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This study was undertaken to address how near-surface soil water content (SWC) patterns have varied across diverse agroecological regions (AERs) of mainland India from 1979 to 2022 (44 years) and how these variations relate to environmental factors. Grid-wise trend analysis using the Mann–Kendall (MK) trend test and Sen's slope was conducted to determine the trends and their magnitudes. Additionally, we used Spearman's rank correlation (ρ) to explore the relationships of ESA CCI's near-surface SWC data with key environmental variables, including rainfall, temperature, actual evapotranspiration, and the normalized difference vegetation index (NDVI). The results revealed significant variations in SWC patterns and trends across different AERs and months. The MK trend test indicated that 17.96% of the area exhibited a significantly increasing trend (p < 0.1), while7.6% showed a significantly decreasing trend, with an average annual Sen's slope of 0.9 × 10−4 m3 m−3 year−1 for mainland India. Areas with the highest decreasing trends were AER-16 (warm per-humid with brown and red hill soils), AER-15 (hot subhumid to humid with alluvium-derived soils), and AER-17 (warm per-humid with red and lateritic soils). In contrast, increasing trends were the most prominent in AER-5 (hot semi-arid with medium and deep black soils), AER-6 (hot semi-arid with shallow and medium black soils), and AER-19 (hot humid per-humid with red, lateritic, and alluvium-derived soils). Significant increasing trends were more prevalent during monsoon and post-monsoon months while decreasing trends were noted in pre-monsoon months. Correlation analysis showed strong positive correlations of SWC with rainfall (ρ = 0.70), actual evapotranspiration (ρ = 0.74), and NDVI (ρ = 0.65), but weak or negative correlations with temperature (ρ = 0.12). This study provides valuable insights for policymakers to delineate areas based on soil moisture availability patterns across seasons, aiding in agricultural and water resource planning under changing climatic conditions. [ABSTRACT FROM AUTHOR]

Climate change has increased the frequency and severity of weather extremes, including droughts, heat waves and compound drought and heatwave (CDHW) events. CDHW events profoundly impact water availability, agriculture, public health and energy production, particularly in the Horn of Africa (HOA). This study examined the historical spatiotemporal patterns of CDHW periods in the HOA during three periods (1951–1980, 1971–2000 and 1991–2020) using the ERA5 reanalysis dataset. This study utilized monthly Standardized Precipitation Evapotranspiration Index (SPEI) data to detect droughts and daily maximum temperature data to identify heatwaves for characterizing the duration, severity and magnitude of CDHW events. The results show a substantial increase in the duration of CDHW events in recent years, with durations reaching up to 25 days. The average duration of heat waves also increased from 7 days before 1993 to 18 days by 2011, culminating in a record‐breaking 43‐day heat wave in 2019. Most areas experienced a significant increase in heatwave duration by more than 12 days from the early period of 1951–1980 to the late period of 1991–2020. Although around 76% and 69% of the HOA exhibited insignificant heatwave trends in the first two periods, Ethiopia and Kenya experienced substantial increases of more than 18 days during the most recent period, with some durations exceeding 25 days in recent decades. The magnitude of CDHW events generally decreased as drought duration intensified, but specific areas, particularly southwest Kenya and Eritrea, exhibited higher CDHW values in the last period. These findings underscore the urgent need to understand and address CDHW events in the HOA. Targeted interventions for disaster risk reduction and resilience‐building are needed to mitigate the adverse effects of these events in this vulnerable region. This study provides a basis for future research and policy formulation in the HOA. [ABSTRACT FROM AUTHOR]

This research aims to assess the climatic temporal suitability over the year and identify the appropriate season for open-air tourism in ten Romanian cities. It was evaluated using the Enhanced Tourism Climatic Index on a temporal scale of one day and then aggregated to 10 days over 61 years (1961–2021). Daily mean and maximum temperature, mean and minimum relative humidity, wind speed, precipitation, and sunshine hours were employed in the investigation. The Mann–Kendall test and Sen's slope were used for trend detection in the frequency, season duration, and first/last suitable day during the year for outdoor tourism. Acceptable or better weather conditions usually begin in the last part of April and end in mid-October, with Good or better conditions lasting between 260 and 310 days/year. The trend shows a shift of Good conditions earlier in the year (0.3–9.0 days/decade), resulting in a longer season duration (0.8–13.0 days/decade) for open-air activities. The trend is statistically significant mainly for the extra-Carpathian regions. Big differences in open-air events number during the climatically suitable season have been identified among the cities considered (2–19 events/year). This study is useful for better planning open-air events and activities for tourism and recreation. [ABSTRACT FROM AUTHOR]

Changes in vegetation status generally also represents changes in the ecological health of islands and reefs (IRs). However, studies are limited of drivers and trends of vegetation change of Nansha Islands, China and how they relate to climate change and human activities. To resolve this limitation, we studied changes to the Normalized Difference Vegetation Index (NDVI) vegetation-greenness index for 22 IRs of Nansha Islands during normal and extreme conditions. Trends of vegetation greenness were analyzed using Sen' s slope and Mann-Kendall test at two spatial scales (pixel and island), and driving factor analyses were performed by time-lagged partial correlation analyses. These were related to impacts from human activities and climatic factors under normal (temperature, precipitation, radiation, and Normalized Difference Built-up Index (NDBI)) and extreme conditions (wind speed and latitude of IRs) from 2016 to 2022. Results showed: 1) among the 22 IRs, NDVI increased/decreased significantly in 15/4 IRs, respectively. Huayang Reef had the highest NDVI change-rate (0.48%/mon), and Zhongye Island had the lowest (−0.29%/mon). Local spatial patterns were in one of two forms: dotted-form, and degradation in banded-form. 2) Under normal conditions, human activities (characterized by NDBI) had higher impacts on vegetation-greenness than other factors. 3) Under extreme conditions, wind speed (R2 = 0.2337, P < 0.05) and latitude (R2 = 0.2769, P < 0.05) provided limited explanation for changes from typhoon events. Our results provide scientific support for the sustainable development of Nansha Islands and the United Nations 'Ocean Decade' initiative. [ABSTRACT FROM AUTHOR]

The prediction of land use and land cover (LULC) patterns is essential for land use planning, natural resources management, and climate change mitigation. In this study, the artificial neural network (ANN) technique and QGIS software have been proposed to project 5- and 10-year future LULC for the Mahanadi River basin in Chhattisgarh, India. The results reveal that slope, aspect, and hillshade maps have significant effects on LULC. The projection of the future LULC for the years 2027 and 2032 reveals that the grassland and cropland areas would significantly decline and increase, respectively, in the study region. The results from trend analysis show an increasing trend for forests, permanent wetlands, cropland, urban built-up land, waterbodies, and cropland/natural vegetation, while decreasing trends for the remaining classes. The overall finding from this study suggests that remote sensing enabled with the ANN technique has the potential to project the future LULC with high accuracy. [ABSTRACT FROM AUTHOR]

