Sen's Slope Research Articles

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.

Analysis of spatio-temporal variability of temperature, relative humidity and temperature humidity index in Naivasha Sub-county, Kenya

The aim of the study was to understand the environmental conditions impacting the reproductive performance of Sahiwal cattle, focusing on temperature, relative humidity, and relative humidity index (RHI). These factors significantly influence reproductive rates, highlighting the importance of spatio-temporal variability studies for informed herd management decisions. This study analyzed climate variability trends from 1998 to 2019 at the Kenya Agricultural and Livestock Research Organization (KALRO) Dairy Research Institute (DRI)-Naivasha, in Malewa Ward, Naivasha Sub-County, Kenya. Daily minimum and maximum temperature (T) and relative humidity (RH) data sourced from the Kenya Meteorological Department were utilized. The Temperature-Humidity Index (THI) was computed using the THI equation developed by Mader. Time series analyses, including the Mann-Kendall test and Sen's slope estimates, were employed to assess seasonal variability. Results indicated significant decreasing trends in temperature across all the four seasons studied (DJF, MAM, JJA, and SON), with rates of cooling at -0.0625, -0.0175, -0.0125, and -0.0342 °C per year, respectively. In contrast, relative humidity showed statistically significant increases in all seasons, with rates of +0.5977, +0.999, +1.4493, and +1.0499 for DJF, MAM, JJA, and SON, respectively. Seasonal THI exhibited significant decreases in DJF and JJA, potentially impacting livestock reproductive performance. These findings are crucial for policymakers to create adaptation and mitigation methods to address climate change effects on livestock and livelihoods.

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.

Spatial-temporal seasonal and annual rainfall trends and variability assessment in the Pangani Basin, East Africa

Although in situ rainfall data remains the most accurate, the gauge network density in East Africa is sparse. It lacks continuity, thus making it inadequate to assess the spatial and long-term rainfall trend and variability accurately. As such, rainfall remote sensing data are normally used instead of ground station data. This study evaluates the capabilities and limitations of remote sensing data compared with ground-based observations in Tanzania's Pangani Basin in assessing the seasonal and annual rainfall trends and variability. Data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and twenty-three ground stations were analyzed, comprising a time series from 1990 to 2022. Trend analysis was conducted using the Mann-Kendall test, while the spatial distribution of precipitation was determined using Sen's slope method. CHIRPS annual rainfall showed good agreement with station rainfall data, with NSE, R2, slope, Pbias, MAE, and RMSE values of 0.84, 0.92, 0.92, 7.55%, 297.4, and 397.1, respectively. The coefficient of variation from station data indicated extreme variability, exceeding 30% for annual rainfall, while CHIRPS data showed moderate variability, ranging from 20% to 30%. Both station data and remote sensing data showed an increasing trend for annual and seasonal rainfall at least 10 stations. However, stations like Maji Moshi, Kiungu Primary School, Segera C. Tank, and Kibong'oto exhibited an increasing trend for vuli rainfall with CHIRPS data, while station data indicated a significant decreasing trend.The study highlights the necessity for calibration and validation to avert misinterpretations in climate trend analyses, especially at the basin level.

Influence of precipitation and temperature on burned areas in the agricultural and cattle ranching frontier of the Brazilian Amazon.

Fire occurrence, intensity, and spread are highly influenced by climatic variables. This study investigates the correlation between burned area, precipitation, and temperature in Rondônia, an agricultural frontier in the southwestern Brazilian Legal Amazon, from 2001 to 2022. The analysis utilized climatological data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and MODIS product MOD11A1.061 for temperature, along with MODIS product MCD64A1 for burned area. The study was conducted on a monthly scale, employing the cross-correlation function to determine the lagged effects of temperature and precipitation on burned areas. Trend analysis was performed using the Mann-Kendall test, with the magnitude of trends estimated by Sen's Slope. Results indicated a significant negative correlation between burned areas and precipitation, with a 2-month lag and an R2 of - 0.51. In contrast, temperature exhibited a significant positive correlation with burned areas, showing a 1-month lag and an R2 of 0.55. Trend analysis revealed a decrease in precipitation by - 0.0542mm.month-1, temperature increased by 0.006°C.month-1, while burned areas decreased by - 111.13 km2.month-1. These findings underscore the intricate relationship between climate variables and fire occurrences, highlighting the urgent need for policies addressing climate change and environmental degradation in the Amazon.

