Number Of Rain Days Research Articles

Community Adaptation to Mitigate the Impact of Flooding in Kaoliaw, Sybounhuengthong and Sysomsuen Vilages, Sykhottabong District, Vientiane Capital

Kaoliaw, Sybounhuengthong and Sysomsuen Vilages, Sykhottabong District, Vientiane Capital is one of the areas have been affected by floods every year. The main reason may be due to the geography and the location is a flat area. The construction of embankment blocked the amount of water in the area unable to be drained out, which resulted of flooding in the area. The flood problem caused people need to adapt themselves to survive. Therefore, the purpose of this research is to study the various factors that affect the occurrence of flooding and how the people adapt in the flood area. The research method analyzed the annual rainfall and the maximum of flood using Log Pearson Type III. The population sample interview was used in total of 136 families (3 villages), community's flood adaptation data is analyzed using SPSS. Research results found that the amount of annual rainfall and the number of rain days tend to decrease. SPSS illustrated that the main cause of flooding is inefficient drainage system, followed by heavy rain, which accounts for 44.5% and 20.5% of the total sample group, respectively. The maximum duration of flooding is more than 10 days, but most of them are in the range of 4 to 7 days, which accounts for almost 40% of the sample group. For the community's flood adaptation before the flood, they followed the weather news through television and social media, installed water pumping and built waterproof walls around the house; During the flood, many families had to drain the water from their houses and they have to drop their vehicles outside the flood area. After the flood, people improved their house to make it more flood resistant and plan to deal with the next flood. In addition, the study results can be an important database for relevant organizations to analyze and investigations the solution plan to deal with future floods.

Grain Yield and Quality Losses Caused by Tan Spot in Wheat Cultivars in Australia

Tan spot (TS), known in Australia as yellow leaf spot, is caused by the fungal pathogen Pyrenophora tritici-repentis, a major foliar disease of wheat. Four experiments were conducted between 2016 and 2017 in two different climatic zones of southeastern Australia to determine the impact of TS on grain yield and quality in six wheat cultivars with different resistance ratings. In each experiment, high and low TS disease scenarios were applied to each cultivar. Disease severity was assessed as either whole-plot percentage leaf area affected (%LAA) or top three leaf (flag to flag-2) %LAA. Whole-plot %LAA was analyzed using both repeated measurements and area under the disease progress curve (AUDPC). For whole-plot %LAA, the repeated measurements identified differences in epidemic progression at key growth stages not identified through AUDPC. Both AUDPC and end-of-season flag to flag-2 %LAA were negatively correlated with grain yield and screenings. Increased rainfall impacting disease development and number of rain days above 5 mm increased TS severity across both climatic zones. Cultivar resistance ratings influenced grain yield loss, with moderately resistant, moderately susceptible, and susceptible cultivars losing up to 6, 18, and 24% grain yield, respectively. High disease significantly increased screenings by up to 1, 3, and 5% for moderately resistant, moderately susceptible, and susceptible cultivars, respectively. This study demonstrated that TS can cause significant grain yield losses in southeastern Australia, and a minimum cultivar rating of moderate resistance to moderate susceptibility was required to prevent severe TS and associated grain yield and quality losses. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

A goodness-of-fit test for the compound Poisson exponential model

On the basis of bivariate data, assumed to be observations of independent copies of a random vector (S,N), we consider testing the hypothesis that the distribution of (S,N) belongs to the parametric class of distributions that arise with the compound Poisson exponential model. Typically, this model is used in stochastic hydrology, with N as the number of raindays, and S as total rainfall amount during a certain time period, or in actuarial science, with N as the number of losses, and S as total loss expenditure during a certain time period. The compound Poisson exponential model is characterized in the way that a specific transform associated with the distribution of (S,N) satisfies a certain differential equation. Mimicking the function part of this equation by substituting the empirical counterparts of the transform we obtain an expression the weighted integral of the square of which is used as test statistic. We deal with two variants of the latter, one of which being invariant under scale transformations of the S-part by fixed positive constants. Critical values are obtained by using a parametric bootstrap procedure. The asymptotic behavior of the tests is discussed. A simulation study demonstrates the performance of the tests in the finite sample case. The procedure is applied to rainfall data and to an actuarial dataset. A multivariate extension is also discussed.

