Search

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 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]

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.;

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.

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]

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]

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]

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]

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]

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]

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.

The objective of this study is to analyze and visualize the spatial distribution of trends for 74 climate indices on a monthly time-scale in direction, magnitude, and significance level at a resolution of 0.1° during the period of 1950–2021 over the European region. The Mann–Kendall and Sen's slope estimators reveal that growing degree days with mean air temperature >4 °C (gd4) and heating degree days with mean air temperature

ABSTRACTDetecting 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.

Water resource management is highly impacted by variations in rainfall, maximum and minimum temperature, and potential evapotranspiration. The rice area is also a key aspect for groundwater declination due to high-water consuming crop. Groundwater in Central Punjab has declined at an alarming rate over the last two decades. The decisions regarding water resource management need accurate information for the groundwater level. Therefore, to explore the main reason for the depletion of groundwater, it is essential that the most influential factors responsible for groundwater depletion should be addressed. A study was conducted in Central Punjab by using artificial neural network (ANN) and multiple linear regression (MLR) models during 1998–2018 to forecast the groundwater depth. ANN performed better than MLR. The sensitivity analysis showed that tubewell density, rice area, and rainfall are highly responsible for groundwater fluctuation. HIGHLIGHTS In the present study, both climatic and human-induced factors were taken for groundwater modeling.; Artificial neural network, a complex phenomenon was used to forecast groundwater depth.; Python was used for groundwater modeling.; ANN was found to be more accurate than MLR.;

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.

Based on data from 1966 to 2018, the present study analyses precipitation trends in the Zohreh–Jirahi basin in Iran. The homogeneity of data was estimated based on the Kolmogorov–Smirnov method. Missing data were reconstructed using inverse distance weighted (IDW) and ordinary-linear-kriging methods. The results were evaluated by the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). A multidimensional raster was generated containing precipitation values related to the statistical period years, and time-series data were produced in array format per unit area. Based on the period range, 34,267 points underwent the time-series analysis, and the trend change was assessed based on the Mann–Kendall method. The Sen’s slope was evaluated using yearly and monthly scales in these points, followed by producing raster maps. The IDW was selected with an R2 value of 0.95 as the optimal method of missing data reconstruction in this time period based on the model evaluation. The results showed no significant trends on a yearly scale, but on the monthly scale, December–April–November–August–September showed the highest ascending trends, respectively, February–December–March–May exhibited the highest descending trend, respectively, and June–July–September showed no trends. The maximum and minimum mean Sen’s Slope were estimated for December (0.213) and February (–0.68), respectively. Water resources management is inevitable, particularly in the agricultural sector as the main consumer with macro socioeconomic dimensions. Given the notable impact of water supply time to optimise and increase productivity, the current study can contribute to revising the cultivation patterns and time in the region. The recharge of aquifers, the storage process, and the consumption process should be compatible with forthcoming changes. [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]

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]

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]

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 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]

Fluctuations in the precipitation pattern often tend to have an impact on the availability of water, making it necessary to explore spatiotemporal variations in rainfall. This study explores the time series analysis of the rainfall from 1952 to 2019. The trend was analyzed using the modified Mann–Kendall test (MMK), and innovative trend analysis (ITA). The analysis showed that the northern region received the least rainfall while the southern region received the maximum rainfall except that one of the stations had a positive kurtosis. The kurtosis of the rainfall histogram ranges from −0.69 to 24.13. The trend was very well defined by all the methods, though MMK z statistics showed more occurrences of significant changes in the rainfall. The northeast monsoon carried a significantly decreasing trend at Chikkanayakanahalli station where the z value of MMK and ITA_R test showed values of −1.33 and −2.23, respectively, while all of the significantly increasing trends were defined by the MMK test in the annual and southwest monsoon season. The homogeneity test showed the most correlation between Pettitt and Buishand tests in comparison to SNHT. Later, the ARIMA model was run for the precipitation to predict the rainfall value from 2019 to 2029. HIGHLIGHTS The semi-arid region in Karnataka is prone to drought conditions and lacks the presence of any major rivers.; There are hardly any studies in the area which can contribute to policy making and mitigations method.; MMK, Sen Slope and ITA methods visualize the historical trend.; Homogeneity tests determine the changepoint and the ARIMA model forecasts the rainfall for the next decade.;

