Detailed Estimates Research Articles

Kinlessness at older ages: Prevalence and heterogeneity in 27 countries.

We provide recent and detailed estimates of the prevalence of kinlessness (i.e., people lacking [close] kin) among older individuals in 27 countries. We add to the literature by considering a broad range of kinship ties and examining within-country variabilities by age, sex, education, and rurality of the residential area. Using data from Wave 8 (2019-2020) of the Survey of Health, Ageing, and Retirement in Europe (SHARE), we estimated the prevalence of kinlessness among individuals aged 65 and over in 26 European countries and Israel. We considered different definitions of kinlessness, from a less restrictive (based on the absence of both a partner and children) to a more restrictive one (absence of partner, children, grandchildren, parents, and siblings). Kinlessness varied considerably across countries. The percentage of people aged 65 and over who lacked a partner and children ranged between 2-3.5% in the Czech Republic, Romania, Israel, and Bulgaria to more than 8% in Switzerland, Spain, Belgium, and Malta. The percentage of older people lacking all close kin considered (partner, children, grandchildren, parents, and siblings) ranged from 0.1 to 4.1%. In addition, there was substantial heterogeneity in kinlessness in some countries by age, sex, and rurality of the residential area, whereas, except for a few cases, we did not find significant differences by education. Understanding the prevalence of kinless older adults is critical for policymakers and healthcare providers to design appropriate support systems for this potentially vulnerable group of people.

Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories

Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracking methods, such as Long Short-Term Memory networks (LSTM), offer a promising approach for trajectory prediction in such scenarios. This study combines satellite observations and idealized simulations to compare the predictive performance of LSTM with a resource-dependent dynamic tracking method (DT). The results indicate that when driven solely by historical drifter paths, LSTM achieves better trajectory predictions when trained and tested on relative trajectory intervals rather than the absolute positions of individual trajectory points. In general, LSTM provides a more accurate geometric pattern of trajectories at the initial stages of forecasting, while DT offers superior accuracy in predicting specific trajectory positions. The velocity and curvature of ocean currents jointly influence the prediction quality of both methods. In regions characterized by active sub-mesoscale dynamics, such as the fast-flowing and meandering Kuroshio Current and Kuroshio Current Extension, DT predicts more reliable trajectory patterns but lacks precision in detailed position estimates compared to LSTM. However, in areas dominated by the fast but relatively straight North Equatorial Current, the performance of the two methods reverses. The two methods also demonstrate different tolerances for noise and sampling intervals. This study establishes a baseline for selecting machine learning methods for marine drifter prediction and highlights the limitations of AI-based predictions under data-scarce and resource-constrained conditions.

Joint Optimization-Based Texture and Geometry Enhancement Method for Single-Image-Based 3D Content Creation

Recent studies have explored the generation of three-dimensional (3D) meshes from single images. A key challenge in this area is the difficulty of improving both the generalization and detail simultaneously in 3D mesh generation. To address this issue, existing methods utilize fixed-resolution mesh features to train networks for generalization. This approach is capable of generating the overall 3D shape without limitations on object categories. However, the generated shape often exhibits a blurred surface and suffers from suboptimal texture resolution due to the fixed-resolution mesh features. In this study, we propose a joint optimization method that enhances geometry and texture by integrating generalized 3D mesh generation with adjustable mesh resolution. Specifically, we apply an inverse-rendering-based remeshing technique that enables the estimation of complex-shaped mesh estimations without relying on fixed-resolution structures. After remeshing, we enhance the texture to improve the detailed quality of the remeshed mesh via a texture enhancement diffusion model. By separating the tasks of generalization, detailed geometry estimation, and texture enhancement and adapting different target features for each specific network, the proposed joint optimization method effectively addresses the characteristics of individual objects, resulting in increased surface detail and the generation of high-quality textures. Experimental results on the Google Scanned Objects and ShapeNet datasets demonstrate that the proposed method significantly improves the accuracy of 3D geometry and texture estimation, as evaluated by the PSNR, SSIM, LPIPS, and CD metrics.

