Search Results (761)

Central Asia (CA) faces growing Water–Energy–Food (WEF) Nexus challenges, due to its complex transboundary water management, legacy Soviet-era water infrastructure, and increasing climate and socio-economic pressures. This study presents the development of a System Dynamics Model (SDM) to evaluate WEF interdependencies across the Aral Sea Basin (ASB), including the Amu Darya and Syr Darya river basins and their sub-basins. Different downscaling strategies based on the area, population, or land use have been applied to process open-access databases at the national level in order to match the scope of the study. Climate and socio-economic assumptions were introduced through the integration of already defined Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The resulting SDM incorporates more than 500 variables interacting through mathematical relationships to generate comprehensive outputs to understand the WEF Nexus concerns. The SDM was successfully calibrated and validated across three key dimensions of the WEF Nexus: final water discharge to the Aral Sea (Mean Absolute Error, MAE, <5%), energy balance (MAE = 4.6%), and agricultural water demand (basin-wide MAE = 1.2%). The results underscore the human-driven variability of inflows to the Aral Sea and highlight the critical importance of transboundary coordination to enhance future resilience. Full article

Legume crops are sensitive to shifting environmental conditions, as they depend on a narrow range of climatic stability for growth and nitrogen fixation. This research sought to assess the sustainability of Faba Asturiana (FA) cultivation under current and future climatic scenarios by establishing generalized linear mixed models (GLMMs). Specifically, it aimed to (1) investigate the effects of significant climatic stressors, including higher nighttime temperatures and extended drought periods, on crop viability, (2) analyze future scenarios based on Representative Concentration Pathways (RCP 4.5 and RCP 8.5), and (3) recommend adaptive measures to mitigate threats. Six spatial GLMMs were developed, incorporating variables such as extreme temperatures, precipitation, and the drought duration. Under present-day conditions (1971–2000), all the models exhibited strong predictive performances (AUC: 0.840–0.887), with warm nights (tasminNa20) consistently showing a negative effect on suitability (coefficients: −0.58 to −1.16). Suitability projections under future climate scenarios revealed considerable variation among the developed models. Under RCP 4.5, Far Future, Model 1 projected a 7.9% increase in the mean suitability, while under RCP 8.5, Far Future, the same model showed a 78% decline. Models using extreme cold, drought, or precipitation as climatic stressors (e.g., Models 2–4) revealed the most significant suitability losses under RCP 8.5, with the reductions exceeding 90%. In contrast, comprising variables less affected by severe fluctuations, Model 6 showed relative stability in most of the developed scenarios. The model also produced the highest mean suitability (0.130 ± 0.207) in an extreme projective scenario. The results highlight that high night temperatures and prolonged drought periods are the most limiting factors for FA cultivation. ecological niche models (ENMs) performed well, with a mean AUC value of 0.991 (SD = 0.006) and a mean TSS of 0.963 (SD = 0.024). According to the modeling results, among the variables affecting the current distribution of Protected Geographical Indication-registered AF, prspellb1 (max consecutive dry days) had the highest effect of 28.3%. Applying advanced statistical analyses, this study provides important insights for policymakers and farmers, contributing to the long-term sustainability of PGI agroecosystems in a warming world. Full article

This study quantifies the uncertainty in monthly streamflow predictions under future climate scenarios in two periods (near and far future) for the Riu Mannu di Narcao basin in Sardinia, Italy. The sources of uncertainty include the hydrological model structure, model parameters, and variability in climatic inputs derived from global and regional climate models (GCM-RCM coupling) and representative concentration pathways (RCPs). Three conceptual and lumped hydrological models (GR3M, ABCD, and IHACRES) were combined with four climate models and two RCPs (RCP 4.5 and RCP 8.5) to assess future streamflow. Monte Carlo simulations were performed to evaluate parameter uncertainty, and the analysis of variance (ANOVA) method was applied to quantify the different sources of uncertainty. The results reveal that, as a single source, GCM-RCM coupling is the largest contributor, accounting for 47.32% (54.64%) of total near (far) future monthly streamflow projection uncertainties, followed by the hydrological model structure at 16.02% (21.09%), RCP scenarios at 15.35% (8.54%), and parameter uncertainty at 0.79% (1.39%). A consistent decline in median monthly streamflow is projected, especially during winter months (December to February), raising a concern about water availability in the region. Our study quantified different sources of uncertainty in monthly streamflow predictions under climate change, disentangling the roles of the hydrological model, model parameters, climate model, and climate scenario for reliable future streamflow projections. Full article

