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Water, ecology, energy and food are indispensable resources for human survival and social progress, as well as the core of the regional sustainable development. They are interdependent and closely related. The importance of ecology support and feedback capability has been overlooked in traditional studies of the water–energy–food nexus. In this paper, the concept of water–ecology–energy–food (WEEF) nexus was proposed. Then, a comprehensive evaluation indicator system of WEEF nexus was established from three criteria of stability, coordination and sustainability based on synergy theory. A comprehensive variable fuzzy evaluation model was constructed to evaluate the coordinated development level of WEEF nexus in Tianjin, China. And the main obstacle factors that constrained the coordinated development of WEEF nexus were analyzed through the obstacle degree model. The results showed that: from 2010 to 2021, the comprehensive coordinated development level of the WEEF nexus showed an upward trend, with an average growth rate of 4.0%. The stability, coordination and sustainability all significantly affected the comprehensive coordinated development of regional WEEF nexus. The coordination of water–ecology, energy–ecology and food–ecology subsystems was relatively low. Food self-sufficiency rate, average water consumption for energy production, annual rainfall and ecological environment index were the main obstacle factors of WEEF nexus. In summary, the nexus framework and evaluation method proposed in this paper can provide a new perspective on the basic theory and application of regional WEEF nexus. [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]

Intelligent Transport System (ITS) is a typical class of Cyber-Physical Systems (CPS), and due to the special characteristics of such systems, higher requirements are placed on system security. Runtime verification is a lightweight verification technique which is used to improve the security of such systems. However, current runtime verification methods often ignore the effects of the physical environment (e.g., the effects of rain, snow, and other weather changes on road conditions), which results in the inability of the monitor to effectively monitor the system according to the changes in the environment. To address this problem, this paper proposes a method for constructing a runtime monitor with environmental context-awareness capability. First, the physical environment factors affecting the system are formally described and constructed into an environment model, then the system statute is transformed into a Büchi automaton, and then a synthesis algorithm combining the environment model and the Büchi automaton is designed based on the network of automatons, and the corresponding monitor is generated. Finally, the proposed method is applied and verified on simulation and real objects. The experimental results show that the monitors generated based on the method of this paper can effectively monitor unsafe events in different environments, thus improving the safety of intelligent driving systems. [ABSTRACT FROM AUTHOR]

Systematic and timely documentation of triggered (i.e. event) landslides is fundamental to build extensive datasets worldwide that may help define and/or validate trends in response to climate change. More in general, preparation of landslide inventories is a crucial activity since it provides the basic data for any subsequent analysis. In this work we present an event landslide inventory map (E-LIM) that was prepared through a systematic reconnaissance field survey in about 1 month after an extreme rainfall event hit an area of about 5000 km2 in the Marche-Umbria regions (central Italy). The inventory reports evidence of 1687 triggered landslides in an area of ~550 km2. All slope failures were classified according to type of movement and involved material, and documented with field pictures, wherever possible. The database of the inventory described in this paper as well as the collection of selected field pictures associated with each feature is publicly available at figshare. [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]

The rainwater tank is a critical component of the rainwater harvesting system and has a significant impact on the non-potable water saving efficiency and economic acceptability of the rainwater harvesting system. The choice of a suitable tank design to suit an existing catchment and local conditions is critical. In this paper, capacity is considered as a function of local rainfall data, unit price of tap water, roof area, non-potable water demand and an estimate of the cost of constructing a rainwater harvesting system. Analytical equations are derived that allow direct calculation of the desired cistern volume in terms of roof area and non-potable water demand. The validity of the analytical equations was demonstrated by comparing their results with the results of the sequential simulation, and the analytical equations proved to be adequate for the estimation of cisterns, but it was necessary to exclude some extreme scenarios due to the high deviation rate of the calculation results in the buildings with extremely high demand rates or extremely low roof areas. The proposed approach provides 'easy-to-use' design tools for engineers, architects, local authorities and rainwater tank manufacturers to estimate or recommend suitable sizes of rainwater cisterns. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature 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.)

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]

Heavy rainfall and precipitation play a massive role in shaping the socio-agricultural landscape of a country. Being one of the key indicators of climate change, natural disasters, and of the general topology of a region, rainfall prediction is a gift of estimation that can be used for multiple beneficial causes. Machine learning has an impressive repertoire in aiding prediction and estimation of rainfall. This paper aims to find the effect of ensemble learning, a subset of machine learning, on a rainfall prediction dataset, to increase the predictability of the models used. The classification models used in this paper were tested once individually, and then with applied ensemble techniques like bagging and boosting, on a rainfall dataset based in Australia. The objective of this paper is to demonstrate a reduction in bias and variance via ensemble learning techniques while also analyzing the increase or decrease in the aforementioned metrics. The study shows an overall reduction in bias by an average of 6% using boosting, and an average reduction in variance by 13.6%. Model performance was observed to become more generalized by lowering the false negative rate by an average of more than 20%. The techniques explored in this paper can be further utilized to improve model performance even further via hyper-parameter tuning. [ABSTRACT FROM AUTHOR]

