scholarly journals Inhomogeneity detection in the rainfall series for the Mae Klong River Basin, Thailand

2021 ◽  
Vol 11 (9) ◽  
Author(s):  
Alamgir Khalil
Keyword(s):  
River Basin ◽  
Curve Analysis ◽  
Rainfall Series ◽  
Rainfall Data ◽  
Homogeneity Test ◽  
Significance Level ◽  
Von Neumann ◽  
Standard Normal Homogeneity Test ◽  
Double Mass ◽  
Climate Studies

AbstractAn accurate and complete rainfall record is prerequisite for climate studies. The purpose of this research study was to evaluate the homogeneity of the rainfall series for the Mae Klong River Basin in Thailand. Monthly rainfall data of eight stations in the Mae Klong River Basin for the period 1971–2015 were used. The double mass curve analysis was used to check the consistency of rainfall data, whereas the absolute homogeneity was assessed using the Pettitt test, standard normal homogeneity test, Buishand test, and von Neumann test at a 5% significance level. The results of these tests were qualitatively classified as ‘useful’, ‘doubtful’, and ‘suspect’ according to the null hypothesis. Results of the monthly time series indicated the rainfall data as ‘useful’ for 75% of the stations, while two stations’ data were classified as ‘doubtful’ (Stn130221) and ‘suspect’ (Stn376401). On an annual scale, seven out of eight stations data were classified as ‘useful,’ while one station (Stn376401) data were classified as ‘suspect’. Double mass curve analysis technique was used for the adjustment of inhomogeneous data. The results of this study can help provide reliable rainfall data for climate studies in the basin.

HOMOGENEITY ANALYSIS OF RAINFALL IN KELANTAN, MALAYSIA

2015 ◽  
Vol 76 (15) ◽  
Author(s):  
Ng Jing Lin ◽  
Samsuzana Abd Aziz ◽  
Huang Yuk Feng ◽  
Aimrun Wayayok ◽  
Md Rowshon Kamal
Keyword(s):  
River Basin ◽  
Monthly Rainfall ◽  
Rainfall Series ◽  
Rainfall Data ◽  
Seasonal Rainfall ◽  
Homogeneity Test ◽  
Ratio Test ◽  
Homogeneity Analysis ◽  
Standard Normal Homogeneity Test ◽  
Kelantan River Basin

Good quality of rainfall data is required for the hydrological studies, water resources planning and sustainable environmental management. Consequently, the assessment of the homogeneity of rainfall data at different region is becoming increasing popular in the past few decades. In this study, the homogeneity analysis of rainfall data was carried out in Kelantan River Basin, Malaysia. The methods, namely standard normal homogeneity test (SHNT), Buishand range test, Pettitt test and von Neumann ratio test were applied to the monthly, yearly and seasonal data. The historical rainfall data from 10 rainfall stations covering the study period from 28 to 60 years were selected. The four tests were applied to 120 monthly series, 10 yearly series and 40 seasonal series. ‘Useful’, ‘doubtful’ and ‘suspect’ were used to classify the results of the four tests. The results showed that 94.17% of the monthly rainfall series, 70% of yearly rainfall series and 97.5% of seasonal rainfall series are labelled ‘useful’. There is 5% of monthly rainfall series, 30% of yearly rainfall series and 1% of seasonal rainfall series are classified as ‘doubtful’. Meanwhile, there is only 0.83% of monthly rainfall series and no yearly rainfall series and seasonal rainfall series detected in the class ‘suspect’. Overall, the percentage of inhomogeneity detected in the monthly, yearly and seasonal rainfall data series of Kelantan River Basin is very small, thus most of the data is suitable to be used for further hydrological and variability analysis.

scholarly journals Assessing Inhomogeneities in Extreme Annual Rainfall Data Series by Multifractal Approach

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1030 ◽  
Author(s):  
Amanda García-Marín ◽  
Javier Estévez ◽  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
José Ayuso-Muñoz ◽  
...  
Keyword(s):  
Central Italy ◽  
Extreme Rainfall ◽  
Break Point ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Data Series ◽  
Homogeneity Test ◽  
Umbria Region ◽  
Standard Normal Homogeneity Test ◽  
Break Points