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The high variability of the sub-desert climate, especially in the south-eastern region of Morocco, has severe socio-economic impacts on the inhabitant’s way of life, as is the case in the Daoura watershed, where this variability is associated with droughts or exceptional rainfall. The data collected from the Guir-Ziz-Rheriss hydraulic agency were processed, corrected and analyzed using the Climatol package (version 4.0.0) developed in R software to homogenize rainfall data. Through this work, we defined the significance and amplitudes of annual and monthly rainfall trends using the Mann-Kendall test and Sens's slope estimator, while comparing the results of raw and homogenized data. The Daoura watershed has a sub-desert climate, and the homogenization process revealed a few rainfall stations with significant positive trends at confidence levels ranging from 90 % to 95 %. According to the raw and homogenized data, the majority of these stations are located in the High Atlas (CR1) and Anti-Atlasic (CR2) zones, where there is considerable spatiotemporal variability in rainfall from one year to the next. The essential objective of this scientific paper is to analyze the spatiotemporal variability of monthly and annual precipitation and to study their trends through rainfall data homogenized by the climatol package (version 4.0.0) from 13 stations over a period (1957-2018). This study's contribution to science is the rainfall data it offers, which is useful for managing natural resources in sub-desert areas.

This study examines the spatial and temporal changes in temperature and precipitation extremes from 1951 to 2020 in the West African transboundary Benue River Basin, which frequently experiences impacts from extreme events. The existing studies in the region have relied on sparse observation data, hindering a comprehensive understanding of the spatial variability of climate extremes. High-resolution gridded climate data can be used to explore how climate extremes vary spatially and temporally across transboundary river basins. This study is the first attempt to employ 0.25° × 0.25° daily ERA5 temperature and precipitation data to comprehensively assess spatiotemporal changes of climatic extremes in a West African transboundary river basin. The study applied the non-parametric Mann-Kendall test and Sen's slope method to analyze temperature and precipitation extremes in the Benue River Basin. Results show a significant increase in mean maximum temperature (Tmax) of approximately ≥ 2 °C, indicating rising mean Tmax. Analysis of temperature indices reveals significant positive trends, with the number of warm days (TX90p) increasing by 8%. In contrast, evaluations of maximum 1-day (RX1day) and 5-day (RX5day) precipitation exhibit widespread reductions of 22.5% and 45%, respectively, indicating decreasing rainfall over most areas. Examinations of consecutive dry days (CDD) display higher mean changes in the northern basin extending to western Chad (70 to ≥ 110 days), revealing a link between lower annual precipitation and increased temperatures. However, we also found a basin-wide decrease in CDD ranging from ≤-2 to ≥-10 days. Overall, this study shows that the maximum temperature increases faster than the minimum temperature in the study area and that extreme temperature changes are more pronounced than extreme precipitation. These findings hold crucial implications for understanding the evolving climate dynamics in this vulnerable basin, emphasizing the urgency for adaptive measures in the face of escalating climate challenges. [ABSTRACT FROM AUTHOR]

Groundwater has been the largest accessible freshwater source globally. The overall dimensions of every basin's water budgeting are closely linked to the quantity quality of available surface and subsurface water. In this paper, the groundwater levels trend in six districts within the Baitarani River Basin, Odisha State, India has been analyzed. Data on the depth to groundwater level for pre and post-monsoon season from 32 wells spread over six different regions from 1997 to 2020 were analyzed. Sen's slope techniques and creative trend analysis were used to investigate the behavior of groundwater level changes in pre- and post-monsoon periods to identify both monotonic and non-monotonic behaviors. The findings showed that, throughout the pre-monsoon and post-monsoon periods, the depth to groundwater level for four Tubewell, five Borewell, and seven Dugwell locations increased. However, only three tubewell stations Champua, Bhadrak, and Karanjia exhibited declining trends in the pre- and post-monsoon periods. In the case of borewell, only one station (Anadapur) has a negative trend in post-monsoon. For Dugwell, during pre-monsoon Banspal represents the –ve trend, and Bhandaripokhari, Banspal, Anandapur, and Joda show negative trends in post-monsoon seasons. A significant decline in groundwater level was observed in three stations for tubewell, one station for borewell, and four stations for dugwell observed in the current study. Accordingly, spatial maps of increasing and decreasing trends of groundwater levels were prepared, which can be used by water resource managers to identify the causes of declining groundwater levels for managing the resources effectively. [ABSTRACT FROM AUTHOR]

Drought is the most complex natural hazard that can occur over large spatial scales and during long time periods. It affects more people than any other natural hazard, particularly in areas with a dry climate, such as the semiarid region of the Brazilian Northeast (NEB), which is the world's most populated dry area. In this work, we analyzed trends and the spatial distribution of drought characteristics (frequency, affected area, and intensity) based on the Standardized Precipitation Index (SPI) on annual (SPI-12) and seasonal (SPI-3) scales. The study used monthly precipitation data recorded between 1962 and 2012 at 133 meteorological stations in Pernambuco State, Brazil, which is located in the eastern part of the NEB and has more than 80% of its territory characterized by a semiarid climate. The regions of Sertão, Agreste, and Zona da Mata of Pernambuco were considered for comparison. The Mann–Kendall and Sen's slope tests were used to detect the trend and determine its magnitude, respectively. The results indicated that annual droughts in the state of Pernambuco became more frequent from the 1990s onwards, with summer having the greatest spatial coverage, followed by winter, autumn, and spring. Sertão presented a greater number of stations with a significant positive trend in drought frequency. Regarding the drought-affected area, global events occurred in a greater number of years on an annual scale and during the summer. Trend analysis pointed to an increase in areas with drought events on both scales. As for the drought intensity, the entire state of Pernambuco experienced drought events with high intensity during the autumn. The relationship between drought characteristics indicated an increase in the affected area as the result of an increase in drought intensity. [ABSTRACT FROM AUTHOR]

