Journal articles on the topic 'Annual rainfall data'
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1
RAGHAVENDRA, V. K. "Trends and periodicities of rainfall in sub-divisions of Maharashtra State." MAUSAM 25, no. 2 (February 7, 2022): 197–210. http://dx.doi.org/10.54302/mausam.v25i2.5194.
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The Maharashtra State of India is divided into four meteorological sub-divisions, viz., Konkan, Madhya Maharashtra, Marathwada and Vidarbha. Of these, Madhya Maharashtra and Marathwada are prone to droughts. The principal rainy season is the monsoon season of June to September when over 80 per cent of the annual rainfall is received. The coefficient of variation is about 20 per cent for the annual and monsoon rainfall except in Marathwada where it is 25 per cent. The annual and monsoon rainfalls follow the normal distribution for their yearly frequencies. In this region the annual and the monsoon rainfall series are highly correlated. In the loss drought prone sub-division of Konkan, the annual and monsoon rainfalls show a 100 year cycle. In all the sub-divisions the successive years' rainfalls are not dependent. The trend as revealed by fitting of orthogonal polynomials is shown as a quadratic curve for the annual and monsoon rainfalls of Konkan and Madhya, Maharashtra, the sub-divisions on either side of the Western Ghats. The low pass filter and Mann-Kendall test against randomness confirmed the trend in Konkan rainfall. The power spectral analysis of the data indicates the existence of long term trend for monsoon rainfall of Konkan, 60 year cycle for the annual rainfall of Konkan and Madhya Maharashtra, 30.year cycle for the annual and monsoon rainfall or Vidarbha, 20-year cycle for the monsoon rainfall of Marathwada, 15-year cycle for the monsoon rainfall of Madhya Maharashtra, 7.5-year cycle for the annual and monsoon rainfall of Marathwada.
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Stefanidis, Stefanos, and Dimitrios Stathis. "Spatial and Temporal Rainfall Variability over the Mountainous Central Pindus (Greece)." Climate 6, no. 3 (September 6, 2018): 75. http://dx.doi.org/10.3390/cli6030075.
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In this study, the authors evaluated the spatial and temporal variability of rainfall over the central Pindus mountain range. To accomplish this, long-term (1961–2016) monthly rainfall data from nine rain gauges were collected and analyzed. Seasonal and annual rainfall data were subjected to Mann–Kendall tests to assess the possible upward or downward statistically significant trends and to change-point analyses to detect whether a change in the rainfall time series mean had taken place. Additionally, Sen’s slope method was used to estimate the trend magnitude, whereas multiple regression models were developed to determine the relationship between rainfall and geomorphological factors. The results showed decreasing trends in annual, winter, and spring rainfalls and increasing trends in autumn and summer rainfalls, both not statistically significant, for most stations. Rainfall non-stationarity started to occur in the middle of the 1960s for the annual, autumn, spring, and summer rainfalls and in the early 1970s for the winter rainfall in most of the stations. In addition, the average magnitude trend per decade is approximately −1.9%, −3.2%, +0.7%, +0.2%, and +2.4% for annual, winter, autumn, spring, and summer rainfalls, respectively. The multiple regression model can explain 62.2% of the spatial variability in annual rainfall, 58.9% of variability in winter, 75.9% of variability in autumn, 55.1% of variability in spring, and 32.2% of variability in summer. Moreover, rainfall spatial distribution maps were produced using the ordinary kriging method, through GIS software, representing the major rainfall range within the mountainous catchment of the study area.
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Saha, Sudip. "Trend analysis of rainfall data Rangpur, Bangladesh." International Journal of Advanced Geosciences 8, no. 1 (July 25, 2020): 81. http://dx.doi.org/10.14419/ijag.v8i1.30786.
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The study area lies in Bangladesh that is in the tropical area. The analysis of rainfall data reveals that the average annual rainfall in Rangpur was 2099.25 mm that varies from 427 mm to 3748 mm within the investigated period of time. The highest amount of annual rainfall was recorded in 1984. Heavy rainfall occurs in the month of July of the year. The highest amount of total monthly rainfall was recorded in July, 1987 and measured as 1314 mm. The rainfall trend can be expressed as monsoon rainfall > pre-monsoon rainfall> post monsoon rainfall. The total amount of annual rainfall is strongly significantly positively correlated with the total monthly rainfall of the months of June, July August, September and October and significantly positively correlated with the total monthly rainfall of the month April. The analysis shows that it rained in every year in the month of June as the minimum monthly value for June is greater than zero. Pearson correlation index shows that the mean daily temperature is significantly negatively correlated with daily rainfall which implies that fall of daily mean temperature with the daily rainfall. The increasing trend of annual rainfall suggests the climate change in Rangpur within the investigated period of time. In Rangpur, the value of skewness for all rainfall data are positive that indicate the data are skewed to the right. The positive values of kurtosis indicate that the distribution is not normal.
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Yoo, Chulsang, Minkyu Park, Hyeon Jun Kim, and Changhyun Jun. "Comparison of annual maximum rainfall events of modern rain gauge data (1961–2010) and Chukwooki data (1777–1910) in Seoul, Korea." Journal of Water and Climate Change 9, no. 1 (October 6, 2017): 58–73. http://dx.doi.org/10.2166/wcc.2017.110.
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Abstract In this study, the annual maximum rainfall event series were constructed and compared for both the modern flip-bucket type rainfall data, collected since 1961 (the modern data), and the old Chukwooki rainfall data, collected from 1777 to 1910 (the Chukwooki data). First, independent rainfall events were derived, by applying the same rainfall threshold of 2 mm and data collection time interval of 2 hours, to both the Chukwooki and the modern data. Annual maximum rainfall event series were then constructed, by applying Freund's bivariate exponential distribution annually. Finally, bivariate frequency analysis was done for the annual maximum rainfall event series constructed, by applying the bivariate logistic model to evaluate and quantify their characteristics. The results are in summary: (1) characteristics of the Chukwooki rainfall events and modern rainfall events are very similar to each other; (2) the annual maximum rainfall events of modern data are slightly larger than those of the Chukwooki data. The total rainfall depth per rainfall event for any given return period is thus estimated to be a little higher for the modern data than that of the Chukwooki data. However, based on the findings in this study, it could not be concluded that the rainfall characteristics have significantly changed during the last 200 years.
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Rao, Srinivas G., and A. Ramachandra Rao. "Run Analysis of Rainfall Data Affected by Urbanization." Hydrology Research 17, no. 1 (February 1, 1986): 47–64. http://dx.doi.org/10.2166/nh.1986.0004.
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Urbanization generally increases the amount of rainfall in urban areas and/or downwind areas of urban centers. This paper investigates whether urbanization significantly changes the run properties of rainfall. Three run properties, the run-length, run-sum and run-intensities of positive (surplus) and negative (deficit) runs, of the annual and monthly rainfall are evaluated by using the theory of runs. The annual rainfall is assumed to be normally or gamma distributed, and the monthly rainfall is assumed to follow a two-state Markov chain with stationary, transition probabilities. The methology is applied to annual and monthly rainfall data at La Porte and three surrounding stations in Indiana. The results indicate that these assumptions are adequate to characterize the annual and monthly rainfall. The study demonstrates that although urbanization has inadvertently increased the rainfall amounts at La Porte in relation to its surrounding stations (the so-called La Porte anamoly) the run properties at La Porte are not significantly different from those at the surrounding stations. Further it is found that the run-intensity is a better indicator than run-length or run-sum for evaluating the effects of urbanization on rainfall.
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SINGH, VARTIKA, and PRAMENDRA DEV. "50 year rainfall data analysis and future trend in Saharanpur region." MAUSAM 63, no. 1 (December 31, 2021): 55–64. http://dx.doi.org/10.54302/mausam.v63i1.1455.
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The environmental implications of rainfall pattern in replenishment of ground water system of Saharanpur region, located in western Uttar Pradesh, have been discussed. The mathematical analysis of rainfall dissimilarity of Saharanpur region for a period of 50 year (1959 to 2008) display a quite good range from 497.70 to 4357.5 mm with an annual average rainfall value of 1209.8 mm. The positive trend of departure from the computer value of average annual rainfall exhibits appropriate periods for recharge of ground water reservoir. The recorded data of annual rainfall during the last 3 year reveal values below the calculated annual average rainfall, pointing out negative trend. The statistical analysis of rainfall data involves computations of various statistical parameters, which also support the negative trend of rainfall. The prediction of expected future rainfall trend for a period up to 2018 has been made, which indicates a negative trend. The proposal have been incorporated to implement a plan for augmentation of ground water resource and also to develop possibilities of rainwater harvesting.
