Search Results (787)

Changes in land use and land cover (LULC) and climate increasingly influence flood occurrences in the Gumara watershed, located in the Upper Blue Nile (UBN) basin of Ethiopia. This study assesses how these factors impact return period-based peak floods, flood source areas, and future high-flow extremes. Merged rainfall data (1981–2019) and ensemble means of four CMIP5 and four CMIP6 models were used for historical (1981–2005), near-future (2031–2055), and far-future (2056–2080) periods under representative concentration pathways (RCP4.5 and RCP8.5) and shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). Historical LULC data for the years 1985, 2000, 2010, and 2019 and projected LULC data under business-as-usual (BAU) and governance (GOV) scenarios for the years 2035 and 2065 were used along with rainfall data to analyze flood peaks. Flood simulation was performed using a calibrated Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) model. The unit flood response (UFR) approach ranked eight subwatersheds (W1–W8) by their contribution to peak flood magnitude at the main outlet, while flow duration curves (FDCs) of annual maximum (AM) flow series were used to analyze changes in high-flow extremes. For the observation period, maximum peak flood values of 211.7, 278.5, 359.5, 416.7, and 452.7 m³/s were estimated for 5-, 10-, 25-, 50-, and 100-year return periods, respectively, under the 2019 LULC condition. During this period, subwatersheds W4 and W6 were identified as major flood contributors with high flood index values. These findings highlight the need to prioritize these subwatersheds for targeted interventions to mitigate downstream flooding. In the future period, the highest flow is expected under the SSP5-8.5 (2056–2080) climate scenario combined with the BAU-2065 land use scenario. These findings underscore the importance of strategic land management and climate adaptation measures to reduce future flood risks. The methodology developed in this study, particularly the application of RF-MERGE data in flood studies, offers valuable insights into the existing knowledge base on flood modeling. Full article

In this study, the Soil and Water Assessment Tool (SWAT) model was first initialized for the Qinglongshan Irrigation Area (QLS). We aimed to assess the impacts of climate and land use (LULC) changes between 1980 and 2020 on several hydrological parameters in the QLS, including actual evapotranspiration (ET), soil water (SW), soil recharge to groundwater (PERC), surface runoff (SURQ), groundwater runoff (GWQ), and lateral runoff (LATQ). We predicted the trends in hydrological factors from 2021 to 2050. Based on the S1 scenario, the precipitation and the paddy field area decreased by 42.28 mm and 1717.65 km², respectively; hydrological factors increased by 91.53, 104.28, 50.66, 21.86, 55.93, and 0.79 mm, respectively, in the QLS. Climate changes contributed 6.10%, −7.58%, −54.11%, 26.90%, −121.17%, and −31.66% to changes in hydrological factors, respectively; LULC changes contributed −2.19%, 3.63%, 11.61%, −2.93%, 25.89%, and 16.86%, respectively; and irrigation water volume changes contributed 96.09%, 103.95%, 142.50%, 76.03%, 195.28%, and 114.80%, respectively. Irrigation and water intake were the main factors affecting the changes in hydrological elements. This was followed by climatic changes and LULC. In natural development scenarios, the QLS is anticipated to face challenges, including increased actual ET, reduced seepage and groundwater contribution, and declining groundwater levels. Full article

Drought is one of the most severe natural disasters globally, with its frequency and intensity escalating due to climate change, posing significant threats to agricultural production. This is particularly critical in major food-producing regions, where drought profoundly impacts crop yields. Such impacts can trigger food crises in affected regions and disrupt global food trade patterns, thereby posing substantial risks to global food security. Based on historical data, this study examines the yield response characteristics of key crops—maize, rice, soybean, spring wheat, and winter wheat—under drought conditions during their growth cycles, highlighting variations in drought sensitivity among major food-producing countries. The findings reveal that maize and soybean yield in China, the United States, and Brazil are among the most sensitive and severely affected by drought. Furthermore, using precipitation simulation data from CMIP6 climate models, the study evaluates drought trends and associated crop yield risks under different future emission scenarios. Results indicate that under high-emission scenarios, crops face heightened drought risks during their growth cycles, with China and the United States particularly vulnerable to yield reductions. Additionally, employing copula functions, the study analyzes the probability of simultaneous drought occurrences across multiple countries, shedding light on the evolving trends of multicountry drought events in major food-producing regions. These findings provide a scientific basis for assessing global food security risks and offer policy recommendations to address uncertainties in food supply under climate change. Full article

