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The aim of this paper is to report on fatal landslides resulting from two extraordinary storm events in January and February 2023, in Auckland, New Zealand, including the characteristics of the terrain, the site geology, and the storms. The January 27 Auckland Anniversary storm was from an "atmospheric river," dumping Summer's worth of rain (265 mm) in one day. This was the highest 24-h total on record, estimated a 1 in 200-year event, with a peak 2-min rainfall of 4.2 mm, coinciding with Friday's evening "rush hour." This led to widespread landsliding throughout Auckland, including a fatal landslide in Parnell, central Auckland. Notably, this occurred only meters from a similarly destructive landslide in 1997. With January 2023 Auckland's wettest January on record, and with the region still in a state of emergency, Tropical Cyclone Gabrielle crossed the region on February 13–14. This also caused widespread landsliding, including a fatal landslide at Muriwai on the west Auckland coast. Fatal landslides from the same escarpment had occurred in 1965, close to the February 13, 2023, fatal landslide. Taken together, both storm events caused > 140,000 landslides across the North Island, and flooding, making hundreds of people homeless, with damage estimates of > US$8.6 billion (3.4% of NZ's GDP). For future disaster risk reduction, attention should be paid to revising legislation and local planning, which currently allows people to legally build (and re-build) houses in landslide-prone areas of New Zealand. [ABSTRACT FROM AUTHOR]

The potential collapsible deformation of loess surrounding rock is not conducive to the safety and stability of the tunnel, especially under rainfall conditions, which may induce severe engineering disasters. This paper analyzes the regularity of water infiltration in loess and failure response of tunnel structure under rainfall conditions. Rainfall infiltration in loess strata includes unsaturated infiltration and dominant channel infiltration. The dominant channels enhance soil permeability, leading to the overall downward movement of the wetting front, which provides conditions for the instability and failure of surrounding rock of loess tunnel. Through case statistics, the loess tunnel engineering disasters caused by rainfall are analyzed, elucidating the mechanism of tunnel instability and failure. Finally, according to the characteristics of loess surrounding rock and the response regularity of rainfall to tunnel structure, a series of engineering measures to prevent the deformation of surrounding rock and the specific construction method of loess tunnel structure safety warning system under the condition of rainfall are proposed. [ABSTRACT FROM AUTHOR]

Evaluating the exceedance probability within a time period (EPT) of the runout distance of rainfall-induced landslides is important for the quantitative risk assessment (QRA) of rainfall-induced landslides. However, assessing the EPT of the runout distance of rainfall-induced landslides using a mechanics-based method remains a challenging problem since it requires considering uncertainties in both soil properties and rainfall. This paper proposes a novel mechanics-based method to assess the EPT of the runout distance of rainfall-induced landslides with explicit consideration of the above two types of uncertainties. A two-stage numerical approach, which combines the finite element method (FEM) and the material point method (MPM), is first developed for the large deformation analysis to obtain runout distances of landslides under given rainfalls. To further enhance the computational efficiency, a machine learning-based surrogate model is built to predict the exceedance of the runout distance, and the EPT of the runout distance is finally estimated via Monte Carlo simulation. The proposed method is applied to a sandy slope under rainfall. The results show that the EPT increases as the time period becomes longer, and the runout distance of the landslide is controlled by the first failure of the slope caused by rainfall. This study contributes to the development of a general and efficient tool to support the QRA of rainfall-induced landslides. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed and demonstrated a 100 Gbps data transmission system over hybrid single mode fiber (SMF)-free space optics (FSO) link using optical comb lines in wavelength-division-multiplexing technique. A continuous wave laser, two dual drive Mach-Zehnder modulators and four electrical attenuators are employed to generate optical frequency comb lines (OFCL). The OFCL generator generates 52 comb lines by optimizing biasing voltages. The OFCL lines with a side mode suppression ratio of ∼ 50dB and flatness ∼∼ 0.01dB are generated and used as an optical carrier in the proposed system. The performance of the system is investigated in different weather conditions, such as clear weather, hazy weather, heavy rain, light fog, moderate fog, and heavy fog. [ABSTRACT FROM AUTHOR]

Landslide event inventories are one of the most critical datasets to increase knowledge on landslide occurrences. However, they are rarely available in various regions, especially in countries of the Global South. This study aims to generate rainfall-induced landslide event inventories and define the rainfall thresholds responsible for landslide occurrence at the national scale of Malawi, Africa. We mainly followed a three-step methodology to generate landslide inventories. First, we went through media reports to identify documented landslide events. Second, we used Sentinel-2 images to identify possible areas affected by landslides using automated change detection algorithms based on vegetation indices. Third, we manually went through optical images provided by Planet Lab and Google Earth and mapped landslides via visual image interpretation. Overall, we mapped 27 rainfall-induced landslide inventories between 2003 and 2022, with a total of 4709 individual landslides. We then analysed the Malawian terrain and identified two different landscape clusters (i.e. Cluster 1 and Cluster 2) showing similar morphometric and climatic conditions. Ultimately, we calculated the rainfall threshold for each landscape cluster. The minimum rainfall amounts responsible for landsliding correspond to 66 mm/two-day and 51 mm/day in Clusters 1 and 2, respectively. In this context, our paper not only presents and shares the first national-scale, digital rainfall-induced landslide event inventory database of Malawi but also suitable rainfall thresholds to be potentially exploited for a national scale landslide early warning system. A similar framework could be applied to generate landslide inventories for other data scarce regions. [ABSTRACT FROM AUTHOR]

The traditional decomposed ensemble prediction model decomposes the entire rainfall sequence into several sub-sequences, dividing them into training and testing periods for modeling. During sample construction, future information is erroneously mixed into the training data, making it challenging to apply in practical rainfall forecasting. This paper proposes a novel stepwise decomposed ensemble coupling model, realized through variational mode decomposition (VMD) and bidirectional long short-term memory neural network (BiLSTM) models. Model parameters are optimized using an improved particle swarm optimization (IPSO). The performance of the model was evaluated using rainfall data from the Southern Four Lakes basin. The results indicate that: (1) Compared to the PSO algorithm, the IPSO algorithm-coupled model shows a minimum decrease of 2.70% in MAE and at least 2.62% in RMSE across the four cities in the Southern Four Lakes basin; the IPSO algorithm results in a minimum decrease of 25.58% in MAE and at least 28.19% in RMSE for the VMD-BiLSTM model. (2) When compared to IPSO-BiLSTM, the VMD-IPSO-BiLSTM based on the stepwise decomposition technique exhibits a minimum decrease of 26.54% in MAE and at least 34.16% in RMSE. (3) The NSE for the testing period of the VMD-IPSO-BiLSTM model in each city surpasses 0.88, indicating higher prediction accuracy and providing new insights for optimizing rainfall forecasting. [ABSTRACT FROM AUTHOR]

Landslides represent a prevalent geological hazard in reservoir areas, posing significant risks to the lives and properties of residents. It is widely acknowledged that rainfall and the fluctuation of reservoir water level are two primary factors triggering reservoir landslides. However, there has been limited research on multi-sliding zone reservoir bank landslides, and existing studies fail to adequately characterize the evolutionary process of this type of landslide. In this paper, the Outang landslide located in Three Gorges Reservoir (TGR) was taken as a case study to evaluate its stability and failure probability. Sensitivity analyses were conducted on statistical mechanical parameters and autocorrelation functions initially, followed by a comparative analysis of annual stability and reliability. The results indicate that the stability of the Outang landslide is predominantly influenced by the water level fluctuation, and the lower sliding zone plays a pivotal role in affecting the overall stability compared with other regions in the landslide. Furthermore, variations in failure probability are notably influenced by the frictional angle and the vertical scale of fluctuation, with autocorrelation functions playing a more significant role when the coefficient of variation is small and the vertical scale of fluctuation is large. This study could provide valuable guidance for the prevention and mitigation of analogous multi-sliding zone reservoir bank landslides. [ABSTRACT FROM AUTHOR]

