Sen's Slope Estimator Research Articles

Mann-Kendall and Sen's Slope Estimator Test to Analyze Trends for Temperature and Rainfall in Eastern Himalayas of Arunachal Pradesh, India

In the past ten years, climate change has gained significant attention. Global climate change does not always affect various places in the same way. One important weather factor associated with climate change is rainfall. An appropriate first step in assessing how climate change may impact food security and water availability is to examine patterns in temperature and rainfall. The temperature, precipitation, and changes at several Arunachal Pradesh districts from 2001 to 2021 are examined in this study. We examined fourteen districts using the Mann-Kendall (MK) and Sen's Slope estimator test, which may identify long-term monotonic trends as well as variations in temperature and rainfall. The maximum and lowest temperature data revealed notable upward and decrease patterns, indicating that the region had experienced a change in climate. Nonetheless, there was an increase in rainfall patterns.

Trend Analysis of Rainfall and Temperature in the Kharun Watershed: Implications for Climate and Water Management

Rainfall and temperature are critical meteorological variables for understanding climate change and its potential impacts on water resources. Rainfall and Temperature are the most important for climatic parameters. The result of this study will help to take necessary steps and measures for agricultural production. This study focuses on analyzing rainfall and temperature trends in the Kharun watershed, Chhattisgarh, using the Mann-Kendall (MK) test and Sen's slope (SS) estimator. The analysis covers seasonal (summer, winter, and monsoon) and annual trends across key stations. Results for precipitation trends reveal mixed outcomes. At Dhamtari, a significant increasing trend in summer rainfall (z = 3.21, p < 0.01, Sen's slope = 0.77) was observed, though the annual trend remains insignificant. In Patharidih, summer rainfall also shows a significant upward trend (z = 2.04, p = 0.04), while Raipur exhibits a significant decline in both annual and summer precipitation (z = -1.04, p = 0.03, Sen's slope = -0.27). Temperature trends indicate no significant changes in maximum temperature across most stations, though slight positive trends are observed in summer and monsoon seasons. However, minimum temperature trends show a significant decline during winter at Dhamtari and Raipur (z = -2.12, Sen's slope = -0.04). These findings highlight localized seasonal variability in rainfall and temperature, underlining potential challenges for water resource management and climate adaptation in the region.

Trends in Vegetation Seasonality in the Iberian Peninsula: Spatiotemporal Analysis Using AVHRR-NDVI Data (1982–2023)

Vegetation seasonality is a critical indicator of ecological responses to global climate change, especially in the Iberian Peninsula, where the intersection of human activity and climate variability amplifies these effects. Understanding these changes is vital for adopting ecogeographical sustainability and developing effective climate adaptation strategies. This study examines trends in vegetation seasonality in the Iberian Peninsula from 1982 to 2023, based on weekly AVHRR NDVI data (2184 images). By integrating Seasonal Trend Analysis (STA) with Robust Trend Analysis (RTA)—including the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test (α = 0.05), and false discovery rate (FDR) control—we identified significant phenological shifts and widespread vegetation greening. The results reveal a regional response to global patterns of climate change, with 94.2% of the study area exhibiting significant trends, particularly in the Mediterranean ecoregion, where earlier growing seasons are becoming increasingly common. These shifts highlight the urgent need for sustainable land and resource management in the face of accelerating global change. Our findings provide critical insights into the ecological dynamics of the Iberian Peninsula, offering a robust foundation for formulating policies that promote environmental sustainability and enhance resilience to climate change.

Establishing Climate Change on Rainfall Trend, Variation and Change Point Pattern in Umuahia, Nigeria

This study investigates the prevailing conditions of changing climatic trends and change point dates in Umuahia, Nigeria, using daily annual maximum series (AMS) rainfall data spanning 31 years. The Indian Meteorological Department (IMD) was employed to downscale the time series data into durations ranging from 5 minutes to 24 hours. Mann-Kendall (MK) trend and Sen's Slope Estimator (SSE) tests revealed a statistically significant increasing trend across all durations. The Sen's Slope magnitude ranged from 0.1373 mm/year for 5-minute duration to 0.9045 mm/year for 24-hour duration, corresponding to a variation rate of 9.05 mm/decade for the 24-hour duration. Trend change-point analysis, utilizing Distribution-free CUSUM and Sequential Mann-Kendall tests, identified 2002 as the initial rainfall change-point date. These results provide robust evidence of positive changing climatic conditions for rainfall in Umuahia, indicating that non-stationary Intensity-Duration-Frequency (IDF) modelling may be more appropriate for flood risk management and urban planning in this area.

Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline

We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management.

Analyzing the Spatiotemporal Changes in Climatic Extremes in Cold and Mountainous Environment: Insights from the Himalayan Mountains of Pakistan

This study assessed the past changes in extreme precipitation and temperature events across the Himalayan Mountains of Pakistan. This cold and mountainous environmental region has witnessed a significant increase in climate-related disasters over the past few decades. Spatiotemporal changes in extreme temperature and precipitation events were analyzed using 24 indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). For this study, in situ data of 16 national meteorological stations were obtained from the Pakistan Meteorological Department (PMD) for the past three decades (1991–2020). The significance of the trends was assessed using the modified Mann–Kendall (MMK) test, and the Theil–Sen (TS) slope estimator was used to estimate the slope of the trend. The results showed that there has been a consistent decline in the total precipitation amount across the Himalayan Mountains of Pakistan. The trend exhibited a decrease in the annual average precipitation at a rate of −6.56 mm/year. Simultaneously, there was an increasing trend in the annual average minimum and maximum temperatures at rates of 0.02 °C/year and 0.07 °C/year, respectively. The frequencies of consecutive wet days (CWDs) and maximum 5-day precipitation (RX5day) have decreased significantly, with decreasing rates of −0.40 days/year and −1.18 mm/year, respectively. The amount of precipitation during very wet days (R95p) and extremely wet days was decreased by −19.20 and −13.60 mm/decade, respectively. The warm spell duration (WSDI) and the frequency of warm days (TX90p) across the Himalayan Range both increased by 1.5 and 1.4 days/decade. The number of cold days (TX10p) and cold nights (TN10p) decreased by 2.9 and 3.4 days/decade. The average temperature of the hottest nights (TXn) and the diurnal temperature range (DTR) were increased by 0.10 and 0.30 °C/decade. The results indicated an increasing tendency of dry and warm weather in the Himalayan region of Pakistan, which could have adverse consequences for water resources, agriculture, and disaster management in the country. Therefore, it is essential to prioritize the implementation of localized adaptation techniques in order to enhance sustainable climate resilience and effectively address the emerging climate challenges faced by these mountainous regions.

Analysis of Diurnal Air Temperature Trends and Pattern Similarities in Highland and Lowland Stations of Italy and UK

ABSTRACTIn this study, an analysis of hourly air temperatures in four groups of 32 stations from the UK highland (5 stations), UK lowland (4 stations), Italian highland (11 stations), and Italian lowland (12 stations) at different altitudes was carried out over the period from 2002 to 2021. The study aimed to examine the trends of each hour of the day during this period, over different averaging time windows (10‐day, 30‐day, and 60‐day). The trends were computed using the Mann–Kendall trend test and Sen's slope estimator. The similarity of trends within and across the groups of stations was assessed using the hierarchical clustering with dynamic time warping technique. An additional analysis was conducted to show the correlation of trends among the group of stations using the correlation distance matrix. Hierarchical clustering and distance correlation analysis show trend similarities and correlations, also indicating dissimilarities among different groups. Using 30‐day averages, significant warming trends in specific months at the Italian stations are evident, especially in February, July, August, and December. The UK highland stations did not show statistically significant trends, but clear pattern similarities were found within the groups, especially in certain months. The ultimate goal of this article is to provide insights into temperature dynamics and climate change characteristics on regional and diurnal scales.