This paper presents the 1000 ensemble flood simulations using ensemble rainfalls simulated by 4D LETKF. The number of ensemble rainfall members is large as 1000 compared to the operational rainfall products of two-digit numbers to avoid sampling errors in the three-dimensional meteorological simulation based on chaotic theory. Using the large data set, 1000 ensemble rainfall–runoff for dam catchments and high-resolution inundation simulations of large area are carried out focusing on the Kumagawa river catchment. Herewith, the comparisons were carried out with 21-member ensemble rainfalls of an operational forecast by Japan Meteorological Agency and 100-member 4D-LETKF ensemble rainfalls simulated independent of 1000-member 4D-LETKF. At the same time, the accuracy of selective 100-member ensembles out of 1000 members is investigated. As a result, although many previous research works show a large number of ensemble simulations are necessary for three-dimensional meteorological field, the number could be reduced in the catchment-average rainfall–runoff and 2.5-dimensional inundation simulations given that the rainfall prediction has a certain level of accuracy since improving the discharge prediction accuracy with lower dimension is sometimes possible by adjusting the horizontally/vertically integrated model parameters determined by topography and soil characteristics in advance against the observed rainfall. Also, the 1000 ensembles could be classified into several patterns in horizontally accumulated 2D rainfall field. Likewise, the flood flow moves toward the low elevation area and river; thus, the resultant 2.5-dimensional flood field does not show much variety as three-dimensional meteorological simulation. The paper summarizes these studies. [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]

One of significant tasks in autonomous vehicle technology is traffic signs recognizing. It helps to avoid traffic violations on the road. However, recognition of traffic signs becomes more complicated in bad weather such as lack of light, rain, fog. Those bad weather conditions cause low accuracy of detecting and recognizing. In this paper, we aim to build a model to recognize and classify the traffic signs in different bad weather conditions by applying deep learning technique. Weather data are collected from variety types as well as generated from different techniques. Collected data are trained on the YOLOv5s, YOLOv7 model. In order to increase the accuracy, those YOLOv5s are improved on different models by replacing Squeeze-and-Excitation (SE) attention module or Global Context(GC) block. On the test set: the accuracy of YOLOv5s is 76.8%, the accuracy of YOLOv7 is 78% the accuracy of YOLOv5s+SE attention module is 78.4% and the accuracy of YOLOv5s+C3GC is 79.2%. The results show that YOLOv5s+C3GC model significantly improves the accuracy in recognition of blurred-distant-objects. [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]

Monitoring water resources requires accurate predictions of rainfall data. Our study introduces a novel deep learning model named the deep residual shrinkage network (DRSN)—temporal convolutional network (TCN) to remove redundant features and extract temporal features from rainfall data. The TCN model extracts temporal features, and the DRSN enhances the quality of the extracted features. Then, the DRSN–TCN is coupled with a random forest (RF) model to model rainfall data. Since the RF model may be unable to classify and predict complex patterns and data, our study develops the RF model to model outputs with high accuracy. Since the DRSN–TCN model uses advanced operators to extract temporal features and remove irrelevant features, it can improve the performance of the RF model for predicting rainfall. We use a new optimizer named the Gaussian mutation (GM)–orca predation algorithm (OPA) to set the DRSN–TCN–RF (DTR) parameters and determine the best input scenario. This paper introduces a new machine learning model for rainfall prediction, improves the accuracy of the original TCN, and develops a new optimization method for input selection. The models used the lagged rainfall data to predict monthly data. GM–OPA improved the accuracy of the orca predation algorithm (OPA) for feature selection. The GM–OPA reduced the root mean square error (RMSE) values of OPA and particle swarm optimization (PSO) by 1.4%–3.4% and 6.14–9.54%, respectively. The GM–OPA can simplify the modeling process because it can determine the most important input parameters. Moreover, the GM–OPA can automatically determine the optimal input scenario. The DTR reduced the testing mean absolute error values of the TCN–RAF, DRSN–TCN, TCN, and RAF models by 5.3%, 21%, 40%, and 46%, respectively. Our study indicates that the proposed model is a reliable model for rainfall prediction. [ABSTRACT FROM AUTHOR]