Testing the homogeneity in extreme rainfall data series is an important step to be performed before applying the frequency analysis method to obtain quantile values. In this work, six homogeneity tests were applied in order to check the existence of break points in extreme annual 24-h rainfall data at eight stations located in the Umbria region (Central Italy). Two are parametric tests (the standard normal homogeneity test and Buishand test) whereas the other four are non-parametric (the Pettitt, Sequential Mann–Kendal, Mann–Whitney U, and Cumulative Sum tests). No break points were detected at four of the stations analyzed. Where inhomogeneities were found, the multifractal approach was applied in order to check if they were real or not by comparing the split and whole data series. The generalized fractal dimension functions Dq and the multifractal spectra f(α) were obtained, and their main parameters were used to decide whether or not a break point existed.

scholarly journals Analysis of Precipitation and Temperature Extremes over the Muda River Basin, Malaysia

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 283 ◽  
Author(s):  
Mou Leong Tan ◽  
Narimah Samat ◽  
Ngai Weng Chan ◽  
Anisah Jessica Lee ◽  
Cheng Li
Keyword(s):  
River Basin ◽  
Climate Extremes ◽  
Quality Data ◽  
Maximum Temperature ◽  
Homogeneity Test ◽  
Temperature Extremes ◽  
Monthly Precipitation ◽  
Northeast Monsoon ◽  
Standard Normal Homogeneity Test ◽  
Precipitation And Temperature

Trends in precipitation and temperature extremes of the Muda River Basin (MRB) in north-western Peninsular Malaysia were analyzed from 1985 to 2015. Daily climate data from eight stations that passed high quality data control and four homogeneity tests (standard normal homogeneity test, Pettitt test, Buishand range test, and von Neumann ratio test) were used to calculate 22 Expert Team on Climate Change Detection and Indices (ETCCDI) extreme indices. Non-parametric Mann–Kendall, modified Mann–Kendall and Sens’ slope tests were applied to detect the trend and magnitude changes of the climate extremes. Overall, the results indicate that monthly precipitation tended to increase significantly in January (17.01 mm/decade) and December (23.23 mm/decade), but decrease significantly in May (26.21 mm/decade), at a 95% significance level. Monthly precipitation tended to increase in the northeast monsoon, but decrease in the southwest monsoon. Mann–Kendall test detected insignificant trends in most of the annual climate extremes, except the extremely wet days (R99p), mean of maximum temperature (TXmean), mean of minimum temperature (TNmean), cool days (TX10p), cool nights (TN10p), warm days (TX90p) and warm nights (TN90p) indices. The number of heavy (R10mm), very heavy (R20mm), and violent (R50mm) precipitation days changed at magnitudes of 0~2.73, −2.14~3.33, and −1.67~1.29 days/decade, respectively. Meanwhile, the maximum 1-day (Rx1d) and 5-day (Rx5d) precipitation amount indices changed from −10.18 to 3.88 mm/decade and −21.09 to 24.69 mm/decade, respectively. At the Ampangan Muda station, TNmean (0.32 °C/decade) increased at a higher rate compared to TXmean (0.22 °C/decade). The number of the cold days and nights tended to decrease, while an opposite trend was found in the warmer days and nights.

scholarly journals Improving the performance of streamflow forecasting model using data-preprocessing technique in Dungun River Basin

2018 ◽  
Vol 34 ◽  
pp. 02014
Author(s):  
Ervin Shan Khai Tiu ◽  
Yuk Feng Huang ◽  
Lloyd Ling
Keyword(s):  
Water Level ◽  
River Basin ◽  
Data Preprocessing ◽  
Water Levels ◽  
Rainfall Series ◽  
Rainfall Data ◽  
Forecasting Model ◽  
Northeast Monsoon ◽  
Streamflow Forecasting ◽  
Preprocessing Technique