Analysis of Pre-monsoon and Post-monsoon Seasonal Rainfall Trends by Mann-Kendall and Sen’s Slope Estimator Test in Himachal Pradesh, India

The purpose of the current study is to determine the trends and patterns of precipitation occurring in the periods before and after the monsoon season at 12 meteorological stations located in the twelve districts of the Himachal Pradesh region. The timeframe of the analysis is from 1991 to 2020. For this study, we used the Mann-Kendall test and estimator Sen's slope estimator test. Precipitation is an essential climatic factor that can act as a signal of climate change. In order to understand how climate change may affect water resource planning and management, it is crucial to understand rainfall pattern. The results indicate that there is a growing tendency in the pre-monsoon season at some sites, while some stations are seeing a declining trend. Keylong has the most significant falling trend, with a Sen slope of -4.154mm whereas Manali had the highest increasing trend at Sen's Slope (1.471mm). In contrast, the analysis of post-monsoon season data reveals an upward tendency in all studied regions, except for Dharamshala, where Sen's slope (-0.042mm) shows a slight downward trend whereas Manali had the highest increasing trend with the value of 1.771mm. According to the research, agricultural and horticultural sectors in Himachal Pradesh are at risk from heavy rainfall and landslides during the periods before and after the monsoon season, respectively, if the patterns continue to rise. Conversely, a decrease in these patterns might result in water scarcity. To tackle adverse climate changes, the general public must embrace adaptation measures based on long-term series data.

Analyzing trend and forecasting of temperature and rainfall in Shimla district of Himachal Pradesh, India using non-parametric and bagging REPTree machine learning approaches

The changing pattern of climate variables has caused extreme weather events and severe disasters, especially in mountainous regions. Such events have a detrimental impact on resources, environment and society. Thus, it has become imperative to examine the trends and forecasts of meteorological variables using a scientific modelling approach. This study investigates temperature and rainfall trends using the modified Mann-Kendall test and Sen's slope estimator between 1980 and 2021. A Bagging-REPTree machine learning model was utilized for forecasting temperature and rainfall trends for the next 30 years (2022–2051) to understand the temporal dynamics in Shimla district of the Indian Himalayan state. The mean absolute percentage error, mean absolute error, root mean squared error and correlation coefficient were determined to assess the effectiveness and precision of the model. The findings revealed that the frequency of intense rainfall in the district has increased during the monsoon season (June–September) from 1980 to 2021. Significant trends were found in annual rainfall, maximum, minimum and mean temperatures while rainfall during the winter, summer and post-monsoon seasons has shown a declining trend. The forecast analysis revealed a significant trend for rainfall during the monsoon season and an increasing trend in the maximum temperature has been observed during the winter and summer seasons. The analysis has provided sufficient evidence of variability and uncertainty in the behavior of meteorological variables. The outcome of the study may help in devising suitable adaptation and mitigation strategies to combat climate change in hilly regions. The methodology adopted in the study may help in the future progression of the research in different geographical regions for trend and climate forecasting.

Trend analysis on CO2 emissions and their implications: a comparative study between India and China.

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.

Climate change and watershed hydrology: assessing variability in water balance components and groundwater flow patterns

ABSTRACT This study aimed to quantify the effect of climate change on water balance and groundwater flow systems over the Walga–Darge watersheds. In this paper, Water and Energy Transfer between Soil, Plants, and Atmosphere under quasi-Steady State and MODFLOW were used to assess the hydrological impact of climate change. The Mann–Kendall test and Sen's slope estimator were used to analyze climate change. The mean annual temperature shows increasing trends, whereas the mean annual precipitation indicates decreasing trends. The simulated annual mean surface runoff is projected to decrease by 17.18, 22.04, and 31.37 for 2040, 2060, and 2080, respectively, compared to the 1980s. The model also indicates a reduction in precipitation and increased temperature causes a relative change in recharge, ranging from a decrease of 2.65, 38.82, 50.91, 61.57, and 75.49 in the 2000s, 2010s, 2040s, 2060s, and 2080s, respectively. The MODFLOW outputs furthermore show that annual groundwater discharge to the stream has decreased by −26.46% from 1985 to 2020 and is expected to decrease by −34.02% by 2080 due to climate change. The results of the study indicate that the increasing trends in temperature and decreasing rainfall amounts pose a significant threat to the sustainable use of water resources.