Vegetation canopies intercept and redistribute rainfall into throughfall and stemflow, which transfer substantial amounts of elements into the soil, influencing soil microbial community, plant survival, and plant community succession. Despite advancements in ecohydrological research, the implication of nutrient enrichment resulting from this redistribution of rainfall by canopies remains largely unexplored. To address this gap, we conducted a systematic review of 1020 papers published between 2000 and 2022, gathering data on nutrient concentration and enrichment for critical ions (including K+, Na+, Ca2+, Mg2+, NH4+, Cl−, NO3− and SO42−) from the Web of Science and Chinese Knowledge Infrastructure databases. We aimed to synthesize the mechanisms, quantify the enrichments, and identify global patterns of nutrient enrichment in stemflow and throughfall across climate zones, and vegetation types and ecosystems. The results of this study indicate that stemflow exhibits, on average, 2.1 times greater ion concentration (6.13 mg L−1) compared to throughfall. In particular, among the investigated ions, SO42− (12.45 and 6.32 mg L−1) for stemflow and throughfall, respectively, and Cl− (9.21 and 4.81 mg L−1) exhibit the highest concentrations in both rainfall redistribution components, while K+ (13.7 and 5.8) and Mg2+ (5.6 and 2.8) have the highest enrichment factors. Across climate zones, throughfall and stemflow show the lowest ion concentrations but the highest enrichment factors in extremely humid regions. Along the temperature gradient, ion concentrations are the highest in cold climates with no clear patterns observed for enrichment factors with increasing temperature. In addition, shrubs, conifers, mixed forests, and artificial ecosystems demonstrate enrichment factors 1.1 to 3.0 times greater than those of trees, broad- leaved plants, pure forests, and natural ecosystems. These findings emphasize the need for increased attentions to artificial ecosystems, such as urban and agricultural ecosystems, which often received limited research focus, especially regarding shrubs and conifers exhibiting stronger nutrients enrichment capabilities. Future investigations should integrate soil moisture analysis to better understand the impact of rainfall redistribution on the nutrient enrichment processes, patterns, and nutrient balance in global terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Metaphoric glyphs are intuitively related to the underlying problem domain and enhance the readability and learnability of the resulting visualization. Their construction, however, implies an appropriate modification of the base icon, which is a predominantly manual process. In this paper, we introduce the parametric contour-based modification (PACEMOD) approach that lays the foundations of automated, controllable icon manipulations. Technically, the PACEMOD parametric representation utilizes diffusion curves, enriched with new degrees of freedom in arc-length parameterization, which allows for manipulation of the icon contours' geometry and the related color attributes. Moreover, we propose an implementation of our generic approach for a specific, automated design of metaphoric glyphs, based on periodic, wave-like contour modifications. Finally, the practicality of such periodic contour modifications is demonstrated by two visualization examples, which comprise uncertainty visualization of a rain forecast and gradient glyphs applied to COVID-19 data. In summary, with the PACEMOD approach we introduce an instrument that facilitates a user-centered design of metaphoric glyphs and provides a generic basis for potential further implementations according to specific applications. [ABSTRACT FROM AUTHOR]

The frequency of extreme events is increasing on a global scale due to various factors, accounting for natural disasters such as landslides, floods, and droughts. The Heilongjiang province, which is agriculturally important for China, has suffered from several extreme rainfall events in the past as a high-latitude plain region. Analysis of historical rainfall characteristics of the Heilongjiang region helps to understand and predict the behavior of extreme rainfall events and provides a reference for reducing agricultural economic losses. Precipitation data from 1974 to 2017 is selected to estimate whether rainfall series in Heilongjiang obey the Gumbel-Logistic Model. We compared the Gumbel-Logistic Model based on the data length of 5 years, 10 years, and 20 years with the original 44-year data to determine the appropriate data partition length and explore the reasons for errors. Combining the empirical frequency and KS test, this paper concludes that the proposed model is appropriate for the representation of the joint distribution of monthly extreme rainfall and annual rainfall. The proposed model improves the rainfall assessment of these two positively correlated variables in Heilongjiang. Univariate and bivariate return periods and correlation coefficients are derived as the basis for assessing the risk of extreme rainfall. The proposed model obtained from the 5-year data length has the largest error, while the distribution curve is progressively smoother as the data length increases. [ABSTRACT FROM AUTHOR]

This paper analyzes the correlation between precipitation and deforestation and trends in the Brazilian Amazon Biome from 1985 to 2021 using rain gauge measurements. A total of 187 stations were selected after filtering time series with less than 10% of missing data. Deforestation data was acquired using the MapBiomas dataset. Results show that deforestation rates vary across different locations and may be influenced by various factors. The analysis also found significant negative correlations between precipitation and deforestation, with a range of − 0.49 to − 0.33, as well as significant positive correlations, with a range of 0.34 to 0.57. These findings underscore the importance of continued monitoring of precipitation patterns in the region and the urgent need for measures to mitigate deforestation, which is critical for the hydrological cycle of the region. The highest deforestation rates and values were found between latitude 0 and − 12.5, where the arc of deforestation lies. Additionally, this study found a significant negative correlation between precipitation patterns and deforestation, indicating that deforestation led to a reduction in rainfall in the region. [ABSTRACT FROM AUTHOR]

The reactivation of ancient landslides, a persistent issue that hinders urban development and threatens human safety, is the central focus of this paper. Specifically, this paper offers a detailed description and analysis of an ancient landslide that reactivated on February 26, 2021, in Guoye town, Zhouqu County, Gansu Province, China, and has remained slow-moving since then. With a volume of approximately 21.2 million m3 of rock and soil masses involved, this landslide poses a significant risk to the safety of 1119 individuals from 282 households in Moli village and Guoye village. A combination of field investigations, remote sensing interpretation, in situ monitoring, and interferometric synthetic aperture radar (InSAR) was used to investigate the landslide characteristics and reactivation mechanisms. The results revealed that the presence of sliding-prone rock formations and tectonic movement were the primary contributors to the formation of the Moli landslide. Furthermore, the accumulation of bedrock blocks in the quarry and collapsed rock masses caused by fault activity at the rear parts, along with the additional load from buildings on the middle and rear sections, increased the sliding force. Simultaneously, the erosion of the Dashuiba River resulted in a reduction in the anti-slip force, thereby directly facilitating landslide reactivation. The notable increase in annual rainfall in recent years has played a crucial role in ancient landslide reactivation by elevating the groundwater level and softening the shear strength of the sliding zone soil. The findings of this study contribute to an enhanced understanding of the reactivation mechanisms in giant, ancient landslides situated within areas characterized by highly complex geological conditions. [ABSTRACT FROM AUTHOR]

This paper explores the operationalization of climate-related indicators in violent conflict research. The climate-conflict narrative gained traction in recent decades and climate change is often referred to as a 'threat multiplier' by both policy makers and scholars. Yet, the relationships between climate-related phenomena and violent conflict are complex and context-specific. However, limited attention has been given to the climatic indicators applied in climate-conflict research. This paper addresses that gap by analyzing 32 studies published from 2004 to 2020 on the operationalization of climatic indicators and their relationship with violent conflict. It first categorizes climate indicators operationalization into five clusters: natural disasters, basic climate variability, advanced climate variability, freshwater availability, and the ENSO. The study evaluates the climate indicators for each cluster and shows that at an aggregate level these clusters examine 68 different climate representations. When paired with their respective conflict types, it finds a total of 113 climate-conflict combinations. Most operationalizations represent various forms of climate-related phenomena and variability rather than climate change. Some indicators are advancements over time, for example moving from changes in average rainfall to standardized precipitation indices. However, other indicators refer to various natural processes, making it challenging to determine whether climatic variability impacts conflict. The paper then demonstrates a discrepancy between the pathways through which climate may affect violent conflict and the representation of these pathways in the selected climate indicators. It discusses how the selection and operationalization of climate indicators requires careful consideration, and the phenomena researched should be well-specified in research findings. [ABSTRACT FROM AUTHOR]

