Search Results (1,111)

Hydrometeorological changes profoundly influence runoff generation and evolution in river basins. It is of great significance to carry out runoff prediction research to ensure water resources security and improve disaster prevention and mitigation capabilities. In this paper, the Ganhe River Basin in Northeast China was taken as the research object. Based on the hydrometeorological and runoff data from 1980 to 2022, a variety of statistical methods were used to systematically study the climate change, runoff evolution characteristics and driving mechanism of the basin. Combined with BP neural network model and CMIP6 climate scenario data, the future runoff changes were predicted. The results showed that the precipitation and relative humidity showed a downward trend, while the temperature, sunshine and evapotranspiration showed an upward trend during the study period. The runoff showed a non-significant upward trend, and an abrupt change occurred in 2009. After the abrupt change, the runoff increased by 38.7% compared with the baseline period. The change in land use was the most significant from 1990 to 2000, and the area of cultivated land increased significantly. Correlation analysis showed that precipitation was the dominant meteorological factor affecting runoff change, and the contribution rate of human activities was 88.51%, which was much higher than that of climate change. The BP neural network model demonstrated satisfactory simulation performance, and the training set and test set R2 reached 0.88 and 0.82, respectively. In the future, both temperature and precipitation will increase under different SSP scenarios. On this basis, the BP neural network prediction results show that the runoff of the basin is generally increasing, and the increase is the most significant under the high emission scenario, and the risk of extreme hydrological events may be further aggravated. These findings provide scientific support for water resources management and ecological conservation in the Ganhe River Basin. Full article

A central premise of climate adaptation is that better information and smarter decisions can keep escalating hazards within manageable bounds. We test this premise for one of the most information-sensitive decisions in disaster management—ordering a hurricane evacuation—and find that it has limits. Taking Hurricane Irma (2017), the storm behind Florida’s largest evacuation (6.5 million people, 4 million vehicles), as a reference event, we add Coupled Model Intercomparison Project Phase 6 (CMIP6) perturbations to the historical storm and use the Pangu-Weather artificial intelligence (AI) forecasting system to generate 20,000 ensemble members for present-day and future climates (Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP5-8.5; 2050s and 2080s). As the climate warms, storm intensity rises by 15–20% and forecast uncertainty roughly doubles. A reinforcement learning (RL) framework that optimizes evacuation orders under these conditions then exposes a paradox: although RL’s advantage over fixed policies grows from 7% today to 17% under the 2080s SSP5-8.5, absolute evacuation performance still deteriorates by 44% despite optimization. The optimized future climate outcome (objective: 0.239) is in fact worse than that of suboptimal fixed policies today (0.178)—better decisions cannot compensate for a decision environment that has itself degraded. This is direct, scenario-specific evidence that optimization-based adaptation has a ceiling, with consequences for the long-term sustainability of hazard-exposed coastal regions: keeping such communities safe and livable will require coupling evacuation optimization with structural risk reduction, equitable access to decision-support technology, and aggressive greenhouse gas mitigation that holds future risk within adaptable—and therefore sustainable—bounds. The framework supplies quantitative support for sustainable disaster risk reduction and resilient infrastructure planning aligned with global sustainability goals. Full article

Urban Heat Islands (UHIs) intensify extreme heat, raise energy demand, and risk citizen thermal comfort in densely built cities. However, spatially detailed, scenario-differentiated estimates of future UHI intensity are still limited for complex coastal mountainous cities. This study set out to forecast UHI intensity variations in Busan, South Korea, under SSP2-4.5 and SSP5-8.5 scenarios. Daily temperatures from 19 automatic weather stations (2010–2014) were spatially interpolated using Empirical Bayesian Kriging Regression (EBKR), which included elevation and coastline distance variables. Among the 16 CMIP6 Global Climate Models (GCMs) tested, CNRM-CM6-1 (r = 0.902, RMSE = 4.937 °C) was chosen and bias-corrected using Empirical Quantile Mapping (EQM). The results reveal that mean maximum UHI intensity rises gradually, with ΔUHI (compared with the 2010–2014 baseline of 0.90 °C) reaching +7.03 °C (SSP2-4.5) and +9.60 °C (SSP5-8.5) in the far future, roughly 1.43 times more under the high-emission scenario. Summer through autumn has a UHI intensity increase, whereas long-term warming concentrates in Busan’s urban core. These findings inform targeted urban heat adaptation strategies, prioritizing green infrastructure, cool urban surfaces, and energy-resilient city planning to protect human well-being. Full article