This research analyses the long-term vegetation trends in Shada Mountain across six elevation zones, utilizing Landsat 5, 7, 8, and 9 imageries processed via Google Earth Engine and R. The study managed differences in images resolution through meticulous calibration and image processing techniques. The study is structured around two objectives: examining the relationship between vegetation and its proximity to streams and land surface temperature and analyzing trends in the Normalized Difference Vegetation Index (NDVI). Regression analysis revealed a negative correlation between vegetation and proximity to streams in lower zones (1-3), with no significant effect in higher zones (4-6). NDVI trend analysis indicated an overall increase in vegetation across most zones, with the exception of zone 5, which displayed a negative trend (slope -0.0025). The findings reveal that the decline is particularly pronounced among key tree species such as Ficus cordata subsp. salicifolia and Acacia asak, suggesting potential impacts from climate change and land use alterations. These zone-specific insights deepen our understanding of the dynamic ecological processes in semi-arid environments and guide targeted environmental management and conservation efforts. [ABSTRACT FROM AUTHOR]

The high variability of the sub-desert climate, especially in the south-eastern region of Morocco, has severe socioeconomic impacts on the inhabitant's way of life, as is the case in the Daoura watershed, where this variability is associated with droughts or exceptional rainfall. The data collected from the Guir-Ziz-Rheriss hydraulic agency were processed, corrected and analyzed using the Climatol package (version 4.0.0) developed in R software to homogenize rainfall data. Through this work, the significance and amplitudes of annual and monthly rainfall trends were defined using the Mann-Kendall test and Sens's slope estimator, while comparing the results of raw and homogenized data. The Daoura watershed has a sub-desert climate, and the homogenization process revealed a few rainfall stations with significant positive trends at confidence levels ranging from 90% to 95%. According to the raw and homogenized data, the majority of these stations are located in the High Atlas (CR1) and Anti-Atlasic (CR2) zones, where there is considerable spatiotemporal variability in rainfall from one year to the next. The essential objective of this scientific paper was to analyze the spatiotemporal variability of monthly and annual precipitation and to study their trends through rainfall data homogenized by the climatol package (version 4.0.0) from 13 stations over a period (1957-2018). The contribution of this study to science is the rainfall data it offers, which is useful for managing natural resources in sub-desert areas. [ABSTRACT FROM AUTHOR]

We observed the effect of the La Niña phenomenon on the concentration of water vapor and ozone in the Indonesian region. This aims to the value of water vapor and ozone concentrations due to the La Niña phenomenon using Microwave Limb Sounder (MLS) data from 2004-2022. The La Niña phenomenon was chosen because during La Niña, the sea surface temperature in Indonesia is warmer than normal, thus increasing the evaporation of sea water which result is an increase in the concentration of water vapor in the atmosphere. Concentration values are observed at altitudes of (25.7;30.5;35.3;40.1) km because there are trends in water vapor and ozone concentrations at these altitudes. The La Niña phenomenon is used to see anomalies in water vapor and ozone concentrations from their normal state. La Niña phenomenon is observed based on the ONI index. We found that during La Niña, the water vapor concentration increased from its normal state while the ozone concentration decreased from its normal state. These two concentration values were used to find trends using Mann Kendall and Sen's Slope methods. We found that the trend of water vapor concentration is statistically significant while the trend of ozone concentration is the opposite.

Understanding the spatiotemporal dynamics of climatic conditions within a region is paramount for informed rural planning and decision-making processes, particularly in light of the prevailing challenges posed by climate change and variability. This study undertook an assessment of the spatial and temporal patterns of rainfall trends across various agro-ecological zones (AEZs) within Wolaita, utilizing data collected from ten strategically positioned rain gauge stations. The detection of trends and their magnitudes was facilitated through the application of the Mann–Kendall (MKs) test in conjunction with Sen's slope estimator. Spatial variability and temporal trends of rainfall were further analyzed utilizing ArcGIS10.8 environment and XLSTAT with R programming tools. The outcomes derived from ordinary kriging analyses unveiled notable disparities in the coefficient of variability (CV) for mean annual rainfall across distinct AEZs. Specifically, observations indicated that lowland regions exhibit relatively warmer climates and lower precipitation levels compared to their highland counterparts. Within the lowland AEZs, the majority of stations showcased statistically non-significant positive trends (p > 0.05) in annual rainfall, whereas approximately two-thirds of midland AEZ stations depicted statistically non-significant negative trends. Conversely, over half of the stations situated within highland AEZs displayed statistically non-significant positive trends in annual rainfall. During the rainy season, highland AEZs experienced higher precipitation levels, while the south-central midland areas received a moderate amount of rainfall. In contrast, the northeast and southeast lowland AEZs consistently received diminished rainfall across all seasons compared to other regions. This study underscores the necessity for the climate resilient development and implementation of spatiotemporally informed interventions through implementing region-specific adaptation strategies, such as water conservation measures and crop diversification, to mitigate the potential impact of changing rainfall patterns on agricultural productivity in Wolaita.

Central Punjab includes over 40% of the state of Punjab. In Central Punjab, groundwater levels are dropping by more than 1 m every year. The primary cause of groundwater depletion in Central Punjab between 1998 and 2019 is presented in this paper. In order to detect any sudden changes in the area under paddy and the depth of groundwater, Pettitt's test was used. Based on change points and trends, the entire duration was split into two time periods: 1998–2008 (T1) and 2009–2019 (T2); rainfall, potential evapotranspiration, and paddy area were assessed in both periods with 1998–2019 (T3). The slope methodology developed by Mann–Kendall and Sen demonstrated the trend analysis. The findings showed that changes in climate were caused by humans during the 1998–2008 era, changes in meteorological variables were caused by humans during the 2008–2019 period, and changes in climate were caused by humans during the T3 period. The major growing trend of groundwater depth loss is thought to be caused by the combined effects of rainfall variations, an increase in the maximum temperature, cropping intensity, and the number of pumping units. HIGHLIGHTS Paddy area has been increasing in Punjab for the last 20 years, and this increase is causing a huge decrease in groundwater levels.; Pettitt's test was applied to see in which year an abrupt change happened.; Mann–Kendall and Sen's slope were applied to analyze the trend.; Rainfall was not enough for groundwater recharge as compared to declination due to the paddy area.; Central Punjab must evolve decision-making tools for arresting groundwater (GW) decline.;

