Geographic Information System Framework Research Articles

An approach to demarcate prospective areas for groundwater recharge a case study of Upper Thurinjalar Tamil Nadu, India

India, as the world's largest consumer of groundwater, faces an alarming water crisis, particularly in southern regions like Upper Thurinjalar, Tamil Nadu. The relentless extraction of groundwater has severely depleted reserves, making it increasingly difficult to meet the water demands of households, agriculture, and industry. This unsustainable usage endangers the region's water security, emphasizing the urgent need for sustainable management practices. Immediate action is essential to safeguard these vital resources and secure a sustainable water future for the region. The present study addresses the critical need to identify optimal groundwater recharge areas to mitigate the adverse effects of groundwater depletion. Utilizing Remote Sensing (RS) and Geographic Information Systems (GIS), this research focuses on delineating suitable recharge zones in Upper Thurinjalar, Tamil Nadu, to promote sustainable water resource management. Through geospatial analysis, thematic maps were generated, incorporating variables such as geomorphology, geology, subsurface lithology, lineaments, land use, drainage density, soil types, and slope. Utilizing satellite data within the GIS framework, groundwater recharge zones were classified into three categories: highly suitable (61.06 km2), moderately suitable (214.18 km2), and least suitable (48.07 km2). Additionally, prioritized interventions for 18 rural reservoirs within the same catchment area included sediment removal, depth enhancement, and infrastructure upgrades. The application of artificial groundwater recharge in these targeted areas is anticipated to significantly alleviate irrigation water deficits, thereby advancing sustainable development and rehabilitating degraded land. The use of a weighted overlay technique within Geographic Information Systems (GIS) has proven to be a highly effective method for optimizing water resource management, facilitating the sustainable development of groundwater resources. The results emphasize the importance of promptly undertaking focused interventions, such as prioritizing tank improvements and deploying artificial recharge procedures, in order to guarantee the long-term availability of water. Implementing these proactive measures is essential for reducing water scarcity and promoting the development of unused land in the designated recharge areas.

Suitability Assessment and Optimization of Small Dams and Reservoirs in Northern Ghana

Water shortages, exacerbated by erratic rainfall, climate change, and population growth, pose significant challenges globally, particularly in semi-arid regions like northern Ghana. Despite the construction of numerous small dams in the region that were intended to provide reliable water for domestic and irrigation purposes, critical water issues persist during dry periods. Key drivers in this failure are attributed to the lack of studies and/or the number of inadequate studies on suitable dam siting. This study focused on assessing the sites of selected small dams in northern Ghana, employing various methods such as stream order analysis and the Analytic Hierarchy Process within a Geographic Information System framework. Results showed that many existing dams are poorly sited, with over half located far from major stream networks, resulting in drying out during the dry season and failing to meet sustainable water storage standards. This study proposed new dam locations that would allow achieving a significant increase in storage capacities from 30% to 60%. These results highlight the necessity for decision-makers to adopt research-based approaches to address water shortages effectively, balancing agricultural, domestic, economic, and environmental needs. Future research should integrate climate change considerations, long-term monitoring, environmental impact assessments, and advanced decision-making techniques such as machine learning.

Application of Bivariate Statistical Method for Landslide Susceptibility Assessment along Srinagar-Bandipora Highway, North Kashmir Himalaya

The present study aims to create and evaluate a map indicating the potential of landslides along the Srinagar-Bandipora highway in the North Kashmir Himalaya region. Therefore, the study utilized the Bivariate Statistical Relative effect (RE) method to establish the correlation between casual factors (CFs) contributing to landslides and the actual landslide incidences. Additionally, fifteen different CFs were analyzed within a Geographic Information System (GIS) framework to assess the impact of these factors on landslides. Further, a comprehensive GIS-based inventory map was compiled, incorporating both primary and secondary data sources, to identify landslide locations. A landslide susceptibility zonation (LSZ) map was developed by integrating all identified landslide CFs along with their respective effect function values. Moreover, the results revealed that 21.06% of the study area was classified as having a very high to high susceptibility to landslides. To validate the accuracy of the produced map, 30% of landslide validation data was employed and the receiver operating characteristics (ROC) curve method was utilized, yielding an excellent result with an area under curve (AUC) value of 0.919 for the study area. Consequently, the findings of this study hold significant potential for mitigating landslide-related hazards and guiding land use planning projects.

