Standard Normal Homogeneity Test 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.

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.

Precipitation Anomalies and Trends Estimated via Satellite Rainfall Products in the Cananeia–Iguape Coastal System, Southeast Region of Brazil

The objective of this research is to select the best orbital sensor for rainfall estimates (monthly and annual scales) and to analyze the frequency and magnitude of extreme rainfall events and their trends and disruptions based on the use of satellite rainfall product data for the Cananeia–Iguape Coastal System (CICS). Data from four satellite rainfall products were used to identify the correspondence with seven points on the surface of the study area. Statistical metrics were used to identify the best satellite rainfall product. After identifying the sensor with the best performance in estimating orbital precipitation, extreme events were identified by the Standardized Precipitation Index (SPI) on a one-month (SPI-1), three-month (SPI-3), and twelve-month (SPI-12) scale. Trend and rupture detection in the time series were performed using different statistical techniques (Mann–Kendall, Pettitt, Standard Normal Homogeneity Test, or Buishand test). Among the satellite rainfall products, CHIRPS had the best measurements for the analyzed points on the surface. The year 1983 was characterized as very rainy, also marked by the occurrence of El Niño, and was marked by the rupture of the rains at all points (IDs 1, 2, 3, 4, 5, 6, and 7) analyzed in the month of June. The decrease in monthly rainfall was more significant in the months of February (at points IDs 1, 2, 3, 5, and 7) and April (IDs 1, 3, 5, and 7). Decreased rainfall may cause CICS mangrove shrinkage. These results showed the importance of studying rainfall in an area with mangroves in order to understand the dynamics of vegetation in the face of climate change.

Spatiotemporal variability and trends of droughts in the Mediterranean coastal region of Türkiye

AbstractDrought, defined as the period when water availability considerably remains below normal levels for a certain time period, has devastating social, economic and environmental impacts. It is significant to closely monitor the temporal and spatial variability of drought in order to implement policies to mitigate the drought risk and ensure the sustainable management of water resources. In this study, three different drought indices, standard precipitation index (SPI), standardized precipitation‐evapotranspiration index (SPEI) and reconnaissance drought index (RDI), are calculated for 3‐, 6‐ and 12‐month time scales from the precipitation and temperature data measured in 11 meteorological observation stations within the Mediterranean coastal region of Türkiye during 1972–2020. Initially, two absolute homogeneity tests were applied, namely the standard normal homogeneity test (SNHT) and the Pettitt test, to detect abrupt changes in drought index time series. Then, trend analysis was carried out with the innovative trend analysis (ITA) and improved visualization of ITA (IV‐ITA) methods to determine the temporal variation of the drought in the region. A graphical representation is developed for the drought classifications based on IV‐ITA. As a result of the SNHT and Pettitt test, significant change points were found at a 5% significance level in 91.92% and 85.86% of all indices, respectively. In both tests, breaks were detected at similar times (between 1972 and 1974) at Marmaris station, while at Alanya station, changes were found between 1972 and 1973 for the SNHT and in 2008 for the Pettitt test. The trend differences between extreme conditions (extremely dry and wet) decreased with an increase in time scale, distinctively. The ITA trends were almost identical for 3‐month time scales, and they started to differ with time scale increases. Particularly, the presence of negative differences in data points contributes to the augmentation of drought periods, despite a few instances of extreme cases and minor fluctuations that can also be observed in IV‐ITA. The findings obtained from the study are expected to provide valuable information about the recent shifts in drought in the Mediterranean basin.

Homogenizing Monthly Rainfall and Temperature Data Series in Maharashtra & Goa

Annual rainfall and temperature data series of all climate stations in Maharashtra & Goa are statistically tested for data homogeneity. To inspect homogeneity of a station, a two-step approach is followed. First, four homogeneity tests Standard normal homogeneity test, Pettit’s test, Buishand’s range test and Von Neumann ration test at 5% level of significance are used to determine test hypothesis for homogeneity on testing parameters of annual rainfall and temperature. Second, results from all these four tests aggregated together into three different classes as ‘useful’, ‘doubtful’ and ‘suspect’. Here 30 rainfall, 29 maximum and minimum temperature climate stations were tested. The results showed 80% stations as ‘useful’, 7% as ‘suspect’ and 13% as ‘doubtful’ for rainfall, for maximum temperature series these results are 17% as ‘useful’, 7% as ‘suspect’ and 76% as ‘doubtful’, while for minimum temperature series these results are 21% as ‘useful’, 10% as ‘suspect’ and 69% as ‘doubtful’. Further, in this study an attempt is also made to correct the monthly rainfall and temperature data series for homogeneity. Stations categorised as ‘useful’ are used as reference series to remove inhomogeneities from ‘suspect’ and ‘doubtful’ stations. To correct rainfall series ratio’s method is used while for temperature series addition method is used. Correction results showed significant improvement in ‘suspect’ category stations. After correction of inhomogeneous series, the results shows all 100% of rainfall stations and more than 65% of temperature stations are now in ‘useful’ category. The corrected stations may be included in further climate research studies.