The main goal of this study is to present future changes in various precipitation indices at a kilometer-scale resolution for Bulgaria on an annual and seasonal basis. Numerical simulations were conducted using the Non-Hydrostatic Regional Climate Model version 4 (RegCM4-NH) following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol for three 10-year periods (1995–2004, 2041–2050, and 2090–2099), with horizontal grid resolutions of 15 km and 3 km, on the petascale supercomputer HPC Discoverer at Sofia Tech Park. Data from the Hadley Centre Global Environment Model version 2 (HadGEM2-ES), based on the Representative Concentration Pathway 8.5 (RCP8.5) scenario, were used as boundary conditions for the regional climate model (RCM) simulations, which were subsequently downscaled to the kilometer-scale (3 km) simulations using a one-way nesting approach. High-resolution model data were compared with high-resolution observational datasets as well as lower-resolution (15 km) data. Future changes in precipitation indices were analyzed on both annual and seasonal scales, including mean daily and hourly precipitation, the frequency and intensity of wet days (>1 mm/day) and wet hours (>0.1 mm/hour), extreme daily precipitation (99th percentile, p99), and extreme hourly precipitation (99.9th percentile, p99.9) for both future periods. Additionally, changes in near-surface (2 m) temperature and surface snow amount were also presented. There is no substantial difference in projected temperature change between the resolutions. A positive trend in annual mean precipitation is expected in the near future. Extreme precipitation (p99 and p99.9) is projected to increase in spring and winter, accompanied by a rise in daily and hourly precipitation intensity across both future periods. An increase in surface snow amount is observed in the central Danubian Plain, Thracian Lowland, and parts of the Rila and Pirin mountains for the near-future period. However, surface snow amount is expected to decrease by the end of the century. Full article

This study aimed to evaluate the performance and robustness of the GR2m “Génie Rural à 2 paramètres au pas du temps Mensuel” rainfall–runoff model for simulating streamflow under past and future hydrometeorological shifts in the Medjerda, a data-scarce Mediterranean catchment in northern Tunisia characterized by limited hydrometeorological records and high climate variability. The evaluation was conducted across three subcatchments characterized by contrasting climatic conditions and representing the hydrometeorological pattern of the Medjerda catchment. To assess the model’s robustness, a calibration–validation process was applied. This method alternated between dry and wet periods and evaluated model performance through various criteria. Subsequently, GR2m was adopted to simulate projected discharge, using projections from the “Model for Interdisciplinary Research on Climate 5” (MIROC5) under Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Standardized climate indices (SCIs) were employed to assess climate change impacts. The results demonstrate that GR2m performs well in simulating streamflow across different climatic conditions within the Medjerda catchment and maintains satisfactory performance when calibrated over a non-stationary climate period. The findings indicate a continuous decline in projected runoff and suggest a significant increase in extreme drought events. Full article

The planthopper Pentastiridius leporinus, commonly called reed glass-winged cicada, transmits the pathogens “Candidatus Arsenophonus phytopathogenicus” and “Candidatus Phytoplasma solani”, which are infesting sugar beet and, most recently, also potato in the Upper Rhine valley area of Germany. They cause the “Syndrome Basses Richesses” associated with reduced yield and sugar content in sugar beet, leading to substantial monetary losses to farmers in the region. No effective solutions exist currently. This study uses statistical models to understand to what extent the abundance of cicadas depends on climate regions during the vegetation period (April–October). We further investigated what influence temperature and precipitation have on the abundance of the cicadas in sugar beet fields. Furthermore, we investigated the possible impacts of future climate on cicada abundance. Also, 22 °C and 8 mm/day were found to be the optimal temperature and precipitation conditions for peak male cicada flight activity, while 28 °C and 8 mm/day were the optimum for females. By the end of the 21st century, daily male cicada abundance is projected to increase significantly under the worst-case high greenhouse gas emission scenario RCP8.5 (RCP-Representative Concentration Pathways), with confidence intervals suggesting a possible 5–15-fold increase compared to current levels. In contrast, under the low-emission scenario RCP2.6, male cicada populations are projected to be 60–70% lower than RCP8.5. An understanding of the influence of changing temperature and precipitation conditions is crucial for predicting the spread of this pest to different regions of Germany and other European countries. Full article