Spatiotemporal climate variability and food security implications in the Central Ethiopia Region

Studies focusing on the spatiotemporal distribution of climatic parameters and meteorological drought are of paramount significance for countries like Ethiopia, where climate change and variability cause major losses to rain-dependent agriculture. In this study, the National Meteorology Institute of Ethiopia provided an Enhanced National Climate Services (ENACTS) dataset at a spatial resolution of approximately 4 km by 4 km over 38 years (1981–2018) was used to study climate trends, spatiotemporal variability, and meteorological drought in the Central Ethiopia Region. Coefficient of variation (CV), Standardized Rainfall Anomaly (SRA), Standardized Precipitation Index (SPI), Mann-Kendall trend test, and Sen's slope were used for the analysis. The findings suggest that Belg rainfall (also known as "small-rain") varied greatly in space and time over the study area, with area-averaged CV of 29 % and pixel-level CVs ranging from 63 to 93 %. The average precipitation during Belg experienced a 15 % decrease from 2000 to 2019 compared to the preceding two decades, from 1981 to 1999. The maximum temperature has increased significantly during the Annual, Belg, and Bega seasons. The SPI and SRA showed that there have been multiple drought episodes with rising negative rainfall anomalies, with a drought occurring every 2.9 years during the Kiremt (also called "big rain", spanning from June to September) and Belg seasons. The growing negative rainfall anomaly, high CV, and highly significant increase in mean maximum temperature during the Belg season is concerning for food security and poverty eradication. The notable rise in rainfall during the June (sowing period) and November (harvesting period) also hurts crop production during the main cropping season. Thus, developing appropriate adaptation strategies and policies oriented toward climate-resilient agriculture is crucial to meet the global sustainable development goals (SDGs) and Africa Union's Agenda 2063.

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.

Climatic temperature indices for the coastal resilience observatory in Tabasco (Dos Bocas)‒LANRESC

This study focused on calculating climate change indices derived from maximum and minimum temperature variables for the Coastal Resilience Observatory in Tabasco, Dos Bocas, located in Paraíso, Tabasco, Mexico. The indices were computed using CLIMPACT2, 2007 version, and a total of 26 indices were obtained for the period from 1940 to 2022. Subsequently, significant trends were calculated using statistical tests such as Mann‒Kendall, trend-free prewhitening, bias correction applied to prewhitening, and variance correction (by two methods). Sen's slope method was employed to determine the extent of alteration in extreme climate indices. Increasing trends were found in the indices tn90p (%), tx90p (%), tr (days), txgt50p (%), wdsi (days), su (days), tmge10 (days), tmge5 (days), gddgrow (days), and gsl (days), indicating the presence of local warming and providing evidence of climate change detection. These results contribute to updating climate information in the area and serve as a proposal for replication in the other six Coastal Observatories of the National Coastal Resilience Laboratory in Mexico, as part of measures related to climate stressors in coastal areas where these observatories are located. This information is valuable for decision-makers and the general population, as it will support socio-environmental adaptation and mitigation measures in the face of climate change as part of efforts to enhance the resilience of socioecosystems in coastal areas.

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.

