Rainfall Time Research Articles

Diurnal variations of the meridional overturning circulations over West Africa during the premonsoon and monsoon seasons

AbstractDiurnal variations in the shallow and deep meridional overturning over West Africa during the premonsoon (April–May) and monsoon (July–September) seasons were analyzed using ECMWF Reanalysis 5th Generation (ERA5) reanalysis and Tropical Rainfall Measuring Mission (TRMM)/Global Precipitation Measurement (GPM) satellite radar data from 1998 to 2019. We found that a daytime sea breeze and two nocturnal low‐level jets have varying impacts on the diurnal cycle of convection and the meridional circulations over West Africa, depending on the season. During the premonsoon, the zonal rain belt is centered over the Gulf of Guinea and the sea breeze initiates intense deep convection (and thus deep overturning) over the coastal region in the afternoon that propagates northward into the early evening. The nocturnal Sahel/Sahara low‐level jet (SLLJ) then drives low‐level convergence into the West African heat low, producing a strong, dry shallow overturning over land at night. During the monsoon, the rain belt is centered over land (N) and the sea breeze is forced by weaker temperature gradients, producing less intense convection that does not propagate as far inland as during the premonsoon. However, organized convection inland maintains deep daytime overturning. At night, a coastal low‐level jet strengthens northward moisture transport from the Gulf of Guinea into the active monsoon region after the sea breeze dissipates, helping maintain convective systems with large stratiform components and continued deep meridional overturning over land. The nocturnal SLLJ still drives low‐level convergence into the West African heat low, but the dry shallow meridional overturning is farther north and weaker than in the premonsoon. While ERA5 winds and moisture‐flux convergence are generally consistent with diurnal variations in the TRMM/GPM precipitation, ERA5 precipitation is not. The reanalysis indicates significant biases in the timing and magnitude of rainfall over the Gulf of Guinea and West Africa in each season, with the convective and large‐scale rain fields both contributing to the disparate precipitation patterns.

Study on Fluid–Solid Coupling Numerical Simulation and Early Warning of Weathered Granite Landslides Induced by Extreme Rainfall

Rainfall is the main factor inducing landslides. Clarifying rainfall-induced landslides’ mechanisms and establishing adequate warning and prevention measures are crucial for regional disaster prevention and sustainable development. The weathering degree of fully weathered granite slopes is high, and the engineering mechanics property is poor, so it is easy to lose stability under extreme rainfall conditions. In this paper, the Fanling fully weathered granite landslide in Laoshan Scenic Spot in eastern China is taken as the research object, and the fluid–solid coupling landslide numerical model is established using ABAQUS 2022. The numerical simulation is carried out under five different rainfall intensity and time conditions, and the seepage field response, deformation response, and stability of the slope are analyzed. The research results indicate that (1) the fully weathered granite landslide in Fanling is a thrust-type landslide, and the response of horizontal deformation is greater than that of vertical deformation. (2) Compared with a long-term small rainstorm, a short-term heavy rainstorm is more harmful, and the slope is more prone to instability and damage. (3) The established unstable and under-stable rainfall warning curves for fully weathered granite landslides can provide a reference for the warning and prevention of similar regional landslides.

GEV Analysis of Extreme Rainfall: Comparing Different Time Intervals to Analyse Model Response in Terms of Return Levels in the Study Area of Central Italy

