Von Neumann Ratio Research Articles

An insight from homogeneity testing of long-term rainfall datasets over East Java, Indonesia

Robust, reliable, and trustworthy ground observation datasets are the preliminary requirement for assessing the impact of climate change over regions. Principal testing to assess the quality of ground observation rely on the missing data and homogeneity result. The study used 40 years of monthly rainfall documented from different topographical features in the monsoonal region of East Java, Indonesia. The test included annual rainfall, early rainy season (October-November-December), and primary rain season (January-February-March). The homogeneity of rainfall determined by absolute technique: Pettitt’s test, the Standard Normal Homogeneity Test, the Buishand Rank Test, and the von Neumann Ratio. Among the time series, October-November-December observation results in better homogeneity. However, the rainfall datasets during primary rainy season showed the worst homogeneity. By performing annual and seasonal homogeneity test from 67 rainfall stations: 5 stations out of data length required, 5% stations ‘rejected’, 11% ‘suspect’, 11% ‘doubtful’, and 73% were ‘trusted’. Therefore, a total of 45 stations can be used as metadata for relative comparison and 7 stations can be considered to be useful for analysis despite ‘doubtful’. The remaining 10 stations need careful consideration to be used for future water management. Change point detected particularly between the year of 1997 through 2000. Pettitt’s test has outstanding results in the case of extreme climatic anomaly, but less sensitive of continuous abrupt change. The von Neumann test could detect abnormal data, but was not suitable for datasets containing few extreme values. The insights from homogeneity testing were: a) it is important to remove any outliers in the datasets before conducting homogeneity testing, b) both parametric and nonparametric homogeneity tests should be performed, and c) comparisons should be made with surrounding rainfall stations. Comparison with trusted long-term rainfall data is valuable for stations labeled as ‘doubtful’ or ‘suspect’ to mitigate false detections in individual homogeneity tests. The identified ‘useful’ rainfall data can then serve as reference stations for relative homogeneity tests. These findings suggest that reference stations should be assessed within similar rainfall zones.

Temporal Trend Analysis of Annual, Maximum, and Seasonal Rainfall in Selected Weather Stations in Region IV-A, Philippines

Temporal variability analysis of rainfall is important for planning agricultural crop production activities, designing water resource systems, and conducting climatological research, particularly climate change analysis. This study aimed to analyze the temporal trends of annual (total and 1-d maximum) and seasonal (total) rainfall observed in Region IV-A. Historical rainfall data were collected from seven weather stations scattered all over the region. Each dataset was initially subjected to a series of homogeneity tests (standard normal homogeneity, Pettitt’s, Buishand’s range, and von Neumann ratio), followed by trend analysis using both linear regression analysis and non-parametric methods [Mann-Kendall (MK), modified MK (MMK), and Sen’s slope (SS) estimator]. All data series proved to be homogeneous except for the annual and seasonal (DJF) total rainfall in Sangley Point station. The results of the homogeneity test in Sangley Point in annual and DJF timescale suggest the consideration of the breakpoint on the years of 1984 and 1999, respectively, for trend analysis. Temporal trend based on linear regression showed a weak correlation between rainfall of various time scales and time in years, with only three stations showing a significant trend at a 5% significance level – namely, Los Baños (DJF, 2.86 mm/yr), Tayabas (DJF, 7.99mm/yr), and Sangley Point (JJA, 9.32 mm/yr). On the other hand, both MK and SS indicates a significant increase in the seasonal (DJF) total rainfall in both stations of Ambulong (1.04 mm/yr) and Los Baños (2.68 mm/yr). Overall, the results of trend analysis revealed an increasing trend in the cumulative rainfall across timescales in most stations in the region which has an implication on the timing of the optimum cropping patterns in agricultural areas and on the hydrology of the watersheds in the region. This study has generated new knowledge and information that could prove useful for agricultural production management and for proper planning, design, and management of water resource systems.

