Search Results (315)

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

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

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 research focuses on analyzing historical climate variability over the period 1982–2022, as well as future projections of climate change over the period 2025–2099, with regard to the Souss-Massa watershed, a semi-arid region with high dependency on agricultural activities. Precipitation and temperature data were collected annually from five meteorological stations, Agadir, Amaghouz, Amsoul, Aoulouz, and Taroudant, in order to analyze long-term climatic trends and predict possible scenarios of climate change. A trend analysis was carried out using a combination of the Mann–Kendall test and Sen’s slope estimator. The findings of this study indicate that there is an increase in mean annual temperature that is statistically significant (p < 0.05) across all stations, ranging from +0.28 °C per decade at Agadir, which is located along the coastal region of Morocco, to as high as +0.45 °C per decade at Taroudant, which is located inland. Conversely, the precipitation trend is decreasing and not statistically significant (p > 0.05). For projecting future climatic conditions, we used the Statistical Down-Scaling Model (SDSM v4.2.9) with global climate models using outputs from CanESM2 under two emission scenarios, namely RCP 4.5 and RCP8.5. The calibration period (1982–2001) and the validation period (2002–2022) were satisfactory, as indicated by the high values of the coefficients of determination (R² > 0.6) for temperature and moderate values (R² = 0.5–0.6) for precipitation. Projections indicate an increase in temperature, with the mean temperature change ranging from +4.8 °C and +8.7 °C by 2099 depending on the station’s location. Projected precipitation decreases are found under both scenarios, but with stronger decreases under RCP8.5, especially along the coastal regions, with decreases as large as −53.8% at Agadir. However, the precipitation projections have to be used with caution due to the limitations associated with the downscaling methods and the use of a single global climate model. All the projections indicate a trend towards arid conditions, emphasizing the need for adaptive water resources management and improving the ensemble models for climate projections. Full article

Carbon emissions (CEs) are a primary driver of global climate change, particularly pronounced in China’s Yangtze River Delta (YRD) region, where rapid economic development and urbanization have led to a substantial increase in CEs. At fine spatial scales (e.g., county level) or in regions with limited statistical data, traditional methods for CE accounting are constrained by data gaps and inconsistencies, which hinders the accurate characterization of regional disparities. Therefore, this study proposes a CE spatial downscaling method based on nighttime light (NTL) data. By integrating remote sensing data with the IPCC emission inventory model, energy consumption-related carbon emissions (ECCEs) across the YRD region from 2000 to 2020 were quantified. Through global spatial autocorrelation analysis and standard deviation ellipse (SDE) analysis, the spatial distribution characteristics and temporal variation trends of ECCEs were revealed. Results indicate that total CEs increased significantly over the study period. CE hotspots were concentrated in the Hangzhou Bay area and the Shanghai–Nanjing corridor, while coldspots were identified in southwestern Anhui and Zhejiang. From 2010, the CE centroid shifted toward the southwest or northwest, and the regional CE distribution evolved from a point pattern to a band-shaped pattern. These findings offer a novel approach for CE monitoring and can provide scientific support for low-carbon development policies and precise emission reduction strategies in data-scarce regions of developing countries. Full article

Numerical weather prediction (NWP) models are essential for precipitation forecasting but are constrained by coarse spatial resolutions (10–50 km), which fail to capture fine-scale variations required for regional disaster prevention, particularly in complex terrain. While statistical and machine learning downscaling methods have been developed to bridge this resolution gap, they predominantly operate as “black boxes” without explicit physical guidance, leading to predictions that violate meteorological principles and systematic underestimation of extreme precipitation events. To address these limitations, this study aims to develop a Physics-Informed Machine Learning framework that explicitly integrates multi-scale topographic modulation and physical consistency constraints into precipitation downscaling. Specifically, a Random Forest model enhanced with Multi-Scale Structural Similarity (MS-SSIM) loss and Physical Constraint Enhancement (MSSSIM-PCE-RF) was constructed. The model introduces elevation gradient weights at low-resolution layers and micro-topographic parameters (slope, surface roughness) at high-resolution layers, while enforcing physical consistency between precipitation intensity, radar reflectivity, and ground observations via the Z-R relationship. Based on hourly data from 2252 meteorological stations in Jiangxi Province (2021–2022), coupled with topographic factors (DEM, slope, aspect) and Normalized Difference Vegetation Index (NDVI), a technical framework of “data fusion–feature synergy–machine learning–spatial reconstruction” was established. Results demonstrate that the MSSSIM-PCE-RF model achieves a validation R² of 0.9465 and RMSE of 0.1865 mm, significantly outperforming the conventional RF model (R² = 0.9272). Notably, errors in high-altitude, steep-slope, and high-vegetation areas are reduced by 45.3%, 42.0%, and 43.1%, respectively, with peak precipitation period errors decreasing by 37.2%. Multi-scale topographic analysis reveals significant orographic lifting effects at 250–1000 m elevations, peak precipitation at 12–15° slopes, and abundant precipitation on south/southeast aspects. By explicitly embedding topographic modulation and physical consistency constraints, the model effectively alleviates systematic underestimation of extreme precipitation in complex terrain, providing high-resolution data support for transmission line disaster prevention and micro-meteorological risk assessment. Full article