The extreme rainfall events of recent years in central Italy are producing an increase in hydrogeological risk, with disastrous flooding in terms of human lives and economic losses, as well as triggering landslide phenomena in correspondence with these events. A correct prediction of 100-year return levels could encourage better land planning, sizing works correctly according to the expected extreme events and managing emergencies more consciously through real-time alerts. In the recent period, it has been observed that the return levels predicted by the main forecasting methods for extreme rainfall events have turned out to be lower than observed within a few years. In this context, a model widely used in the literature, the generalised extreme value (GEV) with the “block maxima” approach, was used to assess the dependence of this model on the length of the collected precipitation time series and the possible addition of years with extreme events of great intensity. A total of 131 rainfall time series were collected from the Adriatic slope in central Italy comparing two periods: one characterised by 70 years of observations (1951–2020), the other by only 30 years (1991–2020). At the same time, a decision was made to analyse what the effect might be—in terms of the 100-year return level—of introducing an additional extreme event to the 1991–2020 historical series, in this case an event that actually occurred in the area on 15 September 2022. The results obtained were rather surprising, with a clear indication that the values of the 100-year return level calculated by GEV vary according to the length of the historical series examined. In particular, the shorter time series 1991–2020 provided higher return level values than those obtained from the 1951–2020 period; furthermore, the addition of the extreme event of 2022 generated even higher return level values. It follows that, as shown by the extreme precipitation events that have occurred in recent years, it is more appropriate to consider a rather short period because the ongoing climate change does not allow true estimates to be obtained using longer time series, which are preferred in the scientific literature, or possibly questioning the real reliability of the GEV model.

Effects of climate variability on respiratory diseases in the Western Region of Bahia, Brazil

ObjectivesThis study aimed to analyse hospitalisations for respiratory diseases in the Western Region of Bahia, Northeast Brazil, from 2010 to 2019, and to explore possible correlations with meteorological data. Study designThis descriptive, epidemiological, ecological study analysed data from 37 municipalities in the Western Bahia health macro-region, defined according to geographical, administrative, demographic, epidemiological, social and cultural criteria, and accounting for availability of health resources. MethodsHospitalisation data for respiratory diseases, including total admissions and disease frequency, mean and prevalence, were obtained from DATASUS (Ministry of Health). The data were evaluated by sex, age group and city. Statistical tests, such as the Chi-squared test and analysis of variance, were used for data analysis. Meteorological data were compared using the t-test and Mann–Whitney test. Correlations between health indicators and weather data were assessed using the Pearson and Spearman correlation coefficients. ResultsOver the investigated period, there were 536,195 hospitalisation records in the region, with respiratory diseases accounting for 17.1% of admissions. Notably, 40% of respiratory hospitalisations were among children aged 0–9 years. The most prevalent respiratory conditions were pneumonia and asthma, which together constituted 73% of all respiratory hospitalisations. A significant negative correlation was observed between respiratory diseases and rainfall (r = −0.70, P = 0.011). ConclusionsPneumonia and asthma remain important causes of hospitalisation among children in the Western Bahia Region. The study findings suggest that respiratory diseases are influenced by rainfall, possibly due to increased atmospheric pollutants during time of low rainfall. These findings emphasise the importance of environmental factors in the development and exacerbation of respiratory diseases.

A catastrophe identification method for rainfall time series coupled sequential Mann-Kendall algorithm and Bernaola Galvan algorithm: a case study of the Qinglong River watershed, China

AbstractThe identification of rainfall catastrophe characteristics is important for rainfall consistency testing in hydrostatistical analysis. In this study, a new classification method (trend, mean and change-rate catastrophe) was proposed and applied to the Qinglong River watershed, Northern China. Two groups of algorithms were compared to obtain the optimal algorithm: the Cumulative-anomaly method and the Sequential Mann-Kendall (SQ-MK) algorithm, the Pettitt algorithm and the Bernaola-Galvan heuristic segmentation (B-G) algorithm. Its parameters were optimized and its robustness was tested. Results revealed that: (1) The SQ-MK algorithm was suitable for trend catastrophe and sensitive to the length of the time series. The most significant point of trend catastrophe in the Qinglong River watershed was in 2012. (2) The sensitivity of parameter P0 (Range value (R) = 4.333) in the B-G algorithm was greater than that of parameter l0 (R = 2.889). (3) The B-G algorithm was suitable for identifying mean catastrophes and insensitive to the length of the time series. In the Qinglong River watershed, mean catastrophe points were identified in 1997, 2002, 2004, 2006, 2009, 2012, and 2018. (4) There was no change-rate catastrophe point in the Qinglong River watershed. Trend catastrophe and mean catastrophe do not necessarily lead to change-rate catastrophe.

