Slope Estimator Research Articles

Studying the impact of rainfall and LULC changes in the reservoir water level of the Hatia dam, Ranchi, India

ABSTRACT This study aimed to analyse the pattern of rainfall and land use land cover (LULC) changes and their impact on the water level of the Hatia dam, a crucial source of the water supply for Ranchi city in Jharkhand, India. High-resolution daily-gridded rainfall and temperature data from 1991 to 2023 were collected from IMD Pune and analyzed using Mann–Kendall and Sen's slope estimator. The results showed a significant decline in annual, monsoon, post-monsoon, and winter rainfall, while pre-monsoon rainfall increased during the study period. The Google Earth Engine was used to observe LULC changes at 5-year intervals. The analysis revealed significant changes in the waterbody, built-up, forest cover, barren land, and agriculture before and after the monsoon. The analysis show that during the pre-monsoon season, the waterbody decreased from 8.68 to 4.94%, while the built-up area increased from 2.71 to 11.08%. Similar trends were observed during the post-monsoon season, with the waterbody decreasing from 10.06 to 7.3% and the built-up area increasing from 2.74 to 11.27%. It is observed from the rainfall analysis that the annual rainfall is decreasing from 1,561 to 703 mm and the reservoir surface area has also decreased from 126.31 to 37.44 ha. The findings highlight the need for sustainable water management strategies.

Analysis of the Spatiotemporal Evolution Patterns and Driving Factors of Various Planting Structures in Henan Province Based on Mixed-Pixel Decomposition Methods

Understanding the spatiotemporal evolution patterns and drivers of cropping structures is crucial for adjusting cropping structure policies, ensuring the sustainability of land resources, and safeguarding food security. However, existing research lacks sub-pixel scale data on planting structure, where planted area data are mainly derived from manual counting results. In this study, remote sensing technology was combined with geostatistical methods to realize the spatiotemporal evolution of crop planting structure at sub-pixel scale. Firstly, the spatial distribution of the multiple cropping structure in Henan Province was extracted based on a mixed-pixel decomposition model, and spatiotemporal evolution of the crop planting structure was analyzed using a combination of Sen’s slope estimator and Mann–Kendall trend analysis, as well as centroid migration. Then, Pearson correlation coefficients were calculated to explore the contribution of driving factors. The results indicate the following: (1) from 2001 to 2022, the cropping structure in Henan Province shows a slightly obvious increase. (2) The centroid of different cropping structures migrates to the main production areas as a whole. (3) Among the driving factors, there was a positive correlation with the labor force and a negative correlation with the urbanization rate. This study provides new insights into the evolution of large-scale crop planting structures and offers significant theoretical and practical value for sustainable agricultural development and the optimization of agricultural planting structures.

Assessing the Snow Water Equivalent Dynamics of the Brahmaputra River Basin

ABSTRACT The Brahmaputra River Basin holds a crucial position in South Asia’s hydrological framework, serving as a vital source of water for a substantial population. Snow Water Equivalent (SWE), a pivotal metric reflecting water availability in regions dominated by snow, assumes a significant role in shaping the hydrological cycle of the basin. Thus, this present work aims to conduct a comprehensive study of the SWE dynamics across the Brahmaputra River Basin from 1980 to 2021. The present study obtains the data of elevation, ERA5-Land and SWE and SWE maximum, snowfall, snow cover area, snow depth, and air temperature, in a Geographic Information System (GIS) environment. Seasonal and annual trends are analyzed using the Theil-Sen slope estimator and the Mann-Kendall significance test. Correlations and linear regressions are conducted to assess the relationships among the variables. The findings unveil intricate SWE distribution patterns across the study area, with high elevations exhibiting prolonged snow cover area and substantial SWE values. Trend analysis shows a consistent decrease in SWE and SWE maximum across all seasons, ranging from -0.006 (post-monsoon) to -0.034 m (pre-monsoon) water equivalent/decade. Similarly, a declining trend (at a rate of -0.014 m water equivalent/decade) is also observed in annual SWE. The declining seasonal and annual SWE is possibly linked to escalating air temperature, diminishing snowfall, snow cover area, and snow depth during the study period. The implications of these findings underscore the significance of incorporating SWE variations into water management and the assessment of ecosystem health in the region.

