Monthly Rainfall Research Articles

Rainfall is associated with divorce in the socially monogamous Seychelles warbler.

Divorce-terminating a pair bond whilst both members are alive-is a mating strategy observed in many socially monogamous species often linked to poor reproductive success. As environmental factors directly affect individual condition and reproductive performance, they can indirectly influence divorce. Given current climate change, understanding how environmental fluctuations affect partnership stability has important implications, including for conservation. Yet, the relationship between the environment and divorce remains largely unstudied. We examined the influence of temporal environmental variability on the prevalence of within- and between-season divorce and the possible underlying mechanisms in a socially monogamous passerine. Analysing 16 years of data from a longitudinal dataset, we investigated the relationship between rainfall and divorce in the Seychelles warbler (Acrocephalus sechellensis). First, we performed climate window analyses to identify the temporal windows of rainfall that best predict reproductive success and divorce. Then, we tested the effects of these temporal windows of rainfall on reproductive success and divorce and the influence of reproductive success on divorce whilst controlling for covariates. Annual divorce rates varied from 1% to 16%. The probability of divorce was significantly associated with the quadratic effect of 7 months of total rainfall before and during the breeding season, with divorce increasing in years with low and high rainfall. This quadratic relationship was driven by a heavy rainfall event in 1997, as excluding 1997 from our analyses left a significant negative linear relationship between rainfall and divorce. Although the same temporal window of rainfall predicting divorce significantly influenced reproductive success, we found no significant correlation between reproductive success and divorce. Our findings suggest that rainfall impacts divorce. Given that this effect is likely not directly mediated by reproductive success, we discuss other possible drivers. Although the 1997 super El Niño event shows how heavy rainfall may affect socially monogamous partnerships, more data are required to estimate the robustness of this effect. By adding to the growing body of literature showing that environmental conditions influence the stability of socially monogamous partnerships, we provide novel insights that may also be important for conservation efforts in times of climate change.

Combined impacts of climate and land use scenarios on sediment loads in small agricultural watershed in Tunisia

This study investigates the impact of conservation practices, specifically mulching and terracing, on soil erosion in the Kamech watershed (2.6 km²) situated in the Cap Bon region of northeastern Tunisia, under both current and future climate scenarios. Utilizing the Soil and Water Assessment Tool (SWAT) model, the research evaluates the effectiveness of these Best Management Practices (BMPs) in reducing soil loss, revealing significant reductions of 1.5 t ha⁻¹ yr⁻¹ for mulching and 2 t ha⁻¹ yr⁻¹ for terracing. The findings indicate that these practices play a crucial role in enhancing soil conservation amidst climate change challenges, including altered precipitation patterns and increased temperatures. Additionally, the study highlights a notable decline in average monthly rainfall projected for the period 2051 to 2100 under the RCP8.5 scenario, further emphasizing the importance of implementing effective soil management strategies. This paper indicates that both methods, mulching and terracing, significantly contribute to reducing soil erosion under future scenarios (2051-2100). Their implementation proves essential for effective soil conservation, helping to mitigate the impacts of climate change and maintain soil health in the Kamech watershed. The results contribute to a better understanding of BMP performance in addressing soil erosion, water quality, and hydrological responses in the face of evolving environmental conditions. By enhancing our understanding of these interactions, this research aims to provide valuable insights for sustainable watershed management, particularly in regions prone to water scarcity and land degradation.

Diversity and composition of Coreidae (Hemiptera: Heteroptera) in a seasonal forest of central Mexico

ABSTRACT The diversity of the Coreidae family in the tropical deciduous forest (TDF) of Coaxitlán, Tlaquiltenango, Morelos, Mexico, is presented. From the collection carried out during an annual cycle, 34 species, 18 genera, 8 tribes, and 2 subfamilies were recorded. The sample coverage was 99% and according to the Chao 1 richness estimator, 90% of the species in the study area were represented. The greatest richness, abundance, and diversity occur during the months of greatest rainfall. The faunal similarities between months show groupings in species composition corresponding to the TDF’s seasonality. The community structure reflects a high diversity, integrated by a high proportion (>60%) of species with a low abundance. The data show that the diversity of Coreidae in the TDF is high, but the environmental heterogeneity of this type of vegetation suggests that the species composition could still be different in other sites, which shows the need to continue documenting it.