Intended and unintended impacts of nitrogen-fixing microorganisms and microbial inhibitors on nitrogen losses in contrasting maize cropping systems.

Efforts to mitigate the nitrogen (N) footprint of maize production include using N-fixing microbes (NFM) and/or microbial inhibitors. We quantified the effects of NFM, the nitrification inhibitor (NI) DMPSA, and the urease inhibitor (UI) NBPT, each applied by itself or paired with another additive, on nitrous oxide (N2 O) emissions, nitrate (NO3 - ) leaching, and crop performance in contrasting irrigated and rainfed maize systems over two growing seasons. We also used published emission factors to estimate indirect N2 O emissions from leached NO3 - that can be converted to N2 O. Agronomic effects were relatively small; the NI+NFM treatment increased N use efficiency and grain yield and protein content in some cases by 11-14% relative to a treatment receiving only urea. Most of the additive treatments reduced direct (in-field) N2 O emissions, most consistently for treatments that contained NI which reduced emissions by 24-77%. However, these beneficial effects were counteracted by increased NO3 - leaching, which occurred most consistently with UI or NFM applied as single additives or with NI. In these treatments, NO3 - leaching increased during at least one growing season, and at both sites, by factors of 2 to 7. In three site-years, increased NO3 - leaching with NFM and NI+NFM offset large reductions in direct N2 O, such that total direct + indirect N2 O emissions were not different from that in the urea-only treatment. These unintended effects may have resulted from unfavorable rainfall timing, varying crop N demand, and declining additive effectiveness. Use of these soil additives requires caution and further study. This article is protected by copyright. All rights reserved.

Studying the evolution of Uttarkashi cloudburst event from reanalysis datasets–A case study

In recent years, the frequency and severity of cloudburst considerably increased over southern rim of Himalayas due to hot climate that leads to loss of human lives and damage properties. The observed rainfall data shows that cloudburst events with heavy rainfall ∼ 100–200 mm/day are common over the Himalayan region during the summer monsoon period. It is very necessary to understand the mechanisms associated with such type of short span of high impact localised weather events over the regions where observations are limited. Therefore, one of best way to study the mechanism associated with the formation and development of cloudburst is using the available high resolution reanalysis datasets. An effort is made to understand the role of atmospheric conditions that control the evolution of cloudburst event by considering two reanalyses datasets such as high resolution IMDAA and ERA5 reanalyses. The present study analysed a cloudburst case that occurred on 3rd August 2012 at 10 pm with heavy rainfall of ∼ 100 mm in a very short span of time over the Uttarkashi district. Various dynamic and thermodynamic parameters are calculated from the two datasets with an aim of determining the best representation of severity of the cloudburst event. It is noticed that the evolution of dynamic and thermodynamic variables is well represented in the high resolution IMDAA dataset as compared to the ERA5 dataset. The amount and spatial distribution of rainfall from IMDAA reanalyses are well comparable with satellite estimated rainfall (GPM), having better correlation (0.60) with the observed rainfall as compared to the ERA5 (0.28). The rainfall time bias over the Uttarkashi district is larger in ERA5 reanalyses (∼ 5 h) than in the IMDAA (∼ 3 h). The ERA5 is not able to capture such type of localise high rainfall event due to its low resolution, compared to high resolution reanalyses (12 km) of IMDAA. The observations also indicate that the moisture flux from the Bay of Bengal (BoB) and Arabian Sea interacted with northwesterly dry air over Uttarkashi and the orographic uplifting resulted the cloudburst. Overall, results show that the eveolution and mechanism associated with the cloudburst is better represented in IMDAA than the ERA5. More cases are required to be studied to further support the findings of this study.