This study aimed to analyze the trend in land surface temperature (LST) over time using the entire archive of the available cloud-free Landsat images from 1986 to 2022 for Jordan and its nine local climate zones (LCZs). Two primary datasets were used (i) Landsat-5; -8 imagery, and (ii) map of LCZs of Jordan. All LST images were clipped, preprocessed, and checked for cloud contamination and bad pixels using the quality control bands. Then, time-series of monthly LST images were generated through compositing and mosaicking processes using cloud computing functions and Java scripts in Google Earth Engine (GEE). The Mann–Kendall (MK) test and Sen's slope estimator (SSE) were used to detect and quantify the magnitude of LST trends. Results showed a warming trend in the maximum LST values for all LCZs while there was annual fluctuation in the trend line of the minimum LST values in the nine zones. The monthly average LST values showed a consistent upward trajectory, indicating a warming condition, but with variations in the magnitude. The annual rate of change in LST for the LCZs showed that the three Saharan zones are experiencing the highest rate of increase at 0.0184 K/year for Saharan Mediterranean Warm (SMW), 0.0185 K/year for Saharan Mediterranean Cool (SMC), and 0.0169 K/year for Saharan Mediterranean very Warm (SMvW), indicating rapid warming in these regions. The three arid zones came in the middle, with values of 0.0156 K/year for Arid Mediterranean Warm (AMW), 0.0151 for Arid Mediterranean very Warm (AMvW), and 0.0139 for Arid Mediterranean Cool (AMC), suggesting a slower warming trend. The two semi-arid zones and the sub-humid zone showed lower values at 0.0138, 0.0127, and 0.0117 K/year for the Semi-arid Mediterranean Cool (SaMC), Semi-arid Mediterranean Warm (SaMW) zones, and Semi-humid Mediterranean (ShM) zones, respectively, suggesting the lowest rate of change compared to other zones. These findings would provide an overall understanding of LST change and its impact in Jordan's LCZs for sustainable development and water resources demand and management. [ABSTRACT FROM AUTHOR]

1- Introduction One of the important purposes of flood spreading plans is to feed groundwater tables and increase quality of these resources. One of the most important indicators for evaluation of artificial recharge plans is to examine their impact on quality of groundwater resources. The main aim of current research is to investigate effects of implementation of artificial aquifer recharge plan (Herat flood spreading) on temporal changes of groundwater quality parameters in the region. 2- Methodology Harat watershed area is 1413 square kilometers in south of Yazd province. The location of flood spreading sites implemented at the end of Harat basin outlet and with an area of 490 hectares, which includes four implementation areas. In order to investigate and evaluate the effects of flood spreading projects on groundwater resources, statistics of parameters of 17 wells were used from the area of aquifer of Fathabad affected by these projects, based on available information, parameters of calcium, carbonate, magnesium, sodium, chlorine, and total dissolved solids and salt was extracted. The wells were classified into two groups, control and affected, based on their location relative to sites and the direction of flow in waterways of area. According to length of period, in order to evaluate the impact of water withdrawals and consequently impact of sedimentation on the efficiency of the sites, the entire period was divided into two periods so that 2003-2011 is first period and 2012-2021 is second period. Minitab software was used to check linear trend of average groundwater quality parameters. For this purpose, the average of all types of quality parameters of water resources, first and second periods were entered in software and the change process for each parameter was obtained. In order to investigate temporal trend of groundwater quality parameters, Mann-Kendall test was used at 95% confidence level and Sen's slope by XLSTAT 2019 Software. 3- Results The results related to time trend of water quality factors through Mann-Kendall method and Sen's slope showed that calcium factor among nine wells located downstream of the project, three wells have no trend and the rest have a negative trend. The results of checking trend of control wells showed that calcium had a positive trend in all but one well during statistical period under review. Factors of sodium, bicarbonate and magnesium in the downstream of projects often have a negative trend and also in the control area they often show a positive trend, but factors of salinity, chlorine and total dissolved solids in the control area do not have the same situation as other factors. So that the number of wells without trend in the control range is more than the rest of the factors. This means that the condition of the aquifer in terms of three indicators is neutral and does not have a negative or positive tendency, but the investigation of trend related to affected area showed that flood spreading projects have been able to improve the status of these factors, which are part of the important factors affecting agricultural water. lead to improvement. In order to investigate effects of flood spreading projects, at the beginning and after several years, linear trending of the average factors was done in two time periods. Due to lack of data in the last of years of implementation of flood spreading to evaluate effectiveness of groundwater quality from flood spreading project, the number of available years was divided into two almost equal parts so that impact of these projects can be investigate in the initial period and after a few years. The results of linear trend of the first and second periods showed that total dissolved solids and calcium were decreasing with a high slope in the first period, but in second period and after a few years, effectiveness decreased, but it is still decreasing with a gentle slope. Bicarbonate and magnesium factor have had an increasing trend in first period, and in second period, magnesium has decreased towards a negative slope, but bicarbonate follows an increasing trend with a higher slope than in first period. In the case of other factors such as salinity, chlorine, sodium, trends are almost similar to each other. So, in the first period, they have decreased with a slight slope, but in second period, this slope has increased proportionally. This means that the infiltration of flood water through these projects has been able to improve status of all factors except for bicarbonate, but the passage of time has left different effects on the manner and extent of effect, which requires more detailed investigations. 4- Discussion & Conclusions According to results of investigations of time trend of various factors in this region, flood spreading has been able to have a positive effect on quality of groundwater. So that in determining trends of whole aquifer, except for bicarbonate factor, which had a positive trend, in other factors, there was a decreasing trend and no trend, which indicates positive effect of flood spreading on the control of quality factors. The results of this research showed that state of trend of all factors in some wells of witness area showed an increase and, in some wells, there was no trend, but in the affected area (downstream) all factors had a decreasing trend. The location of wells and distance from the waterways and flood spreading sites have a great impact on the vulnerability of wells. So that B9 well, which is located in the farthest part of the affected aquifer and is far from flood spreading sites, is among wells that had the most negative trend in most factors. Also, B3 and B2 wells are among wells that have the highest decreasing slope. Investigations showed that B9 well was located at a very short distance from the main waterway and B2 and B3 wells are also located near the distribution sites in addition to position relative to waterway and are affected by several flood distribution sites. So, distance of wells from waterway and the location of the sites can be very effective. [ABSTRACT FROM AUTHOR]

The effects of global warming and climate change appear as increasing or decreasing trends in hydrometeorological records in different locations. Identifying trends in long-term (more than 30 year) data is possible through a variety of methodologies as well as classical Mann–Kendall (MK), Regression (R), Spearman's Rho (SR) methods in addition to the innovative trend analysis (ITA) approaches. The most used method in the literature is the MK trend determination test, but it has limited assumptions, and the trend slope is calculated according to Sen's median procedure. However, Sen's approach is an empirical methodology that considers a single median slope from all possible consecutive lag slopes. This paper provides the theoretical probability distribution function (PDF) that matches Sen's slopes, and an innovative probabilistic trend slope methodology is proposed with a set of slope risk levels, rather than statistical slope calculation. The application of the proposed methodology is presented for long-term Danube River annual discharge data in Romania and precipitation records at Antalya resort center in the south along the Mediterranean coast of Turkey. A set of trend lines, objectively different risk levels, are obtained. It has been determined that there are decreasing and increasing monotonic trends in each historical time series record. Therefore, for extreme events, it is possible to consider a particular risk level trend characteristic, i.e., floods and droughts, rather than the classical mean or median level trend definition. [ABSTRACT FROM AUTHOR]

