Frequency Of Heavy Rainfall Research Articles

IMPROVEMENT OF METHODS AND TECHNICAL MEANS OF WATER EROSION CONTROL DURING CULTIVATING POTATOES ON A PROFILED FIELD SURFACE

BACKGROUND: Potato is a crop that requires the creation of a fine-grained structure of the upper tuber-inhabited soil layer to form tubers of the correct shape, as well as to ensure conditions for good soil separation during harvesting. For this purpose, most technologies for cultivating this crop provide for the formation of a profiled field surface. One of the results of global climate change is an increase in the frequency of heavy rainfall during the growing season. At the same time, the presence of a profiled surface on fields with even a slight slope leads to significant risks of water erosion during heavy rains due to water flowing from the ridge walls into the row spacing. This leads to annual irreparable losses of the fertile soil layer. Therefore, in order to ensure the preservation of the level of natural soil fertility and eliminate the risks of water erosion when using intensive potato production technologies in the context of global climate change, it is necessary to improve the technological methods and technical means used to form a profiled field surface. AIM: The aim of the study is to protect soil from water erosion during potato cultivation on a profiled field surface by improving technological methods and technical means used to form a profiled field surface, as well as substantiating the parameters and modes of their operation. METHODS: The object of the research is a non-powered rotary dyker mounted on a row-crop cultivator-subsoiler. To select rational parameters of the dyker's blades theoretical studies were conducted on the basis of which the rotor diameter of its blades was selected. The following assumptions were adopted as initial data for determining the technological parameters of the dyker: intensity of rainfall; depth of the loosening tines of the row-crop cultivator-subsoiler; the rate of rain absorption by capillaries on medium-loamy soils at a certain degree of field slope. The theoretical calculation of the technological parameters of thedyker was performed on the basis of the constructed trajectories of the rotor center and its blades during the work process. The calculation of the parameters of the dyker was carried out taking into account that the front and rear walls of the mini pit are formed by its blade by pushing loose soil during rolling with a step t relative to a fixed point at a certain depth h, the step of the dyker's blades t is determined by the design parameters of the rotor: diameter D and the number of blades on it. RESULTS: To determine the number of mini pits per linear meter the volume of water that gets between the rows during a downpour was calculated depending on their interrow width. The calculation results showed that with a precipitation intensity of 15 mm/h the number of mini tip per linear meter of the profiled surface of the field varies from 2.4 to 3.1 pcs/m. These data made it possible to determine the rational parameters of the dyker for protection against water erosion of the fields located on slopes when cultivating potatoes on the profiled surface. CONCLUSION: An effective method for preventing water erosion on the profiled surface of a field when cultivating potatoes is deep loosening between rows with simultaneous formation of mini pits at the bottom of the furrow. For this purpose it is proposed to use a non-powered rotary pit cultivator. When using a dyker with a rotor diameter of 600 mm the number of mini pits per linear meter varies from 2.4 pcs/m with a inter row spacing of 70 cm to 3.1 pcs/m with a inter row spacing of 90 cm. For reliable protection of soils from water erosion with a inter row spacing of 70 cm it is enough to install 4 blades on the rotor, with inter row spacing of 75 and 80 cm best result available with 5 blades per rotor and with a width of 90 cm 6 blades per rotor will be required.

The Impact of Rainfall on Water, Energy, Industry and Economic Growth—Based on Empirical Data from 29 Provinces in China

Sustainable urban development requires good interaction between water, energy, infrastructure and socio-economic areas. In the context of more frequent heavy rainfall and flooding events, managing the subsystems within the city in an integrated manner and realizing sustainable development have become popular research topics. Based on the above analysis, this paper constructs a water, energy, industry and economic growth system. It also introduces rainfall as an exogenous variable into the model in order to simulate the process of interactions between subsystems within a city and achieve sustainable development. By measuring the dynamic changes and spatial distribution characteristics of the efficiency values of the total water–energy–industry and economic growth system and each subsystem in 29 provinces in China, the following conclusions are drawn: (1) Most of the provinces are in the situation of “high-efficiency–negative growth” or “low-efficiency–positive growth”, and the constraints for them to reach the state of “high efficiency–positive growth” are due to the water subsystem. (2) The low-efficiency provinces are mainly concentrated in the central region, and the spillover effect of the low-efficiency provinces on the neighboring regions is more notable than that of the high-efficiency provinces. (3) The addition of rainfall improves the total efficiency in most provinces, with the most obvious improvement in the efficiency of the water subsystem. (4) The efficiency value of the industry and economic growth subsystem is relatively less affected by the amount of rainfall, but excessive rainfall will also have a negative impact. Finally, relevant policy recommendations are made to inform the relevant government departments in formulating policies related to addressing climate change and achieving sustainable urban development.

