Buishand Range Test Research Articles

Trend and change-point analyses of meteorological variables using Mann–Kendall family tests and innovative trend assessment techniques in New Bhupania command (India)

ABSTRACT Climate change (CC) significantly influences agricultural water productivity, it is advisable to consider the adapting irrigation regimes to observed changes in precipitation patterns. This study aim is to assess trends and change point analysis of weather variables, namely temperature (T), precipitation (R), and reference evapotranspiration (ETo), utilizing 31 years of long-term data for a semi-arid climate. The analysis was carried out using Mann-Kendall (MK), Modified Mann-Kendall (MMK), Innovative Trend Analysis (ITA), and Innovative Polygon Trend Analysis (IPTA) methods. Homogeneity tests, including Pettitt's test, Standard Normal Homogeneity Test (SNHT), Buishand range test, and Von Neumann Ratio Test (VNRT), were employed to detect change points (CPs) in the time series data. The results indicated that, for maximum temperature (Tmax), MK and MMK revealed a positive trend for September and July, respectively, while minimum temperatures (Tmin) indicated Increasing trends in August and September. Precipitation exhibited an increasing trend during the Zaid season (April-May). ETo exhibited a negative trend in January. ITA and IPTA displayed a greater potential to detect the trends across months and seasons. Change point analysis revealed that for Tmax, the CP occurred in 1998 for April month time series data. Likewise, for Tmin, change points for April and August time series found in 1997. This study underscores shifting climatic parameters, emphasizing the importance of accounting for these changes in agricultural and water management strategies to ensure sustainability and resilience.

Changes in mean and extreme homogeneous precipitation in China during 1960–2020

This study aims to explore the observed national and regional changes in the seasonal mean and extreme precipitation in mainland China between 1960 and 2020 using data from 678 stations. Tests of homogeneity (e.g. standard normal homogeneity test, Pettitt's test and Buishand range test) are applied to identify homogeneous stations for the trend analysis. The non-parametric Mann-Kendall test and a modified Theil-Sen slope estimator were then used to examine changes in the precipitation characteristics. Our results show that despite a decreasing trend in the frequency of wet days, the mean precipitation has actually increased. Extreme precipitation has also increased, and in many locations, at greater rates than those of the mean. This is principally due to a greater rate of increase per degree of warming, of 8% for extreme precipitation compared to 7% for the mean. However, considerably regional and seasonal variability has been seen in the trends. Mean and extreme precipitation trends are positive throughout in western China for all seasons, but they vary in eastern China. Mean precipitation increased slightly in the southeast but decreased in the northeast of the country. Extreme precipitation increased in both regions, but the increases were greater in the southeast. Trends, additionally, also differed within regions, especially over eastern China. In southeast China, precipitation increased in the east and decreased in the west. In northeast China, precipitation increased in the north and decreased in the south. Using a multiple regression model, we found that changes during summer precipitation clearly dominate the overall annual changes in most regions. Most long-term changes over eastern China appear to be linked to those of the East-Asian and South-Asian Summer Monsoons.

Trend analysis of hydro-climate variables in the Jemma sub-basin of Upper Blue Nile (Abbay) Basin, Ethiopia

The change in climate elements such as rainfall and temperature are determinant factors of hydrological components (e.g., streamflow, water yield, evapotranspiration). Thus, understanding the trends of hydro-climate variables are imperative for planning water resources management measures. This study examines the trends of precipitation and temperature (1980–2014) as well as streamflow (1990–2008) changes in the Jemma sub-basin of the Upper Blue Nile (Abbay) Basin. A homogeneity test was performed for pre-processing data to inspect the statistical nature of data homogeneity viz., Pettitt’s, SNHT, Buishand range test, and Von Neumann test (VNT). The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Sen’s slope estimator, and Innovative trend analysis (ITA) were applied to compute the existence of trend and the magnitude of change at an annual timescale. In a nutshell, the study portrays a change in the trends of hydro-climate variables when using different statistical tests. The result showed that the mean annual temperature in many stations depicted upward trends. There is a significant increasing trend (p < 0.05) by 0.029 °C per year in the mean annual temperature of all climatic stations. Based on Sen’s slope estimator, the annual precipitation and streamflow were increased by 1.781 mm/year and 0.085 m3/s, respectively. But no significant trends were detected in precipitation and streamflow when using Sen’s slope estimator test. The results of this study are worthwhile for evaluating the trends of hydro-climatic variables in other areas of Ethiopia in particular and elsewhere in the world in general, which are fundamental for planning water resource management measures.