Indonesia’s long-term climate strategy targets net-zero emissions by 2060. In this context, this paper develops a simulation for the Java–Madura–Bali (Jamali) grid to quantify the joint impact of electric vehicle (EV) uptake and rooftop photovoltaic (PV) integration on system performance from 2025 to 2060. Historical statistics and national planning projections were used to calibrate annual capacity, peak load, and energy trajectories, which were downscaled to hourly resolutions. EV charging demand, generated using state-of-charge-dependent Alternating Current (AC) and Direct Current (DC) load profiles, and PV output were modeled across a 36-year span under a 5 × 5 policy matrix, producing a 900-scenario-year. These scenarios range from Business-as-usual (BAU) to aggressive interventions (including subsidies, regulation, and smart management). The scenarios were evaluated using a min–max composite index weighting supply–demand balance, production–consumption balance, and policy cost. Based on this simulation inputs, results indicate that the scenario combining regulated EV growth with BAU PV adoption achieves the highest average composite score. While charge-time management strategies provided the best adequacy, highly interventionist EV–PV packages were the most expensive without delivering proportional benefits. The study concludes that, with this current parameter input, moderate and regulation-driven strategies outperform aggressive interventions when adequacy, balance, and cost are jointly considered. Full article

Climate change can alter both the amount and timing of inflows to water supply reservoirs while also increasing heat-driven demand and the likelihood of stressful warm-season conditions. Climate-driven changes in inflow to Eagle Mountain Lake Reservoir (Texas, USA) were quantified by integrating (i) a calibrated SWAT model evaluated at four USGS stream gauges, (ii) statistically downscaled CMIP6 daily precipitation and minimum/maximum temperature at seven stations/grid points for a historical baseline (2003–2022) and two future windows (2031–2050 and 2081–2100) under SSP1-2.6, SSP2-4.5, and SSP5-8.5, and (iii) observed reservoir operations (lake level, water supply releases, and flood discharge; 1990–2022). A standard watershed climate workflow is reframed through an operations-focused lens, wherein projected inflow changes are translated into decision-relevant indicators via the utilization of observed thresholds and operating mode signals. Included within this framework are spring refill-season inflow shifts, a hot–dry month metric, and storage threshold performance measures, which are coupled with screening-level probabilities linked to multi-year inflow deficits. Across models and stations, mean annual temperature increases by 0.7–1.9 °C in the 2030s and by 0.7–6.1 °C in the 2080s, while annual precipitation changes remain uncertain (−24% to +55%). Daily projections show a strong increase in extreme heat days (daily Tmax above the historical 95th percentile), from about 18 days yr⁻¹ historically to about 30–33 days yr⁻¹ in the 2030s and about 34–82 days yr⁻¹ by the 2080s. Hot–dry months (monthly mean Tmax above the historical 90th percentile and monthly precipitation below the historical median) increase modestly by mid-century and rise to about 1.5 months yr⁻¹ on average by the 2080s under SSP5-8.5. SWAT simulations indicate that the mean annual inflow declines by 17–20% across scenarios, with the largest reductions during the spring refill period (March–June). Historical operations show that hot–dry months are associated with approximately double the mean water supply release (7.2 vs. 3.5 m³/s) and a lower monthly minimum lake level (about 0.30 m; about 1.0 ft lower on average). Flood discharges occur almost exclusively when lake elevation is at or above about 197.8 m and follow multi-day rainfall clusters (cross-validated AUC = 0.99). Together, these results indicate that earlier-season inflow reductions and more frequent hot–dry stress will tighten the operational margin between refill, summer demand, and flood management, underscoring the need for adaptive drought response triggers and integrated drought–flood planning for the Dallas–Fort Worth region. Full article