In his article Willems (Clim Chang 120(4):931-944, ) proposed a methodology to analyse extremes in rainfall series. When applying it to artificially generated, non-cyclic random variables we were able to detect cyclic behavior. Therefor we had a closer look on the methodology. Here we discuss our considerations, why this method generates cycles, depending on chosen subperiods and their coherence between detected cycle lengths. To verify these relationships some examples based on random data samples are given. [ABSTRACT FROM AUTHOR]

Accumulation landslides are prone to occur during the continuous infiltration of heavy rainfall, which seriously threatens the lives and property safety of local residents. In this paper, based on the Green-Ampt (GA) infiltration model, a new slope rainfall infiltration function is derived by combining the effect of air resistance and lateral seepage of saturated zone. Considering that when the soil layer continues to infiltrate after the saturation zone is formed, the air involvement cannot be discharged in time, which delays the infiltration process. Therefore, the influence of air resistance factor in soil pores is added. According to the infiltration characteristics of finite long slope, the lateral seepage of saturated zone is introduced, which makes up for the deficiency that GA model is only applicable to infinite long slope. Finally, based on the seepage characteristics of the previous analysis, the overall shear strength criterion is used to evaluate the stability of the slope. The results show that the safety factor decreases slowly with the increase of size and is inversely correlated with the slope angle and initial moisture content. The time of infiltration at the same depth increases with the increase of size and slope angle, and is inversely correlated with the initial moisture content, but is less affected by rainfall intensity. By comparing with the results of experimental data and other methods, the results of the proposed method are more consistent with the experimental results than other methods. [ABSTRACT FROM AUTHOR]

The failure of soil slopes is usually triggered by rainfall infiltration. Under the rainfall infiltrating, soils transform from unsaturated state to saturated state, the matric suction gradually disappears, pore water pressure increases, and the safety factor of soil slopes decreases. In this paper, a novel rigorous limit equilibrium method is proposed to investigate the stability of unsaturated–saturated soil slopes under rainfall infiltration. The force equilibrium conditions along three coordinate axes and the overall moment equilibrium conditions around three coordinate axes are all strictly satisfied, which are more coincident with the real force state of the slopes. In addition, the seepage fields of soil slopes are investigated by using SEEP/W software. Then, the seepage fields are introduced to the rigorous limit equilibrium method. The effects of rainfall intensity, rainfall patterns and rainfall duration on the stability of the unsaturated–saturated soil slopes are investigated using the proposed method. For the unsaturated soils, in which there exists the matric suction, and the matric suction can improve the stability of soil slopes. The safety factors of soil slops under the different rainfall intensity and rainfall patterns are investigated. Moreover, the stability of Fanlingqian slope under rainfall infiltration is analyzed to verify the correctness and precision of the proposed method. [ABSTRACT FROM AUTHOR]

Dynamical prediction of monsoon rainfall has been an important topic and a long-standing issue in both research and operational community. This paper provides a comprehensive evaluation of the subseasonal-to-seasonal (S2S) prediction skill of the East Asian summer monsoon (EASM) rainfall using the hindcast record from the Beijing Climate Center Climate System Model, BCC CSM1.1m, during the period 1983–2019. The model exhibits reasonable skills for predicting the EASM rainfall at all lead times with the skill dropping dramatically from the shortest lead time of about 2 weeks (LM0) to 1-month lead (LM1), and then fluctuating remarkably throughout 2-month to 12-month lead times. Over the EASM domain, the rapid decline of the S2S rainfall prediction skill from LM0 to LM1 is mainly caused by the inferior skills over Central China in July and over Northeast China in August. Composite analysis based on hindcast records suggest that these inferior skills are directly tied to the model's difficulties in capturing above-normal precipitation over eastern Central China and Northeast China in the respective months, which are further shown to be associated with anomalous weakening and meridional movement of the Northwestern Pacific subtropical high and the activity of large-scale teleconnection pattern hard to be predicted over northeastern Asia in summer, respectively. These findings inform the intrinsic limits of the S2S predictability of the EASM rainfall by a dynamical model, and strongly suggest that the level of confidence placed upon S2S forecasts should be stratified by large-scale circulation anomalies known to significantly affect the prediction skill, e.g., the subtropical high and high-latitude teleconnection patterns for summer monsoon rainfall prediction in this region. [ABSTRACT FROM AUTHOR]

The Himalayan Mountain range is prone to landslides and its influential factors vary from region to region. To identify these factors, it is essential to have comprehensive information about the study area. This study aims to assess Landslide Susceptibility Mapping in parts of the Aglar watershed in the Lesser Himalayas using the frequency ratio method in the GIS environment. An inventory map in the first place of landslide events was prepared using satellite data and field survey studies. In this study, pertinently, sixteen conditioning factors were considered as slope, aspect, profile curvature, plan curvature, elevation, relative relief, geology, distance to stream, distance to road, normalized difference vegetation index, stream power index, topographic wetness index, rainfall, distance to lineaments, geomorphology, and land use land cover based on the local topography and climate. To gain a comprehensive understanding of the study area, subsequently, the frequency ratio of each factor was analyzed. This determined the correlation between landslide classes and each class of conditioning factors. A thorough analysis was conducted to select the most significant factors using the higher and lower prediction ratio values. This study was eventually conducted by comparing three susceptibility maps that were created based on prediction ratio values. To confirm the comparison, the ROC–AUC curve was utilized. The accuracy of the results was evaluated through a prediction rate and success rate curve, resulting in 78.5 and 80.7%, respectively, demonstrating a commendable performance. In this paper, the significance of different conditioning factors is evident in the increase of the very high susceptible area from 2.44 to 5.99% and eventually to 8.44%. [ABSTRACT FROM AUTHOR]

Landslides are frequent hillslope events that may present significant risks to humans and infrastructure. Researchers have made ongoing efforts to assess the potential danger associated with landslides, intending to ascertain the location, frequency, and magnitude of these events in a given area. This study is meant to supplement the previous study (Part I), which explored empirical and physically based causative thresholds. In this paper (Part II), a systematic review is used to conduct an in-depth study of existing research on prediction models. Deterministic physical approaches were investigated for local-scale landslides. Next, national-scale landslide susceptibility models are discussed, including qualitative and quantitative models. Consequently, key findings about rainfall-induced landslides are reviewed. The strategy selection is generally governed by data and input factors from a macroscopic perspective, while the better prediction model is defined by dataset quality and analysis model performance from a microscopic perspective. Physically based causative thresholds can be used with limited geotechnical or hydrological data; otherwise, numerical analysis provides optimal accuracy. Among all statistical models, the hybrid artificial intelligence model achieved the best accuracy. Finally, current challenges have concentrated on integrating AI and physical models to obtain high accuracy with little data, prompting research suggestions. Advanced constitutive models for real-time situations are lacking. Dynamic and spatiotemporal susceptibility maps are also used, although their subjectivity needs further research. This study analyses how to choose the best model and determine its key traits. This research provides valuable insights for scholars and practitioners seeking innovative approaches to lessen the severity of landslides. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of deformation and failure on loess slopes, predominantly triggered by rainfall, by evaluating the efficacy of various slope protective measures. Recognizing the escalating preference for ecological protection techniques, this paper focuses on identifying optimal strategies that align with local rainfall patterns. A comprehensive analysis of loess slopes under diverse rainfall conditions was conducted, encompassing scenarios with prime soil, stabilized soil, and no protection. Utilizing the finite element software ABAQUS, a sophisticated rainfall infiltration slope model was developed and validated against experimental data. The investigation reveals that under low rainfall intensities, the choice of protective material—whether stabilized or prime soil—exerts limited influence on slope pore pressure. In contrast, at higher rainfall intensities, the impact of these protective materials on slope stability becomes significantly more pronounced. [ABSTRACT FROM AUTHOR]

