Search

The deformation behavior of slow‐moving large landslides is often governed by rainfall characteristics. Based on observational data such as precipitation, deformation measurement, and pore water pressure measurements in the slip zone, in many cases a strong correlation between strong rainfall events, a time‐delayed increase of pore water pressures in the slip zone, and, simultaneously to this, an increase of the deformation rate of the landslide can be found. Based on such detailed data, calculation models, which couples the relation between rainfall characteristics and the development of pore water pressures in the slip zone on one hand and the deformation behavior of the slope on the other, can be developed and be used for a better understanding and a prediction of deformation behavior of such slow‐moving landslides. Climate change issues will lead to a change in rainfall frequency and magnitude and annual temperature distribution characteristics in several regions worldwide, which will also lead to changes in the deformation behavior of such large landslides. In this contribution, satellite‐based interferometric synthetic aperture radar (InSAR) data are discussed to be used as source for deformation measurements as bases for prediction models describing the rainfall‐triggered deformation behavior of slow‐moving landslides. [ABSTRACT FROM AUTHOR]

BACKGROUND: The Songhua River Basin, a vital grain‐producing area in China, faces challenges due to the uneven distribution of water resources and the intensive water demands of agriculture. To enhance agricultural development and effectively manage water scarcity, it is essential to identify the water‐saving potential of major staple crops – corn, wheat, and rice. This study enhances the World Food Studies (WOFOST) model by refining the day of year for the developmental vegetative stage (DVS), thereby improving the representation of phenological stages for spring maize, spring wheat, and rice within the model. This refinement offers a detailed analysis of the potential and rainfed yields. RESULTS: The results from the modified WOFOST model show promising simulation outcomes for the biomass and yield of maize, wheat, and rice, with Nash–Sutcliffe efficiency (NS) and index of agreement (IoA) values all exceeding 0.7. An analysis of photothermal potential yields (Yp) and rainfed yields (Yr) revealed minimal differences in yields for spring maize and rice across various rainfall frequencies. Specifically, the average photothermal utilization rates (LTs) are 93.57% for maize and 85.25% for rice. In contrast, the rainfed yield for wheat is lower than its photothermal yield, with an LT of 43.66%. CONCLUSIONS: These findings suggest that in the Songhua River Basin, maize and rice offer greater potential for water conservation compared to wheat. It is recommended to judiciously reduce irrigation during the growing seasons of spring maize and rice to help alleviate agricultural water use pressures. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Waterlogging is expected to become a more prominent yield restricting stress for barley as rainfall frequency is increasing in many regions due to climate change. The duration of waterlogging events in the field is highly variable throughout the season, and this variation is also observed in experimental waterlogging studies. Such variety of protocols make intricate physiological responses challenging to assess and quantify. To assess barley waterlogging tolerance in controlled conditions, we present an optimal duration and setup of simulated waterlogging stress using image-based phenotyping. Six protocols durations, 5, 10, and 14 days of stress with and without seven days of recovery, were tested. To quantify the physiological effects of waterlogging on growth and greenness, we used top down and side view RGB (Red-Green-Blue) images. These images were taken daily throughout each of the protocols using the PSI PlantScreen™ imaging platform. Two genotypes of two-row spring barley, grown in glasshouse conditions, were subjected to each of the six protocols, with stress being imposed at the three-leaf stage. Shoot biomass and root imaging data were analysed to determine the optimal stress protocol duration, as well as to quantify the growth and morphometric changes of barley in response to waterlogging stress. Our time-series results show a significant growth reduction and alteration of greenness, allowing us to determine an optimal protocol duration of 14 days of stress and seven days of recovery for controlled conditions. Moreover, to confirm the reproducibility of this protocol, we conducted the same experiment in a different facility equipped with RGB and chlorophyll fluorescence imaging sensors. Our results demonstrate that the selected protocol enables the assessment of genotypic differences, which allow us to further determine tolerance responses in a glasshouse environment. Altogether, this work presents a new and reproducible image-based protocol to assess early stage waterlogging tolerance, empowering a precise quantification of waterlogging stress relevant markers such as greenness, Fv/Fm and growth rates. [ABSTRACT FROM AUTHOR]

We propose the lag‐1 autocorrelation of daily precipitation as a simple diagnostic of tropical precipitation variability in climate models. This metric generally has a relatively uniform distribution of positive values across the tropics. However, selected land regions are characterized by exceptionally low autocorrelation values. Low values correspond to the dominance of high frequency variance in precipitation, and specifically of high frequency convectively coupled equatorial waves. Consistent with previous work, we show that CMIP6 climate models overestimate the autocorrelation. Global kilometer‐scale models capture the observed autocorrelation when deep convection is explicitly simulated. When a deep convection parameterization is used, though, the autocorrelation increases over land and ocean, suggesting that land surface‐atmosphere interactions are not responsible for the changes in autocorrelation. Furthermore, the metric also tracks the accuracy of the representation of the relative importance of high frequency and low frequency convectively coupled equatorial waves in the models. Plain Language Summary: Rainfall in the tropics is influenced by many atmospheric processes that depend on geographic location. We use the lag‐1 autocorrelation as a metric for the day‐to‐day persistence of rainfall. We find that rainfall is very persistent in most parts of the tropics with a few exceptions over land, for example, the Sahel, where high frequency rainfall events dominate. Our results show that models with a horizontal resolution of a few kilometers reproduce the autocorrelation, in contrast to coarser climate models. We also analyze atmospheric waves and find that they are important for the autocorrelation pattern in the observations and the simulations. Key Points: The lag‐1 autocorrelation pattern of daily precipitation in the tropics is robust across different observation‐based data setsThe lag‐1 autocorrelation reflects the relative variance of high frequency and low frequency convectively coupled equatorial wavesKilometer‐scale models capture the observed autocorrelation, but models with parameterized deep convection overestimate it [ABSTRACT FROM AUTHOR]

The frequency of snowfall and rainfall is expected to change due to the warming climate. However, trends in liquid and solid phases are not linearly related to air temperature trends. This paper discusses the impact of thermal properties of circulation types (CTs) on the trends in snowy and rainy days in Poland in the period 1966–2020. The visual observations from 42 synoptic stations, which constitute the most-reliable information on precipitation type, were used to identify the precipitation phase. In most CTs, the air temperature increased between 1966–1985 and 2001–2020, but at various rates depending on the type of circulation. Positive tendencies in the thermal properties of CTs contributed to decreasing trends in winter snowfall and increasing trends in winter rainfall. The rate of tendencies in the probability of the precipitation phases depended on the average temperature and the intensity of warming, in particular CTs. In winter, both the snowfall and rainfall tendencies were the strongest for those CTs with average air temperatures (ATs) close to the freezing point, particularly when the average had crossed that threshold between the years 1966–1985 and 2001–2020. The most rapid tendencies in winter snowfall and rainfall, and in the spring mixed phase were induced by N and NW air advection under cyclonic conditions, bringing air from the rapidly warming Arctic. No trends in the winter mixed precipitation probability resulted from its various tendencies in particular CTs. The probability of snowfall increased during air advection from the southeastern sector, particularly in winter. [ABSTRACT FROM AUTHOR]

The qualitative analysis of farmers' use of Indigenous knowledge on climate change adaptation across farming systems and agroecological zones of Sierra Leone was conducted using a combination of interpretive phenomenological analysis, Focus Group Discussions, and participant observation, which was analyzed with a qualitative interpretative approach. Farmers respond to climate change through the use of wind and cloud patterns, animal and bird behavior, moon shape, and position of the sun to predict changes in temperature, intensity, drying up of rivers, and frequency of rainfall, as well as the incidence of pests and diseases. Other Indigenous knowledge used by farmers were rotational grazing, migration, crop-livestock integration, and the use of manure for composting with herbal and biological treatment for disease management. The study recommended that in the current context of climate change, the promotion of adaptation strategies should explore the interdependency of different knowledge systems and knowledge hybridity in agriculture. [ABSTRACT FROM AUTHOR]

