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Summary: This paper evaluates the performance of a spectral‐amplitude‐coding optical code division multiple access (SAC‐OCDMA) system over a free space optical (FSO) communication link under various weather conditions such as fog, snow, and rain. The system uses different code families, including modified quadrature congruence (MQC), random diagonal (RD), and diagonal eigenvalue unity (DEU), to address user codes and reduce the effects of multiple access interference (MAI). The system's bit error rate (BER) performance has been analyzed taking into account receiver noises and MAI, and calculates the required optical power for a BER value of 10−9 under different weather conditions. The results show that fog has the most significant impact on the system's performance, while rainy conditions require the lowest optical power. The performance of the system also declines with increasing transmission length and concurrent user count. Finally, the paper compares the performance of different code families under unfavorable weather conditions and concludes that DEU performs better than MQC and RD in bad weather. [ABSTRACT FROM AUTHOR]

An array of atmospheric profile observations consists of three‐dimensional vectors representing pressure, temperature, and humidity, with each profile forming a continuous curve in this three‐dimensional space. In this paper, the Signature method, which can quantify a profile's curve, was adopted for the atmospheric profiles, and the accuracy of profile representations was investigated. The description of profiles by the signature was confirmed with adequate accuracy. The machine‐learning‐based model, developed using the signature, exhibited a high level of annual accuracy with minimal absolute mean differences in temperature and water vapor mixing ratio (<2.0 K or g kg−1). Notably, the model successfully captured the vertical structure and atmospheric instability, encompassing drastic variations in water vapor and temperature, even during intense rainfall. These results indicate the Signature method can comprehensively describe the vertical profile with information on how ordered values are correlated. This concept would potentially improve the representation of the atmospheric vertical structure. Plain Language Summary: The atmospheric profile can be visualized as a three‐dimensional curve representing pressure, temperature, and humidity. By utilizing the Signature method, we can measure and quantify the profile's curve, allowing for comprehensive modeling of the atmosphere. In this paper, we confirmed the accuracy of atmospheric profile representation using signatures and introduced the characteristics of signatures revealed through machine‐learning models. The description of profiles by the signature was confirmed with adequate accuracy. Moreover, the model demonstrated robust annual accuracy, with minimal temperature and water vapor discrepancies. It effectively captured the vertical structure and instability of the atmosphere, even during heavy rainfall, characterized by significant temperature and water vapor content fluctuations. Key Points: The utilization possibility of the Signature method for atmospheric profiles was confirmedBy utilizing the Signature method, we can measure and quantify the profile's curve, allowing for comprehensive modeling of the atmosphereThe machine‐learning model developed with the signature can predict the profiles with high annual accuracy [ABSTRACT FROM AUTHOR]

Sunken green space is one of the urban rainwater collection facilities, which belongs to Low Impact Development (LID) techniques. It plays a key role in the construction of sponge city, and the amount of runoff collection is usually affected by the area of the sunken green space, the infiltration rate of the soil, and the annual runoff collection rate. To determine the minimum soil infiltration rate of sunken green space considering the annual runoff collection ratio of sponge cities, this paper selects a residential district in Hefei city, China, as the case study. Based on 45 years of precipitation data, the designed rainfall corresponding to annual runoff collection ratios of 75%, 80% and 85% is 21.3 mm, 23.4 mm and 27.5 mm, respectively. The characteristics of rainfall infiltration in sunken green space are analyzed by using the water balance model and runoff yield and conflux model. The results reveal that the soil infiltration rate is 1.16×10−4 cm/s~3.88×10−3 cm/s when the sunken depth is 0.1 m~0.3 m and that the ratio of green space area is 5%~25%. The runoff collection of the reconstructed sunken green space is 2.87 times and 1.98 times that of the nonsunken green space and the nonreconstructed sunken green space, respectively. That is to say, under the comprehensive performance of the sunken depth, sunken green space area, the steady soil infiltration rater of the reconstructed sunken green space cannot be less than the value obtained in this paper. Otherwise, the requirements of annual total runoff reduction ratio of the sponge city cannot be met. Therefore, this study provides references for realizing the ratio of annual runoff collection and sponge city construction in similar urban areas. It can also be applied to optimal selection of sunken green space in some sponge city projects. [ABSTRACT FROM AUTHOR]

As arid lands expand across the globe, scholars increasingly turn to the archaeological record for examples of sustainable farming in extreme environments. The arid north coast of Peru was the setting of early and intensive irrigation-based farming; it is also periodically impacted by sudden, heavy rainfall related to the El Niño Southern Oscillation (ENSO) phenomenon. While the sociopolitical effects, technologies, and engineering expertise of these irrigation systems have been thoroughly examined and theorized, little is known about how farmers managed periods of water stress. The aim of this study is to test whether arid zone farming was supported by hybrid, intermittent flood and perennial water source systems in the prehispanic past. An arroyo in the Chicama Valley was selected for preliminary data collection, and these data are presented here: (1) drone photography of the arroyo capturing the aftermath of a recent (2023) rain event; and (2) potassium (K) soil test kit results from samples collected near suspected prehispanic check dam features in the same area. The paper combines these data with comparative examples from the literature to suggest that the prehispanic features functioned as water-harvesting infrastructure. The paper concludes that sustainable, arid zone farming can be supported by hybrid, intermittent flood and perennial water source systems. [ABSTRACT FROM AUTHOR]