Rainfall Erosivity Projection in South‐East Australia Using the Improved Regional Climate Simulations

ABSTRACTRainfall erosivity is one of the most dynamic factors in the soil erosion process. The increase in soil erosion caused by high rainfall erosivity, and the subsequent loss of soil nutrients, can lead to reduced food production and ecosystem services. This research program under the New South Wales (NSW) Climate Change Adaptation Strategy, assesses rainfall pattern change, rainfall erosivity and erosion risk across NSW under future climate conditions. Daily rainfall erosivity and erosion risk were modelled by Revised Soil Loss Universal Equation (RUSLE) approach and compared with that driven by observed rainfall data. Future rainfall erosivity and soil erosion risk change were investigated from daily precipitation projection of the updated NSW and Australian Regional Climate Modelling (NARCliM1.5) for two future scenarios, RCP4.5 and RCP8.5, from the historical (1986–2005) to far future (2060–2079) periods. The annual average rainfall erosivity is projected to increase about 8% under RCP 4.5 and further decrease 5% under RCP 8.5 in NSW due to the predicted temperature rises. More frequent heavy rainfall events are projected to occur during summer (December–January–February), and the rainfall from these extreme rainfall events is expected to account for 51% of the total annual rainfall in the far future. NARCliM‐derived results underestimate annual rainfall erosivity compared with observation‐derived erosivity. There are greater instability (root mean squared error [RMSE]: 803.2) and erosivity uncertainty (Bias: 16%~48%) in high rainfall zones. At a monthly scale, dry months (June–July–August) are becoming drier, while wet months (December–January–February) are becoming wetter and more erosive. 67% of NSW is predicted to experience increased rainfall erosivity under RCP4.5, whereas most of NSW will shift to drought and its consequent effects under the high‐end emission scenario (RCP 8.5). To address the dual challenges of excessive wetness in coastal and north‐east NSW and increasing aridity in Western NSW, it is necessary to develop climate change adaptation management strategies based on high‐risk areas and monthly or seasonal conditions. With the emerging launch of NARCliM2.0, we anticipate further improvements of these predictions will be achieved by more accurate models and data at higher spatial and temporal resolutions.

Economic impacts of floods in China and adaptation strategies under climate change

Abstract Climate change has intensified the frequency and severity of heavy rainfall and flooding in China, posing serious threats to economic and social stability. Over the past 40 years, the increasing trend of these events has led to significant economic losses, highlighting the urgent need to develop effective adaptation strategies in both research and policy. This study used the Adaptive Regional Input-Output model to comprehensively assess the economic impacts of the July 2021 Henan flood, with a main focus on indirect economic losses that are often underestimated in traditional assessments. The findings revealed that the indirect economic losses were up to $37.9 billion, nearly twice the direct economic losses. Additionally, a sensitivity analysis showed that increasing production capacity and the timing required to achieve it were two key factors to reduce indirect economic losses. Based on these insights, this study proposes an adaptation strategy framework tailored to China’s current development context, where systemic governance, strategic investments, and technological innovation are accounted for. This framework offers practical strategies to reduce the impacts caused by floods, thereby providing valuable guidance for sustainable disaster risk reduction and long-term economic stability in China.

