Search Results (812)

The availability of high-resolution oceanographic data is critical for evidence-based coastal environmental management and climate resilience planning, yet it remains constrained by observational gaps and the prohibitive computational cost of fine-scale hydrodynamic modeling. While downscaling techniques provide a viable pathway, current data-driven approaches often lack statistical physical associations, overlook multivariate environmental interactions, and struggle to represent complex coastal topography. To address these limitations, we present MEOFGAN—an environmentally informed downscaling framework that integrates multivariate empirical orthogonal function (MEOF) decomposition with a generative adversarial network (GAN). The model extracts physically interpretable spatial modes of coupled ocean variables, learns their cross-scale transitions through adversarial training, and systematically incorporates high-resolution bathymetry as a static environmental constraint to enhance spatial fidelity. When applied to the Bohai Sea, MEOFGAN successfully downscales sea surface temperature (SST) and sea surface height (SSH) from 1/4° to 1/12°, achieving error reductions of 30–68% compared to benchmark methods while preserving ecologically relevant structural patterns (SSIM > 0.92). The framework demonstrates strong generalization by reconstructing 500 m resolution distributions of chlorophyll-a (Chl-a), dissolved oxygen (DO), and salinity in Bohai Bay, capturing fine-scale environmental gradients during a documented algal bloom event. This work establishes a methodological framework that can be transferred as a paradigm for generating high-resolution coastal datasets. Rather than serving as a universally transferable pre-trained model, the framework requires region-specific training and application. Data generated in this manner can directly support water quality monitoring, eutrophication assessment, habitat mapping, and regionally tailored climate adaptation strategies. Full article

Understanding carbon dioxide (CO₂) emissions from buildings is critical for shaping effective policies toward sustainable urban development. Previous studies mainly applied bottom-up methods for small areas or top-down downscaling at national, provincial or grid scales. However, limited research has explored the relationship between building functions and CO₂ emissions at a larger scale. To bridge this gap, this study employed ridge regression to disaggregate monthly CO₂ emissions to the level of different functional buildings across England in 2022 and investigated the relationship between building functions and CO₂ emissions. Results show that commercial buildings rank highest in CO₂ intensity, reaching 1.49 kg per volume in February, while residential buildings rank lowest, reaching 0.25 kg per volume in July at the national scale, and industrial buildings have the largest total emissions. In addition, regional disparities in economic development and industrial structure contribute to emission differences among buildings of the same function. Temporally, all functional buildings exhibited lower emissions during summer compared to winter. Overall, this study offers a scalable and interpretable framework for understanding urban carbon emissions at high spatial and functional granularity. The findings may offer valuable insights to support government decision-making in urban planning and spatial policy design, thereby contributing to low-carbon development goals. Full article

Climate change poses significant challenges to the operation and safety of dam and reservoir (D&R) systems, particularly in regions characterized by water scarcity and high climate variability. This study presents a structured methodology for climate risk assessment that integrates regional climate projections, system-specific thresholds, and a semi-quantitative risk matrix approach. A key innovation is the explicit linkage between climate indicators and system performance through physically based thresholds, combined with empirically derived exceedance probabilities from high-resolution climate projections. The methodology is applied to the Almopeos D&R system in northern Greece, using an ensemble of statistically downscaled CMIP6 simulations under two emission scenarios (SSP2-4.5 and SSP5-8.5) and two future periods (2041–2060 and 2081–2100). Three climate indicators are analyzed: TX35 (temperature extremes), CDD (consecutive dry days), and Rx1day (extreme precipitation). Results indicate that temperature increase is the dominant climate risk hazard, leading to increased irrigation demand and reduced system reliability, with risks classified as high to very high. Drought conditions represent a secondary but important risk, becoming critical during prolonged dry periods affecting reservoir storage, while extreme precipitation events exhibit low likelihood but potentially high consequences for dam safety. Adaptation measures are prioritized using a qualitative multi-criteria approach, highlighting the effectiveness of operational measures, while structural and monitoring interventions remain essential for ensuring system safety. The proposed methodology provides a transparent and transferable framework for climate-resilient planning of water infrastructure systems. Full article