Imaging disdrometers are widely used in field campaigns to provide information on the shape of hydrometeors, together with the diameter and the fall velocity, which can be used to derive information on the shape–size relations of hydrometeors. However, due to their higher price compared to laser disdrometers, their use is limited to scientific research purposes. The 3D stereo (3DS) is a commercial imaging disdrometer recently made available by Thies Clima and on which there are currently no scientific studies in the literature. The most innovative feature of the 3DS is its ability in capturing images of the particles passing through the measurement volume, crucial to provide an accurate classification of hydrometeors based on information about their shape, especially in the case of solid precipitation. In this paper. the performance of the new device is analyzed by comparing 3DS with the Laser Precipitation Monitor (LPM) from the same manufacturer, which is a known laser disdrometer used in many research works. The data used in this paper were obtained from measurements of the two instruments carried out at the Casale Calore site in L'Aquila during the CORE-LAQ (Combined Observations of Radar Experiments in L'Aquila) campaign. The objective of the comparison analysis is to analyze the differences between the two disdrometers in terms of hydrometeor classification, number and falling speed of particles, precipitation intensity, and total cumulative precipitation on an event basis. As regards the classification of precipitation, the two instruments are in excellent agreement in identifying rain and snow; greater differences are observed in the case of particles in mixed phase (rain and snow) or frozen phase (hail). Due to the different measurement area of the two disdrometers, the 3DS generally detects more particles than the LPM. The performance differences also depend on the size of the hydrometeors and are more significant in the case of small particles, i.e., D < 1 mm. In the case of rain events, the two instruments are in agreement with respect to the terminal velocity in still air predicted by the Gunn and Kinzer model for drops with a diameter of less than 3 mm, while, for larger particles, terminal velocity is underestimated by both the disdrometers. The agreement between the two instruments in terms of total cumulative precipitation per event is very good. Regarding the 3DS ability to capture images of hydrometeors, the raw data provide, each minute, from one to four images of single particles and information on their size and type. Their number and coarse resolution make them suitable to support only qualitative analysis of the shape of precipitating particles. [ABSTRACT FROM AUTHOR]

In the face of humanitarian crises such as torrential rainfall, resulting floods, and landslides, rapid rescue operations are often imperative. However, owing to the inherent dangers and unpredictability of such circumstances, immediate on-site aid delivery is frequently unfeasible. In such challenging scenarios, mobile robot systems have emerged as the optimal solution for aiding search and rescue efforts. The 'Informational system for management of flood and landslide disaster areas using a distributed heterogeneous robotic team' project, initiated by the International e-ASIA Joint Research Program, united research teams from Japan, Russia, and Thailand, each contributing unique expertize and experience towards common objectives. Drawing upon our extensive theoretical and practical knowledge in disaster response research, we developed an integrated international operational framework for disaster site management, centered on dispersed, heterogeneous semi-autonomous Unmanned Aerial Vehicles (UAVs), Semi-Autonomous Rough Terrain Vehicles (UGVs), and Semi-Autonomous Surface Vehicles (USVs). This paper provides an overview of the methodologies, models, and algorithms employed throughout the successful three-year project conducted by the Thailand team while also directing interested readers to supplementary research papers for in-depth insights. [ABSTRACT FROM AUTHOR]

Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-yr return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical-cyclone-related 5-yr rainfall events is increasing, which makes it a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing 2 decades from the previous active era (1951–70) with two from the current era (2001–20). This comparison also shows that both the mean rainfall and the maximum rainfall associated with tropical cyclones are increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians. Significance Statement: The purpose of this study is to analyze the changes in frequency and magnitude of extreme precipitation events associated with tropical cyclones with the goal of developing a new indicator for climate change. This is important because heavy rainfall and associated flooding is one of the primary causes of tropical cyclone destruction and fatalities, especially in inland locations away from where storms initially make landfall. Our results show that both the frequency and magnitude of extreme rainfall events from tropical cyclones have increased over the CONUS. The strongest storms (major hurricanes) also show more of an increase in extreme rainfall than storms of weaker intensities. [ABSTRACT FROM AUTHOR]

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

The precipitation estimation with high spatial and temporal resolution over Tibet is of great significance for monitoring the stability of the plateau ecosystem and early warning of natural disasters. In response to the problem of the unsatisfactory accuracy in traditional real-time precipitation inversion methods and quantitative accuracy estimation products in this region. This paper adopts deep-learning methods to explore the corresponding relationship between precipitation and multi-band observation data from geostationary satellite imagery. The U-High Resolution Network (U-HRNet) as backbone is fully exploited to generate multi-scale features and fused using attention gates to extract high-resolution spatial representations representing precipitation clouds, thereby achieving precipitation estimation of target clouds at different scales. In addition, in response to the characteristics of high precipitation intensity and drastic cloud cluster changes in summer convective weather within a short period of time, a multi-head attention mechanism is introduced to extract temporal information from time series inputs and fuse it with spatial features to improve the accuracy of real-time precipitation estimation. Compared with the operational precipitation products and baseline deep-learning models, the classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR) and Critical success index (CSI) show that the proposed model (U-HRNet+) has better performance in test set and has high generalization ability for estimating short-term heavy rainfall over Tibet and its surrounding areas in summer. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. [ABSTRACT FROM AUTHOR]