Study on Multi-Scenario Rain-Flood Disturbance Simulation and Resilient Blue-Green Space Optimization in the Pearl River Delta

In the face of global climate change and rapid urbanization, the Pearl River Delta is confronted with frequent river floods and heavy rainfall, which leads to substantial economic losses and casualties. Enhancing the role of blue-green space in rain-flood resilience is crucial for mitigating such damages in this new era. Firstly, based on an analysis of the current status quo of blue-green space in the Pearl River Delta and the identification of potential areas at risk from rain and floods, this paper elucidates that resilient blue-green space in the Pearl River Delta should be guided by a systematic, bottom-line, and forward-looking orientation while considering spatial characteristics such as multi-scale network connectivity, redundancy and diversity/multi-functionality. Secondly, an optimization route is proposed based on steps of analysis of existing blue-green space, identification of inundated areas prone to rain and flood damage and optimization of blue-green spaces. Strategies for optimizing blue-green space are put forth including enhancing water corridor connectivity, optimizing ecological barriers and corridors, as well as constructing water gates to control hydrological flow direction. Simulation results demonstrate that under similar rain-flood disaster conditions, optimized blue-green space exhibits smaller sizes and lower depths of potential inundated areas compared to the original ones.

Uncovering Below Cloud Rain‐Vapor Interactions During Tropical Rain Events Through Simultaneous and Continuous Real‐Time Monitoring of Rain and Vapor Isotopes

AbstractDue to limited water vapor measurements, vapor isotopes have been traditionally estimated under the assumption of isotopic equilibrium between rain and vapor below cloud base. However, recent advancements in analytical instruments allow more vapor isotopic measurements that have challenged this assumption. To enhance our understanding of rain‐vapor interactions below cloud base in tropical regions, we established an automated system to measure rain and vapor isotopes simultaneously and continuously in real time at minute intervals in Singapore. Among 324 rain events monitored from 2016 to 2019, 81% exhibited a substantial departure of rain and vapor isotopes from the expected equilibrium. This departure suggests that raindrop evaporation plays a larger role in determining their isotopes. The conclusion is supported by the generally lower slopes of the local meteoric water line. Seasonal variations in rain event characteristics indicate changing influences of rain‐vapor interactions: during monsoons, more frequent heavy rainfall maintains relatively high humidity below cloud base, favoring rain‐vapor isotopic equilibrium, whereas during inter‐monsoons, more light rain events lead to pronounced rain evaporation and larger isotopic differences. Furthermore, rain‐vapor interactions below cloud base significantly modulated their isotope evolution during individual events. As events progressed, reduced humidity favored evaporation, increasing rain isotope values and decreasing its d‐excess, whereas vapor isotope values decreased and its d‐excess increased. Our study introduces a new approach to capturing real‐time high‐resolution rain and vapor isotopes at minute intervals to understand the dynamics of rain‐vapor interactions below cloud base. Findings underscore the crucial role of these interactions in influencing rain and vapor isotopes during tropical rain events.

Partitioning of Heavy Rainfall in the Taihang Mountains and Its Response to Atmospheric Circulation Factors

The spatial and temporal distribution of heavy rainfall across the Taihang Mountains exhibits significant variation. Due to the region’s unstable geological conditions, frequent heavy rainfall events can lead to secondary disasters such as landslides, debris flows, and floods, thus intensifying both the frequency and severity of extreme events. Understanding the spatiotemporal evolution of heavy rainfall and its response to atmospheric circulation patterns is crucial for effective disaster prevention and mitigation. This study utilized daily precipitation data from 13 meteorological stations in the Taihang Mountains spanning from 1973 to 2022, employing Rotated Empirical Orthogonal Function (REOF), the Mann–Kendall Trend Test, and Continuous Wavelet Transform (CWT) to examine the spatiotemporal characteristics of heavy rainfall and its relationship with large-scale atmospheric circulation patterns. The results reveal that: (1) Heavy rainfall in the Taihang Mountains can be categorized into six distinct regions, each demonstrating significant spatial heterogeneity. Region I, situated in the transition zone between the plains and mountains, experiences increased rainfall due to orographic lifting, while Region IV, located in the southeast, receives the highest rainfall, driven primarily by monsoon lifting. Conversely, Regions III and VI receive comparatively less precipitation, with Region VI, located in the northern hilly area, experiencing the lowest rainfall. (2) Over the past 50 years, all regions have experienced an upward trend in heavy rainfall, with Region II showing a notable increase at a rate of 14.4 mm per decade, a trend closely linked to the intensification of the hydrological cycle driven by global warming. (3) The CWT results reveal significant 2–3-year periodic fluctuations in rainfall across all regions, aligning with the quasi-biennial oscillation (QBO) characteristic of the East Asian summer monsoon, offering valuable insights for future climate predictions. (4) Correlation and wavelet coherence analyses indicate that rainfall in Regions II, III, and IV is positively correlated with the Southern Oscillation Index (SOI) and the Pacific Warm Pool (PWP), while showing a negative correlation with the Pacific Decadal Oscillation (PDO). Rainfall in Region I is negatively correlated with the Indian Ocean Dipole (IOD). These climatic factors exhibit a lag effect on rainfall patterns. Incorporating these climatic factors into future rainfall prediction models is expected to enhance forecast accuracy. This study integrates REOF analysis with large-scale circulation patterns to uncover the complex spatiotemporal relationships between heavy rainfall and climatic drivers, offering new insights into improving heavy rainfall event forecasting in the Taihang Mountains. The complex topography of the Taihang Mountains, combined with unstable geological conditions, leads to uneven spatial distribution of heavy rainfall, which can easily trigger secondary disasters such as landslides, debris flows, and floods. This, in turn, further increases the frequency and severity of extreme events.