This study evaluates the impacts of Solar Radiation Management (SRM) on precipitation-related climate extremes in Southeast Asia. Using simulations from the Geoengineering Large Ensemble (GLENS), we assess spatial anomalies and differences in extreme precipitation indices—number of wet days (RR1), very heavy precipitation days (R20mm), maximum 5-day precipitation (Rx5day), consecutive dry days (CDD), and consecutive wet days (CWD)—relative to historical (1980–2009) and Representative Concentration Pathway 8.5 (RCP8.5) baselines. The results reveal that SRM induces highly heterogeneous precipitation responses across the region. While SRM increases rainfall frequency in parts of Indonesia, it reduces the number of wet days and lengthens dry spells over Vietnam, Thailand, and the Philippines. Spatial variations are also observed in changes to heavy precipitation days and multi-day rainfall events, with potential implications for flood and drought risks. These findings highlight the complex trade-offs in hydrological responses under SRM deployment, with important considerations for agriculture, water resource management, and climate adaptation strategies in Southeast Asia. Full article

Upland fields are a significant source of N₂O emissions. Thus, an accurate estimation of these emissions is essential. This study employed four classical modeling approaches—the Stepwise Regression Model, Decision Tree Regression, Support Vector Machine, and Random Forest (RF)—to simulate soil N₂O emissions from Chinese upland fields. The upland crops considered in this study covered food crops, oil crops, cash crops, sugar crops, fruits, and vegetables, excluding flooded rice. Comparative analysis revealed that the RF algorithm performed the best, with the highest R² at 0.66 and the lowest root mean square error at 0.008 kg N₂O ha⁻¹ day⁻¹. The application rate of mineral nitrogen fertilizers, mean temperature during the growing season, and soil organic carbon content were the key driving factors in the N₂O emission model. Utilizing the RF model, total N₂O emissions from Chinese upland fields in 2020 were estimated at 183 Gg. Future projections under Representative Concentration Pathway (RCP) scenarios indicated a 2.80–5.92% increase in national N₂O emissions by 2050 compared to 2020. The scenario analysis demonstrated that the proposed nitrogen reduction strategies fail to counteract climate-driven emission amplification. Under the combined scenarios of RCP8.5 and nitrogen reduction strategies, a net 4% increase in national N₂O emissions was projected, highlighting the complex interplay between anthropogenic interventions and climate feedback mechanisms. This study proposes that future attention should be paid to the development of nitrogen optimization strategies under the impact of climate change. Full article

Buildings account for a significant portion of global energy consumption and are increasingly vulnerable to the adverse effects of climate change, including rising greenhouse gas emissions and shifting weather patterns. These disruptions significantly impact energy demand, necessitating proactive measures to ensure buildings remain functional, sustainable, and energy efficient. This study offers a novel contribution by systematically comparing the energy performance of office building prototypes using a simulation-based method across all U.S. climate zones under projected Representative Concentration Pathways (RCPs) 4.5 (moderate emissions) and 8.5 (high emissions) for the years 2050 and 2080. This multi-scale and multi-scenario simulation provides a nationally comprehensive view of how building size and climate conditions interact to influence vulnerability to future energy demand shifts. The findings reveal that medium-sized office buildings are the most vulnerable to climate change, with an average Energy Unit Intensity (EUI) increase of 12.5% by 2080 under RCP 8.5, compared to a 7.4% rise for large office buildings and a slight decline of 2.5% for small office buildings. Hot and humid cities such as Miami, FL, experience the highest increases, with EUI projected to rise by 21.2% for medium offices, while colder regions like Fairbanks, AK, show reductions of up to 18.2% due to decreasing heating demands. These results underscore the urgent need for climate-compatible building design strategies, particularly in high-risk areas. As climate change intensifies, integrating resilience-focused policies will safeguard energy systems and ensure long-term occupant comfort. Full article

Persistent climate change and anthropogenic activities have caused the degradation of urban ecosystems and the fragmentation of landscapes in the Loess Plateau region, situated in northern China. Ecological networks have been considered an effective measure for reducing urban habitat fragmentation, enhancing landscape connectivity, and identifying priority areas for ecological restoration. However, research on the spatiotemporal dynamics of ecological networks in cities in the Loess Plateau region, especially multi-scenario ecological networks under future climate change scenarios, and the drivers affecting these network elements are still limited. This study analyzed the spatiotemporal dynamic changes in the ecological networks in Shenmu City from 2000 to 2035, and used GeoDetector to explore the driving factors influencing changes in ecological source distribution. The results showed the following: (1) The ecological sources in Shenmu City continued to shrink from 2000 to 2020, while landscape fragmentation increased. By 2035, the results of scenario modeling will differ for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs), with the ecological source area increasing under scenarios SSP119 and SSP245, and continuing to decrease under scenario SSP585. (2) From 2000 to 2020, the α, β, and γ indices increased and then declined, while the ecological networks of the SSP119 and SSP585 scenarios will stabilize. (3) Under the optimal scenario SSP119, 27 ecological pinch points and 40 ecological barrier points will be identified, which are priority areas for the future execution of ecological restoration initiatives. (4) Precipitation is the primary factor that affects the distribution of ecological sources, followed by temperature. This study proposes a new research perspective on ecological networks, and provides a guideline for ecological restoration and conservation in cities (counties) in the Loess Plateau region. Full article