Developing effective adaptation and mitigation strategies depends on climate predictions capable of representing future conditions across multiple temporal scales. Decadal climate predictions bridge seasonal forecasting and long-term climate projections, providing near-term climate information for decision-making and adaptation planning at multi-year timescales. This study assesses the predictive skill of CMIP6 decadal precipitation forecasts from the Decadal Climate Prediction Project for three strategic catchments in state of Ceará, in the Brazilian semi-arid region. Forecast skill was assessed using deterministic and probabilistic metrics for three averaging horizons corresponding to years 1, 1–5, and 1–10 after initialization. Systematic biases were assessed and corrected. The results indicate that predictive skill varies across forecast systems, averaging horizons, and catchments. While skill was generally lower for the 1–5-year averaging horizon, several forecast systems showed positive skill relative to climatology for the 1-year and the 1–10-year averaging horizons, especially for below-normal and above-normal precipitation categories. Although bias correction reduced effectively systematic errors, it did not consistently improve forecast skill. These findings suggest potentially useful predictive skill at decadal timescales and highlight the potential of decadal climate information to provide complementary information for near-term water resources planning and drought preparedness in the Brazilian semi-arid region. Full article

The management and efficient use of water resources are crucial to the sustainable development of agriculture in arid regions. The Manas River Basin faces severe water shortages due to its arid climate and heavy reliance on irrigation water. Therefore, based on water footprint theory, this study comprehensively utilized the CROPWAT model, pathway analysis, and CMIP6 data to construct an integrated “assessment–driving–prediction” framework for crop water footprints, with the aim of revealing the evolution patterns and driving mechanisms of water footprints in river basins. The results showed that the cultivated area of crops in the Manas River Basin exhibited a nonlinear expansion trend from 1990 to 2020, with a total increase of 143.56% over the 30-year period. Among all crops, cotton occupied the largest cultivated area, accounting for 60.34% of the total. During the study period, the crop water footprint, crop blue water footprint, and crop green water footprint in the Manas River Basin showed overall upward trends, increasing by 1.07 × 10⁹ m³, 1.04 × 10⁹ m³, and 3.0 × 10⁷ m³, respectively. Total agricultural machinery power and per capita grain production are the main factors influencing changes in crop water footprint. Under future climate scenarios, the crop water footprint in the Manas River Basin is projected to follow the order SSP2-4.5 > SSP5-8.5 > SSP1-2.6. By 2100, the crop water footprint under the SSP2-4.5 scenario is expected to increase by 37.01% relative to 2020, posing substantial challenges to agricultural water resource management in the basin. In contrast, the crop water footprint under the SSP1-2.6 scenario remains relatively stable, indicating a more sustainable development pathway. Full article

Accurate building energy prediction is essential for climate-resilient design, retrofit planning, and long-term energy management. However, most machine-learning models are developed using historical weather data, implicitly assuming that future climatic conditions will remain similar to the past. This assumption is increasingly challenged by climate change, which is altering temperature patterns, solar exposure, humidity levels, and the frequency of extreme weather events. This study presents a climate-scenario-aware artificial intelligence framework that integrates future climate conditions into simulation-driven machine-learning development and validation. Using a UK hotel case study based on the Hilton Watford context, future weather scenarios were derived from CIBSE datasets informed by UKCP18 and CMIP6 climate projections. EnergyPlus version 23.2.0 simulations were performed under baseline, moderate-warming, high-warming, and heatwave stress-test scenarios to generate hourly building energy data. Random Forest, XGBoost 2.1.1, Multiple Linear Regression, and Multi-Layer Perceptron models were trained and evaluated using both Historical-Only and Climate-Scenario-Aware training approaches. Results show that models trained exclusively on historical conditions maintain high present-day accuracy but experience notable performance degradation under future climate scenarios, particularly for cooling demand and peak-load prediction. In contrast, Climate-Scenario-Aware models demonstrated improved robustness, reduced prediction errors, and greater physical consistency during extreme heatwave conditions while maintaining comparable performance under current climatic conditions. The proposed framework provides a reproducible methodology for developing climate-resilient AI models for building energy prediction and highlights the importance of incorporating future climate scenarios into model training and validation. The findings suggest that climate stress-testing should become a standard component of AI-based building energy analytics, digital twins, and long-term energy planning tools. Full article