Estimating precipitation over large spatial areas remains a challenging problem for hydrologists. Satellite-based remote sensing rainfall products have the advantage of large-scale synchronous coverage, but the reliability of their inversion still needs to be improved. Correcting the bias of satellite-based precipitation estimates (SPEs) is a major challenge in applications such as environmental modeling, hydrology, and water resource management. In this paper, a new bias correction method—a Gaussian process regression (GPR) model method based on rain intensity classification—is proposed to improve the accuracy of a satellite precipitation product—the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run (FR) product—at the daily scale in the Guangdong-Hong Kong-Macao Greater Bay Area. By comparing the effects of the proposed method with those of other existing classical correction methods, namely, quantile mapping (QM), the support vector machine (SVM) approach and direct GPR, it is found that all four methods improve the accuracy of the FR product to varying degrees. GPR based on rain intensity classification has the best effect of FR product improvement, with the CORR increasing from 0.55 to 0.59, the RMSE ranging from 14.37 to 12.79 mm/day, and the BIAS ranging from − 0.14 to 0.03 during the validation period. GPR without rain intensity classification also yields good results, with the QM and SVM methods being the least effective. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature 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 literature review analyzed more than 100 publications on soil erosion in the Central Russian Upland, one of the most erosion-prone regions of Russia. The selection of scientific papers was carried out from open web resources, domestic and international citation databases. The following parameters have been analyzed: time; geographical position; scale and methods of research; soil and geomorphological features; anti-erosion measures; type of erosion and rates of soil washout/accumulation; bibliographic information about the publication. There is a shortage of studies at the small-scale and medium-scale levels. The relationship of large-scale studies to the main watershed of the Central Russian Upland was revealed. There are discrepancies in the estimates of soil erosion by different authors, especially at different scale levels. An analysis of changes in soil erosion over time indicates a decrease in the rate of soil erosion in general on the Central Russian Upland, mainly due to climate change and a reduction in the area of arable lands. A lack of studies of rainfall, tillage and wind erosion of soils in this area has been revealed. [ABSTRACT FROM AUTHOR]

The article discusses a research about the use of tree rings in providing information about the amount of snow or rainfall. It references a study by Kerstin Treydte and colleagues published within the issue. They concluded that the latter part of the twentieth century was by far the wettest period, suggesting that humans may play a role in the climate of the world.

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]

The latest report by the Intergovernmental Panel on Climate Change (IPCC) warns of an increase in heavy rainfall events due to global warming and climate change, which can result in significant economic costs for insurance companies and businesses. To address this challenge, insurance companies are focusing on developing new risk management strategies and offering new products such as flood insurance. However, the article argues that effective and feasible coordination shortens recovery time and can therefore drastically reduce the financial costs of a crisis—that is, the insurance costs. The paper analyses the deficit in crisis management during heavy rain events in Germany, based on the 2021 Ahr valley flood. The analysis is conducted based on document analysis and interviews and focuses on three areas of deficit: coordination between crisis staffs and (1) civil society, (2) emergency responders, and (3) political leaders. The paper highlights the importance of coordination during a crisis, which can help to address the crisis more efficiently and effectively, minimise damage and get communities back on their feet faster. The paper recommends policy changes to improve interface management and disaster management coordination. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature 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.)

Mudstone, as a typical soft rock with wide distribution, has been endangering the slopes containing mudstone by its water-sensitivity of swelling and weakening strength when encountering water. To comprehensively understand the water-sensitivity of mudstone and reveal its influence on slope stability, we took the working slope containing water-sensitive mudstone of Shengli No.1 open-pit coal mine in Xilinhot, Inner Mongolia, China, as an example. Mudstone samples taken from the working slope were remodeled and saturated, and then triaxial tested to obtain the effective cohesion and effective internal friction angle. The filter paper method was used to obtain the soil–water characteristic curve of unsaturated mudstone. The pore structure of mudstone samples with different water contents were analyzed using the mercury intrusion porosimetry tests combined with the fractal dimension. The total pore content of the mudstone sample with lower water content is greater than that of the mudstone sample with higher water content. The mesopores are more in the mudstone sample with lower water content, while the small pores are more in the mudstone sample with higher water content. The variation of water content will change the complexity of mudstone pore structure. The higher the water content, the simpler the mudstone pore structure and the smoother the pore surface. Numerical calculations were conducted on the stability of the working slope under different rainfall conditions. The effective saturation on the mudstone layer surface changed and the plastic strain all occurred on the mudstone steps under different rainfall conditions. The key to preventing landslide of the slope containing water-sensitive mudstone in Shengli No.1 open-pit coal mine is to control the deformation and sliding of the mudstone layer. [ABSTRACT FROM AUTHOR]

The water weakening effect refers to the gradual deterioration of soil mechanical properties under long-term saturation. This paper analyzed the impact of water weakening on slope stability under alternating excavation and rainfall. The field investigation speculated shale hydration as the cause of overall slope instability. The mechanical parameters of hydrated shale were determined by the parameter inversion and empirical estimation methods. The simulations were used to restore the process of parameter weakening and slope failure, and confirmed the fact that hydration causes landslide. Furthermore, the failure mechanism of slope and support structure under hydration were investigated. The results show that the mechanical parameters of the slope decreased linearly, whereas the plastic strain–time and total displacement-maximum shear stress curves of the hydrated shale exhibited three stages: slow initial growth, rapid growth in the middle term, and rapid increase in the later period; the rate of slope deformation and the factor of safety reduction also gradually increase over time; under middle stage of hydration, the middle to rear of shale were extruded, while the front first underwent tensile shear deformation, forming a plastic zone of at rear and front excavated slope; In the late stage, hydrated shale quickly reaches its yield limit (maximum shear stress of 270 kPa), the middle and rear shale is damaged and compresses the front, causing it to transform from tensile shear to compressive shear failure. The plastic zone in the rear extends forward and connects with the front ones, forming an overall landslide. Besides, preventing deep landslides caused by hydration through waterproofing, drainage, protection, and support for excavated slopes has proven difficult. Therefore, it is necessary to redesign treatment schemes based on the characteristics of stress, strain, and seepage. [ABSTRACT FROM AUTHOR]

