Search Results (20)

Against the backdrop of the increasing frequency of extreme hydrological events and persistent over-extraction of groundwater, the North China Plain (NCP) is facing significant land subsidence. This study systematically analyzed the surface subsidence response patterns and mechanisms of the NCP during extreme rainfall events by integrating Gravity Recovery and Climate Experiment (GRACE) data, Global Navigation Satellite System (GNSS) observations, environmental load models, well data, and precipitation records. The main findings are as follows: (1) From 2002 to 2020, the groundwater storage change (GWSC) in most of the study area declined at an average rate of trend about 5 cm/yr, while from 2021 to 2024, influenced by heavy rainfall recharge, GWSC recovered with a mean rate of trend about 7 cm/yr; (2) During the extreme rainfall event from 1 July to 31 August 2023, the environmental loading model effectively captured the vertical deformation caused by hydrological loading, showing general consistency with GNSS monitoring results in spatial distribution. Most GNSS stations experienced rapid subsidence during the event (GNSS: 5 mm, model: 2 mm), followed by a gradual rebound after the extreme rainfall, consistent with elastic theory; (3) The deformation at the TJBH station exhibited anomalies attributable to porous elastic effects; (4) Integrated well data confirmed that rainfall recharge primarily influences shallow groundwater. This study reveals the multiple mechanisms underlying extreme hydrological induced land subsidence in the NCP. Full article

Against the backdrop of increasingly evident climate change and frequent extreme weather events, flash floods have emerged as a major challenge for flood disaster prevention and mitigation in China. Flash flood early warning systems are crucial means to address this challenge, primarily comprising rainfall-based warnings (RW) and discharge-based warnings (DW). To support precise flash flood warnings, this study compares the effectiveness of RW and DW and summarizes their applicable scenarios through both case study analysis and model simulations. The results demonstrate that DW outperforms RW under the following scenarios: ① During persistent moderate-intensity rainfall events when antecedent soil moisture is moderate to high, RW is prone to missed or delayed warnings. ② When rainfall exhibits significant spatial heterogeneity, RW tends to produce false alarms. Conversely, RW outperforms DW in the following scenarios: ① For localized short-duration heavy rainfall events, DW is prone to missed or delayed warnings. ② In basins where numerous small- and medium-sized reservoirs exist upstream without operational data, DW is prone to false alarms. ③ When sparse or unevenly distributed rain gauges result in poor representativeness of areal rainfall, DW is prone to missed warnings. To enhance flash flood disaster management, future warning systems should integrate both RW and DW approaches to deliver more timely, reliable, and scientifically grounded warning information for local authorities. Full article

The intensification of global climate change has led to an increased frequency of extreme rainfall and temperature events, posing severe threats to China’s ecosystems and socio-economic systems. This study, based on multi-year daily precipitation, monthly surface air temperature, and daily near-surface temperature datasets, employs multi-year averaging, EOF mode analysis, Mann–Kendall testing, and R/S analysis. By selecting heavy-rain days, rainfall amount, rainfall intensity, and drought indices, it explores the spatiotemporal evolution and driving mechanisms of extreme rainfall, drought, and compound events across China. The analysis of extreme rainfall reveals that precipitation in China shows a “more in the southeast, less in the northwest; abundant in the southeast, sparse in the northwest” pattern. EOF analysis identifies two spatial modes for rainfall parameters, the “Eastern Coordination Mode” and the “North–South Antiphase Mode,” corresponding to heavy rainfall days, rainfall amount, and rainfall intensity. The Mann–Kendall test shows that some regions in the eastern monsoon zone have experienced a significant increase in heavy rainfall parameters, while certain areas in the northeast, southern China, and northwest have also undergone significant changes. By contrast, parts of the southwest have seen a decrease. R/S analysis reveals that the Hurst index is high in the eastern monsoon region, indicating a strong likelihood of continued upward trends in the future, while regions in the western arid and semi-arid zones and parts of the Tibetan Plateau exhibit stronger randomness in trends, leading to more alternating drought and flood events. The analysis of the drought index (SPI-3) reveals synchronized drought patterns in the central-eastern and northern regions, with “synergistic consistency,” “Northwest–Northeast Antiphase,” and “Northern–Central-South Antiphase” characteristics. The Mann–Kendall test indicates a “north-wet, south-dry” differentiation, with significant wetting in the northern regions and parts of the Tibetan Plateau, and significant drying in the central-eastern and southwestern regions. R/S analysis shows high Hurst indices across most of the northwest and northern regions, indicating stronger drought persistence, while coastal areas in the east are more prone to dry–wet transitions. In terms of compound events, high-temperature and heavy rainfall events have increased from northwest to southeast over the past 40 years, with southern China experiencing more than 200 days of such events. Significant changes have been observed in the eastern and southern coastal regions, with high Hurst indices and strong persistence in the eastern coastal areas. Low-temperature and heavy rainfall events are more frequent in the eastern coast and southwestern regions, with higher Hurst indices in the eastern and central regions, indicating strong persistence. Full article