The study investigates the projected impact of climate change on water runoff in the upper Parsęta catchment, a postglacial lowland basin located in northwestern Poland. In the first step of the analysis, hydrological simulations for the period 2005–2022 were conducted using the Soil and Water Assessment Tool (SWAT). Model calibration and validation, performed in SWAT-CUP with the SUFI2 algorithm, yielded satisfactory performance (R² = 0.66–0.80; PBIAS = 0.43–13.87). Based on the calibrated model, projected simulations were performed for three future decades (2021–2030, 2031–2040, and 2041–2050) under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Climate input data were derived from the KLIMADA 2.0 national database, which was developed using down-scaled regional climate model output from the EURO-CORDEX ensemble and statistical bias-correction methods to generate high-resolution projections. Under RCP4.5, mean annual runoff increased by approximately 13–26%, while under RCP8.5, the changes were more variable, ranging from 2% to 28% relative to the 2011–2020 baseline. Seasonal analyses revealed enhanced autumn–winter runoff and lower spring–summer flows. The findings highlight that moderate climate forcing can lead to substantial alterations in hydrological regimes in postglacial lowland catchments, in certain decades comparable in magnitude to those projected under extreme forcing, underscoring the need for adaptive water management in northern Poland. Full article

China’s age structure is undergoing profound demographic shifts, making accurate spatial information on age-stratified populations essential for policy-making, resource allocation, and risk assessment. However, census data are primarily aggregated by administrative units, offering coarse spatial resolution that constrains their integration and application with other gridded datasets. Using township-level population counts for four age groups (0–14, 15–59, 60–64, and ≥65 years) from the 2020 Seventh National Population Census across 38,572 townships, we developed an age-stratified downscaling framework. This framework integrates a random forest model with age-filtered Points of Interest (POI) data and other multi-source geospatial covariates to generate a 100 m resolution age-stratified population density weighting layer. Through township-level data dasymetric mapping, we produced the township-based 100 m Age-Stratified Population Grid Data (Township-ASPOP). Since township-level data represent the finest publicly available spatial unit of demographic statistics in China, we further validated the accuracy of Township-ASPOP by generating County-based 100 m Age-Stratified Population Grid Data (County-ASPOP) through dasymetric mapping using county-level age-stratified population data. The results demonstrate that County-ASPOP achieves superior predictive accuracy, with R² values of 0.95, 0.95, 0.85, and 0.86, and Root Mean Square Error (RMSE) values of 1743, 6829, 900, and 2033 persons per township for the four age groups, respectively—significantly outperforming the contemporaneous WorldPop dataset (R² = 0.69, 0.72, 0.64, and 0.60). The accuracy of Township-ASPOP is no less than that of County-ASPOP and effectively captures realistic spatial settlement patterns. This study establishes a reproducible framework for generating age-stratified population grid data and provides critical data support for policy formulation and resource allocation. Full article

Arctic regions are warming rapidly, elevating extinction risks and accelerating ecosystem change, yet widely used bioclimatic datasets rarely represent polar-specific ecological constraints. Here we present BioClimPolar_2300 v1.0, a raster bioclimatic dataset designed for terrestrial Arctic biodiversity research under climate change. The dataset includes 33 gridded bioclimatic layers at a 10 km spatial resolution, covering seven discrete temporal intervals from 2010 to 2300 AD. In addition to conventional variables used globally, BioClimPolar_2300 incorporates three polar-relevant constraint domains: (1) polar day–night phenomena (PDNs), including degree-day metrics during polar night and polar day; (2) temperature-defined seasonal cycles (TSCs), including seasonal temperature, precipitation, aridity, and season length; (3) hot/cold stresses (HCSs), capturing indices of extreme summer heat and winter cold. Precipitation during snow-melting days (P_melting) is also included due to its relevance for species depending on subnivean habitats. Climate fields were extracted from CMIP6 models and statistically downscaled to 10 km using a change-factor approach under a polar projection. Monthly fields were linearly interpolated to derive daily grids, enabling the computation of variables that require daily inputs. Validation against observations from 30 Arctic weather stations indicates performance suitable for biodiversity applications, and two exemplar range shift case studies (one animal and one plant) illustrate biological relevance and provide practical guidance for data extraction and use. BioClimPolar_2300 fills a key gap in Arctic bioclimatic resources and supports more realistic biodiversity assessments and conservation planning through 2300. Full article