Spatiotemporal Analysis of Drought and Its Driving Factors in the Yellow River Basin Based on a Standardized Precipitation Evapotranspiration Index

As a natural disaster, drought can endanger global ecology, socio-economic systems, and sustainable development. To address sudden droughts in the future, assess drought disasters, and propose mitigation measures, in-depth research on the spatiotemporal variations in and driving factors of meteorological drought is essential. To study drought in the Yellow River Basin, we calculated the multi-scale Standardized Precipitation Evapotranspiration Index (SPEI), derived from monthly meteorological data recorded at weather stations from 1968 to 2019. We examined the features of drought and its driving factors using the trend-free pre-whitening Mann–Kendall (TFPW-MK) test and Sen’s slope estimator, as well as a drought frequency analysis, center of gravity migration model, standard deviation ellipse model, and geographic detector. Our analysis shows that (1) from 1968 to 2019, the Yellow River Basin exhibited a shift from aridity to increased moisture on an annual basis, with the smallest SPEI of −1.47 in 2002 indicating a moderate drought; SPEI3 showed a growing tendency in all seasons, particularly in winter (0.00388/year), followed by spring (0.00214/year), summer (0.00232/year), and fall (0.00196/year). The SPEI3 exhibited higher fluctuations in frequency compared to the annual-scale SPEI12; (2) in terms of spatial variability, there was no significant change in drought conditions at any scale, with the probability of a drought event being greater in the eastern and northwestern portions of the watershed. The epicenter of the drought exhibited a tendency to migrate southwestward; (3) among the seven driving factors, land use and night lighting were the dominant factors affecting drought conditions, with driving force values of 0.75 and 0.63, respectively.

PRESENT TRENDS IN AIR TEMPERATURE IN THE EAST OF KAZAKHSTAN

According to world research, over the past decades there has been a tendency for air temperature to rise and an increase in the frequency of extreme weather events. The study of the climatic predisposition of individual regions to extreme events, in particular fires, is an urgent problem of our time. The main purpose of this study was to define current trends in air temperature in Eastern Kazakhstan on an annual, seasonal and monthly scale. The non-parametric Mann-Kendall test and Sen’s slope estimation were used for the analysis, and the co-kriging method was used to interpolate the data to obtain areal distribution. As a result of the study, it was revealed that in most of Eastern Kazakhstan, there has been an increasing trend in average and maximum temperatures significantly. It was also noted that the main statistically significant changes are observed in the spring and summer seasons. Changes at some stations were reached from 0,2 to 1,0 °C in ten years. The most consistent and significant trends in temperature increase were recorded in March and April.

Extreme Weather Patterns in Ethiopia: Analyzing Extreme Temperature and Precipitation Variability

Climate change is significantly altering Ethiopia’s weather patterns, causing substantial shifts in temperature and precipitation extremes. This study examines historical trends and changes in extreme rainfall and temperature, as well as seasonal rainfall variability across Ethiopia. In this study, we employed the Mann–Kendall test, Sen’s slope estimator, and empirical orthogonal function (EOF), with data from 103 stations (1994–2023). The findings provide insights into 16 climate extremes of temperature and precipitation by utilizing the climpact2.GUI tool in R software (v1.2). The study found statistical increases were observed in 59.22% of the annual maximum value of daily maximum temperature (TXx) and 77.67% of the annual maximum value of daily minimum temperature (TNx). Conversely, decreasing trends were found in 51.46% of the annual maximum daily maximum temperature (TXn) and 85.44% of the diurnal temperature range (DTR). The results of extreme precipitation found that 72.82% of yearly total precipitation (PRCPTOT), 73.79% of consecutive wet days (CWD), and 54.37% of the number of heavy precipitation days (R10mm) showed increasing trends. In contrast, at most selected stations, 61.17% of consecutive dry days (CDD), 55.34% of maximum 1-day precipitation (RX1day), 56.31% of maximum 5-day precipitation (RX5day), 66.02% of precipitation from very wet days (R95p), and 52.43% of precipitation from extremely wet days (R99p) were decreasing. The results of seasonal precipitation variability during Ethiopia’s JJAS (Kiremt) season found that the first three EOF modes accounted for 59.78% of the variability. Notably, EOF1, which accounted for 35.84% of this variability, showed declining rainfall patterns, particularly in northwestern and central-western Ethiopia. The findings of this study will help policymakers and stakeholders understand these changes and take necessary action, as well as build effective adaptation and mitigation measures in the face of climate change impacts.

