scholarly journals Local Rainfall Variability - A Potential Bias for Bioecological Studies in the Central Amazon

1984 ◽  
Vol 14 (1-2) ◽  
pp. 159-174 ◽  
Author(s):  
Maria de Nazaré Góes Ribeiro ◽  
Joachim Adis
Keyword(s):  
Rainy Season ◽  
Rainfall Variability ◽  
Dry Season ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Annual Precipitation ◽  
Potential Bias ◽  
Central Amazon ◽  
Rainfall Patterns ◽  
Local Rainfall

Rainfall data registered betwe en 1910 and 1979 at Manaus confirm the existence of a dry season between June and November (monthly rainfall: 42-162mm) and a rainy season from December until May (monthly rainfall: 211-300mm). Annual precipitation amounted to 2105mm with about 75% of the rainfall recorded during the rainy season. Rainfall data collected over 12 months at eigth stations in the vicinity of and at Manaus are compared. Annual precipitation was lower in Inundation Regions (1150-2150mm) compared with Dryland Regions (2400-2550mm). Considerable differences are found in rainfall patterns (intensity, frequency and time of rainfall). This is also truefor neighbouring stations, even if data of a 11-year record period are compared. Thus, it is highly recommended that preciptation data for bioecological studies be collected at the study site.

scholarly journals IDENTIFIKASI PERUBAHAN IKLIM BERDASARKAN DATA CURAH HUJAN DI WILAYAH SELATAN JATILUHUR KABUPATEN SUBANG, JAWA BARAT (Identification of Climate Change Based on Rainfall Data in Southern Part of Jatiluhur, Subang District, West Jawa)

2015 ◽  
Vol 35 (01) ◽  
pp. 98 ◽  
Author(s):  
Dyah Susilokarti ◽  
Sigit Supadmo Arif ◽  
Sahid Susanto ◽  
Lilik Sutiarso
Keyword(s):  
Climate Change ◽  
Rainy Season ◽  
Statistical Tests ◽  
Initial Period ◽  
Climatic Conditions ◽  
Dry Season ◽  
T Test ◽  
Rainfall Data ◽  
Normal Period ◽  
Rainfall Patterns

Indonesian region is strongly influenced by the monsoon climatic conditions have obvious difference between wetseason and dry season. Climate variability and extreme climate phenomenon that often happens lately caused climatechange. Climate change is characterized by changes in rainfall patterns and its causes shifting early in the season thatmake it difficult to plan cultivation. It is therefore necessary to study the behavior of the climate through rainfall timeseries analysis. Statistical tests performed using the F test and t test. This study aims to identify climate change throughpattern trends, distribution and similarity of rainfall data at different timescales, using rainfall data rainy season (Octoberto March) and the dry season (April to September) year period from 1975 to 2012. Data obtained from 6 (six) graduatedrainfall stations around the study site those are Kalijati, Curugagung, Cinangling, Dangdeur, Subang and Pegaden. Dataare grouped in 10-year period with a 4-year timing differences in accordance with the rules of the moving average. Theperiod 1975 -1984 was indicated as an initial period as a basis to look for changes in rainfall patterns that occur. F testshows there has been a change in the distribution of rainfall in every period than normal period. T test showed there hasbeen a change in the pattern of rainfall in the dry season period from 1987 to 1996. While the rainy season is startingto look at the period from 1995 to 2004. Rainy season and the dry season period (1995-2004) shows a similar patternwith the normal period (1975 -1984) so that it is possible in a certain period of climate change on the location of thecycle is approaching normal conditions.Keywords: Time seriesanalysis,precipitation, climatechange, Subangdistrict ABSTRAKWilayah Indonesia sangat dipengaruhi oleh kondisi iklim monsun yang mempunyai perbedaan yang jelas antaramusim basah dan musim kering.Variabilitas iklim dan adanya fenomena iklim ekstrim yang sering terjadi akhir akhirini menyebabkan terjadinya perubahan iklim. Perubahan iklim ditandai adanya perubahan pola curah hujan yangmenyebabkan terjadinya pergeseran awal musim tanam sehingga sulit membuat perencanaan budidaya tanaman. Olehkarena itu perlu dilakukan kajian prilaku iklim melalui analisis deret waktu curah hujan.Uji statistik dilakukan denganmenggunakan uji F dan uji t. Penelitian ini bertujuan untuk mengidentifikasi terjadinya perubahan iklim melalui polakecenderungan, distribusi dan kesamaan data curah hujan pada rentang waktu yang berbeda, menggunakan data curahhujan musim hujan (Oktober – Maret) dan musim kemarau (April – September) periode tahun 1975 – 2012. Datadiperoleh dari 6 stasiun penakar curah hujan di sekitar lokasi penelitian yaitu stasiun Kalijati, Curug agung, Cinangling,Dangdeur, Subang dan Pegaden. Data dikelompokkan dalam periode 10 tahunan dengan beda waktu 4 tahun sesuaidengan aturanmovingaverage. Periode tahun 1975 -1984 menjadi periode awal sebagai dasar untuk melihat perubahanpola curah hujan yang terjadi. Uji F menunjukkan telah terjadi perubahan distribusi curah hujan disetiap periodedibanding periode normalnya. Uji t menunjukkan telah terjadi perubahan pola curah hujan musim kemarau sejakperiode tahun 1987 – 1996. Sedangkan musim hujan mulai terlihat pada periode tahun 1995 – 2004. Musim hujandan musim kemarau periode (1995-2004) menunjukkan pola yang sama dengan periode normal (1975-1984) sehinggadimungkinkan pada periode tertentu siklus perubahan iklim pada lokasi ini mendekati kondisi normal.Kata kunci: Analisis deret waktu, curah hujan, perubahan iklim, kabupaten Subang