Groundwater level prediction for excessive agricultural irrigation to achieve SDGs: Vulnerability assessment using field-based empirical method

Excessive over-drafting and unsystematic management of groundwater brings different geo-environmental problems; thus precise prediction of groundwater (GW) status in large scale becomes prime concern for efficient GW management. This present study primarily focused on estimating the current groundwater status of agro-rich Murshidabad district using a GIS-based empirical approach including trend analysis of groundwater depth (2001–2021) by using Modified Mann Kendall test (MMKT) and Sen's slope estimator; the most prominent water level fluctuation (WLF) method employed in the estimation of groundwater recharge scenario of the last three decades. The groundwater recharge potential zone (GWRPZ) also delineated through widely used logistic regression (LR) method. The current research depicts that eight CD Blocks and twenty-five CD Blocks out of twenty-six CD Blocks are facing notable declining trend in groundwater depth in pre-monsoon and post-monsoon period accordingly, with a 95% confidence level; the lowering down of groundwater recharge (GWR) also alarming, the recharge amount in 2001, 2011 and 2021 was 210958.70 ham, 240168.80 ham and 201921.90 ham respectively due to variability in rainfall; the predicted GWRPZ shows that 674 (12.6%) Km2 and 1392 Km2 (26.02%) come under very high and high recharge zone which is predominantly found in the eastern and south-eastern part of this study region, validated through most reliable method Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) with 0.954 value. Unsystematic over-drafting of groundwater and intense cropping intensity are severely responsible for this alarming condition. Thus, sustainable groundwater management is essential to fulfil SDG-6 by ensuring SDG-2 and policymakers and planners will be benefited from this study in formulating the proper mitigation measures for this agronomic Murshidabad district.

Comparative study between observed and numerical downscaled data of surface air temperature

Surface Air temperature, being one of the key parameters of climate, plays an important role in occurrence severe environmental issues like droughts, floods, rate of evaporation etc. In this study, an endeavor has been made to investigate the experiments available in Educational Global Climate Model (EdGCM) at Sylhet Agricultural University for recognizing best suited experiment to predict the changing pattern of surface air temperature at Sylhet region. Downscaling is a cutting-edge procedure to produce high-goals atmosphere change expectation from coarse to fine goals of worldwide atmosphere model outcomes and a conspicuous device for estimating future atmosphere situation at the local scale for some information alarm zone like Bangladesh. Maps are generated from EdGCM simulation run to collect data and depict global temperature variations. Transform software have been connected to downscale the outcomes of temperature data by Doubled_CO2, Global_Warming_01, Ice_Age_21kya, IPCC_A1F1_CO2, Modern_PredictedSST and Modern_SpecifiedSST. The trend analysis was performed using the Mann-Kendall Trend test and Sen-slope estimator. Temperature changes are significant for observed results, Doubled_CO2 and IPCC_A1F1_CO2 results where p-value is less than alpha 0.05. Doubled_CO2 results are more suited for dry season and IPCC_A1F1_CO2 provides better results for monsoon. The investigation could have been unquestionably progressively broad in the event that more situations of EdGCM information were utilized, and local boundary conditions were used.

Precipitation dynamics and its interactions with possible drivers over global highlands

The climate and ecosystem in the highland regions are fragile and vulnerable to climate change. This study used ERA5, CRU, and CHIRPS at 0.5° × 0.5° resolution to assess spatiotemporal precipitation trends over global highlands from 1981 to 2021. The Standardized anomaly index (SAI) is used to evaluate the comparative anomalies between the datasets, while the Mann-Kendall and Sen Slope estimator tests the precipitation trends. Empirical orthogonal function (EOF), pixel-wise correlation, and detrended cross-correlation (DCCA) were employed to investigate the dominant precipitation patterns and their relationships with regional climatic impacts, while the wavelet analysis determines ocean-atmospheric factors in the time-frequency domain. The SAI correlation strongly relates to CRU-CHIRPS for precipitation estimates in North America (NA) R2 = 0.84, Europe R2 = 0.71, and South America (SA), whereas CRU-ERA5 showed better results in the Asian highlands R2 = 0.29, highlighting regional differences in the precipitation dynamics. For precipitation, EOF1 showed a positive variance in most highland regions, but a negative variance prevailed in the Tibetan Plateau (TP), whereas EOF1–2 explained ∼32%, 33%, and 30% variance globally for ERA, CRU, and CHRIPS, respectively. A strong positive correlation is observed between precipitation and mean temperature (TMP) in North America (NA) and the European Alps. However, a significant negative correlation prevailed in the TP and some parts of NA, indicating that these factors can significantly influence the precipitation dynamics in the highlands. Additionally, Detrended Cross-Correlation Analysis (DCCA) has verified that there are positive correlations between precipitation and key climatic factors such as total column water vapor (TCWV), potential evaporation (PEV), and soil moisture (SM). In addition, the influence of ocean-atmospheric (interannual and decadal) coherence patterns originating from the Pacific and Atlantic Oceans indicates a significant impact on precipitation variability, especially in the Asian highlands. This study can contribute to a better understanding of mechanisms and serve as a reference for forecasting precipitation dynamics to develop corresponding strategies in global highlands.