Extremely persistent flash heavy rainfalls (EPHRs) over the Sichuan Basin in China are influenced by both multiscale weather systems and complex underlying surfaces, making it difficult to understand the favorable dynamic mechanisms and to further improve operational numerical forecasting skills. In this study, EPHRs from 2010 to 2024 are objectively identified and then classified into three categories based on the SOM method. Precipitating characteristics for each category are further investigated from the perspective of the diurnal cycle and spatial features with the use of rain-gauge-based observations. Evaluations of the ERA5 reanalysis dataset, MSWX bias-corrected meteorological product, and CMORPH satellite-based precipitation product are performed to determine their capabilities in representing precipitating characteristics of different EPHR categories at different stages. The following results are obtained. During EPHR events, CMORPH outperforms MSWX and ERA5 in capturing heavy precipitation distribution, diurnal cycles, and evolution over the central basin. Both MSWX and ERA5 miss the central precipitation core, with MSWX showing premature peaks and ERA5 generating secondary evening peaks while overestimating precipitation duration. During events influenced by small-scale weather systems, all three products exhibit minimal false alarms but show the largest errors in intensity and diurnal variation. Under certain circulation types, MSWX and ERA5 significantly underestimate precipitation development with comparable metrics, while CMORPH achieves superior accuracy in precipitation intensity and correlations, yet it underestimates nighttime precipitation occurrences in steep western terrain. This study may help to facilitate not only theoretical studies but also numerical model developments for precipitation extremes. Full article

Precipitation concentration reflects the uneven temporal distribution of rainfall. It plays a critical role in water resource management and flood–drought risk under climate change. However, its long-term trends, associations with atmospheric teleconnections as potential drivers, and links to extreme heavy precipitation events remain poorly understood in complex basins like the Yangtze River Basin. This study analyzes these aspects using ground station data from 1960 to 2019 and conducts a comparison using the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG) satellite product. We calculated three indices—Daily Precipitation Concentration Index (PCID), Monthly Precipitation Concentration Index (PCIM), and Seasonal Precipitation Concentration Index (SPCI)—to quantify rainfall unevenness, selected for their ability to capture multi-scale variability and associations with extremes. Key methods include Mann–Kendall trend tests for detecting changes, Hurst exponents for persistence, Pettitt detection for abrupt shifts, random forest modeling to assess atmospheric teleconnections, and hot spot analysis for spatial clustering. Results show a significant basin-wide decrease in PCID, driven by increased frequency of small-to-moderate rainfall events, with strong spatial synchrony to extreme heavy precipitation indices. PCIM is most strongly associated with El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). GPM IMERG captures PCIM patterns well but underestimates PCID trends and magnitudes, highlighting limitations in daily-scale resolution. These findings provide a benchmark for satellite product improvement and support adaptive strategies for extreme precipitation risks in changing climates. Full article

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period. Full article

Mapping flooded areas immediately after heavy rainfall is particularly challenging when sediment-laden floodwaters dominate the landscape. Traditional indices, such as the Normalized Difference Water Index (NDWI), are designed to detect water-covered areas but fail to identify muddy zones with high turbidity, which are common during extreme flood events. These muddy floodwaters often blend spectrally with surrounding land, leading to significant misclassifications. This study introduces the Flood Mud Index (FMI), a novel spectral index specifically developed to detect debris-laden flooded areas using only the red and blue bands. Landsat 8 imagery was utilized to validate the FMI, and its performance was evaluated through confusion matrices. The index achieved an overall accuracy of 97.86%, outperforming existing indices and demonstrating exceptional precision in delineating muddy floodplains. By relying solely on red and blue bands, the FMI is applicable to any platform equipped with RGB sensors, offering versatility for flood monitoring. Its compatibility with low-cost drones makes it especially valuable for rapid post-flood assessments, enabling immediate data collection even in scenarios with persistent cloud cover. The FMI addresses a critical gap in flood mapping, providing an effective tool for emergency response and management in sediment-rich environments. Full article