Enhancing groundwater resource detection in Ahmednagar District through an integrated GIS framework utilizing AHP, SI, and FR models

ABSTRACT Groundwater resources affected by climate change exacerbate groundwater level declines in areas relying on erratic monsoons and finite surface water supplies. This research developed and compared an integrated geographic information system (GIS) with the analytical hierarchy process (AHP), the statistical index model (SI), and the frequency ratio model (FR). Multiple layers of the GIS framework have captured surface and subsurface characteristics, including rainfall, slope, elevation, land use/land cover, soil, geology, and geomorphology. 30% of the 69 wells in the study area are the testing dataset, and the remaining 70% make up the training dataset. Based on AHP, the results show that 35.34% of the region has low potential, 43.47% has moderate potential, and 21.09% has high potential. While SI modeling reveals 9.60, 57.36, and 24.79% for low, moderate, and high potential, respectively, FR analysis suggests 25.50% low potential, 52.74% moderate potential, and 21.60% high potential areas. The superior reliability of the SI model has been indicated by the validation of the GWPZ map using receiver operating characteristic area under the curve values. The results of this study are crucial for planning sustainable water supplies and managing groundwater resources in areas susceptible to climate change and groundwater depletion.

Effects of localized development on land use and coastline dynamics: A focus on recent changes along the Lekki Peninsula

Human-induced activities significantly impact the natural coastlines of island cities, primarily due to the processes of urbanization and population concentration in developing nations. Over the past decades, affluent communities have rapidly urbanized the Lekki Peninsula on the Nigerian Coast and provided adequate coastal protection. However, little attention is paid to its effects on its adjacent coastlines. This study highlights these coastline alterations and corresponding patterns of land use/land cover (LULC) along the ∼88 km stretches of the study area. The study employs six multispectral Landsat 7 ETM+ and Landsat 8 OLI images within the geographic information system (GIS) framework to analyze these changes. Results reveal that urbanized areas increased from 5.36 % in 2002 to 42.37 % in 2022, accompanied by a significant retreat of approximately 56 % (49 km) along the entire coast over the entire study period. These changes are resultant effects of few protected and isolated major infrastructures exhibiting developmental levels significantly higher than the rest of the coastline. Therefore, addressing these pressing issues is essential for sustainable environmental management and the well-being of ecosystems and human societies.

Geographic information systems adoption model: A partial least square-structural equation modeling analysis approach

The ability of Geographic Information System (GIS) to organize, analyze, visualize and integrate spatial data has been at the top of its primary uses among professional industries. However, considering the extensive adoption of Information System (IS) throughout history for government organizations’ or citizens' disaster response, the implementation of geographical elements is still minimal. Previous GIS models and framework studies, particularly in developing countries, were affected by pandemic pressure, competitiveness pressure, change management, and security factors. Thus, this study aims to develop a model for the successful adoption of GIS using the Technology Acceptance Model (TAM), and De Lone and Mc Lean Information Success Model and analyze the applicability of the existing factors to enhance the performance of Public Sector Organizations (PSOs). From the study, a new conceptual framework was proposed to examine the effects of factors on GIS adoption that impact performance among PSOs from the perspective of Saudi Arabia. Quantitative methods were used to collect data through a questionnaire distributed to 350 respondents from PSO, and only 272 were found to be valid. Partial Least Square Structural Equation Modeling (PLS-SEM) validated the GIS model. The finding revealed that system quality, service quality, change management, competitiveness pressure, perceived ease of use, perceived usefulness, and security factors significantly and positively affected GIS adoption. The study also showed that GIS adoption substantially affected PSO performance. The proposed model provides insight into how GIS adoption can eventually enhance performance among PSOs. In essence, the study contributes to the running of PSO and the decisions taken by policymakers.

A geospatial approach-based assessment of soil erosion impacts on the dams silting in the semi-arid region

Soil erosion significantly impacts dam functionality by leading to reservoir siltation, reducing capacity, and heightening flood risks. This study aims to map soil erosion within a Geographic Information Systems (GIS) framework to estimate the siltation of the K'sob dam and compare these estimates with bathymetric observations. Focused on one of the Hodna basin’s sub-basins, the K'sob watershed (1477 km2), the assessment utilizes the Revised Universal Soil Loss Equation (RUSLE) integrated with GIS and remote sensing data to predict the spatial distribution of soil erosion. Remote sensing data were pivotal in updating land cover parameters critical for RUSLE, enhancing the precision of our erosion predictions. Our results indicate an average annual soil erosion rate of 7.83 t/ha, with variations ranging from 0 to 224 t/ha/year. With a typical relative error of about 13% in predictions, these figures confirm the robustness of our methodology. These insights are crucial for crafting mitigation strategies in areas facing high to extreme soil loss and will assist governmental agencies in prioritizing actions and formulating effective soil erosion management policies. Future studies should explore the integration of real-time data and advanced modeling techniques to further refine these predictions and expand their applicability in similar environmental assessments.