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

Groundwater extraction in the Kapran Sub-basin (KSB) has been intensified in recent decades due to population growth, urbanization, industrial activities, and agricultural expansion. Assessing water resources in KSB has become vital for researchers and local authorities to develop sustainable groundwater management strategies. This study aims to evaluate the spatio-temporal changes and trends of groundwater level using Innovative Trend Analysis (ITA), supported by non-parametric test techniques like Mann-Kendall (MK), modified Mann-Kendall (mMK) and Sen's slope estimator, along with Cumulative Rainfall Departure (CRD) to assess the relationship between groundwater level fluctuations and rainfall-recharge. Data from 10 monitoring wells (2000–2023), three rainfall stations (1941–2022), and 1053 production wells (2008 and 2023) were analyzed using geostatistical method. The results illustrate a strong linear relationship between the water level and topography, highlighting the aquifer's heterogeneity. The ITA and non-parametric analysis reveal a significant decline in groundwater levels, exacerbated by reduced rainfall (0.05–1.89 mm/year) and increased extraction (1.0 × 10^6 m³/day), with a decline rate ranging from 0.7 to 5.4 m/year. These findings provide (1) a robust scientific foundation for the stakeholders to efficiently develop sustainable strategies for integrated water resource management (IWRM) in order to mitigate the effect of climate change and GW abstraction, and (2) it provides local authorities and government in resolving groundwater scarcity in KSB.

Future warming of the Gulf of Oman: SST trends under contrasting climate scenarios by 2050

This study investigated potential changes in sea surface temperature (SST) within the Gulf of Oman over the next three decades (2020–2050) under contrasting climate scenarios. Monthly SST data from three Coupled Model Intercomparison Project Phase 6 (CMIP6) models were obtained from the Earth System Grid Federation (ESGF) database for a defined spatial grid (0.5° x 0.5°) encompassing the Gulf of Oman. Future changes (2020–2050) in SST were analyzed under two contrasting Shared socioeconomic Pathways (SSPs): SSP245 (optimistic) and SSP585 (pessimistic).A time series analysis employing a weighted average approach across all three models was conducted. This analysis revealed a projected increase in SST for most seasons and months, except for summer. Further trend analysis was performed using the non-parametric Mann-Kendall test and Sen's slope estimator. This analysis indicated a moderate warming trend in the Gulf of Oman's SST over the next 30 years, with an average increase of approximately 1°C. Spatial analysis of SST was also conducted, identifying minimum and maximum temperature values in shallow regions, the Strait of Hormuz, and its connection to the Persian Gulf. The findings of this study suggest a potential for significant warming in the Gulf of Oman over the coming decades, with implications for regional marine ecosystems and weather patterns. Understanding these projected changes is crucial for informing adaptation strategies and mitigating the potential negative impacts of climate change in the region.

Xiaojiang River Basin Ecological Environmental Quality Spatiotemporal Pattern and Evolutionary Trend Analysis Using GEE from 1990 to 2022