In this work, we formulate a regionalization framework for rainfall intensity–time scale–return period relationships which is applied over the Greek territory. The methodology for single-site estimation is based on a stochastic framework for multi-scale modelling of rainfall intensity which is outlined in the companion paper. Five parameters are first fitted independently for each site and the resulting parameter variability is assessed. Following a systematic investigation of uncertainty and variability patterns, two parameters, i.e. the tail-index and a time scale parameter, are identified as constant in space and estimated using data pooling techniques. The other three parameters are regionalized over Greece by means of spatial interpolation and smoothing techniques that are assessed through cross-validation in a multi-model framework. The regionalization scheme is implemented in a sequential order that allows exploiting rainfall information both from rainfall stations with sub-daily resolution and from the more reliable network of daily raingauges. [ABSTRACT FROM AUTHOR]

This work presents a stochastic framework for the construction of rainfall intensity–time scale–return period relationships, which was applied in the recent regionalization of design rainfall curves over the Greek territory, described in a companion paper. The methodology outlined herein builds upon a widely-used mathematical framework, which has been recently revisited and upgraded, and incorporates two different versions: (a) a theoretically consistent stochastic model applicable for rainfall intensity over any scale of interest; and (b) a simplified version valid over small scales, which makes parameter estimation easier. Special attention is given to the presentation of the simplified version, which suffices for most engineering tasks. Parameter estimation approaches are presented in detail, including the K-moments framework that allows for reliable high-order moment estimation and handling of bias due to spatiotemporal dependence. [ABSTRACT FROM AUTHOR]

As the Wind blows over the ocean surface, the generated waves are the primary factor affecting the microwave backscatter of the remote sensing radar. In addition, there are also some influencing factors, such as rainfall, which is the best-known atmospheric phenomenon. This paper proposes a wind and rain backscatter model whose angle of incidence is from 1 to 6 deg. The model is developed by considering the influence of ocean surface wind, atmospheric rain attenuation, rain volume backscatter and interactions between raindrops and ocean surface. The wind-induced component of the model adopted a quasi-specular theoretical model for the isotropic rough surface of Gaussian statistics. Rain attenuation is expressed as a coefficient of the model, and rain volume backscatter is expressed as a function of the rain volume backscattering coefficient, the thickness of precipitation layer and the incidence angle. Interactions between raindrops and ocean surface are complicated and deduced from the collocated dataset from the Precipitation Radar onboard the Tropical Rainfall Measuring Mission satellite and the European Centre for Medium-Range Weather Forecasts. Based on the collocated dataset, the result shows the proposed model can estimate reasonably accurate estimates surface backscatter within wind speeds range from 8 to 18 m s−1. [ABSTRACT FROM AUTHOR]

Rainfall can cause seepage erosion of the fine particles in soil (FPS) within the wide grading of particles present in a deposit slope, reducing its strength and eventually leading to a landslide. Unfortunately, there is no effective method for simulating this phenomenon; therefore, using the finite-element method (FEM), the whole process for establishing the slope stability analysis method is described considering the effect of the erosion of FPS, which includes equations of the FPS erosion process, introducing the real seepage parameters obtained by large-scale soil-water characteristic curve (SWCC) tests, and establishing correlations among water content, fine particle content, and slope strength. The findings of the application of this method to a real project reveal that FPS erosion occurs most readily on the first and second levels of a subslope, causing shallow instability of the slope and a reduction in the slope factor of safety (FOS); however, the higher the rainfall intensity, the lower the slope FOS. The method in this paper is suitable for the deposit slope with a certain content of FPS. [ABSTRACT FROM AUTHOR]

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]

Earth – satellite microwave links such as TV-SAT can help for rainfall monitoring and could be a complement or an alternative to ground-based weather radars, rain gauges or satellites dedicated to Earth observation. Rain induced attenuation which is harmful for telecommunication is exploited here as an opportunistic way to estimate rain rate along the path link. This technique makes it possible to obtain rain measurements at a fine temporal resolution (a few tens of seconds) and with a spatial resolution of few kilometers, which is a good compromise for human activities such as civil security (watershed monitoring, flash flood), agriculture or transport. Among the advantages of this technique, one can note the low cost of the hardware used (which can be commercial) as well as that of its maintenance on site. However, measured attenuation does not directly provide rain intensity and some parameters have to be estimated. Among these, it is necessary to take into account the contribution of the natural radiation of the atmosphere. In this paper, we detail a theoretical framework allowing to estimate rainfall from the measurements of a low-cost sensor operating simultaneously over two parts of the Ku-frenquency band. Then this framework is assessed in a densely instrumented area in south of France, where it is shown that very good results are obtained when compared to rain gauges measurements, both in terms of overall rain accumulation and in terms of rainfall rate distribution. Then we apply this dual channel method in Ivory Coast, in the metropolitan area of Abidjan, where such an approach is very promising. It is shown that this technique when compared to rain gauge measurements give results far better than a single-channel naive approach neglecting natural radiation of atmosphere, but that there are still significant errors in the rain assessment, leading to a persistent underestimation of rain accumulation. Finally we discuss various effects that could lead to this remaining underestimation, opening the door to further studies. [ABSTRACT FROM AUTHOR]