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Lompi, Marco, Paolo Tamagnone, Tommaso Pacetti, Renato Morbidelli, and Enrica Caporali. "Impacts of Rainfall Data Aggregation Time on Pluvial Flood Hazard in Urban Watersheds." Water 14, no. 4 (February 11, 2022): 544. http://dx.doi.org/10.3390/w14040544.
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Pluvial floods occur when heavy rainstorms cause the surcharge of the sewer network drainage, representing one of the most impacting natural hazard in urban watersheds. Pluvial flood hazard is usually assessed considering the effect of annual maxima rainfall of short duration, comparable with the typical concentration times of small urban watersheds. However, short duration annual maxima can be affected by an error of underestimation due to the time resolution as well as the aggregation time of the rainfall time series. This study aims at determining the impact of rainfall data aggregation on pluvial flood hazard assessment. Tuscany region (Central Italy) is selected as a case study to perform the assessment of the annual maxima rainfall underestimation error, since the entire region has the same temporal aggregation of rainfall data. Pluvial flood hazard is then evaluated for an urban watershed in the city of Florence (Tuscany) comparing the results obtained using observed (uncorrected) and corrected annual maxima rainfall as meteorological forcing. The results show how the design of rainfall events with a duration of 30 min or shorter is significantly affected by the temporal aggregation, highlighting the importance of correcting annual maxima rainfall for a proper pluvial flood hazard evaluation.
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Liao, Yifan, Bingzhang Lin, Xiaoyang Chen, and Hui Ding. "A New Look at Storm Separation Technique in Estimation of Probable Maximum Precipitation in Mountainous Areas." Water 12, no. 4 (April 20, 2020): 1177. http://dx.doi.org/10.3390/w12041177.
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Storm separation is a key step when carrying out storm transposition analysis for Probable Maximum Precipitation (PMP) estimation in mountainous areas. The World Meteorological Organization (WMO) has recommended the step-duration-orographic-intensification-factor (SDOIF) method since 2009 as an effective storm separation technique to identify the amounts of precipitation caused by topography from those caused by atmospheric dynamics. The orographic intensification factors (OIFs) are usually developed based on annual maximum rainfall series under such assumption that the mechanism of annual maximum rainfalls is close to that of the PMP-level rainfall. In this paper, an alternative storm separation technique using rainfall quantiles, instead of annual maximum rainfalls, with rare return periods estimated via Regional L-moments Analysis (RLMA) to calculate the OIFs is proposed. Based on Taiwan’s historical 4- and 24-h precipitation data, comparisons of the OIFs obtained from annual maximum rainfalls with that from extreme rainfall quantiles at different return periods, as well as the PMP estimates of Hong Kong from transposing the different corresponding separated nonorographic rainfalls, were conducted. The results show that the OIFs obtained from rainfall quantiles with certain rare probabilities are more stable and reasonable in terms of stability and spatial distribution pattern.
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Noorunnahar, M., and MA Hossain. "Trend Analysis of Rainfall Data in Divisional Meteorological Stations of Bangladesh." Annals of Bangladesh Agriculture 23, no. 1 (June 15, 2020): 49–61. http://dx.doi.org/10.3329/aba.v23i1.51473.
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Sixty four years, 1952-2016, rainfall data (monthly rainfall and annual total rainfall) were analyzed using non-parametric methods like Mann-Kendall and Sen’s T test to detect the recent trends in rainfall pattern over seven divisions of Bangladesh. Sen’s non-parametric estimator of slope was frequently used to estimate the magnitude of trend, whose statistical significance was assessed by the Mann–Kendall test. Station basis trend analysis was performed for rainfall data. For rainfall of Bangladesh most of the stations, viz. Dhaka, Sylhet, Rangpur, Khulna showed significant upward trend. There were rising rates of rainfall in some months such as April in Rangpur and September in Khulna and a decreasing trend in some other months as in January in Sylhet were obtained by these statistical tests suggested overall significant changes in rainfall trend in these areas. Monthly rainfall and annual total rainfall were found to decrease at the rates of 4.94 mm/year and 16.11 mm/year, respectively, where the downward trend of monthly total rainfall was insignificant but the trend of annual total rainfall was significant with 5% level of significance.
Ann. Bangladesh Agric. (2019) 23(1) : 49-61
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SEETHARAM, K. "Rainfall models – a study over Gangtok." MAUSAM 61, no. 2 (November 27, 2021): 225–28. http://dx.doi.org/10.54302/mausam.v61i2.819.
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In this paper, the Pearsonian system of curves were fitted to the monthly rainfalls from January to December, in addition to the seasonal as well as annual rainfalls totalling to 14 data sets of the period 1957-2005 with 49 years of duration for the station Gangtok to determine the probability distribution function of these data sets. The study indicated that the monthly rainfall of July and summer monsoon seasonal rainfall did not fit in to any of the Pearsonian system of curves, but the monthly rainfalls of other months and the annual rainfalls of Gangtok station indicated to fit into Pearsonian type-I distribution which in other words is an uniform distribution. Anderson-Darling test was applied to for null hypothesis. The test indicated the acceptance of null-hypothesis. The statistics of the data sets and their probability distributions are discussed in this paper.
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Ledingham, Jamie, David Archer, Elizabeth Lewis, Hayley Fowler, and Chris Kilsby. "Contrasting seasonality of storm rainfall and flood runoff in the UK and some implications for rainfall-runoff methods of flood estimation." Hydrology Research 50, no. 5 (August 14, 2019): 1309–23. http://dx.doi.org/10.2166/nh.2019.040.
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Abstract Using data from 520 gauging stations in Britain and gridded rainfall datasets, the seasonality of storm rainfall and flood runoff is compared and mapped. Annual maximum (AMAX) daily rainfall occurs predominantly in summer, but AMAX floods occur most frequently in winter. Seasonal occurrences of annual daily rainfall and flood maxima differ by more than 50% in dry lowland catchments. The differences diminish with increasing catchment wetness, increase with rainfalls shorter than daily duration and are shown to depend primarily on catchment wetness, as illustrated by variations in mean annual rainfall. Over the whole dataset, only 34% of AMAX daily flood events are matched to daily rainfall annual maxima (and only 20% for 6-hour rainfall maxima). The discontinuity between rainfall maxima and flooding is explained by the consideration of coincident soil moisture storage. The results have serious implications for rainfall-runoff methods of flood risk estimation in the UK where estimation is based on a depth–duration–frequency model of rainfall highly biased to summer. It is concluded that inadequate treatment of the seasonality of rainfall and soil moisture seriously reduces the reliability of event-based flood estimation in Britain.
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Nashwan, Mohamed Salem, Shamsuddin Shahid, and Xiaojun Wang. "Uncertainty in Estimated Trends Using Gridded Rainfall Data: A Case Study of Bangladesh." Water 11, no. 2 (February 19, 2019): 349. http://dx.doi.org/10.3390/w11020349.
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This study assessed the uncertainty in the spatial pattern of rainfall trends in six widely used monthly gridded rainfall datasets for 1979–2010. Bangladesh is considered as the case study area where changes in rainfall are the highest concern due to global warming-induced climate change. The evaluation was based on the ability of the gridded data to estimate the spatial patterns of the magnitude and significance of annual and seasonal rainfall trends estimated using Mann–Kendall (MK) and modified MK (mMK) tests at 34 gauges. A set of statistical indices including Kling–Gupta efficiency, modified index of agreement (md), skill score (SS), and Jaccard similarity index (JSI) were used. The results showed a large variation in the spatial patterns of rainfall trends obtained using different gridded datasets. Global Precipitation Climatology Centre (GPCC) data was found to be the most suitable rainfall data for the assessment of annual and seasonal rainfall trends in Bangladesh which showed a JSI, md, and SS of 22%, 0.61, and 0.73, respectively, when compared with the observed annual trend. Assessment of long-term trend in rainfall (1901–2017) using mMK test revealed no change in annual rainfall and changes in seasonal rainfall only at a few grid points in Bangladesh over the last century.
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Khatun, Khadija, MA Samad, and Md Bazlur Rashid. "Time Series Analysis of Temperature and Rainfall Data of Dhaka Division." Dhaka University Journal of Science 65, no. 2 (July 5, 2017): 119–23. http://dx.doi.org/10.3329/dujs.v65i2.54519.