Homogeneity Analysis of Turkish Rainfall Intensity Series

In this study, homogeneity checking the annual maximum rainfall intensity series for periods ranging from 30 to 78 years were obtained from 103 stations operated by the Turkish State Meteorological Service. Absolute homogeneity tests namely Standard Normal Homogeneity Test (SNHT), Buishand Range Test (BRT), Pettitt Test (PT), and Von Neumann Ratio Test (VNRT) were applied at a confidence level of 95%. Stations were classified inhomogeneous if at least one of the standard durations data classified suspect or doubtful. Assuming that the factor destabilizing the homogeneity is the trend, a detrended methodology (DFA) was performed. After the implementation of DFA, absolute homogeneity tests were reapplied to the series of rainfall intensity. As a result of this study 49 of 103 stations called useful all 14 standard durations. 45 of the remaining 54 stations classified useful all standard durations after trend components separated from rainfall intensity series. As a result of the analysis, it was determined that the remaining 8 of 103 stations had inhomogeneous values after the trend components were separated.

Homogeneity and trend analysis of rainfall and droughts over Southeast Australia

This study investigates rainfall and drought characteristics in southeastern Australia (New South Wales and Victoria) using data from 45 rainfall stations. Four homogeneity tests are adopted to determine inhomogeneities in the annual total rainfall (ATR) and monthly rainfall data, namely The Pettitt test, the SNHT, the Buishand range test and the Von Neumann ratio test at significance levels of 1%, 5%, and 10%. Temporal trends in rainfall (ATR, monthly, and seasonal) and droughts are examined using autocorrelated Mann–Kendall (A-MK) trend test at 1%, 5%, and 10% significance levels. We also assess meteorological droughts by using multiple drought indices (3-, 6-, 9-, 12-, 24-, and 36-month Standardized Precipitation Index (SPI) and Effective Drought Index (EDI)). Furthermore, spatial variability of temporal trends in rainfall and drought are investigated through interpolation of Sen’s slope estimator. The results represent an increasing trend in ATR between 1920 and 2019. However, southeast Australia is highly dominated by a significant negative trend in the medium term between 1970 and 2019. Winter is found to be dominated by a significantly negative trend, whereas summer and spring seasons are dominated by a positive trend. April is detected as the driest month according to magnitude of Sen’s slope and the A-MK test result. Positive trends on droughts are observed at inner parts of the study area, whereas a negative trend is detected in the south, southeast, and northeast of the study area based on SPIs and EDI. The findings of this study help to understand changes in rainfall and droughts in southeastern Australia.

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

Change-point detection and trend analysis in monthly, seasonal and annual air temperature and precipitation series in Bartın province in the western Black Sea region of Turkey

Studies associated with climate change and variability are of great importance at both the global and local scale in the global climate crisis. In this study, change-point detection and trend analysis were carried out on mean, maximum, minimum air temperatures and total precipitation based on monthly, seasonal and annual scale in Bartın province located in the western Black Sea Region of Turkey. For this aim, 4-different homogenei-ty tests (von Neumann test, Pettitt test, Buishand range test and standard normal homogeneity test) for change-point detection, Modified Mann–Kendall test and Şen’s innovative trend test for trend analysis, and Sen’s slope test for the magnitude estimation of trends were used. According to the test results, the summer temperatures in particular show increasing trends at the 0.001 significance level. Mean maximum temperature in August, mean minimum temperature in June and August, and mean temperature in July and August are in increasing trend at the 0.001 significance level. Over a 51 year period (1965–2015) in Bartın province, the highest rate of change per decade in air temperatures is in August (0.55°C for Tmax, 0.46°C for Tmin and 0.43°C for Tmean) based on Sen’s slope. However, the study showed that apart from October precipitation, there is no significant trend in monthly, seasonal and annual precipitation in Bartın. Increasing trends in mentioned climate variables are also visually very clear and strong in Şen’s innovative trend method, and they comply with the statistical results. As a result, the study revealed some evidence that temperatures will increase in the future in Bartın and its environs.