Two Centuries of Monthly Rainfall in Barcelona (NE Spain): Disparity Trends, Correlation of Autumnal Rainfall with the WeMO Index and Its Contribution to Annual Amounts

Rainfall irregularity in Mediterranean regions is a characterizing feature of their climate. The aim of this manuscript is to analyze, in a climate change context, the evolution of this irregularity in Barcelona. A very long monthly database (1786–2023) enables detailed analysis of rainfall evolution, with its irregularity quantified using the concept of disparity, the trends of which are assessed using moving windows and a modified Mann–Kendall test. The relationship between disparity and the Western Mediterranean Oscillation index (WeMOi) is also explored. Additionally, the study compares rainfall amounts to the 1961–1990 reference period and evaluates autumn’s contribution to annual totals. A significant and increasing disparity trend over the years is detected for the autumn months. While correlations between disparity and WeMOi are limited, the WeMOi and monthly precipitation are significantly correlated for two autumn months, October and November, and for December, aligning with previous studies. This suggests the potential influence of the WeMOi fluctuations on future rainfall during these three months. Recent evidence of the increasing autumn irregularity is seen in the consecutive low-rainfall years of 2021, 2022 and 2023, which stand out as the driest since 1835, with the last two autumns ranking among the 5% driest.

Kernel-Based Versus Tree-Based Data-Driven Models: On Applying Suspended Sediment Load Estimation

River sediment load estimation poses a critical challenge for water engineers due to its complex and nonlinear hydrological processes. This study assessed the amount of suspended sediment at the Bagh-e-Kalayeh hydrometric station on the Alamut River in the Qazvin province of Iran using two hydrological and meteorological variables, including discharge and rainfall, by considering three scenarios (discharge, discharge + monthly rainfall, and discharge + monthly rainfall + daily rainfall). For modeling, kernel-based data-driven methods, including Gaussian process regression (GPR) and support vector regression (SVR), and tree models, including the M5 tree, random forest (RF), random tree (RT), extra trees, reduced error pruning tree (REPT), and multi-search methods, were used. The results showed that the best performance was achieved by the SVR, with r = 0.948, Wilmot index = 0.965, and RMSE = 0.011 in the first scenario (only discharge). Discharge had the most significant impact on sediment estimation compared to rainfall. It was determined that the suspended sediment load in the Alamut River can be successfully estimated by the SVR method, where only the discharge was used as the input parameter. Additionally, the results indicated that given its characteristics and inherent features, the multi-search method can be used as a complementary approach in sediment modeling, especially in situations where the data volume is not extensive.

LEVANTAMENTO DE PERFIL EPIDEMIOLÓGICO DA DENGUE EM ARAGUAÍNA NO ESTADO DO TOCANTINS

Analyzing the epidemiology of dengue in Brazil, focusing on the city of Araguaína in Tocantins, exploring the characteristics of the different virus serotypes, their clinical implications, and the importance of early diagnosis. A quantitative study was conducted with a time series analysis covering the period from 2014 to 2023. Using data from the Notification Diseases Information System (Sinan), the research correlated the number of dengue cases in Araguaína with variables such as sex, age, and months of the year. The objective was to ensure the accuracy of the results, avoiding distortions in interpretation. The research aims to provide information on the seasonality of the infection and identify more vulnerable groups, contributing to preventive actions. The analyzed data from SINAN indicate a significant increase in the incidence of dengue in Araguaína TO, especially during the months of higher rainfall, when conditions for the proliferation of Aedes aegypti are ideal. The research confirmed that the lack of adherence by the population to prophylactic measures contributes to this increase, reflecting the need to intensify awareness campaigns. It was also observed that the majority of reported cases are among women, suggesting a greater tendency to seek care in health units. Early identification of symptoms is crucial for effective treatment, helping to reduce mortality. Thus, the importance of continuous and appropriate preventive actions for dengue control is reinforced.