Model Hibrid Harmonik, ARMA dan Outlier Curah Hujan di Surabaya, Malang dan Banyuwangi

The main factors affecting the climate in Indonesia are monsoons, El Nino Southern Oscillation (ENSO) and sunspot cycles. Another influence is local characteristics, namely the geography and topography of each region. The harmonic model is an applied statistical model of Fourier series analysis. Because the harmonic model is a deterministic model with residuals that often still have autocorrelation, it is necessary to add a stochastic Autoregressive Integrated Moving Average (ARIMA) model. With the existence of 3 main factors that influence the climate in Indonesia with different seasonal periods, plus local influences, the bulk modeling with Seasonal ARIMA, Double S ARIMA is not enough so that a combination of harmonics, ARMA and Additive Outliers (AO) appears. The purpose of this study is to find the periodicity of rainfall and model rainfall time series data with hybrid harmonics, ARMA outliers. Periodogram analysis of monthly rainfall data for three cities in East Java shows a 1-year seasonal return period influenced by monsoons, an 11-year return period influenced by sunspots that cannot be detected. The monthly rainfall model for the city of Surabaya is the 2nd order Harmonic Hybrid Model, ARMA([1,9,11],0,0) and the outlier type , for the city of Banyuwangi is the 1st order Harmonic Hybrid Model, ARMA (1,0,0) and 2 outliers of type , , Malang city are first order Harmonic Hybrid models, ARMA([6,7],0,0) and outliers of type . First order Harmonic Hybrid models, ARMA and Outlier are better than ARIMA and Outlier models.
 Key words: Monsoon, ENSO, sunspot, harmonics, ARMA and outliers.

Roles of soil surface roughness in surface–subsurface flow regulation and sediment sorting

Tillage-induced soil surface roughness (SSR) and rainfall interactions determine the process of water erosion. In this study, long-duration rainfall simulations were conducted to investigate the effect of SSR on the processes of surface–subsurface flow generation and soil loss, flow hydrodynamics, sediment sorting and transport on the steep red soil sloping farmland, southern China. Rainfalls with intensities of 30, 60, and 90 mm h−1 with duration of 180 min were applied to two soil surfaces (smooth and rough) with the slope gradient of 15°. Lesser surface flow and more subsurface flow were generated on the rough surface, which also accelerated the initial flow response and erosion stage advancement. The reduction effect of SSR on the sediment yield was significant under light rain or in the sheet erosion stage, while was limited when the rainfall intensity became stronger or entering the rill erosion stage. Differences in the spatial variability of soil crusts and the spatial distribution of rill networks were the main agents. Stream power took roughness factor into consideration well, thus was identified as the best hydraulic parameter for the prediction of soil loss rate. The investigation of particle size distribution of the eroded sediment showed the contents of sand, silt and clay changed from 15.9%, 64.9%, and 19.2% (average value of the original soil) to 22.9%, 53.1%, and 24% for the smooth surface, and to 37.8%, 42.1%, and 20.2% for the rough surface (average value of the eroded sediment). The selective and degrading effects of SSR on sediment particles were represented well. The rough surface was more inclined to transport clay particles than the smooth surface in the former period of erosion. Sand particles instead become more enriched with the extension of rainfall time. The results are beneficial for controlling the water erosion on agricultural sloping farmland.