A rainfall prediction model based on ERA5 and Elman neural network

The heightened frequency and intensity of heavy rainfall, brought about by global climate warming, significantly affect various regions. Rainfall prediction plays a pivotal role in ensuring societal security and fostering development. There has been limited exploration of the impact of input parameters on the accuracy of rainfall predictions. Despite the advantages of Elman neural network models in handling non-linear relationships and spatiotemporal data, their application in weather forecasting is restricted. This paper assesses the accuracy of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) with a dataset from meteorological stations. It introduces a novel rainfall forecasting model based on the Elman neural network and ERA5 reanalysis data. The study also identifies optimal input parameters through factor correlation analysis. Experimental results showcase the precision and stability of ERA5 across various rainfall conditions. Meteorological parameters, such as Precipitable Water Vapor (PWV), exhibit noticeable correlations with temporal variables and precipitation volume. The seven-factor model, including PWV, Zenith Tropospheric Delay, temperature, relative humidity, day-of-year, and hour of day, outperforms in precision evaluation. It achieves a mean critical success index of 57.06 %, a correct forecast rate of 91.39 %, and a false alarm rate of 39.48 %. This rainfall forecasting model introduces a novel approach and empirical research to enhance predictive accuracy, holding significant implications for the amelioration of meteorological alert systems, risk mitigation, and the safeguarding of life and property.

Navigating planetary human entanglements through climate change-induced human mobility in Zimbabwe: An Afrocentric perspective from the global south

The central question of the 21st century revolves around increasing human entanglement. Humans are finding it increasingly difficult to survive in the changing environment caused by climate-induced disasters such as floods, droughts, storms, and heat waves. In Zimbabwe, this has led to the emergence of human mobility as an adaptation strategy, with individuals (indigenous and local) relocating to areas offering more favorable economic and environmental conditions. This study employed Afrocentric theoretical lenses to describe how both slow and sudden-onset climatic catastrophic events have affected the agro-economic livelihoods of the indigenous Ndau people, forcing them to seek better living conditions and safety. As an Afrocentric study, this research examines how historical and cultural factors influence the Ndau people’s mobility decisions. It employed the philosophical sagacity interview method and talking cycles to collect data from seven wards in Chimanimani, Zimbabwe. The findings reveal that the impacts of climate change – both gradual and abrupt – have increased in frequency, intensity, duration, and location. The Ndau people have suffered frequent cyclones, storms, and heavy rainfall, leading to landslides and floods. These conditions have driven both short-term and long-term climate-induced mobility. Individuals moved locally and regionally to find livelihood opportunities and their decisions were most influenced by historical and cultural ties through kinship. The study advocates for enhancing communities’ preparedness and adaptability to reduce vulnerabilities. It highlights the importance of strong governance, resilience strategies, environmental protections, economic diversification, and social support to mitigate disasters; prevent unwanted displacement; and manage emigration. Furthermore, European narratives often dominate discussions of African climate-related agro-migration, even though most of these migrants move within their own countries and regions. As a consequence, this study aims to amplify African narratives on human mobility and climate change adaptation.