The detection of nonstationarities in partial duration time series (PDS) depends on several factors, including the length of the time series, the selected statistical test, and the heaviness of the tail of the distribution. Because of the more limited attention received in the literature when compared to the trend detection on block maxima variables, we perform a Monte Carlo simulation study to evaluate the performance of different approaches: Spearman's rho, Mann–Kendall, ordinary least squares (OLS), Sen's slope estimator (SEN), and the nonstationary generalized Pareto distribution fit to identify the presence of trends in PDS records characterized by different sample sizes (n), shape parameter (ξ) and degrees of nonstationarity. The results point to a power gain for all tests by increasing n and the degree of nonstationarity and by reducing ξ. The use of a nonparametric test is recommended in samples with a high positive skew. Furthermore, the use of sampling rates greater than one to increase the PDS sample size is encouraged, especially when dealing with small records. The use of SEN to estimate the magnitude of a trend is preferable over OLS due to its slightly smaller probability of occurrence of type S error when ξ is positive. [ABSTRACT FROM AUTHOR]

In recent decades, an increase in the earth's atmospheric temperature has been noticed due to the augmentation of the volume of gases with the greenhouse effect (GHG) released into the atmosphere. To reduce this effect, the European Union's directives indicate the action directions for reducing these emissions, among which carbon dioxide (CO2) recorded the highest amount. In this context, the article analyzes the CO2 series reported in 1990–2021 by 30 European countries. The Kruskal-Wallis test rejected the hypothesis that the series comes from the same underlying distribution. The Anderson-Darling test rejected the normality hypothesis for seven series out of thirty, and Sen's procedure found a decreasing trend slope only for 17 series. ARIMA models have been built for all individual series. Grouping the series (by the k-means and hierarchical clustering) provided the base for building the Regional series (RegS), which describes the CO2 pollution evolution over Europe. The advantage of this approach is to provide the synthetic image of the regional evolution of the CO2 emission volume (mt), incorporating information from 30 series (one for each country) in only one—RegS. It is also shown that selecting the number of clusters involved in building RegS and assessing their stability is essential for the model's goodness of fit. [ABSTRACT FROM AUTHOR]

Investigating the temporal dynamics of rainfall in a changing climate, especially in rainfed agriculture regions, is crucial for analyzing climate-induced changes and offering adaptation options. Since Mizoram experiences unfavorable impacts of rain nearly every year, the region rainfall has been altering over the years, and vital climatic activity is becoming uncontrollable. The current study is primarily concerned with the changing trend of rainfall over Mizoram, which includes both short-term persistence (STP) and long-term persistence (LTP) of rainfall in seasonal and annual time series of rainfall overseeing for the period of 25 years of daily average rainfall from 1996 to 2020 collected collectively from the seven stations over the study area of Mizoram. Four different Mann-Kendall method iterations were used to analyze rainfall trends: the original or conventional method (without autocorrelation) (MnKn1), removing lag-1 autocorrelation (trend-free pre-whitening), considering multiple lag autocorrelation (more than lag-1 autocorrelation) (MnKn3), and Hurst coefficient or LTP (MnKn4). In the analysis, the study found that during monsoon, station Lawngtlai (LT) observed the highest rainfall having a Z value of 1.986, increased by 0.466 cm/year, while station Serchhip (SC) observed the lowest rainfall having Z value of −2.282, decreased by −0.163 cm/year. After applying modified MnKn4, we observed LTP of rainfall in winter at station Lawngtlai (LT) with an increasing trend and other stations observing STP in almost all seasons either increasing or decreasing trend. Therefore, possible climate change adaptation measures should be made to optimize rainfall use for various applications for the states of Mizoram. [ABSTRACT FROM AUTHOR]

In order to continuously promote water conservation efforts to alleviate the pressure of water diversion, water footprint (WF) is used as an effective tool to measure water utilization in the water-receiving areas of the Middle Route and Eastern Route of the South-to-North Water Diversion Project (SNWDP). The tempo-spatial variations of WF and spatial equilibrium of water footprint intensity (WFI) in the study area are quantified using the Mann–Kandle trend test, Sen's slope, and Dagum Gini coefficient decomposition method for the years of 2005–2020. The results show that WF has a significant decreasing trend (Sen's slop < 0, significant level < 0.05) in 17 cities in the study area, whereas WF shows a significant increasing trend (Sen's slop > 0, significant level < 0.05) in 10 cities. Cities in the water-receiving areas are categorized into three types based on the contribution of the water utilization sector to changes in WF as follows: agriculture water-dominated city (AD), domestic and ecological water-dominated city (DED), and virtual water trade-dominated city (VWTD). Accordingly, targeted water conservation recommendations are made for these three kinds of cities, and it is suggested that AD, DED, and VWTD cities need to focus on advanced irrigation technologies, water reuse, and trade restructuring, respectively. The overall Gini coefficient of WFI fluctuates between 0.219 and 0.267 in the water-receiving areas of the Middle Route, which is dominated by the differences in city level. However, it fluctuates between 0.412 and 0.278 in the water-receiving areas of the Eastern Route, which is dominated by the differences in provincial level. Accordingly, water conservation hotspots are determined at the city level in the Middle Route and at the provincial level in the Eastern Route with different water management policies. These results provide a scientific support for water conservation management in the water-receiving areas of the SNWDP, as well as a methodological reference for the tempo-spatial characteristics of WF and their implications for water conservation. [ABSTRACT FROM AUTHOR]

Analyzing trends can provide insights into price behavior to predict future change. The primary objective was to examine prospective changes in monthly price over the entire period, between 2008 and 2021 using parametric and non-parametric techniques. Four domestic markets were chosen based on the maximum arrival of onions (Allium cepa L.) in Maharashtra and the Indian export market. Some markets fit the linear regression model; the errors do not conform to parametric assumptions, requiring application of distribution-free techniques. Using the mean of prices, Innovative Polygon Trend Analyses (IPTA) was applied. The IPTA plots for all domestic markets indicated upward trends in almost all months with regular polygons, with the exception of the export market. Plots indicated peak arrival months in markets exhibiting a narrower polygon structure, while lean arrival months showed a broader polygon. When results of trend analysis based on other tests were compared to the IPTA technique, there was good agreement across all approaches. The export market exhibited the least variability in prices, and the average monthly price ranges from 127.90 to 334.56 USD∙Mt−1. Post-monsoon and harvest months exhibited the highest average prices. Based on comparisons of trend analysis techniques, the IPTA methodology places a strong emphasis on identifying trends and offers insight through a visual examination of trend patterns. [ABSTRACT FROM AUTHOR]