Landscape heterogeneity increases the stability of wild ungulate populations facing climatic variability in Mediterranean ecosystems

Mediterranean environments are characterized by strong intra- and inter-annual fluctuations in plant primary production, which are likely to regulate the carrying capacity and density-dependent responses of ungulate populations. These effects may, however, vary across spatial and temporal scales. Habitat heterogeneity, particularly when associated to differentiated phenological responses, may allow wild ungulates to mitigate temporal fluctuations in plant production by using different resources along the year. In this work, we use a 15-years dataset (including remote-sensing data on vegetation distribution, phenology and production, as well as ungulate population counts) to assess how temporal variability in plant primary production and livestock abundance influence the population dynamics of two wild ungulates: native red deer, Cervus elaphus, and introduced fallow deer, Dama dama. Results show that temporal alternation in the phenological cycles of the four different vegetation types increased plant production, thus food availability for ungulates, within each year. Furthermore, complementarity in the responses of different vegetation types to variations in the amount and timing of rainfall increased the predictability of food availability across different years. This complementarity effect was further increased by the contrasting responses of ungulate populations to variation in the production of different vegetation types. Furthermore, domestic ungulates had positive effects on wild ungulate density at low to intermediate abundances, but high livestock densities decreased ungulate density and constrained the stability of the plant-ungulate system in response to the impact of climatic variation, particularly under climate change. Our findings deepen the knowledge on vegetation-ungulate interactions in Mediterranean areas, potentially contributing to develop better management strategies of ungulate populations and adapt them to ongoing climate change.

Accuracy dominant frequencies of Lomb - Scargle periodogram in modelingmonthly rainfall

In the field of hydrology, many researchers have developed time series models. Usually, the modelling uses time series data, such as daily rainfall, monthly rainfall, etc. In order to produce a good model, the frequencies used in the model must represent the data being modelled. Otherwise, the inaccurate frequencies used to cause the resulting model to be inaccurate. Several methods or techniques have been used to estimate the frequencies contained in time series data. These methods are the Fast Fourier Transform and Lomb-Scargle periodogram methods. This study used the Lomb-Scargle periodogram method to predict monthly rainfall time series data. In this research, the Lomb periodogram results in rainfall frequencies. The frequencies are created by using monthly rainfall time series data. Using the frequencies, the dominant frequencies can be selected. Periodic modelling of monthly rainfall time series can be adequately simulated using the dominant frequencies. The correlation coefficient between the monthly rainfall data with the monthly rainfall model can be used to measure the accuracy of the monthly rainfall model.

Homogeneity and change point detection of hydroclimatic variables: A case study of the Ghba River Subbasin, Ethiopia

In most studies on hydroclimatic variability and trend, the notion of change point detection analysis of time series data has not been considered. Understanding the system is crucial for managing water resources sustainably in the future since it denotes a change in the status quo. If this happened, it is difficult to distinguish the time series data’s rising or falling tendencies in various areas when we look at the trend analysis alone. This study’s primary goal was to describe, quantify, and confirm the homogeneity and change point detection of hydroclimatic variables, including mean annual, seasonal, and monthly rainfall, air temperature, and streamflow. The method was employed using the four-homogeneity test, i.e., Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test at 5% significance level. In order to choose the homogenous stations, the test outputs were divided into three categories: “useful”, “doubtful”, and “suspect”. The results showed that most of the stations for annual rainfall and air temperature were homogenous. It is found that 68.8% and 56.2% of the air temperature and rainfall stations respectively, were classified as useful. Whereas, the streamflow stations were classified 100% as useful. Overall, the change point detection analyses timings were found at monthly, seasonal, and annual time scales. In the rainfall time series, no annual change points were detected. In the air temperature time series except at Edagahamus station, all stations experienced an increasing change point while the streamflow time series experienced a decreasing change point except at Agulai and Genfel hydro stations. While alterations in streamflow time series without a noticeable change in rainfall time series recommend the change is caused by variables besides rainfall. Most probably the observed abrupt alterations in streamflow could result from alterations in catchment characteristics like the subbasin’s land use and cover. These research findings offered important details on the homogeneity and change point detection of the research area’s air temperature, rainfall, and streamflow necessary for the planers, decision-makers, hydrologists, and engineers for a better water allocation strategy, impact assessment and trend analyses.