A Comparative Spatiotemporal Analysis for Long-Term Trends of Hydrometeorological Variables in Maritsa River Basin

Revealing long-term trends in hydrometeorological variables plays a critical role in the sustainable management and planning of water resources. These analyses are necessary to understand climate change impacts, taking precautions for natural disasters, plan agricultural activities, and develop water management strategies. The aim of this study is to examine the changes in monthly and annual total precipitation and evapotranspiration values in the Maritsa River Basin, a transboundary water basin between Bulgaria, Greece, and Türkiye. For this, precipitation values for the 1982-2023 water years were taken from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data set, and evapotranspiration values for the 1982-2023 water years were taken from the European Reanalysis 5th Generation-Land (ERA5-Land) data set. The Mann-Kendall, Sen's slope estimator, and Innovative Trend Analysis (ITA) methods were used to determine trends. According to the test results, there is a statistically significant increase in annual total precipitation values within the 95% confidence interval and in annual total evapotranspiration values within the 99% confidence interval. Specifically with all three methods positive and statistically significant trends are observed in precipitation in October, January, May and June. In the monthly evapotranspiration trend analysis, a statistically significant increase is observed except for November, December, June and July. Trend increases were visualized using the graphical method ITA. Significant increasing trends in both monthly and annual precipitation and evapotranspiration reveal changes in the hydrological cycle of the basin. The test results can be used in planning and solving problems related to the basin area.

Using a Neural Network to Model the Incidence Angle Dependency of Backscatter to Produce Seamless, Analysis-Ready Backscatter Composites over Land

In order to improve the current standard of analysis-ready Synthetic Aperture Radar (SAR) backscatter data, we introduce a machine learning-based approach to estimate the slope of the backscatter–incidence angle relationship from several backscatter statistics. The method requires information from radiometric terrain-corrected gamma nought time series and overcomes the constraints of a limited orbital coverage, as exemplified with the Sentinel-1 constellation. The derived slope estimates contain valuable information on scattering characteristics of different land cover types, allowing for the correction of strong forward-scattering effects over water bodies and wetlands, as well as moderate surface scattering effects over bare soil and sparsely vegetated areas. Comparison of the estimated and computed slope values in areas with adequate orbital coverage shows good overall agreement, with an average RMSE value of 0.1 dB/° and an MAE of 0.05 dB/°. The discrepancy between RMSE and MAE indicates the presence of outliers in the computed slope, which are attributed to speckle and backscatter fluctuations over time. In contrast, the estimated slope excels with a smooth spatial appearance. After correcting backscatter values by normalising them to a certain reference incidence angle, orbital artefacts are significantly reduced. This becomes evident with differences up to 5 dB when aggregating the normalised backscatter measurements over certain time periods to create spatially seamless radar backscatter composites. Without being impacted by systematic differences in the illumination and physical properties of the terrain, these composites constitute a valuable foundation for land cover and land use mapping, as well as bio-geophysical parameter retrieval.

DYNAMICS OF RAINFALL AND TEMPERATURE IN NORTH SUMATRA PROVINCE: COMPREHENSIVE ANALYSIS OF TEMPORAL TRENDS

This research aims to analyze the temporal trends of rainfall and temperature in North Sumatra Province, focusing on the Medan and Deli Serdang regions. The data used in this study was obtained from the Central Statistics Agency of North Sumatra (BPS Sumut) and spans the period from January 2000 to December 2022. The Mann-Kendall test was applied to identify trends, Sen's Slope Estimator measured the trend slope, and Pearson correlation analysis assessed the relationship between rainfall and temperature. Key findings indicate that Medan has a higher monthly rainfall average than Deli Serdang, with both regions showing a significant increasing trend in rainfall, although the rise is gradual. Additionally, a positive trend in temperature was identified, reflecting broader climate change patterns. However, the correlation between rainfall and temperature was weak, indicating minimal direct interaction between these variables in the study areas. These results contribute valuable insights into climate dynamics and are critical for the development of climate change adaptation strategies in North Sumatra Province.