Rainfall characteristics are changing due to several reasons and change/trend detection is required. Literature survey reveals many relevant studies whose outcomes are divergent, possibly because different data series and different methodologies have been applied. This paper presents a critical appraisal of past studies and methodologies for trend analysis. Results of trend analysis of Indian rainfall data are presented. Data for all of India and for five homogenous regions (North-West, Central North-East, North-East, West Central, and Peninsular India) for 1871–2016 were used. The Pettitt change point test, regression, Mann-Kendall (MK), and Wavelet Decomposition were used to study different aspects of changes. Results of the change point test showed that most rainfall series had change points around 1957–65, possibly due to large-scale land use, cultivation, irrigation, and industrial changes in this period. Generally, rainfall for most homogenous regions and sub-divisions show falling trends; some are statistically significant. Series was also decomposed by the wavelet method. Approximate and detailed components of some decomposed series showed a significant declining trend. This work has focused on the magnitude of rainfalls; trends in rainfall intensities are also important. It is necessary to establish denser observation networks to collect short-term data and analyze. HIGHLIGHTS Detection of trends in data series helps in projections.; We present the results of trend analysis using long-term quality-controlled rainfall data.; Data series at country and regional levels at annual and monsoon scales were studied.; We found that most rainfall data have change points around 1957–1965; some are statistically significant.; Multi-resolution analysis highlighted periods of high variabilities in the data.;

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.

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.

The wind has considerable effects on the ecosystem and evaporation as an essential parameter of the hydrological cycle. Therefore, determining historical changes in the wind will help to specify these effect levels. Although there are studies on the determination of wind speed trends by several researchers in Turkey, it is necessary to investigate the changes in the trend structure with recent data. For this purpose, the trends of monthly surface wind speed data from 1970 to 2021 belonging to 199 meteorology observation stations in Turkey are determined in the present study. The nonparametric Mann–Kendall test and Sen's slope method are used in the trend analysis accounting for serial correlation effects. The trend analysis results of wind speed data are evaluated temporally and spatially for seven geographical regions within Turkey. As a result of this study, a prominent part of stations in Turkey shows a decrease or significant decrease trend. In addition, as a result of comparisons made with previous studies, it is determined that the trend structure of the wind speed in the country has changed. In the annual and monthly wind speeds, it is observed that the number of stations has a "significant trend" decreased considerably. [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]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rainfall is the primary resource for freshwater. Changes in the rainfall trend pattern influence the economic development of the country by tremendously affecting different economic sectors due to the absence or shifting of rainfall at particular periods of the year. This research aims to find the monthly, seasonal, and annual trend in the precipitation pattern and the relationship between the precipitation and time in Keragala, Bingiriya, and Mahawilachchiya in Sri Lanka in the Agroecological Region WL3, IL1a, DL1f, respectively. Daily Rainfall data were collected from 1961 to 2005 from the selected rainfall gauging stations. The Mann-Kendall test and the Augmented Dickey-Fuller test indicate the stationarity of rainfall. Keragala and Bingiriya show the annually insignificant increasing trend and Mahawilachchiya shows the annually insignificant decreasing rainfall trend. Different seasons show different trends in different agroecological regions. These changes show that climate change has influenced the rainfall trends and hence some impacts are faced by the agriculture and aquaculture sector in the country due to these trend changes, even though those are statistically insignificant. Climate change has taken up an inordinate primary place in the water management system. [ABSTRACT FROM AUTHOR]

Riparian systems across the Southwest United States are extremely valuable for the human and ecological communities that engage with them. However, they have experienced substantial changes and stresses over the past century, including non-native vegetation expansion, vegetation die-offs, and increased fire activity. Vegetation management approaches, such as ecological restoration, may address some of these stressors as well as reduce the risk of future impacts. We apply remote sensing to inform restoration priorities along the upper Gila River within the San Carlos Apache Reservation and Upper Gila River watershed. First, we develop a spatially and temporally explicit trend analysis across three observed climate periods (1985–1993, 1993–2014, 2014–2021) using the Landsat-derived indices to quantify changes in riparian vegetation conditions. These maps can be used to identify areas potentially more at risk for degradation. Second, we analyze changes in riparian vegetation within a climate framework to better understand trends and the potential effect of climate change. Vegetation greenness has largely increased throughout the watershed despite intensifying drought conditions across our study period, though areas within the lower watershed have shown increased stress and higher rates of wildfire and other disturbances over the past 5-years. Nevertheless, small-scale restoration activities appear to show improving vegetation conditions, suggesting efficacy of these restoration activities. Results from this study may be integrated with restoration objectives to develop a restoration plan that will help riparian vegetation communities adapt to change.