Powering sustainability: A techno-economic analysis of photovoltaic systems in dairy farming

ABSTRACT Dairy farming plays an indispensable role in the socio-economic growth of rural India. But, the modernization and sustainable growth of dairy farming relies critically upon the availability of reliable and affordable renewable electric energy. However, lack of access to electricity is a major challenge that needs to be addressed to encourage sustainable dairy farming. In this direction, the deployment of solar photovoltaic (PV) systems in dairy farms has enormous potential to combat the existing critical challenges of fossil fuels and rural energy crises. This investigation assesses the techno-economic viability of PV systems through detailed mathematical modeling and electrical load estimation using the MATLAB simulation environment. The study determines the availability of maximum of solar radiations at each instant, and optimizes the inclined surface to extract maximum solar power. The outcomes of the study reveal that deploying an off-grid PV system consisting of 20 PV modules of 335 Wp, 24 batteries of each 150 Ah, and a 10 kVA inverter meets the average electric load of 9.5 MWh/year for twenty-five years with one-day autonomy. The economic analysis comprised the life-cycle cost (LCC), annualized life-cycle cost (ALCC), and costs of electricity (COE) of the study under reference as US$13050, US$323.1, and US$ 0.02596/kWh, respectively. The study results in saving of 0.285 MT CO2 emissions for the total life span. The resulted stand-alone PV system is both economically and technically viable. The present results reinforce the dispersal of green energy measures for their implementation in rural dairy farms having similar climate conditions in the Indian context.

Process integration and electrification through multiple heat pumps using a Lorenz efficiency approach

This paper introduces a novel method for targeting the minimum shaft work required for process electrification employing principles of Pinch Analysis and detailed heat pump performance estimations. The method deviates from conventional Pinch Analysis by dividing the grand composite curve (GCC) into net heat sink and source profiles to enable the placement of heat pumps exploiting the heat pockets of the GCC. Additionally, it employs Lorenz efficiency over exergy efficiency, offering an accurate description of heat pump performance and highlighting the importance of integration between processes. The method is applied to two case studies. The first case, milk evaporator, focused on the placement of heat pumps in the heat pockets of the GCC. The results showed that while the maximum direct heat recovery was 20,447 kW, the optimal configuration limited the heat recovery to 12,199 kW, reducing the shaft work from 1,930 kW to 1,010 kW. The second, a spray dryer case, focused on the integration of electric boilers and the partial process electrification when available excess heat is limited. In this case, 1,520 kW out of 4,640 kW were covered by an electric boiler, with a biomass boiler replacing the electric boiler to cover 3,570 kW if available.

Impact and Effectiveness of COVID-19 Vaccines Based on Machine Learning Analysis of a Time Series: A Population-Based Study.

Background: Although confirmed cases of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been declining since late 2020 due to general vaccination, little research has been performed regarding the impact of vaccines against SARS-CoV-2 in Spain in terms of hospitalizations and deaths. Objective: Our aim was to identify the reduction in severity and mortality of coronavirus disease 2019 (COVID-19) at a nationwide level due to vaccination. Methods: We designed a retrospective, population-based study to define waves of infection and to describe the characteristics of the hospitalized population. We also studied the rollout of vaccination and its relationship with the decline in hospitalizations and deaths. Finally, we developed two mathematical models to estimate non-vaccination scenarios using machine learning modeling (with the ElasticNet and RandomForest algorithms). The vaccination and non-vaccination scenarios were eventually compared to estimate the number of averted hospitalizations and deaths. Results: In total, 498,789 patients were included, with a global mortality of 14.3%. We identified six waves or epidemic outbreaks during the observed period. We established a strong relationship between the beginning of vaccination and the decline in both hospitalizations and deaths due to COVID-19 in all age groups. We also estimated that vaccination prevented 170,959 hospitalizations (CI 95% 77,844-264,075) and 24,546 deaths (CI 95% 2548-46,543) in Spain between March 2021 and December 2021. We estimated a global reduction of 9.19% in total deaths during the first year of COVID-19 vaccination. Conclusions: Demographic and clinical profiles changed over the first months of the pandemic. In Spain, patients over 80 years old and other age groups obtained clinical benefit from early vaccination. The severity of COVID-19, in terms of hospitalizations and deaths, decreased due to vaccination. Our use of machine learning models provided a detailed estimation of the averted burden of the pandemic, demonstrating the effectiveness of vaccination at a population-wide level.