Changes in extreme daily rainfall characteristics in South Africa: 1921–2020

Many areas in South Africa are prone to localized flooding. With climate change already said to affect the intensity of rainfall, there is a need to investigate if there is a change in the probability of significant to extreme daily rainfall across South Africa. This was investigated through the analysis of the daily time series of 70 manual rainfall stations, over the period 1921 to 2020. The analysis period was divided into two equal periods of 50 years for comparison. With the application of the gamma distribution, it is shown that most stations experienced an increase in the probability of receiving more than 50 mm per day, defined as significant rainfall, in the latter half of the analysis period. Also, most stations showed an increase in their 1:50- and 1:100-year return period values, with some stations over the eastern parts showing increases of over 100 mm. There was also an increase in the probability of “heavy rainfall” (>75 mm) and “very heavy rainfall” events (>115 mm) between the first and second half of the analysis period for most stations over the country when applying the Peak-Over-Threshold approach. In summary, the results indicate that, although the number of rain days has remained near-constant over the 1921–2020 period, the probability of experiencing significant and extreme daily rainfall events has increased generally for most regions in South Africa. This is of concern as rainfall of this nature can have serious consequences in terms of flooding, erosion, and damage to agriculture and infrastructure.

Rethinking Botswanas Water Policies: People to the Water not Water to the People

Botswana is a landlocked country situated in central southern Africa (approximately 20 -28° E and 18 - 26° S). It is a water-stressed country. Mean Annual Rainfall (MAR) ranges from under 140 mm to just under 600 mm. The average number of rain days receiving 5mm or more ranges from 18 to 26 days per year [1]. This means the average number of rainless days ranges from 339 to 357 days per year (Figure 1).

The Late-Eighteenth-Century Climate of Cape Town, South Africa, Based on the Dutch East India Company “Day Registers” (1773–91)

Abstract We introduce the Dutch East India Company “day registers” as one of the world’s longest known pre-nineteenth-century corporate chronicles (1652–1791) containing near-continuous, systematic, noninstrumental daily weather information for Cape Town, South Africa. This transcript provides the longest-known continuous seventeenth- to eighteenth-century daily weather record for Africa and the Southern Hemisphere. An 18-yr (1773–91) climate chronology from this record is presented, thus providing unique insight to the late-eighteenth-century climate of Cape Town. Extraction of daily weather information for basic statistical analysis includes precipitation, wind, sky conditions, and accounts of storms, drought, and floods. From this, we provide monthly and annual number of rain days, a rain index (relative rainfall amount), hot and cold days, and occurrence of storm-strength winds. Results show extreme weather and climate variability in Cape Town during the mid- to late 1780s.

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow.

Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data.

Spatio-temporal and trend analysis of rain days having different intensity from 1901 – 2020 at regional scale in Haryana, India

Our study has investigated spatio-temporal distribution of rainfall and rain day trends during different seasons for all districts of Haryana, India. Gridded rainfall dataset of 120 years (1901 to 2020) from India Meteorological Department (IMD) was analysed using mean rainfall, rainfall deviation, seasonal rainfall ratio (SRR), coefficient of variation (CV), number of rain days, rainfall intensity, trends of rain days, Empirical Orthogonal Functions (EOF) and Principal Component (PC) analysis. Districts lying in eastern Haryana have experienced more rainfall (less variability) than the ones lying in western Haryana during each season. SRR and CV analysis depicted most consistent rainfall during monsoon and maximum variability during post-monsoon season. Highest number of rain days was observed during monsoon season followed by pre-monsoon, winter and post-monsoon season in Haryana. Innovative trend analysis method (ITAM) shows a declining trend in number of rain days during winter and post-monsoon season while an increasing trend was observed during pre-monsoon season. Overall, monsoon season has shown a falling trend in moderate while rising trend was observed in both light and heavy rainfall intensity categories in most of districts. Dominant EOF explained maximum variability during post-monsoon season followed by winter, pre-monsoon, and monsoon season, respectively. PC analysis captured inter-annual variability in rainfall during each season. Our findings highlight qualitative and quantitative aspects of seasonal rainfall dynamics at districts level. This study is beneficial in understanding impact of climate change and climate variability on rainfall dynamics in Haryana which may guide policymakers and beneficiaries in optimizing use of hydrological resources.