scholarly journals ANALISIS POLA HUJAN DAN DISTRIBUSI HUJAN BERDASARKAN KETINGGIAN TEMPAT DI PULAU FLORES

2020 ◽  
Vol 5 (2) ◽  
pp. 118-128
Author(s):  
Elisabet Marlin Lesik ◽  
Hery Leo Sianturi ◽  
Apolinaris S Geru ◽  
Bernandus Bernandus
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Rainy Season ◽  
Geographic Information ◽  
Rainfall Data ◽  
Mean Values ◽  
Randomized Design ◽  
Average Rainfall ◽  
Completely Randomized Design ◽  
Rainfall Patterns

Abstrak Telah dilakukan analisis pola dan distribusi hujan berdasarkan ketinggian tempat di pulau Flores. Data rata-rata bulanan untuk mendapatkan pola curah hujan, data curah hujan harian ke dasarian untuk mendapatkan data curah hujan dan data periode curah hujan selama musim hujan. Penelitian ini menggunakan software Geographic Information System (GIS) untuk membuat peta distribusi curah hujan dan di analisis menggunakan metode Rancangan Acak Lengkap (RAL) untuk mendeteksi perbedaan nilai tengah variabel pengamatan pada elevasi yang berbeda. Berdasarkan grafik pola hujan yang ada di pulau Flores adalah pola hujan monsunal. Hasil dari perhitungan menggunakan RAL, diperoleh nilai populasi pengamatan P1 pada ketinggian tempat (0-300 m dpl) dengan curah hujan rata-rata 851,75 mm dan periode musim hujan rata-rata 10,50 dasarian. P2 pada ketinggian tempat (301-600 m dpl) memiliki curah hujan rata-rata 1367,75 mm dan periode musimhujan rata-rata 13,75 dasarian. P3 pada ketinggian tempat (601-900 m dpl) memiliki curah hujan rata-rata 1875,25 mm dan periode musim hujan rata-rata 15,75 dasarian. P4 pada ketinggian tempat (901-1200 m dpl) memiliki curah hujan rata-rata 3164,50 mm dan periode musim hujan rata-rata 22,25 dasarian. Hal ini menunjukan ketinggian tempat memiliki pengaruh terhadap curah hujan dan periode musim hujan di pulau Flores.Kata Kunci: Pola hujan; curah hujan; periode musim hujan; Geographic Information System (GIS); Rancangan Acak Lengkap (RAL). Abstract An analysis of rainfall patterns and distribution based on altitude on the island of Flores has been done. Monthly average data to get rainfall patterns, daily to basic rainfall data to get rainfall data, and rainfall period data during the rainy season. This study used Geographic Information System (GIS) software to create rainfall distribution maps and is analyzed using the Completely Randomized Design (CRD) method to detect differences in mean values of observational variables at different elevations. Based on a chart of rain patterns on Flores island is a monsoonal rain pattern. The results of calculations using RAL, observational population obtained values P1 at altitude (0-300 m asl) with an average rainfall of 851.75 mm and an average rainy season period of 10.50 dasarian. P2 at altitude (301-600 m asl) has an average rainfall of 1367.75 mm and an average rainy season period of 13.75 dasarian. P3 at altitude (601-900 m above sea level) has an average rainfall of 1875.25 mm and an average rainy season period of 15.75 dasarian. P4 at altitude (901-1200 m asl) has an average rainfall of 3164.50 mm and an average rainy season period of 22.25 dasarian. This shows that altitude has an influence on rainfall and the rainy season period on Flores Island. Keywords: Rain patterns; rainfall, periods of the rainy season; Geographic Information System (GIS); Completely Randomized Design (CRD).