• High volume precipitation does not translate into increased moss-associated nitrogen (N) fixation rates. • N fixation increased more in a tropical moss compared to a subarctic moss under all rainfall regimes. • Less than 1 % of fixed N was leached from mosses within a 1-month period. • N leaching from mosses was more sensitive to watering volume than frequency. Nitrogen (N) fixation by moss-cyanobacteria associations has been recognized as an important N input pathway in many ecosystems from arctic tundra to tropical forests. However, the transfer of fixed N 2 from mosses to the soil as well as the effects of rainfall frequency and volume on this N transfer has hardly been studied – even though mosses can leach nutrients upon rewetting. In this study, we investigated the transfer of fixed N 2 by moss-cyanobacteria associations in one month under four watering regimes with a combination of high and low volume and frequency. For this, we used two morphologically similar moss species collected from ecosystems with different climate and N availability (subarctic - Hylocomium splendens ; and tropical - Thuidium delicatulum). Acetylene reduction assays were conducted as a measure of N 2 fixation rates in mosses, and 15N-N 2 tracing was used to follow the fixed N 2 from moss to the underlying substrate. Nitrogen fixation rates were higher in T. delicatulum than in H. splendens , but rainfall volume and frequency did not show strong effects on N 2 fixation rates. Nonetheless, the extent of N leached from mosses was more sensitive to an increase in rainfall volume than to an increase in frequency, and more N was lost from T. delicatulum under high volume precipitation than from H. splendens. Both total nitrogen and 15N enrichment results demonstrate that the fixed N 2 was mostly stored in moss tissues with less than 1 % leached to the substrate. Our results show that both moss species retain almost all fixed N 2 within their tissues under small rainfall disturbances within one month, while increased N availability under higher precipitation volume renders some moss species an important N source for the soil. [Display omitted] [ABSTRACT FROM AUTHOR]

The short and biased observational record of tropical cyclones (TCs) limits scientific understanding of how these destructive storms respond to climate forcing. Paleohurricane records use natural archives (tree rings, coarse‐grained sediment) to reconstruct TC properties (frequency and intensity of rainfall, wind) over the past few hundreds to thousands of years. However, different sensitivities and sampling biases in the various paleohurricane proxies restrict our ability to compile these records into regional or basin‐scale TC estimates. Here we test how well pseudo tree‐ring records of paleohurricanes capture TC rainfall and occurrence. Using a large set of statistically downscaled storms forced with the Max Planck Institute (MPI‐ESM‐P) model as boundary conditions for the past millennium, we generate a 1000‐member ensemble of pseudo tree‐ring records of latewood width from southern Mississippi using a Poisson process‐based random draw. Pseudo records convert synthetic TC rainfall into latewood width using a previously published statistical calibration and seasonal sensitivity. We show that fourth quantile thresholds applied to pseudo latewood data successfully identify years with TC strikes. Comparing pseudo tree‐ring records with pseudo sediment records from the Gulf Coast indicates promise in combining proxies sensitive to TC rainfall with proxies sensitive to storm overwash. Sediment records that are sensitive to lower intensity storms (≥Saffir Simpson Category 1) are more compatible with tree‐ring records, suggesting a need for more of these low intensity threshold records in the Gulf to facilitate future multi‐proxy efforts to reconstruct past TC properties. Key Points: Applying quantile thresholds to latewood tree‐ring records from the Gulf Coast identifies years with at least one tropical cyclone (TC)Low threshold paleohurricane sediment proxies strongly covary with tree ring proxies suggesting promise in future multiproxy comparisonsWe need a wider network of low‐threshold sediment proxies combined with tree ring sites to reconstruct regional TC strikes [ABSTRACT FROM AUTHOR]

The export of dissolved organic matter (DOM) from rivers is essential for linking terrestrial and marine carbon reservoirs in the global carbon cycle. However, there is limited knowledge regarding how the molecular composition of riverine DOM changes under different hydrological conditions, especially during extreme rainfall events. Moreover, the factors beyond hydrology that impact DOM composition have not been well defined. To address these gaps, samples were collected from a human‐impacted medium‐sized subtropical monsoonal river across various hydrological conditions throughout a complete hydrological cycle. Utilizing high‐resolution mass spectrometry, it was discovered that the solid‐phase extractable DOM (SPE‐DOM) during the high‐flow (1 < runoff (Q): annual mean runoff (Qm) < 3) and extreme‐rain (Q:Qm > 3) periods exhibited a higher number of molecular formulae, lower H/C, higher O/C, and a higher proportion of carboxylic‐rich alicyclic molecules compared to the low‐flow period (LFP) (Q:Qm < 1). These alterations were attributed to input from more diverse sources, particularly a greater input from soil organic matter with higher oxidation degrees. Additionally, the P‐containing formulae were more enriched during the extreme‐rain period, likely from agricultural lands and sediment release. Conversely, the fraction of S‐containing formulae was significantly higher during the LFP, possibly due to the amplified influence of anthropogenic input. Furthermore, the DOM aromaticity did not fluctuate with runoff but was significantly associated with temperature. In summary, the study indicated that the composition of DOM varied significantly under different hydrological conditions, with temperature and anthropogenic activities identified as crucial factors influencing riverine DOM export. Plain Language Summary: Rivers play a crucial role in the global carbon cycle by transporting dissolved organic matter (DOM) from land to the ocean. With the increasing frequency of extreme rainfall events attributed to climate change, there is a growing interest among researchers in understanding how hydrological conditions influence the seasonal variation of DOM. In this study, we investigated a subtropical river in China to explore how the molecular composition of DOM changes under different hydrological conditions and the various factors that contribute to shaping DOM composition. Our findings revealed that the composition of DOM during periods of high discharge and extreme discharge were similar. Still, extreme water discharge, including rainfall events, led to an increased export of heteroatom‐containing DOM. In addition to riverine discharge, factors such as temperature and human activities, including agriculture and sewage discharge, influenced DOM composition from different perspectives. For instance, sewage discharge predominantly introduced S‐containing compounds, while agricultural activities were associated with abundant P‐containing compounds. This study highlights the importance of considering not only changes in sources related to hydrology but also the influences of temperature and human activities when examining how riverine DOM responds to climate pressures. Key Points: The S‐containing formulae were more enriched during the low‐flow periodDissolved organic matter (DOM) exported during both high‐flow and extreme‐rain event periods were higher in O/C and lower in H/CDOM exported during the extreme‐rain event was more diverse, and enriched in P‐containing formulae [ABSTRACT FROM AUTHOR]

This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin. [ABSTRACT FROM AUTHOR]

Regional‐scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics‐based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three‐dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad‐scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km2) using 3D, limit‐equilibrium (LE)‐based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back‐calculated from inventoried landslides using 3D LE‐based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA. Plain Language Summary: Landslides are potentially destructive natural hazards that may impact topography, ecology, and important infrastructure. Often, previously triggered landslides are used to better understand where landslides may occur in the future, but in some regions, previous landslides may be poorly documented or characterized. In lieu of these data, models capturing the physics tied to landsliding may be paired with digital topography to predict landslide potential across landscapes. However, existing models are limited in their ability to efficiently capture the realistic geometry of three‐dimensional (3D) landslides across large landscapes. This study presents RegionGrow3D, a new model that identifies slope instabilities throughout large regions, and then grows those instabilities into landslides of previously unknown geometry by balancing forces within the underlying soil. The model is applied to a region in the Oregon Coast Range, USA to develop empirical relationships between landslide area, volume, and frequency for a range of rainfall scenarios and to determine the amount of rainfall required to trigger landslides on specific terrain features. Furthermore, because soil parameters are highly uncertain at this spatial scale (≥1,000 km2), a 3D landslide model is used to forensically assess soil strength from previously documented landslides in the area. Key Points: RegionGrow3D is a regional‐scale three‐dimensional slope stability model that grows discrete landslide volumes to achieve force equilibriumRegionGrow3D is parameterized using a range of input parameters to quantify the size, location, and likelihood of landslide initiationModeled landslides are used to quantify geomorphic scaling laws and rainfall thresholds that consider model input uncertainty [ABSTRACT FROM AUTHOR]

Flood is a natural phenomenon but flooding causes damage to human society. In recent decades, flood risk has been intensified due to rapid urbanization and climate change. Furthermore, it becomes more complicated in regions where social vulnerability evolves with mechanisms such as population aging. This paper provides an analysis of flood risk in Japan in relation to climate change and aging. It highlights changes in hazard and exposure, which resulted in higher vulnerability. Especially, it detected a turning point in the frequency of heavy rainfalls in Japan. It also assessed the latest strategies the Japanese government took to counteract the impacts of climate change. These analyses shed new light on Japan's challenges to achieve better flood risk management. Although a large amount of research on flood risk is available in the literature, the present study provides new thoughts and directions for further studies. [ABSTRACT FROM AUTHOR]

