Assessment of groundwater level fluctuation using integrated trend analysis approaches in the Kapran sub-basin, North East of Iraq

To manage groundwater resources and develop an action plan, it is crucial to understand the long-term behavior of groundwater level (GWL) fluctuations. In this study, Geographic Information System (GIS) and non-parametric statistical tests were applied for detecting long-term (1973 to 2020) spatio-temporal variations and trends in GWL from 137 observation wells evenly distributed across the south-western part of Punjab. This region has experienced significant changes in GWL over the decades. The non-parametric statistical tests included Mann-Kendall (MK), Sens's SlopeEstimator (SSE), and Innovative Trend Analysis (ITA). The study observed significant trends in GWL fluctuations before and after monsoon. The MK and SSE tests showed a statistically increasing trend in observation wells with about 65.7% and 67.2% increase before and after monsoon, respectively. The innovative trend analysis (ITA) also revealed a statistically increasing trend in observation wells with an increase of about 63.5% and 65.7% pre and post-monsoon season, respectively. The results indicate lowering of GWL in the northern districts of southwestern Punjab, while the southern districts experience rising GWLs. This discrepancy can be attributed to diverse agricultural activities and reduced over-exploitation of groundwater in the southern district due to soil salinity and the presence of brackish groundwater. These findings provide valuable insights into the dynamics of GWL in the studied region, highlighting notable trends associated with seasonal variations.

Due to a sharp increase in groundwater demands in coastal regions, groundwater levels are getting depleted day by day. The local groundwater table fluctuation eventually speeds up sea water intrusion. The present study investigates spatial and temporal groundwater level fluctuations to identify the area affected by sea water intrusion as well as for sustainable water resource management in coastal stretch of Sindhudurg district, Maharashtra, which is about 121 km. Using groundwater level data collected from Central Groundwater Board (CGWB), Sindhudurg for the period from 1991 to 2022, fluctuations of groundwater levels were examined. The collected levels were evaluated for monsoon, pre-monsoon & post-monsoon seasons and all levels were interpolated over the study area using the Inverse Distance Weighted Method as a spatial interpolation technique in QGIS 3.22.1, in order to obtain the actual differences in groundwater levels. According to the results, the groundwater level varies from 1.88m to 13.45 m below ground level (bgl) in the pre-monsoon season, from 0.1m to 7.05 m (bgl) in the post-monsoon season and from 1m to 10.6m (bgl) in the monsoon season. Using statistical tests like Mann-Kendall test & Sen's slope estimator, the pattern of variation of groundwater levels was anticipated. According to our findings, over 80% of the basin has a falling water table during the monsoon season. It was observed that the groundwater level in the pre-monsoon season was more as compared to those in the monsoon and the post-monsoon seasons in the entire study area. The overall outcome of the study demonstrates that the groundwater level fluctuations were non-uniform in the study area, which may cause an increase in the sea water intrusion, leading to a decrease in crop production. Keywords: GIS, Groundwater level, Sea water intrusion, Mann Kendall test, Spatial interpolation, Sen’s slope estimator.

Rainfall trend analysis provides useful information for effective planning, and management of water resources, and gives an insight into climate change of a region. This study investigates trends in annual and seasonal rainfall over Sri Lanka using an Innovative Trend Analysis (ITA) and Mann–Kendall (MK) test with Sen's slope estimator. The MK test showed increasing trends in annual rainfall at 24 stations (65%) with five stations showing significant increasing trend. Annual rainfall at 13 locations (35%) showed decreasing trend, but none were significant (p < .05). ITA results for annual rainfall showed increasing trend at 67% stations while 33% stations showed decreasing trend. MK test results for seasonal rainfall indicated increasing trend at 76, 51, 32, and 86% of stations during First Inter‐Monsoon (FIM), Second Inter‐Monsoon (SIM), South West Monsoon (SWM), and North East Monsoon (NEM) seasons, respectively. Seasonal analysis of rainfall trend using ITA method showed increasing trend in 81, 70, 32, and 65% stations during FIM, SIM, SWM, and NEM, respectively. ITA and MK tests exhibited similar trend results for 80% of the stations. Moreover, Spearman's rho correlation coefficient between ITA and MK test trends showed significant (p < .05) positive correlation. In general, eastern, south eastern, north and north central regions of the country showed increasing rainfall trend over the last 31 years (1987–2017) while western, part of north western and central part of the country indicated a decreasing rainfall trend during the same period.