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.

Long-term trends in PM2.5 mass and particle number concentrations in urban air: The impacts of mitigation measures and extreme events due to changing climates

Urbanisation and industrialisation led to the increase of ambient particulate matter (PM) concentration. While subsequent regulations may have resulted in the decrease of some PM matrices, the simultaneous changes in climate affecting local meteorological conditions could also have played a role. To gain an insight into this complex matter, this study investigated the long-term trends of two important matrices, the particle mass (PM2.5) and particle number concentrations (PNC), and the factors that influenced the trends. Mann-Kendall test, Sen’s slope estimator, the generalised additive model, seasonal decomposition of time series by LOESS (locally estimated scatterplot smoothing) and the Buishand range test were applied. Both PM2.5 and PNC showed significant negative monotonic trends (0.03–0.6 μg m−3. yr−1 and 0.40–3.8 × 103 particles. cm−3. yr−1, respectively) except Brisbane (+0.1 μg m−3. yr−1 and +53 particles. cm−3. yr−1, respectively). For the period covered in this study, temperature increased (0.03–0.07 °C.yr−1) in all cities except London; precipitation decreased (0.02–1.4 mm. yr−1) except in Helsinki; and wind speed was reduced in Brisbane and Rochester but increased in Helsinki, London and Augsburg. At the change-points, temperature increase in cold cities influenced PNC while shifts in precipitation and wind speed affected PM2.5. Based on the LOESS trend, extreme events such as dust storms and wildfires resulting from changing climates caused a positive step-change in concentrations, particularly for PM2.5. In contrast, among the mitigation measures, controlling sulphur in fuels caused a negative step-change, especially for PNC. Policies regarding traffic and fleet management (e.g. low emission zones) that were implemented only in certain areas or in a progressive uptake (e.g. Euro emission standards), resulted to gradual reductions in concentrations. Therefore, as this study has clearly shown that PM2.5 and PNC were influenced differently by the impacts of the changing climate and by the mitigation measures, both metrics must be considered in urban air quality management.

Trend of extreme precipitation indices and analysis of long-term climate variability in the Upper Awash basin, Ethiopia

Assessing the spatial and temporal variability of climate data is one of the basic requirements to solve the hydrological and water management problems. In this manuscript, a comprehensive assessment of data quality, precipitation concentration index (PCI), Mann-Kendall (MK) trend test, and extreme climate change indices analyses were performed. The standard normal homogeneity test (SNHT), the Pettitt test, the Buishand range test, and the Von Neumann ratio test were used for homogeneity analysis, and most of the climate stations exhibited homogenous precipitation series. The annual mean and dry season PCI values demonstrated higher spatial and temporal variability or irregular distribution of precipitation, whereas PCI in the wetter seasons indicated low precipitation concentration in the Upper Awash basin. The MK trend test exhibited a significantly increasing trend of maximum temperature; however, the trend of mean annual and extreme precipitation events was insignificant for the majority of stations. Moreover, the magnitude and trend of ten main extreme precipitation indices were constructed. Unlike the mean annual precipitation, larger values of the extreme 1-day (Rx1day) and 5-day (Rx5day) annual precipitation were obtained in the downstream areas of the basin which could signify the prevalence higher flood risk in this portion of the basin. The highest predicted 100-year annual maximum daily (AMD) precipitation was obtained at Zikuala station as 128.5 mm. Statistical analysis of the magnitude and trend of extreme precipitation indices and spatial variability of climate data is imperative for the integrated water resource planning and management decisions.