High-resolution estimates of water availability for the Iberian Peninsula under climate scenarios

Water availability is of paramount importance for sustainable development and environmental planning, specifically in regions such as the Iberian Peninsula, renowned for diverse landscapes and varying climatic conditions. Due to climate change, understanding the potential impacts on water resources becomes essential for effective water management strategies. This research effort aims to assess future potential water availability for the Iberian Peninsula in different climate scenarios, employing cutting-edge water resource modelling techniques integrated within a geographic information system (GIS) framework. In this study, potential water availability is defined as the annual demand for water that can be satisfied at a specific point in the fluvial network with certain reliability. An ensemble of state-of-the-art climate models is utilised to project runoff for the Iberian Peninsula during the mid- and late-twenty-first century periods. These climate projections were subsequently processed using the GIS-based water resource management model, WAAPA, to derive potential water availability under a range of realistic hypotheses. The results indicate that anticipated shifts in precipitation patterns will lead to alterations in hydrological regimes across the region, significantly impacting future water availability. By using GIS-based methodologies, we can facilitate the identification of vulnerable areas susceptible to changes in water availability, offering spatially explicit information along the main rivers of the Iberian Peninsula for decision-makers and stakeholders. High-resolution spatial outputs from this research and detailed water availability estimates serve as valuable input for integrated water resource management and climate change adaptation planning. By combining advanced GIS-based hydrological modelling with climate scenarios, this research presents a robust framework for assessing water resources amidst a changing climate, applicable to other regions struggling with analogous challenges. Ultimately, our study provides vital insights for policymakers and stakeholders, empowering them to make informed decisions and devise adaptive measures to ensure sustainable use of water resources despite uncertain future climatic conditions.

Assessment of soil erosion and sediment yield in the Peddavagu watershed, India, using a revised universal soil loss equation model (RUSLE) and GIS techniques

ABSTRACT The present investigation was carried out within the Peddavagu watershed, which is located in India. The necessary datasets, including soil, land use land cover, rainfall, and digital elevation model, were processed and analysed within a Geographic Information System framework. To evaluate soil loss within the watershed, the present investigation employed the revised universal soil loss equation (RUSLE) model. Subsequently, the sediment yield is estimated based on the sediment delivery ratio (SDR). The average annual soil loss was estimated at 17.91 tonnes/hectare/year, which is high soil erosion risk. The RUSLE model's accuracy is 82.1%. Moreover, the findings revealed that sub-watersheds (SW) 9 and SW 3 exhibited the maximum and minimum average annual soil loss. The Peddavagu watershed's SDR was 0.210. Annually, 3.76 tonnes/hectare/year of sediment were transported to the Peddavagu watershed outlet. The findings revealed that SW 9 and SW 5 exhibited the maximum and minimum average annual sediment yield. The model's performance was evaluated by comparing its predictions with gauge data for validation. The observed actual data indicated a yield of 3.66 tonnes/hectare/year, while the model predicted a yield of 3.76 tonnes/hectare/year. This resource offers significant insights for policymakers and decision-makers on sustainable watershed management techniques.

Micro Hydro Power Site Characterization in Indonesia: Variable Optimization for Site Selection Using GeoDetector and RFE-Random Forest

Abstract Micro hydropower (MHP) systems are a promising alternative renewable and sustainable energy source to conventional fossil fuels, particularly in regions with abundant water resources like Indonesia. The success of MHP initiatives is contingent upon identifying suitable sites and remains challenging related to influencing parameters in site selection for the regional/national scale. Therefore, this study aimed to determine the essential influencing variables for MHP site selection by evaluating multiple variables related to the existing MHPs. The method used for analysis was the GeoDetector and Recursive Feature Elimination-Random Forest (RFE-RF) approach in the Geographic Information System (GIS) framework. Combining GeoDetector and RFE-RF models proves to be a potent tool for essential influencing variables screening in MHP site selection. The eight essential variables were obtained, down from nineteen original variables, with a better performance statistically. This hybrid approach considers spatial patterns in data for variable selection, ensuring alignment with the chosen machine learning method. This study result is expected to assist decision-makers in the preliminary evaluation stage of MHP site exploration and promote Indonesia’s transition to a cleaner, more renewable energy future and participatory forest conservation.