The wave energy resource along the Azores coast is evaluated for the present (1990–2019) and future (2030–2059) periods using the third-generation wave model WAVEWATCH III, forced by winds and sea-ice cover from the RCP8.5 EC-Earth integration dynamically downscaled with the Weather Research and Forecasting model. The results indicate that the region is characterized by a high-energy wave climate, with mean wave power values typically ranging between 30 and 40 kW/m. A statistical comparison between the two periods shows a moderate reduction in wave energy potential under future conditions, with strong spatial variability. The performance of four wave energy converters (AquaBuoy, Wavestar, Oceantec, and Atargis) is analyzed, revealing significant differences in energy production and capacity factor depending on device–site matching. A techno-economic evaluation is performed by estimating the LCOE, accounting for capital expenditure, operational costs, device lifetime, and annual energy production (AEP). The results demonstrate that economic performance is primarily driven by energy production rather than capital cost alone, and that wave energy exploitation in the Azores remains viable under near-future climate conditions. Full article

Water planning in semi-arid regions depends on climate information that resolves both seasonal timing and topographic gradients. This study evaluated 15 CMIP6 models over Zacatecas, Mexico, and produced a 1 km historical dataset for 1985–2014 by statistically refining bias-corrected daily fields from NEX-GDDP-CMIP6. Downscaling was referenced to the CHELSA climatology: temperature was refined using an elevation-informed hybrid spline approach, whereas rainfall was downscaled with geographically weighted regression (GWR) to represent orographic gradients. The resulting fields were assessed against two independent observational baselines: an automated INIFAP network (2004–2014) and a conventional CONAGUA network (1985–2014). For temperature, BCC-CSM2-MR showed the highest performance, with a Pearson correlation of R = 0.94 for both T_max and T_min. A consistent network-dependent bias pattern was identified: the downscaled models overestimated the diurnal temperature range relative to INIFAP but underestimated it relative to CONAGUA, highlighting the influence of instrumentation and observational protocols on model evaluation. For rainfall, ACCESS-ESM1-5 reproduced the seasonal cycle and dominant orographic patterns, with a correlation of R = 0.611 despite the intrinsic stochasticity of semi-arid rainfall. The resulting 1 km fields provide a spatially consistent baseline for regional applications, including stochastic weather generation and impact models in complex semi-arid regions. Full article

This study examines the effects of climate change on precipitation and drought conditions in Greece, focusing on basin-level hydrological analysis. It builds on existing evidence that the Mediterranean region is highly vulnerable to global warming, experiencing reduced rainfall, extended droughts, and increased hydro-climatic extremes. Using high-resolution down-scaled climate projections under multiple RCP scenarios, the research quantifies precipitation volume within specific hydrological basins, incorporating detailed basin geometries and spatial statistical methods. Alongside precipitation estimates, consecutive dry days and drought frequency, assessed via the Standardised Precipitation Index, offer a multi-indicator view of climate stress. This basin-specific framework connects climate modelling with water resource management, supporting more targeted adaptation strategies. The findings provide new spatial insights into how precipitation redistributes across basins under future climate conditions, with implications for drought-prone regions in Greece. Full article