The harmful effects of factors such as human-induced effects, greenhouse gases, fossil fuel consumption, urbanization and destruction of agricultural lands on the environment and climate change are increasing day by day. All these changes can create undesirable and permanent effects on human health, living species and the environment. The analysis of climate parameters on a regional basis is very important in understanding the effects of climate change and taking the necessary predictable steps to reduce its harmful effects. This study analyzes changes in various climate parameters of Ardahan province, such as temperature, precipitation amount, wind speed and sunshine duration. For this purpose, the changes in the three-year averages of the climate parameters measured by Ardahan Meteorological Station between 2001-2021 are analyzed. In addition, non-parametric Mann Kendall and Sen slope methods are applied to climate parameters, and monthly, seasonal and annual values of each parameter are evaluated according to the statistical significance level of 5%. As a result, increasing trends in the temperature values of the region and decreasing trends in the average precipitation, relative humidity and average wind speed values are determined. Thus, this study aims to understand the impacts of climate change and manage risks, as well as to provide the necessary climate data for thermal energy projects planned to be carried out in the region. [ABSTRACT FROM AUTHOR]

Studying the spatiotemporal precipitation characteristics in North Macedonia (1951–2010) is important as no spatially concurrent precipitation changes across the Balkan Peninsula have been identified. North Macedonia lies at the intersection between Mediterranean and continental climate zones and an improved understanding might help to better understand the regional precipitation patterns. The analysis shows a spatially consistent, high inter-annual variability, which makes trend detection difficult. Statistically significant decreasing trends were only found in seasonal precipitation at three stations. Changes in all other precipitation series were non-significant. Trends in winter, spring, and at annual scale are generally decreasing, whereas in summer are increasing. To better understand possible mechanisms behind the observed variability and change, correlations with the North Atlantic Ocsillation (NAO) were assessed. Significant and regionally concurrent correlations were detected. A strong correlation of the previous winter NAOindex with spring precipitation was found, which is valuable information for anticipatory water resources management in the region. [ABSTRACT FROM AUTHOR]

Climate change has been designated the most serious environmental challenge of the twenty-first century, and it will remain so in the future. To adapt to the ill effects of climate change and variability, location-specific climate analysis is essential to provide actionable evidence for informed decision making. The goal of this research was to evaluate current and future rainfall and temperature trends in central Ethiopia. We took 33 (1986–2018) years of daily rainfall and temperature data from ten stations from the National Meteorological Services Agency. The Markov chain model was used to fill in the missing values. Trends of climate variables and their extremes were analyzed using the Mann–Kendall trend test. The results showed that rainfall exhibited significant increasing trends (p < 0.05) at 80% of the stations included in the study. The start of the Kiremit season varied from June (Jun 4 at Bako) to July (Jul 1 at Shashemane) among stations; it starts June 4, June 6, and June 24 at Bako, Ambo, and Melkassa. For Asgori, the cessation date was October 1, and for Ambo, it was October 17. The average growing season varies from 95 to 136 days, and we found the shortest and longest growing seasons at Melkassa (95 days) and Ambo (136 days). The Sen's slope results showed that rainfall increased in most of the study sites from 0.2* to 12.44*, whereas it decreased from − 0.12* to − 0.21*. This has implications for agricultural practices and crop and variety choices for major crops grown in the area. To solve this problem, policymakers must work on creating awareness for the local community, which can help them withstand future climate change. [ABSTRACT FROM AUTHOR]

Agricultural intensification is increasing global demand for water, with groundwater especially susceptible given its year-round reliability. Climate change impacts on groundwater recharge exacerbate uncertainties for future access and use, especially for large aquifers across alluvial plains such as the Indus Basin of Pakistan. To generate better understanding of climate change impacts on groundwater balances in such contexts, we used MODFLOW 2005 to quantify the groundwater budget of the Northern Rohri Canal Command Area under RCP 4.5 and 8.5 climatic scenarios, while also taking climatic regionalisation into account. Under a baseline scenario, total annual pumping in the northern Rohri command was estimated to be 3.619 billion cubic metres (BCM), and the total net loss in storage over the simulation period from October 2010 to April 2014 was estimated at 1.244 BCM per year. By 2047, net decline in storage is projected to more than double to 2.185 per year under RCP 4.5 scenario and 2.214 under RCP 8.5. Our estimates suggest that a sustainable yield across the command area should be managed at approximately 3 ± 0.3 BCM per year to ensure sufficient adaptive reserves of groundwater for access during times of drought and inadequate surface supply, while also reducing waterlogging impacts from high watertables. This first-time estimate of sustainable yield provides irrigation system managers with an overall guide from which divisional-scale measures to achieve the goal can be identified through stakeholder engagement. [ABSTRACT FROM AUTHOR]

Study region: Mimoso Alluvial Valley, Semiarid Brazil. Study focus: This study aims to assess the influence of climate variability and exploitation on groundwater accessibility, quantity and quality, based on a spatiotemporal data analysis from long-term monitoring field campaigns, conducted monthly from 2000 to 2019. This study successfully identified representative stable monitoring points, piezometers and wells, for piezometric and salinity levels employing the technique of relative differences, taking into account aquifer hydraulic properties. Trend analysis was then carried out adopting the Mann-Kendall Method, Sen's Slope test, Pettitt test, and the Seasonal Trend decomposition through the Loess (STL) method. Principal Component Multivariate Analysis (PCA) was also employed to validate long-term analysis of annual groundwater levels, rainfall, evapotranspiration, and pumping rates. New hydrological insights for the region: The spatial-temporal variability of salinity is closely linked to the salt balance in regions with low hydraulic permeability and groundwater use for irrigation. Groundwater salinity presented a strong link between rainfall and groundwater levels. A temporal rise in evapotranspiration alongside declines in rainfall and groundwater levels was observed, potentially exacerbating groundwater scarcity alongside intensified aquifer exploitation. Furthermore, groundwater salinity presented a decreasing trend for the pumping wells and no trends for piezometers. This could potentially disrupt future irrigation plans and cause long-term water shortages in a region already under severe water scarcity. Hence, groundwater pumping constitutes a positive management alternative for controlling groundwater salinity in the domain.