Homogeneity and change point detection of hydroclimatic variables: A case study of the Ghba River Subbasin, Ethiopia

In most studies on hydroclimatic variability and trend, the notion of change point detection analysis of time series data has not been considered. Understanding the system is crucial for managing water resources sustainably in the future since it denotes a change in the status quo. If this happened, it is difficult to distinguish the time series data’s rising or falling tendencies in various areas when we look at the trend analysis alone. This study’s primary goal was to describe, quantify, and confirm the homogeneity and change point detection of hydroclimatic variables, including mean annual, seasonal, and monthly rainfall, air temperature, and streamflow. The method was employed using the four-homogeneity test, i.e., Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test at 5% significance level. In order to choose the homogenous stations, the test outputs were divided into three categories: “useful”, “doubtful”, and “suspect”. The results showed that most of the stations for annual rainfall and air temperature were homogenous. It is found that 68.8% and 56.2% of the air temperature and rainfall stations respectively, were classified as useful. Whereas, the streamflow stations were classified 100% as useful. Overall, the change point detection analyses timings were found at monthly, seasonal, and annual time scales. In the rainfall time series, no annual change points were detected. In the air temperature time series except at Edagahamus station, all stations experienced an increasing change point while the streamflow time series experienced a decreasing change point except at Agulai and Genfel hydro stations. While alterations in streamflow time series without a noticeable change in rainfall time series recommend the change is caused by variables besides rainfall. Most probably the observed abrupt alterations in streamflow could result from alterations in catchment characteristics like the subbasin’s land use and cover. These research findings offered important details on the homogeneity and change point detection of the research area’s air temperature, rainfall, and streamflow necessary for the planers, decision-makers, hydrologists, and engineers for a better water allocation strategy, impact assessment and trend analyses.

Observed trends and coherent changes in daily rainfall extremes over Greater Mumbai, 1985–2020

Climate extremes are predicted to become more frequent and severe in the future, owing to rising greenhouse gas emissions in the atmosphere. Using appropriate trend indices, this study intends to analyze the effects of climate change on rainfall variability and extreme rainfall events in the millennium city of Mumbai. The analysis was carried out for the period 1985–2020 for 2 stations and 23 stations were selected (2006–2020) later from the dense rain gauge network of Municipal Corporation of Greater Mumbai (MCGM). To comprehend the trend and behaviour of rainfall and extreme events, three statistical models were used: Mann–Kendall test, Sen’s slope estimator, and simple linear regression; for abrupt change point detection, Pettitt’s test, standard normal homogeneity test, Buishand’s range test, and von Neumann ratio test were used. The results indicated that average annual rainfall in the study area is 2208.82 mm which is increasing at the rate of 5.18 mm/year (at 99% confidence level). The frequency of heavy (> 120 mm/day) and extreme heavy rainfall events (250 mm/day) increased over Santacruz after 1994 and in the case of Colaba, it increased after 2005. The examination of rainfall and temperature data series for abrupt change point identification suggests that several change points exist, with the highest change points occurring between 2001 and 2005. Extreme climate event scenarios shown here could have large-scale negative consequences for the ecosystem and ecological resources of the study area.

Making the most of data: Quantum Monte Carlo postanalysis revisited.

In quantum Monte Carlo (QMC) methods, energy estimators are calculated as (functions of) statistical averages of quantities sampled during a calculation. Associated statistical errors of these averages are often estimated. This error estimation is not straightforward and there are several choices of the error estimation methods. We evaluate the performance of three methods (the Straatsma method, an autoregressive model, and a blocking analysis based on von Neumann's ratio test for randomness) for the energy time series given by three QMC methods [diffusion Monte Carlo, full configuration interaction Quantum Monte Carlo (FCIQMC), and coupled cluster Monte Carlo (CCMC)]. From these analyses, we describe a hybrid analysis method which provides reliable error estimates for a series of various lengths of FCIQMC and CCMC's time series. Equally important is the estimation of the appropriate start point of the equilibrated phase. We establish that a simple mean squared error rule method as described by White [K. P. White, Jr., Simulation 69(6), 323 (1997)10.1177/003754979706900601] can provide reasonable estimations.