Radar signatures of rain over the ocean have a complex structure since they receive contributions from surface scattering and volume scattering and attenuation by hydrometeors in the atmosphere. These contributions overlap and are often difficult to detangle. While most of the mechanisms contributing to radar signatures of rain over the ocean are well understood, there is one remaining issue that has been discussed controversially in the literature for a long time. It is the question what scattering mechanism causes the areas of strongly enhanced radar backscatter, also called 'bright blobs' or 'bright patches', which are frequently observed on spaceborne C-band SAR images acquired over tropical and subtropical oceans in the presence of convective rain. Recently, papers have been published in which it is hypothesized that they are caused by radar backscattering at hydrometeors in the melting layer (ML). Although many observational facts seem to support this hypothesis, there exists one strong argument against this hypothesis: It is the observation that the position of the ML radar signatures (bright blob) in the SAR image is not shifted in anti-range from the position, where the rain column hits the sea surface. This absence of a shift is observed when 1) comparing Sentinel-1 SAR images on which rain cells are visible with quasi-concurrently acquired weather radar images and 2) when inter-comparing of SAR images of rain cells acquired concurrently at different frequencies and polarizations. Based on these observations, we discard the hypothesis that the bright blobs are due to volume scattering at hydrometeors in the ML and hypothesize instead that they are due to scattering at splash products at the sea surface. This hypothesis is supported by radar backscattering measurements carried out in the laboratory and from a shore-based platform, which show that, at C-and X-band, strong rain can give rise to strong radar returns also at cross-polarization. [ABSTRACT FROM AUTHOR]

Rainfall is an important phenomenon for various aspects of human life and the environment. Accurate prediction of rainfall is crucial for a wide range of sectors, including agriculture, water resources management, energy production, disaster management and many more. The ability to predict rainfall in an accurate fashion enables stakeholders to make informed decisions and take necessary actions to mitigate the impacts of natural disasters, water scarcity and other issues related to rainfall. In addition, advances in rainfall prediction technologies have the potential to contribute to sustainable water management and the preservation of water resources by providing the necessary information for decision‐makers to plan and implement effective water management strategies. Hence, it is important to continuously improve the accuracy of rainfall prediction. In this paper, the integration of the maximum overlap discrete wavelet transform (MODWT) and machine learning algorithms for daily rainfall prediction is proposed. The main objective of this study is to investigate the potential of combining MODWT with various machine‐learning algorithms to increase the accuracy of rainfall prediction and extend the forecast time horizon to 3 days. In addition, the performances of the proposed hybrid models are contrasted with the models hybridized with commonly used discrete wavelet transform (DWT) algorithms in the literature. For this, daily rainfall raw data from three rainfall observation stations located in Turkey are used. The results show that the proposed hybrid MODWT models can effectively improve the accuracy of precipitation forecasting, based on model evaluation measures such as mean square error (MSE) and Nash‐Sutcliffe coefficient of efficiency (CE). Accordingly, it can be concluded that the integration of MODWT and machine learning algorithms have the potential to revolutionize the field of daily rainfall prediction. [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]

This paper describes a novel methodology for establishing the relative moisture performance of renders used to protect a novel walling system combining traditional dense cob with a low-density thermal cob. A new method of enhancing the thermal insulation of cob walls has been developed by an European Union (EU)-funded joint UK and French project called CobBauge. The external surface of traditional cob walls (comprising subsoil, fibre and water) is normally protected from driving rain by placing the walls on a short wall called a plinth, a pronounced eaves overhang and a breathable exterior render. As the low-density fibre and clay insulating layer in the CobBauge system differs from traditional cob, a method of establishing the moisture-related performance of a range of renders needed to be instituted to aid the materials selection process. A series of test panels were constructed with the thermal cob infill, faced by a number of render types. The panels were hung in an open position in a high exposure zone and the moisture content monitored using wood-block sensors, electrical resistivity and gravimetric measurements over a period of six months. The wood-block and electrical resistivity moisture measurements showed a good level of agreement with gravimetric measurements and showed a clear differentiation between the various render choices. [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 prediction and prevention of landslide hazard is a challenging topic involving the assessment and quantitative evaluation of several elements: geological and geomorphological setting, rainfalls, and ground motion. This paper presents the multi-approach investigation of the Nevissano landslide (Asti Province, Piedmont, NW Italy). It shows a continuous and slow movement, alongside few paroxysmal events, the last recorded in 2016. The geological and geomorphological models were defined through a field survey. An inventory of the landslide's movements and rainfall records in the period 2000–2016 was performed, respectively, through archive investigations and the application of "Moving Sum of Daily Rainfall" method, allowing for the definition of rain thresholds for the landslide activation (105 mm and 193 mm, respectively, in 3 and 30 days prior to the event). The displacements over the last 8 years (2016–2023) were monitored through an innovative in-continuum monitoring inclinometric system and Earth Observation (EO) data (i.e., relying on Interferometric Synthetic Aperture Radar, or InSAR data): it gave the opportunity to validate the rainfall thresholds previously defined. This study aims to provide information to public authorities for the appropriate management of the site. Moreover, the proposed workflow could be adopted as a guideline for investigating similar situations. [ABSTRACT FROM AUTHOR]