scholarly journals Analysis of Genesis Rain in Spring Transition in Makassar

2020 ◽  
Vol 6 (2) ◽  
pp. 177
Author(s):  
Candra Febryanto Patandean
Keyword(s):  
Daily Rainfall ◽  
Secondary Data ◽  
Gumbel Distribution ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Analysis Method ◽  
Descriptive Research ◽  
Transition Season ◽  
Rainfall Patterns ◽  
The City

Extreme weather in this case heavy rains is common in the city of Makassar, both of which resulted in a flood or no flood.  This type of research is descriptive research that aims to describe the incidence of rain in the transition season in Makassar. The source of data used in obtaining data on research in Makassar is secondary data. His research methods such as analysis method is based on monthly rainfall data to determine the monthly rainfall pattern using the Log Pearson III distribution methods and daily rainfall data duration of 3 hours early to analyze the frequency of rain by using Gumbel distribution methods. Based on the results in a graph of monthly rainfall patterns in the city of Makassar in the year (1985-2014) for 30 years and chart the frequency of daily rainfall duration 3 hours late in the year (2005 to 2014) for 10 years in the transition season in the city of Makassar, we can conclude that monthly rainfall patterns in Makassar is a monsoonal pattern with the second-largest peak intensity of rainfall occurs in January and December and the smallest intensity of rainfall occurs in August.

scholarly journals Variabilidade e Tendências das Precipitações Pluviais em Rio do Sul – SC

2019 ◽  
Vol 12 (6) ◽  
pp. 1996
Author(s):  
Pedro Murara ◽  
Magaly Mendonça
Keyword(s):  
Present Article ◽  
Rainy Season ◽  
Rainfall Variability ◽  
Winter Season ◽  
Extreme Rainfall ◽  
Summer Season ◽  
The State ◽  
Rainfall Data ◽  
Statistical Techniques ◽  
Santa Catarina