Statistical analysis of annual and seasonal temperature regime change in Rasina River basin, Serbia

In this research, changes in annual and seasonal trends of mean temperatures were analyzed on the territory of the Rasina River basin (central Serbia). We used data from four meteorological stations during three periods: 1961–1989, 1990–2018, and 1979–2013. The change detection analysis has been conceded using the Pettitt’s test, von Neumann ratio test, Buishand’s range test, and standard normal homogeneity (SNH) test, while the linear regression, Mann-Kendall, and Sen's slope tests have been applied for trend analysis. The results show that the change in summer temperatures occurred shortly after 1980. The analysis results showed that inhomogeneous structures are generally observed between 1976 and 1984, between 1997 and 1998, and in 2006. The trend of all the data on annual basis showed positive increasing trend. The analysis indicated that the average annual, winter, and summer temperatures show significant increasing trend both in the longer period (1961–2018) and in the second part of the period (1990–2018). In the first part of the period (1961–1989), autumn temperatures in Kruševac, summer and autumn temperatures in Blace, as well as winter and autumn temperatures in Goč showed significant decreasing trend. The significant rising trend in the summer and winter months in the last 30 years may affect water availability and water demands in the region.

OUTLIER AND HOMOGENEITY ANALYSIS OF EXTREME RAINFALL SERIES IN KANO, NIGERIA

Data preparation is one of the most important steps in the field of hydrology since the results of any research depend on whether the input data were reliable and rightly collected or not. Reliability and quality of long-term rainfall data are required before carrying out any study in hydrology. For this reason, outlier identification techniques and homogeneity tests have proved helpful. This study aimed at carrying out outliers and homogeneity analysis of extreme rainfall data series for the Kano rainfall gauging station. XLSTAT, 2019 was used to identify outliers through Grubb’s test for outliers/two-tailed test and Dixon test for outliers/two-tailed statistic test and Standard Normal Homogeneity Test (SNHT), Buishand Range (BR) Test, Pettitt Test, and Von Neumann Ratio (VNR) Test were used for homogeneity analysis. Four outliers were identified and treated accordingly. The results of homogeneity tests showed that the maximum daily rainfall data of the Kano gauging station is homogenous and classified as useful. The maximum daily rainfall data of Kano station can be used for any study in water resources and environmental engineerings, such as in Development of Intensity Duration Frequency (IDF) Curves and Rainfall-Runoff Simulations.Keywords: Outliers test, homogeneity tests, rainfall data, XLSTAT 2019

Trend and change-point detection analyses of rainfall and temperature over the Awash River basin of Ethiopia

Awash River basin (ARB) as a system is in a state of continuous change that requires successive studies to discern the changes or trends of climatic elements through time due to climate change/variability, and other socio-economical developmental activities in the basin. The livelihood of communities in the ARB is primarily based on rainfall-dependent agriculture. Effects of rainfall anomalies such as reduction of agricultural productivity, water scarcity, and food insecurity are becoming more prevalent in this area. In recent years, ARB has been experiencing more frequent rainfall anomalies that change-point detection test and trend analyses of basin rainfall associated with sea surface temperature is crucial in providing guidance to improve agricultural productivity in ARB. Change-point detection tests such as Pettit's, the von Neumann ratio (VNR), Buishand's range (BR) and standard normal homogeneity (SNH) plus trend analysis Mann-Kendall (MK) test of rainfall and temperature data from 29 meteorological stations in the ARB were carried out from 1986 to 2016. A significant increasing trend of annual and seasonal temperature was found. The temperature change-points for the annual and major rainy season (MRS) were detected in 2001, while for the minor rainy season (mRS) in 1997. A significant decreasing trend, shift, and high variability of rainfall were detected in the downstream part of the ARB. The BR and SNH results showed that the mRS rainfall change-point was in 1998, with a subsequent mean annual decrease of 52.5 mm. The increase (decrease) of rainfall in the annual and MRS was attributable to La Niña (El Niño) events. The significant decreasing trend and change-point of rainfall in the mRS was attributable to the steady warming of the Indian and Atlantic Oceans, local warming, and La Niña events. With this knowledge of the current trends and change-point for rainfall and temperature in the ARB, it is therefore essential that appropriate integrated water management and water-harvesting technologies are established, especially in the downstream areas. Moreover, early detection of El Niño episodes would provide invaluable warning of impending rainfall anomalies in the ARB and would enable better preparations to mitigate its negative effects.