This study evaluates the resource, technical, economic, and CO₂ mitigation potentials of terrestrial hydropower, wind power, and photovoltaic (PV) power in China under historical and future SSP(Shared Socioeconomic Pathways) climate scenarios. By integrating hydro-meteorological observations, land-use information, digital elevation data, nature-reserve constraints, and CMIP6 climate outputs, we estimate renewable-energy potentials through a consistent national-scale screening framework and cost–supply curve analysis. The results show clear spatial heterogeneity among the three energy sources. Hydropower potential is concentrated mainly in the Yangtze River basin, Pearl River basin, and Southwestern International Rivers. Wind-power potential is relatively high in northwestern, northeastern, and plateau regions, while PV potential is particularly large in northwestern, northern, northeastern, and selected southeastern regions. Under the adopted assumptions, PV shows the largest resource and technical potential, followed by wind power and hydropower; however, this ranking reflects resource potential rather than comprehensive deployment superiority. Practical development is also constrained by ecological flow requirements, land-use competition, grid integration, storage demand, transmission capacity, curtailment risk, and regional demand matching. The findings provide a national-scale comparative reference for renewable-energy planning and CO₂ mitigation, while highlighting the need for future work that incorporates dynamic land use, system-level integration costs, detailed turbine or power-curve modeling, and dynamic grid-emission factors. Full article

Global warming is driving large-scale shifts in the climatically suitable habitats of many species. However, climate-only species distribution assessments may overestimate the spatial availability of future suitable habitats when dynamic land-use change is not considered. To assess potential spatial overlaps between climate-driven habitat suitability shifts and human land-use patterns, this study focuses on Quercus L. as a widely distributed keystone forest taxon in China. The genus-level assessment was designed to identify broad-scale habitat–land-use conflict patterns under multiple climate pathways and territorial spatial planning scenarios, rather than to predict species-specific distribution responses. We developed a soft-coupled framework integrating the Maximum Entropy (MaxEnt) model and the Patch-generating Land-Use Simulation (PLUS) model, and applied the Habitat–Land-Use Conflict Index (HLCI) as a categorical spatial overlay framework to classify potential overlaps between projected suitable habitats and future land-use categories across 16 exploratory scenario combinations integrating Shared Socioeconomic Pathway (SSP)-based climate projections and land-use/land-cover (LULC) scenarios for the 2040s at the grid scale. The results indicate that: (1) climate warming may reshape Quercus habitat suitability, characterized by northward/westward expansion and southward contraction in some low-latitude regions; (2) future land-use patterns may reduce the spatial availability of projected suitable habitats by increasing their overlap with built-up land and cultivated land. Under high-emission scenarios, potential newly suitable habitats overlapped with built-up land by up to 5.90 × 10⁴ km² and with cultivated land by up to 36.42 × 10⁴ km²; and (3) the Ecological Protection scenario showed lower overlap with non-ecological land-use categories and a larger area of potentially realizable habitat expansion. This study provides a scenario-based spatial assessment of where future Quercus habitat suitability may overlap with human land-use patterns, offering broad-scale support for adaptive forest conservation and territorial spatial planning. Full article