As inundações urbanas compreendem uma das principais problemáticas socionaturais. Conhecer a vulnerabilidade da população atingida pelas inundações é fundamental para a compreensão de ações que visem a mitigar a ocorrência desse fenômeno. Um dos primeiros passos da investigação da vulnerabilidade a inundações é conhecer a dinâmica pluvial da localidade. Nesse contexto, o presente artigo objetivou caracterizar a variabilidade das precipitações pluviais e sua relação com a ocorrência de inundações no município de Rio do Sul, localizado na mesorregião do Vale do Itajaí, no Estado de Santa Catarina. A metodologia envolveu uma intensa análise de dados de precipitações pluviais e registros de inundações, por meio de técnicas estatísticas e aplicação dos índices ClimPACT. Os resultados identificaram a estação de verão como a mais chuvosa e o outono como o período de diminuição nos totais de chuva, no entanto, as chuvas mais intensas ocorrem na estação de inverno, período em que há os maiores registros de inundações. Por fim, evidenciou-se que a região apresentou aumento nos registros dos totais pluviais e tendência à ocorrência de eventos extremos de chuvas.  Variability and Trends of Rainfall Precipitation in Rio do Sul - SC A B S T R A C TUrban floods comprise one of the main socialnatural problems. Knowing the vulnerability of the population affected by floods is fundamental for understanding actions that aim to mitigate the occurrence of this phenomenon. One of the first steps in the investigation of vulnerability to floods is to know the pluvial dynamics of the locality. In this context, the present article aimed to characterize the rainfall variability and its relation with the occurrence of floods in the municipality of Rio do Sul, located in the mesoregion of the Itajaí Valley, in the State of Santa Catarina. The methodology involved an intense analysis of rainfall data and flood records, using statistical techniques and application of the ClimPACT indices. The results identified the summer season as the rainy season and autumn as the period of decline in rainfall totals, however, the most intense rains occur in the winter season, when there are the highest flood records. Finally, it was evidenced that the region presented an increase in rainfall total records and tendency to extreme rainfall events.Keywords: Rainfall, Historic Serie, Software R, ClimPACT.

scholarly journals Modelling Long-Term Monthly Rainfall Variability in Selected Provinces of South Africa: Trend and Extreme Value Analysis Approaches

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 70
Author(s):  
Vusi Ntiyiso Masingi ◽  
Daniel Maposa
Keyword(s):  
South Africa ◽  
Rainfall Variability ◽  
Location Parameter ◽  
Extreme Value ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Eastern Cape ◽  
Kwazulu Natal ◽  
Distribution Class

Extreme rainfall events have made significant damages to properties, public infrastructure and agriculture in some provinces of South Africa notably in KwaZulu-Natal and Gauteng among others. The general global increase in the frequency and intensity of extreme precipitation events in recent years is raising a concern that human activities might be heavily disturbed. This study attempts to model long-term monthly rainfall variability in the selected provinces of South Africa using various statistical techniques. The study investigates the normality and stationarity of the underlying distribution of the whole body of rainfall data for each selected province, the long-term trends of the rainfall data and the extreme value distributions which model the tails of the rainfall distribution data. These approaches were meant to help achieve the broader purpose of this study of investigating the long-term rainfall trends, stationarity of the rainfall distributions and extreme value distributions of monthly rainfall records in the selected provinces of South Africa in this era of climate change. The five provinces considered in this study are Eastern Cape, Gauteng, KwaZulu-Natal, Limpopo and Mpumalanga. The findings revealed that the long-term rainfall distribution for all the selected provinces does not come from a normal distribution. Furthermore, the monthly rainfall data distribution for the majority of the provinces is not stationary. The paper discusses the modelling of monthly rainfall extremes using the non-stationary generalised extreme value distribution (GEVD) which falls under the block maxima extreme value theory (EVT) approach. The maximum likelihood estimation method was used to obtain the estimates of the parameters. The stationary GEVD was found as the best distribution model for Eastern Cape, Gauteng, and KwaZulu-Natal provinces. Furthermore, model fitting supported non-stationary GEVD model for maximum monthly rainfall with nonlinear quadratic trend in the location parameter and a linear trend in the scale parameter for Limpopo, while in Mpumalanga the non-stationary GEVD model with a nonlinear quadratic trend in the scale parameter and no variation in the location parameter fitted well to the monthly rainfall data. The negative values of the shape parameters for Eastern Cape and Mpumalanga suggest that the data follow the Weibull distribution class, while the positive values of the shape parameters for Gauteng, KwaZulu-Natal and Limpopo suggest that the data follow the Fréchet distribution class. The findings from this paper could give information that can assist decision makers establish strategies for proper planning of agriculture, infrastructure, drainage system and other water resource applications in the South African provinces.