Defoliation and Die-back an Emerging Threat to Cashew Cultivation in the Konkan Region of Maharashtra, India

Cashew is a million-dollar crop, and it is the second most important cash crop in the Konkan region of Maharashtra after mango. Till 2019, the cashew plantations in the Konkan region were free from destructive diseases leading to economic losses. But in July and August 2019, the total monthly rainfall was 1711 and 1312 mm respectively, supported by cloudy weather and high humidity above 93% which led to the sudden outbreak of defoliation followed by dieback of the new vegetative flush of the plants, throughout the region. To combat this unexpected natural calamity thorough investigations were conducted. Extensive surveys conducted during the season revealed that in the primary phase of the disease, new as well as old cashew plants suffered from severe defoliation. On the establishment of the pathogen, the infection progressed to the newly emerged main and lateral branches which consequently lost vigour, turned brown, and then black. The colonization of pathogens within the tissues of the host leads to the formation of small white encrustations on the upper surface of the dead twigs. The pathogen was isolated and identified as Cylindrocladium spp.

Behavior of Slab-on-Grade Resting on Expansive Soil

Abstract This paper discusses a case study of a residential building in Benghazi that features slab-on-grade foundations supported by expansive soils. Although such structures are uncommon in the area, they can suffer serious damage due to volumetric changes in the soil caused by wetting and drying. The building has a total plan area of 200 m², with tie beams located 1.2 m above the formation level, and expansive soil used as backfill. After being moistened and lightly compacted, 100 mm thick concrete slabs were poured between the tie beams, resulting in varying slab sizes. Following a month of heavy rainfall, significant cracking—longitudinal, diagonal, and warping—occurred in the slabs due to uneven deformation from the expansive soil. The paper includes photographs of these deformations and cracks, along with an analysis of the geotechnical properties of the expansive soil, including its swelling and shrinking characteristics. It also employs an approach to predict the pressure exerted on the slabs due to differential heave.

Effects of sea surface temperatures on monthly and dekadal Kiremt rainfall variability in the contrasting topographical regions of northern Ethiopia

Effects of sea surface temperatures on monthly and dekadal Kiremt rainfall variability in the contrasting topographical regions of northern Ethiopia

Comparative evaluation of multiscaler drought indices under different climatic conditions in Gujarat state of India.

The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), two drought indices, have been compared for interchangeability and reliability under various climatic conditions in Gujarat, India. As the quality of the input is crucial for the accuracy of the index, weather records from surface observatories are preferable over grid and reanalysis data. This study found that on a short timescale (1-3months), SPEI diagnosed mild and moderate droughts more frequently than SPI, particularly in stations with relatively heavy rainfall. The indices in all timescales at all sites throughout the monsoon months displayed strong positive associations (r > 0.8). The correlation decreases but remains positive as the temporal scale is extended up to 8months. On a 9-months or longer scales that encompassed active monsoon rainfall months at all stations, correlation coefficients were between 0.8 and 0.9 for all months of the year. During monsoon months, high fractional matches were observed on a short scale. The months after the monsoon show a generalized diagonal pattern of high fractional match with the timescale for all stations. The kappa statistic followed a broad pattern comparable to the match fractions. The instances with poor agreements (R, Match and kappa < 0.3) had proportional bias between the indices. SPEI recognized more drought events at all stations in the short time periods, while the agreements increased with longer time scales. However, SPI detects high intensities in the subhumid.

Rainfall characteristics over the Congo Air Boundary Region in southern Africa: A comparison of station and gridded rainfall products