In the last two decades, the utilization of solar energy has been growing rapidly worldwide, mainly due to the increasing adoption of photovoltaic (PV) systems. Since solar energy is one of the most weather-dependent renewable energy sources, an increasing number of meteorological studies have focused on PV potential (PV_pot) and its projected changes under global warming. GCM outputs disseminated through the Coupled Model Intercomparison Project (CMIP) are often applied in energy-related urban climate studies, as they can be downscaled either statistically or dynamically. It is essential to evaluate raw (not bias-corrected) GCM data, which helps to determine the uncertainties in the GCM simulations before downscaling. Despite their coarse resolution, some studies even rely directly on the GCM grid cell time series to represent individual locations. Accordingly, this study evaluates 10 CMIP Phase 6 (CMIP6) GCMs with respect to some atmospheric variables (air temperature, solar radiation, and wind speed, which are the primary drivers of PV_pot) in four lowland grid cells representing four major urban areas in Central and Southeast Europe: Belgrade (Serbia), Budapest (Hungary), Vienna (Austria), and Prague (Czechia). The use of solar energy has increased significantly in most of these regions in recent years; however, it remains less studied than in Western Europe. ERA5 reanalysis is used as the reference dataset. We analyzed the boreal summer (JJA) days of three overlapping 30-year time periods: 1971–2000, 1981–2010, and 1991–2020. Our main findings are as follows: GCMs tend to overestimate solar radiation and underestimate maximum near-surface air temperature relative to ERA5 in all time periods and in all the four urban areas, which leads to a significant overestimation of the number of JJA days with high PV_pot (PV_pot,90). PV_pot,90 is increasing from 1971–2000 to 1991–2020 in the vast majority of GCMs, in all the four regions. EC-Earth3 and its different configurations (EC-Earth3-Veg, EC-Earth3-CC) are considered the most accurate GCMs relative to ERA5. Full article

High-spatiotemporal-resolution precipitation data are essential for studies on regional hydrology, meteorology, and ecological conservation. Because the Qaidam Basin is a data-scarce region with a few ground stations and coarse-resolution remote sensing products, its utility in regional research is constrained. Therefore, high-resolution precipitation data are urgently needed. Here, longitude, latitude, the normalized difference vegetation index (NDVI), the digital elevation model (DEM), daytime and nighttime land surface temperature, slope, and aspect were selected as environmental variables. Four machine learning methods, Artificial Neural Network (ANN), Cubist, Random Forest (RF), and Support Vector Machine (SVM), were used to downscale Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 to 1 km in the Qaidam Basin and validated using ground observation stations. For annual downscaling, the accuracy ranked as Cubist > ANN > RF > SVM, and residual correction further improved performance. The Cubist model produced the best results, generating finer spatial patterns and reducing outliers in both annual and monthly products. Longitude, latitude, the DEM, and the NDVI were important contributors to the Cubist model. The resulting high-resolution dataset provides valuable support for hydrological and climate change research in the Qaidam Basin. Full article

Large-scale photovoltaic (PV) deployment in arid deserts may alter land–atmosphere interactions and influence groundwater systems, yet such impacts remain poorly quantified due to limited high-resolution observations. To overcome the coarse spatial resolution of GRACE data, this study develops a CNN-LSTM-Attention deep learning framework to downscale terrestrial water storage anomalies (TWSA) from 0.25° × 0.25° to 0.1° × 0.1° over the Huadian PV base in the Tengger Desert, China, during 2004–2024. Groundwater storage anomalies (GWSA) were derived using a water-balance approach, and piecewise linear regression was applied to detect trend shifts associated with PV development. Results show a persistent decline in TWSA and GWSA before 2022, followed by short-term recovery signals afterward. Groundwater responses exhibit greater magnitude and delayed behavior relative to soil moisture. Spatial analysis reveals stronger variability and more frequent deficits in the western subregion, indicating intra-base heterogeneity. A seasonal phase analysis identifies an approximately six-month lag between soil moisture and groundwater, highlighting constraints from deep vadose-zone processes. The findings suggest that groundwater dynamics reflect the combined effects of climate variability, infiltration lag, and PV-related land surface modification rather than a single driver. This study demonstrates the potential of deep-learning-based GRACE downscaling for groundwater monitoring in human-modified arid regions and provides insights for sustainable water management under renewable energy development. Full article