Trend detection and change point analysis of inflows in Karuppanadhi and Gundar dams of Chittar River Basin, Tamil Nadu, India

Abstract Gaining insights into the patterns of hydroclimatic elements such as inflows to dams is essential to adeptly strategize and oversee water resource planning. For this study, we focused on Karuppanadhi and Gundar dams within the Chittar River Basin in Tamil Nadu. We employed various time series trend assessment methods – Mann–Kendall (MK), modified Mann–Kendall (MMK), Sen slope estimator, and innovative trend analysis (ITA). Furthermore, we conducted change point detection analysis using homogeneity tests, namely Pettitt's test, standard normal homogeneity test (SNHT), and Buishand test. The analysis was carried out across three-time scales: monthly, seasonal, and annual for 30 years (1991–2020) span. Results revealed declining trends significantly across all three timescales for the Karuppanadhi dam. Whereas for the Gundar dam, notable trends included increased patterns in January, March, December, and the winter season, while other months/seasons showed statistically decreasing trends. ITA exhibited greater sensitivity in identifying trends at monthly and seasonal scales, indicating its superior trend detection over MK and MMK methods. Change point analysis of trends identified a rising trend in August post-2017 for both dams. Other change points indicated decreasing inflow trends thereafter. Analysing trends and change points in dam inflows aid decisions on water resources management.

Homogeneity Assessment and Correction Methodology for the 1980–2022 Daily Temperature Series in Padua, Italy

Meteorological observations over the last four decades are of paramount importance to investigating ongoing climate change. An important issue is the quality and reliability of the climatic series, which are fundamental prerequisites to drawing the correct conclusions. Homogeneity tests are used to detect discontinuities whose interpretation is facilitated by metadata availability. In this work, daily minimum and maximum temperature measurements collected in Padua, Italy, between 1980 and 2022 are examined. During this period, the weather station of Padua center underwent many changes in location or instruments; therefore, some tests have been used to identify and remove their effects and obtain homogeneous series. Some well-known absolute tests have been applied to investigate the shift in the mean value: Standard Normal Homogeneity test (SNH), Buishand U and range tests, Pettitt test, F-test, and STARS. Relative tests have been applied too, using several stations nearby Padua and two reanalysis datasets (ERA5 and MERIDA) as reference series to enhance the picture of the local situation and provide more robust conclusions. The applied tests identify change-points in the years in which a change in instrument or the location of the station has occurred, confirming that these changes have compromised the homogeneity of the series. The sub-series obtained, splitting the observations in correspondence with these change-points, have been homogenized with respect to a selected period. The corrected series of the minimum and maximum temperatures are more coherent with the modern warming trend. The transfer functions to be applied to future measurements of minimum temperature have been calculated, while the series of maximum temperature measurements can be directly extended.

Analysis of variations and trends of temperature over Niger central hydrological area, Nigeria, 1911–2015

Adequate information about the variations and trends of climatic variables is indispensable for the better management of the water resources and agricultural sectors. In this study, the variability and trends of the monthly, seasonal and annual temperatures ((TMIN, TMAX and TMEAN) time series were studied over Niger Central Hydrological Area, Nigeria (NCHA) during 1911–2015 using the Climate Research Unit (CRU) data. Different statistical operations analysis comprising descriptive statistics, linear regression model, Mann–Kendall test, Pettitt's test and standard normal homogeneity test (SNHT) were employed for the analysis. The tests were used to examine the temperature variability, presence of monotonic trends and change points of the time series. The monthly TMEAN variability over the entire area was between 2.10% and 3.61%. Periods 1914–1925 and 1974–1980 showed cooling trends, while 1930–1935, 1981–1988 and 2001–2015 exhibited warming trends. The annual TMIN and TMEAN showed positive trends across the 33 sub-basins of NCHA, while the TMAX showed mixed trends. The increasing trend was most significant on the monthly, seasonal and annual timescales of the TMIN, followed by TMEAN. In contrast, the mixed trends in TMAX were not significant. The global rate of change trends of TMIN, TMAX and TMEAN are 0.091, −0.007 and 0.043 °C/decade respectively. The overall warming of TMEAN also had a significant upward shift change of 0.43 °C since 1980 and the change spatially is characterized by a SW-NE orientation. There is a need to embrace green settlement and a green economy to reduce the impact of the ever-increasing warming trend over the region.