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In this paper, thirty five years’ (1981-2015) temperature and rainfall data have been studied to detect the recent trends in temperature and rainfall over Dhaka division of Bangladesh. Data of climatic factors such as annual average maximum temperature (MAXT), minimum temperature (MINT), mean temperature (MEANT), monsoon total rainfall (MTR) and annual total rainfall (ATR) have been analyzed. Sen’s non-parametric estimator of slope has been frequently used to estimate the magnitude of trend, whose statistical significance is assessed by the Mann–Kendall test. For this purpose, data from four meteorological stations (Dhaka, Mymensingh, Tangail and Faridpur) have been used. It is observed that annual average maximum, minimum and mean temperature of the study area are increasing at the rates 0.0170C/year, 0.0090C/year and 0.0130C/year respectively and the upward trend is statistically stable with 10% level of significance. On the other hand, monsoon total rainfall and annual total rainfall are decreasing at the rates of 4.94 mm/year and 16.11mm/year respectively where the downward trend of MTR is insignificant but the trend of ATR is significant with 10% level of significance.
Dhaka Univ. J. Sci. 65(2): 119-123, 2017 (July)
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Junges, Amanda H., Carolina Bremm, and Denise C. Fontana. "Rainfall climatology, variability, and trends in Veranópolis, Rio Grande do Sul, Brazil." Revista Brasileira de Engenharia Agrícola e Ambiental 23, no. 3 (March 2019): 160–66. http://dx.doi.org/10.1590/1807-1929/agriambi.v23n3p160-166.
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ABSTRACT The objective of this study was to characterize the rainfall climatology in Veranópolis, Rio Grande do Sul, Brazil, through analyses of means, variabilities related to El Niño Southern Oscillation (ENSO), and temporal trends, using a 60-year data series (1956-2015). Descriptive statistics of annual, monthly and seasonal rainfall were used to characterize the rainfall climatology. The differences between seasons, and influence of ENSO were evaluated using analysis of variance and the Duncan’s test. Rainfall trends were evaluated by the Mann Kendall test. The local average annual rainfall is 1,683 mm and the average monthly rainfall is 140 mm, varying from 109 (May) to 182 mm (September). The annual rainfall has high interannual (standard deviation of 327 mm), monthly (60-100 mm) and seasonal (124-183 mm) variabilities, which should be considered in non-irrigated agricultural systems using rainfall as the main source of water supply to plants. Although autumn presents lower average rainfall (346 mm) than the other seasons, its average percentages were similar to the total annual rainfall (21-28%), and the rainfalls are well-distributed in the seasons. Differences between ENSO events occurred in the spring; La Niña years showed lower rainfall (385 mm) than El Niño (549 mm) and neutral (481 mm) years. The annual rainfall tended to increase by 6.3 mm per year (p < 0.01), with increases of 2.5 mm in spring and 1.9 mm in winter (p < 0.10) in the period analyzed.
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B. V. Asewar, Ramesh Bethala, M. S. Peneke K. K. Dakhore, and M. G. Jadhav A. M. Khobragade. "Taluka Wise Study of Rainfall Pattern in Nanded District of Marthwada Region (Maharashtra) Using Rainfall Data." International Journal of Current Microbiology and Applied Sciences 10, no. 9 (September 10, 2021): 333–50. http://dx.doi.org/10.20546/ijcmas.2021.1009.039.
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About 60 per cent of the total cultivable area of the country is rainfed. However, prolonged dry periods affect the final crop production. Monsoon is an important season for water supplies, from surface reservoir. Uneven distribution of rainfall, affect the agricultural production remarkably. The daily rainfall data was collected for each taluka of Nanded district for the period of 20 years (1998-2017) and it was to be summed up on meteorological weekly, monthly, seasonally, annual basis for each taluka of Nanded district basis for the study of rainfall characterization. The results indicated that weekly mean annual basis total rainfall was ranged between 720.0 mm in Deglur and 1009.9 mm in Mahur. The weekly highest rainfall on annual basis was recorded in Himayat Nagar (53.7 mm) in the 30th MW amongst all the taluka considering monsoon period (23 to 42 MW). The monthly mean rainfall indicated that the lowest and highest monthly mean rainfall amongst all the taluka was observed in Nanded, Kandhar, Loha, Hadgaon, Bhokar, Kinwat, Mahur, Dharmabad, Ardhapur, Naigaon talukas (0.0 mm) in the December month and in the Mahur taluka (283.1 mm) in July month. The seasonal distribution of Nanded district was obtained in winter season (6.1 mm), in summer (15.5 mm), in monsoon (578.3 mm), in post monsoon (216.6 mm). The annual rainfall data is statistical analyzed for Nanded district and within the year and taluka to taluka ranged C.V. (%) were between 25.0 to 46.9 %. The data of taluka-wise annual normal of weather parameter (i.e. rainfall and rainy days) calculated. Here, the results indicated that the onset of monsoon was observed in 23th MW and withdrawal in 43rd MW in Nanded district. It showed that average rainfall of Nanded district is 816.4 mm with 45.0 rainy days per year. The results clearly indicated the onset of monsoon in 23th MW and withdrawal of monsoon in 43rd MW for the Nanded district should be considered. The statistical analysis for rainfall variability was worked out and it was intra-annual as well as intra-taluka variation in Nanded district. It was ranged between 19.0 to 51.0 per cent with annual mean 45.0 rainy days per year.
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Ahmad, Aimi Athirah, Fadhilah Yusof, Muhamad Radzali Mispan, and Hasliana Kamaruddin. "Rainfall, evapotranspiration and rainfall deficit trend in Alor Setar, Malaysia." Malaysian Journal of Fundamental and Applied Sciences 13, no. 4-1 (December 5, 2017): 400–404. http://dx.doi.org/10.11113/mjfas.v13n4-1.844.
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Rainfall and potential evapotranspiration are important variables in water balance study. Rainfall data were obtained from Malaysian Meteorological Department while estimates of potential evapotranspiration were calculated using Penman-Monteith method. Trend analysis of monthly and annual rainfall, potential evapotranspiration and rainfall deficit are essential to manage irrigation system in agricultural systems. This is because changes in trend of these parameters may affect the water cycle and ecosystem. Annual and monthly values of these variables were analysed from 1980-2009. Results indicated increasing trends of 16.2 mm yr-1 and 3.01 mm yr-1 for both annual rainfall and potential evapotranspiration, respectively. Consequently, these trends resulted in annual rainfall deficit of 1.69 mm per year.
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Javanmard, S., A. Yatagai, M. I. Nodzu, J. BodaghJamali, and H. Kawamoto. "Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran." Advances in Geosciences 25 (May 17, 2010): 119–25. http://dx.doi.org/10.5194/adgeo-25-119-2010.
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Abstract. To evaluate satellite rainfall estimates of Tropical Rain Measurement Mission (TRMM) level 3 output (3B42) (TRMM_3B42) over Iran (20°–45° N, 40°–65° E), we compared these data with high-resolution gridded precipitation datasets (0.25°×0.25° latitude/longitude) based on rain gauges (Iran Synoptic gauges Version 0902 (IS0902)). Spatial distribution of mean annual and mean seasonal rainfall in both IS0902 and TRMM_3B42 from 1998 to 2006 shows two main rainfall patterns along the Caspian Sea and over the Zagros Mountains. Scatter plots of annual average rainfall from IS0902 versus TRMM_3B42 for each 0.25°×0.25° grid cell over the entire country (25°–40° N, 45°–60° E), along the Caspian Sea (35°–40° N, 48°–56° E), and over the Zagros Mountains (28°–37° N, 46°–55° E) were derived. For the entire country, the Caspian Sea region, and the Zagros Mountains, TRMM_3B42 underestimates mean annual precipitation by 0.17, 0.39, and 0.15 mm day−1, respectively, and the mean annual rainfall spatial correlation coefficients are 0.77, 0.57, and 0.75, respectively. The mean annual precipitation temporal correlation coefficient for IS0902 and TRMM_3B42 is ~0.8 in the area along the Zagros Mountains, and ~0.6 in the Caspian Sea and desert regions.
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Obi, Lawrence Echefulechukwu. "Application of Hydrological Computations in Predicting Rainfall Trends in Imo State of Nigeria." European Journal of Engineering Research and Science 2, no. 9 (September 23, 2017): 36. http://dx.doi.org/10.24018/ejers.2017.2.9.367.