Integrating Indicators in Agricultural Vulnerability Assessment to Climate Change

AbstractClimate change stands as one of the most pressing challenges confronting global ecosystems and human livelihoods. The agriculture sector of Anuradhapura district, Sri Lanka, well renowned for its pivotal role in the nation’s food production, faces an increasing threat from the changing climate. This study aims to incorporate the indicator system method which uses a set of indicators to assess the agricultural vulnerability (AV) to climate change in Anuradhapura district. The AV assessment used in this study involves three principal components exposure, sensitivity, and adaptive capacity. The indicators are normalized to allow spatial analysis and smooth integration within a geographic information system (GIS) framework. The AV of Anuradhapura district ranged from 0.32 to 0.67 and was divided among five levels very low, low, moderate, high, and very high. The findings suggest that Anuradhapura’s agricultural sector was significantly impacted by climate change as the majority of the total area was found to have very high, high, and moderate levels of AV with 25, 28, and 24%, respectively. The results obtained from this study are essential for formulating plans to improve Anuradhapura’s agricultural sector resilience and adaptability to ensure food security and livelihood sustainability considering the ever-changing climate.

Disaster management with cloud-based geographic information systems: site selection of landfill areas after Kahramanmaraş, Türkiye earthquake sequence

On February 6, 2023, the earthquakes centered on Kahramanmaraş-Pazarcık and Kahramanmaraş-Elbistan affected several provinces in the region and generated a vast amount of demolition waste. This study aims to develop a site selection model for the most suitable landfill areas after the earthquake using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM). First, the study identifies decision criteria with a Delphi expert panel. Next, it determines the weights using the BWM based on pairwise comparison. Spatial data of the determinants were obtained from open data portals, and proximity, overlay, and slope analyses were carried out for the disaster region. Combining the output data layer of each criterion, a suitability map of the landfills was produced. As a result, 36,2% (29,826 kms square) of the study area was classified as suitable and very suitable. In this study, a cloud GIS-based decision support platform was developed to act quickly and initiate recovery processes within the scope of disaster management of the destructive earthquake. The suitability map and potential sites were shared with the responsible teams in the disaster area so that the potential sites could be displayed on the map and the suitable areas could be navigated on appropriate routes. The findings of the study revealed that the use of open data and cloud GIS framework in the disaster management process enables quick, reliable, and cost-effective actions in the immediate aftermath of disasters.

Application of GIS in land unit mapping and evaluation of land suitability for agriculture in Uong Bi district – Quang Ninh province

This study aims to apply Geographic Information Systems (GIS) in land unit mapping and the evaluation of land suitability for agriculture in Uong Bi District, Quang Ninh Province, Vietnam. Given the complex and spontaneous changes in land usage driven by market fluctuations and environmental challenges, assessing land quality becomes crucial for sustainable agricultural development. Integrating soil, topographic, and climatic data within a GIS framework led to the creation of land unit maps for 40 different units in Uong Bi district, classified based on soil type, slope, soil depth, soil fertility, and hydrological conditions. The assessment of land suitability for seven main crops, including Litchi, Citrus, Dragon fruit, Cinnamon, Vegetables, Pine, and Macca, was conducted using the FAO framework for land evaluation. The results indicate that Pine and Cinamon have the highest arable area across all crops (with 15,895.40 ha and 15,502.40 ha respectively) while Vegetables show the lowest total arable area (4,063 ha). The findings provide valuable insights into the suitability of land for specific crops, facilitating informed decision-making to optimize land use and improve agricultural production.

Implementing a vegetation-based risk index to support management actions in Mediterranean coastal dunes