This paper reports the chemistry of fine (PM2.5) and coarse (PM10) aerosols sampled over a period of three years during 2018–2021 at a semi -arid tropical location in the rain shadow region of the peninsular India. The data is classified in to dry (December to May) and wet (June to November) periods. Scavenging effect due to rains have culminated in to less concentrations of both fine and coarse aerosols and their ionic components in the wet period. Significantly high concentrations of the crustal components such as Ca, Na, K and Mg from the local dust resulted in the alkaline pH in both dry and wet periods with Ca and Mg emerging as major neutralizing components. Overall, < 20% samples of both fine and coarse aerosols depicted acidic pH. Concentration of SO4 was comparatively more than NO3 indicating towards more presence of stationary sources (industrial/domestic emissions) than mobile (vehicular emissions) sources. Combustion generated and highly absorbing black carbon aerosols showed high concentration during the dry period. Local activities comprising residential, agricultural, vehicular and industrial emissions were the major sources of aerosols at Solapur however, the contribution from the distant sources were also found to contribute as inferred from the cluster analysis and concentration weighted trajectories (CWT). The observed abundances of the alkaline dust aerosols that could act as cloud condensation nuclei or ice nuclei will have important implications on the studies related to cloud aerosol precipitation interaction over this region. [ABSTRACT FROM AUTHOR]

Event-based rainfall-runoff models are practical tools commonly used to predict catchments' response to a rainfall event. However, one of the main concerns is that the characteristics of rain events are neglected in the model development. This paper develops a novel event-based rainfall-runoff equation to incorporate rainfall characteristics into account. The performance of the new equation is evaluated based on the root mean square error, Nash–Sutcliffe efficiency coefficient, and per cent bias for 13,339 rainfall-runoff events between 2005 and 2020 over 23 catchments across New Zealand and Australia with oceanic, mediterranean, tropical, subtropical, and semiarid climates. Compared to the previous event-based models, the new equation shows an improvement in runoff estimation in almost all case studies. Furthermore, considering the new equation is simple, efficient, and takes the rain event duration into account, the new equation has the potential to become a robust alternative method to the conventional curve number method in hydrological engineering projects. [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]

The Western Ghats (WG) is one of the important biodiversity hotspots and rainfall intense regions in south Asia. In this paper, the long-term trend analysis is carried out during southwest monsoon rainfall over different latitudinal regions of the WG. Further, the gridded daily rainfall data (0.25 × 0.25 spatial grid resolution) is classified into six intensity bins: dry, low, moderate, heavy, very heavy, and extreme, from 1951 to 2015. A detailed trend and variability analysis of rainfall and its frequency in different intensity bins along with their relative contribution were carried out. The results of different rainfall intensity bins depict that the moderate and low intensity bins contribute a maximum share in seasonal rainfall with 45% and 20%, respectively, from the 30% and 50% rainy days in all the regions. Heavy and very heavy intensity bins together contribute about 30% of the seasonal rainfall from about 9% rainy days. Extreme intensity bin recorded an alarming share of 6% with only 1% rainy day occurrences. In general, the relative contribution shows an ascending pattern of moderate, low, very heavy, heavy, extreme, and dry intensity bins. A decreasing trend in rainfall was observed in heavy and very heavy intensity bins, while other intensity bins illustrated mixed trends in different study regions. Extreme rainfall events marked a major shift upward in the WG region during the study period. Overall, our result portrays a negative trend in rainfall during the southwest monsoon season over all the study regions during the study period. [ABSTRACT FROM AUTHOR]

The rainfall over the Indian region, governed majorly by the monsoonal flow, is a point of research in the perspective of climate change. In this paper, we compute the change points in the rainfall series at every grid of the India Meteorological Department (IMD) daily gridded rainfall data for a period of 120 years (1901 to 2020). The map shows clearly demarcated regions indicating different zones, where the rainfall statistics have altered at different periods. It is observed that in a major part of central India, the shift in rainfall intensity is mainly associated with the time frame 1955–1965; in the Indo-Gangetic plain, the changes are found to be more recent (1990), while the latest changes (post 2000) are observed particularly for North Eastern region and some parts along the East Indian coast. The changeover years are significant at a 95% confidence level for most part of the Indian landmass. The causes may be surmised due to moisture transport from the Arabian Sea (Central India), the presence of aerosol (Gangetic Plain), and the possible revival of monsoon due to land–ocean gradient (Eastern coast and North East India). This is the first-ever study which provides a comprehensive daily rainfall change point map over India using 120 years of gridded station data. [ABSTRACT FROM AUTHOR]