An accurate streamflow forecasting model is important for the development of flood mitigation plan as to ensure sustainable development for a river basin. This study adopted Variational Mode Decomposition (VMD) data-preprocessing technique to process and denoise the rainfall data before putting into the Support Vector Machine (SVM) streamflow forecasting model in order to improve the performance of the selected model. Rainfall data and river water level data for the period of 1996-2016 were used for this purpose. Homogeneity tests (Standard Normal Homogeneity Test, the Buishand Range Test, the Pettitt Test and the Von Neumann Ratio Test) and normality tests (Shapiro-Wilk Test, Anderson-Darling Test, Lilliefors Test and Jarque-Bera Test) had been carried out on the rainfall series. Homogenous and non-normally distributed data were found in all the stations, respectively. From the recorded rainfall data, it was observed that Dungun River Basin possessed higher monthly rainfall from November to February, which was during the Northeast Monsoon. Thus, the monthly and seasonal rainfall series of this monsoon would be the main focus for this research as floods usually happen during the Northeast Monsoon period. The predicted water levels from SVM model were assessed with the observed water level using non-parametric statistical tests (Biased Method, Kendall’s Tau B Test and Spearman’s Rho Test).

scholarly journals Simulating rainfall IDF curve for flood warnings in the Ca Mau coastal area under the impacts of climate change

2020 ◽  
Vol 12 (5) ◽  
pp. 705-715
Author(s):  
Seung Kyu Lee ◽  
Truong An Dang
Keyword(s):  
Return Period ◽  
Extreme Rainfall ◽  
Rainfall Data ◽  
Data Series ◽  
Homogeneity Test ◽  
Content Type ◽  
Pettitt Test ◽  
Standard Normal Homogeneity Test ◽  
Intensity Duration Frequency ◽  
The Right

Purpose The purpose of this study is to evaluate the rainfall intensities and their limits for durations from 0.25 to 8 h with return periods from 2 to 100 years for Ca Mau City in Vietnam. Design/methodology/approach First, the quality of the historical rainfall data series in 44 years (1975–2018) at Ca Mau station was assessed using the standard normal homogeneity test and the Pettitt test. Second, the appraised rainfall data series are used to establish the rainfall intensity-duration-frequency curve for the study area. Findings Based on the findings, a two-year return period, the extreme rainfall intensities (ERIs) ranged from 9.1 mm/h for 8 h rainstorms to 91.2 mm/h for 0.25 h. At a 100-year return period, the ERIs ranged from 18.4 mm/h for 8 h rainstorms to 185.8 mm/h for 0.25 h. The results also show that the narrowest uncertainty level between the lower and upper limits recorded 1.6 mm at 8 h for the two-year return period while the widest range is at 42.5 mm at 0.25 h for the 100-year return period. In general, the possibility of high-intensity rainfall values compared to the extreme rainfall intensities is approximately 2.0% at the 100-year return period. Originality/value The results of the rainfall IDF curves can provide useful information for policymakers to make the right decisions in controlling and minimizing flooding in the study area.

scholarly journals Mudança no Regime Temporal da Temperatura do Ar e Precipitação Pluviométrica na Região de Rio Largo, Alagoas (Change in the Temporal Regime of Air Temperature and Rainfall in the Region of Rio Largo, Alagoas, Brazil)

2014 ◽  
Vol 7 (4) ◽  
pp. 662
Author(s):  
Henderson Silva Wanderley ◽  
André Luiz de Carvalho ◽  
Ronabson Cardoso Fernandes ◽  
José Leonaldo de Souza
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Change Point ◽  
Statistical Tests ◽  
Linear Regression Analysis ◽  
Homogeneity Test ◽  
Significance Level ◽  
Standard Normal Homogeneity Test ◽  
Standard Normal ◽  
History Of