Renewable energy is an important pillar of decarbonization in reducing the impact of climate change. Among the renewable energy sources, solar photovoltaic energy is one of the fastest-growing across West Africa, especially in Côte d’Ivoire. However, its dependence on weather and climate could affect future power system operations. This study aims to quantify how climate change could affect future solar PV potential in Côte d’Ivoire under the RCP8.5 scenario. For this purpose, we used three regional climate model simulations (RCMs) generated by the new high-resolution Coordinated Regional Climate Downscaling Experiment (CORDEX) for the Africa domain (AFR-22). Future changes were computed for two time slices: the near future (2021–2040) and the middle future (2041–2060), relative to the reference period (1986–2005). The performance of the RCMs and their ensemble mean in simulating relevant climate variables was first evaluated with respect to the ERA5 reanalysis and satellite-based (SARAH-2) data during the reference period. Our results indicate that all available RCMs and their ensemble mean reasonably simulate the annual cycle and the spatial patterns features of surface solar radiation, near-air temperature and solar PV potential in Côte d’Ivoire. We also conclude that Côte d’Ivoire is expected to experience a moderate decrease in annual mean solar PV potential during the mid-21st century. The average decrease in solar PV potential over Côte d’Ivoire could range from 0.55% to 2.16% in the near future and from 1.30% to 3.50% during the middle future, according to the considered RCMs. This decline in solar PV potential will be particularly noticeable during the period from June to October in all climatic zones. Overall, these findings provide valuable information for renewable energy planners to ensure the long-term success of solar PV energy projects in the context of climate change in Côte d’Ivoire. Full article

Extreme precipitation events are projected to change under climate change in terms of frequency, intensity and duration, which would cause serious impacts on water resources, agriculture, urban systems and socioeconomic conditions in the future. Based on 10 CMIP5 simulations statistically downscaled to 0.25° resolution through the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) initiative, seven precipitation climate extreme indices, as well as the probability ratio (PR) calculated by the Generalized Extreme Value (GEV) model for daily precipitation, were analyzed under scenarios RCP4.5 and RCP8.5. The results show that: (1) Annual precipitation is projected to increase significantly across China during the 21st century. The increasing rates are 1.4%/decade under RCP4.5 and 2.9%/decade under RCP8.5, respectively. The Tibetan Plateau exhibits the largest increase, particularly over the Karakoram Mountain area. Precipitation will also significantly increase in winter (13.59%/decade and 16.40%/decade) and spring (4.30%/decade and 6.33%/decade). (2) Precipitation extremes are projected to intensify markedly across China, with pronounced intensification in Southwest China and the Tibetan Plateau. (3) The more extreme the precipitation events, the greater the projected increase in the probability ratio (PR). It should be noted that the magnitude of the PR increase under RCP4.5 is significantly larger with respect to RCP8.5. These findings enhance the understanding of climate change and provide detailed regional-scale information to support adaptive policy-making. Full article

Precipitation downscaling aims to enhance coarse-resolution data to higher resolutions. Due to the similarity between downscaling and super-resolution (SR), deep learning-based SR approaches have been increasingly adopted in this domain. However, single-image super-resolution (SISR) methods applied to precipitation data face two main challenges: weak high-frequency signals and highly skewed distributions in precipitation datasets, which often lead to overly smooth reconstructions, failure to capture precipitation extremes, and loss of fine-scale variability with predictions biased toward mean values. To address these issues, we propose EdgeGeoDiff, a two-stage diffusion model for precipitation downscaling that leverages both edge information and geographical priors (e.g., terrain-related factors such as elevation). In the first stage, a residual network reconstructs an initial high-resolution precipitation field with preliminary structural details. In the second stage, edge features extracted using the Laplacian operator, together with geographical priors, guide a diffusion model to generate residuals that enhance fine-scale precipitation structures. Experimental results on real-world precipitation datasets show that EdgeGeoDiff effectively reconstructs fine-scale details while preserving large-scale patterns and outperforms conventional SISR methods in terms of its RMSE, PSNR, SSIM, and CSI, particularly demonstrating superior performance in the high-frequency region of the spectrum. Full article