Accurately evaluating the failure process of soil slope can mitigate its negative influence on both human lives and economic loss. However, it is still a very difficult and important task. In this paper, a novel nonlinear rheological model with nonstationary parameter creep (NPCNRM) was proposed to represent the tertiary creep characteristics of slope soil. One-dimensional constitutive equations and three-dimensional (3D) model of NPCNRM were derived, and its 3D finite element program was developed in Fortran. The feasibility of the proposed model was partially verified by the uniaxial creep test of argillaceous slate. Through the application of Liangyeshan landslide located in Wuping county, Fujian province, China, the results show that using the proposed model can scientifically evaluate the creep failure mechanism of saturated granite residual soil slope; the accelerated creep of soil near the top of the slope is more significant than that at slope toe, which will aggravate the disintegration and loosening of soil and the continuous infiltration of rainfall, and then leads to further deterioration of soil strength. [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]

The mountainous areas are vulnerable to climate change and may have many socio-economic and environmental implications. The changing pattern of meteorological variables has deleterious effects on natural resources and livelihood. This paper makes an attempt to analyse trend and forecast metrological variables in Nainital district of India. Monthly, seasonal, and annual trends in rainfall and temperature were examined by Modified Mann–Kendall during 1989–2019. The magnitude of trend in temperature and rainfall was determined using Sen's slope estimator. Ensemble machine learning model was utilized for forecasting the variables for the next 16 years (2020–2035). The effectiveness of the model was examined through statistical performance assessors. The results revealed a significant increasing trend in the rainfall (at the rate of 9.42 mm/year) during 1989–2019. Increasing trend in the mean, minimum, and maximum temperatures on an annual basis was observed in the district. A remarkable increase in the rainfall and temperature was forecasted during various seasons. The findings of the study may help the stakeholders in devising suitable adaptation measures to climate variability. The bagging approach has shown its effectiveness in forecasting meteorological variables. The other geographical regions may find the methodology effective for analyzing climate variability and lessening its impact. [ABSTRACT FROM AUTHOR]

Climate change is a major concern, as it profoundly affects many facets of our lives. It has brought about several issues, including declining water supply, reduced agricultural yields, increased drought occurrences, and increased heat waves. Amidst these challenges, the influence of short-term drought events on plant growth and irrigation schedule emerges as a critical concern. However, despite these evident consequences, a nuanced understanding of the intricate relationship between the severity and duration of short-period drought/deficit events still needs to be explored. This paper analyses fortnightly water deficit periods over different regions of India, which would be more relevant to re-scheduling the irrigation events than monthly or longer duration in preventing crops from reaching the permanent wilting point. Hence, this work considers analyzing 15-day Standardized Precipitation Index (SPI) trends and drought characteristics using conventional methods and Innovative Trend Analysis (ITA) techniques. The analysis uses gridded rainfall data from the India Meteorological Department (IMD), which has a spatial resolution of 0.250-E and 0.250-N. The data spans the period from 1970 to 2021. The ITA and Mann Kendall (MK) displayed nearly identical areas of increasing and decreasing trends, but ITA effectively identified significant trends. While MK and Modified Mann Kendall (MMK) could only indicate significant trends for 12.94%, 9.57%, and 9.9% of grid points for SPI, drought severity, and duration, respectively, ITA was able to identify significant trends at 44.31%, 10.9%, and 10.1% on an annual scale. The ITA method effectively identified the significant trends and magnitudes of fortnightly SPI and drought characteristics. The Tropical monsoon (Am), Tropical savannah (Aw), Arid desert hot (BWh), Arid steppe hot (BSh), and Temperate dry winter warm summer (Cwb) climatic zones have shown a significant increase in annual drought severity. Similarly, a significant increase in monsoon drought severity is observed across several states, including Gujarat and Mizoram, impacting diverse geographic extents. The present study can help policymakers and water resource managers decide on water allocation, irrigation, and crop management practices. [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]

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]

It is often hypothesized that adaptive capacity leads to increased yield. However, the veracity of this assumption in quantitative terms has largely remained understudied by researchers. Also, adaptation depends on the vulnerability of the agricultural sector, climate risks, biocapacity of the area, readiness and adaptive capacities of stakeholders. Adaptation can only take place if stakeholders are ready to respond and resources are also available. To understand these relationships, the paper examined the impacts of climate change, food vulnerability, adaptive capacity, biocapacity and readiness on yields of major cereal crops in thirty-five African countries. We used the panel autoregressive distributive lag model to analyse publicly available panel data obtained from FAOSTAT, Global Foot Print Network, and Notre Dame Adaptation Index databases, and World Bank Group Climate Change Knowledge Portal. The results show that biocapacity, adaptive capacity, food sector vulnerability, and temperature decreased rice yield in the long. Biocapacity increased maize and sorghum yields, while adaptive capacity decreased their yields in the long run. Food sector vulnerability and temperature decreased maize yield in the long run, while temperature increased sorghum yield in the long run. The results suggest that improvement in adaptive capacity is an important policy tool to increase the yield of some cereal crops. [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]