KONYA KAPALI HAVZASI (TÜRKİYE) YILLIK SICAKLIK VERİLERİNİN TREND ANALİZİ VE DEĞİŞİM NOKTALARININ BELİRLENMESİ

Climate change is expressed as major changes in the average climate which exist for many years. Although climate change occurs on a global scale, the impact of climate change varies from region to region. Therefore, the analysis of the variations in meteorological variables is a very vital issue in monitoring of climate change. In this study, assignment of the change points and trends of annual mean temperature, maximum temperature and minimum temperature data of the Konya Closed Basin in Turkey is examined. For this aim, air temperature data of 11 meteorology station were used. The change point of temperature time series data was examined using the Pettitt test, Standard Normal Homogeneity test and Buishand Range test. The Mann-Kendall, Spearman Rho and Innovative Şen Trend methods were used to determine trends of air temperature.

Cellular time series: a data structure for spatio-temporal analysis and management of geoscience information

Abstract Geoscientists are continuously confronted by difficulties involved in handling varieties of data formats. Configuration of data only in time or space domains leads to the use of multiple stand-alone software in the spatio-temporal analysis which is a time-consuming approach. In this paper, the concept of cellular time series (CTS) and three types of meta data are introduced to improve the handling of CTS in the spatio-temporal analysis. The data structure was designed via Python programming language; however, the structure could also be implemented by other languages (e.g., R and MATLAB). We used this concept in the hydro-meteorological discipline. In our application, CTS of monthly precipitation was generated by employing data of 102 stations across Iran. The non-parametric Mann–Kendall trend test and change point detection techniques, including Pettitt's test, standard normal homogeneity test, and the Buishand range test were applied on the generated CTS. Results revealed a negative annual trend in the eastern parts, as well as being sporadically spread over the southern and western parts of the country. Furthermore, the year 1998 was detected as a significant change year in the eastern and southern regions of Iran. The proposed structure may be used by geoscientists and data providers for straightforward simultaneous spatio-temporal analysis.

Long–term spatio–temporal warming tendency in the Vietnamese Mekong Delta based on observed and high–resolution gridded datasets

The Vietnamese Mekong Delta is among the most vulnerable deltas to climate–related hazards across the globe. In this study, the annual mean and extreme temperatures from 11 meteorological stations over the Vietnamese Mekong Delta were subjected to normality, homogeneity and trend analysis by employing a number of powerful statistical tests (i.e. Shapiro–Wilk, Buishand Range test, classical/modified Mann–Kendall test and Sen’s slope estimator). As for spatio–temporal assessment, the well–known (0.5° × 0.5°) high–resolution gridded dataset (i.e. CRU TS4.02) was also utilized to examine trend possibilities for three different time periods (i.e. 1901–2017, 1951–2017 and 1981–2017) by integrating spatial interpolation algorithms (i.e. IDW and Ordinary Kriging) with statistical trend tests. Comparing the calculated test–statistics to their critical values (a = 0.05), it is evident that most of the temperature records can be considered to be normal and non–homogeneous with respect to normality and homogeneity test respectively. As for temporal trend detection, the outcomes show high domination of significantly increasing trends. Additionally, the results of trend estimation indicate that the magnitude of increase in minimum temperature was mostly greater than mean and maximum ones and the recent period (1981–2017) also revealed greater increasing rates compared to the entire analyzed period and second half of the 20th century. In general, these findings yield various evident indications of warming tendency in the Vietnamese Mekong Delta over the last three decades.