This study investigates the regional characteristics and land/sea differences in precipitation and wind along the coast of China, utilizing the Weather Research and Forecasting (WRF) model for continuous 10-year (2009–2018) simulations at 4 km resolution. The results indicate that the WRF model, operating at convection-permitting resolution, can accurately simulate annual precipitation and rainfall frequency, but tends to underestimate the precipitation over the ocean while overestimate the frequency of drizzle rain. The study also reveals that, along the Chinese coastal region, the sub-daily scale precipitation events are most frequent in Eastern China (EC), and WRF tends to underestimate the frequency of events lasting longer than 3 h. The WRF simulations show relatively slight difference in maximum precipitation between land and sea areas. The smallest deviation from the observation of maximum hourly precipitation is over the sea, below 20%. Furthermore, observations show that the diurnal cycle of precipitation peaks at 16:00–17:00 local time over land, with most afternoon precipitation lasting approximately 1 to 6 h. The WRF model successfully captures the observed diurnal variation of precipitation over land and sea in different regions. However, the WRF simulations indicate shorter duration of afternoon precipitation, lasting less than 5 h and an earlier peak. In addition, WRF adequately reproduces the annual and seasonal mean windspeeds for each region, with a slight overestimation during summer. WRF also reasonably captures the frequency distribution of windspeed and wind direction and can identify the occurrence frequency of land-sea breeze in each sub-region. Overall, the study offers valuable insights into the regional characteristics of precipitation and wind along the coast of China and highlights the WRF model's effectiveness in accurately simulating these phenomena. Key points: Convection permitting model (CPM) performs well in simulating the climate characteristics and regional differences in the Chinese coastal areas. There are significant land-sea contrast in the amount, frequency and intensity of precipitation along the eastern coast in China. Water vapor flux convergence and vertical motion due to topographic effects in the model lead to overestimated precipitation over land. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presented an assessment of climate extremes in the Southeast Asia (SEA) region, utilizing downscaled climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs). The study outputs uncovered statistically significant trends indicating a rise in extreme precipitation and temperature events throughout SEA for both the near-term (2021–2060) and long-term (2061–2100) future under both SSP245 and SSP585 scenarios, in comparison to the historical period (1950–2014). Moreover, we investigated the seasonal fluctuations in rainfall and temperature distributions, accentuating the occurrence of drier dry seasons and wetter rainy seasons in particular geographic areas. The focused examination of seven prominent cities in SEA underscored the escalating frequency of extreme rainfall events and rising temperatures, heightening the urban vulnerability to urban flooding and heatwaves. This study's findings enhance our comprehension of potential climate extremes in SEA, providing valuable insights to inform climate adaptation, mitigation strategies, and natural disaster preparedness efforts within the region. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geomechanics is the property of Journal of Geomechanics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study examines mesoscale convective systems (MCSs) in relation to 1‐min automatic weather station data for 2 years over the city of Yaounde. The focus is on characterising the atmospheric variability associated with MCS activity while distinguishing the days with and without MCS activities. This paper aims to determine the diurnal cycles of occurrence frequencies and percentages of rainfall, relative humidity, dew point temperature, solar radiation, temperature, and wind speed for days with and without MCSs. There are more than 623 MCS events during the study period (over 150 events per rainy season). The link between MCS activity and regional‐scale circulation and atmospheric instability is investigated. The diurnal cycle of the number of MCSs shows a maximum in the afternoon (around 1,600–2,200 LT), a morning minimum (around 0700–1,300 LT), and substantial activity during the night. Surface relative humidity is 5% lower on non‐MCS days, surface dew point 2% higher on MCS days between 0700 and 1800 hr, and solar radiation higher on MCS days between 0500 and 1000 hr. The percentage of rainfall associated with MCSs can exceed 60% on an annual scale and up to 80% on a seasonal scale. MCS activity is associated with instability in the lower troposphere, and this convective instability is maximal during the peak of the MCS activity. [ABSTRACT FROM AUTHOR]

Rainstorm is one of the global meteorological disasters that threaten the safety of transportation infrastructure and the connectivity of transportation system. Aiming to support the resilience assessment of transportation infrastructure in three representative regions: Sichuan–Chongqing, Yangtze River Delta, and Beijing-Tianjin-Hebei-Shandong, rainfall data over 40 years in the three regions are collected, and the temporal distribution of rainfall are analyzed. Prediction equations of rainfall are established. For the purpose of this, the probabilistic density function (PDF) is assigned to the rainfall by fitting the frequency distribution histogram. Using the assigned PDF, the rainfall data are transformed into standard normal space where regression of prediction equations is performed and the prediction accuracy is tested. The results show that: (1) The frequency of rainfall in the three regions follows a lognormal distribution based on which the prediction equations of rainfall can be established in standard normal space. The error of regression shows no remarkable dependence on self-variables, and the significance analysis indicates that the equations proposed in this paper are plausible for predicting rainfalls for the three regions. (2) The Yangtze River Delta region has a higher risk of rainstorm disaster compared to the other two regions according to the frequency of rainfall and the return period of precipitation concentration. (3) Over the period of 1980–2021, the Sichuan–Chongqing region witnessed an increase in yearly rainfall but a decrease in rainstorm disasters, whereas the other two regions experienced a consistent rise in both metrics. [ABSTRACT FROM AUTHOR]

Convective-permitting ensemble simulations are used to understand the roles of thermodynamic and dynamic processes in changing intense storms over the West African Sahel due to increases in atmospheric greenhouse gas concentrations. Ensemble simulations with 16 members represent recent August conditions during the height of the boreal summer monsoon season over the Sahel. They are compared with 5 Future-Warming ensemble simulations with increased greenhouse gas concentrations under the late-21st -century high-emission SSP5-8.5 scenario and initial/boundary conditions from the Current-Climate data plus the multi-model mean anomalies derived from CMIP6 experiments. The Current-Climate simulations reproduce observed precipitation and environmental conditions over the Sahel well. The frequency of heavy rainfall events with 24-hr rainfall > 77 mm (the 99.9th percentile) increases by 38.2% in the Future-Warming simulations. While the low- to mid-level vertical wind shear increases in the Future-Warming simulations, we find no significant correlations between the environmental shear strength and peak storm rain rates. In contrast, lower (middle) tropospheric moisture and temperature are correlated (anticorrelated) with peak rain rates and/or the maximum updraft velocity of intense events, consistent with significant correlations between the increased atmospheric instability and storm intensity. Thus, thermodynamic processes and not dynamical (shear-related) processes dominate the rainfall intensification over the Sahel in the simulations. Nevertheless, the enhanced shear strength is associated with larger rain-shield areas and propagation speeds of intense storms in Future-Warming. Wind shear strength is also correlated with pre-storm atmospheric instability, which grows less/more under strong/weak shear with greenhouse gas increases and is relevant for sub/super Clausius-Clapeyron scaling of precipitation. [ABSTRACT FROM AUTHOR]

In this study, UAV (Unmanned Aerial Vehicle) photogrammetry was used to monitor the ground displacement on the slope below a coal waste-rock stockyard and to investigate the role of rainfall on its displacement. The study area is a mountainous region in Korea, where coal mining is active, and coal waste-rock is stored on the slopes of the mountain. In this area, material removal work was undertaken to reduce the steepness of the slope and driving forces in order to prevent disasters, but the strategy requires continuous monitoring to confirm the stabilization of the slope. For slope monitoring, a total of six UAV photogrammetry campaigns were conducted between April 2019 and August 2020. As a result of data processing, an orthoimage and DSM (Digital Surface Model) were generated. The ground displacement was estimated through four steps: slope extraction, displacement area evaluation, horizontal displacement, and vertical displacement analysis. During the study period, the maximum vertical displacement was 3.3 m and the maximum horizontal displacement was 3.5 m. The horizontal displacement was effectively evaluated through the measurement of the movement of the drainage system using orthoimages captured with a periodic survey. The effects of rainfall on ground displacement were also investigated. A very clear linear relationship between rainfall intensity higher than 20 mm/day and ground displacement was identified. The accumulated amount of rainfall also showed a good correlation with slope movement, but the frequency of rainfall intensity below 20 mm/day showed a relatively low correlation with ground displacements. [ABSTRACT FROM AUTHOR]