Influence of the fracture on the seepage stability of loess slopes

The development of fractures in loess slopes often has a great influence on the stability of slopes. In this paper, taking the homogeneous slope model as an example, fractures with different distances and depths from the slope top angle are provided at the top of the slope. Firstly, the influence of single fracture location and depth on slope stability is analyzed, and after the most unfavorable location and depth of the fractures are determined, the influence of the slope top fractures on the slope seepage field and slope stability under the conditions with and without rainfall is studied secondly. The results show that with the increase of the distance from the fracture to the top angle of the slope, the safety coefficient of the slope shows the change law of decreasing first and then increasing. The critical fracture depth corresponding to the fractures at different locations is different, and when the critical fracture depth is exceeded, the slope safety factor tends to be stable. At the end of the rainfall moment through 1-1 section to analyze, it is found that at this time the pore water pressure and volume water content of the soil at the bottom of the fracture at different depths are greater than those of the upper soil, and the shallow fracture has less influence on the seepage field of the slope, and through 2-2 section it is found that the pore water pressure and volume water content in the soil far from the fracture are greater than those of the soil near the location of the fracture. When 288 h after the end of rainfall, the pore water pressure and volumetric water content of the soil around the fracture in section 2-2 were greater than those far from the fracture location. With the passage of time, the pore water pressure at the location of the fracture gradually dissipated and the corresponding volumetric water content gradually decreased, indicating that the fracture had an obvious influence on the characteristics of the seepage field in the soil at different times of rainfall. Under the condition of no rainfall, there exists a critical depth of fractures affecting slope safety stability, while under the condition of rainfall, the slope safety coefficient continuously decreases with the increase of fracture depth, and there is no critical fracture depth. The research results promote the understanding of the safety and stability of loess fracture slopes and provide a reference for slope management.

Assessment of Regression Model for Rainfall in Saudi Arabia (1979-2011) Using Dummy Variables

Objectives: This study aims to analyze rainfall in Saudi Arabia by designing models based on data from 20 stations across the Kingdom from 1979 to 2011.
 Methods: The analysis employed a multiple linear regression model with rainfall as the dependent variable and annual quarters as the independent variables. Dummy variables were utilized in the analysis. The regression model provided valuable insights into the impact of rainfall rates in different quarters across Saudi Arabia. Monthly data was collected from each region of the Kingdom during the study period and categorized into five groups based on average rainfall: Group 1 (5-15 mm), Group 2 (15-25 mm), Group 3 (25-35 mm), Group 4 (35-45 mm), and Group 5 (45-70 mm). Each group was represented by a separate regression model. To reduce the number of dummy variables in the model, the monthly data was converted to quarterly data.
 Results: A significant finding of this study is that all models were statistically significant, indicating that rainfall distribution is influenced by the annual quarters. Furthermore, it was observed that the average rainfall in most quarters across different regions was statistically significant, except for the fourth quarter in Group 5 and the third quarter in Groups 1, 2, and 4.
 Conclusions: The inclusion of dummy variables as independent variables in the multiple linear regression model proved to be a novel and effective approach for analyzing rainfall time series. The results can serve as a foundation for future studies, enabling prediction and informed decision-making based on the findings.

Distribution Characteristics of the Geoelectric Field in Waste Dump Slopes during the Evolution of Instability Sources under Rainfall Conditions

To study the evolution of geological hazard sources of waste dump slopes under rainfall conditions, a physical model of a rainfall-affected slope was designed. The apparent resistivity of the slope rock and soil mass at different rainfall times was measured via the high-density resistivity method, and the formation process of internal disaster sources of the rainfall-affected slope was obtained. The variation characteristics of the resistivity of the rain-affected slope were analyzed when it had a weak surface and crack development. Based on the three-water model and Maxwell conductivity formula, the evolution process of geological hazard sources of the rainfall-affected slope was summarized. A resistivity response mechanism equation for rainfall-induced slope hazard sources was derived and compared to the Archie formula, verifying the model rationality. The test results showed that the behavior of the rainfall-affected slope conforms to the saturated–unsaturated dynamic cycle process. The apparent resistivity was positively correlated with the development of slope pores and cracks and negatively correlated with the water content in the slope. The apparent resistivity increased during fracture development and decreased during water seepage. In the slope failure and disaster process, the apparent resistivity varies under the coupling effect of crack development and water seepage. During the formation of geological hazard sources, the apparent resistivity abruptly changes and fluctuates. Therefore, according to the abrupt changes and abnormal fluctuations in the apparent resistivity detected, the development of geological hazard sources of slopes can be determined.