Analysis of spatiotemporal distribution, variability, and trends of rainfall in Wollo area, Northeastern Ethiopia.

Ethiopia's agriculture is mostly dependent on rain, though the rainfall distribution and amount are varied in spatiotemporal context. The study was conducted to analyze the distribution, trends, and variability of monthly, seasonal, and annual rainfall data over the Wollo area from 1981 to 2022. To accomplish this, the study utilized the Climate Hazards Group Infrared Precipitation with Stations version two (CHIRPS-v2) data. Standard Rainfall Anomaly Index (SRA) and Coefficient of Variation (CV) were employed to examine rainfall variability and develop drought indices over southern Ethiopia. The Modified Mann Kendall (MMK) test, Sen's slope estimator and the innovative trend analysis (ITA) were employed to detect temporal changes in rainfall trends over the study period. The study found that the area experienced considerable rainfall variability and change, resulting in extended drought and flood events within the study period. Results from SRA and CV revealed interannual and seasonal rainfall variability, with the proportions of years below and above the long-term mean being estimated at 56% and 44%, respectively. The MMK test showed that the annual rainfall during the Kiremt (summer-main rainy season) had an increasing trend. On the other hand, rainfall for the Belg (short rain season for the study area) season and the Bega (winter) season showed a significantly decreasing trend (p < 0.05). Results from the innovative trend analysis (ITA) also revealed that the annual and seasonal rainfall trends exhibited different trends in varied magnitude for different stations. On a spatial basis, the eastern and northeastern regions of the study area showed trends of increasing rainfall during the Kiremt (JJA). Decision-makers and development planners need to design strategies to mitigate the risks posed by changes in rainfall variability and distribution and enhance community adaptation and mitigation capacities in Wollo, Ethiopia.

Beware of Validation by Eye: Visual Validation of Linear Trends in Scatterplots.

Visual validation of regression models in scatterplots is a common practice for assessing model quality, yet its efficacy remains unquantified. We conducted two empirical experiments to investigate individuals' ability to visually validate linear regression models (linear trends) and to examine the impact of common visualization designs on validation quality. The first experiment showed that the level of accuracy for visual estimation of slope (i.e., fitting a line to data) is higher than for visual validation of slope (i.e., accepting a shown line). Notably, we found bias toward slopes that are "too steep" in both cases. This lead to novel insights that participants naturally assessed regression with orthogonal distances between the points and the line (i.e., ODR regression) rather than the common vertical distances (OLS regression). In the second experiment, we investigated whether incorporating common designs for regression visualization (error lines, bounding boxes, and confidence intervals) would improve visual validation. Even though error lines reduced validation bias, results failed to show the desired improvements in accuracy for any design. Overall, our findings suggest caution in using visual model validation for linear trends in scatterplots.

Seasonal Trend Analysis of Humidity using Multi-Method Detection Techniques of Silchar, Assam (1986-2022)

This study explores the extended form of methodologies for trend detection in meteorological time series, focusing on one of the critical climatic variables, Humidity, which is collected from Silchar station of Assam, India. To assess the presence and magnitude of trends, a comprehensive approach is adopted, employing the Mann-Kendall (M-K) test, its modified version, and Sen’s slope estimation, alongside the Innovative Trend Analysis (ITA) tech-nique. These methods together offer a robust framework for detecting and quantifying trends in the dataset. The monthly data of 37 years from 1986-2022 at 3 pm and at12 pm has been processed to find out the variability of humidity for which M-K test, modified M-K test and ITA method with trend magnitude and slopes is adopted to check the seasonal variation of humidity at 3 pm and 12 pm. Based on the statistical outcomes derived from the three trend detection methods and their corresponding visualizations, the analysis reveals both increasing and decreasing trends in humidity across different seasons includes a significant trend in humidity measurements has been identified during the winter, monsoon, and postmonsoon seasons at 3 PM, with a corresponding pattern similarly apparent in the monsoon season 12 PM observations. These variations are observed in the humidity values recorded at both 3 pm and 12 pm, highlighting a seasonal divergence in humidity pat-terns, with certain periods exhibiting rising rates, while others demonstrate declining or no trends.