In the early stages, uncontrolled groundwater extraction led to the Hangjiahu (HJH) Plain becoming one of the areas with the most severe land subsidence in China. Since the beginning of this century, comprehensive measures have been taken to control the continuous aggravation of large land subsidence patterns in some areas; however, urban land subsidence issues, influenced by various factors, still persist and exhibit complex geographical distribution characteristics. In this study, we utilized Sentinel-1A images and the SBAS-InSAR technique to capture surface deformation over the HJH Plain in Zhejiang from 16 March 2017 to 20 January 2023. Through a comparative analysis with geological conditions, changes in surface mass loading, rainfall and groundwater, and land use types, we discussed the contributions of natural and anthropogenic factors to land subsidence. Augmented with optical remote sensing images and field investigations, we conducted a correlation analysis of the land subsidence status. The preliminary findings suggest that changes in surface mass loading and short-term heavy rainfall under extreme weather conditions can lead to periodic land subsidence changes in the region. Additionally, extensive infrastructure construction triggered by urbanization has resulted in significant and sustained land subsidence deformation. The research findings play an important guiding role in formulating scientifically effective strategies for land subsidence prevention and control, as well as urban planning and construction. Full article

Water majorly contributes to soil erosion. Considering Japan’s humid and rainy climate, severe soil erosion challenges persist even though forests are the country’s dominant land type. Although numerous studies have emphasized the impact of factors such as land use, soil type, and slope steepness on sediment yield, the synergetic effects of slope gradient with varying land cover and soil types are underexplored. Herein, we used the Soil and Water Assessment Tool (SWAT) on a steep catchment to identify high sediment yield areas—as well as factors influencing high sediment yield—and evaluate the effect of slope gradient on the sediment yield of different land cover and soil types. The findings reveal an average annual sediment yield of 0.55 tons ha⁻¹ yr⁻¹ in the Takahashi catchment, with yields tripling in some western subbasins under heavy rainfall. Furthermore, the slope gradient effect is most considerable in bare land, agriculture, and rice land cover, with the average sediment yield of bare land resulting in 2.2 tons ha⁻¹ yr⁻¹ at slope > 45%. Meanwhile, deciduous forests on steep slopes exhibit extreme sediment yield, peaking at 7.2 tons ha⁻¹ yr⁻¹ at slope > 45%. The regosol soil type has one of the highest sediment yield variations in all soil types due to slope gradient. Full article

Understanding the spatiotemporal distribution of extreme rainfall and meteorological drought on a watershed scale could be beneficial for local management of any water resources system that supports dam operation and river conservation. This study considered the watershed of Angat as a case, given its economic importance in the Philippines. A series of homogeneity tests were initially conducted on each rainfall dataset from monitoring stations in and near the watershed, followed by trend analysis to determine the rate and direction of change in the annual and seasonal rainfall extreme indices in terms of intensity, duration, and frequency. Three indices, using the rainfall deviation method (%DEV), percent of normal rainfall index (PNRI), and Standardized Precipitation Index (SPI), were also used to identify meteorological drought events. Generally, rainfall in the watershed has an increasing annual PCPTOT (4–32 mm/year), with increasing frequency and intensity in heavy rainfall and wet days. A significant increasing trend (α = 5%) in the seasonal PCPTOT (7–65 mm/year) and R10mm (1.7–10.0 days/decade) was particularly observed in all stations during the Amihan Monsoon Season (Dec–Feb). The observed increasing rainfall intensity and frequency, if it continues in the future, could have an implication both for the water resources operation to satisfy the multiple objectives of Angat Reservoir and for the flood operation that prevents damage in the downstream areas. The effect of each ENSO (El Niño- Southern Oscillation) phase on the rainfall is unique in magnitude, intensity, and duration. The seasonal reversal of the ENSO in the extreme rainfall and meteorological drought signals in Angat Watershed was also evident. The identified meteorological drought events in the watershed based on SPI-12 persisted up to 12–33 months, could reduce more than 60% (PNRI < 40%) of the normal rainfall. Insights from the study have implications for the hydrology of the watershed that should be considered for the water resources management of the Angat Reservoir. Full article