Flooding poses a significant hazard to urban areas, particularly under the pressures of climate change and rapid urbanization. This study evaluates the flood risk in the Wadi Ibrahim watershed, located in Makkah Al-Mukarramah City, Kingdom of Saudi Arabia (KSA), by analyzing the impacts of climate change on flood hazards. The analysis incorporates projections from the Coordinated Regional Climate Downscaling Experiment (CORDEX) regional climate model (RCM) for three climate scenarios: representative concentration pathway (RCP) 2.6, RCP 4.5 and RCP 8.5. A novel aspect of this study is the integration of 2D HEC-RAS rain-on-grid (RoG) hydrodynamic modeling with climate change projection analysis, which has not been previously applied in this watershed. Flood risk maps are generated for each scenario at three return periods: 50, 100, and 200 years. The results indicate an increasing flood volume and depth under future climate scenarios. The flood risk mapping shows an expansion of medium- and high-risk zones compared to current conditions. Under the current climate, the low-risk areas (0–0.5 m) slightly decrease from 13.9 km² (50 years) to 13.8 km² (200 years), while the medium- (0.5–2 m) and high-risk areas (>2 m) increase from 6.5 km² to 7.0 km² and from 7.2 km² to 9.8 km², respectively. Under RCP 2.6, the low-risk zones decline from 13.6 km² to 13.0 km², the medium-risk zones grow from 14.5 km² to 16.2 km², and the high-risk zones rise from 4.3 km² to 6.5 km². The higher emissions scenarios show greater risk increases, with the high-risk areas expanding from 5.3 km² to 12.0 km² under RCP 4.5, and from 9.5 km² to 16.6 km² under RCP 8.5. These findings underscore the escalating flood risks due to climate change and highlight the need for mitigation in the Wadi Ibrahim watershed. Full article

The current changing climate requires the development of water–energy–food (WEF) nexus-oriented systems capable of mainstreaming climate-smart innovations into resource management. This study demonstrates the cross-sectoral impacts of climate change on interlinked sectors of water, energy, and food in Narok County, Kenya, and Vhembe District, South Africa. This study used projected hydroclimatic extremes across past, present, and future scenarios to examine potential effects on the availability and accessibility of these essential resources. The projected temperature and rainfall are based on nine dynamically downscaled Coupled Model Intercomparison Project Phase 5 (CMIP 5) of the Global Climate Models (GCMs). The model outputs were derived from two IPCC “Representative Concentration Pathways (RCPs)’’, the RCP 4.5 “moderate scenario”, and RCP 8.5 “business as usual scenario”, also defined as the addition of 4.5 W/m² and 8.5 W/m² radiative forcing in the atmosphere, respectively, by the year 2100. For the climate change projections, outputs from the historical period (1976–2005) and projected time intervals spanning the near future, defined as the period starting from 2036 to 2065, and the far future, spanning from 2066 to 2095, were considered. An ensemble model to increase the skill, reliability, and consistency of output was formulated from the nine models. The statistical bias correction based on quantile mapping using seven ground-based observation data from the South African Weather Services (SAWS) for Limpopo province and nine ground-based observation data acquired from the Trans-African Hydro-Meteorological Observatory (TAHMO) for Narok were used to correct the systematic biases. Results indicate downscaled climate change scenarios and integrate a modelling framework designed to depict the perceptions of future climate change impacts on communities based on questionnaires and first-hand accounts. Furthermore, the analysis points to concerted efforts of multi-stakeholder engagement, the access and use of technology, understanding the changing business environment, integrated government and private sector partnerships, and the co-development of community resilience options, including climate change adaptation and mitigation in the changing climate. The conceptual climate and WEF resource modelling framework confirmed that future climate change will have noticeable interlinked impacts on WEF resources that will impact the livelihoods of vulnerable communities. Building the resilience of communities can be achieved through transformative WEF nexus solutions that are inclusive, sustainable, equitable, and balance adaptation and mitigation goals to ensure a just and sustainable future for all. Full article