Identifying and predicting the impacts of climate change are crucial for various purposes, such as maintaining biodiversity, agricultural production, ecological security, and environmental conservation in different regions. In this paper, we used the surface pressure (SP), surface temperature (ST), 2-m air temperature (AT), 2-m dewpoint temperature (DT), 10-m wind speed (WS), precipitation (PRE), relative humidity (RH), actual evapotranspiration (ETa), potential evapotranspiration (ETP), total solar radiation (TRs), net solar radiation (NRs), UV intensity (UVI), sunshine duration (SD), convective available potential energy (CAPE) as factors in our climate modeling. The spatiotemporal distribution characteristics of the climate factors were analyzed and identified based on historical data for China from 1950 to 2020 using factor analysis and a grey model (GM (1,1)), and their future change characteristics were predicted. The results show that there is a strong correlation between climate factors. ST, AT, DT, PRE, RH, and ETa are the main factors that have the potential to cause heavy rain, thunderstorms, and other severe weather. Meanwhile, PRE, RH, TRs, NRs, UVI, and SD are among the major factors linked to climate change. Specifically, SP, ST, AT, and WS are among the minor factors in most areas. The top ten provinces in terms of combined factor scores are Heilongjiang, Neimenggu, Qinghai, Beijing, Shandong, Xizang, Shanxi, Tianjin, Guangdong, and Henan. The trend of climate factors in China is expected to remain relatively stable over the next 30 years, with a noteworthy decrease observed in CAPE compared to the past 71 years. Our findings can help to better mitigate the risks associated with climate change and enhance resilience; they also provide a scientific basis for environmental, ecological, and agricultural systems to cope with climate change. [ABSTRACT FROM AUTHOR]

This paper investigated whether rainfall promotes dilution or increase in nutrient concentrations and which land use indicators are the main predictors of nutrients in intermittent rivers in a large Brazilian semiarid region. The total phosphorus (TP) and total inorganic nitrogen (TIN) were monitored between 2013 and 2018 at 92 river water quality monitoring sites. The monthly rainfall (Rn) was obtained from 575 rain gauges. Pearson's correlation (R) between Rn and nutrient concentration was performed. The correlation patterns were also analysed based on land use data: urban area (%), agricultural field area (%), demographic density (inhabitants/km2), sewer system coverage (%), and reservoir density (reservoir/km2). Backward stepwise regression was performed to identify predictors of nutrient concentrations. The results revealed a marginal effect of rainfall on nutrients when the effects of urbanisation outweigh all other aspects. However, in regions with greater accumulated rainfall and lower reservoir density, the rainfall was related to a linear increase in nutrient concentrations (R > 0.8). Contrastingly, in the basins with less accumulated rainfall and greater inter-basin hydrological disconnection, there was a linear reduction in nutrient concentration (R < − 0.5). In the backward stepwise regression, sewer system coverage and Rn had the greatest influence for TP, and the urban area was the strongest predictor for TIN. Importantly, our results demonstrated that in semiarid rivers in densely populated regions, there is no single pattern of variability in nutrient concentration, on a wide scale of assessment. Therefore, adaptative and decentralised management can be more effective in improving water quality in these regions. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature 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.)

Background: The Cerrado is the most biodiverse savanna and maintains other biomes. Aware of its significance, this paper evaluated the Brazilian Cerrado's climatic, environmental, and socioeconomic aspects using remote sensing data and spatial statistics (correlation analysis and principal components analysis—PCA). Following the measures of sample adequacy (MSA) and Kaiser–Meyer–Olkin (KMO) tests, seventeen variables were evaluated. Results: The MSA revealed that the dataset had a good quality (0.76), and nine variables were selected: elevation, evapotranspiration, active fires, Human Development Index (HDI), land use and land cover (LULC; shrubland and cropland/rainfed), rainfall (spring and autumn), and livestock. The correlation matrix indicated a positive (negative) association between HDI and autumn rainfall (HDI and active fires) with a value of 0.77 (− 0.55). The PCA results determined which three principal components (PC) were adequate for extracting spatial patterns, accounting for 68.02% of the total variance with respective values of 38.59%, 16.89%, and 12.5%. Due to economic development and agribusiness, Cerrado's northern (central, western, and southern) areas had negative (positive) score HDI values, as shown in PC1. Climatic (rainfall—spring and fall) and environmental (cropland/rainfed and shrubland) aspects dominated the PC2, with negative scores in northern and western portions due to the transition zone between Amazon and Cerrado biomes caused by rainfall variability. On the other hand, environmental aspects (LULC-shrubland and elevation) influenced the PC3; areas with high altitudes (> 500 m) received a higher score. Conclusion: Agricultural expansion substantially affected LULC, leading to deforestation-caused suppression of native vegetation. [ABSTRACT FROM AUTHOR]