Management strategies for maximizing the ecohydrological benefits of multilayer blue-green roofs in mediterranean urban areas

Multilayer Blue-Green Roofs are powerful nature-based solutions that can contribute to the creation of smart and resilient cities. These tools combine the retention capacity of traditional green roofs with the water storage of a rainwater harvesting tank. The additional storage layer enables to accumulate the rainwater percolating from the soil layer, that, if properly treated, can be reused for domestic purposes. Here, we explore the behavior of a Multilayer Blue-Green Roof prototype installed in Cagliari (Italy) in 2019, that have been equipped with a remotely controlled gate to regulate the storage capacity of the system. The gate installation allows to manage the Multilayer Blue-Green Roof in order to increase the flood mitigation capacity, minimizing the water stress for vegetation and limiting the roof load with adequate management practices. In this work, 10 rules for the management of the Multilayer Blue-Green Roof gate have been investigated and their performances in achieving different management goals (i.e., mitigating urban flood, increasing water storage and limiting roof load on the building) have been evaluated, with the aim to identify the most efficient approach to maximize the benefits of this nature based solution. An ecohydrological model have been calibrated based on field measurements carried out for 6 months. The model has been used to simulate the system performance in achieving the proposed goals, using as input nowdays and future rainfall and temperature time series. The analysis reveled the importance of the correct management of the gate, highthing how choosing and applying a specific management rule helps increasing the performance in reaching the desired goal.

Prediction of Rainfall Time Series Using the Hybrid DWT-SVR-Prophet Model

Accurate rainfall prediction remains a challenging problem because of the high volatility and complicated essence of atmospheric data. This study proposed a hybrid model (DSP) that combines the advantages of discrete wavelet transform (DWT), support vector regression (SVR), and Prophet to forecast rainfall data. First, the rainfall time series is decomposed into high-frequency and low-frequency subseries using discrete wavelet transform (DWT). The SVR and Prophet models are then used to predict high-frequency and low-frequency subsequences, respectively. Finally, the predicted rainfall is determined by summing the predicted values of each subsequence. A case study in China is conducted from 1 January 2014 to 30 June 2016. The results show that the DSP model provides excellent prediction, with RMSE, MAE, and R2 values of 6.17, 3.3, and 0.75, respectively. The DSP model yields higher prediction accuracy than the three baseline models considered, with the prediction accuracy ranking as follows: DSP > SSP > Prophet > SVR. In addition, the DSP model is quite stable and can achieve good results when applied to rainfall data from various climate types, with RMSEs ranging from 1.24 to 7.31, MAEs ranging from 0.52 to 6.14, and R2 values ranging from 0.62 to 0.75. The proposed model may provide a novel approach for rainfall forecasting and is readily adaptable to other time series predictions.