A long-lasting rainfall event exceeding historical extremes took place in Jiangxi, China, from May 4 to 6, 2023. Because of the concentrated duration of precipitation, it led to significant water accumulation in the northern, central, and southern regions of Jiangxi. The objective of this study was to investigate the weather mechanisms underlying this extreme rainstorm in Jiangxi. By examining detailed observational data, the mesoscale weather characteristics and environmental conditions of the event can be obtained. These findings offer valuable insights for future weather forecasting and warnings. It was observed that after the Huanghuai cyclone moved eastward into the sea, the cold air on its western side shifted northward and converged with the warm, moisture-laden air mass in Hunan and Jiangxi provinces. This convergence of air masses triggered the heavy rainstorm event. The peak precipitation period occurred from midnight on May 5 to 0800 BJT on May 6. Concerning the macroscopic precipitation characteristics, multiple mesoscale convective systems (MCSs) originated in Hunan during this period and progressed eastward along the shear line toward the central part of Jiangxi. As for the microscopic precipitation features, the total precipitation amount was closely linked to the duration of heavy rain droplets. The rainfall distribution in the raindrop spectrum also served as a valuable reference for understanding the persistence and size of precipitation. The temporal pattern of the combined reflectivity echo along 27.5° N indicated that from 2000 BJT on May 5 to the early morning of May 6, there was a rapid development of a weaker MCS after passing through the Luoxiao Mountains. This development resulted in a “train effect” in the central region of Jiangxi. The presence of a 200 hPa divergence area, high vertical ascent rate, and abundant water vapor contributed to the formation of a narrow area of heavy rainstorms in central Jiangxi. Additionally, the falling area of heavy rain coincided with the front of the 500 hPa low trough. In the northern part of Jiangxi, the occurrence of heavy precipitation was influenced by the equivalent temperature front area. Favorable conditions, including water vapor, dynamics, and thermal factors, further supported the occurrence of heavy precipitation. Full article

An hourly rainfall of 201.9 mm fell in Zhengzhou on 20 July 2021, breaking the hourly rainfall record of mainland China and causing severe urban flooding and human casualties. This observation-based study investigates the associated convective-scale and mesoscale dynamics and microphysical processes using disdrometer and polarimetric radar observations aided by retrievals from the Variational Doppler Radar Analysis System. The synoptic flow forcing brought abundant moisture from the oceans and converged at Zhengzhou; then, the extreme rainfall was produced by a slow-moving convective storm that persisted throughout the hour over Zhengzhou. Unusually high concentrations of raindrops of all sizes (showing combined properties of maritime and continental convection) are revealed by the disdrometer data, whereas the polarimetric radar data suggest that both ice-based and warm rain processes were important contributors to the total rainfall. High precipitation efficiency was achieved with an erect updraft at the low levels, whereas enhanced easterly inflows kept the storm moving slowly. The interaction between convective-scale and mesoscale dynamics and microphysical processes within the favorable synoptic conditions led to this extremely heavy rainfall. Full article