Trend and Change-Point Detection Analyses of Rainfall and Temperature Over the Awash River Basin of Ethiopia

Awash River basin (ARB) as a system is in a state of continuous change that requires successive studies to discern the changes or trends of climatic elements through time due to climate change/variability, and other socio-economical developmental activities in the basin. The livelihood of communities in the ARB is primarily based on rainfall-dependent agriculture. Effects of rainfall anomalies such as reduction of agricultural productivity, water scarcity, and food insecurity are becoming more prevalent in this area. In recent years, ARB experienced more frequent and intense spatio-temporal rainfall anomalies, which make the shift and trend analyses of rainfall associated with sea surface temperature crucial in providing guidance to improve agricultural productivity. Change-point detection tests (Pettit’s, the von Neumann ratio (VNR), Buishand’s range (BR) and standard normal homogeneity (SNH)) and Mann-Kendall (MK) trend analysis of rainfall and temperature data from 29 meteorological stations in the ARB were carried out from 1986 to 2016. A significant increasing trend of annual and seasonal temperature was found. The temperature change-points for the annual and major rainy season (MRS) were detected in 2001, while for the minor rainy season (mRS) in 1997. A significant decreasing trend, shift, and high variability of rainfall were detected in the downstream part of the ARB. The BR and SNH results showed that the mRS rainfall change-point was in 1998, with a subsequent mean annual decrease of 52.5 mm. The increase (decrease) of rainfall in the annual and MRS was attributable to La Nina (El Nino) events. The significant decreasing trend and change-point of rainfall in the mRS was attributable to the steady warming of the Indian and Atlantic Oceans, local warming, and La Nina events. With this knowledge of the current trends and change-point for rainfall and temperature in the ARB, it is therefore essential that appropriate integrated water management and water-harvesting technologies are established, especially in the downstream areas. Moreover, early detection of El Nino episodes would provide invaluable warning of impending rainfall anomalies in the ARB and would enable better preparations to mitigate its negative effects.

Evaluating the Quality of Spatial Data for the Analysis of Climate Variability in the Coastal Region of Nigeria

The lack of truly reliable data for climate change analyses and prediction presents challenges in climate modeling. Needed data are required to be hydrologically/statistically reliable to be useful for hydrological, meteorological, climate change, and estimation studies. Thus, data quality and homogeneity screening are preliminary analyses. In this study, the homogeneity of the climatic data used for analyses of climate variability was conducted in the coastal region of Nigeria. Climatic Research Unit (CRU 0.5× 0.5) gridded monthly climatic data for sixty years (1956- 2016) for nine states of the coastal region of Nigeria obtained from internet sources were validated with the Nigerian Meteorological Agency (NiMet) data to assure adequacy for use. The data were tested for normality using the Shapiro-Wilk (S-W) test, D’Agostino-Pearson omnibus test, and skewness test. Four homogeneity test methods were applied to 257 locations in the nine states of the coastal region of Nigeria and they include Pettit’s, Standard Normal Homogeneity Test (SNHT), Buishand’s and Von Neumann Ratio (VNR) tests. The results of the validity analysis indicated that the CRU data are very reliable and thus justified their use for the further analysis carried out in the study. Also, the results obtained indicated that CRU climatic data series were normally distributed and parametric methods could be used in further analysis of the data. Rainfall data homogeneity was detected for Bayelsa, Delta, Edo, Lagos, Ogun, and Ondo states and inhomogeneity for Akwa Ibom, Cross Rivers, and Rivers States. Also, temperature data inhomogeneity was detected for all the states in the study area.