Compreender como as alterações no clima têm modificado a temperatura do ar e a precipitação pluvial de uma região é essencial, sobretudo para regiões como o Nordeste brasileiro, que apresentam vasto histórico de secas e altas temperaturas. No entanto, estudos com esse fim são escassos ou até mesmo inexistentes para essa região. Deste modo, objetivou-se identificar mudanças ocorridas no regime temporal da temperatura diurna e noturna e na precipitação na região de Rio Largo, Alagoas. Para isto, utilizaram-se dados de temperatura diurna (máxima) e noturna (mínima) compreendidos entre 1973 e 2002, e de precipitação dispostos entre 1973 e 2008. As séries temporais foram submetidas ao teste estatístico SNHT (Standard Normal Homogeneity Test) para identificar possíveis pontos de mudança na média. A análise de regressão linear simples foi utilizada para identificar alterações nas séries temporais, testada por meio do teste t de Student, adotando-se nível de significância estatística de 0,05%, para ambos os testes estatísticos. A análise mostrou que as temperaturas demostraram pontos de mudanças significativos, no entanto, foi observada uma defasagem de quase dez anos entre os pontos. A tendência identificada entre as temperaturas foram opostas entre si, sendo de aumento para a temperatura diurna e de redução para a noturna. A precipitação demostrou tendência de redução, no entanto, não apresentou mudança estatística significativa.  ABSTRACTUnderstanding how changes in climate have changed air temperature and rainfall in a region is essential, especially for regions such as the Brazilian Northeast, which have long history of drought and high temperatures. However, studies for this purpose are scarce or even nonexistent for this region. Thus, this study aimed to identify changes in the temporal regime of daytime and nighttime temperature and rainfall in the region of Rio Largo, Alagoas, Brazil. For this, it was used data of daytime temperature (maximum) and night (minimum) ranging from 1973 to 2002, and rainfall arranged between 1973 and 2008. Time series were submitted to SNHT (Standard Normal Homogeneity Test) statistical test to identify possible change point in average. A simple linear regression analysis was used to identify changes in time series, tested using the Student t test, adopting a significance level of 0.05%, for both statistical tests. The analysis showed that temperatures demonstrated significant change points, however, there was a gap of almost ten years between the points. The trend identified among the temperatures was opposed to each other, with increasing daytime temperature and reduction of nighttime temperature. Rainfall demonstrated trend of reducing, however, showed no statistically significant change.Keywords: daytime and nighttime temperature, SNHT, trend, change point. 

scholarly journals Statistical analysis of temperature regime change on the example of Sokobanja basin in eastern Serbia

2015 ◽  
Vol 19 (suppl. 2) ◽  
pp. 323-330
Author(s):  
Aleksandar Radivojevic ◽  
Natasa Martic-Bursac ◽  
Milena Gocic ◽  
Ivan Filipovic ◽  
Mila Pavlovic ◽  
...  
Keyword(s):  
Air Temperature ◽  
Statistical Tests ◽  
Local Level ◽  
Homogeneity Test ◽  
Von Neumann ◽  
Early 21St Century ◽  
Scientific Disciplines ◽  
Standard Normal Homogeneity Test ◽  
Standard Normal ◽  
Almost All

The changes and oscillations in air temperature during the second half of the 20th and in the early 21st century, have become one of the major concerns of almost all scientific disciplines. Such changes are noticeable both at the local and global level. The objective of this paper is to point out that the changes of this climate element can also be detected at the local level. The research underlying this paper is, in terms of territory, limited to the Sokobanja basin in Eastern Serbia. The analysis relies on the data on air temperature for the period 1946-2012 taken from the Meteorological weather station located in Sokobanja. The obtained data were processed in line with the recommendations of the World Meteorological Organization (WMO). The evidenced statistically significant changes in air temperature were examined using the following statistical tests: Pettit test, Standard Normal Homogeneity test, Buishand range test, and von Neumann test.