PCDD/Fs and PCBs in Mytilus galloprovincialis from a contaminated area in Italy: the role of mussel size, temperature and meteorological factors

Mussels represent a food at risk of contamination because they are filter-feeding organisms with high bioaccumulation and low biotransformation potential for organic contaminants. Taranto, Southern Italy, is of particular relevance in this context due to the presence of industrial sources of POPs. The aim of this study was to analyse the role of mussel size, temperature and other meteorological factors in determining PCDD/Fs and PCBs concentrations in mussels. Mussels were collected on a monthly basis with random sampling between 2012 and 2020 according to the extraordinary monitoring plan of the Prevention Department. Samples were analysed by the National Reference Laboratory for Halogenated POPs in food and feed. A total of 787 mussel samples were collected between 2012 and 2020. Higher median concentrations were observed in Mar Piccolo First Inlet: PCDD/Fs 1.49 pg WHO-TEQ/g ww; PCDD/Fs + DL-PCBs 6.35 pg WHO-TEQ/g ww; DL-PCBs 4.74 pg WHO-TEQ/g ww; NDL-PCBs 65.10 ng/g ww. In all basins, PCDD/Fs + DL-PCBs, DL-PCBs and NDL-PCBs showed higher concentrations in mussels ≥ 5 cm. Temperature showed positive association with all pollutants in all basins. Higher relative humidity was associated with higher concentrations of some pollutants, while number of rain days was associated with lower values. Our findings showed higher PCDD/Fs and PCBs concentrations in adult mussels, with dependence on temperature and some other meteorological factors. These results could help guide public health strategies and mussel sampling according to mussel size and meteorological conditions in areas affected by POPs contamination.

THE INFLUENCE OF CLIMATE FACTORS ON DIARRHEA CASES IN CHILDREN UNDER FIVE YEARS BEFORE AND AFTER THE COVID-19 PANDEMIC IN BANJARMASIN

Abstract: Background: Diarrhea still become one of the leading causes of morbidity and mortality in children, especially under 5 years. Anthropogenic climate change, mainly due to global warming process, is expected to change the epidemiological pattern of infectious diseases, including diarrhea. COVID-19 pandemic that has occurred globally since March 2020 may also have an impact. Banjarmasin as a city in a wetland area with tropical savanna climate that also affected by COVID-19 pandemic is also at risk. We conducted a research to study the influence of climate factors on diarrhea cases in children under 5 years before and after the COVID-19 pandemic in Banjarmasin.Methods: Data of monthly diarrhea cases in children under 5 years at public health centers from Banjarmasin City Health Office and monthly climate data (amount of rainfall, number of rain days, temperature, humidity, sunshine duration, wind speed) from BMKG and BPS between January 2010 to April 2021 were collected. Statistical analysis was carried out to determine the correlation between climate factors and diarrhea cases before and after COVID-19 pandemic.Results: There was a decrease in the average monthly visits of children under five years with diarrhea before (497 ±47.4) and after (132 ±45.8) COVID-19 pandemic. Among climate factors, amount of rainfall (r=-0.753; p=0.005), number of rain days (r=-0.774; p=0.003), and humidity (r=-0.590; p=0.044) were negatively correlated, while sunshine duration (r=0.674; p=0.016) was positively correlated with diarrhea cases in the period before COVID-19 pandemic. No correlation was found between any climate factor and diarrhea cases in the subsequent period.Conclusion: Diarrhea cases in children under 5 years in Banjarmasin tend to increase in dry season, however during COVID-19 pandemic there were some changes in society, such as working from home, reluctance to go to health facilities which might affect the usual trend. Keywords: Anthropogenic climate change; Climate factors; COVID-19 pandemic; Diarrhea under 5 years; Wetland