The Indian economy and food grain production are intricately tied to the spatial distribution and the amount of rainfall during the summer monsoon period (June to September). Numerous studies have delved into modeling methods for forecasting Indian summer monsoon rainfall. This paper utilizes monthly rainfall data spanning 72 years (1950–2021) to analyze, model, and predict the monsoon over Northwest India using Dynamic Linear Modeling (DLM). In view of this, the well-accepted dynamic linear model with significant components is selected from the parsimonious models of DLM based on Bayesian Information Criteria (BIC), significant tests, and statistical fit. The dynamic linear model comprises a significant linear time trend component, a deterministic trigonometric seasonal component, a deterministic cycle component, and a stochastic irregular component has been selected. The validation of the selected model passed through the normal and correlation diagnostics of model residuals. The analysis reveals a subtle decreasing trend in monsoon rainfall, showcasing consistent behaviour in deterministic seasonality, and highlighting the deterministic nature of cyclical component with a period of approximately 2.5 years, alongside irregular variations. Utilizing this model, future monsoon rainfall over Northwest India has been forecasted for the years 2022–2027, providing a 95% confidence interval. [ABSTRACT FROM AUTHOR]

Removing adverse weather conditions from images, such as haze, fog, rain, and snowfall, is a significant issue in several scenarios. Many techniques have been described in the literature that only involve removing specific types of adverse weather degradation. A convolutional neural network (CNN)-based all-in-one dehaze network was recently presented to remove all adverse weather conditions. But, this method contains many variables because it employs many encoder blocks for each adverse weather removal operation, and its efficiency still has to be improved. This paper concentrates on creating an effective solution to remove adverse weather from the foggy and rainy real-time images. The proposed research presented a single encoder–decoder-based transformer fusion with a multi-head attention module for real-time image dehazing. Also, the proposed method introduces a separated patches module fusion with a deep residual attention module to improve the different weather degradation problems and minimize the feature loss of degraded pixels in the transformer encoder block. The proposed method is validated and tested on real-time foggy and rainy images. The qualitative and quantitative evaluation demonstrates that the proposed method is more efficient than other methods. [ABSTRACT FROM AUTHOR]

Under global warming and climate change (CC), the vulnerable piece of land is India's Eastern-Himalayan-Region (EHR), where millions' livelihood activities are at risk. Quantifying the degree of CC-induced vulnerability over the highly climate-sensitive mountain zone is challenging due to data inadequacy. This paper attempted to assess the CC perception of kiwifruit growers under EHR and the impact of CC on its cultivation. Perceived and scientific data were used for accurate decision-support information. The climatic variables of two locations were analyzed for trend using Mann–Kendall's test. Results suggested that the rainfall trend was found non-significant for both locations, but there was a negative trend during winter for Pasighat and during all seasons for Itanagar. Maximum temperature exhibited an increasing trend for annual, and minimum temperature showed an increasing trend across seasons for the Pasighat region. For the Itanagar region, the maximum temperature was found to have a significantly increasing trend in all seasons, while the minimum temperature showed an increasing trend during winter and annual seasons. Morning relative humidity showed an increasing trend for annual data for Pasighat, while annual evening relative humidity was significantly increased for Itanagar. The study shows that the indigenous farming communities were following shifting cultivation and diversifying their traditional agriculture to kiwifruit cultivation as an adaptation strategy. The community had a clear perception of CC trends and an inadequate perception of the impact on agriculture, therefore, engaging indigenous communities and other stakeholders to postulate a comprehensive adaptation and mitigation strategy for CC. [ABSTRACT FROM AUTHOR]

The real-time and accurate monitoring of severe weather is the key to reducing traffic accidents on highways. Currently, rainy day monitoring based on video images focuses on removing the impact of rain. This article aims to build a monitoring model for rainy days and rainfall intensity to achieve precise monitoring of rainy days on highways. This paper introduces an algorithm that combines the frequency domain and spatial domain, thresholding, and morphology. It incorporates high-pass filtering, full-domain value segmentation, the OTSU method (the maximum inter-class difference method), mask processing, and morphological opening for denoising. The algorithm is designed to build the rain coefficient model Prain coefficient and determine the intensity of rainfall based on the value of Prain coefficient. To validate the model, data from sunny, cloudy, and rainy days in different sections and time periods of the Jinan Bypass G2001 line were used. The aim is to raise awareness about driving safety on highways. The main findings are: the rain coefficient model Prain coefficient can accurately identify cloudy and rainy days and assess the intensity of rainfall. This method is not only suitable for highways but also for ordinary road sections. The model's accuracy has been verified, and the algorithm in this study has the highest accuracy. This research is crucial for road traffic safety, particularly during bad weather such as rain. [ABSTRACT FROM AUTHOR]

Wind and rain are considered main drivers for mobilization and transport of macroplastics on land, yet there is a lack of empirical data that quantifies this. We present lab experiment results on land-based macroplastic mobilization and transport. We placed four types of macroplastics on terrains with varying surface roughness and slope angles, and exposed them to changing wind speeds and rain intensities. In general, we find that the mobilization probability and transport velocity of macroplastics strongly depend on the combination of the terrain characteristics and material properties. At Beaufort 3, 100% of the plastic bags were mobilized, whereas for the other plastic types less than 50% were mobilized. We found 1.4 (grass) to 5 times (paved surface) higher mobilization probabilities on land than assumed by existing plastic transport models. Macroplastic transport velocities were positively correlated with wind speed, but not with rain intensity. This suggests that macroplastics are not transported on land by rain unless surface runoff develops that can bring the macroplastics afloat. Macroplastic transport velocities were, driven by wind, 1.9 and, driven by rain, 4.9 times faster on paved surfaces than on grass. This study enhances our understanding of land-based macroplastic transport and provides an empirical basis for models. [ABSTRACT FROM AUTHOR]

Based on the big data GIS system, this paper first analyzes the methods and research results of the visualization of soil and water conservation level data at home and abroad. And for the proposed visual protection plan for land and water resources, a relatively excellent data architecture and the latest persistence plan have been designed. Based on this point, this paper investigates the cause of rainfall erosion and fluctuation in the mountain environment of S city and studies and determines the important technical points of water and soil conservation data images. Based on the results of this kind of research, the article further analyzes the temporal and spatial characteristics of the data and, based on the RUSLE model, conducts a directional consideration of the distribution characteristics of environmental rainfall erosion in mountainous environments: that is, comprehensively examines the impact of rainfall erosion in mountainous environments from both natural and social factors. The main factor of the pattern. Using multi-linear regression equations, combined with environmental variables and bioclimatic variables, the spatial distribution of land in Q mountainous areas is predicted under the background of global change. Finally, the article conducted an in-depth study on the people flow planning of the Leshan Giant Buddha Scenic Spot, introduced the construction of the Leshan Giant Buddha Scenic Spot Project Network, and adopted comprehensive technical solutions such as "portrait photography reconnaissance surveillance camera + online photography AI portrait recognition technology" to build the number of passengers identify and plan the design of the system and elaborate on the system's functions, such as visitor statistics, hotspot analysis, travel route analysis, video tracking, and data display. The article applies the research results of rainfall erosion in mountain environment based on big data GIS to the research on the people flow planning of Leshan Giant Buddha, which promotes the rapid development of scenic spots. [ABSTRACT FROM AUTHOR]