Dark matter induced baryonic feedback in galaxies

We demonstrate that nongravitational interactions between dark matter and baryonic matter can affect structural properties of galaxies. Detailed galaxy simulations and analytic estimates demonstrate that dark matter which collects inside white dwarf stars and ignites type Ia supernovae can substantially alter star formation, stellar feedback, and the halo density profile through a dark matter induced baryonic feedback process, distinct from usual supernova feedback in galaxies. Published by the American Physical Society 2024

Development and implementation of cost control strategies in oil and gas engineering projects

This review delves into the development and implementation of cost control strategies in oil and gas engineering projects, an area critical for maintaining project viability and profitability amidst fluctuating market conditions. Effective cost control is essential in the oil and gas sector, where projects are often capital-intensive and subject to economic and operational uncertainties. This review examines various cost control methodologies and their application throughout the project lifecycle, from initial planning and budgeting to execution and closure. The paper highlights key cost control strategies, including detailed project cost estimation, budget forecasting, and variance analysis. It discusses the use of advanced project management tools and technologies, such as software for real-time cost tracking and predictive analytics, which enhance decision-making and financial oversight. The review also covers the importance of risk management practices in identifying and mitigating potential cost overruns, emphasizing the role of contingency planning and risk assessment in maintaining financial control. Case studies demonstrate how successful implementation of these strategies can lead to significant cost savings, improved project efficiency, and enhanced financial performance. The challenges associated with cost control, including dealing with unexpected price fluctuations, scope changes, and regulatory compliance issues, are also addressed. Solutions to these challenges, such as adopting flexible budgeting techniques and leveraging data-driven insights, are discussed. The review concludes with recommendations for refining cost control practices in oil and gas engineering projects, including the need for continuous monitoring and adjustment of cost control measures, integrating cost management with overall project strategy, and fostering collaboration among project stakeholders to ensure alignment and accountability.

Estimation of Tax Benefit of US Nonprofit Hospitals

Nonprofit hospitals are under increasing scrutiny to justify the generous tax benefit they receive due to their tax-exempt status. Quantifying the value of the tax benefit they receive at the federal, state, and local levels is critical for designing informed public health policies and ensuring nonprofit hospitals' taxpayer accountability. To estimate the financial benefit that nonprofit hospitals derive from their tax-exempt status and assess how the benefit is distributed across state and local communities. Using methodologies and measures consistent with current tax law and practice and data from 2021 Medicare Cost Reports, this study calculated the total financial benefit from nonprofit hospitals' tax-exempt status for all US nonprofit hospitals with the required Cost Reports data. Nonprofit hospitals' total tax benefit, which equals the sum of federal and state income tax, sales tax, property tax, the fair market value of charitable contributions from donors, savings from tax exemptions on issued bonds, and federal unemployment tax. A total of 2927 US nonprofit hospitals received a $37.4 billion total tax benefit in 2021 from federal income tax ($11.5 billion; 31%), sales tax ($9.1 billion; 24%), property tax ($7.8 billion; 21%), state income tax ($3.7 billion; 10%), charitable contributions ($3.2 billion; 8%), bond financing ($2.1 billion; 6%), and federal unemployment tax ($200 million; <1%). Tax benefit varied substantially across states, from $25 098 (Delaware) to $159 464 (Massachusetts) per hospital bed and from $19 (Alabama) to $275 (Massachusetts) per capita. Tax benefit was highly concentrated, with 7% (n = 212) of hospitals accounting for half of the total amount. This study highlights the wide variation of nonprofit hospitals' tax benefit across states, its high concentration among a small number of hospitals, and the primary role played by state and local taxes. Policy efforts to strengthen nonprofit hospitals' taxpayer accountability are likely to be more effective when pursued at the local level. The detailed standardized estimation road map can be used by various stakeholders to estimate tax benefit for external valuation and reporting purposes, updated as laws change, and improved upon as better data sources become available.

