Advances in Climate Change Research最新文献

To address data scarcity on long-term glacial discharge and inadequacies in simulating and predicting hydrological processes in the Tien Shan, this study analysed the observed discharge at multiple timescales over 1980s–2017 and projected changes within a representative glacierized high-mountain region: eastern Tien Shan, Central Asia. Hydrological processes were simulated to predict changes under four future scenarios (SSP1, SSP2, SSP3, and SSP5) using a classical hydrological model coupled with a glacier dynamics module. Discharge rates at annual, monthly (June, July, August) and daily timescales were obtained from two hydrological gauges: Urumqi Glacier No.1 hydrological station (UGH) and Zongkong station (ZK). Overall, annual and summer discharge increased significantly (p < 0.05) at both stations over the study period. Their intra-annual variations mainly resulted from differences in their recharge mechanisms. The simulations show that a tipping point in annual discharge at UGH may occur between 2018 and 2024 under the four SSPs scenarios. Glacial discharge is predicted to cease earlier at ZK than at UGH. This relates to glacier type and size, suggesting basins with heavily developed small glaciers will reach peak discharge sooner, resulting in an earlier freshwater supply challenge. These findings serve as a reference for research into glacial runoff in Central Asia and provide a decision-making basis for planning local water-resource projects.

Flood frequency in river source regions is significantly affected by rainfall and snowmelt as part of climatic changes. A traditional univariate flood frequency analysis cannot reflect the complexity of floods, and when used in isolation, it can only underestimate flood risk. For effective flood prevention and mitigation, it is essential to consider the combined effects of precipitation and snowmelt. Copula functions can effectively quantify the joint distribution relationship between floods and their associated variables without restrictions on their distribution characteristics. This study uses copula functions to consider a multivariate probability distribution model of flood peak flow (Q) with cumulative snowmelt (CSm) and cumulative precipitation (CPr) for the Hutubi River basin located in northern Xinjiang, China. The joint frequencies of rainfall and snowmelt floods are predicted using copula models based on the Coupled Model Intercomparison Project Phase 6 data. The results show that Q has a significant positive correlation with 24-d CSm (r = 0.559, p = 0.002) and 23-d CPr (r = 0.965, p < 0.05). Flood frequency will increase in the future, and mid- (2050–2074) and long-term (2075–2099) floods will be more severe than those in the near-term (2025–2049). The probability of flood occurrence is higher under the SSP2-4.5 and SSP1-2.6 scenarios than under SSP5-8.5. Precipitation during the historical period (1990–2014) led to extreme floods, and increasing future precipitation trends are found to be insignificant. Snowmelt increases with rising temperatures and occurs earlier than estimated, leading to an earlier flood period in the basin and more frequent snowmelt floods. The Q under the joint return period is larger than that during the same univariate return period. This difference indicates that neglecting the interaction between precipitation and snowmelt for floods leads to an underestimation of the flood risk (with underestimations ranging from 0.3% to 22%). The underestimations decrease with an increase in the return period. The joint risks of rainfall or snowmelt according to various flood periods should be considered for rivers with multi-source runoff recharge in flood control design. This study reveals the joint impact of precipitation and snowmelt on extreme floods under climate change in river source regions. This study also provides a scientific basis for regional flood prevention and mitigation strategies, as well as for the rational allocation of water resources.

Mountainous areas are of special hydrological concern because topography and atmospheric conditions can result in large and sudden floods, posing serious risks to water-related safety in neighbouring countries. The Yarlung Zangbo (YZ) River basin is the largest river basin on the Tibetan Plateau (TP), but how floods will discharge in this basin and how the role of glacier melt in floods will change throughout the 21st-century under shared socioeconomic pathways scenarios (SSP2-4.5 and SSP5-8.5) remain unclear. Here, we comprehensively address this scientific question based on a well-validated large-scale glacier-hydrology model. The results indicate that extreme floods was projected to increase in the YZ basin, and was mainly reflected in increased duration (4–10 d per decade) and intensity (153–985 m³ s⁻¹ per decade). Glacier runoff was projected to increase (2–30 mm per decade) throughout the 21st-century, but there was also a noticeable decrease or deceleration in glacier runoff growth in the late first half of the century under the SSP2-4.5, and in the latter half of the century under the SSP5-8.5. Glacier melt was projected to enhance the duration (12%–23%) and intensity (15%–21%) of extreme floods under both SSPs, which would aggravate the impact of future floods on the socioeconomics of the YZ basin. This effect was gradually overwhelmed by precipitation-induced floods from glacier areas to YZ outlet. This study takes the YZ basin as a projection framework example to help enrich the understanding of future flood hazards in basins affected by rainfall- or meltwater across the TP, and to help policy-makers and water managers develop future plans.