Assessing the impact of the 2021 flood event on the archaeological heritage of the Rhineland (Germany)

BackgroundArchaeological sites are increasingly threatened by climate-related hazards. In response, heritage management authorities initiated projects to document damage and plan risk assessment measures. We present a project initiated after the heavy rainfall and subsequent flood event of July 2021, which involved extensive fieldwork to document the damage to archaeological sites in the Rhineland. We use this database to characterise and assess the damage and investigate site-specific and geospatial factors to identify potential predictive parameters for site damage.ResultsDuring fieldwork, we found that the flood damaged 19% of the 538 archaeological sites surveyed. The majority of damaged sites are relatively recent, dating from the medieval or modern periods, and are associated with the use of water power. Damage was mainly caused by erosion, floating debris and washout, e.g. mortar. In a case study, we tested the option of comparing pre- and post-disaster Airborne Laser Scanning elevation data to identify damages. It showed that not only the damage detected during fieldwork was found but also additional areas of loss. In general, however, and quantified based on the entire dataset, the ordnance survey Airborne Laser Scanning data were of limited use for monitoring flood-related damage and could not replace fieldwork. Our statistical analysis of possible risk factors, including both site characteristics and geospatial parameters, using Naïve Bayes Modelling and chi-squared tests, showed that no set of parameters could consistently predict the preservation or damage of archaeological sites across all catchments. In contrast, some external geospatial factors correlated with the occurrence of damage.ConclusionsThe study highlights both the strengths and limitations of the approaches used to assess and predict the damage to the archaeological heritage in the 2021 flood zones of the Rhineland. It also demonstrates the complexity of the data and spatial processes involved, which limits generalisation but can still inform decision-making for archaeological site management and on-site protection measures in flood-prone areas. With the prospect of more frequent heavy rainfall due to climate change, the specific needs of the archaeological heritage should be integrated into broader prevention and disaster management plans.

Forecasting climate-associated non-tuberculous mycobacteria (NTM) infections in the UK using international surveillance data and machine learning.

Nontuberculous mycobacteria (NTM) cause skin and lung infections, have high mortality rates, and are resistant to a range of antibiotics and water treatment methods. As NTM reside in environmental reservoirs, they are sensitive to environmental conditions. The suitability of their environmental reservoirs can increase as a result of climate change, subsequently increasing environmental exposure and infection rates. NTM infections are not generally notifiable, including in the UK, but sustained increases have been observed in regions that report NTM infection rates. To assess the burden of NTM infections in the UK under projected climate change, we examined the relationship between climate variables and available NTM surveillance data internationally. Statistically significant increases were found in regions where NTM infections are notifiable, which were positively associated with increased precipitation and temperatures. A random forest regressor was trained using supervised learning from international NTM surveillance data and linked climate variables. The random forest model was applied to UK climate projections, projecting a 6.2% increase in NTM infection rates over the next 10 years, with notable regional variation. Our random forest model predicts that the forecasted impacts of climate change in the UK, including increasing temperatures and frequency of heavy rainfall, will lead to increases in NTM infection rates. Robust surveillance in the future is necessary to increase data available to train models, increasing our predictive power in forecasting climate-associated NTM trends. Our results highlight a novel aspect of how climate change will impact health outcomes in the UK.

Vertical distribution of Pacific oyster Crassostrea gigas larvae and modeling larval transport in Hiroshima Bay, Japan