Background: In Thailand, the Department of Disease Control (DDC) regularly performs visual larval surveys throughout the country to monitor dengue fever outbreaks. Since 2016, the DDC switched from a paper-based to a digital-based larval survey process. The significant amount of larval survey data collected digitally presents a valuable opportunity to precisely identify the villages and breeding habitats that are vulnerable to dengue transmission. Methods: The study used digitally collected larval survey data from 2017 to 2019. It employed larval indices to evaluate the risk of dengue transmission in villages based on seasonal, regional, and categorical perspectives. Furthermore, the study comprehensively scrutinized each container category by employing different measures to determine its breeding preference ratio. Results: The result showed that villages with a very high-risk of dengue transmission were present year-round in all regions, with the highest proportion during the rainy season. The Southern region had more high-risk villages during the winter season due to rainfall. Slums and residential communities were more vulnerable to dengue than commercial areas. All container categories could potentially serve as breeding habitats for dengue-carrying mosquitoes, with abandoned containers being the most significant breeding sites. Conclusions: The risk of dengue transmission was present year-round throughout Thailand. This underscores the importance of community and government initiatives, along with sustained public awareness campaigns and active community engagement, to efficiently and permanently eradicate mosquito breeding habitats. It should be noted that larval indices may not strongly correlate with dengue cases, as indicated by the preliminary analysis. However, they offer valuable insights into potential breeding sites for targeted preventive measures. [ABSTRACT FROM AUTHOR]

The spread of infectious diseases was further promoted due to busy cities, increased travel, and climate change, which led to outbreaks, epidemics, and even pandemics. The world experienced the severity of the 125 nm virus called the coronavirus disease 2019 (COVID-19), a pandemic declared by the World Health Organization (WHO) in 2019. Many investigations revealed a strong correlation between humidity and temperature relative to the kinetics of the virus's spread into the hosts. This study aimed to solve the riddle of the correlation between environmental factors and COVID-19 by applying RepOrting standards for Systematic Evidence Syntheses (ROSES) with the designed research question. Five temperature and humidity-related themes were deduced via the review processes, namely 1) The link between solar activity and pandemic outbreaks, 2) Regional area, 3) Climate and weather, 4) Relationship between temperature and humidity, and 5) the Governmental disinfection actions and guidelines. A significant relationship between solar activities and pandemic outbreaks was reported throughout the review of past studies. The grand solar minima (1450-1830) and solar minima (1975-2020) coincided with the global pandemic. Meanwhile, the cooler, lower humidity, and low wind movement environment reported higher severity of cases. Moreover, COVID-19 confirmed cases and death cases were higher in countries located within the Northern Hemisphere. The Blackbox of COVID-19 was revealed through the work conducted in this paper that the virus thrives in cooler and low-humidity environments, with emphasis on potential treatments and government measures relative to temperature and humidity. Highlights: • The coronavirus disease 2019 (COIVD-19) is spreading faster in low temperatures and humid area. • Weather and climate serve as environmental drivers in propagating COVID-19. • Solar radiation influences the spreading of COVID-19. • The correlation between weather and population as the factor in spreading of COVID-19. [ABSTRACT FROM AUTHOR]

The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model's precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms. [ABSTRACT FROM AUTHOR]

The continuous downward movement exhibited by the Lashagou landslide group in recent years poses a significant threat to the safety of both vehicles and pedestrians traversing the highway G310. By integrating geomorphological interpretation using multi-temporal optical images, interferometric synthetic aperture radar (InSAR) measurements, and continuous global navigation satellite system (GNSS) observations, this paper traced the formation period of the Lashagou landslide group, and explored its kinematic behavior under external drivers such as rainfall and snowmelt. The results indicate that the formation period can be specifically categorized into three periods: before, during, and after the construction of highway G310. The construction of highway G310 is the direct cause and prerequisite for the formation of the Lashagou landslide group, whereas summer precipitation and spring snowmelt are the external driving factors contributing to its continuous downward movement. Additionally, both the long-term seasonal downslope movement and transient acceleration events are strongly controlled by rainfall, and there is a time lag of approximately 1–2 days between the transient acceleration and heavy rainfall events. This study highlights the benefits of leveraging multi-source remote sensing data to investigate slow-moving landslides, which is advantageous for the implementation of effective control and engineering intervention to mitigate potential landslide disasters. [ABSTRACT FROM AUTHOR]