Response of remote sensing-based climatic indicators to drought conditions during El Niño events and implications for water–food–energy nexus

ABSTRACT Hydrometeorological extremes, such as droughts, are a major threat to society and can have extensive damaging effects. In this study, daily rainfall estimates from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) quasi-global rainfall dataset were used to calculate the Standardised Precipitation Index (SPI) for the assessment of meteorological drought in Southern Province, Zambia. Normalised Difference Vegetation Index (NDVI) imagery (250 m resolution) from MODIS-Terra, for the period 2000–2021, were used to derive the Standardised Vegetation Index (SVI) in order to assess agricultural drought. The Mann–Kendall trend test and Sen's slope were used to determine the spatial-temporal trends and their magnitudes. This study demonstrated that the droughts of the Southern Province of Zambia can be classified into two categories: regressive and aggressive droughts. Regressive droughts are associated with moderate to strong El Niño events. Although El Niño events undermine water security, regressive droughts tend to result in resilient vegetation owing to residue soil moisture. In contrast, aggressive droughts are characterised by an increase in drought intensity as the season progresses. Water security prospects in the region should focus on climate-smart approaches, such as managed aquifer recharge, to ensure water availability even under extreme drought conditions.

Unveiling spatio-temporal mysteries: A quest to decode India's Dengue and Malaria trend (2003-2022)

Dengue and malaria are two mosquito-borne diseases that are dangerous globally, especially in tropical and subtropical regions. In India, these two diseases pose severe health issues as they account for 74.37 % of the total vector-borne disease burden in the country. The present study examined the spatio-temporal patterns of prevalence of dengue and malaria across all states in India. Data related to epidemiological statistics were obtained from the Central Bureau of Health Intelligence (CBHI) and the National Vector Borne Disease Control Program (NVBDCP) for 2003–2017 and 2018–2022, respectively. In this study, we have utilized the Mann-Kendall test, Modified Mann-Kendall test, Sens's slope, Innovative trend analysis, and Percent Bias for trend analysis. Furthermore, a hotspot analysis was conducted to compare and examine the evolving patterns of these diseases over space and time. The Mann-Kendall test showed a significant increase in dengue cases throughout India, with Sen's slope showing the fastest growth in Punjab. West Bengal exhibited the most significant ITA slope increase. The PBIAS slope showed a gradual rise from the southern to the northern and north-eastern states. Mann-Kendall results indicated a statistically significant decline in malaria cases, dropping mostly in Odisha, followed by the northern, southern, and north-eastern states. Only Mizoram displayed an insignificant upward trend in malaria cases. Hotspot analysis revealed that dengue fever hotspots expanded in India's central, western, and northern regions, affecting 66.72 % of the country, whereas significant coldspots remain unchanged. Malaria hotspots covered 47.46 % of north-eastern, eastern coastal, and northern areas, while coldspots almost remained unchanged. This study provides valuable insights for health authorities to prioritize and identify the regions that need immediate intervention regarding these two mosquito-borne diseases.

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.

Integrating Maize Yield and Agricultural Drought Analysis for Sustainable Food Security: A Provincial Study in South Africa (1993–2022)

ABSTRACTExtreme climatic events, such as droughts, hinder progress toward achieving the sustainable development goal of food security. South Africa is vulnerable to drought‐related agricultural losses, which have led to food insecurity. However, few studies have focused on the long‐term impacts of drought on crop production at a regional scale. Therefore, we aimed to examine the intensity, magnitude, and trend of rainfall‐based short‐term agricultural drought at the provincial scale in South Africa based on the Standardized Precipitation Index (SPI). Additionally, we analyzed the impact of agricultural drought on maize yield by calculating the Standardized Yield Residual Series (SYRS) and Crop Drought‐Resilience Factor (CDRF). To this end, we collected rainfall data from 29 stations across nine provinces along with maize yield data for the period of 1993–2022. Agricultural drought analyses based on the three‐month (SPI‐3) and six‐month (SPI‐6) SPIs demonstrated dynamic variations in occurrence, with Sen's slope indicating that 10 stations exhibited a significant increase in drought events across South Africa. Notably, SPI‐6 analysis showed that Gauteng, Free State, and North West provinces experienced the highest percentages of severe to extreme drought events during the study period, at 4.17%, 3.89%, and 3.61%, respectively. Furthermore, the majority of provinces in South Africa experienced an extreme SPI‐6 magnitude ranging from −46.03 in Western Cape Province to −61.6 in Free State Province. The dynamic effects of agricultural drought on maize yield revealed that the maximum yield loss of 13% occurred in 1993 in Eastern Cape Province, while some provinces experienced no yield loss during certain years. However, CDRF analyses identified Western Cape (CDRF [SPI‐3] = 0.52, CDRF [SPI‐6] = 0.62) and Mpumalanga (CDRF [SPI‐6] = 0.7) provinces as the most vulnerable to food insecurity due to the severe non‐resilience of maize to drought in these regions. This study reveals the complex interplay between climatic extremes and maize yield variability, providing valuable insights for managing regional food production systems and ensuring future food security in South Africa.