Climate change is profoundly altering precipitation and temperature patterns across high-altitude regions worldwide. The Qinghai–Tibet Plateau (QTP), known as the “Third Pole” and the “Asian Water Tower,” is among the most climate-sensitive regions and plays a critical role in the Asian water cycle, cryospheric stability, and regional ecological security. However, the complex topography and diverse climate of the QTP result in substantial discrepancies among meteorological products over this region, highlighting the necessity of a comprehensive evaluation against in situ observational records. Using records from 85 stations (1960–2022), we evaluated four products: China’s 1 km monthly dataset (CN_1km), the Climatic Research Unit gridded Time Series (CRU TS), the fifth-generation European Centre for Medium-Range Weather Forecasts land reanalysis (ERA5-Land), and TerraClimate—selected for their long-term continuity, diverse product types, and widespread regional applications. Subsequently, we compared these products with Earth System Model (ESM) simulations from the NASA Earth Exchange Global Daily Downscaled Projections based on CMIP6 (NEX–GDDP–CMIP6). This evaluation was conducted using key statistical metrics, including the coefficient of determination (R²), root mean square error (RMSE), Kling–Gupta efficiency (KGE), and bias, together with spatially distributed long-term trend analysis using the Sen’s slope estimator and Mann–Kendall test. Station-based evaluation shows that temperature datasets generally outperform precipitation datasets, with monthly mean temperature yielding R² values of 0.85–0.94, RMSE values of 2.38–4.79 °C, and KGE values ranging from −0.04 to 0.86. Monthly precipitation R² values of 0.74–0.81, RMSE values of 20.60–36.12 mm, and KGE values of 0.42–0.86. For anomalies, temperature performs better (R² = 0.41–0.67; RMSE = 0.80–1.41 °C) than precipitation (R² = 0.28–0.44; RMSE = 16.87–20.73 mm). Overall, CN_1km and TerraClimate provide the most reliable station-based temperature estimates, while TerraClimate shows the most robust precipitation performance. All four datasets consistently indicate warming and wetting trends, with temperature rising at 0.21–0.24 °C decade⁻¹ and precipitation increasing at 4.5–5.8 mm decade⁻¹, featuring stronger warming in the west and greater precipitation increases in the northeast; however, the precipitation trend in ERA5-Land does not reach statistical significance. NEX–GDDP–CMIP6 simulations reproduce comparable warming and moistening signals (0.22–0.23 °C decade⁻¹ and 4.1–4.7 mm decade⁻¹), though their precipitation distribution differs markedly from the other datasets, with the discrepancy primarily reflected in a pronounced latitudinal gradient. These results provide a reference for the selection of climate-forcing datasets in hydrological, ecological, and cryospheric studies across the QTP. Full article

This study presents a BIM-integrated life cycle analysis framework (screening-level) for climate-responsive urban energy performance assessment at district scale. The methodology addresses the need for consistent evaluation of operational energy demand under both design interventions and future climate conditions. A mixed-use district in Milan is used as a case study, where parametric BIM massing models (LOD 200–300) are coupled with building energy simulation to analyze three scenarios: a baseline configuration (S0), an envelope optimization scenario (S1), and a future climate scenario based on CMIP6 morphed weather data (S2). The framework enables comparative assessment of energy performance across consistent geometric, operational, and climatic assumptions. Results indicate that envelope optimization reduces energy use intensity by approximately 15–22% across building typologies. Under future climate conditions, cooling demand increases significantly, while reduced heating requirements result in a total district energy use intensity of 33.6 kWh/m²·year (1.60 GWh/year). An indicative carbon assessment based on simulated energy use highlights cooling-driven electricity as the dominant contributor to operational emissions under future conditions. The findings demonstrate that climate change primarily redistributes energy demand between heating and cooling rather than uniformly increasing total consumption, and confirm the value of BIM-integrated, scenario-based workflows for supporting climate-responsive urban design decisions. Full article