This paper discusses the role of plants in the prevention of shallow landslides induced by rain (soil slips); these phenomena, related to "hydrogeological instability," are among the most feared because their evolutionary processes can cause huge damages and losses of human lives when interacting with anthropized areas and infrastructures. The paper first highlights how the plants interact with the soil; then introduces the G-SLIP (Green – Shallow Landslides Instability Prediction) model, i.e., the simplified physically-based SLIP model, modified to predict soil slips at punctual and large scale taking into account the vegetation effects. The G-SLIP model is thus applied to a case study of the Parma Apennines (Northern Italy) by using the G-XSLIP platform. In this area, during the intense events of rain between the 4th and 5th of April 2013, numerous landslides occurred, provoking huge damages to structures and infrastructures, and consequent economic losses. The stability analyses carried out with G-XSLIP demonstrate that the presence of vegetation in the study area led to a significant reduction in the triggering of shallow landslides. Finally, an attempt at soil slip mitigation through naturalistic techniques (planting of specific vegetation) is presented. [ABSTRACT FROM AUTHOR]

In computer vision, transformers have shown increasing effectiveness for high-level vision tasks. To further cope with low-level vision tasks, we propose a general framework, namely local reasoning back-projection transformer (LRB-T) for removing multiple types of bad weather (rain, haze, rain fog, raindrop, et al.) affecting images. Specifically, this paper first integrates the back-projection mechanism into the transformer architecture, where iterative up- and down-projection modules effectively feed and correct the feature reconstruction errors for spatial information preservation, and reduce computational costs since nearly half of the feature maps participate in the back-projection using half resolution. Besides, the proposed adaptive local reasoning block captures important neighborhood information through multiple local reasoning schemes. It can aggregate adjacent tokens to produce spatial-specific involution kernels, attention weights and dynamic positional encodings for local structure updating, and provide implicit spatial feature transform to achieve spatial-wise feature modulation. A pyramid scale guidance module is also established to enable arbitrary size generation consistent with the input, and generate scale-dependent trainable parameters to enhance skip connections. Extensive experiments on four types of well-known bad weather datasets show that the proposed LRB-T improves the image deraining, dehazing, de-rain fog and de-raindrop performance in terms of PSNR and SSIM effectively and outperforms state-of-the-art task-specific bad weather removal methods. [ABSTRACT FROM AUTHOR]

Dengue is one of the world's most rapidly spreading mosquito-borne viral diseases. As it is found mostly in urban and semi-urban areas, urbanization and associated human activities that affect the environment and larval habitats could become risk factors (e.g., lane width, conditions of street ditches) for the spread of dengue. However, there are currently no systematic studies of micro-scale urbanization-based risk factors for the spread of dengue epidemics. We describe the study area, two micro-scale environmental risk factors associated with urbanization, and meteorological data. Since the observations involve spatial and temporal correlations, we also use some statistical methods for the analysis of spatial and spatial-temporal data for the relationship between urbanization and dengue. In this study, we analyzed data from Kaohsiung, a densely populated city in southern Taiwan, and found a positive correlation between environmental risk factors associated with urbanization (ditches positive for mosquito larvae and closely packed streets termed "dengue lanes") and clustering effects in dengue cases. The statistical analysis also revealed that the occurrence of positive ditches was significantly associated with that of dengue lanes in the study area. The relationship between climate variables and positive ditches was also analyzed in this paper, indicating a relationship between dengue and both rainfall and temperature, with temperature having a greater effect. Overall, this work is immediately relevant and applicable for policymakers in government, who will need to reduce these favorable habitats for vector-born disease spreaders and implement regulations for new urban constructions to thus reduce dengue spread in future outbreaks. [ABSTRACT FROM AUTHOR]

Better understanding on the phases of Indian summer monsoon i.e., onset, progress and withdrawal helps in the decision making and proper management. In this paper, date of onset of monsoon (DOM) and date of withdrawal of monsoon (DWM) is estimated using the long-term daily rainfall data observed by the India Meteorological Department (IMD) and cumulative rainfall anomaly concept, as the onset/withdrawal represents a signature of rainfall transition. The results are promising with a better match between the estimated DOM and IMD announced DOM over Kerala. The length of monsoon season variability is also analysed. The concept of onset at grid point (OGP) in India is also evaluated and progress of monsoon is monitored by considering the latitudinal variation, persistent & strength of rainfall in the continental India. The zonal classification of the spread of monsoon in terms of the intensity and time (duration) is presented. Zone wise pentad analysis of onset clearly signifies the progress of monsoon and its dependency on various physical factors like SST, OLR, TTG, ITCZ, Energy budget and wind circulation. The impact of ENSO on the OGP is brought out, which clearly shows that DOM, DWM and progress is almost influenced by the largescale processes. The intensity–duration–frequency analysis quantified the actual ISM length, seasonal rainfall and rainy days at all locations in India. This study will help modellers in better understanding of onset and progress of monsoon dynamics in India and can be integrated with NWP for the accurate and advance prediction of DOM and the progress. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm for restoring the precipitation intensity over the ocean surface according to data from the MTVZA-GYa Russian microwave sounder is presented. The developed algorithm is based on the ALG'85 regression model in which the precipitation intensity is estimated using the scattering index on a high-frequency radiometric channel (~90 GHz). In this work, the scattering index is simulated based on MTVZA-GYa data and compared with GPM IMERG reanalysis data. To restore the precipitation intensity, it is proposed to use a fourth-degree polynomial. The quantitative estimates show that the RMS spread reaches 50%, and the correlation coefficient does not exceed 0.75. The qualitative comparison indicates a significant difference between the restored rain rate and the GPM IMERG data, as well as the presence of a shift of the precipitation area. As a result of the analysis, it is concluded that the incorrect convergence of the beams of the radiation patterns for different frequency channels of the MTVZA-GYa device might be one of the causes. [ABSTRACT FROM AUTHOR]

This research paper presents the development of a nonlinear Muskingum model which achieves precise flood routing through river reaches while considering lateral inflow conditions. Fourteen pairs of flood hydrograph found at two specific United States Geological Survey (USGS) stations located along the Red River of the North, namely Grand Forks and Drayton, are used for the calibrations and validations of the Muskingum model. To enhance the accuracy of the procedure, a reach is divided into multiple sub-reaches, and the Muskingum model calculations are performed individually for each interval using the distributed Muskingum method. Notably, the model development process incorporates the use of the Salp Swarm algorithm. The obtained results demonstrate the effectiveness of the developed nonlinear Muskingum model in accurately routing floods through the very gentle river with a bed slope of (0.0002–0.0003). The events were categorized into three groups based on their dominant drivers: Group A (Snowmelt-driven floods), Group B (Rain-on-snow-induced floods), and Group C (Mixed floods influenced by both snowmelt and rainfall). For the sub-reaches in Group A, single sub-reach (NR = 1), the Performance Evaluation Criteria (PEC) yielded the highest value for SSE, amounting to 404.9 × 106. In Group B, when NR = 2, PEC results the highest value were SSE = 730.2 × 106. The number of sub-reaches in a model has a significant influence on parameter estimates and model performance, as demonstrated by the analysis of hydrologic parameters and performance evaluation criteria. Optimal performance varied across case studies, emphasizing the importance of selecting the appropriate number of sub-reaches for peak discharge predictions. [ABSTRACT FROM AUTHOR]