Long-Term Homogeneity and Trends of Hydroclimatic Variables in Upper Awash River Basin, Ethiopia

Understanding long-term trends in hydroclimatic variables is important for future sustainable water resource management as it could show the possible regime shifts in hydrology. The main objective of this study was to analyze the homogeneity and trends of hydroclimatic data of Upper Awash Sab-Basin (UASB) in Oromia, Ethiopia, by employing homogeneity tests and Mann-Kendall and Sen’s slope tests. The data consist of 18 rainfall stations, 8 temperature stations, and 8 flow gauging stations across the UASB. Homogeneity and trends in streamflow, rainfall, and temperature variables were analyzed for the time period 1980 to 2017. In order to analyze homogeneity of hydroclimatic variables, we used four homogeneity tests (Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test) at 5% significance level. Based on the outputs of four homogeneity tests, the results were classified into three categories, namely, “useful,” “doubtful,” and “suspect” to select the homogeneity stations. Mann-Kendall (Z) and Sen’s slope tests (Q) were applied for the selected homogeneous time series to detect the trend and magnitude of changes in hydroclimatic variables. The result showed that most of the stations in annual rainfall and streamflow data series were classified as useful. It is found that 58% of the rainfall stations were homogeneous. It is highlighted that 3 out of 8 discharge gauging stations have an inhomogeneity as they failed from one or a combination of the four tests. The MK revealed significant decreasing trends of annual rainfall in Addis Alem (Q = −19.81), Akaki (Q = −5.60), Hombole (Q = −9.49), and Ghinch (Q = −12.38) stations. The trend of annual temperature was a significant increasing trend in Addis Ababa Bole (Q = 0.05), Addis Ababa Tikur Ambessa (Q = 0.03), Tulu Bolo (Q = 0.07), and Addis Alem (Q = 0.06) stations. The results of discharge showed a significant increasing trend in Bega at Mojo (Q = 0.17) and Hombole (Q = 0.03) gauging stations. In general, the results obtained from discharge, rainfall, and temperature series indicated that most of the stations exhibited no trends in both annual and seasonal time series. It can be concluded that decreases in average annual rainfall totals and increases in mean annual temperature will probably drive sub-basin scale changes in discharge. We believe that the results obtained can fill information gaps on homogeneity and trends of hydroclimatic variables, which is very crucial for future water resource planning and management in the face of climate change.

Inhomogeneity detection in the precipitation series: case of arid province of Pakistan

Reliable precipitation data are often required for conducting hydro-climatological assessments. Therefore, the study aims to detect the inhomogeneity in each calendar month, mean annual and monthly time series precipitation data of Balochistan, an arid province of Pakistan. The inhomogeneity was assessed using standard normal homogeneity test, Buishand range test, Pettitt test and von Neumann ratio test at 95% confidence level. The tests were applied over the precipitation data obtained from 14 meteorological stations for the period 1961–2009. The results of the different tests were classified as ‘useful’, ‘doubtful’ and ‘suspect’ based on the null hypothesis. The result of calendar months and annual series indicates that most of the data were ‘useful’. The output of monthly time series precipitation indicates that the data at Jiwani, Lasbela, Nokkunddi, Ormara, Pasni, Turbat and Zhob stations were ‘useful’ and others are classified into ‘doubtful’ and ‘suspect’ class. It was also observed that von Neumann ratio test is sensitive to minor changes in the precipitation data, and standard normal homogeneity test was found less sensitive to changes.

