Search Results (1,773)

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

The simulation accuracy and error sources of Remote Sensing (RS)-derived products, model-derived products, and RS-based assimilation products remain poorly understood across varying terrain conditions. Here, we investigated watershed-scale Soil Moisture (SM) dynamics across different slope positions using RS data assimilation, with the targeted area located in New South Wales, Australia. After evaluating and comparing the accuracy of existing SM products, a daily 1 km-resolution surface SM dataset was generated through data fusion. This product was then integrated with Soil and Water Assessment Tool (SWAT) model simulations using a Kalman filter approach, yielding a 10 m-resolution dataset with enhanced physical mechanism. Our results revealed that physically constrained products generally outperformed standalone RS inversions or hydrological model simulations, with their performance varied across slope positions. Furthermore, we demonstrated that high Soil Moisture Content (SMC) and spatial heterogeneity amplified SWAT model dominance in assimilated outcomes, whereas low SMC and spatial heterogeneity elevated RS contributions; the assimilated dataset consistently overcame limitations of standalone RS and hydrological model simulations across all slope positions. Our results demonstrated significant variations in the accuracy of RS-derived and model-derived products across distinct slope positions. This study systematically analyzed the underlying error mechanisms, contributing to intelligent water resource monitoring and water management decisions. Full article

Informal settlements are home to more than one billion people worldwide, with forecasts suggesting this number may increase to nearly three billion by 2050. Although informal settlements constitute a significant component of urbanization in the Global South, they are unsafe and unhealthy places to live, as residents are exposed to various environmental challenges, including increasing temperatures. However, relative to other climate-related hazards, heat stress in informal settlements is under-researched. This paper, therefore, aims to analyze land surface temperatures (LSTs) in informal settlements relative to those in surrounding areas. Focusing on the study areas of Masiphumelele and Dunoon in Cape Town, South Africa, the study utilized downscaled 10 m resolution satellite imagery from 2020 to 2025. The LST was derived from Landsat 8 Collection 2 Level 2 Surface Reflectance and Surface Temperature products. Four indices were also generated to further analyze the spatial distribution of LSTs: the normalized difference vegetation index, the normalized difference built-up index, the bare soil index, and the normalized difference water index. Showing that heat intensity in informal settlements is a relative phenomenon influenced by many factors, Dunoon had a lower mean LST than the surroundings, while Masiphumelele demonstrated elevated mean LST relative to the surroundings. The study provides empirical evidence of heat-related patterns to inform planning and climate adaptation strategies in informal settlements, including the equitable provision of green and blue infrastructure. Full article

Climate change is reshaping hydrologic regimes in snow-dominated watersheds, with important implications for mine rock drainage quantity and contaminant mobilization. This study quantifies potential long-term changes in drainage quantity by coupling a previously published physics-informed machine learning model with a Monte Carlo framework driven by downscaled monthly climate projections from ClimateNA. The proposed methodology was applied to three drainage monitoring stations at a mine site in Western Canada to assess projected drainage responses over the 2011–2100 period. An ensemble of daily weather sequences was generated by sampling historical within-month variability and scaling the resulting series to match projected monthly climate statistics, which were then used as inputs for the drainage model. Trends were assessed using the Mann–Kendall test modified for serial correlation, and their magnitudes were summarized using the Theil–Sen slopes. The trend analysis results indicate scenario-dependent changes in annual drainage across stations, alongside consistent seasonal shifts toward higher spring (April–May) and lower early-summer (June–July) drainage. These patterns are consistent with earlier snowmelt and earlier snowpack depletion. Corresponding shifts in intra-annual flow timing suggest that a larger fraction of annual drainage occurs earlier in the year. Overall, these findings provide a physics-informed basis for changes in drainage quantity and for guiding monitoring, design, and mitigation strategies under a warming climate. Full article