Understanding vertical distribution of planktonic larvae is essential for elucidating larval dispersal and recruitment processes. We investigated the vertical distribution and horizontal transport of Pacific oyster Crassostrea gigas larvae by field observations and numerical simulations during their main spawning season in Hiroshima Bay, Japan. In field observations, despite horizontal differences and slight diurnal/semi-diurnal changes depending on larval sizes, most larvae were distributed in the upper 3 m layer. The relationship between C. gigas larvae and environmental conditions revealed that larval density increased with increasing temperature and chlorophyll a concentration, and the density peaked at salinity of approximately 20 for all larval sizes. The observed results suggest that the distribution characteristics of C. gigas larvae are suitable for survival in an estuarine area, where environmental conditions are potentially favorable but hydrodynamic conditions can drastically change over the short term due to variations in river discharge. To examine the effect of high river discharge on larval transport, numerical simulations were conducted using a particle-tracking model incorporating the vertical motion of C. gigas larvae. The simulation results reproduced the spatio-temporal dynamics of planktonic and settled larvae after the high river discharge. Although most particles simulating larvae outflowed from the main spawning area, an area of high particle density at the end of simulation corresponded with the offshore area for seedling collection. The present study suggests the role of vertical distribution of C. gigas larvae for recruitment, and the prospect of sustainability in oyster aquaculture with respect to seedling collection despite the frequent heavy rainfall associated with climate change.

Prediction of Adaptability of Typical Vegetation Species in Flood Storage Areas under Future Climate Change: A Case in Hongze Lake FDZ, China

China experiences frequent heavy rainfall and flooding events, which have particularly increased in recent years. As flood storage zones (FDZs) play an important role in reducing disaster losses, their ecological restoration has been receiving widespread attention. Hongze Lake is an important flood discharge area in the Huaihe River Basin of China. Previous studies have preliminarily analyzed the protection of vegetation zones in the FDZ of this lake, but the future growth trend of typical vegetation in the area has not been considered as a basis for the precise protection of vegetation diversity and introductory cultivation of suitable species in the area. Taking the FDZ of Hongze Lake as an example, this study investigated the change trend of the suitability of typical vegetation species in the Hongze Lake FDZ based on future climate change and the distribution pattern of the suitable areas. To this end, the distribution of potentially suitable habitats of 20 typical vegetation species in the 2040s was predicted under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate scenarios using the latest Coupled Model Intercomparison Project CMIP6. The predicted distribution was compared with the current distribution of potentially suitable habitats. The results showed that the model integrating high-performance random forest, generalized linear model, boosted tree model, flexible discriminant analysis model, and generalized additive model had significantly higher TSS and AUC values than the individual models, and could effectively improve model accuracy. The high sensitivity of these 20 typical vegetation species to temperature and rainfall related factors reflects the climatic characteristics of the study area at the junction of subtropical monsoon climate and temperate monsoon climate. Under future climate scenarios, with reference to the current scenario of the 20 typical species, the suitability for Nelumbo nucifera Gaertn decreased, that for Iris pseudacorus L. increased in the western part of the study area but decreased in the eastern wetland and floodplain, and the suitability of the remaining 18 species increased. This study identified the trend of potential suitable habitat distribution and the shift in the suitability of various typical vegetation species in the floodplain of Hongze Lake. The findings are important for the future enhancement of vegetation habitat conservation and suitable planting in the study area, and have implications for the restoration and conservation of vegetation diversity in most typical floodplain areas.

Differing Contributions of Anthropogenic Aerosols and Greenhouse Gases on Precipitation Intensity Percentiles Over the Middle and Lower Reaches of the Yangtze River

AbstractIn June–July 2020, the middle and lower reaches of the Yangtze River (MLYR) were hit by a Meiyu event characterized by a long duration, abundant precipitation, and frequent heavy rainfall, resulting in destructive flooding. We found that the extreme cumulative precipitation in 2020 was mainly contributed by more moderate to heavy daily precipitation rather than extreme daily events. Although some previous studies have been conducted to attribute the 2020 Meiyu event in the MLYR, most of them focused on the cumulative precipitation amount. This attribution case study complements previous attribution analyses and reveals many new features. Our results show that anthropogenic climate change—primarily driven by greenhouse gas (GHG) forcing, anthropogenic aerosol (AA) forcing, and land‐use—has led to a decrease in the number of light to heavy precipitation days, while concurrently increasing the number of extreme precipitation days in the MLYR. Specifically, GHG and AA forcings decreased the frequency of light and moderate precipitation, but only GHG increased the frequency of extreme precipitation. An increasing trend in very heavy precipitation days has been observed. The competitive effects of GHG and AA forcings make it challenging to detect the signal of human activities, which could be intermingled with effects from natural variability. Under the SSP5‐8.5 scenario, the probability of experiencing both light and extreme precipitation events will significantly increase in the MLYR. By 2050–2100, these events are projected to be nearly 4 times more frequent compared to the current climate, which poses significant challenges to water security and economic development decision makers.