Strong meridional rainfall gradients exist between the tropics and subtropics in southwestern Africa, bounded to the north by the moist Congo basin and to the south by the Kalahari Desert. This domain received relatively little scientific attention compared to the rest of southern Africa. In this study, the limited available station data are assessed against six gridded rainfall products (CHIRPS, PERSIANN-CDR, ERA5, GPCC and CPC) for various rainfall characteristics. The nearest neighbour approach was used to match the closest rainfall dataset pixel to each station location, with the assumption that each rain gauge represents observation of various pixels of products, irrespective of product resolution. Results reveal that ERA5, CHIRPS and PERSIANN-CDR tend to represent the monthly rainfall totals and number of rainy days well for most stations although magnitudes and monthly peaks differ. CPC and GPCC tend to perform poorly for magnitudes of rainfall, rainy days and monthly cycles especially for Angolan stations. These products also fail to adequately capture spatial distributions of rainfall, with poor representation of the strong gradients found in the region.Correlations between various gridded rainfall products mostly show good agreement in rainfall totals and rainy days. For early summer (October–November-December) moderate rainy days, ERA5 and CHIRPS products tend to have more days than the stations while CPC and GPCC products perform poorly over Angola and in the south. ERA5 generally overestimates rainfall in mountainous regions, while other products tend to underestimate it. Based on the Simple Daily Intensity Index, it was found that for most of the gridded rainfall products tend to overestimate rainfall during rainy days in the northern and wetter part of the domain. Furthermore, for heavy rainfall, CPC and GPCC tend to compare fairly well with stations in the southern and eastern parts of the domain but poorly with those in the western parts. PERSIANN-CDR tends to underestimate heavy rainy days for most stations in early and late summer (January–February-March-April). However, CHIRPS compares well at several stations, while ERA5 performs well for stations located in the south. This study helps provide useful guidance in choosing suitable rainfall gridded datasets for assessing long term rainfall cycles, daily rainfall characteristics as well as extremes over southwestern Africa.

Can Expert Prediction Markets Forecast Climate-Related Risks?

Abstract Prediction markets use a betting-like mechanism to aggregate information from disparate sources to produce probability forecasts that represent the collective view of participants. They have similarities with financial markets but are designed specifically to discover information rather than transfer assets or risks. Contracts that pay out a fixed amount if a well-defined event occurs are traded in these markets and, under certain conditions, the price at which these event-contracts trade can be interpreted as a probability estimate that the event will occur. This article examines the performance of 24 prediction markets for climate-related variables that have been run over the past 5 years. The predicted variables included the monthly temperature and rainfall for the United Kingdom, an index of El Niño–Southern Oscillation, Atlantic hurricane activity, and U.K. wheat yield. The markets had horizons of 2–12 months. Invitations to participate in the markets were extended to individuals and teams with relevant expertise. Participants did not have to pay to take part but did receive cash rewards based on their performance. Trades were made through an automated market maker overcoming the problems of low activity that have affected previous prediction markets for specialized topics. The predictions of the markets were consistent with good reliability, given the resolving power afforded by the sample size. Whether this level of reliability would persist for longer, multiyear, horizons relevant to climate change cannot be known without running markets on such time scales, something that we strongly advocate.

Analysis of Meteorological Drought in Cases of North Wello Zone, Ethiopia

Drought is a commonly used term, but the most complex and least realized of all the natural hazards affecting more people than any other hazards. The main objective of the study was to investigate the spatial-temporal occurrence of Meteorological drought indices analysis in the study areas. The study was undertaken to investigate the magnitude, frequency, and trends of drought incidence in North Wello Zone Ethiopia using monthly rainfall records for the period 1990 to 2020 of North Wello zone stations and CHIRPS satellite data. Drought Index Calculator used to analyse standard precipitation index (SPI). The correlation between station data and CHIRPS data in this study was 0.89 which shows highly correlation between them. the result of meteorological drought indices map analysis on the years 1999, 2000, and 2008 severely dry to extremely dry conditions in the Belg season, similarly in 19990, 199308, 201507, 201508, and 201509 have got meteorological drought indices with extremely dry Kiremt season on the North Wello Zone. The drought magnitude of different time scales varied from slight to extremely severe in the studied stations. The identification of the temporal and spatial characteristics of droughts in the North Wello Zone will be useful for the development of a drought preparedness plan in the Zone. Generally increasing tendencies of drought were observed during the main rainy season and decreasing tendencies of drought during the short rainy season and annual scale were observed in the study area.

Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting

ABSTRACT The utilisation of modelling tools in hydrology has been effective in predicting future floods by analysing historical rainfall and inflow data, due to the association between climate change and flood frequency. This study utilised a historical dataset of monthly inflow and rainfall for the Terengganu River in Malaysia, and it is renowned for its hydrological patterns that exhibit a high level of unpredictability. The evaluation of the predictive precision and effectiveness of the Optimised Decision Tree ODT model, along with the RF and GBT models, in this study involved analysing several indicators. These indicators included the correlation coefficient, mean absolute error, percentage of relative error, root mean square error, Nash-Sutcliffe efficiency, and accuracy rate. The research results indicated that the ODT and RF models performed better than the GBT model in predicting monthly inflows. The ODT model, as well as the RF and GBT models, showed validation results with average accuracies of 94%, 91%, and 92%, respectively. The R² values were 90.2%, 84.8%, and 96.0%, respectively, and the NES values ranged from 0.92 to 0.94. The results of this research have greater implications, extending beyond the forecasting of monthly inflow rates to encompass other hydro-meteorological variables that depend exclusively on historical input data.

SMRF: a new class-based probabilistic approach for season-ahead monthly rainfall forecasting

SMRF: a new class-based probabilistic approach for season-ahead monthly rainfall forecasting

Assessment of Spatial and Temporal Monthly Rainfall Trend over Iraq

تستكشف هذه الدراسة خصائص الألياف النانوية المغزولة كهربائيًا من بولي ميثيل ميثاكريلات (PMMA) الممزوجة بالكلوروفيل، والتي من الممكن تطبيقها في تصنيع الخلايا الكهروضوئية. تمت إضافة تراكيز مختلفة من الكلوروفيل (0، 0.05، 0.1، 0.15، 0.2، و0.25 بالوزن%) إلى محاليل الغزل الكهربائي لـ PMMA والأسيتون. بعد عملية الغزل الكهربائي وتبخر الأسيتون، احتوت الألياف على تراكيز الكلوروفيل (0، 0.31، 0.63، 0.94، 1.25، و1.56 بالوزن%). تم توصيف الالياف المنتجة بخصائص الريولوجيا، وتحول فورييه بالأشعة تحت الحمراء، والمجهر الإلكتروني الماسح، والتحليل الطيفي للأشعة فوق البنفسجية. أظهرت النتائج أن الكلوروفيل زاد من لزوجة المحلول وقلل من قطر الألياف النانوية بنسبة وزنية تصل إلى 0.8%. تم الحصول على الألياف النانوية الأكثر اتساقًا بنسبة وزنية تبلغ 0.31% من الكلوروفيل، بمتوسط قطر 11.66 ± 7.3 نانومتر. أدت تركيزات الكلوروفيل الأعلى إلى ألياف نانوية أكبر وأكثر انتظامًا وزيادة فجوة النطاق. أنتجت تراكيز الكلوروفيل التي تزيد عن 1% بالوزن أليافًا غير مرغوب فيها ذات خرز. حددت الدراسة النطاق الأمثل لتركيز الكلوروفيل في ألياف PMMA النانوية (0-0.8 بالوزن%) ودرست تأثير الكلوروفيل على اللزوجة والقطر وفجوة النطاق ومورفولوجيا الألياف النانوية. توفر الدراسة معلومات مفيدة للباحثين والمطورين الذين يرغبون في استخدام ألياف PMMA/الكلوروفيل النانوية لأغراض مختلفة.

Machine Language Approach for Modeling and Predicting Rainfall in Different Zones of Kerala

The forecasting of rainfall is said to be the most difficult of other hydrological processes due to sudden changes in atmospheric processes. The rainfall directly and indirectly influences agriculture and allied sectors. Sudden changes in rainfall or an uneven distribution of rainfall can lead to crop loss. In order to avoid such problems and take the necessary precautions, it is mandatory to forecast the rainfall using various models with maximum precision. In this study, rainfall for northern, central and southern Kerala, India, was predicted using an artificial neural network (ANN) with a multi-layer perceptron (MLP) feed-forward neural network and an extreme learning machine (ELM) neural network. The monthly rainfall data was collected for a period of 39 years (1982–2020) from the regional agricultural research stations (RARS), Pilicode and Pattambi, for the northern and central zones of Kerala, respectively, whereas for the southern zone of Kerala, data was collected from RARS, Vellayani, for a period of 36 years (1985–2020). For the rainfall data collected from three different zones of Kerala, the MLP and ELM were applied. The comparison and validation of MLP and ELM models was done based on the error values of mean square error (MSE), root mean square error (RMSE), and mean absolute error(MAE). The results indicated that for three different zones in Kerala, ANN with MLP showed better performance in forecasting rainfall compared to the ELM model. The best-selected model was also used for forecasting the next 5 years rainfall in each zone of Kerala.