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This research employed the empirical method in its approach and workings. Empirical data were collected and various hydrological computations and graphs were engaged through the application of the collected data. The mass curve of rainfall, hyetograph, moving average of annual rainfalls and the computations of recurrence intervals were done by applying the Weibul formular. With computations and its analysis, the recurrent intervals of rainfall magnitudes were determined and rainfall pattern within Imo State were predicted.
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Obi, Lawrence Echefulechukwu. "Application of Hydrological Computations in Predicting Rainfall Trends in Imo State of Nigeria." European Journal of Engineering and Technology Research 2, no. 9 (September 23, 2017): 36–41. http://dx.doi.org/10.24018/ejeng.2017.2.9.367.
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This research employed the empirical method in its approach and workings. Empirical data were collected and various hydrological computations and graphs were engaged through the application of the collected data. The mass curve of rainfall, hyetograph, moving average of annual rainfalls and the computations of recurrence intervals were done by applying the Weibul formular. With computations and its analysis, the recurrent intervals of rainfall magnitudes were determined and rainfall pattern within Imo State were predicted.
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Gu, Zhijia, Detai Feng, Xingwu Duan, Kuifang Gong, Yawen Li, and Tianyu Yue. "Spatial and Temporal Patterns of Rainfall Erosivity in the Tibetan Plateau." Water 12, no. 1 (January 10, 2020): 200. http://dx.doi.org/10.3390/w12010200.
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The Tibetan Plateau is influenced by global climate change which results in frequent melting of glaciers and snow, and in heavy rainfalls. These conditions may increase the risk of soil erosion, but prediction is not feasible due to scarcity of rainfall data in the high altitudes of the region. In this study, daily precipitation data from 1 January 1981 to 31 December 2015 were selected for 38 meteorological stations in the Tibetan Plateau, and annual and seasonal rainfall erosivity were calculated for each station. Additionally, we used the Mann–Kendall trend test, Sen’s slope, trend coefficient, and climate tendency rate indicators to detect the temporal variation trend of rainfall erosivity. The results showed that the spatial distribution of rainfall erosivity in the Tibetan Plateau exhibited a significant decreasing trend from southeast to northwest. The average annual rainfall erosivity is 714 MJ·mm·ha−1·h−1, and varies from 61 to 1776 MJ·mm·ha−1·h−1. Rainfall erosivity was mainly concentrated in summer and autumn, accounting for 67.5% and 18.5%, respectively. In addition, annual, spring, and summer rainfall erosivity were increasing, with spring rainfall erosivity highly significant. Temporal and spatial patterns of rainfall erosivity indicated that the risk of soil erosion was relatively high in the Hengduan mountains in the eastern Tibetan Plateau, as well as in the Yarlung Zangbo River Valley and its vicinity.
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KELKAR, R. R., and A. V. R. K. RAO. "Interannual variability of monsoon rainfall as Estimated from INSAT -1B data." MAUSAM 41, no. 2 (February 22, 2022): 42–47. http://dx.doi.org/10.54302/mausam.v41i2.2522.
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Satellite estimated rainfall using INSA T -lB data is utilised to study the annual variations of monsoon rainfall during the years 1986, 1987 and 1988. Patterns of mean monthly rainfall for the monsoon months and the deviations from the mean of rainfall for each monsoon month are presented.
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Adhikari, R. N., M. S. RAMA MOHAN RAO, and P. BHASKAR RAO. "Analysis of rainfall data for water management In dry land zone of Karnataka." MAUSAM 44, no. 2 (January 1, 2022): 147–52. http://dx.doi.org/10.54302/mausam.v44i2.3812.
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Bellary region is characterized as one of the semi-arid zones of Karnataka, having only 508 mm of annual rainfall distributed over 35 rainy days. The ill-distribution of rainfall creates at least 5 drought years in every decade. The average rainfa1l distribution shows that there is a total failure in Kharif season. However, some assured rainfall received during September and October a better prospect which assumes for rabi season .This problem can be overcome to. certain extent by scientific management of crops and water. This calls for detailed analysis of any Important water resources Issues. Keeping this mind, an attempt made in this paper to analysis short and long period rainfall data. The probabilities analysis of. rainfall for shorter periods for identification of suitable periods for sowing, return period analysis for designing of soil and water conservation structures and determining the size of storage structures, the identification of number of Various rainfall events for designing water harvesting system for crop and water management are carried out and presented in this paper.
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MAPLES, JOSHUA G., B. WADE BRORSEN, and JON T. BIERMACHER. "THE RAINFALL INDEX ANNUAL FORAGE PILOT PROGRAM AS A RISK MANAGEMENT TOOL FOR COOL-SEASON FORAGE." Journal of Agricultural and Applied Economics 48, no. 1 (February 2016): 29–51. http://dx.doi.org/10.1017/aae.2016.3.
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AbstractThe recently implemented Rainfall Index Annual Forage pilot program aims to provide risk coverage for annual forage producers in select states through the use of area rainfall indices as a proxy for yield. This article utilizes unique data from a long-term study of annual ryegrass production with rainfall recorded at the site to determine whether the use of rainfall indices provides adequate coverage for annual forage growers. The rainfall index is highly correlated with actual rainfall. However, it does not provide much yield loss risk protection for our cool-season forage data.
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Wilby, R. L., and D. Yu. "Rainfall and temperature estimation for a data sparse region." Hydrology and Earth System Sciences 17, no. 10 (October 15, 2013): 3937–55. http://dx.doi.org/10.5194/hess-17-3937-2013.
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Abstract. Humanitarian and development agencies face difficult decisions about where and how to prioritise climate risk reduction measures. These tasks are especially challenging in regions with few meteorological stations, complex topography and extreme weather events. In this study, we blend surface meteorological observations, remotely sensed (TRMM and NDVI) data, physiographic indices, and regression techniques to produce gridded maps of annual mean precipitation and temperature, as well as parameters for site-specific, daily weather generation in Yemen. Maps of annual means were cross-validated and tested against independent observations. These replicated known features such as peak rainfall totals in the highlands and western escarpment, as well as maximum temperatures along the coastal plains and interior. The weather generator reproduced daily and annual diagnostics when run with parameters from observed meteorological series for a test site at Taiz. However, when run with interpolated parameters, the frequency of wet days, mean wet-day amount, annual totals and variability were underestimated. Stratification of sites for model calibration improved representation of the growing season's rainfall totals. Future work should focus on a wider range of model inputs to better discriminate controls exerted by different landscape units.
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Wilby, R. L., and D. Yu. "Rainfall and temperature estimation for a data sparse region." Hydrology and Earth System Sciences Discussions 10, no. 6 (June 14, 2013): 7575–618. http://dx.doi.org/10.5194/hessd-10-7575-2013.
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Abstract. Agencies face difficult decisions about where and how to prioritise climate risk reduction measures. These tasks are especially challenging in regions with few meteorological stations, complex topography and extreme weather events. In this study, we blend surface meteorological observations, remotely sensed (TRMM and NDVI) data, physiographic indices, and regression techniques to produce gridded maps of annual mean precipitation and temperature, as well as parameters for site-specific, daily weather generation in Yemen. Maps of annual means were cross-validated and tested against independent observations. These replicated known features such as peak rainfall totals in the Highlands and western escarpment, as well as maximum temperatures along the coastal plains and interior. The weather generator reproduced daily and annual diagnostics when run with parameters from observed meteorological series for a test site at Taiz. However, when run with interpolated parameters, the frequency of wet-days, mean wet-day amount, annual totals and variability were underestimated. Stratification of sites for model calibration improved representation of growing season rainfall totals. Future work should focus on a wider range of model inputs to better discriminate controls exerted by different landscape units.
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Krisnayanti, Denik Sri, Davianto Frangky B. Welkis, Fery Moun Hepy, and Djoko Legono. "Evaluasi Kesesuaian Data Tropical Rainfall Measuring Mission (TRMM) dengan Data Pos Hujan Pada Das Temef di Kabupaten Timor Tengah Selatan." JURNAL SUMBER DAYA AIR 16, no. 1 (May 31, 2020): 51–62. http://dx.doi.org/10.32679/jsda.v16i1.646.