Tropical Africa and the North Atlantic Ocean are significantly influenced by African easterly waves (AEWs), which play a fundamental role in tropical rainfall and cyclogenesis in that region. The dynamics of AEWs can be described in a potential vorticity (PV) framework. The important impact of latent heat release by cloud processes is captured in this framework by the diabatic generation of PV anomalies. This paper introduces an innovative approach for the identification and tracking of PV structures within AEWs. By employing AEW tracking and computing the wave phase of each point within the AEW domain using a Hilbert transform, we are able to effectively identify and collect 3-D PV structures associated with specific AEWs. To facilitate a climatological analysis, performed here over the months of June to October from 2002 to 2022, these structures are subsequently characterized by low-dimensional descriptors, including their location, intensity, and orientation. Our climatological analysis reveals the seasonal evolution and the structural attributes of PV anomalies within AEWs over the study domain. PV feature locations closely align with the African easterly jet's latitudinal shift during the summer season. Analysis of the mean pressure level of the 3-D PV structures shows a remarkable shift during their life cycle, indicating deep moist convection characteristics over land and more shallow convection characteristics over the ocean. On average, PV features identified within AEW troughs tilt downshear over land and equatorward over the ocean. The trough-centered analysis reveals distinct differences between satellite-estimated and model-predicted rainfall. Agreement between the results of a more traditional composite analysis and our new feature analysis provides confidence in our feature approach as a novel diagnostic tool. The feature framework provides a low-dimensional representation of the PV structure of AEWs, which facilitates future statistical analyses of the relation of this structure to, for example, tropical cyclogenesis or the predictability of tropical rainfall. [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]

Indian Space Research Organisation (ISRO) has successfully launched its second civilian C-band Synthetic Aperture Radar (SAR) mission onboard Earth Observation Satellite-04 (EOS-04). The SAR data monitors and measures various atmospheric features and parameters. In this paper, we report on the investigation of EOS-04 data for several atmospheric phenomena. One of the crucial parameters for studying atmospheric manifestations in SAR data is ocean surface winds and an algorithm for its retrieval has been developed using EOS-04 data. The wind speed products thus generated are evaluated using observations from the Advanced Scatterometer and subsequently used to study atmospheric phenomena like boundary layer structures. The EOS-04 SAR data is also demonstrated for studying structures associated with tropical cyclones, coupling of rain and wind imprints and distinct signatures of an atmospheric front. The study outcomes are used to interpret atmospheric phenomena and understand backscattering signals from EOS-04 SAR. This indicates the possibility and potential of a gamut of atmospheric phenomena that can be probed using EOS-04 SAR data. [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]

This paper presents an open-access data set of reanalysed radar reflectivities and rainfall rates at sub-kilometre spatial and minute temporal scales. Variability at these scales is a blind spot for both operational rain gauge networks and operational radar networks. In the urban area of Hamburg, precipitation measurements of a single-polarized X-band weather radar operating at high temporal (30s), range (60m), and azimuthal sampling (1°) resolutions are made available for a period of more than 8 years. We describe in detail the reanalysis of the raw radar data, outline the radar performance for the years 2013 to 2021, and discuss open issues and limitations of the data set. Several sources of radar-based errors were adjusted gradually, affecting the radar reflectivity and rainfall measurements, e.g. noise, alignment, non-meteorological echoes, radar calibration, and attenuation. The deployment of additional vertically pointing micro rain radars yields drop size distributions at the radar beam height, which effectively reduces errors concerning the radar calibration and attenuation correction and monitors the radar data quality. A statistical evaluation revealed that X-band radar reflectivities and rainfall rates are in very good agreement with the micro rain radar measurements. Moreover, the analyses of rainfall patterns shown for an event and accumulated rainfall of several months prove the quality of the data set. The provided radar reflectivities facilitate studies on attenuation correction and the derivation of further weather radar products, like an improved rainfall rate. The rainfall rates themselves can be used for studies on the spatial and temporal scales of precipitation and hydrological research, e.g. input data for high-resolution modelling, in an urban area. The radar reflectivities and rainfall rates are available at https://doi.org/10.26050/WDCC/LAWR_UHH_HHG_v2. [ABSTRACT FROM AUTHOR]

Soil and water conservation and protection are of great importance to China, as the source of the Pearl and Xiang Rivers, and the main rare earth mining area, the ecology of Dongjiang River Basin, is very fragile. In this paper, the Chinese soil loss equation and the Geodetector method are used to analyze the spatial and temporal patterns and the impact factors of soil erosion in this area from 2016 to 2020. The results show that (1) Soil erosion intensity in the Dongjiang River Basin from 2016 to 2020 is mainly mild, with the soil erosion intensity in the Dongjiang River Basin in 2016 being the largest in five years. (2) The erosion modulus shows an increasing trend with the increase of slope. In terms of vegetation, when the vegetation cover is <30%, the soil erosion modulus is the largest, which is easy to induce soil erosion. When the vegetation cover is >60%, the soil erosion modulus is the smallest, which effectively slows down the occurrence of soil erosion. Rainfall is proportional to the soil erosion modulus and soil erosion. (3) The results of Geodetector indicate that the single factor sizes are land use > vegetation > slope > precipitation, and interaction factors all show a nonlinear enhancement. (4) The proper allocation of land use types, the prohibition of steep slopes for cultivation, and the restoration of forestry can effectively prevent and control soil erosion in the area. Core Ideas: Chinese soil loss equation enabled us to quantify soil erosion patterns.Geodetector unravels the underlying mechanisms responsible for spatial heterogeneity.The investigation highlighted the significant impact of intense rainfall events on steep slopes.Our analysis illustrates the shifting landscape of soil erosion within the Dongjiang River Basin.Our research sheds light on the key drivers behind this phenomenon. [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]