Precipitation in Kathmandu Valley: A Spatial-temporal Study

Over the past decade, advancements in atmospheric research have significantly improved the analysis of precipitation monitoring, understanding climate trends and variability, and identifying climatic anomalies associated with dry and wet periods. A key objective of this research is to enhance the understanding of precipitation patterns in the Kathmandu Valley, particularly in relation to the placement of gauges for long-range predictions and subsequent forecasting challenges. This study focuses on the evolving patterns of precipitation across the three districts within the Kathmandu Valley, the most developed and densely populated area in Nepal, encircled by four mountain ranges. We employ interpolation methods in ArcGIS Pro to identify, estimate, and forecast precipitation trends, and use the Mann-Kendall test to reveal statistically significant trends and provide insights into broader climatic processes. The research analyzes trends in both seasonal and annual precipitation and temperature across four time periods—pre-monsoon, monsoon, post-monsoon, and winter—using the Mann-Kendall test. The analysis reveals a decreasing trend in monthly rainfall from 2003 to 2017 at the majority of stations (14 stations out of 19), with areas along the valley slopes receiving significantly more precipitation compared to the central valley. The results of this study contribute to a deeper understanding of climate change in the Kathmandu Valley.

ARIMA-Based forecasting of monthly rainfall in Mandi district, Himachal Pradesh

ABSTRACT To anticipate monthly rainfall time series, autoregressive integrated moving average (ARIMA) modelling was performed using R studio. For a period of 30 years (in mm), the monthly rainfall data were gathered for eight localities in the Mandi district of Himachal Pradesh: Bharol, Chachiyot, Jogindernagar, Karsog, Kataula, Mandi, Sarkaghat, and Sundernagar. The augmented Dickey–Fuller test was performed to determine whether the rainfall data were steady before applying the ARIMA model. The minimal values of the Akaike information criterion, Bayesian information criterion, autocorrelation function, and partial autocorrelation function were used to select the best models. The best fit was determined to be ARIMA (0,0,2) (2,1,0)12 for five stations and ARIMA (0,0,0) (1,1,0)12 for the remaining three stations. A box test is performed to validate the ARIMA model and verify the autocorrelation in the data. Using the ARIMA model, forecasts are made for the 10 years (2021–2030). The validity and performance of the ARIMA models were evaluated using error statistics (mean absolute error = 34.65, root-mean-square error = 33.58, and R2 = 0.93). The projected data's mean and standard deviation were close to the actual data. As a result, the ARIMA model's monthly rainfall prediction parameters were satisfactory.

Factors influencing fumonisin B1 contamination in maize: insights from two production regions in Costa Rica.

Maize (Zea mays L.) is an important cereal crop worldwide. Contaminated maize kernels pose a significant mycotoxin exposure risk for humans in Latin America. Fumonisins, the most prevalent mycotoxin in maize, typically occur during pre-harvest conditions leading to significant economic losses. Various factors, including weather conditions, may influence this contamination. This study aimed to determine the association between fumonisin B1 (FB1) contamination, prevalence of Fusarium verticillioides, weather conditions and kernel quality in the two primary maize production areas in Costa Rica (Brunca and Chorotega). All maize samples (100%) showed FB1 contamination, with higher concentrations in samples from Brunca region, consistent with the presence of F. verticilliodes. Weather conditions appeared to play an important role in this contamination, since Brunca region had the highest mean temperature and relative humidity after maize silking (R1) and the total monthly rainfall in this region was significantly higher during the last two months of maize cultivation (grain-filling and physiological maturity stages R3 to R6). Interestingly, this study found a negative correlation between grain damage and kernel contamination with FB1 and F. verticillioides. The concentration of mineral nutrients in kernels from both regions was largely similar. Most nutrients in kernels exhibited a negative correlation with FB1, particularly nitrogen. Zinc and phosphorus were the only nutrients in kernels showing a positive correlation with FB1 in samples from the Brunca region. The results highlight elevated levels of FB1 contamination in maize and contribute to a better understanding of pre-harvest factors influencing FB1 contamination in tropical conditions.