Effects of Interval Flooding Stress on Physiological Characteristics of Apple Leaves

As a result of the continuous global warming in recent years, the average annual number of rain days in China has been on the decline, while the number of rainstorm days has gradually increased. These conditions make it extremely easy to form a waterlogging environment, which has an adverse impact on plant growth and development. In many apple-producing areas in China, apples are subject to severe flooding during planting. In this study, two-year-old apple rootstock M9T337 was used to explore the effects of interval water stress on the morphological and physiological parameters of apple leaves. The purpose was to determine the plant’s adaptability to waterlogged environments and provide theoretical reference for management and maintenance after waterlogging. The results showed that the effect on flooded (T2) on apple stock was greater than that of waterlogged (T1), Short-term (7 d) waterlogging (T1) did not affect the growth of seedlings but was conducive to the accumulation of dry matter. Furthermore, the initial stress was be imprinted on the plants, which could directly affect their response to later stress. The results of principal component analysis (PCA) revealed that PC1, PC2, and PC3 explained 26.92%, 17.46%, and 13.03% of the physiological changes under water stress, respectively. By calculating the weight of each indicator, we concluded that high-frequency resistance r, relative chlorophyll content (SPAD) and maximum photochemical efficiency Fv/Fm are important parameters for apple rootstocks affected by water stress.

The role of extreme rain events in driving tree growth across a continental‐scale climatic range in Australia

Rainfall regimes in many parts of the world have become increasingly dominated by fewer, but more extreme, rainfall events. Understanding how tree growth responds to changes in the frequency and intensity of rain events is critical to predicting how climate change will impact on forests and woodlands in the future. In this study, we used five tree‐ring records of the native Australian conifer Callitris columellaris that span a large (> 20°) latitudinal and climatic gradient from the mesic (tropical) north to the xeric (semi‐arid) south of Australia to investigate how inter‐annual and spatial variation in the delivery of rainfall (the intensity and frequency of rain events) influences tree growth. In semi‐arid biomes (~300–400 mm rainfall annually), tree growth is most strongly related to the amount of rainfall from heavy (> 75th percentile) rain days or the number of extreme (> 90th percentile) rain days, regardless of differences in the seasonal distribution and average intensity of rainfall among sites. Our findings also indicate that there is likely a minimum threshold amount of daily rainfall (~5 mm) that is required to stimulate tree growth in the semi‐arid zone. In contrast, in the tropics (> 800 mm annual rainfall), inter‐annual variation in growth is best explained by total growing season rainfall or the number of rain days > ~5 mm (~50th percentile of rain days) rather than extreme rainfall. Our findings indicate that not all rain events are important for driving tree growth, which has important implications for interpreting climatic signals in tree rings. Our findings also indicate that projected increases in the intensity of extreme rain events are likely to have contrasting impacts on tree growth across biomes, with greater and positive impacts on growth in semi‐arid biomes and potentially negative impacts on growth in tropical biomes of Australia.