Standardized drought indices such as the Standardized Precipitation Index (SPI) are widely used in both monitoring and climate-change impact assessments. However, SPI values are not uniquely defined unless the reference climatology used for standardization is explicitly stated and justified−a methodological issue that becomes critical under non-stationary climate conditions. Here, we present a methodological assessment of how reference-climatology strategy affects SPI-based drought projections under climate change, using Türkiye’s 26 major basins as a hydroclimatically diverse testbed. These assessments inform sustainable water resources planning, agricultural adaptation, and climate-resilient infrastructure design under non-stationary climate. Daily precipitation projections from 56 GCM-RCM pairs (EURO-CORDEX EUR-11, 0.11° (approximately 12 km at the mid-latitudes of the study domain); CMIP5 RCP8.5) were bias-corrected against ERA5-Land and aggregated to basin means. We computed SPI-9 and compared two commonly used reference strategies: (i) a fixed historical baseline (1970–2005), applied consistently to both historical and future periods (fixed-baseline SPI); and (ii) a period-specific baseline (period-specific SPI; future SPI values are standardized to the climatology of the future evaluation period itself). Using the same climate simulations, the two strategies yield markedly different drought projections. At the country scale, end-of-century drought time reaches 458 months under the fixed-baseline strategy, whereas the period-specific strategy indicates 393 drought months. Corresponding severity summaries are likewise stronger under fixed-baseline standardization. The contrast is even stronger in several Mediterranean basins, where fixed-baseline standardization produces persistently severe drought conditions. These results show that SPI-based drought projections are substantially sensitive to the choice of reference-climatology strategy, and that the same climate ensemble can support materially different drought narratives depending on how anomalies are standardized. Because the two strategies differ in both reference-timing and calibration-window length (36 versus 95 years), the headline contrast should be interpreted as a combined effect rather than as a pure baseline-timing result. In the present implementation, the period-specific strategy uses a single future calibration period (2006–2100), so the comparison should be interpreted as a stress test of reference framing under non-stationary climate rather than as an equal-length baseline experiment. An equal-length late-baseline sensitivity check (1970–2005 versus 2065–2100; both spanning 36 years) shows that the fixed-to-late-baseline contrast is larger than the fixed-to-period-specific contrast in 25 of 27 spatial units, including a 3.0-fold amplification at the national scale, indicating that the reference-timing effect persists when calibration-window length is held constant. Because the analysis is based on a CMIP5-driven RCP8.5 ensemble, the numerical projections should be interpreted as a high-end stress-test envelope rather than as the most likely outcome. We therefore recommend that drought projection studies explicitly report the reference-climatology strategy, justify the calibration window, and distinguish between analyses designed to quantify change relative to a historical climate and analyses designed to describe anomalies relative to an evolving future climate. These methodological choices have direct implications for sustainable water resources management and drought-risk preparedness in water-stressed Mediterranean systems, and contribute to broader sustainability targets such as Sustainable Development Goal 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land). Full article

The Atlantic Meridional Overturning Circulation (AMOC), as the core component of the global thermohaline circulation, exerts a profound influence on the Northern Hemisphere climate. Recent observations show that AMOC intensity has weakened by approximately 15% over the past 40 years, yet the traditional theoretical framework dominated by the North Atlantic Oscillation (NAO) cannot fully explain its spatial heterogeneity. This study systematically quantifies the independent driving mechanism of the Arctic Oscillation (AO) on AMOC decline for the first time by integrating multi-source reanalysis data (ERA5, ORAS5) and CMIP6 model output. Theoretical analysis shows that the AO positive phase regulates the stability of AMOC through two coupled pathways: (1) anomalous wind stress curl leads to the weakening of Ekman suction in the subpolar seas (contribution: 42 ± 6%), inhibiting deep-water formation in the Labrador Sea; and (2) increased freshwater flux through the Fram Strait triggers a negative salinity advection feedback, which leads to shoaling of the North Atlantic high-latitude mixed layer by up to 30 m. The cross-scale interaction reveals that the AO interannual variability amplifies the modulation of the AMOC interdecadal trend. This amplification occurs through the positive feedback of sea-ice albedo. When AO and NAO are locked in opposite phases (AO+/NAO−), the AMOC weakening rate increases to 1.8 Sv/decade (1 Sv = 10⁶ m³/s), whereas the same-phase negative condition (AO−/NAO−) yields a moderate decline of 0.5 Sv/decade. This mechanism corrects the underestimation of the traditional wind-driven circulation theory for high-latitude processes and provides a physical attribution for the CMIP6 models’ systematic underestimation of AMOC sensitivity. The study further constructs the “Arctic Oscillation–subpolar basin–AMOC” three-pole coupling theoretical model and confirms that the Arctic amplification effect enhances the AO–AMOC coupling strength by a factor of 2.3 over the full study period (1979–2020; R² = 0.71, p < 0.01), with an even more pronounced enhancement of 2.1 times during the recent two decades (2000–2020; R² increased from 0.28 to 0.59). These findings have direct implications for coastal risk assessment, as AMOC weakening may accelerate sea-level rise along the North American East Coast and increase the frequency of extreme winter storm surges in European coastal areas. The results provide a dynamic basis for IPCC climate risk assessment and have practical application value for the early warning of extreme cold-wave events. Full article