This paper describes the rainfall and microphysical structure of precipitation associated with Tropical Cyclone Ockhi using polarimetric Doppler weather radar (PDWR) products. The study reports the statistical analysis of precipitation types of tropical cyclone cloud systems over the north Indian Ocean, by combining the observations of the PDWR and disdrometer for the first time. There are few studies that mention initial DWR observations of TC over this low-latitude region below 23.5°N. We tried to further carry out a statistical analysis of precipitating clouds in a cyclone from its depression stage to severe cyclonic stage. This study tries to classify and quantify the contribution of convective and stratiform rain to the total TC rainfall. Precipitating clouds have been classified into convective and stratiform based on reflectivity measurements. The vertical profiles (VPRs) of the radar reflectivity Z and the differential reflectivity ZDR obtained for the stratiform and the convective events are compared. A study of the VPR of the convective events reveals that the Z and ZDR parameters tend to increase as the raindrops descend toward the ground owing to enhanced collision–coalescence processes. The VPR of stratiform rain shows signatures of the bright band (BB). The drop size distribution (DSD) parameters and rainfall rate pertaining to the two different precipitation regimes are estimated from the radar data and have been compared. The Joss–Waldvogel Disdrometer measurements have been used to derive DSD parameters and polarimetric rain-rate relation. [ABSTRACT FROM AUTHOR]

A reliable constitutive model is essential for accurately predicting slope deformation in numerical analysis, which includes assessing slope stability as an integral component. However, calculating slope stability in numerical models can be challenging due to complex boundary conditions and advanced constitutive models, especially when probabilistic and hydro‐mechanical coupled simulations are required. In this paper, we use a vector‐sum method‐based framework for slope stability analysis that incorporates a critical state hypoplastic constitutive model. The rationality and accuracy of the stability results are initially verified through critical slip surface analysis and a factor of safety evaluation. Subsequently, we investigate the probability‐based slope stability and failure characteristics by incorporating the spatial variability of strength parameters into the hypoplastic model. Finally, the Baishuihe landslide in the Three Gorges Reservoir Region is examined as a case study, where hydro‐mechanical coupled simulations are conducted under actual water level and rainfall conditions. The time‐varying stability and deformation characteristics of the landslide are analyzed and compared with the results obtained from the Mohr‐Coulomb model. The analysis results demonstrate the practicality and efficiency of using a hypoplastic model for probabilistic and time‐varying slope stability calculations. Furthermore, it highlights the superior ability of the hypoplastic model to accurately describe reservoir landslide deformation processes. [ABSTRACT FROM AUTHOR]

In this paper, the global distribution of precipitation for 2023, in terms of global totals and regional anomaly patterns, is analyzed using information from the new Global Precipitation Climatology Project (GPCP) V3.2 Monthly product, including how the precipitation amounts and patterns from 2023 fit into the longer record from 1983–2023. The tropical pattern of anomalies for 2023 is dominated by the effect of the El Nino which began during the Northern Hemisphere spring, after three plus years of La Nina conditions. The transition from La Nina conditions through 2022 shows the rapid change in many regional features from positive to negative anomalies or the reverse. Comparison of the observed regional trend maps with climate model results indicates similarity between the observations and the model results forced by observed SSTs, while the "free-running" model ensemble shows only a broad general agreement over large regions. Global total precipitation shows about a 3% range over the span of data, with El Nino and La Nina years prominent as positive and negative features, with 2023 showing a small positive global anomaly. The ITCZ (Inter-Tropical Convergence Zone) latitude band, 0–10° N, sets a record high mean rain rate in 2023 after a steady upward trend over the decades, probably a response related to global warming. [ABSTRACT FROM AUTHOR]

Floods significantly impact the well-being and development of communities. Hence, understanding their causes and establishing methodologies for risk prevention is a critical challenge for effective warning systems. Complex systems such as hydrological basins are modeled through hydrological models that have been utilized to understand water recharge of aquifers, available volume of dams, and floods in diverse regions. Acquiring real-time hydrometeorological data from basins and rivers is vital for establishing data-driven-based models as tools for the prediction of river-level dynamics and for understanding its nonlinear behavior. This paper introduces a hydrological model based on a multilayer perceptron neural network as a useful tool for time series modeling and forecasting river levels in three stations of the Rio Negro basin in Uruguay. Daily time series of river levels and rainfall serve as the input data for the model. The assessment of the models is based on metrics such as the Nash–Sutcliffe coefficient, the root mean square error, percent bias, and volumetric efficiency. The outputs exhibit varying model performance and accuracy during the prediction period across different sub-basin scales, revealing the neural network's ability to learn river dynamics. Lagged time series analysis demonstrates the potential for chaos in river-level time series over extended time periods, mainly when predicting dam-related scenarios, which shows physical connections between the dynamical system and the data-based model such as the evolution of the system over time. [ABSTRACT FROM AUTHOR]