This research analyses the long-term vegetation trends in Shada Mountain across six elevation zones, utilizing Landsat 5, 7, 8, and 9 imageries processed via Google Earth Engine and R. The study managed differences in images resolution through meticulous calibration and image processing techniques. The study is structured around two objectives: examining the relationship between vegetation and its proximity to streams and land surface temperature and analyzing trends in the Normalized Difference Vegetation Index (NDVI). Regression analysis revealed a negative correlation between vegetation and proximity to streams in lower zones (1–3), with no significant effect in higher zones (4–6). NDVI trend analysis indicated an overall increase in vegetation across most zones, with the exception of zone 5, which displayed a negative trend (slope −0.0025). The findings reveal that the decline is particularly pronounced among key tree species such as Ficus cordata subsp. salicifolia and Acacia asak, suggesting potential impacts from climate change and land use alterations. These zone-specific insights deepen our understanding of the dynamic ecological processes in semi-arid environments and guide targeted environmental management and conservation efforts.

South Asia has been experiencing recurring severe air pollution episodes in recent years. While many previous studies investigated such episodes focusing on individual cities and specific events, limited information exists on episode characteristics across multiple cities in this region. This study presents a comparative analysis of the characteristics of ambient PM2.5 pollution episodes in 12 South Asian cities across five countries during 2019−2023. Daily mean PM2.5 mass concentrations were decomposed into trend, seasonal, and residual components, and episodes were identified through anomalies in residuals. Furthermore, pollution episodes were characterized using magnitude, frequency, duration, and a relative severity index. The cities exhibited annual mean PM2.5 mass concentrations ranging from 20.6 ± 2.5 μg m−3 (Colombo) to 116.6 ± 9.3 μg m−3 (Lahore), with six out of 12 cities having annual mean PM2.5 mass concentrations > 50 μg m−3. Additionally, significant increasing trends (p < 0.05) in PM2.5 levels were observed for Dhaka, Chennai, Hyderabad, Kolkata, Islamabad, and Lahore (Sen's slope: 1.00−4.33 μg m−3 y−1), whereas decreasing trends (p < 0.05) were observed for Mumbai (−0.74 μg m−3 y−1) and New Delhi (−2.00 μg m−3 y−1). Mean PM2.5 episode magnitudes varied in a wide range from 49.9 ± 6.1 μg m−3 (Colombo) to 367.1 ± 17.9 μg m−3 (Lahore) across the cities. Likewise, the mean episode frequency ranged from 1.6 y−1 (Kathmandu) to 5.2 y−1 (Dhaka), whereas duration ranged from 1.2 (Mumbai) to 2.6 (Kathmandu) days per episode. Based on the relative index of episode severity, Lahore, Dhaka, and New Delhi exhibited high episode severity, as well as high baseline PM2.5 levels. In contrast, Karachi, Islamabad, Hyderabad, and Kathmandu showed moderate episode severity and moderate baseline PM2.5 levels, whereas Colombo and Mumbai showed low episode severity with low to moderate baseline PM2.5 levels. Moreover, annual PM2.5 episode severity ranks among the cities changed dramatically during 2019−2023. The relative severity of baseline and episodic pollution levels presented in this study may help policymakers prioritize the control strategies targeting pollution episodes, long-term trends, or both, as well as protecting human health through mitigation, preparedness, and forecasting. The findings will also provide insights for formulating regional policies aimed at transboundary cooperation and collaboration to deal with air pollution challenges across South Asia.

Water is one of the most essential elements for human life and must be provided for life requirements. Historical changes in meteorological data are vital for the planning and operation of water management. A total of 516-time series were used to evaluate the characteristics of drought in Elazig in Turkey. In this study, meteorological drought analysis was carried out in monthly and annual periods by using the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI, Innovative Polygon Trend Analysis (IPTA), and China-Z Index (CZI) drought indices. As a result, it was determined that there was an increase in dry periods for all time scales for eight meteorological stations, especially in 2000 and after. A downward trend was detected in precipitation data, while an upward trend was detected in temperature and evaporation data based on a 95% confidence interval. Although normal drought has the highest share among drought categories, very severe drought has the lowest share. it is determined that SPI gives more sensitive results in the very severe drought category than the SPEI index. As a result, the region's trend of rain and temperature will assist water management for resource planning. HIGHLIGHTS Standardized precipitation evapotranspiration index, standardized precipitation index, innovative polygon trend analysis, and China-Z Index drought indices were used for Elazıg in Turkey.; Precipitation intensity-duration-frequency (IDF) curves for return intervals of 2, 5, 10, 25, 50, and 100 years were obtained using precipitation data. Sen's slope, Mann–Kendall, and run test were used for data analysis.;

Abstract The extent and occurrence of extremely low-flow events are necessary to determine the minimum river flow. Since the true probability distribution is usually not known, the best fitting distribution function describing the low flow in the catchment is important for reliable estimation of low flow and its frequency. The Temsa River is one of the most important tributaries of the Abay River Basin in Ethiopia and has a high ecological value for the country that can be affected by land cover changes. Climate influences watershed development, while landscape features control the accumulation and release of water over time, influencing stream flow, such as low flow. Therefore, analyzing the state of river discharge is important for the economic management of water resources. Rapid population growth has raised serious concerns about the adequacy of the Temsa River’s future water intake in terms of quantity and quality. However, future water resources planning requires information on water flow variability and trends. The aim of this study is to identify and analyze the existing Temsa watersheds and their current status based on river water data collected by the Ethiopian Ministry of Water and Energy from 1997 to 2021 GC. Analysis focused on daily flow, mean annual flow, mean monthly flow, and consecutive 7-day mean minimum flow were included in the model. Methods for trend detection and quantification were the Mann–Kendall test (MK) and Sen’s slope estimator (SS). The results of the MK and SS tests indicate the existence of a trend of statistical significance. The study shows a positive trend for two models and a negative trend for the other two models. The daily discharge analysis and the annual average flow analysis show a decreasing trend and the second model shows an increasing trend. BFI results show that the proportion of groundwater in the watershed is moderate, 73.6%, and the lognormal distribution fits the frequency analysis data.

This research aims to assess the climatic temporal suitability over the year and identify the appropriate season for open-air tourism in ten Romanian cities. It was evaluated using the Enhanced Tourism Climatic Index on a temporal scale of one day and then aggregated to 10 days over 61 years (1961–2021). Daily mean and maximum temperature, mean and minimum relative humidity, wind speed, precipitation, and sunshine hours were employed in the investigation. The Mann–Kendall test and Sen’s slope were used for trend detection in the frequency, season duration, and first/last suitable day during the year for outdoor tourism. Acceptable or better weather conditions usually begin in the last part of April and end in mid-October, with Good or better conditions lasting between 260 and 310 days/year. The trend shows a shift of Good conditions earlier in the year (0.3–9.0 days/decade), resulting in a longer season duration (0.8–13.0 days/decade) for open-air activities. The trend is statistically significant mainly for the extra-Carpathian regions. Big differences in open-air events number during the climatically suitable season have been identified among the cities considered (2–19 events/year). This study is useful for better planning open-air events and activities for tourism and recreation.