Climate-driven changes in oceanographic conditions are increasingly affecting the distribution of marine species, particularly vulnerable elasmobranchs. The undulate ray, Raja undulata, is a Near Threatened batoid species distributed throughout the northeastern Atlantic Ocean and parts of the Mediterranean Sea, yet its potential response to future climate change remains poorly understood. This study assessed current and future habitat suitability using species distribution modelling approaches and CMIP6 climate projections under the SSP245 scenario. Species occurrence records were compiled from biodiversity databases and published sources, and environmental predictors were selected following multicollinearity screening. Among twelve evaluated modelling algorithms, MaxEnt showed the highest predictive performance (AUC = 0.99; TSS = 0.95) and was selected for subsequent analyses. Current habitat suitability was concentrated along the Iberian Peninsula, the Bay of Biscay, the English Channel, and parts of the western Mediterranean Sea. Future projections indicated substantial habitat contraction, with habitat loss (57.3%) greatly exceeding habitat gain (2.2%), resulting in a southward redistribution of suitable habitats. Minimum phytoplankton concentration, sea surface temperature, and silicate concentration were identified as the most influential environmental predictors. Areas predicted to remain suitable under both current and future conditions may represent important climate refugia for the species. Overall, the results indicate that R. undulata is highly vulnerable to future environmental change and highlight the need to incorporate climate-driven habitat shifts into conservation planning, fisheries management, and long-term monitoring strategies. Full article

Projecting the spatiotemporal dynamics of the potential distribution of dominant species under climate change is essential for desertification control and vegetation restoration in drylands. Here, we modeled the current (1970–2000) and future (2080–2100) suitable habitats of Caragana korshinskii Kom, an ecologically important shrub species in northwestern China, by constructing an ensemble of eight species distribution models on the Biomod2 platform using three CMIP6 Shared Socioeconomic Pathways (SSP126, SSP370, SSP585) and 40 environmental variables representing climate, soil, topography and drought conditions. Key environmental drivers were identified through variable importance ranking and response curves, while area changes, spatial patterns, and centroid shifts in suitable habitats were quantified. The ensemble model demonstrated good to excellent predictive performance (mean AUC > 0.9, mean TSS > 0.5). Soil base saturation (t-bs) and soil moisture contributed the most (>38%), highlighting the dominant role of edaphic factors. The current total suitable habitat of C. korshinskii is approximately 182.2 × 10⁴ km², with all future scenarios projecting a consistent decline. Under SSP585, habitat loss reached 9.8% with contraction (30.5 × 10⁴ km²) far exceeding expansion (12.6 × 10⁴ km²). The distribution centroid shifted markedly eastward with a minor southward fluctuation, establishing the Ordos–Bayannur region as a stable core habitat. Overall, our findings suggest that the distribution of C. korshinskii is strongly constrained by edaphic and moisture conditions, and future contraction of marginal habitats may compromise ecosystem services. Full article

Shaded Arabica coffee production in agroforestry systems, as opposed to full-sun production, is a nature-based solution improving soil water balance, reducing heat exposure of coffee plants, and supporting sustainable forest management as opposed to deforestation. For this coffee production system in Mexico, which is dominated by smallholders as the largest group of coffee producers, we herein analyze current and estimate future yields. For the first time, to our best knowledge, this is done with a process-based coffee agroforestry model CAF2014 that we adapted for geo-spatial applications and named CAF2014-Rhaobi. Modeling of smallholders’ representative management is based on tree thinning, pruning frequency, and nitrogen supply through fertilizer and litter from nitrogen-fixing shade trees. Modeled historical yields generally agree with the reported numbers; however, there are discrepancies explained by modeling assumptions and simplifications. While shade trees help sustain coffee production, the projected drop in yields under present management is about 30% at the end of the century compared to the present as estimated using an ensemble of CMIP6 SSP5-8.5 climate projections. Economic analysis for three typologies of Mexican small coffee producers (conventional low, high-efficiency, and organic) reveals the major role of farmer associations and organic coffee price premiums in making production economically sustainable. This emphasizes the need for innovative marketing approaches and policies supporting farmers opting for certified production. Full article