Development of Agribusiness in Tiwi, Albay

This study determined the status of agribusiness in Tiwi, Albay, as basis for the development of business plan. Specifically, it sought to answer the following sub- problems: 1) What are the areas of agribusiness in the Municipality of Tiwi, Albay?; 2) What are the status of agribusiness along; crop production, animal production, aquaculture production, and agri-support services and manufacturing?; 3) What are the contributions of the above areas in the agribusiness in Tiwi, Albay?; 4) What are the problems encountered in agribusiness?; and 5) What business plan may be proposed to address the problems encountered in agribusiness? The study involved presenting the agribusiness sectors contributions through tables and charts, emphasizing their economic impact and community significance. It addressed challenges by organizing issues like infrastructure or market access, supported by relevant data or case studies. It proposed a comprehensive business plan targeting specific problems with detailed strategies, cost estimates, and potential outcomes. Descriptive surveys are crucial in agribusiness research, providing detailed insights into agricultural practices and factors affecting productivity within a specific population. These surveys utilize statistical tools such as frequency percentage and weighted mean to accurately depict response distribution and measure the significance of variables. Synthesizing findings and ensuring proper citation and definition of terms in research helps draw meaningful conclusions and contribute to a broader understanding of agribusiness practices in the region. Incorporating literature demands a structured approach, starting with an introduction highlighting the topic's significance, organizing sources thematically or chronologically, and synthesizing findings while ensuring proper citation and defining terms for clarity. Descriptive surveys in agribusiness employed tools like frequency percentage to depict response distribution and weighted mean to gauge variable significance, offering precise insights into agricultural practices and factors affecting productivity within the surveyed population.

Bi-Objective Inventory Policy with Comprehensive Environmental Factors Formulation and Service Level Constraints

As firms and consumers engage with environmental issues, decisions for inventory control need to entail this perspective of sustainability. Most green inventory models employ methods such as carbon caps or taxes for dealing with environmental sustainability. This problem can be more generally tackled via an explicit estimation of the environmental drivers of maintaining inventory in a warehouse, paired with the economic perspective within a transparent multi-objective optimization framework. With this goal, this paper builds on a detailed estimation of environmental and cost factors for a continuous-review inventory policy. The bi-objective problem is tackled by keeping the objective functions separate. In particular, the modeling of greenhouse gas emission or cost performance factors for the inventory encompasses factors that can depend on the decision variables, taking into account aspects such as warehouse location, building characterization, energy usage, and transport requirements. The effects of the emission drivers on the multi-objective optimization decisions are analyzed, considering that the problem can be constrained by multiple service level measures. Stockout response can be multifaceted and different service level measures capture different aspects of inventory shortages, affecting the resulting efficient solutions differently. The results highlight the impact of aspects such as warehouse location and supply capacity on solutions for the multi-objective inventory problem. Managerial decisions are thus influenced by warehousing and supply attributes via a traceable link to specific cost and emission determinants.

International trade of green hydrogen, ammonia and methanol: Opportunities of China's subregions

Global initiatives to reduce carbon emissions are stimulating the demand for green hydrogen, ammonia and methanol. By 2023, the total low-carbon hydrogen production capacity from announced projects worldwide is projected to reach 38 million tonnes. China is experiencing a surge in green hydrogen projects, with a growing number targeting international markets in addition to domestic demand. This study delves into a systematic modeling analysis to uncover the pivotal roles that China's subregions can play in shaping the future global trade of green hydrogen, ammonia, and methanol. Utilizing a dynamic programming model, complemented by an array of supply chain models for detailed cost estimations, the study highlights the unique advantages and vast potential of subregional engagement in both domestic and international trade. Furthermore, the research explores the essential infrastructure demands and assesses the impact of renewable energy costs and policy shifts on the evolving trade landscape.