Marine energy technologies offer renewable alternatives to conventional energy sources by harnessing ocean-based resources such as wave motion, tides, temperature, and salinity gradients. They are particularly promising for coastal and island regions. This paper presents a literature-based assessment of the technical potential and limitations of these resources, with a focus on the Adriatic Sea as a model for low-energy, semi-enclosed basins. Resource availability and technological maturity are systematically reviewed. Results indicate that wave energy offers the highest regional potential, with peak annual mean wave power reachig up to 2.784 kW/m near the southern offshore regions of the Adriatic. However, current resource levels limit feasibility to down-scaled, modular installations. Tidal and thermal energy are constrained by the Adriatic’s microtidal regime and limited temperature gradients. Although still in early development, salinity gradient systems may become viable near major river mouths such as those of the Po and Neretva. In addition to technical analysis, broad environmental and socio-economic considerations are reviewed to inform responsible marine energy development. These findings help define strategic development and research priorities for marine renewables in enclosed seas and other resource-constrained marine environments. Full article

Wetlands in semi-arid regions are critical for ecological resilience but are increasingly degraded. Ecological water consumption (EWC), reflecting wetland water demand, is essential for understanding wetland sustainability. This study investigated the spatiotemporal dynamics of marsh wetland EWC in the Bashang Plateau, China, from 1986 to 2021, and identified its main driving forces. A Random Forest model was used to downscale GLASS evapotranspiration (ET) product from 0.05° to a 250 m monthly resolution, showing good agreement with flux measurements (RMSE = 21.94 mm, R² = 0.83). Marsh wetland EWC was estimated using the downscaled ET and land cover data, and Granger causality analysis was applied to explore driving mechanisms. Results indicate that the marsh wetland area declined by 74% (from 552.81 to 143.69 km²) while forestland expanded by 217%. Correspondingly, marsh wetland EWC decreased by 67.2%, from 125 to 41 million m³. Precipitation and surface water area were identified as direct drivers of marsh wetland EWC decline, whereas groundwater table, forest EWC, and cropland EWC acted as indirect drivers. While cropland water use has been widely reported as an important factor, results suggest that increased forest EWC associated with large-scale afforestation contributed considerably to groundwater table decline, thereby influencing marsh wetland EWC. Full article

Drought–flood abrupt alternation (DFAA) events have emerged as a critical type of compound climate extreme under ongoing climate change, posing increasing risks to water resources and ecosystems in Southwest China. This study investigated the spatiotemporal evolution of DFAA events during the historical period (1995–2024) and the future period (2025–2064), as well as their relationships with evapotranspiration. Daily precipitation was simulated using a CMIP6 multi-model ensemble mean (MME) combined with Delta downscaling, while station observations were used to identify DFAA events and evapotranspiration. Model performance was evaluated using Taylor diagrams and simulation relative bias. The results showed that the downscaled MME substantially improved the simulation of precipitation, evapotranspiration, and cumulative DFAA event occurrences, with relative bias in most regions controlled within ±3%. Compared with the historical period, both drought-to-flood (DTF) and flood-to-drought (FTD) events showed overall increases during 2025–2064. Specifically, under the four SSP scenarios, DTF events increased by 165, 133, 180, and 140 occurrences, respectively, while FTD events increased by 130, 147, 114, and 79 occurrences, respectively. The regional mean trends of DTF events during the near-term period were −0.21, 0.16, −0.45, and 1.24 times·5a⁻¹, whereas the corresponding trends of FTD events were 1.82, 1.17, 0.05, and −1.03 times·5a⁻¹ under the four scenarios. Spatial analyses revealed pronounced regional heterogeneity, with enhanced signals mainly concentrated in eastern Sichuan, Chongqing, and parts of Guizhou. Lagged correlation analyses further indicated significant monthly lag effects between DFAA events and evapotranspiration during the flood season; DTF events generally showed positive correlations with subsequent evapotranspiration, whereas FTD events exhibited predominantly negative correlations. Overall, this study clarifies the future spatiotemporal evolution of DFAA events in Southwest China and highlights the important role of land–atmosphere hydrothermal processes in regulating compound drought–flood extremes. Full article