Characteristics of Norovirus Food Poisoning Outbreaks in Korea in the 2000s

Norovirus food poisoning outbreaks in Korea (South) appeared in the 2000s and have been increasing since then. We aimed to investigate the epidemiological features of norovirus food poisoning outbreaks in Korea from 2002 to 2017, on the basis of official food poisoning statistics and publically reliable reports, and to find any associations with climate factors. Norovirus was the most common cause of food poisoning among known causative substances in Korea during the study period. More than one-third of the outbreaks occurred in group meal service facilities, including school lunch programs. A few of these facilities used groundwater contaminated with noroviruses to wash or cook food, which contributed to outbreaks. Norovirus occurrences showed strong seasonality: cold and relatively dry winter air may help norovirus to flourish. Both norovirus genotypes GI and GII that are infectious to humans were detected, with GII becoming more prevalent than GI. According to our correlation analysis in connection with climate factors, average temperatures, the highest and lowest temperatures, precipitation, the number of rain days, and humidity showed a significant negative correlation with a monthly norovirus occurrence (P < 0.05). The lowest temperature and average temperature had higher coefficients of correlation, -0.377 and -0.376, respectively. The norovirus outbreaks in Korea showed complex etiological characteristics, although more prevailed in wintertime, and are now a major public health problem. The use of groundwater in group meal service settings is a public health issue, as well as a norovirus concern; therefore, groundwater used in food service facilities and businesses should be treated for safety.

Investigation of the optimum planting dates for maize varieties using a hybrid approach: A case of Hwedza, Zimbabwe

Water scarcity and unreliable weather conditions frequently cause smallholder farmers in Zimbabwe to plant maize (Zea mays L.) varieties outside the optimum planting timeframe. This challenge exacts the necessity to develop sowing management options for decision support. The study's objective was to use a hybrid approach to determine the best planting windows and maize varieties. The combination will guide farmers on planting dates, dry spell probability during critical stages of the crop growth cycle and rainfall cessation. To capture farmer's perception on agroclimatic information, a systematic random sampling of 438 smallholders was carried out. An analysis of climatic data during 1949–2012 was conducted using INSTAT to identify the best planting criterion. The best combination of planting criterion and maize varieties analysis was then achieved by optimizing planting dates and maize varieties in the DSSAT environment. It was found that 56.2% of farmers grew short-season varieties, 40.2% medium-season varieties and 3.6% long-season varieties. It was also established that the number of rain days and maize yield had a strong positive relationship (p = 0.0049). No significant association was found amongst maize yield (p > 0.05), and planting date criteria, Depth (40mm in 4 days), the AREX criterion- Agricultural Research Extension (25 mm rainfall in 7 days) and the MET Criterion-Department of Meteorological Services (40 mm in 15 days). Highest yields were simulated under the combination of medium-season maize variety and the AREX and MET criteria. The range of simulated yields from 0.0 t/ha to 2.8 t/ha formed the basis for the development of an operational decision support tool (cropping calendar) with (RMSE) (0.20). The methodology can be used to select the best suitable maize varieties and a range of planting time.

The variation of precipitation and rain days for different intensity classes during the rainy season in the Qilian Mountains, Northwest China

As an important indicator of climate change, precipitation variability is critical to the ecological environment in arid and semi-arid mountains. This study examined the variability and trends of precipitation and rain days in the Qilian Mountains based on meteorological station data during the rainy seasons of 1960–2017 using a non-parametric method. The results revealed the following. (1) Many major precipitation events occurred during the rainy season (precipitation amount and number of rain days accounted for > 80% and > 70% of the annual total, respectively) and the proportion of precipitation increased significantly with altitude. (2) The distribution of different intensity classes of precipitation was complex in different months and at different altitudes. The distribution of rain days of different intensity classes was similar to that of precipitation. Variation of precipitation and rain days was significant (not significant) in central and eastern parts (western parts and in the Hexi Corridor on the northern slopes) of the Qilian Mountains. Regions with significant (non-significant) increasing trends for both light and moderate rain were distributed above the upper portion, i.e., 2600–3200 m (in the Hexi Corridor on the northern slopes) of the Qilian Mountains. (3) Mutations of light rain and rain days occurred before the mid-1980s above the upper portion but late in the first decade of the twenty-first century in the lower portion (2000–2600 m). Mutations of moderate rain and moderate rain days occurred earlier than for light rain and light rain days; the abrupt change points occurred later with decreasing altitude.