scholarly journals KARASTERISTIK CURAH HUJAN DAN ALIRAN DAS LARONA KABUPATEN LUWU TIMUR SULAWESI SELATAN

2013 ◽  
Vol 14 (2) ◽  
pp. 83
Author(s):  
Djazim Syaifullah
Keyword(s):  
Rainy Season ◽  
Daily Rainfall ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Water Reservoirs ◽  
Light Rain ◽  
Daily Rainfall Data ◽  
Moderate Condition

IntisariKarasteristik curah hujan dan aliran DAS Larona telah dilakukan dengan menggunakan data curah hujan dan aliran (inflow). Data curah hujan 7 buah stasiun data bulanan dan harian 10 sampai 29 tahun dan 8 buah stasiun penakar otomatis untuk mendapatkan data jam-jaman. Nilai inflow biasanya dihitung berdasarkan data outflow. Hasilnya menunjukkan bahwa daerah di sekitar Mahalona, bagian tenggara Matano dan bagian Barat Laut Towuti mempunyai konsentrasi curah hujan yang paling besar. DAS ini masuk musim kering pada bulan Agustus dan September, sementara bulan bulan yang lain termasuk bulan basah. Curah Hujan bulanan maksimum terjadi pada bulan April dengan nilai sekitar 360 mm, sedangkan curah hujan bulanan minimum terjadi pada bulan September sekitar 105 mm. DAS Larona didominasi oleh hujan ringan (kurang dari 5 mm dalam satu harinya) dengan durasi hujan  dominan kurang dari 1 jam (rata-rata sekitar 47 % dari total kejadian hujan). Dari nilai koefisien aliran yang berkisar 0.6 menunjukkan bahwa DAS Larona masih berada pada kondisi moderate dalam hal sebagai reservoir air  AbstractPrecipitation and flow charasteristics of the Larona watershed was conducted by use of the rainfall and inflow data. There are monthly and daily rainfall data 10 until 29 year long for 78 automatic rainfall stations. The value of inflow was calculated based on outflow.The results show that the region around Mahalona, the southeastern of Matano and part of Northwest of Towuti have the most concentration of rainfall. This Catchment came into rainy season on August until September, while other month in the rainy season. Maximum monthly rainfall occurs in april with the value of around 360 mm, while the minimum monthly rainfall happened in september around 105 mm. The Llarona catchment was dominated by light rain (less than 5 mm/day) with the duration of rainfall less than 1mm/hour. From the value of the stream coefficients shows that Larona Catchment are still at moderate condition in terms as water reservoirs

scholarly journals ASSESSING CLIMATE RISK AND CLIMATE CHANGE USING RAINFALL DATA – A CASE STUDY FROM ZAMBIA

2011 ◽  
Vol 47 (2) ◽  
pp. 241-266 ◽  
Author(s):  
R. D. STERN ◽  
P. J. M. COOPER
Keyword(s):  
Crop Production ◽  
Markov Chain Model ◽  
Rainfall Variability ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Sub Saharan Africa ◽  
Chain Model ◽  
Wide Range ◽  
Dry Spell ◽  
Rainfall Patterns

SUMMARYRainfall variability, both within and between seasons, is reflected in highly variable crop growth and yields in rainfed agriculture in sub-Saharan Africa and results in varying degrees of weather-induced risk associated with a wide range of crop, soil and water management innovations. In addition there is both growing evidence and concern that changes in rainfall patterns associated with global warming may substantively affect the nature of such risk. Eighty-nine years of daily rainfall data from a site in southern Zambia are analysed. The analyses illustrate approaches to assessing the extent of possible trends in rainfall patterns and the calculation of weather-induced risk associated with the inter- and intra-seasonal variability of the rainfall amounts. Trend analyses use monthly rainfall totals and the number of rain days in each month. No simple trends were found. The daily data were then processed to examine important rain dependent aspects of crop production such as the date of the start of the rains and the risk of a long dry spell, both following planting and around flowering. The same approach is used to assess the risk of examples of crop disease in instances when a ‘weather trigger’ for the disease can be specified. A crop water satisfaction index is also used to compare risks from choices of crops with different maturity lengths and cropping strategies. Finally a different approach to the calculations of these risks fits a Markov chain model to the occurrence of rain, with results then derived from this model. The analyses shows the relevance of this latter approach when relatively short daily rainfall records are available and is illustrated through a comparison of the effects of El Niño, La Niña and Ordinary years on rainfall distribution patterns.