Climate prediction is of fundamental importance to various sectors of society and the economy, as it can predict the likelihood of droughts or excessive rainfall in vulnerable regions. Climate models are useful tools in producing reliable climate forecasts, which have become increasingly vital due to the rising impacts of climate change. As global temperatures rise, changes in precipitation patterns are expected, increasing the importance of reliable seasonal forecasts to support planning and adaptation efforts. In this study, we evaluated the performance of NOAA/GFDL models from CMIP6 simulations in representing the climate of South America under three configurations: atmosphere-only, coupled ocean-atmosphere, and Earth system. Our analysis revealed that all three configurations successfully captured key climatic features, such as the South Atlantic Convergence Zone (SACZ), the Bolivian High, and the Intertropical Convergence Zone (ITCZ). However, coupled models exhibited larger errors and lower correlation (below 0.6), particularly over the ocean and the South American Monsoon System, which indicates a poor representation of precipitation compared with atmospheric models. The coupled models also overestimated upward motion linked to the southern Hadley cell during austral summer and underestimated it during winter, whereas the atmosphere-only models more accurately simulated the Walker circulation, showing stronger vertical motion around the Amazon. In contrast, the coupled models simulated stronger upward motion over Northeast Brazil, which is inconsistent with reanalysis data. Moreover, we provided insights into how model biases may evolve under climate change scenarios. Future climate projections for the mid-century period (2030–2060) under the SSP2-4.5 and SSP5-8.5 scenarios indicate significant changes in the global energy balance, with an increase of up to 0.9 W/m². Additionally, the projections reveal significant warming and drying in most of the continent, particularly during the austral spring, accompanied by increases in sensible heat flux and decreases in latent heat flux. These findings highlight the risk of severe and prolonged droughts in some regions and intensified rainfall in others. By identifying and quantifying the biases inherent in climate models, this study provides insights to enhance seasonal forecasts in South America, ultimately supporting strategic planning, impact assessments, and adaptation strategies in vulnerable regions. Full article

Droughts have always been one of the most dangerous hazards for civilizations, especially when they impact the headwaters of a watershed, as their effects can spread downstream. In this context, observed droughts (1981–2015) and projected droughts (2016–2100) were assessed in Candarave, the headwaters of the Locumba basin. Regarding observed droughts, SPI-3 and SPEI-3 detected seven extreme droughts (1983, 1992, 1996, 1998, 2010, 2011, and 2012), with the most intense occurring in 1992 and 1998. SPI-6 and SPEI-6 identified the same extreme drought events, highlighting 1992 as the most intense. Additionally, it was concluded that the VCI also detected the droughts identified by the SPEI; however, a more detailed analysis of its use is necessary due to the limited availability of suitable satellite images in the area. On the other hand, a high-resolution dataset of climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under the SSP3-7.0 scenario was used to project future droughts. Of the models in that dataset, CanESM5, IPSL–CM6A–LR, and UKESM1–0–LL did not perform well in the study area. SPI and SPEI projected more than ten episodes of extreme drought, indicating that extreme droughts will become more frequent, severe, and intense in the last 30 years of this century. Full article