Coastal dunes play a crucial role in mitigating sea-related impacts and safeguarding coastlines. However, increasing human influence and natural factors such as sea-level rise underscore the need for effective coastal risk assessment methodologies. This study introduces a comprehensive coastal risk index covering 24 km of the Italian coastline within the protected area of San Rossore Park (Tuscany, Italy). The study area, distinguished by its notable coastal dune ecosystems, holds naturalistic, cultural, and economic importance. Nevertheless, diverse uses, zoning, and human impact variables pose challenges.By incorporating geological, socioeconomic, cultural, and ecological parameters, the index integrates a range of data sources and field observations. This research focused on developing and applying a vegetation-based risk index (VRI) within a geographic information system (GIS) framework, recognizing the ecological importance of dune vegetation in mitigating coastal erosion. Analysisrevealed varying risk levels within the study area. Half of the San Rossore Park coastline exhibited low risk values, 37.5% had moderate risk values, and 12.5% had high risk values. The publicly accessible northernmost section displays excellent preservation of dune habitats but faces heightened risk due to anthropogenic impacts. Conversely, the central-southern portion, inaccessible to the public, registers high-risk levels linked to variables associated with coastal erosion.Furthermore, the results highlight areas with heightened cultural and ecological vulnerabilities aligned with elevated risk levels. The index facilitates clear and intuitive cartographic representations of coastal risk, identifying variable categories that substantially influence on risk determination. Tailored strategies, including mitigating human pressure in the northern sector and implementing erosion management in the central-southern region, are recommended.In summary, this study not only provides a practical tool for assessing and managing coastal areas and directing attention to specific threats but also supports stakeholders in informed decision-making. The VRI enhances global sustainable coastal conservation, deepening our understanding of coastal risks and providing valuable insights for effective management strategies.

Integrating Artificial Intelligence and Environmental Science for Sustainable Urban Planning

The rapid urbanization of modern cities presents significant challenges in sustainable development. To address these challenges, there is a growing integration of Artificial Intelligence (AI) and Environmental Science to enhance urban planning processes. This research aims to assess the impact and utility of AI techniques within the framework of Geographic Information Systems (GIS) for sustainable urban planning. Specifically, it investigates how AI-enhanced GIS tools can be employed to improve urban development strategies and enhance sustainability assessments. Employing Spatial Analysis with GIS, this study analyzes data on land use, population density, and environmental indicators across several metropolitan areas. The methodology incorporates machine learning algorithms to predict and simulate urban growth patterns, enabling the assessment of various urban planning scenarios. The findings reveal that AI-enhanced GIS tools significantly improve the precision of development forecasts and sustainability assessments. These tools facilitate more informed decision-making in urban planning by enabling precise predictions about urban expansion and its environmental impacts. The integration of AI with environmental science not only enhances the efficiency of urban planning processes but also contributes to the resilience and sustainability of urban environments. The study provides urban planners and policymakers with advanced tools to forecast and mitigate the environmental impacts of urbanization, thereby setting a benchmark for future studies in the realm of sustainable urban planning. This research demonstrates the practical application of AI in enhancing the capabilities of GIS for complex spatial analyses, contributing significantly to the field of urban planning.

Sea Level Rise, Land Subsidence, and Flood Disaster Vulnerability Assessment: A Case Study in Medan City, Indonesia

Global sea level rise (SLR) has emerged as a pressing concern because of its impacts, especially increased vulnerability of coastal urban areas flooding. This study addresses the pressing concern of SLR and flood vulnerability in the East Coast of North Sumatra (ECNS) and Medan City. We employ a data-driven approach integrating multicriteria analysis, analytical hierarchy process (AHP)-based weighting, and spatial modeling within a geographic information system framework. The analysis considers crucial factors such as slope, land use, soil type, SLR, and land deformation. The study expands the existing framework by incorporating SLR and land subsidence, acknowledging their significant roles in exacerbating flood vulnerability. Future flood-intensity scenarios are simulated based on SLR projections. Data for spatial analysis primarily originated from multisensor satellite imagery, secondary sources from published literature, and field surveys. We validated the consistency of the variable weightings assigned for vulnerability analysis using a consistency ratio threshold (<0.1). Finally, the established flood vulnerability model was validated by comparing its predictions with recorded flood events in the ECNS and Medan City. The ECNS and Medan City areas were classified as very high and highly vulnerable to flooding, respectively.

Estimating Nitrous Oxide (N2O) emissions from managed soils at higher spatial resolution in the Republic of Ireland

This paper aims to define a high spatial resolution model for estimating nitrous oxide (N2O) emissions from agricultural soils in Ireland. In 2020, N2O emissions from the management of agricultural soils represented 10% of the total national agricultural Greenhouse Gas (GHG) emissions. The high spatial resolution model employed here takes account of environmental factors that influence N2O emissions at a more disaggregated spatial scale than the Intergovernmental Panel on Climate Change (IPCC) national inventory-reporting framework. Activity data from the EU Farm Accountancy Data Network (for Ireland) is used in conjunction with biophysical and climatic data in a geographic information system (GIS) framework to estimate N2O emissions at a sub-national level. Results indicate that N2O emissions are 5% lower by applying this high spatial resolution modelling approach at the farm level compared to the baseline model (Tier 2). Nevertheless, 25% of the farms in the sample analysed had an overestimation in their N2O emissions of 20%, and another 25% of the farm sample had an average underestimation in their N2O emissions by 19%, a consequence of the variation of environmental factors among farms. Using a data panel regression, results confirm that the environmental factors examined are statistically significant within the model proposed, and with a simulation, we assessed the switching of 20% of CAN fertiliser for protected urea. According to the high spatial model, this mitigation measure can reduce N2O emissions from inorganic fertilisers by up to 15%. However, the reduction is conditioned by local factors and environmental conditions.