Healthy forests are essential to human life because they provide food, energy, and other benefits including carbon sequestration. The objective of this study was to assess the forests health status in Mediterranean ecosystems, specifically, arid to semi-arid. Four forest reserves directed by Royal Society for the Conservation of Nature, Jordan were evaluated. Plant health indicators [(gas exchange (photosynthesis, stomatal conductance, transpiration), chlorophyll, middy stem water potential (Ψsmd), relative water content], regeneration, lichens, plant disease, as well as soil variables (respiration CO₂-C, electrical conductivity (EC), pH, microorganisms’ abundance) were measured. The Ψsmd values in those semi-arid/arid ecosystems were within the normal ranges (−0.3 to −1.3 MPa) in spring but was under extreme water stress (−1.6 to −5.3 MPa) in summer in three reserves. Similarly, gas exchange variables reduced by 25%–90% in summer (compared to spring) across the studied forests. Although the regeneration (seedling per 1000 m²) was higher than 100 in two forest (Ajloun and Dibbeen), the number of seedlings in hiking sites was extremely low in both forests. Soil health indicators reveled that soil respiration CO₂-C were higher than 25 mg kg⁻¹ in two forests [Ajloun, Dibbeen, (except hiking zone)]. The mean soil saprophytes (number g⁻¹) ranged from 86 to 377 across the forests reserves. In addition, the mean arbuscular mycorrhizal fungi (spores 100g⁻¹ soil) was between 350 and 877. Soil EC was consistently optimal (less than 0.5 dS m⁻¹) and pH was slightly basic (7.5–8.3) across the reserves. The results revealed that the fluctuation of rainfall and anthropogenic pressures (grazing, hiking) led to partial forest degradation. When forests (Dana Biosphere Reserve) received 81 mm annual precipitation, Ψsmd values in Juniperus phoenicea at summer ranged from −4.4 to −5.3 MPa, regeneration and lichens were less than 20 per 1000 m², and several trees were dead after infected with soil and air borne pathogens including wilt diseases and die back. Intensive hiking activities (Dibbeen forests, tourism area) and heavy grazing (Yarmouk frosts) reduced regeneration, lichens and soil respiration. Interestingly, the native species had better water relations (RWC, Ψsmd) and gas exchange performance than the introduced species. Overall, it is better to grow native species, and exclude anthropogenic pressure on the territory of introduced species. The conservation programs must persist to sustain several native historical forest trees including Juniperus phoenicea (>600 year old), Quercus ithaburensis (>500 year old), and Pinus halepensis (>100 year old) at Mediterranean semi-arid forests. Full article

Every year, cut-off low (COL) pressure systems produce severe weather conditions and heavy rainfall, often leading to flooding, devastation and disruption of socio-economic activities in South Africa. COLs are defined as cold-cored synoptic-scale mid-tropospheric low-pressure systems which occur in the mid-latitudes and cause persistent heavy rainfall. As they occur throughout the year, these weather systems are important rainfall producing systems that are also associated with extreme cold conditions and snowfalls. An in-depth review of COLs is critical due to their high impacts which affect some parts of the country regularly, affecting lives and livelihoods. Here, we provide a comprehensive review of the literature on COLs over the South African domain, whilst also comparing them with their Southern Hemisphere counterparts occurring in South America and Australia. We focus on the occurrence, development, propagation, dynamical processes and impacts of COLs on society and the environment. We also seek to understand stratospheric–tropospheric exchanges resulting from tropopause folding during the occurrence of COLs. Sometimes, COLs may extend to the surface, creating conditions conducive to extreme rainfall and high floods over South Africa, especially when impinged on the coastal escarpment. The slow propagation of COLs appears to be largely modulated by a quasi-stationary high-pressure system downstream acting as a blocking system. We also reviewed two severe COL events that occurred over the south and east coasts and found that in both cases, interactions of the low-level flow with the escarpment enhanced lifting and deep convection. It was also determined from the literature that several numerical weather prediction models struggle with placement and amounts of rainfall associated with COLs, both near the coast and on the interior plateau. Our study provides the single most comprehensive treatise that deals with COL characteristics affecting the South African domain. Full article

Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the accuracy of weather and climate extreme event prediction. A transfer learning CNN (Convolutional Neural Networks) classification model is established to classify the circulation patterns, along with the newly reconstructed dataset of regional persistent historical heavy rain events, daily rainfall data of 2474 observational stations, and the NCEP/NCAR global reanalysis data of daily geopotential height field in 1981–2018. Different from previous classifications, usually with one level variable, here, in addition to 500 hPa heights, 200 hPa zonal winds and 850 hPa meridional winds over the key areas are also considered in the model. The results show that the multi-level circulation pattern classification based on the transfer learning CNN network has a higher accuracy in the independent test than the single-level model, with the accuracy reaching 92.5% (while only 85% for the single-level model). The spatial correlation coefficient of precipitation between each typical mode and related patterns obtained by the multi-level transfer learning CNN classification is greater than that obtained by the single-level transfer learning CNN, and the variance of 500 hPa heights between each typical mode and the associated patterns is also greater than that obtained by the single-level transfer learning CNN. These results show that the performance of the classification by the multi-level transfer learning CNN model is better than that by the single-level transfer learning CNN. The study is helpful to develop circulation classifications related to large-scale weather or climate disaster events and then to provide a physical basis for further improving the forecast effect and extending the valid time of the forecast through combining the numerical model products. Full article