The Yellow River Basin is an important grain-production base in China, playing a crucial role in the country’s agricultural production and overall national economy and social development. However, due to the impact of climate change, China’s food security is facing challenges. Therefore, this article takes the Yellow River Basin as an example to reveal the temporal and spatial evolution patterns of the main crop yields in the basin. Based on a coupled statistical downscaling model (SDSM) and ten General Circulation Models (GCMs) from CMIP5, it estimates the future temporal and spatial evolution characteristics of rainfall and evaporation in the basin. Furthermore, a distributed crop-growth model (AquaCrop) is constructed to reveal the temporal and spatial evolution patterns of agricultural irrigation water requirements from a future perspective, clarifying the impact of multi-source uncertainty on the prediction uncertainty of agricultural irrigation water needs. The results indicate that the ten climate models constructed in this study can be effectively applied to the Yellow River Basin, and their ability to capture light-rain events is superior to that of moderate- and heavy-rain events. The simulation accuracy of the AquaCrop model significantly improves with an increase in precipitation frequency. The agricultural irrigation water demand in the middle and upper reaches of the basin is greater than that in the lower reaches, and the uncertainties from GCMs and RCPs have a significant impact on the uncertainty of agricultural irrigation water demand. The research results provide important references for formulating agricultural development plans for irrigation areas under climate-change conditions and for developing response measures for irrigation areas to cope with climate change. Full article

In agricultural production, droughts occurring during the crucial growth periods of crops hinder crop development, while the daily-scale standardized precipitation evapotranspiration index (SPEI) can be applied to accurately identify the drought characteristics. In this study, we used the statistical downscaling method to obtain the daily precipitation (P_r), maximum air temperature (T_max) and minimum air temperature (T_min) during the rice growing season in Heilongjiang Province from 2015 to 2100 under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 in CMIP6, to study the spatial and temporal characteristics of drought during the rice growing season in cold region and the effect of climate change on drought characteristics. The potential evapotranspiration (PET₀) was calculated using the regression correction method of the Hargreaves formula recommended by the FAO, and the daily SPEI was calculated to quantitatively identify the drought classification. The Pearson correlation coefficient was used to analyze the correlation between the meteorological factors (P_r, T_max, T_min), PET₀ and SPEI. The results showed that: (1) Under 3 SSP scenarios, P_r showed an increasing trend from the northwest to the southeast, T_max showed an increasing trend from the northeast to the southwest, and higher T_min was mainly distributed in the east and west regions. (2) PET₀ indicated an overall interannual rise in the three future SSP scenarios, with higher values mainly distributed in the central and western regions. The mean daily PET₀ values ranged from 4.8 to 6.0 mm/d. (3) Under SSP1-2.6, rice mainly experienced mild drought and moderate drought (−0.5 ≥ SPEI > −1.5). The predominant drought classifications experienced were mild, moderate, and severe drought under SSP2-4.5 and SSP8.5 (−0.5 ≥ SPEI > −2.0). (4) The tillering stage experienced the highest drought frequency and drought intensity, with the longest drought lasting 24 days. However, the heading flower stage had the lowest drought frequency and drought intensity. The drought barycenter was mainly in Tieli and Suihua. (5) The PET₀ was most affected by the T_max, while the SPEI was most affected by the P_r. This study offers a scientific and rational foundation for understanding the drought sensitivity of rice in Northeast China, as well as a rationale for the optimal scheduling of water resources in agriculture in the future. Full article

Regular longitude-latitude grids are commonly used in reanalysis and climate prediction model datasets. However, this approach can disproportionately represent high-latitude regions if simple averaging is applied, leading to overestimation of their contribution. To explore the impact of Earth’s curvature on global warming and heat wave frequency, this study analyzed 450 years of surface temperature data (1850–2300) from a climate prediction model. When area weighting was applied, the global average temperature for the 1850–2300 period was found to be 8.2 °C warmer than in the unweighted case, due to the reduced influence of colder temperatures in high latitudes. Conversely, the global warming trend for the weighted case was 0.276 °C per decade, compared to 0.330 °C per decade for the unweighted case, reflecting a moderation of the polar amplification trend. While unweighted models projected a 317% increase in the frequency of global heat waves above 35 °C by 2300 compared to 1850, the weighted models suggested this frequency might be overestimated by up to 5.4%, particularly due to reduced weighting for subtropical deserts relative to tropical regions. These findings underscore the importance of accounting for Earth’s curvature in climate models to enhance the accuracy of climate change projections. Full article