The negative trend of PM2.5 in southwestern Taiwan could be offset in the future because of the stabilisation of the atmospheric layer with the decreasing strength of the East Asian winter monsoons. However, phenomena such as changes in the moisture source and the washout effect can help reduce PM2.5 levels. To assess these phenomena, hourly PM2.5 levels, weather parameters, and surface weather maps were obtained from the relevant government authorities in Taiwan. Trend, multiregression, and basic statistical methods were used in this study. The study period was divided into two separate parts. P1 (1959–1976) represents the averaged period, and P2 represents the warming period (1977–2021). The rainy-day weather patterns suggested that the percentage of moisture in the weak continental high area south of the Yangtze River basin in China and the southern region outside Taiwan Island was more substantial during P2 (28.3%) than during P1 (7.3%) because the frequency and intensity of rainfall increased in P2. The rainfall pattern in the Kaoping area from 2005 to 2021 changed from low rainfall and decreasing rainfall frequency to high rainfall and increasing rainfall frequency. Multiregression models showed that the negative contribution of the washout effect to the variation in the annual mean PM2.5 was more significant than the positive contribution of air dispersion. This suggests that in tropical regions with poor air dispersion and winter monsoons, the frequency and type of rainfall affect the variations in PM2.5 under climate change. [ABSTRACT FROM AUTHOR]

In the Southern Central Highlands of Vietnam, droughts occur more frequently, causing significant damage and impacting the region's socio-economic development. During the dry season, rivers, streams, and reservoirs often face limited water availability, exacerbated in recent years by increasing drought severity. Recognizing the escalating severity of droughts, the study offers a novel contribution by conducting a comprehensive analysis of surface water resource distribution in Lam Dong province, focusing on assessing water demand for agricultural production, a crucial factor in ensuring sustainable crop growth. Two scenarios, Current-2020 (SC1) and Climate Change-2025 (SC2), are simulated, with SC2 based on climate change and sea level rise scenarios provided by the Ministry of Natural Resources and Environment (MONRE). These scenarios are integrated into the MIKE-NAM and MIKE-HYDRO basin models, allowing for a thorough assessment of the water balance of Lam Dong province. Furthermore, the study utilizes the Keetch–Byram Drought Index (KBDI) to measure drought severity, revealing prevalent dry and moderately droughty conditions in highland districts with rainfall frequency ranging from 50 to 85%. Severe drought conditions occur with a rainfall frequency of 95%, indicating an increased frequency and geographic scope of severe droughts. Additionally, the study highlights that under abnormally dry conditions, water demand for the winter-spring crop is consistently met at 100%, decreasing to 85%, 80%, and less than 75% for moderate, severe, and extreme droughts, respectively. These findings offer insights into future drought conditions in the Lam Dong province and their potential impact on irrigation capacity, crucial for adaptation strategies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Economics & Social Sciences is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The forest headwater streams are important hubs for connecting terrestrial and aquatic ecosystems, with plant litter and sediments as the major carriers for material migrations; however, until now we knew little about the dynamics of trace elements such as iron (Fe) and aluminum (Al) in forest headwater streams. Here, we quantitatively identified the spatiotemporal dynamics of Fe and Al storages in plant litter and sediments and their influencing factors in a subtropical forest headwater stream, and assessed the potential pollution risk. The results showed that: (1) the mean concentrations of Fe and Al in plant litter (sediments) were 5.48 and 8.46 (7.39 and 47.47) g·kg−1, and the mean storages of Fe and Al in plant litter (sediments) were 0.26 and 0.43 (749.04 and 5030.90) g·m−2, respectively; (2) the storages of Fe and Al in plant litter and sediments significantly fluctuated from January to December, and showed a decreasing pattern from the source to mouth; and (3) storages of Fe and Al had no significant correlation with riparian forest type and the present of tributary and the Fe and Al storages in plant litter were mainly affected by water temperature and water alkalinity, and their storages in sediments were mainly affected by water temperature and frequency of rainfall; and (4) there were no anthropogenic pollution in Fe and Al in the forest headwater stream. Our study revealed the primary factors of concentrations and storages of Fe and Al in plant litter and sediments in a forest headwater stream, which will improve our understanding of the role of headwater streams in forest nutrient storage and cycling along with hydrological processes. [ABSTRACT FROM AUTHOR]

Typhoons are recurring meteorological phenomena in the southeastern coastal area of China, frequently triggering debris flows and other forms of slope failures that result in significant economic damage and loss of life in densely populated and economically active regions. Accurate prediction of typhoon-triggered debris flows and identification of high-risk zones are imperative for effective risk management. Surprisingly, little attention has been devoted to the construction of physical vulnerability curves in typhoon-affected areas, as a basis for risk assessment. To address this deficiency, this paper presents a quantitative method for developing physical vulnerability curves for buildings by modeling debris flow intensity and building damage characteristics. In this study, we selected the Wangzhuangwu watershed, in Zhejiang Province of China, which was impacted by a debris flow induced by Typhoon Lekima on August 10, 2019. We conducted detailed field surveys after interpreting remote sensing imagery to analyze the geological features and the mechanism of the debris flow and constructed a comprehensive database of building damage characteristics. To model the 2019 debris flow initiation, entrainment, and deposition processes, we applied the Soil Conservation Service-Curve Number (SCS-CN) approach and a two-dimensional debris flow model (FLO-2D). The reconstructed debris flow depth and extent were validated using observed debris flow data. We generated physical vulnerability curves for different types of building structures, taking into account both the degree of building damage and the modeled debris flow intensity, including flow depth and impact pressure. Based on calibrated rheological parameters, we modeled the potential intensity of future debris flows while considering various recurrence frequencies of triggering rainfall events. Subsequently, we calculated the vulnerability index and economic risk associated with buildings for different frequencies of debris flow events, employing diverse vulnerability functions that factored in uncertainty in both intensity indicators and building structures. We observed that the vulnerability function utilizing impact pressure as the intensity indicator tends to be more conservative than the one employing flow depth as a parameter. This comprehensive approach efficiently generated physical vulnerability curves and a debris flow risk map, providing valuable insights for effective disaster prevention in areas prone to debris flows. [ABSTRACT FROM AUTHOR]

Controlled irrigation and drainage technology for rice is crucial water management that has been widely promoted in northeastern China. It is of great significance to clarify the response mechanism of the drainage volume and nitrogen loss load in the paddy field for realizing water saving, emission reduction, pollution control and high yield in rice-planting areas. In this study, we conducted field experiments and simulations on drainage volume and nitrogen loss load regulations of paddy fields in a cold black soil region under different hydrological years and irrigation and drainage modes. The key parameters for simulating drainage volume and nitrogen loss load in paddy fields using DRAINMOD-NII were determined by combining field experiments, data analysis, and numerical simulation. The results showed that the simulated drainage volume and nitrogen loss load showed a high coefficient of determination with the observed results, which were all above 0.83. The Nash–Sutcliffe efficiency coefficient ranged from 0.72 to 0.97 in model calibration and verification, indicating that the model effectively simulated drainage volume and nitrogen loss load in paddy fields under controlled irrigation and drainage in the cold black soil region. The paddy field drainage volume was not only influenced by rainfall frequency but also by the distribution of rainfall. Compared with traditional irrigation and drainage, the controlled irrigation and drainage significantly reduced the irrigation amount by 39.07% and increased rainwater utilization efficiency by 13.07%. It also reduced the drainage volume by 44.71% and NO3−-N and NH4+-N loss load by 59.38% and 44.96%. The controlled irrigation and drainage mode optimized natural rainfall resources and increased irrigation water productivity and rice yield by 97.85% and 16.88%, respectively. Controlled irrigation and drainage outperformed the traditional mode in different hydrological years, with more pronounced effects in dry years, which highlights its significant value in practical agricultural production. [ABSTRACT FROM AUTHOR]