Assessment and monitoring of the quality of the ecological environment in the area is a very important fundamental task in the development of ecological civilization in the Xiaojiang River Basin in Yunnan Province, which serves as a demonstration area for ecological restoration in the upper reaches of the Yangtze River. The Landsat remote sensing images from 1990, 1995, 2000, 2005, 2010, 2014, 2018, and 2022 were chosen, and the four indexes of greenness （NDMVI）, humidity （WET）, dryness （NDBSI）, and heat （LST） were extracted. The remote sensing ecological index （RSEI） was created using the principal component analysis method, then the spatial and temporal patterns and trends of ecological quality in the Xiaojiang River Basin between 1990 and 2022 were examined using the GEE platform, ArcGIS 10.7 platform, and Python platform, combining the analysis methods of geographic information mapping, coefficient of variation, Mann-Kendall trend test, Sen's slope estimation, and Hurst's index. The findings demonstrated that： ① the ecological quality of the study area had more obvious geographic differentiation spatially, and by 2022, the areas with excellent and good ecological quality grades were primarily distributed in the areas with better alpine vegetation cover, and those with poor ecological quality were primarily distributed in the areas of the mudslide ravines with relatively low terrain. On a time scale, the study area's RSEI index increased from 0.41 in 1990 to 0.55 in 2022, with a fluctuating overall trend of ecological quality improvement and an average increase of 0.048（10 a） -1； this progress was directly related to a number of ecological construction initiatives that have been energetically carried out, such as converting farms to forests, preventing mudslides, saving soil and water, managing heavy metal contamination, etc. ② The RSEI was more appropriate for the evaluation of ecological quality in alpine ravine areas because, in comparison to the NDVI index, the NDVMI adopted in this study was more sensitive to vegetation information in topographic undulation areas, especially in shaded areas, and could more accurately and quantitatively describe the vegetation information. ③ The RSEI in the Xiaojiang River Basin had a mean coefficient of variation of 0.202. Overall, its volatility was low, and its high volatility was mostly concentrated in the mudslide gully area along both sides of the Xiaojiang River fracture zone, where the surface was made up of bare rocks and sediment that was easily impacted by the changing of the seasons, the climate, and human activity. ④ The quality of the ecological environment in the region was significantly improving, with the rising area reaching 85.72% of the total area and the declining area accounting for approximately 10.15% of the total area. The future trend of change will be dominated by ongoing improvement and future degradation, accounting for 44.75% and 39.97%, respectively. It is important to pay close attention to areas that could potentially degrade. The findings of this study can serve as a theoretical foundation for additional ecological environmental conservation, management, and sustainable development in the Xiaojiang River Basin.

Exploring trends and variability of climate change indices in the agro‐ecological zones of Pakistan and their driving mechanisms

AbstractAnthropogenic climate change induced weather and climate extremes have led to frequent heat waves, droughts and floods threatening water resources and food security for an agricultural country like Pakistan. Despite their significance, the trends and variability of extreme temperature and precipitation indices and associated large‐scale drivers in the agro‐ecological zones (AEZs) of Pakistan remain unknown and need urgent attention because of abrupt climate change. The present study documents the spatiotemporal variations of climate change indices together with the elevation‐dependent variability trends over various AEZs in Pakistan for the period of 42 years (1979–2020). Nonparametric Mann–Kendall (MK) and Sen's slope (SS) estimator tests have been employed for trend estimation. Results indicated linearly increasing (warming) and statistically significant trends in Tmean, TNx, WSDI and TR20 whereas significant decreasing (cooling) trends in cool nights (−1.73 days·decade−1) and cold spells (−1.28 days·decade−1). The spatial distribution of temperature indices trends depicts robust warming over southwestern and central zones while cooling trends over northern zones. Regarding precipitation extremes, all indices have shown increasing (wetter) trends with a significant increase in PRCPTOT and RX5day. The stations in northern and subhumid AEZs received more precipitation compared to other zones. Elevation‐dependent trends in temperature indices exhibited a statistically significant positive (negative) relationship with cold (warm) tails. Most of the extreme precipitation indices have a weak, but positive association with elevation except SDII. The weakening of South Asian subtropical upper‐level jet by a high‐pressure system over northeast Pakistan resulted in amplified land surface temperatures. However, the spatial patterns of zonal winds indicate a trough over Pakistan's southern and central parts, with warmer sea‐surface temperature, low sea‐level pressure and easterly anomalies, favour moisture transport and precipitation in Pakistan. The outcomes of present study will be useful in addressing various climate‐induced disasters occurring in various AEZs of Pakistan.