Changes in rainfall of different intensities due to urbanization-induced land-use changes in Shenzhen, China

Shenzhen city, located in southern China and governed by the East Asian monsoon climate system, has experienced rapid urbanization since the beginning of the 1980s, and had become an international metropolis by the 2010s. Urban-induced changes in rainfall of various intensity grades over Shenzhen are investigated using nested numerical integrations at 3.3 km spatial resolution, within which urban surface distributions and expansions are well represented by satellite-based data. The urban-induced impact on subregion average of daily rainfall (DRAIN) and cumulative volumetric rainfall (VRAIN) intensity and variability, as well as rain areas and number of rain days, are evaluated. For total rainfall amounts, significant changes are only detected in VRAIN and DRAIN variability on seasonal scales. However, for different intensity grades of rainfall, urban-induced changes in VRAIN and DRAIN intensity and variability of both annual and seasonal results are more pronounced. Significant changes are detected only for heavy storm rain on annual scales, whereas marked subregional characteristics are found for various grades of rainfall on seasonal scales. The increased DRAIN/VRAIN intensity and variability for heavy storm rain due to urban surface expansion from the 1980s to the 2010s can be linked to intensified vertical circulation and changes in moisture flux. A reduced local evaporation-related moisture flux and a weakened southeasterly water vapor transport result in a decreased relative humidity in the near-surface layers. However, an intensified southwesterly moisture flux induces an increased relative humidity in the low-middle troposphere.

Temporal Variations in Rainfall Volume in the Southwest of Western Australia

AbstractRainfall in the southwest of Western Australia (SWWA) has decreased significantly over recent decades. Previous studies documented this decrease in terms of the change in rainfall depth or decrease in the frequency of rainfall events for selected sites. Although rainfall volume is of vital importance to determine water resources availability for a given region, no study has yet been undertaken to examine the change in rainfall volume in SWWA. The aim of this study is to examine the spatiotemporal changes in rainfall volume and to attribute this change to the changes in wet area and rainfall depth. Gridded daily rainfall data at 0.05° resolution for the period from 1911 to 2018 were used for an area of 265 952 km2 in SWWA. For the whole region and most zones, rainfall volume decreased, which was mostly due to a decrease in the wet area, despite an increase in the mean rain depth. In the regions near the coast with mean annual rainfall ≥ 600 mm, 84% of the decrease in rainfall volume could be attributed to a decrease in the wet area, whereas the decrease in rainfall depth only played a minor role. The regions near the coast with a higher number of rain days showed a decreasing trend in wet area, and the regions farther inland with a lower number of rain days showed an increasing trend in wet area. On the coast, the rate of decrease in rainfall has been reduced, and heavy rainfall, in fact, has increased over the past 30 years, although there was no concurrent change in the southern annular mode (SAM). This suggests that the relationship between SAM and rainfall could have changed and that other climate drivers could also be responsible for the recent rainfall trend and variations in the coastal regions of SWWA.

On the use of mean and extreme climate indices to predict sugar yield in western Fiji

Sugarcane is one of Fiji's largest commercial agricultural crops and greater than 80% of the raw sugar produced is exported. Few sugar-producing countries are as dependant on the contribution of sugar to the export market as Fiji. There has been a statistically significant decline in sugar yield since 1975. The proportion of sugar extracted from sugarcane has also declined as shown by the positive trend in the tonnes cane to tonnes sugar ratio (+0.07/year, p < 0.001). The role of climate in these changes was investigated by first using principal component analysis then stepwise regression to predict sugarcane and sugar yield. ‘Mild drought conditions’, an increase in the diurnal temperature range and cool conditions during the ripening and maturation period are favourable for sugar yield. The impact of future warmer, wetter and drier conditions on sugar yield was also examined, in the absence of adaptive measures. Results show declines in sugar yield with an increase in mean and extreme temperature. Results also show an increase in the number of rain days in March offsets the increase in temperature suggesting that an increase the number of rain days in the late growing season in a future climate may counter the influence of higher temperatures. As for Australia and other developed sugar producing countries, irrigation in the late growing season may be an option to increase yields and/or adapt to a warmer climate.