Climate Variability and Adaptation Strategies in a Pastoralist Area of the Eastern Bale Zone: The Case of Sawena District, Ethiopia

This study was conducted in Sawena district, located in the Eastern Bale Zone of Ethiopia, with the aim of analyzing climate variability and identifying adaptation strategies. Secondary data covering the period from 1984 to 2023 were utilized, along with structured and unstructured questionnaires. Primary data were gathered from 350 pastoralist households across six kebeles through a household survey. This study used the Mann–Kendall test, Sen’s slope estimator, the coefficient of variation, descriptive statistics, and a multivariate probit model to analyze climate variability and adaptation strategies. The Mann–Kendall test, Sen’s slope estimator, and coefficient of variation analysis results showed significant rainfall increases in September, October, and November, with high winter variability and an upward autumn trend. Temperature analysis revealed consistent warming, with the greatest increases in September (0.049 °C/year) and summer (0.038 °C/year), and an annual mean rise of 0.034 °C per year, indicating climate shifts affecting pastoralist and agro-pastoral livelihood strategies and water resources that lead the area toward vulnerability. The descriptive results indicated that pastoralist households have adopted various adaptation strategies: 45.1% participate in seasonal livestock migration, 26.3% rely on productive safety net programs, 19% pursue livelihood diversification, and 9.7% engage in agroforestry. Multivariate analysis indicates that education, age, credit access, livestock ownership, asset value, and media exposure influence these strategies. The findings highlight the importance of policies to enhance climate resilience through diversification, sustainable land management, and improved access to resources like credit and markets, alongside strengthened education and targeted extension services.

Attribution Identification of Runoff Changes Based on the Budyko Elasticity Coefficient Method: A Case Study of the Middle and Upper Reaches of the Jinghe River in the Yellow River Basin

The impacts of climate change and human activities on water resources are a complex and integrated process and a key factor for effective water resource management in semi-arid regions, especially in relation to the Jinghe River basin (JRB), a major tributary of the Yellow River basin. The Sen’s slope estimator and the Mann–Kendall test (M–K test) are implemented to examine the spatial and temporal trends of the hydrological factors, while the elasticity coefficient method based on Budyko’s theory of hydrothermal coupling is employed to quantify the degree of runoff response to the various influencing factors, from 1971 to 2020. The results reveal that the runoff at Pingliang (PL), Jingchuan (JC), and Yangjiaping (YJP) hydrological stations shows an obvious and gradual decreasing trend during the study period, with a sudden change in about 1986, while precipitation shows a fluctuating and increasing trend alongside a potential evapotranspiration-induced fluctuating and decreasing trend. Compared to the previous period, a change of −29%, in relative terms, in the runoff at the YJP hydrological station is observed. The interaction of human activities and climate change in the watershed contributes to the sharp decrease in runoff, with precipitation, potential evapotranspiration, and human activities accounting for −14.3%, −15.1%, and 70.6% of the causes of the change in runoff, respectively. Human activities (e.g., construction of water conservancy projects), precipitation, and potential evapotranspiration are the main factors contributing to the change in runoff.

Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning

We conducted an in-depth exploration of the use of different machine learning (ML) for regression algorithms, including Linear, Ridge, LASSO, Bayesian Ridge, Decision Tree, and a variety of Deep Neural Network (DNN) architectures, to estimate the slope of the high-frequency feature (HFF), a prominent emergent feature found in the gravitational wave (GW) signals of core collapse supernovae (CCSN). We created a data set of CCSN GW signals generated by an analytical model that mimics the characteristics of the signals obtained from numerical simulations, particularly the HFF. This enabled us to simulate a wide range of HFF slope values and analyze their properties. We opted to employ ML for regression techniques, particularly a supervised learning approach, to analyze the data set due to the parameter chosen for estimating the slope of the HFF. This type of architecture is ideal for this purpose as it can detect the connections between input and output data. In addition, it is suitable for handling high-dimensional input data and produces efficient results with low computational cost. We evaluated the efficiency and performance of the ML algorithms using a set of metrics to measure their ability to accurately predict the HFF slope within the data set. The results showed that a DNN algorithm for regression exhibits the highest accuracy in estimating the slope of the HFF.

Impact of Changes in Rainfall and Temperature on Production of Darjeeling Tea in India

Globally, there has been a lot of focus on climate variability, especially variability in annual precipitation and temperatures. Depending on the area, different climate variables have different degrees of variation. Therefore, analyzing the temporal and spatial changes or dynamics of meteorological or climatic variables in light of climate change is crucial to identifying the changes induced by climate and providing workable adaptation solutions. This study examined how climate variability affects tea production in Darjeeling, West Bengal, India. It also looked at trends in temperature and rainfall between 1991 and 2023. In order to identify significant trends in these climatic factors and their relationship to tea productivity, the study used a variety of statistical tests, including the Sen’s Slope Estimator test, the Mann–Kendall’s test, and regression tests. The study revealed a positive growth trend in rainfall (Sen’s slope = 0.25, p = 0.001, R2 = 0.032), maximum temperature (Sen’s slope = 1.02, p = 0.026, R2 = 0.095), and minimum temperature (Sen’s slope = 4.38, p = 0.006, R2 = 0.556). Even with the rise in rainfall, there has been a decline in tea productivity, as seen by the sharp decline in both the tea cultivated area and the production of tea. The results obtained from the regression analysis showed an inverse relationship between temperature anomalies and tea yield (R = −0.45, p = 0.02, R2 = 0.49), indicating that the growing temperatures were not favorable for the production of tea. Rainfall anomalies, on the other hand, positively correlated with tea yield (R = 0.56, p = 0.01, R2 = 0.68), demonstrating that fluctuations in rainfall have the potential to affect production but not enough to offset the detrimental effects of rising temperatures. These results underline how susceptible the tea sector in Darjeeling is to climate change adversities and the necessity of adopting adaptive methods to lessen these negative consequences. The results carry significance not only for regional stakeholders but also for the global tea industry, which encounters comparable obstacles in other areas.

Spatio-temporal rainfall variability and trends using a Kriging-interpolation and Innovative trend analysis approach: the case of Wolaita zone, south Ethiopia

Climate change is one of the worst environmental issues, with a negative impact on most developing countries across the globe and in some regions, including Ethiopia. This study seeks to establish the temporal and spatial changes in rainfall for the period 1987–2021. In this study, ordinary statistical measures, such as the mean and coefficient of variation, precipitation concentration index (PCI), and standardized anomaly index (SAI), were applied to investigate the rainfall variation. Concerning the estimation of the spatiotemporal distribution and magnitude of changes in, non-parametric Mann-Kendall (MK) tests, Sen’s slope estimator, and innovative trend analysis (ITA) were also conducted in ArcGIS 10.8 environment and XLSTAT/R. The study showed significant fluctuations in rainfall in the Wolaita zone, with minimum mean annual rainfall in 1997 and maximum rainfall in 2003. All but the Belg season had more negative seasonal anomalies than positive ones. The annual rainfall for each of the AEZs in different parts of the country ranged from one another; it was 969.03 mm in a southeast lowland AEZ and 1648.75 mm in the northwest highland AEZ. Rainfall was not uniformly distributed throughout the year and study area; the highland AEZs received more rainfall in the Belg and Kermit seasons than in the lowland seasons. In the ordinary kriging results, the extent of variability in the CV of the mean annual rainfall for each zone was identifiable. The southwest lowland AEZ produced a CV of 24.22% with a decrease in rainfall amounts, and the northeast highland AEZ produced a CV of 31.63% of the rainfall distribution amounts. This area includes lowland and highland AEZs of the northeastern part of the study area’s rainfall, which is moderately distributed by PCI. This information is helpful when attempting to associate development and cropping systems with temporal and spatial climatic patterns with respect to rainfall for agro-climatological activities and projects or flood regulatory measures.