Trend and homogeneity test analysis for rainfall over a 121-year time period in the desert district of Western Rajasthan, India

Abstract The Thar Desert receives very scarce and scanty rainfall; hence, water is given the top priority and importance in daily life. During the past decades, there has been a variation in the trend of the rainfall, climate, and other atmospheric conditions in the Jaisalmer district. An analytical study has been carried out to investigate this changing environmental phenomenon. Sixteen grids have been selected to study the trend and magnitude of the slope for rainfall using the MMK (Modified Mann–Kendall) test and Sen's Slope estimator. Pettitt's test, SNHT (Standard Normal Homogeneity Test), and Buishand's test have been used to find trend change year in 121-year time series of rainfall. Mean annual rainfall for all grids shows positive values of Kendall's tau and Sen's Slope, which indicate that trend interpretation is rising, and test interpretation is increasing. Most of the results obtained from grids show the year ‘1992’ as the trend-changing year. A significant rise in mean annual rainfall has been noticed after the change of year in the study area. This type of study has not been conducted earlier in the region to identify the rainfall trend and change of year of trend. This study will help policymakers to take necessary action.

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.

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.

Climate Variability and Trends in Imotski, Croatia: An Analysis of Temperature and Precipitation

This paper examines the long-term changes in temperature and precipitation in the karst region of Imotski, Croatia, which is of particular interest due to its abundance of karst phenomena. This study analyses temperatures and precipitation on monthly and annual scales at two climatological stations in the region, Imotski and Ričice. Linear regression, the Theil–Sen estimator (β), and the Mann–Kendall test were used to determine the trends and statistical significance. The homogeneity of the data was checked using the Standard Normal Homogeneity Test (SNHT), and the F-test and t-test were used to test the significance of the mean shift between the two subseries. Additionally, the coefficient of variability, standardized rainfall anomaly, and precipitation concentration index were employed to analyze the precipitation variability. The study found a statistically significant (p < 0.05) upward trend in the mean (β = 0.0437) and maximum (β = 0.0590) annual air temperature at the Imotski station and the mean (β = 0.0387) annual temperature at the Ričice station. The SNHT test showed a statistically significant (p < 0.05) shift in the mean annual temperatures after 2007 and maximum annual temperatures after 1998 at the Imotski station. Similarly, a statistically significant (p < 0.05) shift in the mean annual temperatures after 2011 and the maximum annual temperatures after 1998 was found at the Ričice station. A seasonal distribution of precipitation is observed at both the Ričice and Imotski stations, with a downward trend (β = −2.7693) at Ričice and an upward trend (β = 6.0575) at Imotski; however, neither trend is statistically significant (p > 0.05). An increase in the intensity of dry periods and the occurrence of extreme events was also noted. The climatological analysis, conducted for the first time in this area, is a crucial step toward understanding local climate patterns and making informed decisions toward sustainable development and adaptation strategies.

Case study of rainfall and temperature assessment through trend and homogeneity analyses in Vadodara and Chhotaudepur district of Gujarat State, India

This study aims to assess the climate change impact on the rainfall and temperature data of the Vadodara-Chhotaudepur district of India and to focus on the environmental challenges related to the rainfall and temperature in the present state of development, land use, industrialization, and urbanization. The study utilized nine trend analysis methods, namely linear regression (LR), Sen's robust slope estimator (SS), Mann-Kendall test (MK), Spearman's rank correlation (SRC), the trend-free pre-whitening (TFPW), variance correction approach by Hamed and Rao (1998) with MK test (MK-CF1), variance correction approach by Yue and Wang (2004) with MK test (MK-CF2), block bootstrap with MK test (BBS-MK), and graphical method as innovative trend analysis (ITA), applied on monthly, annual, and seasonal scales. Additionally, the study also employed four homogeneity analysis methods, including Pettitt's test, standard normal homogeneity test (SNHT), Buishand's test, and Von Neumann Ratio test (VNRTs). The IMD (Indian meteorological department) gridded long-term rainfall data from 1901 to 2019 and temperature data from 1951 to 2019 are used in the present study to assess the homogeneity and trends of the data series. Results showed a warming trend of maximum temperature (MaxT) and minimum temperature (MinT) at the monthly, annual, and seasonal time scale and significant (at 5%) warming trend in annual MinT in the entire study area. Annual rainfall showed negative trend in the study area with significant (at 5%) negative trend in eastern deforested area western industrial and area adjacent to thermal power station. The change point is detected in annual rainfall time series in eastern forest area in 1959 and western area in 1983, i.e., after development of the industries and commissioning of thermal power station in the western study area. The trend rate of MaxT and MinT has been recognized as 0.004°C/year and 0.019°C/year, respectively, for the data period of 1951 to 2019. The annual rainfall trend rate has been observed as -0.743mm/year for the data range 1901 to 2019. All trend analysis methods revealed consistent results except MK-CF2 method, which portraits greater number of significant trends in trend analysis methods.