Cloudburst is a transient phenomenon, where intense rainfall occurs in a localised area in a very short period, resulting in catastrophic effects like landslides and floods. The Western Himalayas region is found to be more vulnerable to cloudburst events. In this paper, the robustness of the alerts provided by the nowcasting model NETRA (Nowcasting of Extreme Orographic Rain) has been evaluated for the Uttarakhand Region. These alerts are routinely provided over Indian Space Research Organisation (ISRO) web portal www.mosdac.gov.in. It was found that the alerts generated from NETRA compare very well with actual events with district-level accuracy. The results also indicate that May month is more vulnerable to cloudburst events. From the perspective of location, the districts of Chamoli, Rudra Prayag, and Uttarkashi are found to be the most vulnerable. Among all districts, NETRA gave the best location accuracy for the district of Uttarkashi. Moreover, Apriori-based analysis is used to find the co-occurrence patterns in a cloudburst. The co-occurrence analysis brings out the spatial linkage between districts concerning cloudburst. We find out that Pauri and Uttarkashi exhibit the maximum co-occurrence probability. [ABSTRACT FROM AUTHOR]

The present research study aims to analyze flood frequency to relate flood magnitudes with corresponding return periods. These estimates are crucial for the development of flood-preventive hydrological infrastructures and flood plain zoning, etc. Basin-wide high intensity of rainfall and high discharge from the upper riparian region during monsoon months creates flooding in the middle and lower reaches of the Ghagra River basin. Therefore, the flood estimates for three gauging sites situated on the mainstream of the Ghagra River viz., Elginbridge, Ayodhya, and Turtipar, have been comprehended using the Gumbel distribution (Extreme Value Type I) method and the Log-Pearson Type III distribution method. Flood estimates are calculated for 1.01, 1.05, 1.11, 1.25, 2, 5, 10, 20, 50, 100, 200, and 500 years return periods considering flood time series of 50 years to reduce estimation uncertainty. The findings of flood frequency analysis (FFA) revealed that the probability of occurrence of the flood is more than 80% at all three sites because Ghagra River can carry around 7800 to 9000 m3/s of water discharge without posing a high risk of levee break. The upper and lower limits of discharge carrying capacity depend on the river's desiltation process. Hence, the flood occurs almost every year in the basin; its only variation is its severity. The rainfall-runoff relationship is estimated by integrating a simple linear model and for rainfall trend analysis Mann-Kendall is applied. Linear regression analysis-based rainfall-runoff relationship outcomes revealed a significant relationship with a positive correlation coefficient i.e., 0.2722 for Elginbridge, 0.39624 for Ayodhya, and 0.4844 for Turtipar gauging site in the monsoon season but other factors like a high amount of water discharge from dams in upper riparian regions, etc. are also responsible for flooding in the middle and lower reaches. The Mann-Kendall trend analysis shows a decrease in annual average rainfall and rainfall in the monsoon season. Discharge variability indicates the direct relationship between flood fury and changes in climatic patterns in recent decades. This paper identifies that future research is needed to better inform the policy planners who strive to design sustainable infrastructure. [ABSTRACT FROM AUTHOR]

Quantifying the extent of change in climatic and hydrological variables in the past and the future is essential for climate change-resilient development, especially in the climate change sensitive region of Nepal. This paper analyzed future climatic trends and extremes, historical hydrological extremes and their linkage with historical precipitation extremes, and discussed the impacts of climate change on various sectors in the less-studied Small and Medium-sized Eastern River Basins (SMERB) of Nepal. Applying an ensemble of five Coupled Model Intercomparison Project phase 6 (CMIP6) global climate models (GCMs), we evaluated 10 precipitation and 13 temperature extreme indices using Climpact2 for the historical (1979–2020), near-future (2021–2045), mid-future (2046–2070) and far-future (2071–2100) under two Shared Socioeconomic Pathway (SSP), SSP245 and SSP585. Hydrological extreme indices were assessed using Indicators of Hydrologic Alteration tool. Projected climate revealed significant increase (9–73%) in annual and seasonal rainfall except winter; frequent intense rainfall extremes but prolonged dry spells; significant increasing minimum and maximum temperature trends (0.4–5 °C); rise in extreme heat events; increasing minimum and maximum discharge extreme trends in most hydrological stations; as well as strong association between maximum 1 day precipitation (Rx1day) and 1 day max flow for all stations. Growing dry periods but intense rainfall in few wet days, coupled with warming pattern all-over SMERB with frequent extreme events indicate high risk for future climate-related disasters. The harsher climate will potentially have damaging implications, especially in climate-induced disasters, food security, and water and sanitation infrastructure. [ABSTRACT FROM AUTHOR]

The construction of expressways in soil and their interaction with railway tunnelling blast excavation faces risks due to limitations on safety vibration velocity regulation on soil landslides. This paper reports on a potential soil landslide coupled with rainfall and blast in an expressway area underneath a railway tunnel in Guangzhou, China. Field monitoring was conducted to obtain the landslide’s geometric features, magnitude, crack distribution, and potential sliding surface. Using the unbalanced force transfer coefficient method, a mechanical model with normal and rainfall conditions was employed to analyse slope stability. Numerical modelling was employed to study the dynamic features of soil slope induced by the blast effect. The triggering factors on topography, tectonics, lithology, rainfall, and blast effects were discussed in detail. A slope mitigation method was proposed, and 6-month surface deformation monitoring was conducted to examine the mitigation effect. The novel results show that the slope was unstable under coupled effect on rainfall and blast conditions, with a potential push-type soil landslide from the top-level slide to the second level. The safety regulation velocity of 3 cm/s for rock slope is too large for safety control on soil slope. [ABSTRACT FROM AUTHOR]

This paper investigates the process behind climate model biases, especially in rainfall, during the southwest (June to August; JJA) and northeast monsoon (December to February; DJF) seasons over the Philippines using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-SEA). Biases in the surface and near-surface climate are assessed over land in eastern (EastPH) and western (WestPH) Philippines using observation and reanalysis datasets. Results show that models with well-represented sea surface temperature (SST) provided better added values after downscaling. Although the CMIP5-driven models tend to overestimate both surface energy and water partitioning, simulations with well-represented SST have better surface energy partitions. In contrast to the reference simulation driven by ERA-Interim reanalysis, the CMIP5-driven simulations tend to underestimate (overestimate) moisture flux convergence during the DJF (JJA) season, which might have contributed to the overall underestimation (overestimation) of rainfall in this season. Further investigation along a vertical transect shows the differences in governing mechanisms for each season. During DJF (JJA) season, the simulation with underestimated (overestimated) SST, near-surface air temperature, and mixing ratio results in underestimated (overestimated) rainfall over EastPH (WestPH). In addition, the CMIP5-driven models were found to overestimate low-level winds, which may have induced the deep convection regardless of season, suggesting the role of low-level winds in the convection scheme used in RegCM4 aside from topography and land cover. [ABSTRACT FROM AUTHOR]

Stratospheric Aerosol Geoengineering (SAG) is one of the solar geoengineering approaches that have been proposed to offset some of the impacts of anthropogenic climate change. Past studies have shown that SAG may have adverse impacts on the global hydrological cycle. Using a climate model, we quantify the sensitivity of the tropical monsoon precipitation to the meridional distribution of volcanic sulfate aerosols prescribed in the stratosphere in terms of the changes in aerosol optical depth (AOD). In our experiments, large changes in summer monsoon precipitation in the tropical monsoon regions are simulated, especially over the Indian region, in association with meridional shifts in the location of the intertropical convergence zone (ITCZ) caused by changes in interhemispheric AOD differences. Based on our simulations, we estimate a sensitivity of − 1.8° ± 0.0° meridional shift in global mean ITCZ and a 6.9 ± 0.4% reduction in northern hemisphere (NH) monsoon index (NHMI; summer monsoon precipitation over NH monsoon regions) per 0.1 interhemispheric AOD difference (NH minus southern hemisphere). We also quantify this sensitivity in terms of interhemispheric differences in effective radiative forcing and interhemispheric temperature differences: 3.5 ± 0.3% change in NHMI per unit (Wm−2) interhemispheric radiative forcing difference and 5.9 ± 0.4% change per unit (°C) interhemispheric temperature difference. Similar sensitivity estimates are also made for the Indian monsoon precipitation. The establishment of the relationship between interhemispheric AOD (or radiative forcing) differences and ITCZ shift as discussed in this paper will further facilitate and simplify our understanding of the effects of SAG on tropical monsoon rainfall. [ABSTRACT FROM AUTHOR]