In China, the middle and lower reaches of the Yangtze River basin (MLRYRB) is a core region suffering frequent devastating floods triggered by heavy precipitation during warm seasons, exerting serious impacts on society. However, the physical mechanisms responsible for the increasing flood-inducing rainfall (FIR) frequency over MLRYRB during warm seasons remain unclear. Based on objective definition procedures, the present study investigates the salient atmospheric and oceanic signals tied to the interannual fluctuations of warm-season FIR frequency over MLRYRB. The results show that the suppressed convection from the remote western Pacific to the east of the Philippines could serve as a salient synchronous atmospheric signal for the increased FIR frequency. Moreover, the sea surface temperature (SST) warming over the tropical Indian Ocean (TIO) and the preceding wintertime El Niño-related SST anomaly pattern are deemed as salient contemporaneous and precursory oceanic signals linking the enhancement of the warm-season FIR frequency over MLRYRB on the interannual timescale, respectively. Further observational evidence and tropical Pacific pacemaker experiment results based on the Community Earth System Model Version 2 (CESM2) suggest that the mature El Niño in the prior winter can exert a delayed impact on the enhanced FIR frequency over MLRYRB during the subsequent warm season by exerting vital contributions to the FIR-favorable systems (i.e., southwestward-shifted western North Pacific anomalous anticyclone and the southward-displaced East Asian subtropical westerly jet). The basin-wide positive TIO SST anomalies act as El Niño's capacitor to relay its impact. These signals have important implications for seasonal prediction of FIR frequency over MLRYRB, and it is essential to place a high requirement on consideration of the better-known El Niño's cross-season atmospheric teleconnection. [ABSTRACT FROM AUTHOR]

With the frequency of extreme rainfall increasing, the risk of waterlogging is significantly exacerbated in subway systems. It is imperative to first identify the rainfall threshold for waterlogging risk for subway stations in order to develop effective waterlogging prevention and control plans. This study focuses on Line 11 of the Beijing Subway, using InfoWorks ICM to construct a model of the research area and simulate waterlogging at various subway stations under different rainfall scenarios. The results indicate that there is a risk of waterlogging at Jinanqiao station, Moshikou station, and Beixinan station on Line 11. The accumulated water may enter the subway station through exits A, B, C, and D of Jinanqiao Station. The inlet sequence of Jinanqiao Station always follows A(B), C, and D, and the difference in waterlogging time for each outlet does not exceed 10 min. We derived the rainfall threshold formula for waterlogging risk at Jinanqiao subway station. Among the three influencing factors of topographic features, step height, and drainage capacity of the pipeline network, step height has a significant effect on increasing the rainfall threshold for waterlogging risk. The conclusions obtained can provide reference for the refined management of waterlogging risks in subway stations. [ABSTRACT FROM AUTHOR]

Estimating the effects of extreme drought on the photosynthetic rates (Pn) of dominant plant species is crucial for understanding the mechanisms driving the impacts of extreme drought on ecosystem functioning. Extreme drought may result from either reduced rainfall amount or decreased rainfall frequency, and the impacts of different patterns of extreme drought may vary greatly. In addition, different grasslands likely appear various sensitivity to different extreme drought patterns. However, there have been no reports on the effects of different extreme drought patterns on dominant species Pn in different grassland types. Here, we conducted multi-year extreme drought simulation experiments (reducing each rainfall event by 66% during the growing season, CHR vs. completely excluding rainfall during a shorter portion of the growing season, INT) in two different grasslands (desert grassland vs. typical grassland) from 2014. The Pn of two dominant species in each grassland was measured in July and August 2017. Both CHR and INT significantly decreased dominant species Pn, with INT causing more negative impacts on Pn regardless of grassland types. The response ratios of Pn in desert grassland were generally higher than that of typical grassland, especially for Leymus chinensis in CHR. These results demonstrated that decreased rainfall frequency had a more negative effect on Pn compared with reduced rainfall amount, with grassland types changing the magnitude, but not the direction, of the effects of extreme drought patterns. These findings highlight the importance of considering extreme drought patterns and grassland types in ecosystem management in the face of future extreme droughts. [ABSTRACT FROM AUTHOR]

A larger antecedent effective precipitation (AEP) indicates a higher probability of a debris flow (Pdf) being triggered by subsequent rainfall. Scientific topics surrounding this qualitative conclusion that can be raised include what kinds of variation rules they follow and whether there is a boundary limit. To answer these questions, Jiangjia Gully in Dongchuan, Yunnan Province, China, is chosen as the study area, and numerical calculation, a rainfall scenario simulation, and the Monte Carlo integration method have been used to calculate the occurrence probability of debris flow under different AEP conditions and derive the functional relationship between Pdf and AEP. The relationship between Pdf and AEP can be quantified by a piecewise function. Pdf is equal to 15.88 %, even when AEP reaches 85 mm, indicating that debris flow by nature has an extremely small probability compared to the rainfall frequency. Data from 1094 rainfall events and 37 historical debris flow events are collected to verify the reasonability of the functional relationship. The results indicate that the piecewise functions are highly correlated with the observation results. Our study confirms the correctness of the qualitative description of the relationship between AEP and Pdf , clarifies that debris flow is a small-probability event compared to rainfall frequency, and quantitatively reveals the evolution law of debris flow occurrence probability with AEP. All the above discoveries can provide a clear reference for the early warning of debris flows. [ABSTRACT FROM AUTHOR]

Growing urban population and the distinct strategies to accommodate them lead to diverse urban development patterns worldwide. While local evidence suggests the presence of urban signatures in rainfall anomalies, there is limited understanding of how rainfall responds to divergent urban development patterns worldwide. Here we unveil a divergence in the exposure to extreme rainfall for 1790 inland cities globally, attributable to their respective urban development patterns. Cities that experience compact development tend to witness larger increases in extreme rainfall frequency over downtown than their rural surroundings, while the anomalies in extreme rainfall frequency diminish for cities with dispersed development. Convection-permitting simulations further suggest compact urban footprints lead to more pronounced urban-rural thermal contrasts and aerodynamic disturbances. This is directly responsible for the divergent rainfall responses to urban development patterns. Our analyses offer significant insights pertaining to the priorities and potential of city-level efforts to mitigate the emerging climate-related hazards, particularly for countries experiencing rapid urbanization. Cities that experience compact development tend to witness more extreme rainfall over downtown than their rural surroundings, while the anomalies in extreme rainfall frequency diminish for cities with dispersed development patterns. [ABSTRACT FROM AUTHOR]

The impact of hydrological and geological disasters has resulted in significant social, economic, and human losses, which added climate change impacts, and such events have become more frequent and intense. Therefore, our objective is to analyze the extreme rainfall (trends) in the Metropolitan Region of the Paraiba do Sul Valley and North Coast of Sao Paulo (RMVPLN). This analysis will support the most affected areas by landslides identification, which mainly impact roads and their population. In addition, evaluate the atmosphere conditions that supported these extreme rainfall events. To achieve our objectives, we have surveyed historical landslide data reported by the Brazilian government and information related by press and media. The precipitation evaluation used CHIRPS v.2 data and ETCCDI indices and the vertically integrated moisture flow and wind speed were calculated by ERA5 reanalysis. Our results show that the frequency and intensity of rainfall indicators such as seasonal PRCPTOT, R20mm, R30mm, and SDII have increased, particularly in the coastal and mountainous regions of São Paulo. This is due to positive anomalies of moisture transport and an increase of ocean winds influenced by the intense South Atlantic Subtropical Anticyclone (SASA). The region with the highest susceptibility to landslides triggered by extreme rainfall is the one that combines deforested areas, high slope topography, and excessive anthropic intervention. The presence of mountainous regions increases the risk of landslides, which can damage local infrastructure and expose the vulnerability of populations in these risk areas. [ABSTRACT FROM AUTHOR]

Many alluvial rivers worldwide are plagued with significant geomorphology changes due to severe riverbank collapse. This study developed an approach for quantifying riverbank erosion and outlined a structure for riverbank preservation based on insights gained from field investigations conducted in the lower Yellow River. Using the erosional forces, the newly introduced assessment method enables convenient measurement of riverbank erosion at various locations, even without cross-sectional profiles or channel-specific data. Abundant evidence highlights the severe riverbank collapses in the lower Yellow River, exemplified in the Jiyang Reach, where the left riverbank retreated at 26.0 m/yr from March 31, 2016 to May 10, 2018. The presence of weak soil erosion resistance is an inherent cause of riverbank collapse, with the critical shear stress of the soil being only 0.1 N/m2, significantly lower than that of the flow of 3.1 N/m2. Climate change and human activities, such as increased heavy rainfall frequency, reduced incoming water-sediment conditions and changes in riverbank boundary conditions, have triggered riverbank collapse. To prevent the occurrence of riverbank collapse, this study proposes a theoretical framework for a riverbank protection structure that simultaneously addresses the need for flood control and ecological conservation. [ABSTRACT FROM AUTHOR]