Groundwater is a precious natural element which ensures global water, food, and environmental security in the twenty-first century. Systematic monitoring, sustainable utilization, preservation and remediation are critical aspects of efficient groundwater resource management. This study deals with the analysis of spatial variability and trend in groundwater chemistry as well as identification of possible contamination sources in a coastal alluvial basin of eastern India. Pre-monsoon season data of 14 groundwater-quality variables measured in 'leaky confined' and 'confined' aquifers were analyzed for ten years (2012-2021). Mann-Kendall (M-K) test with the Sen's Slope Estimator, Spearman Rank Order Correlation (SROC) and Innovative Trend Analysis (ITA) tests were employed to assess decadal (2012-2021) trends. The analysis of the results indicated that the 'critical' water-quality parameters exceeding the acceptable limits for drinking are TDS, EC, TH, pH, Mg2+, Na+, K+, Fe2+, HCO3-, Cl- and NO3-. Weak negative correlations between rainfall and groundwater elevation for both the aquifers reveal poor rainfall recharge into the aquifers. Therefore, a reduction in groundwater abstraction and augmentation of groundwater recharge is recommended. Trend analysis results indicated that the concentrations of TH, Mg2+ and Fe2+ exhibit significant increasing trends in the 'leaky confined aquifer'. In contrast, significant rising trends in TH, Mg2+, Na+, Fe2+, HCO3- and NO3- concentrations are identified in the 'confined aquifer'. Further, the SROC test could not detect the trends in groundwater quality in most blocks and for many parameters. On the other hand, the ITA test revealed significant trends in most of the parameters of the two aquifers in almost all the blocks. Trend magnitudes of the groundwater-quality parameters based on the Sen's Slope Estimator and the ITA test vary from -63.7 to 58.65mg/L/year for TDS, -14 to 39.07mg/L/year for TH, -1.49 to 4.83mg/L/year for Mg2+, -7.14 to 22.96mg/L/year for Na+, -0.32 to 0.44mg/L/year for Fe2+, -8.33 to 20.75mg/L/year for HCO3-, -26.52 to 31.01mg/L/year for Cl- and 1.29 to 3.76mg/L/year for NO3- over the study area. The results of M-K and ITA tests were found in agreement in all the blocks for both the aquifers. Groundwater contamination in both the aquifers can be attributed to weathering, geogenic processes, mineral dissolution, seawater intrusion, poor recharge pattern and injudicious anthropogenic activities. It is strongly recommended that concerned authorities urgently formulate efficient strategies for managing groundwater quality in the 'leaky confined' and 'confined' aquifers which serve as vital sources of drinking and irrigationwater suppliesin the study area.

This research paper aims to provide a comparative trend analysis of CO2 emissions from the two largest emitters, India and China. The analysis focuses on the main sources of CO2 emissions-coal, oil, cement, and gas and their annual data and global share percentages from 1960 to 2019. The study uses non-parametric trend analysis methods, which do not rely on assumptions of normality, outliers, or data length. Pettitt's test, a well-established non-parametric method, was used to detect sudden shifts in the data. The Mann-Kendall (MK) test and Sen's slope estimator were then applied to identify the presence or absence of monotonic linear trends and assess the magnitude of the slopes. In addition, the innovative trend analysis (ITA) method was used, which is particularly effective in detecting and visualizing monotonic, non-monotonic, and sub-trends. The ITA method has the advantage of presenting these trends in a graphical format. According to the results of Pettitt's test, an abrupt change was detected in India in 1989 for all sources of CO2 emissions. In China, however, an abrupt change was detected in 1989 for coal and gas-related sources, while other sources showed a change point in 1990. The results of the MK test and the ITA method showed that all sources show only monotonic increasing trends. Based on the results of Sen's slope estimator, the average rate of change of CO2 emissions is significantly higher in China than in India in all categories after the detection of the abrupt change point. Policymakers should promote the adoption of renewable energy sources, such as solar, wind, hydro, and geothermal, and also implement strategies to control deforestation to counteract the abnormal increase in CO2 emissions. Finally, this research lays a solid foundation for future studies on CO2 emissions.

Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as specified by some researchers. Şen_ITA is a new trend detection method and does not require independent and normally distributed time series, but has never been used to detect trend change points. In the literature, multiple, half-time and multi-durations ITA methods are used to calculate partial trends in a time series without identifying trend change points. In this study, trend change points are detected using the Şen_ITA method and named ITA_TCP. This approach may allow researchers to identify trend change points in a time series. Diyarbakır (Turkey) is selected as a study area, and ITA_TCP has detected trends and trends change points in monthly average temperatures. Although ITA detects only a significant upward trend in August, given the 95% statistical significance level, ITA_TCP shows three upward trends in June, July and August, and a decreasing trend in September. Critical trend slope values are obtained using the bootstrap method, which does not require the normal distribution assumption.

Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as specified by some researchers. Şen_ITA is a new trend detection method and does not require independent and normally distributed time series, but has never been used to detect trend change points. In the literature, multiple, half-time and multi-durations ITA methods are used to calculate partial trends in a time series without identifying trend change points. In this study, trend change points are detected using the Şen_ITA method and named ITA_TCP. This approach may allow researchers to identify trend change points in a time series. Diyarbakır (Turkey) is selected as a study area, and ITA_TCP has detected trends and trends change points in monthly average temperatures. Although ITA detects only a significant upward trend in August, given the 95% statistical significance level, ITA_TCP shows three upward trends in June, July and August, and a decreasing trend in September. Critical trend slope values are obtained using the bootstrap method, which does not require the normal distribution assumption.

ABSTRACTIt has become important to understand the dynamic nature of hydro‐meteorological phenomena, especially rainfall, as rainfall is considered to be the principal source of water in the hydrological cycle. Purulia, the westernmost district of West Bengal, India, is part of the Chotanagpur Plateau fringe with its undulating topography, varying slope, hard rock aquifer, limited surface and subsurface water resources, and sub‐humid dry climate. Drought has been a recurring phenomenon for years, and the majority of its residents practise rain‐fed agriculture, solely relying on the monsoonal rain. Therefore, it is crucial to study the nature and pattern of annual and seasonal rainfall. The objective of the study is to bring out the long‐term nature of the rainfall trend along with the short term and to understand the characteristics of the rainfall over the region. This study has used the India Meteorological Department (IMD) provided daily gridded rainfall dataset for 1961 to 2020, non‐parametric Mann‐Kendal (M‐K) test, Modified version of M‐K test, and Sen's slope estimator to determine the trend of rainfall in long‐term and short‐term time series; the recently developed approach, i.e., innovative trend analysis (ITA) is also applied to determine the underlying trend and its stability in the long‐term time series. For the purpose of change point identification, this paper has applied the sequential version of M‐K test (SQMK). Both the long term (1961–2020) and two short term (1961–1990 and 1991–2020) time series have been analysed annually and seasonally. To understand the long‐term variation in the character of rainfall temporally and spatially, three indices, i.e., precipitation concentration index (PCI), rainfall deviation index (RDI) and modified Fournier index (MFI) have also been implemented. The ITA approach provides a better understanding of the trend as it can determine the trend whilst the M‐K test failed to determine it in some cases. In contrast to the long‐term (1961–2020) and first‐half (1961–1990) series, the second half of the time step (1991–2020) had the largest falling trend; i.e., 90% of the total stations have recorded a downward trend during the monsoon season. PCI and RDI, as well as SSE, identified the western half of the district as being the driest, and MFI revealed that the eastern section of the district has high rainfall intensity. This study may help the planners and policymakers to frame policies for its people and their livelihood; the comprehension of the previous hints will be used to predict the future.

Exploring recent groundwater level changes using Innovative Trend Analysis (ITA) technique over three districts of Jharkhand, India

Climate variability is one of the major factors affecting the supply of ecosystem services and the well-being of people who rely on them. Despite the substantial effects of climate variability on ecosystem goods and services, empirical researches on these effects are generally lacking. Thus, this study examines the spatiotemporal impacts of climate variability on selected ecosystem services in Maze National Park and its surroundings, in southwestern Ethiopia. We conducted climate trend and variability analysis by using the Mann-Kendall (MK) trend test, Sen's slope estimator, and innovative trend analysis (ITA). Relationships among ecosystem services and climate variables were evaluated using Pearson's correlation coefficient (r), while partial correlation was used to evaluate the relationship among key ecosystem services and potential evapotranspiration (PET). The MK tests show a decreasing trend for both mean annual and main rainy season rainfall, with Sen's slope (β) = -0.721 and β = -0.1.23, respectively. Whereas, the ITA method depicted a significant increase in the second rainy season rainfall (Slope(s) = 1.487), and the mean annual (s = 0.042), maximum (s = 0.024), and minimum (s = 0.060) temperature. Spatial correlations revealed significant positive relationships between ecosystem services and the mean annual rainfall and Normalized Difference Vegetation Index (NDVI), while negative correlations with the mean annual temperature. Additionally, temporal correlations highlighted positive relationships among key ecosystem services and the main rainy season rainfall. The maximum and minimum temperatures and ecosystem services were negatively correlated; whereas, there was strong negative correlations between annual (r = -0.929), main rainy season (r = -0.990), and second rainy season (r = -0.814) PET and food production. Thus, understanding the spatiotemporal variability of climate and the resulting impacts on ecosystem services helps decision-makers design ecosystem conservation and restoration strategies to increase the potential of the ecosystems to adapt to and mitigate the impacts of climate variability.