The HST Key Project galaxies NGC 1326A, NGC 1425, and NGC 4548: New variable stars and massive star population

Studies of the massive star population in galaxies beyond the Local Group are the key to understanding the link between their numbers and modes of star formation in different environments. We present the analysis of the massive star population of the galaxies NGC 1326A, NGC 1425, and NGC 4548 using archival images obtained with the Hubble Space Telescope Wide Field Planetary Camera 2 in the F555W and F814W filters. Through high-precision point spread function fitting photometry for all sources in the three fields, we identified 7640 candidate blue supergiants, 2314 candidate yellow supergiants, and 4270 candidate red supergiants. We provide an estimate of the ratio of blue to red supergiants for each field as a function of galactocentric radius. Using Modules for Experiments in Stellar Astrophysics (MESA) at solar metallicity, we defined the luminosity function and estimated the star formation history of each galaxy. We carried out a variability search in the V and I filters using three variability indexes: the median absolute deviation, the interquartile range, and the inverse von Neumann ratio. This analysis yielded 243 new variable candidates with absolute magnitudes ranging from MV = −4 to −10 mag. We classified the variable stars based on their absolute magnitude and their position on the color–magnitude diagram using the MESA evolutionary tracks at solar metallicity. Our analysis yielded 8 candidate variable blue supergiants, 12 candidate variable yellow supergiants, 21 candidate variable red supergiants, and 4 candidate periodic variables.

Analysis of Drivers of Trends in Groundwater Levels Under Rice–Wheat Ecosystem in Haryana, India

In India, especially in the state of Haryana in the northwestern parts, groundwater is the key source of water supply for domestic, industrial and agricultural uses. In recent years, the dependence on groundwater has increased drastically over the state due to speedy expansion of area under rice–wheat cultivation and therefore the states’ aquifers have started experiencing over-exploitation. The aim of this study was to compute the magnitudes of groundwater level trends and abrupt change point detections spatially and temporally based on secondary data obtained from the Groundwater Cell, Department of Agriculture, Government of Haryana with respect to 118 community development blocks for pre- and post-monsoon seasons during 1990–2013. Three statistical models, namely Mann–Kendall test, Sen’s slope estimator and simple linear regression, were applied to understand the trend and rate of change in groundwater level, whereas the abrupt change point detection analysis was carried out using Pettitt’s test, standard normal homogeneity test, Buishand’s range test and von Neumann ratio test. The results indicate that the average depth to groundwater level during pre-monsoon season in the state has ranged from 3.42 to 43.90 m below ground level (mbgl), whereas the average depth to groundwater level during post-monsoon season has ranged from 2.48 to 43.68 mbgl. The mean of groundwater level fluctuations during pre- and post-monsoon seasons was found to be − 6.0 and − 6.7 mbgl, respectively. The study highlights that the negative (falling) trends are much more pronounced (significant at 0.01% probability level) than positive (rising) ones, which corroborate the unsustainable groundwater development in the state. The abrupt change point detection analysis of groundwater levels indicates toward different change points, with maximum change points between the years 2000–2006 during pre- and post-monsoon seasons. This can be attributed to the advanced transplanting of rice crop by farmers much before the start of rainy season. In addition, both anthropogenic and climatic factors have led to groundwater depletion in the study area. These findings may be helpful for the planners and policy makers toward judicious utilization of groundwater resources in the state.

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

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.

Massive star population of the Virgo Cluster galaxy NGC4535

We analyzed the massive star population of the Virgo Cluster galaxy NGC 4535 using archival Hubble Space Telescope Wide Field Planetary Camera 2 images in filters F555W and F814W, equivalent to Johnson V and Kron-Cousins I. We performed high precision point spread function fitting photometry of 24353 sources including 3762 candidate blue supergiants, 841 candidate yellow supergiants, and 370 candidate red supergiants. We estimated the ratio of blue to red supergiants as a decreasing function of galactocentric radius. Using Modules for Experiments in Stellar Astrophysics (MESA) isochrones at solar metallicity, we defined the luminosity function and estimated the star formation history of the galaxy over the last 60 Myr. We conducted a variability search in the V and I filters using three variability indexes: the median absolute deviation, the interquartile range, and the inverse von-Neumann ratio. This analysis yielded 120 new variable candidates with absolute magnitudes ranging from MV = −4 to −11 mag. We used the MESA evolutionary tracks at solar metallicity to classify the variables based on their absolute magnitude and their position on the color-magnitude diagram. Among the new candidate variable sources are eight candidate variable red supergiants, three candidate variable yellow supergiants and one candidate luminous blue variable, which we suggest for follow-up observations.