Concepción Bay is a socio-economic and ecologically important embayment whose hydrographic variability has been historically regulated by wind-modulated seasonal upwelling events during spring–summer and by freshwater from precipitation and river discharges during fall–winter. This system is subject to several anthropogenic and environmental strains due to the intense port activity and the increasing occurrence of extreme natural events. This study determines a new hydrographic regime and characterizes and analyzes the biogeochemical response of the water column to changes in rainfall and upwelling patterns. Despite the intrusion of nitrate-rich upwelled waters that enhance biological productivity remains more intense during spring–summer, the system remains fertilized year-long due to the occurrence of persistent upwelling pulses during fall–winter. The hydrographic structure presented a two-layer water column that was stratified thermally in spring–summer and primarily by freshwater inputs in fall–winter. Nevertheless, the regular pattern of the rainfall has changed (a decrease in precipitation and an increased frequency of extreme rainfall events), together with recurrent upwelling-favorable wind pulses during the non-upwelling season. This new regime has altered the seasonality of the physicochemical conditions and the structure of the microplanktonic communities, with productive and sanitary implications affecting the biogeochemical status of CB. [ABSTRACT FROM AUTHOR]

The historic 22–26 May 2015 flood event in Texas and Oklahoma was caused by anomalous clustered mesoscale convective systems (MCSs) that produced record‐breaking rainfall and $3 billion of damage in the region. A month‐long regional convection‐permitting simulation is conducted to reconstruct multiple clustered MCSs that lead to this flood event. We further use the pseudo global warming approach to examine how a similar event may unfold in a warmer climate and the driving physical factors for the changes. Tracking of MCSs in observations and simulations shows that the historical simulation reproduces the salient characteristics of the observed MCSs. In a warmer climate under a high‐emission (SSP5‐8.5) scenario, the Southern Great Plains is projected to experience a near surface warming of 4–6 K, accompanied by enhanced moisture transport by the strengthened Great Plains low‐level jet. A warmer and moister lower troposphere leads to 36%–59% larger convective available potential energy, supporting wider and more intense convective updrafts and rainfall production. Consistently, MCSs have wider convective areas and stronger rainfall intensities, producing 50% larger rain volumes during the mature stage. Extreme (99.5%) MCS rainfall frequency and amount will increase by threefold. However, MCS stratiform rain area decreases as a result of elevated stratiform cloud bases that lead to stronger sublimation and evaporation of precipitation in response to warming, resulting in reduced weak‐to‐moderate surface precipitation. Results suggest that global warming greatly increases precipitation intensity of clustered MCS events under strong synoptic influence, with much higher potential to produce serious floods without additional climate adaptation. Plain Language Summary: Changes in extreme rainfall produced by thunderstorms in a warmer climate have significant implications to society. However, their projections are highly uncertain due to the inability of the coarse‐resolution global climate models to simulate key processes in thunderstorms. Here, we use a kilometer‐scale regional model to examine how the record‐breaking May 2015 flood event in Texas and Oklahoma may unfold in a warmer climate. By tracking the organized thunderstorms that produced extreme rainfall in observations and simulations, we found that the model reproduced many key characteristics of the observed storms. In a warmer climate where the Great Plains warm by 4–6 K (a low‐climate‐mitigation scenario), these storms produce 50% more total rainfall and the extreme precipitation increase by threefold as a result of the warmer atmosphere that contain more moisture and larger convective instability. A sharpening of the storm is found where wider and stronger convective vertical motions produce more heavy rainfall while areas with weaker rainfall decrease. These results suggest if similar events were to occur in a warmer climate, the probability of more severe flash flooding may increase. Current flood control and mitigation strategies may need to adapt to the possible strengthening of organized thunderstorms in future climate. Key Points: The historic flood‐producing mesoscale convective systems are reproduced in simulations to examine their changes in a warmer climateWarming strengthens the Great Plains low‐level jet to transport warmer and more unstable air that fuels stronger convective stormsStorm systems have wider and stronger updrafts, producing a threefold increase in extreme precipitation frequency with warming [ABSTRACT FROM AUTHOR]

Temporary works are necessary to ensure the construction and operation of railways. These works are characterized by their large scale, numerous locations, and long construction periods. However, suitable land resources for such purposes are extremely limited in mountainous railway areas. Additionally, the selection of sites for these works often overlaps with areas affected by debris flow, leading to high potential risks from geological disasters. Taking the Jinjia Gully watershed as an example, this paper explores a method for assessing debris flow risks in single gullies, including the zoning of debris flow danger areas, vulnerability analysis, and risk assessment. Based on the data obtained from field surveys, they utilize ArcGIS and the Analytic Hierarchy Process (AHP), combined with numerical simulations and indoor experiments, to establish a quantitative risk assessment method for large-scale temporary works. The results indicate that (1) the area of debris flow hazard zones decreases with increasing rainfall frequency, and (2) the vulnerability assessment model can not only reflect the types of individual work, structural materials, and construction quality but also the shielding effect of building clusters. In the direction of flow, the shielding effect range of buildings on debris flow accumulation fans is approximately 37.5 times the size of the buildings. In the direction of extension, when the angle between current and rear buildings exceeds 0.674 radians, the shielding effect can be neglected. (3) At a rainfall frequency of p = 5%, more than 80% of large-scale temporary works are in extremely low or low-risk zones, indicating that the study area is at a low risk level. [ABSTRACT FROM AUTHOR]

In recent years, there has been a common use of automatic precipitation mapping based on various interpolation methods. Based on multiple linear regression, the study primarily focuses on the entire Cheliff basin (43 750 km²), to search a suitable model for mapping mean annual precipitation based on 89 rainfall stations of the observed series (1968/69-2001/2002). Then conducts on the upper part of Cheliff basin downstream the Boughzoul dam (4777 km²) using 11 rainfall stations of annual precipitation series relative to the period 1982/83-2020/2021. The same approach was used to map median annual precipitations and quantiles of rain associated with the return periods of 5 and 10 years, representing both wet and dry periods. Indeed, this work is based on the backward elimination method between the observed annual precipitation and four predictors: smoothed altitude (Zs), longitude (X), latitude (Y) and distance from the sea (D). The model's results are satisfactory with a global correlation coefficient for both regional (R = 0,89) and local scale (R > 0,81). The final maps obtained are produced after interpolating by kriging the residuals. The median annual precipitation map was compared to that elaborated by the National Hydraulic Resources Agency (ANRH). The comparison results of 2296 nodes of the grid covering the Upper Cheliff basin showed negative deviations on average - 10% and reaching - 34% in northern area reflecting a dry trend in precipitation. The final grids of rainfall can be integrated into Geographical Information Systems related to many sectors including water management and climate change. [ABSTRACT FROM AUTHOR]

The Bailong River Basin is one of the most developed regions for debris flow disasters worldwide, often causing severe secondary disasters by blocking rivers. Therefore, the early identification of potential debris flow disasters that may block the river in this region is of great significance for disaster risk prevention and reduction. However, it is quite challenging to identify potential debris flow disasters that may block rivers at a regional scale, as conducting numerical simulations for each debris flow catchment would require significant time and financial resources. The purpose of this article is to use public resource data and machine learning methods to establish a relationship model between debris flow-induced river blockage and key influencing factors, thereby economically predicting potential areas at risk for debris flow-induced river blockage disasters. Based on the field investigation, data collection, and remote sensing interpretation, this study selected 12 parameters, including the basin area, basin height difference, relief ratio, circularity ratio, landslide density, fault density, lithology index, annual average frequency of daily rainfall exceeding 40 mm, river width, river discharge, river gradient, and confluence angle, as critical factors to determine whether debris flows will cause river blockages. A relationship model between debris flow-induced river blockage and influencing factors was constructed based on machine learning algorithms. Several machine learning algorithms were compared, and the XGB model performed the best, with a prediction accuracy of 0.881 and an area under the ROC curve of 0.926. This study found that the river width is the determining factor for debris flow blocking rivers, followed by the annual average frequency of daily rainfall exceeding 40 mm, basin height difference, circularity ratio, basin area, and river discharge. The early identification method proposed in this study for river blockage disasters caused by debris flows can provide a reference for the quantitative assessment and pre-disaster prevention of debris flow-induced river blockage chain risks in similar high-mountain gorge areas. [ABSTRACT FROM AUTHOR]