This study comprehensively investigates how rain and drizzle affect the object-detection performance of non-contact safety sensors, which are essential for the operation of unmanned aerial vehicles and ground vehicles in adverse weather conditions. In contrast to conventional sensor-performance evaluation based on the amount of precipitation, this paper proposes spatial transmittance and particle density as more appropriate metrics for rain environments. Through detailed experiments conducted under a variety of precipitation conditions, it is shown that sensor performance is significantly affected by the density of small raindrops rather than the total amount of precipitation. This finding challenges traditional sensor-evaluation metrics in rainfall environments and suggests a paradigm shift toward the use of spatial transmittance as a universal metric for evaluating sensor performance in rain, drizzle, and potentially other adverse weather scenarios. [ABSTRACT FROM AUTHOR]

Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary: Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points: We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall testRecomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall testNo perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data [ABSTRACT FROM AUTHOR]

A typhoon passing through or making landfall in a coastal city may result in seawater intrusion and continuous rainfall, which may cause urban flooding. The urban flood disaster caused by a typhoon is a dynamic process that changes over time, and a dynamic Gaussian Bayesian network (DGBN) is used to model the time series events in this paper. The scene data generated by each typhoon are different, which means that each typhoon has different characteristics. This paper establishes multiple DGBNs based on the historical data of Macau flooding caused by multiple typhoons, and similar analysis is made between the scene data related to the current flooding to be predicted and the scene data of historical flooding. The DGBN most similar to the scene characteristics of the current flooding is selected as the predicting network of the current flooding. According to the topography, the influence of the surface confluence is considered, and the Manning formula analysis method is proposed. The Manning formula is combined with the DGBN to obtain the final prediction model, DGBN-m, which takes into account the effects of time series and non-time-series factors. The flooding data provided by the Macau Meteorological Bureau are used to carry out experiments, and it is proved that the proposed model can predict the flooding depth well in a specific area of Macau under the condition of a small amount of data and that the best predicting accuracy can reach 84%. Finally, generalization analysis is performed to further confirm the validity of the proposed model. [ABSTRACT FROM AUTHOR]

Extreme weather conditions such as heavy rainfalls have been wreaking havoc not only in urban areas but also in an entire watershed. The development of a flood management plan and flood mitigating structures to alleviate the impacts of flooding is very crucial because it needs intensive and continuous historical data. However, missing data due to equipment failure that gathers the rainfall data could be a problem. Rainfall data is not only useful in designing flood mitigating structures but also in planning our day-to-day activities ahead of time. To address this problem, this paper proposes a predictive model which able to forecast in a short lead-time and predict missing data within the dataset. In this paper, three predictive models will be compared namely recurrent neural network, Gaussian processing regression, and the proposed 6-gene genetic expression-based predictive modeling (MGGP). 29-year 24-hour cumulative rainfall data which were sourced in PAGASA Tacloban city weather station, Philippines, was used. The data were cleaned by removing negative values. Two datasets were created, the first (RFDS1) dataset which makes use of three indices (year, month, and days), and the second (RFDS2) dataset which was orchestrated and transformed to increase correlation and reduce prediction errors which had an additional two datasets (ave(t-1,t-2),t-1). Each method used three and five time-based indices. The result shows an erratic behavior of the model from three methods that used the RFDS1, while RFDS2 had a more stable predictive model. This shows that the data orchestration and transformation greatly improved the correlation and reduced errors. However, MGGP showed the best results among the three methods. [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]

Context dependence arises when ecological relationships vary with the conditions under which they are observed. Context dependence of interactions involving parasites is poorly known, even if it is key to understanding host–parasite relationships and food web dynamics. This paper investigates to which extent predation pressure on an avian ectoparasite (Carnus hemapterus) is context‐dependent. Based on a predator‐exclusion experiment, predation pressure on C. hemapterus pupae in the host's nest for 3 years, and its variation between habitat types are quantified. Variation in precipitation and normalized difference vegetation index (NDVI) is also explored as a likely cause of context dependency. We hypothesize that predation pressure should fluctuate with such surrogates of food availability, so that inter‐annual and intra‐annual differences may emerge. The number of nests with significant reduction of pupae varied widely among years ranging from 24% to 75%. However, average pupae reduction in nests where a significant reduction occurred did not vary between years. No differences in predation rates between habitat types were detected. Precipitation and NDVI varied widely between years and NDVI was consistently lower around nests on cliffs than around nests on trees and farmhouses. Parallels were found between variation in predation pressure and precipitation/NDVI at a wide scale (highest predation the driest year, and much lower the 2 rainier ones), but not at the nest scale. This paper shows clear context‐dependent insect predation pressure on an ectoparasite under natural conditions, and that such interaction changes in signs rather than magnitude between years. The causes for these variations require longer‐term studies and/or well‐designed, large‐scale experiments. [ABSTRACT FROM AUTHOR]

Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from "citizen science" and "crowdsourcing," allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors. Significance Statement: New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems. [ABSTRACT FROM AUTHOR]