The sea level in coastal areas of China reached the second highest in 2021, just after that recorded in 2022. External force and dynamic analyses based on tide gauges, satellite observations, reanalysis data and regional numerical outputs were conducted to understand these abnormally high sea levels and determine their possible causes. Results show that the coastal sea level of China had increased at an annual rate of 3.4 ± 0.3 mm during 1980–2021, with an acceleration of 0.06 ± 0.02 mm per year². The superposition of significant oscillations of quasi-2, 3–7, quasi-9, quasi-11, quasi-19 and 20–30 years contributed to the anomalously high sea levels. The negative-phased El Niño/Southern Oscillation was correlated with the anomalously high sea level and the north‒south anti-phase pattern of the coastal sea level in 2021. Meanwhile, phase lags of 1–4 months occurred with the sea-level response. On a decadal timescale, the Pacific Decadal Oscillation (PDO) was negatively correlated with the anomalous mean sea level (MSL), and the negative-phased PDO contributed to the anomalous sea-level change in 2021. Particularly, the monthly MSL peaked in April and July, and the contribution of wind stress to the anomalously high sea level was 38.5% in the south of the Taiwan Strait in April and 30% along the coast of China in July. These results were consistent with the tide gauge and satellite data. Close agreement was also observed between the coastal sea-level fingerprint and the air and sea surface temperatures.

Freeze‒thaw induced landslides (FTILs) in grasslands on the Tibetan Plateau are a geological disaster leading to soil erosion. These landslides reduce biodiversity and intensify landscape fragmentation, which in turn are strengthen by the persistent climate change and increased anthropogenic activities. However, conventional techniques for mapping FTILs on a regional scale are impractical due to their labor-intensive, costly, and time-consuming nature. This study focuses on improving FTILs detection by implementing image fusion-based Google Earth Engine (GEE) and a random forest algorithm. Integration of multiple data sources, including texture features, index features, spectral features, slope, and vertical‒vertical polarization data, allow automatic detection of the spatial distribution characteristics of FTILs in Zhidoi county, which is located within the Qinghai‒Tibet Engineering Corridor (QTEC). We employed statistical techniques to elucidate the mechanisms influencing FTILs occurrence. The enhanced method identifies two schemes that achieve high accuracy using a smaller training sample (scheme A: 94.1%; scheme D: 94.5%) compared to other methods (scheme B: 50.0%; scheme C: 95.8%). This methodology is effective in generating accurate results using only ∼10% of the training sample size necessitated by other methods. The spatial distribution patterns of FTILs generated for 2021 are similar to those obtained using various other training sample sources, with a primary concentration observed along the central region traversed by the QTEC. The results highlight the slope as the most crucial feature in the fusion images, accounting for 93% of FTILs occurring on gentle slopes ranging from 0° to 14°. This study provides a theoretical framework and technological reference for the identification, monitoring, prevention and control of FTILs in grasslands. Such developments hold the potential to benefit the management of grassland ecosystem, reduce economic losses, and promote grassland sustainability.

Glaciers have retreated and shrunk in High Mountain Asia since the mid-20th century because of global warming, leading to glacier instability and hazardous ice–snow avalanches. However, the complex relationship between ice–snow avalanches and factors such as climate and potential triggers are difficult to understand because of the lack of observational data. Here, we addressed ice–snow avalanches on the Annapurna II glacier in Nepal, Central Himalaya. We constructed an ice–snow avalanche history using long-term multi-source remote sensing images (1988–2021) and mapped the velocity fields of glaciers using cross-correlation analysis on SAR and optical images. Then, we investigated the impact of climate change and earthquakes on the frequency and size of ice–snow avalanches. The results demonstrate that the frequency of ice–snow avalanches has increased from 10 in 1988 to 27 in 2020, but the average area of ice–snow avalanche deposits has decreased by approximately 70%, from 3.4 $\times$ 10⁵ m² in 1988 to 1.2 $\times$ 10⁵ m² in 2020. The evolutionary characteristic of ice avalanches is linked to the impact of glacier retreat (reduction in ice material supply) and increased activity under climate change. The glacier movement velocity controls the size of ice–snow avalanches and can be set as an indicator for ice–snow avalanche warnings. On the Annapurna II glacier, an ice–snow avalanche occurred when the glacier velocities were greater than 1.5 m d⁻¹. These results offer insights into ice–snow avalanche risk assessment and prediction in high-mountain areas, particularly in regions characterised by dense glacier distribution.