Green smart multifunctional wooden roofs enabled by single-step hydrophobic laser-induced graphene fabrication

Global warming, contributing to the worsening climate crisis, has led to more frequent heavy snowfall and rainfall. This causes corrosion, mold, and structural damage to roofs, which shortens the roofs' lifespan and potentially creates safety hazards. Addressing these challenges, we propose an approach to develop a multifunctional roof crucial for proactive adaptation to extreme weather. In this article, we present hydrophobic laser-induced graphene (LIG) onto wood in a single-step process using femtosecond-laser-direct-writing (FsLDW) technology in ambient air without additional chemical treatment. This hydrophobic LIG, showcasing a high electrical conductivity of 10.0 Ω·sq⁻1 and a high contact angle of 148.8°, seamlessly integrates onto roofs, creating cost-effective, fast, eco-friendly, and smart roofing solutions. The hydrophobic surface of these LIG electrodes incorporates a heating function of LIG showcasing their versatility in providing waterproofing, drying wood, and facilitating de-icing functions. This eco-friendly invention, reliant solely on laser patterning on wood, not only extends roof lifespan but also relieves concerns about electronic waste and recycling, promising the integration of green, smart, and sustainable roofing solutions.

Trend analysis of extreme rainfall indices from CHIRPS precipitation estimates over the Lake Tana sub-basin, Abbay Basin of Ethiopia.

Ethiopia is among the African nations most susceptible to climate change because of its frequent droughts and heavy rainfall. Therefore, hydrological and water management problems require an investigation of regional variability and extreme rainfall patterns. This study analyzed the spatiotemporal trends of extreme rainfall in the Lake Tana sub-basin (LTSB) of Ethiopia's upper Blue Nile basin (UBNB) between 1981 and 2019. The trend and geographic patterns of ten extreme rainfall indices are evaluated using high-resolution data from Climate Hazards Group InfraRed Precipitation Stations (CHIRPS). The researcher used RClimDex, an R software tool, to analyze the ten severe rainfall indices. The variability of the extreme rain indices was also assessed by applying the standard anomaly index (SAI). The trend analysis shows that the majority of rainfall indices decreased in the majority of station locations. Among the rainfall locations, the decreasing trend was only significant in 40% consecutive wet days (CWD), 13.33% (R95p and R99p), and 6.66% highest rainfall amount in a 1-day period (RX1day). In contrast, significant positive patterns were revealed in the incidence of rainfall events of number of heavy precipitation days (R10mm), annual total wet day rainfall (PRCPTOT), and consecutive dry days (CDD), with significant positive trends of 26.66% (R10mm) and 40% (PRCPTOT). Furthermore, a spatial distribution result of extreme rainfall trends reveals considerable variations between stations location. Thus, these findings point to the necessity of creating adaptation and mitigation plans for climate change variability within the sub-basin.

Rainstorm erosion difference and topographical changes induced by heavy rainfall between afforestation and grassland restoration catchments on the Chinese Loess Plateau

The frequency and intensity of heavy rainfall on the Chinese Loess Plateau (CLP) are increasing due to global climate change, which has caused severe soil erosion. However, precise evaluation of heavy rainfall-induced soil erosion, deposition, and related topographical changes at the catchment scale is lacking, and there is uncertainty as to how effectively trees can inhibit erosion, especially during heavy rainfall. In this study, we selected a pair of catchments with afforestation and natural grassland restoration on the CLP. Precise evaluation of erosion and topographic changes induced by heavy rainfall was conducted based on airborne light detection and ranging (LiDAR) data. In the grassland catchment, the gully bed area increased by 17.6 %, the gully slope and inter-gully land area decreased by 1.4 %, and the length of the gully shoulder line decreased by 4.4 %. Conversely, the forestland catchment experienced a decrease in gully bed area of 8.8 %, an increase in gully slope area of 5.3 %, a decrease in inter-gully land area of 9.9 %, and a decrease in the length of the gully shoulder line of 11.4 %. Following heavy rainfall, both catchments experienced a substantial increase in terrain relief. Soil erosion in the gully slope accounted for >90 % of the total erosion in both catchments, while deposition in the same area contributed to >75 % of the total deposition. The grassland catchment experienced a unit area erosion of 233,908 m3 km−2, which was 4.7 times greater than that of the forestland catchment (49,768 m3 km−2). In both catchments, erosion predominantly transpired in the steep slope region adjacent to the gully shoulder line, accounting for >15 % of the gully slope erosion area. The grassland catchment exhibited a continuous distribution pattern of erosion and deposition, whereas the forestland catchment exhibited a scattered and intertwined distribution pattern. This study is essential for further research on erosion and topographic changes induced by extreme weather events and to determine appropriate measures.