Climate change effects on Chickpea yield and its variability in Andhra Pradesh, India

Farmers usually do not know the precise output that is affected by climatic factors such as temperature and rainfall and are characterized by inter-annual variability, part of which is caused by global climate change. No study covers the influences of climate factors on yield and yield risk in the context of chickpea farming in Andhra Pradesh, India. In this context, this study aimed to investigate the trends in climate change variables during Rabi season (October to January, 1996-2020) and evaluated their variability on chickpea yields across different agro-climatic zones in Andhra Pradesh by employing Just and Pope production function. Four non-parametric methods-Alexandersson’s Standard Normal Homogeneity Test, Buishand’s Range Test, Pettitt’s Test and Von Neumann’s Ratio Test are applied to detect homogeneity in the data. Mann–Kendall (MK) test and Sen’s slope (SS) method were employed to analyze monthly rainfall trends and minimum and maximum temperature trends. Results of Just and Pope (panel data) quadratic and Cobb-Douglas methods revealed that monthly minimum temperature positively influenced the mean yield of chickpea (0.22% and 0.16%, respectively). However, rainfall (-0.41% and -0.31%) and maximum temperature (-0.08% and -0.04%) negatively influenced the mean yield of chickpea under quadratic and Cobb-Douglas models, respectively. Accordingly, rainfall (0.08% and 0.06%) and maximum temperature (0.83% and 0.72%) positively influenced the yield variability and minimum temperature (-0.77% and -0.67%) reduced yield variability of chickpea under quadratic and Cobb-Douglas models respectively. In view of these findings, it is imperative to advocate the farmers about the importance of cultivating drought-tolerant chickpea varieties, drought-proofing and mitigation strategies, micro-irrigation practices and improving their access to agro-meteorological information towards sustainable chickpea cultivation in Andhra Pradesh.

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.

Classification of precipitation anomalies in the Rio Grande do Sul in ENSO events in the 20th century

This study investigated the intensity and spatial distribution of precipitation anomalies in Rio Grande do Sul state (RS) during the 20th century by analyzing the influence of El Niño – Southern Oscillation (ENSO) in its three phases. Reanalyzes from the University of Delaware (V5) were used. Precipitation anomalies were divided into three zones (Campaign, Plateau, Coast) to investigate possible differences in precipitation under ENSO influence. Alexandersson's Standard Normal Homogeneity Test was applied to verify possible structural breaks. Wavelets were used to rate the periodicity of precipitation anomalies into three phases. Anomalies that occurred in El Niño and La Niña phases were classified in contingency tables as weak, average, and strong to evaluate the qualitative behavior of these precipitation anomalies. It was found that precipitation anomalies presented a structural break in 1955 when the tendency to positive anomalies increased. From 1955, precipitation anomalies increased at least 0.5 standard deviation while the frequency of these anomalies decreased from 85 to 60 months. Peaks of positive anomalies in El Niño were higher than 200 mm. In neutral cases, anomalies intensified in 0.5 standard deviation since 1970. Negative anomalies did not show specific behavior in any ENSO phase. Contingency tables indicated that La Niña events did not present any visible influence pattern. It could intensify the episodes of positive and negative precipitation anomalies in at least 0.5 (-0.5) standard deviation. Therefore, La Niña events could increase or decrease the monthly anomaly but do not show any tendency to negative anomalies. Weak El Niños tends to contribute to negative precipitation anomalies while strong cases are associated with an average increase of 2 standard deviations in positive anomalies. It was concluded that the influence of ENSO in RS is stronger in El Niño than in La Niña. Still, both may induce negative and positive anomalies, depending on the intensity of each case. Wavelet analysis revealed that cycles that did not coincide with El Niño/La Niña showed an increase (decrease) of 0.5 (-0.5) standard deviation is positive (negative) anomalies. The increase of anomalies in neutral phases indicated that other climatic variability modes and the intensity of meteorological events decreased the 25 and 43 months cycles and increased the precipitation in RS.