The increasingly frequent and severe regional-scale compound heatwave‒drought extreme events (CHDEs), driven by global warming, present formidable challenges to ecosystems, residential livelihoods, and economic conditions. However, uncertainty persists regarding the future trend of CHDEs and their insights into regional spatiotemporal heterogeneity. By integrating daily meteorological data from observations in 1961–2022 and global climate models (GCMs) based on the Shared Socioeconomic Pathways, the evolution patterns of CHDEs were compared and examined among three sub-catchments of the Yangtze River Basin, and the return periods of CHDE in 2050s and 2100s were projected. The findings indicate that the climate during the 2022 CHDE period was the warmest and driest recorded in 1961–2022, with precipitation less than 154.5 mm and a mean daily maximum temperature 3.4 °C higher than the average of 1981–2010, whereas the characteristics in the sub-catchments exhibited temporal and spatial variation. In July–August 2022, the most notable feature of CHDE was its extremeness since 1961, with return periods of ∼200-year in upstream, 80-year in midstream, and 40-year in downstream, respectively. By 2050, the return periods witnessed 2022 CHDE would likely be reduced by one-third. Looking towards 2100, under the highest emission scenario of SSP585, it was projected to substantially increase the frequency of CHDEs, with return periods reduced to one-third in the upstream and downstream, as well as halved in the midstream. These findings provide valuable insights into the changing risks associated with forthcoming climate extremes, emphasizing the urgency of addressing these challenges in regional management and sustainable development.

Understanding how hydrological factors interrelate is crucial when examining the impact of climate warming on snowmelt. However, these connections are often overlooked, leading to an unclear relationship between temperature and snowmelt. This study investigates the complex interplay between temperature and snowmelt in the Tibetan Plateau from 1961 to 2020, focusing on how extreme high-temperature events affect the frequency of extreme snowmelt. Using a structural equation model, we detected three temperature-related factors that predominantly influenced snowmelt and extreme snowmelt. The annual average temperature was found to have a significant indirect impact on snowmelt, mediated by changes in snowfall, snow depth and snow cover. By contrast, high-temperature days (daily maximum temperatures exceeding the 90th percentile) and heat waves (at least three consecutive high-temperature days) negatively affected extreme snowmelt directly or indirectly. The direct effect of increasing extreme temperature events was associated with an earlier onset of high-temperature periods, which accelerated snowmelt and shortened the duration of extreme snowmelt periods. Additionally, the reduction in snow cover owing to warming emerged as a main factor suppressing snowmelt and extreme snowmelt frequencies. We also revealed spatiotemporal variations in the temperature‒snowmelt relationship that highly depended on changes in snowmelt patterns. The study elucidated why warming suppresses snowmelt and extreme snowmelt events in the Tibetan Plateau, highlighting the mediating roles of snow-related and phenological factors. The findings will provide scientific support for climate simulation and water management policymaking in alpine regions worldwide.

Climate change alters snowpack evolution, which in turn influences the likelihood of snow avalanches and flood risks. The lack of systemic observational data on key snow characteristics in high mountains remains a scientific challenge in terms of systematically elucidating the dynamic chain of variations in climate–snowpack–snow disasters. This restricts our understanding and poses challenges in the prediction of snow-related disaster risks. As such, this study analysed the variations of temperature and snowfall and the physical characteristics of snowpacks based on ground-based observations from the Kunse River Valley situated in the Tianshan Mountains from 1967 to 2021. The results reveal that the temperature increased significantly by 0.32 °C per decade (p < 0.01) during the snow season, along with more extreme snowfall events. The snow-cover duration was observed to have been shortened by 4.77 d per decade (p < 0.01) from 1967 to 2021, which is characterised by later snow-cover onset and earlier snowmelt. Concurrently, average and maximum snow depths increased along with an increase in peak snow water equivalent, thus indicating a higher frequency of extremely scarce or abundant snow years. The low snowpack temperature gradient and earlier snowmelt dates in spring lead to earlier occurrences of snowmelt floods and wet avalanches. As the risks of these events increase, they pose greater threats to farmlands, road transportation, water–electricity infrastructure and several other human activities. Therefore, these insights are critical for providing vital information that can deepen our understanding of the impact of climate change on snowpack characteristics and improve management strategies for snow-related disaster prevention and mitigation.

In 2022, the Pakistan witnessed the hottest spring and wettest summer in history. And devastating floods inundated a large portion of Pakistan and caused enormous damages. However, the primary water source and its contributions to these unprecedented floods remain unclear. Based on the reservoir inflow measurements, Multi-Source Weighted-Ensemble Precipitation (MSWEP), the fifth generation ECMWF atmospheric reanalysis (ERA5) products, this study quantified the contributions of monsoon precipitation, antecedent snowmelts, and orographic precipitation enhancement to floods in Pakistan. We found that the Indus experienced at least four inflow uprushes, which was mainly supplied by precipitation and snowmelt; In upper Indus, abnormally high temperature continued to influence the whole summer and lead to large amounts of snowmelts which not only was a key water supply to the flood but also provided favorable soil moisture conditions for the latter precipitation. Before July, the snowmelt has higher contributions than the precipitation to the streamflow of Indus River, with contribution value of more than 60%. Moreover, the snowmelt could still supply 20%–40% water to the lower Indus in July and August; The leading driver of 2022 mega-floods over the southern Pakistan in July and August was dominated by the precipitation, where terrain disturbance induced precipitation account to approximately 33% over the southern Pakistan. The results help to understand the mechanisms of flood formation, and to better predict future flood risks over complex terrain regions.