Standard normal homogeneity test as a tool to detect change points in climate-related river discharge variation: case study of the Kupa River Basin

2019 ◽  
Vol 65 (2) ◽  
pp. 227-241 ◽  
Author(s):  
Krešo Pandžić ◽  
Mira Kobold ◽  
Dijana Oskoruš ◽  
Božidar Biondić ◽  
Ranko Biondić ◽  
...  
2021 ◽  
Vol 11 (9) ◽  
Author(s):  
Alamgir Khalil

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.


2012 ◽  
Vol 516-517 ◽  
pp. 530-535
Author(s):  
Xin Jie Deng ◽  
Yang Sheng You ◽  
Yan Ying Chen ◽  
Xue Mei Yang

The homogeneity test is the first stage to revise the climate records. Its accuracy will directly affect the follow-up work. The classic method SNHT (Standard Normal Homogeneity Test) can only be applied in climatic sequences obey normal distribution, but lots of non-normality climate sequences need to be examined. In this paper, the Smirnov Test was introduced to test the homogeneity of the temperature series, which is a classical method for distribution test, and it can apply for the temperature sequences obey any distribution. The homogeneity test results by testing Chongqing Municipality's temperature sequences show that: the Smirnov Test is better than SNHT


2021 ◽  
Vol 930 (1) ◽  
pp. 012021
Author(s):  
S M Beselly ◽  
R D Lufira ◽  
U Andawayanti

Abstract Quantitative assessment for sustainable watershed management is essential. Hydrological parameters such as stream discharge, surface runoff, infiltration, groundwater recharge, and water quality are susceptible to the changes of the components in the river basin ecosystem. Numerous studies have shown that the Land Use Land Cover (LULC) changes such as deforestation, extensive agriculture, urbanization, and mining are recognized as the main factors to changes in LULC, which are related to the changes of the hydrological components of the river basin of all scale. This paper particularly shows the spatiotemporal variability of LULC in the Upper Brantas Basin and the effects on the river discharge variation. We showed that the changes in LULC, particularly cultivated and managed vegetation and urban/built-up area, contributed significantly to the river discharge. Particularly in the Upper Brantas Basin, it was indicated that almost half of the increased river discharge was explained by the increase of urban/built-up and the decrease in cultivated and managed vegetation area.


2012 ◽  
Vol 51 (2) ◽  
pp. 317-326 ◽  
Author(s):  
Andrea Toreti ◽  
Franz G. Kuglitsch ◽  
Elena Xoplaki ◽  
Jürg Luterbacher

AbstractSudden changes caused by nonclimatic factors (inhomogeneities) usually affect instrumental time series of climate variables. To perform robust climate analyses based on observations, a proper identification of such changes is necessary. Here, an approach (named the “GAHMDI” method, after its components and purpose) that is based on a genetic algorithm and hidden Markov models is proposed for detection of inhomogeneities caused by changes in the mean and variance. Simulated series and a case study (winter precipitation from a weather station located in Milan, Italy) are set up to compare GAHMDI with existing methodologies and to highlight its features. For the identification of a single changepoint, GAHMDI performs similarly to other methods (e.g., standard normal homogeneity test). However, for the identification of multiple inhomogeneities and changes in variance, GAHMDI returns better results than three widespread methods by avoiding overdetection. For future applications and research in the homogenization of climate datasets (temperature and precipitation) the use of GAHMDI is encouraged, preferably in combination with another detection procedure (e.g., the method of Caussinus and Mestre) when metadata are not available. Since GAHMDI is developed in the generic context of time series segmentation, it can be applied to series of generic variables—for instance, those related to economics, biology, and informatics.


2015 ◽  
Vol 76 (15) ◽  
Author(s):  
Ng Jing Lin ◽  
Samsuzana Abd Aziz ◽  
Huang Yuk Feng ◽  
Aimrun Wayayok ◽  
Md Rowshon Kamal

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.


2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Peter Domonkos

Efficiency evaluations for change point Detection methods used in nine major Objective Homogenization Methods (DOHMs) are presented. The evaluations are conducted using ten different simulated datasets and four efficiency measures: detection skill, skill of linear trend estimation, sum of squared error, and a combined efficiency measure. Test datasets applied have a diverse set of inhomogeneity (IH) characteristics and include one dataset that is similar to the monthly benchmark temperature dataset of the European benchmarking effort known by the acronym COST HOME. The performance of DOHMs is highly dependent on the characteristics of test datasets and efficiency measures. Measures of skills differ markedly according to the frequency and mean duration of inhomogeneities and vary with the ratio of IH-magnitudes and background noise. The study focuses on cases when high quality relative time series (i.e., the difference between a candidate and reference series) can be created, but the frequency and intensity of inhomogeneities are high. Results show that in these cases the Caussinus-Mestre method is the most effective, although appreciably good results can also be achieved by the use of several other DOHMs, such as the Multiple Analysis of Series for Homogenisation, Bayes method, Multiple Linear Regression, and the Standard Normal Homogeneity Test.


2018 ◽  
Vol 50 (3) ◽  
pp. 364-381 ◽  
Author(s):  
Arno Adi Kuntoro ◽  
◽  
Muhammad Cahyono ◽  
Edy Anto Soentoro ◽  
◽  
...  

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 283 ◽  
Author(s):  
Mou Leong Tan ◽  
Narimah Samat ◽  
Ngai Weng Chan ◽  
Anisah Jessica Lee ◽  
Cheng Li

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.


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