scholarly journals Diagnóstico e tendência da precipitação pluvial em Bom Jesus - Piauí, Brasil

2016 ◽  
Vol 11 (3) ◽  
pp. 115 ◽  
Author(s):  
Raimundo Mainar Medeiros ◽  
Virgínia Mirtes de Alcântara Silva ◽  
Valneli da Silva Melo ◽  
Hudson Ellen Alencar Menezes ◽  
Hamstrong Ellen Alencar Menezes
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Rainy Season ◽  
Rainfall Variability ◽  
Temporal Distribution ◽  
Annual Precipitation ◽  
Annual Rainfall ◽  
Central Tendency ◽  
Average Value ◽  
The City

<p>Com o objetivo de<strong> </strong>analisar a distribuição temporal e a tendência da precipitação pluvial para o município de Bom Jesus - PI relacionando o estudo com regressão linear e medidas de tendência central e de dispersão dos índices pluviométricos mensais e anuais, a estação chuvosa dura seis meses (novembro a abril) com valor médio do período de 875,1 mm, correspondendo a 88,86% da precipitação anual. Em 55 anos de precipitação observada sua média histórica é de 984,8 mm. Conforme a análise de regressão linear da série histórica de precipitação do período de 1960 a 2014, a tendência de maior variabilidade da precipitação centra-se entre os meses de novembro a abril, e os menores índices pluviométricos centra-se entre os meses de maio a setembro, que possui baixos índices pluviométricos.</p><p align="center"><strong><em>Diagnosis and trend rainfall in Bom Jesus - Piauí, Brazil</em></strong><strong><em></em></strong></p><p><strong>Abstract</strong><strong>: </strong>With the objective of analyse the temporal distribution and trend of rainfall for the city of Bom Jesus - PI related study with linear regression and measures of central tendency and dispersion of the monthly and annual rainfall, the six-month rainy season (November to April) with an average value of875.1 mm period, corresponding to 88.86% of the annual precipitation. In 55 years of rainfall observed its historical average is984.8 mm. As will analysis Linear regression of the time series period of rainfall 1960-2014, the trend of increased rainfall variability focuses during the months from November to April, and the lowest rainfall is centered between the months of May to September, that It has low rainfall.</p>

STUDI BIONOMIK VEKTOR MALARIA DI KECAMATAN KALIBAWANG, KULONPROGO

2018 ◽  
Vol 4 (2) ◽  
Author(s):  
Sardjito Eko Windarso dkk
Keyword(s):  
Land Use ◽  
Malaria Vector ◽  
Rainy Season ◽  
Dry Season ◽  
Physical Factors ◽  
Mosquito Vector ◽  
Anopheles Species ◽  
Commercial Farming ◽  
Study Results ◽  
Dry And Rainy Seasons

The increasing of malaria cases in recent years at Kecamatan Kalibawang has been suspected correspond with the conversion of farming land-use which initiated in 1993. Four years after the natural vegetation in this area were changed become cocoa and coffee commercial farming estates, the number of malaria cases in 1997 rose more than six times, and in 2000 it reached 6085. This study were aimed to observe whether there were any differences in density and diversity of Anopheles as malaria vector between the cocoa and mix farming during dry and rainy seasons. The results of the study are useful for considering the appropriate methods, times and places for mosquito vector controlling. The study activities comprised of collecting Anopheles as well as identifying the species to determine the density and diversity of the malaria vector. Both activities were held four weeks in dry season and four weeks in rainy season. The mea-surement of physical factors such as temperature, humidity and rainfall were also conducted to support the study results. Four dusuns which meet the criteria and had the highest malaria cases were selected as study location. Descriptively, the results shows that the number of collected Anopheles in cocoa farming were higher compared with those in mix horticultural farming; and the number of Anopheles species identifi ed in cocoa farming were also more varied than those in the mix horticultural farming.Key words: bionomik vektor malaria, anopheles,

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