Spatial econometric analysis of the digital divide in Thailand at the sub-district level: Patterns and determinants

This study explores spatial patterns of the digital divide and its determinants at the sub-district level in Thailand, uncovering nuanced patterns often overlooked in the in prior literature. To our knowledge, no studies in the digital divide field have offered such detailed estimations. Based on the Spatially Aware Technology Utilization Model, we constructed a conceptual model tailored to the digital divide's geospatial context in Thailand. Using a spatial econometric approach, we analyzed the 2021 data set on basic household necessities provided by Thailand's Community Development Department. The results show a positive spatial autocorrelation of the household internet access divide (HIAD) across Thailand's sub-districts. This spatial clustering tended to be high–high in the sub-districts located in the northeastern and northern regions, with low–low agglomerations in the central region of Thailand. In addition, the spatial econometric analysis results indicated that the HIAD may be influenced by various factors including demographic, economic, educational, government ICT prioritization, social capital, transportation, and disaster-related variables. This study provides both theoretical and policy implications to enrich geographic knowledge of the digital divide, specifically in the context of developing countries.

Gigaton commercial-scale carbon storage and mineralization potential in stacked Columbia River basalt reservoirs

This work presents a detailed supercritical CO2 storage resource estimation for the stacked basalt reservoirs in the Grande Ronde Basalt of the Columbia River Basalt Group in eastern Washington and Oregon. The assessment aims to derisk the commercialization potential of geologic carbon storage in basalt by leveraging both structural and mineralization trapping of CO2 in basalt. The structural closures formed by anticlinal ridges and synclinal valleys in Yakima Fold Belt are excellent physical traps to accommodate injected supercritical CO2. Rigorous hydraulic testing, well logs and simulation results from the Wallula Basalt Pilot #1 well showed the occurrence of 17 suitable permeable injection zones (up to 2,496 mD) intercalated with dense seals (∼2.6E-10 mD) in the Grand Ronde Basalt. In addition, geochemical studies showed fast reactions between supercritical CO2 and dissolved basalt minerals to form stable carbonates. Our calculation indicates up to 40 gigatons (P90) of mineralization storage resources exist in the Grande Ronde Basalt reservoirs.

Calibrating the global network of gravitational wave observatories via laser power calibration at NIST and PTB

Current gravitational wave (GW) observatories rely on photon calibrators that use laser radiation pressure to generate displacement fiducials used to calibrate detector output signals. Reducing calibration uncertainty enables optimal extraction of astrophysical information such as source distance and sky position from detected signals. For the ongoing O4 observing run that started on 24 May 2023, the global GW detector network is employing a new calibration scheme with transfer standards calibrated at both the National Institute of Standards and Technology (NIST) and the Physikalisch-Technische Bundesanstalt (PTB). These transfer standards will circulate between the observatories and the metrology institutes to provide laser power calibration traceable to the International System of Units (SI) and enable assessment and reduction of relative calibration errors for the observatory network. The Laser Interferometer Gravitational-Wave Observatory (LIGO) project and the Virgo project are currently participating in the new calibration scheme. The Large-scale Cryogenic Gravitational-wave Telescope project (KAGRA) is expected to join in 2024, with the LIGO Aundha Observatory in India joining later. Before implementing this new scheme, a NIST-PTB bilateral comparison was conducted. It validated the scale representation by both laboratories, with a degree of equivalence of −0.2% and an associated expanded uncertainty of 0.32% (k = 2) which is significantly lower than previous studies. We describe the transfer of power sensor calibration, including detailed uncertainty estimates, from the transfer standards calibrated by NIST and PTB to the sensors operating continuously at the interferometer end stations. Finally, we discuss the ongoing calibration of Pcal-induced displacement fiducials for the O4 observing run. Achieved combined standard uncertainty levels as low as 0.3% facilitate calibrating the interferometer output signals with sub-percent accuracy.

