards. For this reason, fit-for-purpose climate services are needed to conduct accurate historical characterisation and projections for interpretative studies on climate- and water-related risks at the local coastal scale. The EU-funded SCORE project (Smart Control of Climate Resilience in European Coastal Cities) utilises climate and marine services for the development of smart technologies that support nature-based solutions to address specific concerns, including rising sea levels, coastal erosion, and coastal flooding due to extreme weather events. As part of the SCORE project, decision-makers will be able to address climate change-related coastal effects in their own cities through novel participatory approaches (Coastal City Living Labs—CCLLs). As part of this framework, this work (i) discusses the main requirements for the identification of fit-for-purpose coastal climate services for local-scale impact studies in European coastal cities based on CCLL requests and prior knowledge and (ii) provides relevant parameters and features that fulfil the users’ needs.

Coastal areas are socially, economically, and environmentally intensive zones. Their risk to various natural coastal hazards like coastal flooding, erosion, and storm surges has increased due to climate-induced changes in their forcing agents or hazard drivers (e.g. sea-level rise). The increased exposure (e.g. dense population living near the coast) and vulnerability (e.g. insufficient adaptation) to these hazards in the coastal areas have complicated the adaptation challenges.Thus, monitoring coastal hazards is essential to inform suitable adaptation to increase the climate resilience of the coastal areas. In monitoring coastal climate hazards to develop coastal climate resilience, both the forcing agents and the coastal responses should be observed.As coastal monitoring is often expensive and challenging, creating a database through a systematic analysis of low-cost sensing technologies, like UAV photogrammetry for monitoring the hazards and their drivers would be beneficial to the stakeholders. Real-time information from these low-cost sensors in complement to the existing institutional sensors will facilitate better adaptation policies including the development of early warning support for building coastal resilience. In addition, it would also provide a valuable dataset for validating coastal numerical models and providing insights into the relationship between these hazards and forcing agents. Additionally, such low-cost sensors would also create opportunities for engaging citizens in the data collection process, for efficient data collection, and increasing scientific literacy amongst the general public. For instance, in the Sensing Storm Surge Project (SSSP), citizen science was used to collect technical data to characterise estuarine storm surges, generating data useable in peer-reviewed Oceanography journals. Coastal areas show complex morphological changes in response to the forcing agents over a wide range of temporal and spatial scales. Thus, monitoring the hazards with a sufficient temporal and spatial resolution is imperative to distinguish the changes in these hazards/drivers due to climate change from natural variability. This will not only help address the response strategies to these hazards but also adjust these response strategies according to the changing vulnerability of a particular region.The database of the low-lost sensors thus created is in no way exhaustive since those have been retrieved through a certain combination of keywords in databases like Sciencedirect, Web of Science, and Scopus, nonetheless it is useful as these are the latest low-cost sensors available to monitor the major coastal hazards in the vulnerable coastal regions.

Unequivocal change in the climate system has put coastal regions around the world at increasing risk from climate-related hazards like storm surge flooding. With studies demonstrating an increase in the frequency of storm surges along the Northwest coast of Ireland, within the EU H2020 project SCORE (Smart Control of the Climate Resilience in European Coastal Cities) a numerical modelling approach has been undertaken using SHYFEM (System of HYdrodynamic Finite Element Model) to simulate the storm surges into 2100 for the NW of Ireland under different Climate scenarios. Using the significant grid flexibility offered by the finite elements within SHYFEM, unstructured grids are implemented for two computational domains. One confined to the region of interest, specifically the Donegal Bay, namely the Limited Area Model (LAM) and the other extended to span the entirety of the North Atlantic Basin namely the Basin Scale Model (BSM). The two model applications are designed to capture the development of storm surges due to wind shear stresses and barometric pressure fluctuations associated with the passage of storm systems across the North Atlantic and to quantify the influence of the tides on the tide surge interaction. Given the large tidal excursion in the region, baroclinicity is neglected and the models are implemented in the barotropic mode. The LAM is forced with surface atmospheric forcing (pressure and winds) from ECMWF and sea surface height (SSH, including eleven tidal components) from CMEMS at its open boundary, whereas the BSM is solely forced with the atmospheric forcing. Extensive calibration of wind drag and bottom friction coefficients for the LAM and BSM using tidal gauge observations for 2020, shows high P. Corr and acceptable RMSE. Only negligible differences, around 3 cm, are seen in the surge magnitudes between the LAM and the BSM implying the negligible non-linear interaction of the tides on the storm surge dynamics in the Donegal Bay. Thus, the BSM has been forced with atmospheric forcing obtained from CMIP6 climate models to predict the impact of climate changes on storm surge intensity and frequency under different climate scenarios up to 2100. Finally, by accounting for the tidal contribution, total water levels projections have been determined thus facilitating the generation of useful statistics (for instance Annual exceedance probabilities) and flood maps to inform relevant policies for coastal resilience.