Investigating changes in compound dry and hot events (CDHEs) and evaluating the associated socioeconomic exposure under climate change are critical for developing effective climate change mitigation and adaptation strategies. However, the socioeconomic exposure and the contributions of various driving factors to socioeconomic exposure under different warming levels remain poorly understood. Using the latest climate experiments from Coupled Model Intercomparison Project Phase 6 (CMIP6), this study assessed future changes in the frequency and socioeconomic exposure of CDHEs and explored the contributing drivers in the Yangtze River Basin (YRB) under 1.5 °C, 2.0 °C, and 3.0 °C global warming scenarios. Results indicate that the occurrences of CDHEs are projected to increase by 2.9, 3.9, and 4.8 times in a 1.5 °C, 2.0 °C, and 3.0 °C warmer world, respectively, compared to the present period (1985–2014). Population exposure to CDHEs increases significantly, with the greatest magnitude occurring at the 2 °C warming scenario. GDP exposure is expected to intensify continuously as the global average temperature rises, with the area experiencing significant increases continuously expanding. Climate change is the dominant driver of total projected changes in population exposure to CDHEs, accounting for approximately 105.6% at 1.5 °C, 110.3% at 2.0 °C, and 141.0% at 3.0 °C. At 1.5 °C, 2.0 °C, and 3.0 °C warming levels, changes in GDP exposure are primarily driven by the synergistic interaction between climate and GDP, accounting for 50.7%, 62.0%, and 64.8%, respectively. These findings provide valuable insights for climate change mitigation and adaptation strategies. Full article

In arid areas, droughts caused by climate change seriously impact wheat production. Therefore, research on spatial and temporal variability of dry and hot wind events and drought risk under different development patterns of future climate can provide a reference for wheat cultivation planning in the study area. Based on meteorological data under three scenarios of the CMIP6 (Sixth International Coupled Model Comparison Program) shared socio-economic path (SSP), we introduced wheat dry hot wind discrimination criteria and calculated the Standardized Precipitation–Evapotranspiration Index (SPEI). Future temperature changes within the Ningxia Province were consistent, increasing at a rate of 0.037, 0.15 and 0.45 °C·(10 a⁻¹) under SSP126, 245 and 585 scenarios, respectively. Simultaneously, average annual precipitation would increase by 17.77, 38.73 and 32.12 mm, respectively. Dry hot wind frequency differed spatially, being higher in northern Ningxia and western Ningxia, and lower in southern Ningxia and eastern Ningxia. During the wheat growing period, there is an obvious increasing drought risk trend under the SSP585 model in May, and the possibility of drought risk in the middle period was highest under the SSP126 model. In June, SPEI was generally higher than in May, and the risk of alternating drought and flood was greater under the SSP585 model, while near-medium drought risk was lower under the SSP126 and SSP245 models. The influence of DHW (dry and hot wind) on wheat yield will increase with the increase of warming level. However, when DHW occurs, effective irrigation can mitigate the harm. Irrigation water can be sourced from various channels, including rainfall, diversion, and groundwater. These results provide scientific reference for sustainable agricultural production, drought risk and wheat meteorological disaster forecast in inland arid areas affected by climate change. Full article