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The construction of the Temef Dam in Oenino Village, Oenino District, and Konbaki Village, Polen District, South Central Timor Regency requires long and reliable rainfall data. To overcome the minimum data or the unavailability of automatic rainfall (ARR) and discharge data in the past decades, the use of Tropical Rainfall Measuring Mission (TRMM) satellite data is foreseen. The accuracy of TRMM data is obtained when the parameters of suitability and compatibility of TRMM are in a good agreement with the ARR. For the Temef watershed, there are six rainfall stations that were reviewed, namely Fatumnasi, Oeoh, Noelnoni, Polen, Nifukani, and Batinifukoko rainfall stations. Direct comparisons of rainfall data were conducted for 20 years (1998-2018) with temporal resolution on a monthly and daily basis. The results of the study show that the rainfall patterns in the TRMM data product (version 3B42V7) tend to be consistent with 3 rainfall stations in the Temef watershed namely Noelnoni, Fatumnasi, and Batinifukoko. A correlation coefficient of 0.505 – 0.813 was obtained from TRMM data calibration at monthly basis while a correction factor level of 0.0056 - 0.0129 was obtained for daily. The calibration on the annual maximum daily rainfall data resulted in a correction factor of 0.0298 - 0.2516. Monthly and daily TRMM data fit well with the data of 3 rainfall stations. However, the Noelnoni rainfall station showed poor results on the annual maximum daily rainfall.Keywords: TRMM data, ARR data, correction factor, correlation coefficient
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Saha, Sudip. "Precipitation concentration index (PCI) a tool to evaluate the distribution of Rainfall, Barishal, Bangladesh." International Journal of Advanced Geosciences 8, no. 2 (September 30, 2020): 193. http://dx.doi.org/10.14419/ijag.v8i2.31074.
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The present research work reveals the mean annual rainfall of Barishal is 2087.34 mm for the investigated period. The maximum annual rainfall was 3390 mm in the year of 1960 and minimum annual rainfall was recorded as 1277 mm in the year of 1964. The annual rainfall is inversely correlated with time. The maximum monthly rainfall is recorded in the month of July. The amount of annual rainfall is strongly significantly positively correlated with the monthly rainfall of May, June, July, August and September. In Barishal, the value of skewness for all rainfall data are positive that indicate the data are skewed to the right. The positive value of kurtosis of the eleven months of the year (except July) means a peaked distribution and a negative value in the month of July reveals the flat distribution with the same mean and standard deviation. The annual PCI value is inversely proportional to the annual rainfall. The analyses of seasonal precipitation concentration index (SPCI) reveals that the rainfall is uniformly distributed in summer monsoon whereas the winter rainfall shows the dominance of strong irregularity in precipitation distribution.
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Shukla, Siddhartha, Sandeep Kumar, Navdeep Sharma, and Dr Manjot Kaur Bhatia. "Data Mining Application: Rainfall Predictions." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 113–18. http://dx.doi.org/10.22214/ijraset.2022.47833.
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Abstract: Data mining is method or process of extracting ( Implicit previously unknown and potentially useful) pattern or information from large amount of Data. It is used to extract relevance knowledge from raw data. Some data mining methods and algorithms or some organizations used this because to enhance their businesses and they found required result. In 1936 Alan Turing introduced this idea first time. And other name of Data Mining is Knowledge Discovery in Database (KDD), because of unknown and potentially important data stored in database. Rainfall is the prime input for wedding seasons or any occasion in India . It is designed for information about the rainfall season on occasion or by taking the previous 10 years data. This is very helpful for primary sector workers for crop planning .The Daily rainfall data for a period of 10 years is used to understand usual rainfall, deficit rainfall, Excess rainfall and Seasonal rainfall This analysis will provide useful facts for water resources planner and formula available is used to evaluate return period of monthly , seasonally and annual rainfall.
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Kaamun and Sahil Arora. "Statistical analysis of seasonal rainfall data in Chandigarh: A case study." IOP Conference Series: Earth and Environmental Science 889, no. 1 (November 1, 2021): 012024. http://dx.doi.org/10.1088/1755-1315/889/1/012024.
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Abstract The following research focuses on Chandigarh’s annual rainfall of past 50 years i.e. from 1968 to 2017. Parameters like Kurtosis, Variance, Goodness of Fit, Mann-Kendall’s Test were performed along with total annual forecast as well as seasonal forecast was predicted. Seasonal rend was also studied so as to study in detail about the past, present, and future of rainfall in Chandigarh. This study was performed with the help of MS-Excel and ExcelStat. A rising trend was found in Chandigarh for total as well as seasonal rainfall with a maximum rainfall of 1510.9 mm in the year of 1996 and a minimum of 371.1 mm in year 1987, other than this Sen.’s slope was 6.431 whereas skewness was found to be 0.6018.
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Mondal, M. Shahjahan, Sara Nowreen, and Mostofa Najmus Sakib. "Scale-Dependent Reliability of Projected Rainfalls over Bangladesh with the PRECIS Model." Climate 8, no. 2 (January 27, 2020): 20. http://dx.doi.org/10.3390/cli8020020.
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The regional climate model, Providing REgional Climates for Impact Studies (PRECIS), has been widely used throughout the world to generate climate change projections for impact studies and adaptations. Its recent application in South Asia also includes the projection of rainfall extremes. In spite of its wide application, a stringent validation of the model is yet to be reported. In this study, we assessed the performance of the model in simulating annual, monthly and extreme rainfalls over Bangladesh by using a number of statistical techniques, e.g., pattern (both spatial and temporal) correlation, root mean square difference (RMSD), mean absolute difference (MAD), Student’s t-test for significance, probability density functions, etc. The results indicated that the PRECIS model could capture the overall spatial pattern of mean annual and monthly rainfalls very well. However, the inter-annual variability was poorly simulated by the model. In addition, the model could not capture the rainfall extremes. A spatial aggregation of rainfall data did not improve the reliability of the model as far as variability and extremes are concerned. Therefore, further improvements of the model and/or its driving global climate model are warranted for its practical use in the generation of rainfall scenarios.
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Srikanthan, R., and T. A. McMahon. "Stochastic generation of annual, monthly and daily climate data: A review." Hydrology and Earth System Sciences 5, no. 4 (December 31, 2001): 653–70. http://dx.doi.org/10.5194/hess-5-653-2001.
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Abstract. The generation of rainfall and other climate data needs a range of models depending on the time and spatial scales involved. Most of the models used previously do not take into account year to year variations in the model parameters. Long periods of wet and dry years were observed in the past but were not taken into account. Recently, Thyer and Kuczera (1999) developed a hidden state Markov model to account for the wet and dry spells explicitly in annual rainfall. This review looks firstly at traditional time series models and then at the more complex models which take account of the pseudo-cycles in the data. Monthly rainfall data have been generated successfully by using the method of fragments. The main criticism of this approach is the repetitions of the same yearly pattern when only a limited number of years of historical data are available. This deficiency has been overcome by using synthetic fragments but this brings an additional problem of generating the right number of months with zero rainfall. Disaggregation schemes are effective in obtaining monthly data but the main problem is the large number of parameters to be estimated when dealing with many sites. Several simplifications have been proposed to overcome this problem. Models for generating daily rainfall are well developed. The transition probability matrix method preserves most of the characteristics of daily, monthly and annual characteristics and is shown to be the best performing model. The two-part model has been shown by many researchers to perform well across a range of climates at the daily level but has not been tested adequately at monthly or annual levels. A shortcoming of the existing models is the consistent underestimation of the variances of the simulated monthly and annual totals. As an alternative, conditioning model parameters on monthly amounts or perturbing the model parameters with the Southern Oscillation Index (SOI) result in better agreement between the variance of the simulated and observed annual rainfall and these approaches should be investigated further. As climate data are less variable than rainfall, but are correlated among themselves and with rainfall, multisite-type models have been used successfully to generate annual data. The monthly climate data can be obtained by disaggregating these annual data. On a daily time step at a site, climate data have been generated using a multisite type model conditional on the state of the present and previous days. The generation of daily climate data at a number of sites remains a challenging problem. If daily rainfall can be modelled successfully by a censored power of normal distribution then the model can be extended easily to generate daily climate data at several sites simultaneously. Most of the early work on the impacts of climate change used historical data adjusted for the climate change. In recent studies, stochastic daily weather generation models are used to compute climate data by adjusting the parameters appropriately for the future climates assumed.
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Martins, Sérgio Gualberto, Junior Cesar Avanzi, Marx Leandro Naves Silva, Nilton Curi, Lloyd Darrell Norton, and Sebastião Fonseca. "Rainfall erosivity and rainfall return period in the experimental watershed of Aracruz, in the coastal plain of Espirito Santo, Brazil." Revista Brasileira de Ciência do Solo 34, no. 3 (June 2010): 999–1004. http://dx.doi.org/10.1590/s0100-06832010000300042.