This study was undertaken to address how near-surface soil water content (SWC) patterns have varied across diverse agroecological regions (AERs) of mainland India from 1979 to 2022 (44 years) and how these variations relate to environmental factors. Grid-wise trend analysis using the Mann–Kendall (MK) trend test and Sen’s slope was conducted to determine the trends and their magnitudes. Additionally, we used Spearman’s rank correlation (ρ) to explore the relationships of ESA CCI’s near-surface SWC data with key environmental variables, including rainfall, temperature, actual evapotranspiration, and the normalized difference vegetation index (NDVI). The results revealed significant variations in SWC patterns and trends across different AERs and months. The MK trend test indicated that 17.96% of the area exhibited a significantly increasing trend (p < 0.1), while7.6% showed a significantly decreasing trend, with an average annual Sen’s slope of 0.9 × 10−4 m3 m−3 year−1 for mainland India. Areas with the highest decreasing trends were AER-16 (warm per-humid with brown and red hill soils), AER-15 (hot subhumid to humid with alluvium-derived soils), and AER-17 (warm per-humid with red and lateritic soils). In contrast, increasing trends were the most prominent in AER-5 (hot semi-arid with medium and deep black soils), AER-6 (hot semi-arid with shallow and medium black soils), and AER-19 (hot humid per-humid with red, lateritic, and alluvium-derived soils). Significant increasing trends were more prevalent during monsoon and post-monsoon months while decreasing trends were noted in pre-monsoon months. Correlation analysis showed strong positive correlations of SWC with rainfall (ρ = 0.70), actual evapotranspiration (ρ = 0.74), and NDVI (ρ = 0.65), but weak or negative correlations with temperature (ρ = 0.12). This study provides valuable insights for policymakers to delineate areas based on soil moisture availability patterns across seasons, aiding in agricultural and water resource planning under changing climatic conditions.

The present study analyzes trends of extreme flows for the Krishna river at the Prakasam barrage, Vijayawada of Andhra Pradesh state, India. Annual maximum and minimum flows are considered using available data from the CWC (Central Water Commission) portal from 1965–1966 to 2019–2020. The trend analysis of extreme flows is carried out using the Mann-Kendall trend test, Sen's slope, and modified Mann-Kendall test, that is, bootstrapped Mann-Kendall trend test with optional bias-corrected pre-whitening. Also, R programming is applied for the trend analysis of extreme flows. Extreme annual flows are in decreasing trend. Annual maximum flow has significant decreasing trend with Sen's slope of 105.52 cumecs per year. Annual minimum flow has insignificant decreasing trend at a magnitude of 0.0002 cumecs per year. Trend results of the present study are useful for the trend analysis of extreme river flows. [ABSTRACT FROM AUTHOR]

Climate change is already a fact, where the intensity and frequency of changes in climate extremes and their impacts on various physical and biological systems contribute to global warming. The United Nations Global Sustainable Development Goal 13 calls for urgent action to address climate change and its impacts. The goals call for strengthening resilience and adaptive capacity at the national and local levels. Goal 13- Climate Action, is one of the nine goals for which India has shown a positive push. Tamil Nadu ranked second with a composite score of 74 and is listed as a Front Runner under the Performance towards SDG's goals in 2020-21. The National Action Plan on Climate Change Assessment declares that the climate parameter is important at the regional and local scale to estimate its impacts on various sectors. Hence in the present study, an attempt is made to analyze the trend and prediction of rainfall and temperature using statistical techniques like-Homogeneity test, Box and Whisker Plot test, Coefficient of Variability, Cluster Dendrogram, Mann-Kendall, Sen's slope, and ARIMA model in Dindigul district, Tamil Nadu, India. The rainfall and temperature data are collected from 15 rainfall stations spread across the district for 39 years, from 1981 to 2020. The results can help prioritize new strategies for managing available water resources to sustain the agriculture sector's dependence in the study area. The ongoing Action Plans of the Government of Tamil Nadu to achieve climate change mitigation measures are listed. These recommendations could be utilized by agriculturalists, agro-climatologists, and decision-makers to scientifically support development in the study area. [ABSTRACT FROM AUTHOR]

Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests. The random forest was the most accurate approach, followed by anomaly detection and the Sen's slope approach, with an overall accuracy of 0.75 (kappa = 0.50), 0.65 (kappa = 0.30), and 0.64 (kappa = 0.30), respectively. The classification results were unaffected by the Landsat acquisition times, indicating that rather, environmental variables may have contributed to the separation of declining and non-declining areas and not the remotely sensed spectral signal of the trees. We conclude that identifying declining forest patches in semi-arid regions using Landsat data is challenging. This difficulty arises from weak vegetation signals caused by limited canopy cover before a bright soil background, which makes it challenging to detect modest degradation signals. Additional environmental variables may be necessary to compensate for these limitations. [ABSTRACT FROM AUTHOR]

The extent and occurrence of extremely low-flow events are necessary to determine the minimum river flow. Since the true probability distribution is usually not known, the best fitting distribution function describing the low flow in the catchment is important for reliable estimation of low flow and its frequency. The Temsa River is one of the most important tributaries of the Abay River Basin in Ethiopia and has a high ecological value for the country that can be affected by land cover changes. Climate influences watershed development, while landscape features control the accumulation and release of water over time, influencing stream flow, such as low flow. Therefore, analyzing the state of river discharge is important for the economic management of water resources. Rapid population growth has raised serious concerns about the adequacy of the Temsa River's future water intake in terms of quantity and quality. However, future water resources planning requires information on water flow variability and trends. The aim of this study is to identify and analyze the existing Temsa watersheds and their current status based on river water data collected by the Ethiopian Ministry of Water and Energy from 1997 to 2021 GC. Analysis focused on daily flow, mean annual flow, mean monthly flow, and consecutive 7-day mean minimum flow were included in the model. Methods for trend detection and quantification were the Mann–Kendall test (MK) and Sen's slope estimator (SS). The results of the MK and SS tests indicate the existence of a trend of statistical significance. The study shows a positive trend for two models and a negative trend for the other two models. The daily discharge analysis and the annual average flow analysis show a decreasing trend and the second model shows an increasing trend. BFI results show that the proportion of groundwater in the watershed is moderate, 73.6%, and the lognormal distribution fits the frequency analysis data. [ABSTRACT FROM AUTHOR]