Sen's Slope Estimator Research Articles

Variation of extreme values of rainfall and temperature in Subarnarekha River basin in India

Abstract In recent years, the Subarnarekha River basin has frequently experienced harsh weather and has also observed extreme floods during monsoons and a shortage of water during lean periods. Hence, in this study, the data of extreme values of rainfall and maximum–minimum temperatures from the years 1990 to 2020 in the Subarnarekha River basin have been analysed. The trend analysis of annual extreme rainfall, maximum–minimum temperatures, extreme diurnal temperature range, and number of rainy days have been determined using the Mann–Kendall test and Sen's slope estimator. Further, the analysis has also been carried out to study the shifting of the onset date of monsoon. The results show that extreme rainfall showed a significant increasing trend at a 95% level of confidence in the upper and middle parts of the basin, whereas it had a decreasing trend in the lower part. Annual extreme maximum temperature indicated a decreasing trend of 0.03 and 0.08 °C/year in the upper and middle parts, respectively, and the decrease in extreme minimum temperature in the lower part was 0.03 °C/year. No significant variations in the number of rainy days have been observed; however, a major shift in the onset date of the monsoon was observed during the selected time-period.

Spatiotemporal variation of water balance components in Mashhad catchment, Iran: Investigating the impact of changes in climatic data and land use

Abstract The research aims to investigate the spatiotemporal changes of water balance components and distinguish the relative impacts of climatic data and land-use on groundwater level in northeastern Iran. This investigation employs the WetSpass-M model to estimate water balance and the Mann-Kendall test alongside Sen's slope estimator to evaluate trend. The study also assesses mean annual water balance components, considering diverse combinations of land-use and soil. The findings offer a hydrological insight, revealing that 14% of precipitation results in runoff, 29% of that recharging the aquifer; the remaining portion is lost through evapotranspiration. The trends in precipitation and simulated water components are not significant but a significant downward trend in groundwater is observed beyond a specific point in time. Based on this outcome, as well as the analysis of land-use changes, it was speculated that human activities in this fast-developing region might be implicated in the decline in groundwater levels. Analysis of water balance components in various soil and land-use combinations indicates that evapotranspiration exhibits greater variability within the land-cover class, while recharge is more influenced by soil texture. These findings enhance our understanding for identifying potential sites for artificial recharge and determining sustainable groundwater withdrawals based on spatiotemporal recharge patterns.

Long-term spatiotemporal dynamics of groundwater storage in the data-scarce region: Tana sub-basin, Ethiopia

Imprudent extraction of groundwater tends to undue stress and portends its sustainability. Spatiotemporal analysis of groundwater storage anomaly (GWSA) is imperative for the judicious use, management, and sustainable development of a region. This study aimed to examine the changes in groundwater storage over the past 20 years in the Tana sub-basin using Gravity Recovery and Climate Experiment (GRACE) assimilated into Global Land Data Assimilation Systems (GLDAS). Validation analysis was carried out to evaluate the accuracy of GWSA against anomalies of Lake Tana water level, precipitation, and in-situ groundwater level. Modified Mann-Kendal test and Sen's slope estimator were applied for trend analysis of the GWSA. The results exhibited that GWSA strongly correlated (Pearson's correlation coefficient, R ranges from 0.75 to 0.96) with the three validation above variables, which elucidated in general, credible GWSA estimation. The net annual GWSA curve showed a non-significant (p > 0.05) decreasing trend from 2003 to 2012. However, years including 2005, 2006, and 2009 were drought periods, which caused 0.49 billion cubic meters (BCM) groundwater loss. In the entire study period (2003–2022), on the other hand, the net annual GWSA revealed a significant increasing trend (p < 0.05) with a rate of 0.333 cm/year. Generally, the Tana sub-basin was nurtured with a net 4.87 BCM groundwater gain in the study period. The most sensitive parts of the study area to large fluctuations of groundwater storage were mainly the nearby southern and eastern directions of Lake Tana.

Assessing exposure to climate extremes over the Arabian Peninsula using ERA5 reanalysis data: Spatial distribution and temporal trends