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Knowledge on the factors influencing water erosion is fundamental for the choice of the best land use practices. Rainfall, expressed by rainfall erosivity, is one of the most important factors of water erosion. The objective of this study was to determine rainfall erosivity and the return period of rainfall in the Coastal Plains region, near Aracruz, a town in the state of Espírito Santo, Brazil, based on available data. Rainfall erosivity was calculated based on historic rainfall data, collected from January 1998 to July 2004 at 5 min intervals, by automatic weather stations of the Aracruz Cellulose S.A company. A linear regression with individual rainfall and erosivity data was fit to obtain an equation that allowed data extrapolation to calculate individual erosivity for a 30-year period. Based on this data the annual average rainfall erosivity in Aracruz was 8,536 MJ mm ha-1 h-1 yr-1. Of the total annual rainfall erosivity 85 % was observed in the most critical period October to March. Annual erosive rains accounted for 38 % of the events causing erosion, although the runoff volume represented 88 % of the total. The annual average rainfall erosivity return period was estimated to be 3.4 years.
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Indarto, Indarto, and Askin Askin. "VARIABILITAS SPASIAL HUJAN DI WILAYAH UPT PSDA DI MALANG." Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) 6, no. 3 (March 28, 2018): 171. http://dx.doi.org/10.23960/jtep-l.v6i3.171-180.
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This study show the spatial variabilit of rainfall (monthly and annual) rainfall in the area of technical implementation unit of water resources management (UPT-PSDA) in Malang. Administrative area of UPT PSDA in Malang include Malang regency, Malang city, Batu, Blitar Regency, Tulungagung Regency, and Trenggalek Regency. Daily rainfall data from 88 pluviometers spread around the areas are used as main input. The research procedures consist of : (1) data pre-analysis; (2) the analyses using ESDA tools (Histogram, voronoi, QQ-Plot); (3) interpolation by using IDW method; (4) producing a thematic map; and (5) interpretation. Analysis using the histogram, voronoi–maps and normal QQ-plots tools illustrates more detail the spatial variability of the monthly and annual rainfall around the regions. Interpolation produces a thematic map of mean monthly-rainfall, between 100 – 400 mm/month. The spatial distribution of annual rainfall was illustrated by a thematic show the average-annual-range from 1000 – 4000 mm/year. Keywords: spatial variability, rainfall, ESDA, IDW, monthly, annual rainfall
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Brychta, Jiří, and Miloslav Janeček. "Determination of erosion rainfall criteria based on natural rainfall measurement and its impact on spatial distribution of rainfall erosivity in the Czech Republic." Soil and Water Research 14, No. 3 (May 27, 2019): 153–62. http://dx.doi.org/10.17221/91/2018-swr.
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Rainfall erosivity is the main factor of the USLE or RUSLE equations. Its accuracy depends on recording precision and its temporal resolution, number of stations and their spatial distribution, length of recorded period, recorded period, erosion rainfall criteria, time step of rainfall intensity and interpolation method. This research focuses on erosion rainfall criteria. A network of 32 ombrographic stations, 1-min temporal resolution rainfall data, 35.6-year period and experimental runoff plots were used. We analysed 8951 rainfalls from ombrographic stations, 100 rainfalls and caused soil losses and runoffs from experimental runoff plots. Main parameter which influenced the number of erosion rainfalls was the precondition AND/OR which determines if conditions of rainfall total (H) have to be fulfilled simultaneously with rainfall intensity (I<sub>15</sub> or I<sub>30</sub>) or not. We proved that if parameters I<sub>15 </sub>> 6.25 mm/15 min AND H > 12.5 mm were fulfilled, then 84.2% of rainfalls caused soil loss > 0.5 t/ha and 73.7% ≥ 1 t/ha. In the case of precondition OR only 44.6% of rainfalls caused soil loss > 0.5 t/ha and 33.9% ≥ 1 t/ha. If the precondition AND was fulfilled, there were on average 75.5 rainfalls, average R factor for each rainfall was 21 MJ/ha·cm/h (without units below in the text, according international unit: 210 MJ/ha·mm/h) and average annual R factor was 45.4. In the case of precondition OR there were on average 279 rainfalls but average R factor for each rainfall was only 9.1 and average annual R factor was 67.4. Therefore if the precondition OR is used, R factor values are overestimated due to a high number of rainfalls with no or very low erosive potential. The resulting overestimated soil losses calculated using USLE/RUSLE subsequently cause an overestimation of financial expenses for erosion-control measures.
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SELVARAJ, R. SAMUEL, S. TAMILSELVI, and R. GAYATHRI. "Fractal analysis: Annual rainfall in Chennai." MAUSAM 61, no. 1 (November 27, 2021): 35–38. http://dx.doi.org/10.54302/mausam.v61i1.774.
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The annual rainfall data of Chennai is analyzed using the Fractal Construction Technique. According to Mandelbrot the dimension of any line including nautical lines may not be Euclidean but Fractional, Mandelbrot, 1982. This fractional dimension leads to a repetitive appearance of any pattern. Climate which is usually periodic by nature can be analyzed through this technique. Efforts are on to search the fractal geometry of climate and to predict its periodicity on different temporal scales. This paper estimates the various parameters like Lyapunov exponent, Maximum Lyapunov characteristic exponent, Lyapunov time, Kaplan-Yorke dimension for the annual rainfall of Chennai.
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YANOS, MAR HEISEN. "Analysis of Rainfall Variability in the Province of Quirino." Journal of Environmental Science and Economics 1, no. 2 (April 2, 2022): 44–50. http://dx.doi.org/10.56556/jescae.v1i2.20.
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The temporal variability of rainfall in Quirino Province was analyzed through the use of rainfall data of seven (7) rain gauges within the neighboring provinces like Nueva Vizcaya and Aurora. The length of record analyzed from 1997 to 2016. In this study, rainfall frequency analysis and consistency of rainfall data from the different stations through the use of double mass curve analysis was performed and analyzed. The annual series was used to screen each station’s annual rainfall data while the province’s average Thiessen rainfall was screened using maximum period series process. It was found out that all the data of the seven (7) rainfall stations were consistent. To attain allowable error in estimation of 10%, 5% and 1% for the mean annual rainfall the number of rain gauge station needed in the province should be 18, 72 and 1799, respectively.
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Caloiero, Tommaso, Roberto Coscarelli, and Gaetano Pellicone. "Trend Analysis of Rainfall Using Gridded Data over a Region of Southern Italy." Water 13, no. 16 (August 19, 2021): 2271. http://dx.doi.org/10.3390/w13162271.
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Climate change is affecting all regions worldwide. Globally, polar ice shields are melting and the sea is rising. Moreover, some regions are facing more common extreme weather events and rainfall, while others are experiencing more extreme heat waves and droughts, causing changes in mean renewable water supplies e.g., precipitation and runoff. In this work, in order to detect possible rainfall trends in the Calabria region (southern Italy), a gridded database has been obtained from a rainfall data set of 129 monthly series collected for the period 1951–2016. In particular, the Inverse Distance Weighed was applied to build 603 rainfall grid series with a spatial resolution of 5 km × 5 km and, for each grid point, the monthly, seasonal and annual rainfall series were analyzed with the Mann–Kendall non-parametric test and the Theil–Sen estimator. Results showed a decreasing trend for the annual and winter–autumn rainfall and an increasing trend for the summer one.
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Krishn, Pragalbh, Gulshan Kumar, and GaneshD Kale. "Trend Analyses in Gridded Rainfall Data over the Sabarmati Basin." MAUSAM 73, no. 2 (March 31, 2022): 295–306. http://dx.doi.org/10.54302/mausam.v73i2.303.
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Sabarmati basin in India has been experiencing decreased rainfall since 1960s. In the monsoon season, maximum reduction in number of rainy days was observed in the Sabarmati basin. Thus, study of rainfall in the Sabarmati basin is necessary. Therefore, gridded rainfall data of the Sabarmati basin corresponding to annual, seasonal and monthly temporal scales is used for trend analyses for the period of 54 years’ (1951 to 2004) through application of Mann-Kendall (MK) test, MK test with block-boot strapping, Sen’s slope test, innovative trend analysis plot and smoothing curve. Results showed that, statistically significant trends are present at very few grid points of annual, winter, pre-monsoon and monsoon rainfall data and almost are decreasing. Thus, if the same trend continues in future, then there will be scarcity of water and more strain on the management of water resources at the given grids in corresponding temporal scales.