The trend analysis of precipitation for four rain gauge stations and runoff for Hayaghat station in the Bagmati river basin is carried out in this work by adopting modified Mann–Kendall and Sen's slope methods. Primary and secondary data are used for finding the monthly, seasonal, and annual trends at four stations. Primary data are the observed rainfall from 1981 to 2013 which were collected from IMD Pune, observed runoff data were collected from CWC Patna and secondary data from the National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis rainfall data for the period 1981–2013. The goals of this research are (i) to determine rainfall and runoff trend analysis, as well as the relationship between observed rainfall data and NCEP/NCAR reanalysis secondary data for four stations and (ii) to find the correlation between observed rainfall data and runoff data for all four seasons. The correlation analysis of observed rainfall and NCEP/NCAR reanalysis data shows a very good correlation ranging between 0.6111 and 0.7435. Rainfall is increasing during and after the monsoon at all selected stations, except during the monsoon season in Dhenge. Correlation analysis of rainfall and runoff shows ranges from 0.3724 to 0.4721 for all the four seasons. The correlation of rainfall and runoff is relatively good in monsoon season. HIGHLIGHTS To determine rainfall and runoff trends, as well as the relationship between observed rainfall data and NCEP/NCAR reanalysis secondary data for four stations.; To find the correlation between observed rainfall and runoff data for all four seasons.;

Abstract Abstract The identification of hydro-meteorological trends is essential for analyzing climate change and river discharge at the watershed level. The Ajora-Woybo watershed in Ethiopia was studied for long-term trends in rainfall, temperature, and discharge at the annual, monthly, and seasonal time scales. The rainfall and temperature data extend 1990 to 2020, whereas the discharge data span from1990 to 2015. The Pettitt and Standard Normal Homogeneity Test (SNHT) tests were used to determine homogeneity. The Mann–Kendall and Sen's slope tests, as well as numerous variability measures, were then employed for trend analysis. The degree of relationship between climate variables and river discharge was determined using Pearson correlation coefficients (r). Inhomogeneity was discovered in annual rainfall data from the Angacha and Areka stations. Rainfall and discharge showed insignificant trends over time, with increasing and decreasing variability across stations. Monthly rainfall decreasing trends were observed to be significantly falling in February and March. Rainfall and runoff increase just insignificantly during the Kiremt season. On the other hand, minimum, maximum, and mean annual temperatures showed significant trends with annual increases of 0.01 °C, 0.04 °C, and 0.025 °C, respectively. In this study, the relationships between discharge and temperature and rainfall were found to be moderate and minimal, respectively. Generally, the results of the long-term examination of the hydrological and climate parameters in the watershed show that water resources vary throughout and over time. As a result, designing strategies require due attention. Article highlights Hydrological and meteorological parameters are essential for analyzing trends in water resource and climate change at the watershed level. Mann-Kendall and Sen's slope tests, along with a number of variability measures, were utilized in conjunction with the time series analysis approach for trend analysis. The analysis of the rainfall, temperature, and discharge in the watershed's data generally demonstrates how the availability of water resource varies over time. Designing suitable plans for water resource management and sustainable development in the watershed is therefore essential.

Abstract Temperature variability may have direct and indirect impacts on the environments of the Accra and Kumasi Metropolises in Ghana. This study analysed temperature and trends in temperature in both cities using in-situ measurements from one meteorological station in both cities from 1986 to 2015. The temperature indices were computed using the RClimdex package from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI). The temperature time series was pre-whitened before the Mann–Kendall trend and Sen’s slope estimator analysis were applied. Initial analysis revealed minimal variation in temperature in both cities. The results from the analysed temperature indices revealed an increase in warm days and a general rise in the minimum temperature compared to maximum temperatures. Mann Kendall and Sen’s slope revealed significant trends in the annual and seasonal (dry and wet seasons) in minimum temperature in both cities. These might lead to an increased rate of heat-stressed diseases and an overall rise in urban warming in both cities. The analysis of temperature, indices and trends provided comprehensive insights into the temperature of Accra and Kumasi. The results highlight the essence of evaluating temperature indices and trends in light of Climate Change concerns. It is recommended that urban green and blue spaces should be incorporated into land use plans as these policy directions can aid regulate the temperature in both cities.

The flood in Kashmir valley has a long history and is considered as recurrent phenomena, though the flood occurred in 2014 was extremely severe due to its extent, intensity and impact on mankind. To understand the variation of this precarious climatic condition, the long-term variability of precipitation over Kashmir valley was analysed over a period of 1901–2018 on an annual, seasonal, monthly and weekly basis to investigate the pattern of precipitation and its linkage to flooding. The mean annual precipitation analysis exhibits only few (06) severe incidences (>1226mm). Sen’s slope estimation exhibited a statistically positive trend during winter season (0.458 mm year−1) as compared to rainy season (-0.661 mm year−1) at the 5% level of significance. The major deficit (> −99%) in precipitation anomaly was recorded forming the September month as anomalous. September 2014 exceptionally confronted one incidence of sudden but continuous weekly high precipitation (>130 mm) with intensity of 22.82 mm day−1 during 15 years.