Variability in the East Asian summer monsoon (EASM) brings the risk of heavy flooding or drought to the Yangtze River basin, with potentially devastating impacts. Early forecasts of the likelihood of enhanced or reduced monsoon rainfall can enable better management of water and hydropower resources by decision-makers, supporting livelihoods and major economic and population centres across eastern China. This paper demonstrates that the EASM is predictable in a dynamical forecast model from the preceding November, and that this allows skilful forecasts of summer mean rainfall in the Yangtze River basin at a lead time of six months. The skill for May–June–July rainfall is of a similar magnitude to seasonal forecasts initialised in spring, although the skill in June–July–August is much weaker and not consistently significant. However, there is some evidence for enhanced skill following El Niño events. The potential for decadal-scale variability in forecast skill is also examined, although we find no evidence for significant variation. [ABSTRACT FROM AUTHOR]

Large parts of southern Africa are influenced by extra-tropical weather systems for most of the year. During late summer (December–March), the circulation over the area becomes distinctly tropical. This paper introduces the Africáne, a synoptic scale tropical low-pressure system which has been shown to cause widespread and heavy rainfall over the southern sub-continent of Africa. The frequency of occurrence of Africánes, their contribution to rainfall and interannual variability are discussed in this paper. Africánes occur most frequently at the longitude of the Caprivi area with a second peak in frequency at around 32.5° E. They mostly occur over Namibia, Botswana and Zimbabwe and only infrequently infiltrate as far south as the borders of South Africa. However, when they do occur over South Africa, they cause widespread heavy rainfall and floods. Rainfall is mostly confined to the eastern flank of Africánes and between 20 and 35% of the annual rainfall over southern Africa in late summer can be attributed to these systems. There are two main synoptic regimes associated with Africánes: a westerly wave or tropical-temperature trough combines with the Africáne to pull rainfall southwards into South Africa. The second, is a mid-level subtropical high pressure, located south of the Africáne, which causes the rainfall to be confined to the north. The interannual variability of Africánes are closely linked to rainfall over southern Africa, such that an above normal number of Africánes in a season causes above normal rainfall over southern Africa. The number of Africánes that form per year is linked to El Niño–Southern Oscillation (ENSO). It is recommended that the predictability of Africánes on different time scales should be investigated. [ABSTRACT FROM AUTHOR]

West African rainfall is an important part of the global climate system that influences the Atlantic thermohaline circulation, hurricane activities, and dust transport. The water cycle is linked to the monsoon and its interannual to decadal variations. Over the past decades, West Africa has seen major climate variability with extended droughts that had negative effects during the 1970s and 1980s. Indeed, when it is too scarce, rain causes shortages, reduces agricultural yields, and leads to migrations. On the other hand, when it is too abundant, it causes catastrophic floods and poses threats to populations, water resources as well as natural and farmlands. In this paper, drought is considered as part of climate-related hazards and one of the main hydrometeorological extreme events occurring in West Africa. The exposure to drought has made the region more vulnerable. Thus, two sites, namely the Niger river basin and the Bandama watershed (Côte d'Ivoire), are studied in this paper to review and analyze the weather and climate extreme events that affect vast areas of West Africa. Grounded in remote sensing, statistical, and socio-anthropological approaches, this work first reviews drought as observed from space; then assesses rainfall and evapotranspiration between 1970 and 2013 as indicators of risks of water resources scarcity in the hydro-system of the Bandama river in Côte d'Ivoire. The results reveal that the West African region is highly vulnerable to this hydrometeorological extreme event with heavy impacts on people and the economy due to a large dependency on rainfed agriculture. Thus, planning and management of drought require a change of paradigm. In addition, more comprehensive studies on hydrometeorological extreme events are necessary and policies must be better designed to significantly improve the tackling of droughts with better mitigation strategies. [ABSTRACT FROM AUTHOR]

Understanding the future patterns of precipitation behaviour in unique geographical areas, largely determined by their orography and local scale, can help lay the foundations for a new precipitation model for the design of the city's main urban drainage infrastructures (intensity-duration-frequency curves, mathematical functions that relate precipitation intensity to duration and frequency of occurrence, hereafter IDF, for the short-, medium- and long-term future). This will definitely contribute to the improvement of the city's resilience to the effects of climate change. In this paper, the projections of a subset of climate change models from both the sixth phase of the Coupled Model Intercomparison Project (CMIP6; with a total of 5 simulations) and Euro-CORDEX (for a set of 51 simulations) have been adjusted to the municipality of Alicante (in the southeast of Spain), using the Climadjust tool (climadjust.com). These projections contain different climatic variables. The rainfall variable has been used to derive a new framework of boundary conditions to help design more resilient infrastructure for torrential rainfall events and urban flooding. The projections corresponding to three climate change scenarios (CMIP6: SSP1-2.6, SSP2-4.5, SSP5-8.5; and Euro-CORDEX: RCP2.6, RCP4.5, RCP8.5) are considered with daily resolution and, by applying statistical techniques of temporal disaggregation (by means of a cascade model), hourly (and sub-hourly, reaching 30-min resolution) disaggregation. The results at hourly and 30-min resolutions are used to construct IDF curves of future climate, grouped into short-term (years 2015 to 2040), medium-term (years 2041 to 2070) and long-term (years 2071 to 2100) sub-scenarios. The selected future climate IDFs for an adverse climate change scenario (SSP2-4.5 and SSP5-8.5) show increases in rainfall intensities, higher the shorter the rainfall duration, for return periods greater than or equal to 25 years, whereas for return periods under 25 years the current IDFs can be representative of future scenarios. Current calculations and future projection of the torrentiality index for severe climate change scenarios, as well as the climate change factors, show an increase in the frequency and magnitude of the heaviest rainfall. This fact corroborates the hypotheses of greater general torrentiality in future rainfall in this specific area of the Spanish Mediterranean coast. [ABSTRACT FROM AUTHOR]

The El Niño Southern Oscillation (ENSO) is a major influence on interannual variability of rainfall in stations in the tropical southwest Pacific. Predictions of seasonal rainfall, especially a season or two ahead, are of great value to these countries. This paper therefore examines the correlations over ~ 60 years between seasonal rainfall and 8 ENSO indicators at 16 island stations, allowing for lead times. The results show the influence on rainfall of the position and movement of the South Pacific Convergence Zone (SPCZ) during ENSO events, and that the Southern Oscillation Index (SOI), the sea surface temperature anomaly in the central Pacific (Niño 3.4), and the warm water volume in the eastern Pacific (WWV1) have longer lead times compared to most other ENSO indicators. These indicators can therefore be used with confidence in SCOPIC, a widely used statistical tool for prediction of seasonal rainfall. (As global climate models generally have systematic errors in their depiction of the SPCZ, they cannot yet be used directly to reliably predict seasonal rainfall in this region.) For several sites near the SPCZ, we find that a moderately good forecast of rainfall in both spring and summer can be made from indicators measured in June–July (i.e., 3–6 months in advance.) [ABSTRACT FROM AUTHOR]