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Suryanto, Joko, and Joko Krisbiyantoro. "Trend Analysis of Rainfall Data in Magelang District Using Mann-Kendall Test and Modification Mann-Kendall Variation." AGRIFOR 17, no. 2 (October 11, 2018): 293. http://dx.doi.org/10.31293/af.v17i2.3616.
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The objective of the research was to analyzed rainfall trends from 6 rainfall stations Kajoran, Mendut, Muntilan, Ngablak, Salaman and Tempuran rainfall station in different time scales (monthly, 3-months periodicityand annual). Identification homogenity of the rainfall data period 1986-2016 for Magelang district using Rescaled Adjusted Partial Sums (RAPS) methode. The three non-parametric tests, Mann-Kendall (MK), modified Mann-Kendall (MMK), trend free prewhitening Mann-Kendall (TFPW-MK) and Sen’s slope wereemployed to assess significance of trends and detecting magnitude of trends.The results shows that monthly rainfall have no significant trend using MK, MMK, and TFPW-MK test at 0.05 level significance. Rainfall 3-month based January-February-March (JFM) period Kajoran station have negative significant trend with magnitude 19.4 mm/3-month. Mendut station have positive trend for April-May-June (AMJ) period with magnitude 6.75 mm/3-month. No significant trends at 0.05 level significance using MK trend test were detected in annual rainfall for 6 rainfall stations.
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Askin, Askin, Indarto Indarto, Dimas Ghufron Ash-Shiddiq, and Sri Wahyuningsih. "Variabilitas Spasial Hujan Tahunan di Wilayah UPT PSDA di Pasuruan, Jawa Timur : Analisis Histogram dan Normal QQ-Plot." Rona Teknik Pertanian 11, no. 1 (April 1, 2018): 35–49. http://dx.doi.org/10.17969/rtp.v11i1.9981.
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Abstrak. Penelitian ini bertujuan untuk menganalisis variabilitas spasial hujan di wilayah UPT PSDA di Pasuruan. Wilayah studi mencakup kabupaten Probolinggo, kota Probolinggo, Kabupaten Pasuruan dan Kota Pasuruan di Jawa Timur. Data hujan tahunan rerata (Hthn_rrt) dan hujan tahunan maksimal (HthnMaks) dihitung dari kumulatif data hujan harian pada 93 stasiun dan dijadikan sebagai input utama untuk analisis. Panjang periode rekaman data yang digunakan dari tahun 1980 sampai dengan 2015 (35 tahun). Tahap penelitian mencakup: (1) pra-pengolahan data, (2) analisis pendahuluan, (3) analisis menggunakan tool histogram dan voronoi map, (4) interpolasi data dan pembuatan peta tematik. Pra-pengolahan data dilakukan menggunakan excel. Analisis histogram dan QQ-Plot dilakukan untuk melihat variabilitas spasial lebih detail per sub-wilayah. Selanjutnya, metode interpolasi digunakan untuk membuat peta tematik hujan tahunan. Peta tematik menunjukkan hujan tahunan rerata (Hthn_rrt) yang terjadi di wilayah tersebut selama 35 tahun terakhir berkisar antara 1200 sd 2600 mm/tahun. Hujan tahunan maksimal yang terjadi berkisar antara 2100 sd 4500 mm/tahun. Penelitian juga menunjukkan adanya korelasi positif antara lokasi stasiun hujan (elevasi) dengan jumlah hujan tahunan yang diterima. Spatial Variability of Annual Rainfall in The Administrative Area of UPT PSDA at Pasuruan, East Java : Analysis Using Histogram and QQ-Plot Abstract. This research aims to analyze the spatial variability of annual rainfall. Daily rainfall data from 93 rain gauge in the administrative area of UPT PSDA Pasuruan were used as the main input. The average annual rainfall and the maximum annual rainfall obtained from the daily rainfall data. Histograms, and QQ-Plot were used to describe the spatial variability in each sub-regions. Next, interpolation methods is used to create a thematic map of the annual rainfall. The results shows that local spatial variability of rainfall can be visualized more detail for each sub-region by means of histogram and QQ-Plot. The thematic map showed that the distribution of average annual rainfall in the region range from 1,200 mm/year up to 2,600 mm/year. Maximum annual rainfall range between 2,100 mm/year up to 4,500 mm/year. The result also show the positif correlation between the altitude of the rain gauge and local annual rainfall received.
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SINGH, VIVEKANAND, and ANSHUMAN SINGH. "Variation of temperature and rainfall at Patna." MAUSAM 68, no. 1 (November 30, 2021): 161–68. http://dx.doi.org/10.54302/mausam.v68i1.445.
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In this paper, the variation of temperature and rainfall at Patna are analysed using simple non-parametric tests. The trends in the annual maximum and minimum daily temperatures, annual rainfall, annual maximum daily rainfall, number of rainy days in a year, the annual average rainfall per rainy day and the ratio of maximum to average rainfall per rainy day at Patna have been examined. Tends in total monthly rainfall, Highest daily rainfall in a month and number of rainy days in a month have also been determined for every month in a year. The monthly trends of data using simple Mann-Kendall test indicated statistically significant changes in rainfall pattern for the city.
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Soldini, Luciano, and Giovanna Darvini. "Extreme rainfall statistics in the Marche region, Italy." Hydrology Research 48, no. 3 (April 6, 2017): 686–700. http://dx.doi.org/10.2166/nh.2017.091.
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A statistical analysis of the rainfalls is carried out for detecting a possible trend in the observed data. The rainfall dataset refers to the historical series collected in the hydrographic basins of the Marche region. On the one hand, the annual maximum daily, hourly and sub-hourly rainfalls have been analysed, on the other hand Climate Change Indices by Expert Team on Climate Change Detection and Indices (ETCCDI) (R1 mm, Rx1day, R20 mm, R95pTOT, PRCPTOT) have been computed to verify an eventual variation of the frequency of the rainfall regime in the Marche region. The time series, selected in the reference period 1951–2013, have been processed by using the non-parametric Mann–Kendall test. The results confirm that most of the series relating to the annual maximum rainfalls do not exhibit any trend. The absence of trend or the presence of negative trend prevail also in the analysis of the ETCCDI indices. The annual average anomalies of the same indices computed with respect to the climatological reference period 1961–1990 are negative since the mid-1980s, but they appear to show an increasing behaviour in the period 2009–2013.
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Singh, Bhim. "Variability and trend analysis of rainfall data of Jhalawar district of Rajasthan, India." Journal of Applied and Natural Science 8, no. 1 (March 1, 2016): 116–21. http://dx.doi.org/10.31018/jans.v8i1.759.
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An attempt has been made to study the variability and trends of annual and seasonal rainfall for the period of 38 years (1973-2010) for all seven tehsils of Jhalawar district of Rajasthan. The mean annual rainfall of the district was found to 910 mm with standard deviation 218 mm and coefficient of variation 24 per cent. Annual rainfall varied from about 831 mm in Khanpur to more than 1022 mm in the Pirawa tehsil of the district. The annual rainfall showed declined trend (-0.23 to -17.41 mm/year) in all seven tehsils of the district. The negative trends at Pirawa (17.407 mm/year), Manoharthana (11.595 mm/year) and Aklera (5.789 mm/year) are statistically significant at less than 0.001, 0.05 and 0.05 levels, respectively. During the study period maximum dry period was recorded during postmonsoon and winter. Also, for the entire 38 years period maximum dry months were recorded during December till April. August was normal month for about 87 per cent followed by July and June for about 84 per cent and 66 per cent respectively. It was evident that the onset of south-west (SW) monsoon took place in the month of June and chancesof drought occurrence during kharif season were very low. Hence, SW monsoon rainfall is found ideal for raising kharif crops like soybeans, urd, moong, jowar, maize, tomato, brinjal, chilli, okra, kharif onion, amaranth, rainfed green gram, red gram, castor, etc in the district.
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Banerjee, Abhishek, Ruishan Chen, Michael E. Meadows, R. B. Singh, Suraj Mal, and Dhritiraj Sengupta. "An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine." Remote Sensing 12, no. 4 (February 21, 2020): 709. http://dx.doi.org/10.3390/rs12040709.
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This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region.
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Adjei, Kwaku Amaning, Li Liang Ren, Emmanuel Kwame Appiah-Adjei, and Samuel Nii Odai. "The Potential Utilization of Satellite Derived Rainfall in a Data-Scarce Basin." Advanced Materials Research 726-731 (August 2013): 3531–37. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.3531.