Rainfall onset dates (ROD), rainfall cessation dates (RCD) and length of rainy season (LRS) are crucial for crop production and food security in Eastern Africa yet scantily documented. This paper seeks to investigate the spatial patterns of these parameters. Data used are Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2.0) and National Oceanic and Atmospheric Administration (NOAA) gridded temperature. Threshold of 1 mm for rainy day, 20 mm over 5 days with at least 3 rain days and dry spell not exceeding 7 days in the next 21 days were used to determine RODs. The Potential Evapotranspiration (PET) and Water Balance (WB) criteria were computed to determine RCDs. The differences between ROD and RCD were used in calculating LRS. The results showed early rainfall cessation over more than 30 counties in Kenya lead to shortened rainy season by 10–20 days during MAM season. Similarly, 20–40 days early onset dates are observed in most counties in upper Nile, Unity, and Jonglei states in South Sudan, while 20–40 days delayed rainfall onset was observed in Khartoum and southern parts of Nile state western Darfur, eastern and Aljazeera states in Sudan, most parts of Ethiopia and Eritrea districts during JJA season. Highlands of western and Nyanza region in Kenya, most parts of Uganda observed rainfall onset by March and no sign of cessation before November. Early cessation over northern Uganda districts was behind shortened LRS, furthermore, the early RODs over western and southwestern Uganda districts increased LRS. Prolonged dry conditions over northern Sudan, southeastern parts of South Sudan, northern Kenya, central Somalia, northern Darfour, Kordofan and northern parts of Sudan exacerbated by significant delayed onset and early cessation of rainfall. These findings are important for rain-fed agricultural planning and food security in the IGAD region of Eastern Africa. [ABSTRACT FROM AUTHOR]

A dense network of rain gauges and considerably large variability of the southwest monsoon precipitation across the country make India a suitable test-bed to evaluate any satellite-based precipitation product. In this paper, three real-time infrared-only precipitation products derived from the INSAT-3D satellite namely, INSAT Multispectral Rainfall (IMR), Corrected IMR (IMC) and Hydro-Estimator (HEM) and three rain gauge-adjusted Global Precipitation Measurement (GPM)-based multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP) and an Indian merged satellite-gauge product (INMSG) have been evaluated over India at a daily timescale for the southwest monsoon seasons of 2020 and 2021. An evaluation against rain gauge-based gridded reference dataset shows noticeable reduction of bias in IMC product over IMR, primarily over the orographic regions. However, INSAT-3D infrared-only precipitation retrieval algorithms have limitations in shallow and convective precipitation estimation. Among rain gauge-adjusted multi-satellite products, INMSG is shown to be the best product in the monsoon precipitation estimation over India due to use of rather larger number of rain gauges than IMERG and GSMaP products. All satellite-derived precipitation products, i.e. infrared-only and gauge-adjusted multi-satellite products underestimate heavy monsoon precipitation by 50–70%. The bias decomposition analysis indicates that a simple statistical bias correction would considerably improve the performance of the INSAT-3D precipitation products over the central India, but the same might not work over the west coast due to rather larger contributions of both positive and negative hit bias components. Although rain gauge-adjusted multi-satellite precipitation products show very small or negligible total biases in the monsoon precipitation estimation, positive and negative hit bias components are considerable over the west coast and central India. Furthermore, rain gauge-adjusted multi-satellite precipitation products underestimate very heavy to extremely heavy precipitation with larger magnitudes than the INSAT-3D derived precipitation products over the central India. Among the rain gauge-adjusted multi-satellite precipitation products, INMSG has smaller bias and error than IMERG and GSMaP products for very heavy to extremely heavy monsoon precipitation over the west coast and central India. Preliminary results of this study would be useful for end users in choosing a better precipitation product for real-time and research applications as well as for algorithm developers in further improving these products. [ABSTRACT FROM AUTHOR]

Demographic outbursts and increased food demands invoke excessive use of pesticides in the agricultural field for increasing productivity which leads to the relentless decline of riverine health and its tributaries. These tributaries are connected to a plethora of point and non-point sources that transport pollutants including pesticides into the Ganga river's mainstream. Simultaneous climate change and lack of rainfall significantly increase pesticide concentration in the soil and water matrix of the river basin. This paper is intended to review the paradigm shift of pesticide pollution in the last few decades in the river Ganga and its tributaries. Along with this, a comprehensive review suggests the ecological risk assessment method which facilitates policy development, sustainable riverine ecosystem management, and decision-making. Before 2011, the total mixture of Hexachlorocyclohexane was found at 0.004–0.026 ng/mL in Hooghly, but now, the concentration has increased up to 0.465–4.132 ng/mL. Aftermath of critical review, we observed maximum residual commodities and pesticide contamination reported in Uttar Pradesh > West Bengal > Bihar > Uttara Khand possibly because of agricultural load, increasing settlement, and incompetency of sewage treatment plant in the reclamation of pesticide contamination. [ABSTRACT FROM AUTHOR]

Australia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature 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.)