Estimation and analysis of the effective rainfall In Tabuk area – Saudi Arabia

this study examined the effective rainfall in Tabuk area by applying the Lang rainfall factor, De Martonne Index and the (UNEP) arid index. The study methodology was based on a statistical tests using the Normality test (Shapiro-Wilk), the Homogeneity test (Leven test), the ANOVA (LSD test) of the monthly mean of rainfall, the daily maximum rainfall and the total number of rainfall days. The multiple comparison (Tehmane’s Test) had been applied between the studied stations. The results of the Shapiro-Wilk test shows that the distribution of monthly rainfall averages follows normal distribution in all stations except stations Tabuk and Al Bad’ and that the maximum daily rainfall distribution at Duba, Shuw?q, Al Kurr stations is also normal distribution. The Leven test results showed that the significance level was greater than 0.05 and the Leven Test was greater than 0.05 for the average monthly precipitation, for the daily maximum rainfall and for the number of rain days and it indicates the homogeneity of the rainfall variances in the studied stations.The ANOVA analysis of the averages and maximum daily rainfall, shows that the significance level is greater than 0.05 and the LSD test is greater than 0.05 indicating that the differences are not significant. While the LSD was greater than 0.05 for the number of rain days, indicating that the differences between the number of rain days and their distribution at the studied stations are statistically significant differences, and therefore there is no homogeneity in their differences. The results of the Tamhane’s Test of multiple comparison confirmed that the mean level of monthly mean rainfall and daily maximum rainfall is greater than 0.05 indicates that the differences between the monthly rain averages of and the daily maximum rainfall amounts are not significant for 22.2% of total comparisons.The effective rainfall estimate of Lang’s rainfall index shows that the threshold of effective mean rainfall is between 1.3 and 27.4 mm and that the threshold of maximum daily rainfall is 154.1 mm at the station (Al Bad’). Also, the effective rainfall estimate by the DeMartonne index shows that the threshold of average effective rainfall is between 1.2 and 15.2 mm and that the threshold of maximum daily rainfall is 80.4 mm at the station (Al Bad’). Therefore, the effective rainfall estimate by the UNEP index shows that the threshold of average effective rainfall is between 1.8 and 30.3 mm and that the threshold of maximum daily rainfall is 130.5 mm at the station (Al Bad’). Finally, the effective rainfall estimate by the difference between the rainfall and Pan Class“A” Evaporation shows that the threshold of average effective rainfall is between 1.2 and 25.8 mm and that the threshold of maximum daily rainfall is 137.1 mm at the station (Al Bad’).- Key Words:Effective rainfall average, Effective maximum daily rainfall , Number of rainy days, Statistical tests, Lang rainfall factor, DeMartonne index, UNEP arid index, Tabuk area, Saudi Arabia.

Trend analysis of rain indicators for the state of Bahia, Brazil

The concern about the climate issue has been increasing since climate change influence the water resources system. For this reason, this study sought to identify the presence of trends in the historic series of two meteorological variables: total annual precipitation and number of rain days, in the state of Bahia, Brazil, in order to verify if the precipitation regime is being affected by the global climate change. Two trend tests were applied: Mann-Kendall and Linear Regression, with significance levels of 5% and 10%, in the historical series of 32pluviometric stations distributed throughout the state. The results of the two testes were similar, which guarantees that both can be used for detection of changes in rain pattern. The study confirms that in a specific region of Bahia, more specifically in the central and eastern areas, there is a reduction trend in the historical series of annual precipitation. Regarding the number of rain days, no trend conclusions were observed.