Polar sea ice is undergoing rapid change, with recent record-low extents in both hemispheres, raising the demand for skillful predictions from days to seasons for navigation, ecosystem management, and climate risk assessment. Accurate sea ice prediction is essential for understanding coupled climate processes, supporting safe polar operations, and informing adaptation strategies. Physics-based numerical models remain the backbone of operational forecasting, but their skill is limited by uncertainties in coupled ocean–ice–atmosphere processes, parameterizations, and sparse observations, especially in the marginal ice zone and during melt seasons. Statistical and empirical models can provide useful baselines for low-dimensional indices or short lead times, yet they often struggle to represent high-dimensional, nonlinear interactions and regime shifts. This review synthesizes recent progress of DL for key sea ice prediction targets, including sea ice concentration/extent, thickness, and motion, and organizes methods into (i) sequential architectures (e.g., LSTM/GRU and temporal Transformers) for temporal dependencies, (ii) image-to-image and vision models (e.g., CNN/U-Net, vision Transformers, and diffusion or GAN-based generators) for spatial structures and downscaling, and (iii) spatiotemporal fusion frameworks that jointly model space–time dynamics. We further summarize hybrid strategies that integrate DL with numerical models through post-processing, emulation, and data assimilation, as well as physics-informed learning that embeds conservation laws or dynamical constraints. Despite rapid advances, challenges remain in generalization under non-stationary climate conditions, dataset shift, and physical consistency (e.g., mass/energy conservation), interpretability, and fair evaluation across regions and lead times. We conclude with practical recommendations for future research, including standardized benchmarks, uncertainty-aware probabilistic forecasting, physics-guided training and neural operators for long-range dynamics, and foundation models that leverage self-supervised pretraining on large-scale Earth observation archives. Full article

Soil erosion is largely driven by climate change and land use dynamics. The objective of this study is to assess the dynamic variation in erosion under the combined effects of precipitation and land use change in the Tigrigra watershed, located in the mountainous region of the Middle Atlas. The RUSLE (Revised Universal Soil Loss Equation) model is used in the methodological approach to estimate soil loss based on various parameters such as precipitation, soil, topography, land cover, and conservation practices. Geographic Information Systems (GIS) and remote sensing tools are essential for applying this method. In addition, the CA-Markov model (cellular automata), which models and predicts land use changes over time, is used to project future land cover scenarios that influence soil erosion dynamics. The research focuses on four previous periods (1991–2000, 2001–2010, 2011–2015, and 2016–2023), as well as a future period (2024–2050), considering two climate scenarios, RCP 2.6 and RCP 4.5. Precipitation data from local weather stations and the CMIP5 climate model were used to calculate the R factor (precipitation erosivity). Land cover analysis was performed using Landsat satellite images (30 m resolution) integrated into the CA-Markov model to calculate the C factor (land cover management). The results show that erosion has gradually decreased over both past and future periods, mainly due to variations in precipitation and vegetation cover. It should be noted that the period from 1991–2000 to 2016–2023 shows higher erosion compared to the future periods, with a maximum value of 17.83 t/ha/year recorded between 1991 and 2000. For the future period 2024–2050, a continuous decrease in erosion is observed under both scenarios, with an average value of 15.30 t/ha/year for the RCP2.6 scenario and 15.86 t/ha/year for the RCP4.5 scenario, with erosion remaining slightly higher under RCP4.5. Overall, erosion decreases across both historical (1991–2023) and projected (2024–2050) periods due to reduced rainfall erosivity. The northern part of the basin is particularly prone to erosion due to the low vegetation cover. The results indicate that areas susceptible to erosion require conservation measures to reduce soil loss. Implementing sustainable agricultural practices is crucial for maintaining long-term soil health and preventing degradation. However, some limitations of the study, such as the lack of data on conservation practices and daily precipitation, might affect the overall robustness of the findings. Full article