Vertical divergence profiles as detected by two wind‐profiler mesonets over East China: Implications for nowcasting convective storms

AbstractLow‐level convergence is one of the most important dynamic variables in governing the convection initiation (CI) and development of storms. However, our ability to obtain high‐resolution horizontal divergence profiles remains limited due to the lack of high‐resolution vertical wind observations. In this study, horizontal divergence (and vertical motion) profiles are derived from horizontal winds measured by two radar wind‐profiler (RWP) mesonets in East China, and then used to examine their relationship with CI occurring within a mesonet and an intense squall line moving across another mesonet. Results show that the RWP mesonets together with automated surface observations could provide realistically the lower‐tropospheric profiles of horizontal divergence (and upward motion) associated with the CI in the presence of the urban heat island effects, and lake and sea breezes; and the evolution of the squall line respectively. High‐resolution surface observations resolve better divergence induced by localized CI than the RWP measurements, whereas the latter capture well significant convergence below 4‐km altitude that precedes the onset of squall precipitation. A statistical analysis of the RWP data during the summer months of 2018 indicates that the lower‐tropospheric convergence can be detected up to about 40 min in advance of the occurrence of heavy rainfall, and the correlation is higher when it is closer to the heavy rainfall time. Nearly 40% of heavy rainfall moments (at 6‐min intervals) are accompanied by strong convergence signals occurring mostly near the top of the boundary layer. This work indicates that the RWP and surface wind measurements should be integrated to aid in nowcasting the location and timing of CI and development of convective storms.

Drought from the 1970s to the 1990s and its Influence in the Tropical City of Beni, Eastern DR Congo

Regional and local climate variability has serious consequences on water resources, ecosystems, and socio-economic activities. With the inexistence of rainfall stations, it is important to reconstruct the past climate with existing data and explore new possibilities for monitoring future climate evolution in the tropical city of Beni. The objective of this paper is to date the 1970-1990 drought and determine the rainfall trend in the tropical city of Beni. To this purpose, a 45-year rainfall time series (period 1974-2019) was analyzed using the Standardized Precipitation Index (SPI). The results of the paper indicate that the average annual rainfall ranges from 693 to 3080 mm with an average of 1827 ± 480.65 mm. The Hubert segmentation procedure applied to the rainfall series reveals three breaks (sub-periods): 1974-1979 (mean 1205.7 mm), 1980-2007 (mean 1736.68 mm), and 2008-2019 (mean 2348.01 mm). After the 2000s, the analysis shows an increasing trend in rainfall. A rainfall deficit of 35.20% is recorded during the period 1974-1979 compared to the period 1980-2007 and 22.61% for the period 1980-2007 compared to the period 2008-2019. The results of this study demonstrate that the city of Beni would have suffered from the drought of the years 1970-1990. Future research should focus on the future evolution of rainfall. However, understanding this future evolution requires long-term data, which is not the case for the tropical city of Beni. Therefore, there is a need for policymakers in collaboration with scientific actors to install meteorological stations to collect climate data.

An Analysis of Landslide Risk Using the Change in the Volumetric Water Content Gradient in the Soil Layer Per Unit Time of Effective Cumulative Rainfall

The topographical characteristics of South Korea are coupled with heavy rain and typhoons concentrated from July to September, which take up two-thirds of the country’s annual precipitation, causing substantial damage from slope failures, landslides, debris flows, and other mountain disasters. This study uses the Land-Atmosphere Modeling Package (LAMP) data to study the area, which has been affected by numerous landslides in the past. Specifically, to analyze the likelihood of slope failure, this study simulates the rainfall events in the area at the time of the landslides as well as changes in the volumetric water content in the soil layer and uses the changes in the volumetric water content per unit time, which are affected by the effective cumulative rainfall within the soil layer. To that end, this study assesses the risk of landslides based on how rainwater infiltration in the saturation depth changes the saturation depth ratio and compares the results with the landslide spots in the past. Moreover, this study analyzes the landslide risk by applying the saturation depth ratio (Ht) to an infinite slope stability analysis model. The landslide threshold proposed in this study was obtained by analyzing the rainfall events that caused the landslides in the study area using the LAMP model. It can then be concluded that, when compared with the spatial information on the previous landslides, if the change in the volumetric water content per unit time during the first and second rainfall events is 0.7, a landslide may occur. These results indicated that the landslide risk increases the volumetric water content in the soil layer, along with the congruence between past landslide risk points and spatial landslide risks.