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.

Assessing forest fire dynamics and risk zones in Central Indian forests: a comparative study of the Khandwa and North Betul forest divisions of Madhya Pradesh.

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22years using Mann-Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments.

Towards understanding climate change impacts: monitoring the vegetation dynamics of terrestrial national parks in Indonesia

Monitoring vegetation dynamics in terrestrial national parks (TNPs) is crucial for ensuring sustainable environmental management and mitigating the potential negative impacts of short- and long-term disturbances understanding the effect of climate change within natural and protected areas. This study aims to monitor the vegetation dynamics of TNPs in Indonesia by first categorizing them into the regions of Sumatra, Jawa, Kalimantan, Sulawesi, and Eastern Indonesia and then applying ready-to-use MODIS EVI time-series imageries (MOD13Q1) taken from 2000 to 2022 on the GEE cloud-computing platform. Specifically, this research investigates the greening and browning fraction trends using Sen's slope, considers seasonality by analyzing the maximum and minimum EVI values, and assesses anomalous years by comparing the annual time series and long-term median EVI value. The findings reveal significantly increasing greening trends in most TNPs, except Danau Sentarum, from 2000 to 2022. The seasonality analysis shows that most TNPs exhibit peak and trough greenness at the end of the rainy and dry seasons, respectively, as the vegetation response to precipitation increases and decreases. Anomalies in seasonality that is affected by climate change was detected in all of the regions. To increase TNPs resilience, suggested measures include active reforestation and implementation of Assisted Natural Regeneration, strengthen the enforcement of fundamental managerial task, and forest fire management.

Interdecadal variations of aerosol and its composition over the Fenwei Plain based on multi-source observations

Understanding the variations and potential source of air pollution is essential for implementing targeted mitigation actions. However, the distribution and long-term trends of Aerosol Optical Depth (AOD) and its components over the Fenwei Plain (FWP) have not been thoroughly investigated. Furthermore, the potential source contribution of AOD loading is still unclear. Thus, maximum synthesis and Mann-Kendall trend (MK) test with Sen's Slope methods are employed to reveal the spatiotemporal variation characteristics of AOD over the FWP. The Potential Source Contribution Function (PSCF) model was applied to analyze the potential source contribution of AOD over the FWP. Results demonstrated that the AOD in spatial pattern exhibited consistency with the topography. AOD over the FWP fluctuated annually from 2000 to 2020, with an increase in the previous decade followed by a gradual decline after 2011. There was a significant monthly variation in AOD with higher values in August (0.47±0.21) and lower in November (0.29±0.12). A positive AOD trend was confirmed from 2000 to 2010 yet a negative trend is identified from 2011 to 2020. The sulfate aerosol (AODSU) exhibited an increasing trend over an extended period. Clear-sky radiation shows a negative trend at the surface and the top of the atmosphere (TOA) from 2000 to 2010, which is consistent with the trend in AOD. The AOD in FWP was primarily influenced by local emissions, with contributions from northern and northwestern sources. This research offers an enhanced overarching comprehension of the distribution and regional climate effects of aerosols over the FWP.

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.