SARS-CoV-2 dynamics in New York City during March 2020-August 2023.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widespread since 2020 and will likely continue to cause substantial recurring epidemics. However, understanding the underlying infection burden (i.e., including undetected asymptomatic/mild infections) and dynamics, particularly since late 2021 when the Omicron variant emerged, is challenging due to the potential for asymptomatic and repeat SARS-CoV-2 infection, changes in testing practices, and changes in disease reporting. Here, we leverage extensive surveillance data available in New York City (NYC) and a comprehensive model-inference system to reconstruct SARS-CoV-2 dynamics therein from the pandemic onset in March 2020 to August 2023, and further validate the estimates using independent wastewater surveillance data. The validated model-inference estimates indicate a very high infection burden totaling twice the population size (>5 times documented case count) but decreasing infection-fatality risk (a >10-fold reduction) during the first 3.5 years. The detailed estimates also reveal highly complex variant dynamics and immune landscape, changing virus transmissibility, and higher infection risk during winter in NYC over this time period. These transmission dynamics and drivers, albeit based on data in NYC, may be relevant to other populations and inform future planning to help mitigate the public health burden of SARS-CoV-2.

Machine learning for gap‐filling in greenhouse gas emissions databases

AbstractGreenhouse gas (GHG) emissions datasets are often incomplete due to inconsistent reporting and poor transparency. Filling the gaps in these datasets allows for more accurate targeting of strategies aiming to accelerate the reduction of GHG emissions. This study evaluates the potential of machine learning methods to automate the completion of GHG datasets. We use three datasets of increasing complexity with 18 different gap‐filling methods and provide a guide to which methods are useful in which circumstances. If few dataset features are available, or the gap consists only of a missing time step in a record, then simple interpolation is often the most accurate method and complex models should be avoided. However, if more features are available and the gap involves non‐reporting emitters, then machine learning methods can be more accurate than simple extrapolation. Furthermore, the secondary output of feature importance from complex models allows for data collection prioritization to accelerate the improvement of datasets. Graph‐based methods are particularly scalable due to the ease of updating predictions given new data and incorporating multimodal data sources. This study can serve as a guide to the community upon which to base ever more integrated frameworks for automated detailed GHG emissions estimations, and implementation guidance is available at https://hackmd.io/@luke‐scot/ML‐for‐GHG‐database‐completion and https://doi.org/10.5281/zenodo.10463104. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Quantifying myelin density in the feline auditory cortex.

The cerebral cortex comprises many distinct regions that differ in structure, function, and patterns of connectivity. Current approaches to parcellating these regions often take advantage of functional neuroimaging approaches that can identify regions involved in a particular process with reasonable spatial resolution. However, neuroanatomical biomarkers are also very useful in identifying distinct cortical regions either in addition to, or in place of functional measures. For example, differences in myelin density are thought to relate to functional differences between regions, are sensitive to individual patterns of experience, and have been shown to vary across functional hierarchies in a predictable manner. Accordingly, the current study provides quantitative stereological estimates of myelin density for each of the 13 regions that make up the feline auditory cortex. We demonstrate that significant differences can be observed between auditory cortical regions, with the highest myelin density observed in the regions that comprise the auditory core (i.e., the primary auditory cortex and anterior auditory field). Moreover, our myeloarchitectonic map suggests that myelin density varies in a hierarchical fashion that conforms to the traditional model of spatial organization in auditory cortex. Taken together, these results establish myelin as a useful biomarker for parcellating auditory cortical regions, and provide detailed estimates against which other, less invasive methods of quantifying cortical myelination may be compared.

Irregular extended target tracking with unknown measurement noise covariance

To address the challenge of tracking irregular extended target while adapting to unknown measurement noise, this paper presents a novel tracking algorithm for irregular extended target. Considering the flexibility of the Gaussian process (GP) model for representing shape, in the proposed algorithm, we utilize the GP model to delineate the radial function of the tracked extended target’s contour. Then, we incorporate the GP model into the variational Bayesian technique to formulate the posterior distribution for both the unknown measurement noise and the shape state of the tracked target. Subsequently, the square-root cubature Kalman filter is employed to tackle the nonlinearity filtering in the problem of irregular extended target tracking (ETT), enabling simultaneous estimation of intricate shape details and the unknown measurement noise covariance. Through extensive experiments using both simulated and real-time lidar data, the proposed algorithm demonstrates superior performance compared to the existing ETT algorithms for extended target tracking in both accuracy and robustness.

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.