Fire plays a critical role in both the formation and degradation of ecosystems; however, there are still significant uncertainties in the estimation of burned areas (BAs). This study systematically evaluated the performance of ten global climate models (GCMs) in simulating global and regional BA during a historical period (1997–2014) using the Global Fire Emissions Database version 4.1s (GFED4s) satellite fire product. Then, six of the best models were combined using Bayesian Model Averaging (BMA) to predict future BA under three Shared Socioeconomic Pathways (SSPs). The results show that the NorESM2-LM model excelled in simulating both global annual and monthly BA among the GCMs. GFDL-ESM4 and UKESM1-0-LL of the GCMs had the highest Pearson’s correlation coefficient (PCC), but they also exhibited the most significant overestimation of monthly BA variations. The BA fraction (BAF) for GCMs was over 90% for small fires (<1%). For small fires (2~10%), GFDL-ESM4(j) and UKESM1-0-LL(k) outperformed the other models. For medium fires (10–50%), CESM2-WACCM-FV2(e) was closest to GFED4s. There were large biases for all models for large fires (>50%). After evaluation and screening, six models (CESM2-WACCM-FV2, NorESM2-LM, CMCC-ESM2, CMCC-CM2-SR5, GFDL-ESM4, and UKESM1-0-LL) were selected for weighting in an optimal ensemble simulation using BMA. Based on the optimal ensemble, future projections indicated a continuous upward trend across all three SSPs from 2015 to 2100, except for a slight decrease in SSP126 between 2071 and 2100. It was found that as the emission scenarios intensify, the area experiencing a significant increase in BA will expand considerably in the future, with a generally high level of reliability in these projections across most regions. This study is crucial for understanding the impact of climate change on wildfires and for informing fire management policies in fire-prone areas in the future. Full article

The ridge–furrow plastic mulching technique has been widely applied due to its benefits of increasing temperature, conserving moisture, reducing evaporation, and boosting yields. Hydrus-2D is a computer model designed to simulate the two-dimensional movement of water in soil characterized by a low cost and high flexibility compared to field experiments. This study, based on field experiment data from Jianping County, Liaoning Province, China, during 2017–2018, developed Hydrus-2D models for two distinct field management practices: non-mulched flat cultivation (NM-FC) and mulched ridge tillage (M-RT). Furthermore, it simulated the dynamic changes in farmland water variations during the summer maize growth period (2021–2100) under climate change scenarios, specifically medium and high emission pathways (SSP2-4.5 and SSP5-8.5), based on the FGOALS-g3 model, which exhibits the highest similarity to the climate pattern of Jianping County in the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and the Shared Socioeconomic Pathways (SSPs). The results showed that in the future FGOALS-g3 model, net radiation exhibited a significant upward trend under the SSP2-4.5 scenario (Z = 2.38), while the average air temperature showed a highly significant increase under both SSP2-4.5 and SSP5-8.5 scenarios, with Z-values of 6.48 and 8.90, respectively. The Hydrus-2D model demonstrated high simulation accuracy in both NM-FC and M-RT treatments (R² ranging from 0.86 to 0.96, with RMSE not exceeding 0.011), accurately simulating the dynamic changes in soil water content (SWC) under future climate change. Compared to NM-FC, M-RT reduced evaporation, increased transpiration, and effectively decreased the leakage caused by increased future precipitation, resulting in a 0.04 and 0.01 cm³/cm³ increase in surface and deep soil SWC, respectively, during the summer maize growing season, significantly improving water use efficiency. Moreover, M-RT treatment reduced the impact coefficients of climate change on various water balance parameters, stabilizing changes in these parameters and SWC under future climate conditions. This study demonstrates the significant advantages of M-RT in coping with climate change, providing key scientific evidence for future agricultural water resource management. These findings offer valuable insights for policymakers and farmers, particularly in developing adaptive land management and irrigation strategies, helping to improve water use efficiency and promote sustainable agricultural practices. Full article