Spatiotemporal variability identification and analysis for non-stationary climatic trends for a tropical river basin of India

The non-stationary behavior of climatic variables has been increasingly recognized as a challenge that disrupts the equilibrium of human-defined climate-based stationary processes, including hydrological and agricultural practices, and irrigation systems. This study aims to investigate long-term trends and non-stationarity in climatic variables across 23 stations of the Krishna River basin, India. Prominent trends in rainfall, temperature, and their extreme indices were identified using the Modified Mann-Kendall (MMK), Bootstrapped Mann-Kendall (BMK), and Sen's Slope Estimator tests, while the Innovative Trend Analysis (ITA) test uncovered hidden trends and potential shifts in climatic patterns. This study addresses a critical research gap by exploring both significant and hidden trends in climatic variables, providing a better understanding of future dynamics. Traditional methods like MMK and Sen's Slope were insufficient to reveal these hidden trends, but ITA offered a more comprehensive analysis. The findings revealed an increase in total annual rainfall for almost 50% of the basin, which aligns with rising maximum temperatures, suggesting enhanced evaporation rates and subsequent fluctuations in rainfall patterns. Seasonal analysis indicated a shift towards decreased rainfall during winter and pre-monsoon seasons, contrasted by increased precipitation during the monsoon and post-monsoon periods, highlighting a clear alteration in rainfall distribution. The Simple Daily Intensity Index (SDII) and other indices suggest intensified rainfall events despite a decrease in the number of rainy days, indicating fewer but more intense events. Temperature analysis showed an overall increase in maximum temperatures, with the Diurnal Temperature Range (DTR) significantly increasing across all stations, implying greater daily temperature variations and potential for intensified water cycles and extreme climatic events. Furthermore, the study simplifies these trends by classifying them into two attributes: intensity and frequency, aiding policymakers in site-specific management of water resources and planning for future climatic scenarios. The presence of non-stationarity in extreme rainfall was confirmed by the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. These findings are significant as they conclude how climate change is altering hydrological patterns at each station. The study emphasizes the necessity for adaptive management strategies to mitigate the adverse impacts on agriculture, infrastructure, and human safety.

Spatiotemporal variability and trends in rainfall and temperature extremes using ERA5 reanalysis and CORDEX–Africa model in Southern Ethiopia

Rainfall and temperature are characterized by spatial and temporal variability in Ethiopia. However, less attention was given for the analysis of climate variability using advanced techniques and multiple sets of data. This study was conducted to examine spatiotemporal variability and trends in rainfall and temperature extremes in Ghibe III Dam watershed. Observational, ERA5, and regional simulation model data sets were used. The coefficient of variation (CV) and precipitation concentration index (PCI) were employed. The trends in rainfall and temperature extremes were examined using the modified Mann-Kendall test and the Sen Slope estimator in R-ClimDex in R 4.2.2 software. The warmest days exhibited temperature from 24.6°C to 40°C in Bele, 28.2°C to 35.43°C in Wolaita Sodo, 33.6 °C–44 °C in Areka, 31.64 °C–36.8 °C in Gesuba, and 29.19 °C–36.15 °C in Gena Bosa. The warmest nights showed temperature ranging from 14 °C to 18.74 °C in Bele and Gena Bosa, respectively. Annual warm days (TX90p) ranges from 11.34 to 57.1 days, with higher heating in the southern parts. The cool days (TX10p) range from 2.79 to 8.41, while the cool nights (TN10p) range from 0.04 to 8.26 days. The areal average temperature maximum and minimum range between 26.37 °C and 13.81 °C, respectively, with mean precipitation of 1446.92 mm.The rainfall extremes indices showed increasing and decreasing trends. The extreme temperature indices showed an overall warming trend. Based on ERA estimates, the rainfall in winter showed higher variability (CV = 72.4%–99.3 %) than the annual rainfall (CV = 33%–79.8 %). PCI showed a moderately (12 %) to very erratic (19.4 %) rainfall distribution. The climate model estimate showed high variability (CV = 20.65 %) in Climate Limited Area Modeling Community (CCLM) under representative concentration paths (RCP) 4.5 and 8.5 and extremely high variability (CV = 93.49 %) in the Regional Atmospheric Climate Model (RACMO) under RCP 4.5. Policymakers should design appropriate adaptation strategies applicable to farmers.