For efficient adaptation strategies, investigation of the variability in climatic data and its impact on meteorological drought is critical, particularly in semi-arid and arid regions. Innovative trend analysis (ITA), Mann–Kendall (MK) and Sen’s slope estimator (SSE) tests were employed to analyze the variations in precipitation (1981–2018) on annual, seasonal and monthly scale across 12 meteorological stations over the selected areas of the lower Indus basin (LIB) of Pakistan. The reliability of the ITA method was also compared and analyzed with both MK and SSE methods for 48 seasonal precipitation times series. Annual precipitation results indicated a significant increasing trend, i.e., 2.09 mm/year, at only one station (Rahim Yar Khan (RYK)-Khanpur), with MK test statistic Zmk = 2.09 and Sen’s slope estimator β = 2.56. On a monthly scale, the maximum number of positive significant trends were noted during June, with Zmk values of 2.01 to 3.24 and β values of 1.06–3.06, while the maximum number of negative trends was found during January, February, November and December. On a seasonal scale, ITA methods showed significant increasing trends during the summer at 12 selected meteorological stations, with trend indicator (B) values ranging from 0.22 to 2.46. Moreover, performance of the ITA method was found to be consistent with both MK and SSE test results at a verified significance level. The results of the study can help to increase our understanding of the annual, seasonal and monthly precipitation variability in the LIB that may be helpful in developing strategies for the proper management of water resources over the area.

The research aims to investigate the spatiotemporal changes of water balance components and distinguish the relative impacts of climatic data and land-use on groundwater level in northeastern Iran. This investigation employs the WetSpass-M model to estimate water balance and the Mann-Kendall test alongside Sen's slope estimator to evaluate trend. The study also assesses mean annual water balance components, considering diverse combinations of land-use and soil. The findings offer a hydrological insight, revealing that 14% of precipitation results in runoff, 29% of that recharging the aquifer; the remaining portion is lost through evapotranspiration. The trends in precipitation and simulated water components are not significant but a significant downward trend in groundwater is observed beyond a specific point in time. Based on this outcome, as well as the analysis of land-use changes, it was speculated that human activities in this fast-developing region might be implicated in the decline in groundwater levels. Analysis of water balance components in various soil and land-use combinations indicates that evapotranspiration exhibits greater variability within the land-cover class, while recharge is more influenced by soil texture. These findings enhance our understanding for identifying potential sites for artificial recharge and determining sustainable groundwater withdrawals based on spatiotemporal recharge patterns.

Innovative trend analysis of annual and seasonal air temperature and rainfall in the Yangtze River Basin, China during 1960–2015

This study presents the first comprehensive assessment of rainfall trends and variability in Ethiopia's Gurage Zone, employing Sen's slope estimator, the Mann–Kendall (MK) test, and innovative trend analysis (ITA), complemented by the coefficient of variation (CV) and standard anomaly index (SAI) metrics. The results revealed high rainfall variability (CV = 18.14–158.8%), particularly in the summer (Kiremt) and spring (Tsedey) seasons within highland agroclimatic regions. Seasonal distribution showed maximum rainfall in the summer (1,045.18 mm) and minimum in the winter (43.29–102.25 mm). Spatially, the northwestern areas received higher rainfall than the eastern parts. The trend analysis indicated a significant decreasing trend in the winter, with Buie (Z = −2.93) and Koshe (Z = −2.94) showing the strongest declines. Additionally, Butajira and Ejersalele stations exhibited significant monthly trends at the 10% level, with January showing the most pronounced decline. Overall, the findings highlight substantial rainfall variability and declining trends, offering critical, novel insights for climate research, policy formulation, and strategic water resource management in an area previously unexamined.

Spatio -temporal trend detection of hydro -meteorological parameters for climate change assessment in Lower Tapi river basin of Gujarat state, India