In 2023, the rapid increase in global temperature of around 0.25 °C caught the scientific community by surprise. Its cause has been investigated largely by exploring variations on a long-term trend, with little success. Building on previous work, this paper proposes an alternative explanation—on decadal timescales, observed temperature shows a complex, nonlinear response to forcing, stepping through a series of steady-state regimes. The 2023 event is nominated as the latest in the sequence. Step changes in historical and modeled global mean surface temperatures (GMSTs) were detected using the bivariate test. Each time series was then separated into gradual (trends) and rapid components (shifts) and tested using probative criteria. For sea surface, global and land surface temperatures from the NOAA Global Surface Temperature Dataset V6.0 1880–2022, the rapid component of total warming was 94% of 0.72 °C, 78% of 1.16 °C and 74% of 1.93 °C, respectively. These changes are too large to support the gradual warming hypothesis. The recent warming was initiated in March 2023 by sea surface temperatures (SSTs) in the southern hemisphere, followed by an El Niño signal further north. Global temperatures followed, then land. A preceding regime shift in 2014 and subsequent steady-state 2015–2022 was also initiated and sustained by SSTs. Analysis of the top 100 m annual average ocean temperature from 1955 shows that it forms distinct regimes, providing a substantial ‘heat bank’ that sustains the changes overhead. Regime shifts are also produced by climate models. Archived data show these shifts emerged with coupling of the ocean and atmosphere. Comparing shifts and trends with equilibrium climate sensitivity (ECS) in an ensemble of 94 CMIP5 RCP4.5 models 2006–2095 showed that shifts had 2.9 times the influence on ECS than trends. Factors affecting this relationship include ocean structure, initialization times, physical parameters and model skill. Single model runs with skill ≥75 showed that shifts were 6.0 times more influential than trends. These findings show that the dominant warming mechanism is the sudden release of heat from the ocean rather than gradual warming in the atmosphere. The model ensemble predicted all regime changes since the 1970s within ±1 year, including 2023. The next shift is projected for 2036, but current emissions are tracking higher than projected by RCP4.5. Understanding what these changes mean for the estimation of current and future climate risks is an urgent task. Full article

To address the growing electricity demand driven by population growth and economic development while mitigating climate change, West and Central African countries are increasingly prioritizing renewable energy as part of their Nationally Determined Contributions (NDCs). This study evaluates the implications of climate change on renewable energy potential using ten downscaled and bias-adjusted CMIP6 models (CDFt method). Key climate variables—temperature, solar radiation, and wind speed—were analyzed and integrated into the Teal-WCA platform to aid in energy resource planning. Projected temperature increases of 0.5–2.7 °C (2040–2069) and 0.7–5.2 °C (2070–2099) relative to 1985–2014 underscore the need for strategies to manage the rising demand for cooling. Solar radiation reductions (~15 W/m²) may lower photovoltaic (PV) efficiency by 1–8.75%, particularly in high-emission scenarios, requiring a focus on system optimization and diversification. Conversely, wind speeds are expected to increase, especially in coastal regions, enhancing wind power potential by 12–50% across most countries and by 25–100% in coastal nations. These findings highlight the necessity of integrating climate-resilient energy policies that leverage wind energy growth while mitigating challenges posed by reduced solar radiation. By providing a nuanced understanding of the renewable energy potential under changing climatic conditions, this study offers actionable insights for sustainable energy planning in West and Central Africa. Full article

Grassland ecosystems cover a high percentage of the terrestrial habitats of Earth and support the livelihood and well-being of at least one-fifth of the human population. Climate change and human activities are causing increasing pressure on arid and semi-arid regions. Land use/cover change significantly affects the function and distribution of grasslands, showing diverse patterns across space and time. The study investigated the spatial distribution of grasslands of Mount Zireia (Peloponnesus, Greece) using MaxEnt modeling based on CMIP6 models (CNRM-CM6 and CCMCC-ESM2) and two Shared Socioeconomic Pathways (SSP 245 and SSP 585) covering the period of 1970–2100. The results from the current (1970–2000) and several future periods (2020–2100) revealed that the MaxEnt model provided highly accurate forecasts. The grassland distribution was found to be significantly impacted by climate change, with impacts varying by period, scenario, and climate model used. In particular, the CNRM-CM6-1 model forecasts a substantial increase in grasslands at higher elevations up to 2100 m asl. The research emphasizes the importance of exploring the combined impacts of climate change and grazing intensity on land use and cover changes in mountainous grasslands. Full article