The Northeast United States (NEUS) has faced the most rapidly increasing occurrences of extreme precipitation within the US in the past few decades. Understanding the physics leading to long‐term trends in regional extreme precipitation is essential but the progress is limited partially by the horizontal resolution of climate models. The latest fully coupled 25‐km GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless system for Prediction and EArth system Research) simulations provide a good opportunity to study changes in regional extreme precipitation and the relevant physical processes. Here, we focus on the contributions of changes in synoptic‐scale events, including atmospheric rivers (AR) and tropical cyclone (TC)‐related events, to the trend of extreme precipitation in the fall season over the Northeast US in both the recent past and future projections using the 25‐km GFDL‐SPEAR. In observations, increasing extreme precipitation over the NEUS since the 1990s is mainly linked to TC‐related events, especially those undergoing extratropical transitions. In the future, both AR‐related and TC‐related extreme precipitation over the NEUS are projected to increase, even though the numbers of TCs in the North Atlantic are projected to decrease in the SPEAR simulations using the SSP5‐8.5 projection of future radiative forcing. Factors such as enhancing TC intensity, strengthening TC‐related precipitation, and/or westward shift in Atlantic TC tracks may offset the influence of declining Atlantic TC numbers in the model projections, leading to more frequent TC‐related extreme precipitation over the NEUS. Plain Language Summary: In recent decades, the densely populated Northeast United States has faced the most rapid increase in the frequency of extreme rainfall within the US. Here, we examine the causes of the increase in extreme rainfall over the Northeast US in both current and future climates. We find that the surge in extreme rainfall since the 1990s is primarily linked to events associated with tropical cyclones. In a future warming climate, based on projections from a high‐resolution climate model, our research shows that we can expect more frequent occurrences of extreme rainfall related to both atmospheric rivers and tropical cyclones. However, the increase in extreme rainfall linked to atmospheric rivers is projected to outpace that associated with tropical cyclones. Given the distinct spatial patterns of extreme rainfall resulting from atmospheric rivers and tropical cyclones, changes in their relative contributions could have profound implications for flood prevention and mitigation strategies. Key Points: The autumn extreme precipitation trend over the Northeast US is primarily attributed to tropical cyclone‐related events since the 1990sIn future projections, extreme precipitation linked to atmospheric rivers increases faster than that associated with tropical cyclonesDespite fewer projected tropical cyclones in the future, extreme precipitation associated with them is projected to increase [ABSTRACT FROM AUTHOR]

Strong positive Indian Ocean Dipole (pIOD) events like those in 1997 and 2019 caused significant flooding in East Africa. While future projections indicate an increase in pIOD events, limited historical data hinders a comprehensive understanding of these extremes, particularly for unprecedented events. To overcome this we utilize a large ensemble of seasonal reforecast simulations, which show that regional rainfall continues to increase with pIOD magnitude, with no apparent limit. In particular we find that extreme rain days are highly sensitive to the pIOD index and their seasonal frequency increases super‐linearly with higher pIOD magnitudes. It is vital that socio‐economic systems and infrastructure are able to handle not only the increasing frequency of events like 1997 and 2019 but also unprecedented seasons of extreme rainfall driven by as‐yet‐unseen pIOD events. Future studies should prioritize understanding the hydrological implications and population exposure to these unprecedented extremes in East Africa. Plain Language Summary: The Indian Ocean Dipole is a pattern of climate variability in the Indian Ocean, characterized by opposite‐signed sea surface temperature differences from normal in the west and east. During positive events (pIOD), the western Indian Ocean is warmer than usual, which disrupts the normal circulation of the atmosphere. When pIOD is strong, extreme rainfall and flooding are common in East Africa. This happened in 1997 and 2019. According to future predictions, the frequency of extreme pIOD events will increase. However, our understanding of these events is limited as there is not a large enough sample of past events in the historical data to study. To address this, we evaluate a large collection of climate model simulations called seasonal reforecasts. The results show no evidence of an upper limit to the impact of pIOD on regional rainfall. The frequency of rain days increases during pIOD, whilst extremely heavy rain days increase even more. This means that more frequent extreme pIOD seasons will likely bring more heavy rainfall impacts. It also means that if pIOD reaches a high magnitude which has not yet been recorded, the rainfall impacts are likely to be even larger than those seen in 1997 or 2019. Key Points: Unprecedented strong positive Indian Ocean Dipole (IOD) events are likely to bring extreme rainfall to East AfricaThe extreme component of the seasonal distribution of rain days is particularly sensitive to the magnitude of the IODThe impact of larger IOD events on extreme rainfall frequency increases non‐linearly with the IOD magnitude [ABSTRACT FROM AUTHOR]

Changes in tropical cyclones due to greenhouse‐gas forcing in the Shanghai area have been studied in a double‐nesting regional model experiment using the Met Office convection‐permitting model HadREM3‐RA1T at 4 km resolution and the regional model HadREM3‐GA7.05 at 12 km for the intermediate nest. Boundary conditions for the experiment have been constructed from HadGEM2‐ES, a General Circulation Model (GCM) from the 5th Coupled Model Intercomparison Project (CMIP5), directly using high‐frequency data for the atmosphere (6‐hourly) and the ocean (daily), for the historical period (1981–2000) and under the Representative Concentration Pathway 8.5 (2080–2099). These choices identify one of the warmest climate scenarios available from CMIP5. Given the direct use of GCM data for the baseline, large scale conditions relevant for tropical cyclones have been analyzed, demonstrating a realistic representation of environmental conditions off the coast of eastern China. GCM large scale changes show a reduction in wind shear in addition to the expected strong increase in sea‐surface temperature. Tropical cyclones from the 4 km historical simulation have a negative bias in intensity, not exceeding Category 4, and a wet bias in the rainfall associated with these cyclones. However, there is a clear improvement in cyclone intensity and rainfall at 4 km in comparison with the 12 km simulation. Climate change responses in the 4 km simulation include an extension of the tropical cyclone season, and strong increases in frequency of the most intense cyclones (approximately by a factor of 10) and associated rainfall. These are consistent with the results from the 12 km simulation. Plain Language Summary: Since global climate models do not have the spatial resolution to simulate tropical cyclones, higher resolution limited area models are commonly used to improve the representation of these phenomena. In this study, where changes in the Shanghai area have been estimated, this was done in two steps, using a regional model at 12 km resolution as the intermediate step to finally reach a 4 km resolution for the region around Shanghai with a model which can explicitly represent convection processes. This was done by using one of the warmest climate scenarios available from the climate projections run under realistic increases of greenhouse‐gas concentrations, to study climate change at the end of this century. The global climate model reproduces large scale conditions relevant for tropical cyclones which compare well with observations in the historical period and projects changes in the atmosphere and the ocean which are relevant for tropical cyclones. The downscaled model at 4 km reproduces the observation with some biases in intensity, not sufficient to reach Category 4, and in the cyclone precipitation, through marked improvements with respect to the 12 km model. The climate change response shows a strong increase in intense tropical cyclone frequency and rainfall, common to both models. Key Points: Double‐nesting experiment with a 4 km convective scale model driven by a high end Coupled Model Intercomparison Project general circulation model to study changes in tropical storms near ShanghaiImprovement at 4 km over the intermediate 12 km nest on rainfall and intensity of tropical storms, but not sufficient to reach category 4Future changes at 4 km show a marked increase in frequency of intense tropical cyclones and in rainfall, well matched by the 12 km model [ABSTRACT FROM AUTHOR]

The predicted intensification of the North American Monsoon is expected to alter growing season rainfall patterns in the southwestern United States. These patterns, which have historically been characterized by frequent small rain events, are anticipated to shift towards a more extreme precipitation regime consisting of fewer, but larger rain events. Furthermore, human activities are contributing to increased atmospheric nitrogen deposition throughout this dryland region.Alterations in rainfall size and frequency, along with changes in nitrogen availability, are likely to have significant consequences for above‐ground net primary production (ANPP) and plant community dynamics in drylands. The conceptual bucket model predicts that a shift towards fewer, but larger rain events could promote greater rates of ANPP in these regions by maintaining soil moisture availability above drought stress thresholds for longer periods during the growing season. However, only a few short‐term studies have tested this hypothesis, and none have explored the interaction between altered rainfall patterns and nitrogen enrichment.To address this knowledge gap, we conducted a 14‐year rainfall addition and nitrogen fertilization experiment in a northern Chihuahuan Desert grassland to explore the long‐term impacts of changes in monsoon rainfall size and frequency, along with chronic nitrogen enrichment, on ANPP (measured as peak biomass) and plant community dynamics.Contrary to bucket model predictions, small frequent rain events promoted comparable rates of ANPP to large infrequent rain events in the absence of nitrogen enrichment. It was only when nitrogen limitation was alleviated that large infrequent rain events resulted in the greatest ANPP. Furthermore, we found that nitrogen enrichment had the greatest impact on plant community composition under the small frequent rainfall regime.Synthesis. Our long‐term field experiment highlights limitations of the bucket model by demonstrating that water and nitrogen availability sequentially limit dryland ecological processes. Specifically, our findings suggest that while water availability is the primary limiting factor for above‐ground net primary production in these ecosystems, nitrogen limitation becomes increasingly important when water is not limiting. Moreover, our findings reveal that small frequent rain events play an important but underappreciated role in driving dryland ecosystem dynamics. [ABSTRACT FROM AUTHOR]