Aridity in the Iberian Peninsula (1960–2017): distribution, tendencies, and changes

Aridity is a climatic characteristic that has a significant impact on many aspects of life, mainly in agriculture but also in other economic sectors, and temperature and precipitation are the variables by which the effects of aridity can be measured. It is necessary to know its distribution and trend in different climate change scenarios. The Iberian Peninsula presents a strong contrasting climate, comprising both humid and very dry regions. Forty-five climatic time series were used to perform a spatial and temporal analysis and to identify possible points of trend change in annual aridity for the period from 1960 to 2017. Two indices were considered: the De Martonne Index (IDM) and the Food and Agriculture Organisation (FAO) Index (IF). By means of ordinary kriging, maps of spatial, temporal, and trend distributions were made. The trends of the aridity indices, annual temperature, and annual precipitation were analysed by applying the techniques of the modified Mann-Kendall test and Sen’s estimator; furthermore, to identify gaps of years in the series, the Buishand range test was used. The results showed a significant average annual temperature increase at all stations. Although annual precipitation decreased across the board, this decrease was not significant. Semi-arid was the dominant class, although the study area showed great variability, i.e. from extremely humid areas to arid zones. Forty-one out of the 45 series presented increasing aridity during the study period, although the trend was significant in only 16 of the series. From the Buishand range test for both aridity indices, the results revealed a noteworthy changing point for IDM and for IF. Although the changing point was variable, it was concentrated around the late 1970s and early 1980s, and from that period, the aridity tendency was dependent on the analysed series.

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.

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.

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

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).

Trend analysis and detection of precipitation fluctuations in arid and semi-arid regions

The most important impacts of climate change relate to temperature and precipitation. Precipitation is particularly important, because changes in precipitation patterns may lead to floods or droughts in different areas. Also, precipitation is a major factor in agriculture and in recent years interest has increased in learning about precipitation variability for periods of months to annual and seasonal trends and change points had been analyzed for 22 rainfall stations in Fars province during 1972 to 2011. Mann-Kendall non-parametric test and Sen’s method had been used to determine positive or negative trends; also Pettitt test, Standard normal homogeneity test, Buishand range test, Von Neumann ratio, for detection of change points in the time series had been implemented. The TFPW approach had been used in order to decline the effects of autocorrelation and serial correlation on Mann-Kendall test. The results of Mann-Kendall test and Sen’s Method showed decreasing trend for all rainfall stations except for the Monje station. But, no significant trends were observed in all stations. Also, the results indicated that the precipitation has not occurred nonhomogeneity; whereas all test indicated there is no change point on precipitation time series. No change and abrupt shift were visible in the precipitation time series except in winter for Ali Abad Khafr; and Ali Abad Khafr; Tangab and Ramjerd based on pettitt test and Standard normal homogeneity test, respectively.

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.

Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation

Climate data homogenisation is of major importance in climate change monitoring, validation of weather forecasting, general circulation and regional atmospheric models, modelling of erosion, drought monitoring, among other studies of hydrological and environmental impacts. The reason is that non-climate factors can cause time series discontinuities which may hide the true climatic signal and patterns, thus potentially bias the conclusions of those studies. In the last two decades, many methods have been developed to identify and remove these inhomogeneities. One of those is based on a geostatistical simulation technique (DSS — direct sequential simulation), where local probability density functions (pdfs) are calculated at candidate monitoring stations using spatial and temporal neighbouring observations, which then are used for the detection of inhomogeneities. Such approach has been previously applied to detect inhomogeneities in four precipitation series (wet day count) from a network with 66 monitoring stations located in the southern region of Portugal (1980–2001). That study revealed promising results and the potential advantages of geostatistical techniques for inhomogeneity detection in climate time series. This work extends the case study presented before and investigates the application of the geostatistical stochastic approach to ten precipitation series that were previously classified as inhomogeneous by one of six absolute homogeneity tests (Mann–Kendall, Wald–Wolfowitz runs, Von Neumann ratio, Pettitt, Buishand range test, and standard normal homogeneity test (SNHT) for a single break). Moreover, a sensitivity analysis is performed to investigate the number of simulated realisations which should be used to infer the local pdfs with more accuracy. Accordingly, the number of simulations per iteration was increased from 50 to 500, which resulted in a more representative local pdf. As in the previous study, the results are compared with those from the SNHT, Pettitt and Buishand range tests, which were applied to composite (ratio) reference series. The geostatistical procedure also allowed us to fill in missing values in the climate data series. Finally, based on several experiments aimed at providing a sensitivity analysis of the procedure, a set of default and recommended settings is provided, which will help other users to apply this method.