Comparative Study of Deep Neural Networks for Landslide Susceptibility Assessment: A Case Study of Pyeongchang-gun, South Korea

With an increase in local precipitation caused by extreme climatic phenomena, the frequency of landslides and associated damage has also increased. Therefore, compiling fine-scale landslide susceptibility assessment maps based on data from landslide-affected areas is essential. Deep neural network (DNN) and kernel-based DNN(DNNK) models were used to prepare landslide susceptibility maps of the mountainous Pyeongchang-gun region (South Korea) within a geographic information system framework. To map landslide susceptibility, datasets of landslide occurrence areas, topography, land use, forest, and soil were collected and entered into spatial databases, and 18 factors were then selected from the databases and used as model inputs. The training and test datasets consisted of 1600 and 400 landslide locations, respectively. The test accuracies of the DNN and DNNK models were 98.19% and 97.53% and 94.11% and 92.22% for the area under the receiver operating characteristic curve and the average precision value of the precision-recall curve, respectively. The location of future landslides can now be quickly and efficiently predicted using remote sensing data at a lower cost and with less labor. The landslide susceptibility maps produced in this study can play a role in sustainability and serve as references for establishing policies for landslide prevention and mitigation.

Utilization of Augmented Reality Technique for Sewer Condition Visualization

Wastewater pipelines are largely buried underground, and techniques for assessing and visualizing their condition are critical for planning and rehabilitation. This paper introduces a framework for integrating Geographic Information System (GIS), 3D-creation platform, augmented reality (AR) techniques, and machine learning algorithms for the dynamic visualization of the condition of sewer networks. A sewer network in Ålesund City, Norway, was used as a case study, and the developed framework was implemented on an Android OS and Microsoft HoloLens. The results show the potential applications of the integrated framework of GIS, AR, and 3D models for sewer condition visualization. The positioning accuracy of the application for 2D objects is equivalent to that of well-designed GPS receivers (approximately 1–3 m), depending on the handheld device used. Loading and locating 3D objects will be limited by the performance of the devices used.

Mapping the vulnerability of groundwater to saltwater intrusion from estuarine rivers under sea level rise

Surface water bodies connected to the ocean such as estuarine rivers can act as pathways for saltwater intrusion (SI, i.e., the displacement of fresh groundwater by saltwater in an aquifer) far inland of the coast, presenting one of the earliest risks associated with relative sea level rise (SLR). However, SI vulnerability mapping approaches have largely focused on SI from the coast and do not consider estuarine surface water bodies, except for GALDIT–SUSI (Kazakis et al., 2019); a weighted indexing approach designed for regional-scale mapping applications using a Geographic Information Systems (GIS) framework. However, GALDIT-SUSI is subjective in ranking the importance of factors that can contribute to SI and combining these into a vulnerability index. A less subjective approach to assessing SI vulnerability than weighted indexing methods involves the use of physically-based analytic solutions such as Strack (1976), but these have not been applied in a GIS framework or along estuaries and rivers previously. Here, these analytic solutions, surface water salinity, and surface water-groundwater freshwater head gradients are used in the development of a new SI vulnerability tier system. This new approach was applied in the low-lying coastal city of Ōtautahi Christchurch, Aotearoa New Zealand, along 70 km of coastal, estuary, and river margins under current sea level and SLR using GIS, then compared to GALDIT–SUSI. The main advantage of the SI vulnerability tiers method is that it considers surface water behaviour, e.g., increased saltwater encroachment up rivers under SLR, which exposed new areas of the aquifer to saltwater and increased SI vulnerability upstream. In contrast, GALDIT-SUSI does not consider surface water conditions and accounts for topography and groundwater level as the main SI vulnerability drivers in this application. The SI vulnerability tiers proposed here are more theoretically robust than GALDIT-SUSI and provide a physically-based, large-scale, relatively low-budget and rapid screening tool to highlight areas most vulnerable to SI under current and future conditions for further monitoring and management; a gap of national and international relevance.