The study examines the impacts of climate change on the operation and capacity of the combined sewer network in the historic center of Thessaloniki, Greece. Rainfall data from three high-resolution Regional Climate Models (RCMs), namely (a) the Cosmo climate model (CCLM), (b) the regional atmospheric climate model (RACMO) and (c) the regional model (REMO), from the MED-CORDEX initiative with future estimations based on Representative Concentration Pathway (RCP) 4.5, are first corrected for bias based on existing measurements in the study area. Intensity–duration–frequency (IDF) curves are then constructed for future data using a temporal downscaling approach based on the scaling of the Generalized Extreme Value (GEV) distribution to derive the relationships between daily and sub-daily precipitation. Projected rainfall events associated with various return periods are subsequently developed and utilized as input parameters for the hydrologic–hydraulic model. The simulation results for each return period are compared with those of the current climate, and the projections from various RCMs are ranked according to their impact on the combined sewer network and overflow volumes. In the short term (2020–2060), the CCLM and REMO project a decrease in CSO volumes compared to current conditions, while the RACMO predicts an increase, highlighting uncertainties in short-term climate projections. In the long term (2060–2100), all models indicate a rise in combined sewer overflow volumes, with CCLM showing the most significant increase, suggesting escalating pressure on urban drainage systems due to more intense rainfall events. Based on these findings, it is essential to adopt mitigation strategies, such as nature-based solutions, to reduce peak flows within the network and alleviate the risk of flooding. Full article

The Maltese Islands, situated at the centre of the Mediterranean basin, are recognised as a climate change hotspot. This study utilises projected changes in temperature and precipitation derived from the World Climate Research Program (WCRP) and analyses outputs from six coupled model intercomparison project phase 5 (CMIP5) models under two Representative Concentration pathways (RCPs). Through statistical and spatial analysis, the study demonstrates that climate change will have significant adverse effects on Maltese agriculture. Regardless of the RCP scenario considered, projections indicate a substantial increase in temperature and a decline in precipitation, exacerbating aridity and intensifying heat stress. These changes are expected to reduce soil moisture availability and challenge traditional agricultural practices. The study identifies the Western District as a relatively more favourable area for crop cultivation due to its comparatively lower temperatures, whereas the Northern and South Eastern peripheries are projected to experience more severe heat stress. Adaptation strategies, including the selection of heat-tolerant crop varieties such as Tetyda and Finezja, optimised water management techniques, and intercropping practices, are proposed to enhance agricultural resilience. This study is among the few comprehensive assessments of bioclimatic and physical factors affecting Maltese agriculture and highlights the urgent need for targeted adaptation measures to safeguard food production in the region. Full article

Understanding how mammals respond to climate change is critical for predicting future biogeographic shifts and implementing effective conservation strategies. In this study, we applied MaxEnt modeling to identify key determinants of the distribution of the Malabar slender loris (Loris lydekkerianus malabaricus), a nocturnal primate endemic to the Western Ghats of India. Using 416 slender loris sightings, spatially thinned at 0.5 km intervals to reduce spatial autocorrelation, we evaluated 19 present bioclimatic variables alongside 10 additional climatic variables. From these, 14 predictor variables with Pearson correlation values above 0.75 were selected for analysis. Future distribution models employed bioclimatic projections from the CNRM-CM5 global climate models under three Representative Concentration Pathways (RCPs): 2.6, 4.5, and 8.5. The current distribution models identified 23 km² as a suitable habitat for slender lorises, with 3 km² suitable for males and 12 km² for females. Projections for 2070 under RCP 2.6, 4.5, and 8.5 scenarios predict habitat reductions of 52%, 13%, and 8%, respectively, signaling significant vulnerability under changing climatic conditions. Precipitation of the warmest quarter, precipitation of the driest month, distance from roads, and elevation were identified as the most influential variables shaping the species’ distribution. This study underscores the pressing need for targeted conservation efforts to mitigate habitat loss and fragmentation, particularly in the context of climate change. By providing a detailed analysis of current and future habitat suitability, it lays the groundwork for similar predictive studies on nocturnal small mammals. As climate change accelerates, the integration of species–specific ecological insights and advanced modeling techniques will be vital in guiding conservation actions and preserving biodiversity in vulnerable ecosystems like the Western Ghats. Full article