As one representative object of bad weather, rain streaks can have a bad influence on image capture. Worse still, computer vision tasks can be badly influenced when working in rainy scenes. Therefore, it makes sense to conduct research on rain removal. This paper introduces a new deraining network (DDRNet). Specifically, rain streaks contain both frequency and spatial information, so we propose a rain removal module (RRM) consisting of a rain channel attention module (RCAM) and a rain spatial attention module (RSAM) for extracting rain information. Furthermore, we design a detail-recovery module (DRM) to extract the background feature so that the original background image details deleted by mistake during the deraining process can be made up by it. Moreover, a two-branch aggregation (TBA) mechanism is adopted to promote the process of information flow that effectively enhances the execution of our DDRNet. The evaluation of our DDRNet on various benchmark datasets has shown the superiority of our algorithm. In addition, a range of low-level experiments, such as object detection and semantic segmentation, provide further evidence of the effectiveness of the proposed DDRNet. [ABSTRACT FROM AUTHOR]

Accurate early warning systems for landslides are a reliable risk-reduction strategy that may significantly reduce fatalities and economic losses. Several machine learning methods have been examined for this purpose, underlying deep learning (DL) models' remarkable prediction capabilities. The long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms are the sole DL model studied in the extant comparisons. However, several other DL algorithms are suitable for time series forecasting tasks. In this paper, we assess, compare, and describe seven DL methods for forecasting future landslide displacement: multi-layer perception (MLP), LSTM, GRU, 1D convolutional neural network (1D CNN), 2xLSTM, bidirectional LSTM (bi-LSTM), and an architecture composed of 1D CNN and LSTM (Conv-LSTM). The investigation focuses on four landslides with different geographic locations, geological settings, time step dimensions, and measurement instruments. Two landslides are located in an artificial reservoir context, while the displacement of the other two is influenced just by rainfall. The results reveal that the MLP, GRU, and LSTM models can make reliable predictions in all four scenarios, while the Conv-LSTM model outperforms the others in the Baishuihe landslide, where the landslide is highly seasonal. No evident performance differences were found for landslides inside artificial reservoirs rather than outside. Furthermore, the research shows that MLP is better adapted to forecast the highest displacement peaks, while LSTM and GRU are better suited to model lower displacement peaks. We believe the findings of this research will serve as a precious aid when implementing a DL-based landslide early warning system (LEWS). [ABSTRACT FROM AUTHOR]

In the paper, the frequency and spatial distribution of atmospheric and hydrological droughts over the territory of Belarus in 1945–2020 is assessed. Aridity is evaluated using the standardized precipitation and streamflow indices (SPI and SSFI). Trends in monthly total precipitation are determined, the features of the streamflow formation during the open-channel period are revealed. The frequency of anticyclones that determines hydrometeorological conditions on the territory of Belarus is calculated. The statistically significant increase in the number of the days with anticyclonic weather over the country during the warm season is found. The increasing frequency of dry conditions in summer months against the background of a rise in the precipitation intensity is highlighted. A simultaneous increase in the frequency of very-low-flow periods on rivers and an increase in the height of warm rainfall floods in some river basins are revealed. [ABSTRACT FROM AUTHOR]

Rural populations are particularly exposed to increasing weather variability, notably through agriculture. In this paper, we exploit longitudinal data for Turkish provinces from 2008 to 2018 together with precipitation records over more than 30 years to quantify how variability in a standardized precipitation index (SPI) affects out-migration as an adaptation mechanism. Doing so, we document the role of three potential causal channels: per capita income, agricultural output, and local conflicts. Our results show that negative SPI shocks (droughts) are associated with higher out-migration in rural provinces. A mediated-moderator approach further suggests that changes in per capita income account for more than one quarter of the direct effect of droughts on out-migration, whereas agricultural output is only relevant for provinces in the upper quartile of crop production. Finally, we find evidence that local conflict fatalities increase with drought and trigger out-migration, although this channel is distinct from the direct effect of SPI shocks on out-migration. [ABSTRACT FROM AUTHOR]

ERA5-Land service has been released recently as an integral and operational component of Copernicus Climate Change Service. Within its set of climatological and atmospheric parameters, it provides soil moisture estimates at different soil depths, represeting an important tool for retrieving saturation degree for predicting natural hazards as shallow landslides. This paper represents an innovative attempt aiming to exploit the use of saturation degree derived from ERA5-Land soil moisture products in a data-driven model to predict the daily probability of occurence of shallow landslides. The study was carried out by investigating a multi-temporal inventory of shallow landslides occurred in Oltrepò Pavese (northern Italy). The achieved results follow: (i) ERA5-Land-derived saturation degree reconstructs well field trends measured in the study area until 1 m from ground; (ii) in agreement with the typical sliding surfaces depth, saturation degree values obtained since ERA5-Land 28–100 cm layer represent a significant predictor for the estimation of temporal probability of occurrence of shallow landslides, able especially to reduce overestimation of triggering events; (iii) saturation degree estimated by ERA5-Land 28–100 cm layer allows to detect soil hydrological conditions leading to triggering in the study area, represented by saturation degree in this layer close to complete saturation. Even if other works of research are required in different geological and geomorphological settings, this study demonstrates that ERA5-Land-derived saturation degree could be implemented to identify triggering conditions and to develop prediction methods of shallow landslides, thanks also to its free availability and constantly updating with a delay of 5 days. [ABSTRACT FROM AUTHOR]

Single image deraining is a fundamental task in computer vision, which can greatly improve the performance of subsequent high-level tasks under rainy conditions. Existing data-driven rain removal methods rely heavily on paired training data, which is expensive to collect. In this paper, we propose a new unsupervised method, called Cycle-Attention-Derain, which removes rain from single images in the absence of paired data. Our method is based on the CycleGAN framework with two major novelties. First, since rain removal is highly correlated with analyzing the texture features of an input image, we propose a novel attention fusion module (AFM) with complementary channel attention and spatial attention, which can effectively learn more discriminative features for rain removal. Second, to further improve the generalization ability of our model, we propose a global–local attention discriminator architecture with an attention mechanism to guide the network training, so that the rain removal results are realistic, both globally and locally. Our proposed model is able to remove rain streaks and raindrops of varying degrees without paired training images. Extensive experiments on synthetic and real datasets demonstrate that the proposed method outperforms most of the state-of-the-art unsupervised rain removal methods in terms of both PSNR and SSIM on Rain800 datasets and achieves slightly close results to other popular supervised learning methods. [ABSTRACT FROM AUTHOR]

This study explores the whole process of deformation–failure–movement of fault-type slopes under rainfall conditions. Taking the Guanling landslide as an example, this paper simulates the whole process of the Guanling landslide based on FLAC/PFC coupling. The research results show that the simulated Guanling landslide accelerates deformation and failure due to the influence of reverse faults and rainfall. In general, the simulated Guanling landslide first fails in the form of sliding on the left side and then collapsing on the right side. The bottom unit mainly consists of tensile–shear composite failure that is supplemented by shear failure. After the simulated Guanling landslide failure, the average peak velocities basically show a trend of first increasing and then decreasing from top to bottom and from left to right. Overall, the landslide bed is basically in a state of compression, and the impact stress is constantly attenuated with the movement process. The σ1 trace of the landslide bed is basically consistent with the movement direction of the landslide body. As burial depth increases, the trace is obviously deflected downwards. According to the law of conservation of energy, the hydrodynamic work and gravitational potential energy are 2.96 × 1012 J and 4.43 × 1012 J, respectively, and the energy consumption of the peak bonding energy, peak kinetic energy, sliding energy and damping energy are 7.77 × 1011 J, 1.41 × 1012 J, 5.55 × 1012 J and 1.66 × 1012 J, respectively. Finally, the accumulation forms of the landslide bodies in each section after the termination of the simulated Guanling landslide are compared, and the simulation results are consistent with the actual situation, which confirms the reliability of the simulation results. These research results provide a reference for studying the failure mechanisms and movement characteristics of similar geological landslide disasters. [ABSTRACT FROM AUTHOR]