Bazhong City is located on stratum dominated by red-bed conditions. This type of weak geological condition with sand and mudstone interbedding is very easily affected by climatic conditions to produce rainfall-type landslides. On the basis of landslide data statistics collected in Bazhong City from 2011 to 2019, this paper uses ERA5-Land rainfall data to statistically analyze the correlation between rainfall and landslide events in Bazhong City. The landslide events in Bazhong City are greatly affected by rainfall events lasting for 10 days. Considering the influence of rainfall seepage and other processes, an effective cumulative rainfall-duration threshold curve for Bazhong City is obtained via median nonlinear fitting. Then, on the basis of the prediction interval, the rainfall threshold formula under different landslide occurrence probabilities is obtained and the critical threshold curve with a non-exceeding probability of 1% in Bazhong City is calculated and verified. Subsequently, a continuous probability distribution fitting function of landslide occurrence is established and a continuous probability distribution surface with a good fitting effect in Bazhong City is obtained. This allows a definite probability of whether future rainfall events will induce landslides to be obtained, providing an important basis for engineering disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Climate change severely affects sub-Saharan African economies in several ways: increased temperatures, erratic rainfall variability patterns, and recurrent droughts and floods. Such adverse climate change effects may result in a greater incidence of crop pests, loss of soil moisture content, rapid soil nutrient depletion and substantial decreases in crop productivity and yields. These effects in combination with lack of access to improved, high-yielding crop varieties, limited agricultural extension services and poor access to irrigation infrastructure could further threaten access to food, limit export earnings and markedly lower net crop revenue. Several definitions, assessment approaches and methods for the concepts related to "vulnerability" and "adaptation" exist due to complex nature of climate impact and strategies used to deal with them. Based on the context of such studies, this paper reviews climate change risks, people's vulnerability to such risks as well as synthesize different approaches, methods and models used to assess vulnerability and adaptation in the area of agriculture in sub-Saharan Africa. As climate change effects are complex and site-specific, the understanding of several concepts, approaches and methods provides detailed information on farmers' vulnerability and adaptation process. Such information would be useful to inform adaptation interventions and agricultural policies that build farmers' resilience. [ABSTRACT FROM AUTHOR]

Rainfall in the Moist Tropical Caribbean Region (MTCR) in Costa Rica occurs practically throughout the year, with the quarters June–August (JJA) and December–February (DJF) concentrating over 70% of annual rainfall. Contrarily, in March–April and September–October, it rains below 100 mm per month. This seasonal rainfall behavior makes the region ideal for producing bananas (Musa spp.) and pineapple (Ananas comosus) for export (10% and 8% of total exports in 2021, respectively). A national-scale study determined that agriculture in the MTCR is one of the most vulnerable sectors to climate changes. However, the climate in this region has been poorly studied so far. This research analyzed the spatial and temporal variability of annual, monthly, and seasonal (DJF, JJA, SO) rainfall in the MTCR and how they change in the study period based on quality-checked series of daily rainfall from 28 weather stations in two periods: 1985–2009 and 1997–2019. The results show that rainfall regimes in the region are variable in space and throughout the year, with peaks occurring close to the mountain range and minimum values close to the coast. Trends were statistically significant in the period 1985–2009 with a predominance of significant positive trends in DJF and significant negative trends in SO. No significant trends (positive or negative) were observed in the period 1997–2019. JJA rainfall has uneven regional distribution and presents a positive and significant trend in the mountain region. This paper contributes to filling the knowledge gap in rainfall seasonality, variability, and trends in a region where banana and pineapple commercial plantations are fundamental to the country's economy thus providing information to decision-making in the agri-food sector to reduce the negative impacts of changing rainfall regimes. [ABSTRACT FROM AUTHOR]

Understanding the temporal and spatial patterns of flood in the Awash River basin, which is located in Ethiopia's Afar region, is crucial. The Awash basin was picked because it is continuously in danger both spatially and temporally. The likelihood of flooding was assessed using eight independent variables: elevation, slope, rainfall, drainage density, land use, soil type, wetness index, and lineament density. Each constituent was assigned a weight based on its susceptibility to the danger, which was classified into four classifications. Exploratory regression analysis showed that the existing land use is the main factor influencing flood susceptibility. For the GIS domain, a total of 31 models were built using exploratory regression. Model number 31 was found to be the best fit model, having the highest Adjusted R2 value of 0.8 and the lowest Akaike's Information criterion value of 1536.8. The spatial autocorrelation tool's Z score and p-value for the standard residuals are, respectively, 0.7 and 0.4, indicating that they were neither clustered nor scattered. The geographic breadth of flood susceptibility and risk is thoroughly examined in this paper, as is the significance of spatial planning in the Awash basin. [ABSTRACT FROM AUTHOR]