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This study assessed the potential for the use of satellite derived rainfall in a data-scarce basin like the Black Volta in West Africa. Using a point to pixel approach, accumulations of ground measurements on daily, monthly and annual time scales were compared with accumulations derived from 0.1° resolution daily gridded satellite Rainfall Estimates (RFE). The results from the analysis of the RFE data showed monthly correlations ranged from 0.76 to 0.92 when compared with the rain gauge measurements. These results obtained, indicate “good” to “very good” in terms of correlation (r) for the monthly and annual datasets. The study found that the use of satellite derived rainfall, like the RFE from FEWSNET, in the basin would be of great benefit considering the difficulties in accessing data both locally and from the other riparian countries; but its analysis and use should be on the monthly and annual scales.
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Chen, Yueli, Xingwu Duan, Minghu Ding, Wei Qi, Ting Wei, Jianduo Li, and Yun Xie. "New gridded dataset of rainfall erosivity (1950–2020) on the Tibetan Plateau." Earth System Science Data 14, no. 6 (June 9, 2022): 2681–95. http://dx.doi.org/10.5194/essd-14-2681-2022.
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Abstract. The risk of water erosion on the Tibetan Plateau (TP), a typical fragile ecological area, is increasing with climate change. A rainfall erosivity map is useful for understanding the spatiotemporal pattern of rainfall erosivity and identifying hot spots of soil erosion. This study generates an annual gridded rainfall erosivity dataset on a 0.25∘ grid for the TP in 1950–2020. The 1 min precipitation observations at 1787 weather stations for 7 years and 0.25∘ hourly European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) precipitation data for 71 years are employed in this study. Our results indicate that the ERA5-based estimates have a marked tendency to underestimate annual rainfall erosivity when compared to the station-based estimates, because of the systematic biases of ERA5 precipitation data including the large underestimation of the maximum contiguous 30 min peak intensity and relatively slight overestimation of event erosive precipitation amounts. The multiplier factor map over the TP, which was generated by the inverse distance-weighted method based on the relative changes between the available station-based annual rainfall erosivity grid values and the corresponding ERA5-based values, was employed to correct the ERA5-based annual rainfall erosivity and then reconstruct the annual rainfall erosivity dataset. The multiyear average correction coefficient over the TP between the station-based annual rainfall erosivity values and the newly released data is 0.67. In addition, the probability density and various quantile values of the new data are generally consistent with the station-based values. The data offer a view of large-scale spatiotemporal variability in the rainfall erosivity and address the growing need for information to predict rainfall-induced hazards over the TP. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.271833; Chen, 2021).
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KAUR, NAVNEET, ABRAR YOUSUF, and M. J. SINGH. "Long term rainfall variability and trend analysis in lower Shivaliks of Punjab, India." MAUSAM 72, no. 3 (October 22, 2021): 571–82. http://dx.doi.org/10.54302/mausam.v72i3.1307.
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The trend analysis of historical rainfall data on monthly, annual and seasonal basis for three locations in lower Shivaliks of Punjab, viz., Patiala-ki-Rao (1982-2015), Ballowal Saunkhri (1987-2015) and Saleran (1984-2017) has been done in the present study using linear regression model, Mann Kendall test and Sen’s slope. Further, the data for annual and seasonal rainfall and rainy days has also been analyzed on quindecennial basis, i.e., for the period of 1986-2000 and 2001-2015. The analysis of data showed that annual rainfall in the region ranged from 1000 to 1150 mm. The trend analysis of the data shows that the monthly rainfall is decreasing at Patiala-ki-Rao and Saleran, however, the trend was significant for May at Patiala-ki-Rao; and in March and November at Saleran. At Ballowal Saunkhri, the decreasing trend is observed from May to October, however, the trend is significant only in August. The decrease in annual and monsoon rainfall is about 13 to 17 mm and 12 to 13 mm per year respectively at three locations in lower Shivaliks of Punjab. The highest annual (1600-2000 mm) and monsoon (1500-1800 mm) rainfall during the entire study period was recorded in the year 1988 at three locations. The decadal analysis of the data shows below normal rainfall during April to October. The analysis of the rainfall and rainy days on monthly, annual and seasonal averages of 15 year basis showed that both rainfall and rainy days have decreased during the 2001-2015 as compared to 1986-2000 during all the seasons of the year.
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Pinheiro, Antonio G., Thais E. M. dos S. Souza, Suzana M. G. L. Montenegro, Abelardo A. de A. Montenegro, and Sérgio M. S. Guerra. "Rainfall pattern and erosion potential in the physiographic regions of the state of Pernambuco, Brazil." Revista Brasileira de Engenharia Agrícola e Ambiental 22, no. 12 (December 2018): 849–53. http://dx.doi.org/10.1590/1807-1929/agriambi.v22n12p849-853.
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ABSTRACT The objective of the present study was to characterize the spatial and temporal (2000-2015) rainfall pattern variability and erosive potential in the different physiographic regions of the state of Pernambuco, Brazil. Rainfall data series (3 to 12 years) from 25 weather stations of the state were analyzed. Erosive rainfall events (more than 10 mm depth) were considered to evaluate the annual erosivity index, monthly erosivity index (EI30), rainfall erosivity factor (R), and rainfall pattern. The inverse distance weighting (IDW) - inverse of the square of the distance - was used to create spatial interpolation and develop maps. The rainfall data from the weather stations showed average annual rainfall of 827 mm and average erosivity of 4,784 MJ mm ha-1 h-1. The Metropolitan region of Pernambuco presented the highest rainfall erosivity index, with annual average of 9,704 MJ mm ha-1 h-1; and the Sertão do São Francisco region had the lowest, with annual average of 4,902 MJ mm ha-1 h-1. The state of Pernambuco presented advanced (42%), intermediate (38%), and delayed (20%) rainfall patterns.
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Hadi, Arzu M. Mohammed, Akram Mohammed, Huda J. Jumaah, Mohammed H. Ameen, Bahareh Kalantar, Hossein Mojaddadi Rizeei, and Zainab Talib Abidzaid Al-Sharify. "GIS-Based Rainfall Analysis Using Remotely Sensed Data in Kirkuk Province, Iraq." Tikrit Journal of Engineering Sciences 29, no. 4 (December 24, 2022): 48–55. http://dx.doi.org/10.25130/tjes.29.4.6.
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This research aims to calculate the rate of rainfall for two consecutive years, 2018 and 2019, to study and analyze the drought periods during these years. The research was conducted in Kirkuk province north of Iraq, by dividing the study area into five regions based on five stations of rainfall records. Two types of data used in investigations; remotely sensed daily precipitations from integrated multi-satellite retrievals and monthly precipitations average rate from NASA power application. The methodology involved the calculation of rainfall rats by three methods; Mean, Thiessen’s polygons, and the Isohyetal technique. Data were analyzed, and final maps were produced using Geographic Information Systems. For the year 2018 the average annual rainfall rate produced by Mean, Thiessen’s polygons, and Isohyetal technique were; 1.376, 1.341, and 1.343 respectively. But, for the year 2019 the average annual rainfall rate produced by Mean, Thiessen’s polygons, and Isohyetal technique were; 0.966, 0.932, and 0.882 respectively. The results showed the scarcity of water and the lack of rainfall during the study years, which has a great impact on the region's exposure to drought, represented by the drying up of rivers.
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SINGH, P. K., L. S. RATHORE, K. K. SINGH, A. K. BAXLA, and B. ATHIYAMAN. "Incomplete Gamma distribution of rainfall for sustainable crop production strategies at Palampur, Himachal Pradesh." MAUSAM 60, no. 1 (November 27, 2021): 73–80. http://dx.doi.org/10.54302/mausam.v60i1.1016.
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The knowledge of rainfall pattern (amount and probability) helps in planning of crops to be grown in a region. Therefore weekly, monthly, seasonal and annual rainfall data for 33 years (1974-2006) for the station Palampur have been collected and its analysis has been attempted. The annual and monthly rainfall data were analyzed for finding out drought normality and abnormality. The analysis indicated that the rainfall is mainly confined in annual rainfall 2343 mm with 25.7 per cent variability. The standard deviation of annual rainfall is 62.8 mm. Each standard week from 26th to 35th receive a rainfall of more than 100 mm, indicating the crop period. Seed sowing in paddy nursery in the Palampur region generally takes places immediately after initiation of monsoon during 23rd - 25th standard meteorological weeks and transplanting is carried out around 27th or 28th standard meteorological week. The tillering, 50 percent flowering and dough stage are observed during 32-33rd, 37-38th and 40-41st standard meteorological weeks respectively.