In the context of human‐induced warming climate, the atmosphere is expected to hold a greater amount of water vapor, leading to heavier precipitation on a global scale. However, the extent to which changes in extreme rainfall can be attributed to human influences varies at regional scales. Here we conduct attribution analyses on 40 extreme precipitation events in different seasons during 1998–2018 over the Pearl River Delta (PRD), by using the Weather Research and Forecasting (WRF) model and applying the pseudo global warming (PGW) method. The model was integrated with the factual and counterfactual conditions separately, with the latter derived from differences between the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical and historical‐natural runs. By comparing parallel experiments, extreme daily rainfall (>95th percentile) in PRD enhanced by 8%–9.5% for 0.9–1.1 K near‐surface warming (nearly Clausius‐Clapeyron, or CC scaling) in the May‐to‐September (MJJAS) and 12.4% at most for 0.6–0.8 K warming (∼2 × CC rate) in non‐MJJAS seasons; the probability of those extremes increased by 10%–30% during MJJAS (20%–40% in other seasons). While moisture‐related thermodynamic effects play a similar role in modulating rainfall, the wind circulation‐related dynamic effects act differently in different seasons. Changes in MJJAS extremes are related to stronger low‐level southerly winds, while non‐MJJAS rainfall is exacerbated by strengthened low‐level wind convergence and updrafts. Moisture budget analysis suggests that thermodynamic effects associated with the increased moisture amount account for the mean rainfall increase, whereas dynamic effects related to wind circulation changes are responsible for extreme precipitation, regardless of seasons. Key Points: Anthropogenic influences lead to around 20% or more increase in frequency of extreme rainfall events over the Pearl River Delta regionVery extreme rainfall in non‐rainy season tends to become more frequent and stronger than in rainy season under anthropogenic warmingBoth precipitable water and vertical motion have positive contributions to human‐induced increase in 95th percentile rainfall intensity [ABSTRACT FROM AUTHOR]

The accelerating pace of climate change has led to unprecedented shifts in surface temperature and precipitation patterns worldwide, with African savannas being among the most vulnerable regions. Understanding the impacts of these extreme changes on ecosystem health, functioning and stability is crucial. This paper focuses on the detection of breakpoints, indicative of shifts in ecosystem functioning, while also determining relevant ecosystem characteristics and climatic drivers that increase susceptibility to these shifts within the semi‐arid to arid savanna biome. Utilising a remote sensing change detection approach and rain use efficiency (RaUE) as a proxy for ecosystem functioning, spatial and temporal patterns of breakpoints in the savanna biome were identified. We then employed a novel combination of survival analysis and remote sensing time series analysis to compare ecosystem characteristics and climatic drivers in areas experiencing breakpoints versus areas with stable ecosystem functioning. Key ecosystem factors increasing savanna breakpoint susceptibility were identified, namely higher soil sand content, flatter terrain and a cooler long‐term mean temperature during the wet summer season. Moreover, the primary driver of changes in ecosystem functioning in arid savannas, as opposed to wetter tropical savannas, was found to be the increased frequency and severity of rainfall events, rather than drought pressures. This research highlights the importance of incorporating wetness severity metrics alongside drought metrics to comprehensively understand climate–ecosystem interactions leading to abrupt shifts in ecosystem functioning in arid biomes. The findings also emphasise the need to consider the underlying ecosystem characteristics, including soil, topography and vegetation composition, in assessing ecosystem responses to climate change. While this research primarily concentrated on the southern African savanna as a case study, the methodological robustness of this approach enables its application to diverse arid and semi‐arid biomes for the assessment of climate–ecosystem interactions that contribute to abrupt shifts. [ABSTRACT FROM AUTHOR]

The intensity and frequency of extreme rainfall events have increased in North America and the world due to climate change. Extreme rainfall events, characterized by a heavy volume of rainfall in a short duration, have triggered the onset of urban flash floods. Over the years, flash flooding has been reported in different cities in Canada, which resulted in many losses. Subsequently, different green roofing systems have been adopted to control urban stormwater runoff as part of Nature-Based Solutions (NBS) to mitigate urban flood and build a flood-resilient city. Currently, no specific widely recognized standard or code is dedicated to determining the hydrological performance of green roofs as a whole system. Moreover, there are no test protocols to regulate the design of green roof systems in the market. A comprehensive literature review examines existing research methods adopted to evaluate influencing parameters affecting the hydrological performance of NBS-CR. The results indicate several limitations in experimental and field investigations. Consequently, to address these limitations, it is essential to formulate a multi-functional work plan to develop a standardized test method that can become a common platform for the roofing industry to test and quantify the hydrological performance of their systems. [ABSTRACT FROM AUTHOR]

Rainfall frequency analysis methods are developed and implemented based on high‐resolution radar rainfall data sets, with the Baltimore metropolitan area serving as the principal study region. Analyses focus on spatial heterogeneities and time trends in sub‐daily rainfall extremes. The 22‐year radar rainfall data set for the Baltimore study region combines reflectivity‐based rainfall fields during the period from 2000 to 2011 and polarimetric rainfall fields for the period from 2012 to 2021. Rainfall frequency analyses are based on non‐stationary formulations of peaks‐over‐threshold and annual peak methods. Increasing trends in short‐duration rainfall extremes are inferred from both peaks‐over‐threshold and annual peak analyses for the period from 2000 to 2021. There are pronounced spatial gradients in short‐duration rainfall extremes over the study region, with peak values of rainfall between Baltimore City and Chesapeake Bay. Spatial gradients in 100‐year, 1 hr rainfall over 20 km length scale are comparable to time trends over 20 years. Rainfall analyses address the broad challenge of assessing changing properties of short‐duration rainfall in urban regions. Analyses of high‐resolution rainfall fields show that sub‐daily rainfall extremes are only weakly related to daily extremes, pointing to difficulties in inferring climatological properties of sub‐daily rainfall from daily rainfall analyses. Changing measurement properties are a key challenge for application of radar rainfall data sets to detection of time trends. Mean field bias correction of radar rainfall fields using rain gauge observations is an important tool for improving radar rainfall fields and provides a useful tool for addressing problems associated with changing radar measurement properties. Key Points: Rainfall frequency analysis tools based on long‐term, high‐resolution radar rainfall fields are developedSub‐daily rainfall extremes for the Baltimore study region exhibit increasing trends over 22‐year period of recordSub‐daily rainfall extremes exhibited pronounced spatial heterogeneities over the Baltimore study region [ABSTRACT FROM AUTHOR]

Given the increased frequency of extreme rainfall events, pre-disaster countermeasures against landslides triggered by heavy rainfall are important to enhance disaster resilience. This study presents a methodology for economic risk assessment of structures affected by rainfall-induced landslides using machine learning (ML). Random Forest and LightGBM algorithms were applied to develop ML-based landslide prediction models considering the spatial distributions of landslide conditioning and triggering factors. The rainfall index was calculated considering the temporal variation in rainfall and was used as a feature associated with rainfall intensity. The rainfall hazard curve, representing the relationship between the rainfall index and its annual exceedance probability, was statistically estimated using a generalised extreme value distribution. Rainfall-induced landslide susceptibility was assessed using an ML-based landslide prediction model and rainfall hazard curve. Finally, the risk curve associated with the economic loss from structures damaged by rainfall-induced landslides was estimated based on landslide susceptibility and structure distribution maps. In this study, LightGBM showed better prediction performance for evaluating rainfall-induced landslide susceptibility than Random Forest. An illustrative example is presented to demonstrate that the proposed methodology can be used to develop an appropriate risk-based disaster mitigation strategy. [ABSTRACT FROM AUTHOR]