Neurogranin (Ng) is a postsynaptic calmodulin-binding protein highly enriched in forebrain neurons and widely implicated in synaptic plasticity. However, whether Ng contributes more broadly to neuronal network maturation and cellular homeostasis remains unclear. Here, we examined the consequences of silencing or restoring Ng to adult physiological levels in primary hippocampal neurons. Ng expression promoted dendritic expansion, increased synaptic number, and shifted the axon initial segment toward the soma, consistent with structural adaptations to enhanced connectivity. Calcium (Ca²⁺) imaging revealed a marked increase in spontaneous neuronal activity and network synchronization, which was confirmed by electrophysiological recordings showing enhanced burst firing and spike synchrony. At the molecular level, Ng altered Ca²⁺/calmodulin (CaM) signaling by increasing total CaM levels, reducing Ca²⁺/CaM-dependent protein kinase II (CaMKII) abundance while increasing its relative autophosphorylation, and downscaling specific ionotropic glutamate receptors. Despite elevated network activity, Ng expression enhanced neuronal metabolic competence and viability, reduced cellular stress signaling and induced modest caspase-3 activation without engagement of apoptotic pathways. Together, these results indicate that Ng promotes neuronal maturation and coordinated network activity while engaging compensatory mechanisms that preserve excitatory balance and neuronal resilience. Our findings identify Ng as a molecular integrator linking Ca²⁺/CaM signaling with the structural and functional maturation of neuronal networks. 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

Understanding the spatiotemporal streamflow patterns under future climate scenarios is critical for sustainable water resource management in large river basins. This study applied the Soil and Water Assessment Tool (SWAT), forced by five downscaled and bias-corrected CMIP6 global climate models, to evaluate historical (2008–2024) and future (2025–2049) streamflow patterns in the Missouri River Basin in the continental United States. Model calibration and validation were satisfactory, with NSE > 0.5, KGE ≥ 0.5, R² > 0.5, and PBIAS within ±25% at most USGS gauge stations. Future projections indicate spatially and temporally variable hydrological responses: The upper basin (Bismarck, North Dakota) is projected to experience lower flows across most percentiles and reduced extreme events, whereas the lower basin (Hermann, Missouri) shows decreased median flows but higher extremes. Recurrence interval analysis of 2-, 5-, 10-, 50-, 100-, and 500-year flows suggests that 100-year flows may decline by 11% at Bismarck and increase by 37.4% at Hermann. These results highlight the importance of integrating percentile-based and extreme event streamflow analyses with hydrologic modeling for assessing the spatiotemporal streamflow patterns under future climate scenarios in large-scale basins. Quantitative insights into future streamflow variability and its implications for flood risk mitigation, water resources management, and adaptive strategies were gained for one of North America’s largest river systems. 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

The inspection of minuscule insulator defects from high-resolution (HR) UAV imagery presents a significant algorithmic challenge. The severe scale mismatch between HR images and low-resolution model inputs often leads to feature distortion for sparsely distributed targets. To address these issues, this paper proposes an integrated data–model collaborative framework. At the data level, an offline label-guided optimal tiling (LGOT) strategy is introduced to alleviate scale mismatch by curating information-dense training tiles. At the model level, we design the semi-decoupled prior-driven detection head (SDPD-Head), which leverages evolutionary priors to stabilize the learning of microscopic spatial features. During inference, an online inference-time adaptive tiling (ITAT) strategy is used to match the spatial scale distribution between training and inference and to reduce feature loss caused by direct downscaling. Experiments on a real-world inspection dataset show that the proposed framework achieves an mAP@50 of 92.9% with 2.17 M parameters and 4.7 GFLOPs. Full article

Climate change is expected to modify precipitation patterns and increase flood hazard in urban areas, potentially affecting critical infrastructures such as wastewater treatment plants (WWTPs), often located in flood-prone zones. This study assesses the impacts of climate-driven changes in extreme rainfall on flood hazard, pedestrian safety, and tangible physical damage at WWTPs in the Metropolitan Area of Barcelona, Spain. Twenty-four future flood scenarios are defined using CMIP6-based downscaled climate projections (SSP126 and SSP585), two time horizons (2041–2070 and 2071–2100), and different climate model percentiles. Climate Change Coefficients derived from updated Intensity–Duration–Frequency curves are applied to hydrodynamic simulations to evaluate flooded and high-hazard areas for plant workers, as well as direct economic damage at the Montcada i Reixac WWTP, used as a case study. Results indicate limited changes under SSP126, while SSP585 leads to systematic increases in hazard extent and damage, particularly for long-term projections (2071–2100) and extreme percentiles (90th). A large dispersion among climate models is also observed, especially for extraordinary flood events. Finally, a site-specific nature-based adaptation measure targeting frequent floods is proposed, demonstrating the potential of integrated assessments to support sustainable adaptation planning and to reduce the Expected Annual Damage in future climate conditions by 93%. Full article