NEURO-FUZZY SYSTEMS APPROACH TO INFILL MISSING RAINFALL DATA FOR KLANG RIVER CATCHMENT, MALAYSIA

2016 ◽  
Vol 78 (6-12) ◽  
Author(s):  
Nadeem Nawaz ◽  
Sobri Harun ◽  
Rawshan Othman ◽  
Arien Heryansyah
Keyword(s):  
Fuzzy System ◽  
Fuzzy Systems ◽  
Rainfall Data ◽  
System Model ◽  
Coefficient Of Determination ◽  
Homogeneity Test ◽  
River Catchment ◽  
Statistical Measures ◽  
Neuro Fuzzy ◽  
Standard Normal Homogeneity Test

Rainfall data can be regarded as the most essential input for various applications in hydrological sciences. Continuous rainfall data with adequate length is the main requirement to solve complex hydrological problems. Mostly in developing countries hydrologists are still facing problems of missing rainfall data with inadequate length. Researchers have been applying a number of statistical and data driven approaches to overcome this insufficiency. This study is an application of neuro-fuzzy system to infill the missing rainfall data for Klang River catchment. Pettitt test, standard normal homogeneity test (SNHT) and Von Neumann Ratio (VNR) tests were performed to check the homogeneity of rainfall data. The neuro-fuzzy model performances were assessed both in calibration and validation stages based on statistical measures such as coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). To evaluate the performance of the neuro-fuzzy system model, it was compared with a traditional modeling technique known as autoregressive model with exogenous inputs (ARX). The neuro-fuzzy system model gave better performances in both stages for the best input combinations. The missing rainfall data was predicted using the input combination with best performances. The results of this study showed the effectiveness of the neuro-fuzzy systems and it is recommended as a prominent tool for filling the missing data. 

scholarly journals Evaluation and Comparison of Satellite-Derived Estimates of Rainfall in the Diverse Climate and Terrain of Central and Northeastern Ethiopia

2021 ◽  
Vol 13 (7) ◽  
pp. 1275
Author(s):  
Girma Berhe Adane ◽  
Birtukan Abebe Hirpa ◽  
Chul-Hee Lim ◽  
Woo-Kyun Lee
Keyword(s):  
River Basin ◽  
Hydrological Cycle ◽  
Rainfall Data ◽  
Data Availability ◽  
Trend Test ◽  
Climate Data ◽  
Significance Level ◽  
Monotonic Trend ◽  
Mk Test ◽  
Satellite Rainfall

Understanding rainfall processes as the main driver of the hydrological cycle is important for formulating future water management strategies; however, rainfall data availability is challenging for countries such as Ethiopia. This study aims to evaluate and compare the satellite rainfall estimates (SREs) derived from tropical rainfall measuring mission (TRMM 3B43v7), rainfall estimation from remotely sensed information using artificial neural networks—climate data record (PERSIANN-CDR), merged satellite-gauge rainfall estimate (IMERG), and the Global Satellite Mapping of Precipitation (GSMaP) with ground-observed data over the varied terrain of hydrologically diverse central and northeastern parts of Ethiopia—Awash River Basin (ARB). Areal comparisons were made between SREs and observed rainfall using various categorical indices and statistical evaluation criteria, and a non-parametric Mann–Kendall (MK) trend test was analyzed. The monthly weighted observed rainfall exhibited relatively comparable results with SREs, except for the annual peak rainfall shifts noted in all SREs. The PERSIANN-CDR products showed a decreasing trend in rainfall at elevations greater than 2250 m above sea level in a river basin. This demonstrates that elevation and rainfall regimes may affect satellite rainfall data. On the basis of modified Kling–Gupta Efficiency, the SREs from IMERG v06, TRMM 3B43v7, and PERSIANN-CDR performed well in descending order over the ARB. However, GSMaP showed poor performance except in the upland sub-basin. A high frequency of bias, which led to an overestimation of SREs, was exhibited in TRMM 3B43v7 and PERSIANN-CDR products in the eastern and lower basins. Furthermore, the MK test results of SREs showed that none of the sub-basins exhibited a monotonic trend at 5% significance level except the GSMap rainfall in the upland sub-basin. In ARB, except for the GSMaP, all SREs can be used as alternative options for rainfall frequency-, flood-, and drought-monitoring studies. However, some may require bias corrections to improve the data quality.

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