Homogeneity analysis of streamflow records in arid and semi-arid regions of northwestern Iran

Homogeneity analysis of the streamflow time series is essential to hydrological modeling, water resources management and climate change studies. In this study, five absolute homogeneity tests and one clustering approach were used to determine the homogeneity status of the streamflow time series (over the period 1960–2010) in 14 hydrometric stations of three important basins (i.e., Aras River Basin, Urmia Lake Basin and Sefid-Roud Basin) in northwestern Iran. Results of the Buishand range test, von Neumann ratio test, cumulative deviation test, standard normal homogeneity test and Pettitt test for monthly streamflow time series detected that about 42.26%, 38.09%, 33.33%, 39.28% and 68.45% of the streamflow time series were inhomogeneous at the 0.01 significance level, respectively. Streamflow time series of the stations located in the eastern parts of the study area or within the Urmia Lake Basin were mostly homogeneous. In contrast, streamflow time series in the stations of the Aras River Basin and Sefied-Roud Basin showed inhomogeneity at annual scales. Based on the overall classification for the monthly and annual streamflow series, we determined that about 45.60%, 11.53% and 42.85% of the time series were categorized into the ‘useful’, ‘doubtful’ and ‘suspect’ classes according to the five absolute homogeneity tests. We also found the homogeneity patterns of the streamflow time series by using the clustering approach. The results suggested the effectiveness of the clustering approach for homogeneity analysis of the streamflow time series in addition to the absolute homogeneity tests. Moreover, results of the absolute homogeneity tests and clustering approach indicated obvious decreasing change points of the streamflow time series in the 1990s over the three basins, which were mostly related to the hydrological droughts.

Non-parametric characterization of long-term rainfall time series

The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851–2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann–Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann–Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann–Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

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

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.

Homogeneity and trends in long-term rainfall data, Kelantan River Basin, Malaysia

ABSTRACTThe Kelantan River Basin, situated in the northeastern Malaysian Peninsula, suffers serious flood/inundation damage, related to the northeast monsoon season (November–January), every few years. In this river basin, rainfall observation systems have been progressively developed since 1948, and long-term time-series data at distributed rainfall stations have been accumulated. This study firstly investigated the homogeneity of the accumulated time-series data for the purpose of constructing a reliable database for various hydrologic analyses. The homogeneity of rainfall time-series data was established using four absolute homogeneity tests: the Pettitt test, standard normal homogeneity test, Buishand range test, and von Neumann ratio test. It was found that among 50 rainfall stations within the river basin, 9 were flagged by the tests. Of these, inhomogeneous time-series data from four stations were omitted from further analysis. Secondly, using the homogenous time-series rainfall data, a trend analysis of annual rainfall variability was conducted by means of the Mann–Kendall (MK) test. To investigate long- and short-term characteristics of rainfall variability, three sampling methods of the MK test were applied: 30-year, sequentially increased, and 10-year moving segmented sampling. The MK test using the 30-year sampling showed a decreasing trend between 1957 and 1987, and an increasing trend between 1981 and 2011. The MK test using the sequentially increased sampling detected neither significant increasing nor decreasing trends through the same 70-year period. The MK test using 10-year moving segmented sampling clearly showed significant rainfall variability, which corresponded to the El Niño and La Niña events of 1972, 1982, 1988, the mid-1990s, and 2000–2004. Extending the plot, by comparing the turning points with the occurrence of El Niño and La Niña events, the results showed that the influence of the El Niño southern oscillation (ENSO) is the most likely to significantly influence rainfall trends in the Kelantan River Basin.