Evaluation of Summer Season Characteristics and Its Changes in Iraq over the Period (1960-2021)

Anthropogenic climate change has essentially affected the characteristics of the thermal seasons, hence the overall ecosystems, and human health. The characteristics of the thermal seasons in Iraq, and how they change are still unclear. This research has been conducted to: (1) evaluate the spatial characteristics of summer season in Iraq over the period (1960-2021). (2) Estimating changes in the length and timing (onset and end) of summer in Iraq using the Mann-Kendall test (MK) and Sen's slope estimator. The findings indicated that there are significant spatial differences in the average length of the summer season in Iraq. The difference reaching more than 30 days between northern and southern Iraq, It is longer in the south. Results of trend analysis showed that summer season length significantly increases by 3.33 and 6.67 days/decade respectively. Our results reveal that, all over Iraq, the onset of thermal summer season occurs earlier by more than 17 days during (1960-2021). The biggest statistically significant changes in summer advancing were observed in northern and western Iraq. However, the results revealed that the only western Iraq has been recorded advancing at the end of the summer, while the end of the summer at all other parts occurred later.

Continuous-Context, User-Independent, Real-Time Intent Recognition for Powered Lower-Limb Prostheses.

Community ambulation is essential for maintaining a healthy lifestyle, but it poses significant challenges for individuals with limb loss due to complex task demands. In wearable robotics, particularly powered prostheses, there is a critical need to accurately estimate environmental context, such as walking speed and slope, to offer intuitive and seamless assistance during varied ambulation tasks. We developed a user-independent and multi-context, intent recognition system that was deployed in real-time on an open-source knee and ankle powered prosthesis (OSL). We recruited 11 individuals with transfemoral amputation, with 7 participants used for real-time validation. Our findings revealed two main conclusions: 1) the user-independent (IND) performance across speed and slope was not statistically different from user-dependent (DEP) models in real-time, and did not degrade compared to its offline counterparts, 2) IND walking speed estimates showed ~0.09 m/s mean absolute error (MAE) and slope estimates showed ~0.95° MAE across multi-context scenarios. Additionally, we provide an open-source dataset to facilitate further research in accurately estimating speed and slope using an IND approach in real-world walking tasks on a powered prosthesis.

Analysis of the Observed Trends in Rainfall and Temperature Patterns in North-Eastern Nigeria

The present study offers a comprehensive evaluation of the monthly rainfall and temperature patterns across nine stations and fifty-nine points in North-Eastern Nigeria using NASA’s Prediction of Worldwide Energy Resources data, spanning four decades (1981–2021). By employing the Mann–Kendall (MK) test and inverse distance weighting (IDW) interpolation, the researchers effectively detected and visualized trends in climate variables. The MK test results indicate contrasting rainfall trends, with notable decreases in Akko, Billiri, Maiduguri, Numan, and Yola, and increases in Gombe, Abadam, Biu, and Mubi. The trends in the maximum temperature were found to be statistically significant across all stations, showing a consistent increase, whereas the minimum temperature trends exhibited a slight but insignificant decrease. The application of the Theil–Sen slope estimator quantified these trends, providing nuanced insights into the magnitudes of changes in climate variables. The IDW results further corroborate the general trend of decreasing rainfall (z = −0.442), modest increases in the maximum temperature (z = 0.046), and a marginal decline in the minimum temperature (z = −0.005). This study makes an important contribution by advocating for the proactive dissemination of climate information. Given the evident climate shifts, particularly the increasing temperatures and fluctuating rainfall patterns, timely access to such information is crucial to enhancing climate resilience in the region. The rigorous statistical methods applied and the detailed spatial analysis strengthen the validity of these findings, making this study a valuable resource for both researchers and policymakers aiming to address climate variability in North-Eastern Nigeria. These research results may also be useful for understanding the climate variabilities in different parts of the world.