An empirical study on assessment of trend analysis: Food grain production in India

Considering the importance of the food grains to be specified-rice, wheat, coarse cereals, and pulses-is vital in developing the Indian economy. This paper carries out change point detection and trend analysis for the annual historical series of food grains in India. The change points were identified by the methods of the Pettitt?s test (PT), the Buishand?s range test (BRT), and the standard normal homogeneity test (SNHT). On the other hand, the Mann-Kendall (MK) test was used to determine the presence and absence of trends in food grain production, and their slopes were assessed by the Sen?s slope estimator. The change point analysis result shows significant change points for food grains at a 95% level. The discrete change points were observed from 1982 to 2009, but the ideal change point, i.e., 1987, was captured in all food grains. In India?s history, agricultural production in the year 1987 was vulnerable due to drought, and thus it is strong evidence of the need to consider methods based on proven facts. The MK test results reveal that the trend in food grain production was statistically significant and had an upward direction in all three segmentation periods after 1987.

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.

Trends in climatic and hydrological parameters in the Ajora-Woybo watershed, Omo-Gibe River basin, Ethiopia

The identification of hydro-meteorological trends is essential for analyzing climate change and river discharge at the watershed level. The Ajora-Woybo watershed in Ethiopia was studied for long-term trends in rainfall, temperature, and discharge at the annual, monthly, and seasonal time scales. The rainfall and temperature data extend 1990 to 2020, whereas the discharge data span from1990 to 2015. The Pettitt and Standard Normal Homogeneity Test (SNHT) tests were used to determine homogeneity. The Mann–Kendall and Sen's slope tests, as well as numerous variability measures, were then employed for trend analysis. The degree of relationship between climate variables and river discharge was determined using Pearson correlation coefficients (r). Inhomogeneity was discovered in annual rainfall data from the Angacha and Areka stations. Rainfall and discharge showed insignificant trends over time, with increasing and decreasing variability across stations. Monthly rainfall decreasing trends were observed to be significantly falling in February and March. Rainfall and runoff increase just insignificantly during the Kiremt season. On the other hand, minimum, maximum, and mean annual temperatures showed significant trends with annual increases of 0.01 °C, 0.04 °C, and 0.025 °C, respectively. In this study, the relationships between discharge and temperature and rainfall were found to be moderate and minimal, respectively. Generally, the results of the long-term examination of the hydrological and climate parameters in the watershed show that water resources vary throughout and over time. As a result, designing strategies require due attention.Article highlightsHydrological and meteorological parameters are essential for analyzing trends in water resource and climate change at the watershed level.Mann-Kendall and Sen's slope tests, along with a number of variability measures, were utilized in conjunction with the time series analysis approach for trend analysis.The analysis of the rainfall, temperature, and discharge in the watershed's data generally demonstrates how the availability of water resource varies over time. Designing suitable plans for water resource management and sustainable development in the watershed is therefore essential.

Evaluation of Potential Changes in Extreme Discharges Over Some Watersheds in Côte d’Ivoire

Climate change has had strong impacts on water resources over the past decades in Côte d’Ivoire, but these impacts on hydrological extremes remain largely unknown in most watersheds. Thus, this work aimed to evaluate the trends and breakpoints in extreme discharge characteristics of five watersheds in Côte d’Ivoire over the period 1970 to 2017. Seven indexes were selected, namely the 5-day maximum flow (QX5-days), peak discharge (Qmaxan), maximum monthly discharge (VCX30), annual minimum discharge (Qminan), average monthly discharge (QMNA), discharge day rate (VCN7), and characteristic of low discharge (WFD). The analysis was done using the modified Mann–Kendall (MMK) test and the standard normal homogeneity test at a 5% significance level for heterogeneous and homogeneous periods of data. The results for the heterogeneous and homogeneous periods were similar, with a predominance of non-significant trends for high discharge, except for the VCX30 index, which showed a significant upward trend at Kahin station. A decreasing trend for QX5-days was found at Loboville station. The variables Qminan, QMNA, VCN7, and WDF show significant upward trends of 33%, 16%, 50%, and 33% for the heterogeneous stations, respectively. A significant breakpoint in almost all variables was obtained, with a strong decrease after 2008. Some differences between the results from the heterogeneous and homogeneous periods of data were found and discussed. This study can help in understanding the behaviour of past hydrological extremes in the study area and in planning for further studies in the future.