Synoptic Control on the Initiation and Rainfall Characteristics of Warm‐Season MCSs Over the South China Coast

AbstractThe South China coast (SCC) experiences frequent heavy rainfall during the warm season (May–September). Objective classification analysis on 925‐hPa geopotential height shows that the majority of warm‐season precipitation (>80%) occurs under three typical synoptic patterns: the southerly monsoon pattern (P1), the southwesterly monsoon pattern (P2), and the low‐level vortex pattern (P3). Using 20 years of satellite observations and cloud tracking, this study highlights that mesoscale convective systems (MCSs) play a pivotal role in generating precipitation under all three synoptic patterns, while the initiation and rainfall characteristics of MCSs are strongly modulated by the background synoptic circulations. The diurnal MCS precipitation under P1 and P2 is predominantly influenced by land‐sea breeze circulation, which is characterized by a morning offshore propagation and an afternoon onshore propagation. The rainfall propagation speeds, however, are strongly modulated by the prevailing low‐level monsoonal flows. Under P3, MCS precipitation initiates near the coast around midnight and then propagates offshore, merging with the widespread offshore precipitation. The analysis also shows that the majority of MCSs contributing to SCC warm‐season precipitation were locally initiated. This underscores the critical role of locally initiated MCSs in driving the SCC precipitation characteristics, as opposed to propagating MCSs. Statistical correlation analysis further indicates that the precipitation area and intensity of locally initiated MCSs are closely related to the lower‐tropospheric moisture transport and the convective available potential energy over the upstream South China Sea, and the deep‐layer wind shear (from surface to 400‐hPa) over the SCC.

AIoT Monitoring Technology for Optimal Fill Dam Installation and Operation

Fill dam structures are generally considered safe, but frequent heavy rainfall in recent years due to climate change has increased their risk of collapse. Technologies for the monitoring and safety management of these structures have attracted considerable attention, and methods to utilize technologies such as artificial intelligence (AI) and Internet of Things (IoT) for maintenance have been investigated. However, the measurement and communication processes of sensors used in the IoT technology are prone to measurement errors. Moreover, the communication environment and frequency range used by unlicensed operators vary within a country. Technologies for accurate interpretation of measurement results and optimal communication are required to address these issues. In this study, a technology for dam safety management and communication environment optimization was developed using AIoT (AI+IoT), and its field applicability was verified.

Biochemical, physiological and molecular aspects of waterlogging tolerance in economically important oilseed crops rapeseed, sesame and soybean

Climate change poses a significant threat to agricultural sustainability. As the frequency of heavy rainfall has increased globally, waterlogging has become a pressing global issue that has a significant impact on the growth and development of oilseed crops. Due to decreased aerobic respiration in the rhizosphere, various physiological processes, including metabolic reactions, hormone production, and signaling cascades, are adversely impacted by waterlogging. These physiological changes impair reproductive health, resulting in decreased oilseed crop yields. In response to waterlogging, the most common resistance mechanisms developed by crop plants are development of aerenchyma, adventitious roots, and radial oxygen loss barrier. Consequently, the identification and selection of parents with resistance mechanisms, as well as their incorporation into breeding programmes, are essential for sustaining crop production. Thus, a better understanding of the physiological and biochemical mechanisms during waterlogging followed by identification of underlying key regulatory molecules would greatly facilitate the oilseed breeding programs. This review systematically summarizes the response of crop plants to waterlogging through adaptations and the strategies for introduction of waterlogging resistance in oilseed crops.