Understanding climate extremes and indices is crucial for addressing climate change impacts, including the increased variability in local weather patterns and extreme events. Previous studies on the Arabian Peninsula (AP) have been limited by sparse station data and restricted spatial coverage. To overcome this limitation, this paper aims to assess the spatial distribution and temporal trends of temperature and precipitation indices suggested by the Expert Team on Climate Change Detection and Indices (ETCCDI) using ERA5 reanalysis data from 1951 to 2020 for the AP. Additionally, this study investigated the changes relative to the reference period (1951–1980). The modified Mann-Kendall test and Sen's Slope estimator are utilized to detect significant trends and estimate their magnitude. Results revealed that ERA5 was consistent with previous studies that utilized in-situ station data, which offers a more detailed picture of the affected areas. The findings indicated a significant warming trend in temperature indices, with an increase of over 1 °C per decade observed across several areas, including the northern AP, and certain regions experiencing a discrepant change of 3 °C increase compared to the reference period. Changes in rainfall indices indicate a shift in rainfall patterns from the AP's fertile southwest regions towards more intense patterns in specific eastern regions, including Oman, Kuwait, Saudi Arabia, and Yemen. However, the precipitation temporal trends are weak in magnitude and variable spatially, with dominant decreases in both intensity (-10 mm per decade) and frequency indices (-5 days per decade). Some locations are subject to the combined effects of most heatwave indicators simultaneously, while flood indicators and yet others by drought indicators influence others. All these locations have been accurately identified in this study. The findings provide valuable information regarding the region's climate change vulnerability and adaptation needs.

Analyzing Coastal Groundwater Variability in Sindhudurg, Maharashtra: A Spatio-temporal GIS Approach+6

Due to a sharp increase in groundwater demands in coastal regions, groundwater levels are getting depleted day by day. The local groundwater table fluctuation eventually speeds up sea water intrusion. The present study investigates spatial and temporal groundwater level fluctuations to identify the area affected by sea water intrusion as well as for sustainable water resource management in coastal stretch of Sindhudurg district, Maharashtra, which is about 121 km. Using groundwater level data collected from Central Groundwater Board (CGWB), Sindhudurg for the period from 1991 to 2022, fluctuations of groundwater levels were examined. The collected levels were evaluated for monsoon, pre-monsoon &amp; post-monsoon seasons and all levels were interpolated over the study area using the Inverse Distance Weighted Method as a spatial interpolation technique in QGIS 3.22.1, in order to obtain the actual differences in groundwater levels. According to the results, the groundwater level varies from 1.88m to 13.45 m below ground level (bgl) in the pre-monsoon season, from 0.1m to 7.05 m (bgl) in the post-monsoon season and from 1m to 10.6m (bgl) in the monsoon season. Using statistical tests like Mann-Kendall test &amp; Sen's slope estimator, the pattern of variation of groundwater levels was anticipated. According to our findings, over 80% of the basin has a falling water table during the monsoon season. It was observed that the groundwater level in the pre-monsoon season was more as compared to those in the monsoon and the post-monsoon seasons in the entire study area. The overall outcome of the study demonstrates that the groundwater level fluctuations were non-uniform in the study area, which may cause an increase in the sea water intrusion, leading to a decrease in crop production. Keywords: GIS, Groundwater level, Sea water intrusion, Mann Kendall test, Spatial interpolation, Sen’s slope estimator.

Trend analysis of rainfall data using Mann-Kendall test and sen's slope estimator

Rainfall is the most important fundamental physical parameter among the climate, as this parameter determines the environmental condition of the particular region, which affects the agricultural productivity. Global warming or climate change, is one of the most important worldwide issues discussed among scientists and researchers. One of the consequences of climate change is the alteration of rainfall patterns and an increase in temperature. The drastic changes in rainfall pattern showed a significant impact on society, and therefore its up-to-date information is needed to estimate the spatial distribution and variability at all points of the territory. In this paper, a study on trend analysis of rainfall data observed at Anasi, Haliyal, Kadra, and Supa rain gauge stations in Karnataka, India, was carried out. For this purpose, the annual 1-day maximum rainfall (AMR) and annual total rainfall (ATR) series were generated from the daily rainfall data and used in trend analysis. A non-parametric Mann-Kendall (MK) test was applied to evaluate the presence of significant trend in AMR and ATR, while the rate of significant trend was computed by Sen's slope estimator (SSE). The MK test results indicated that there is a decreasing trend in AMR series of Anasi, Haliyal, Kadra, and Supa. The study showed that the rate of decreasing trend in the AMR series of Anasi, Haliyal, Kadra, and Supa is computed as 1.4 mm/year, 0.1 mm/year, 0.6 mm/year, and 0.2 mm/year, respectively. For the ATR series, the rate of decreasing trend for Anasi was computed as 23.8 mm/year, whereas 11.2 mm/year for Supa, whereas the rate of increasing trend was 4.3 mm/year for Haliyal and 2.2 mm/year for Supa. This paper illustrated the application of the MK test and SSE for analyzing the trend in AMR and ATR of Anasi, Haliyal, Kadra, with Supa and the results obtained from the study.

Changes in the hydrological regime and Climatic variability assessment within the Belaya River catchment, Russia

The trends of changes in the indicators of the hydrological regime and climatic variability in the catchment area of the Belaya River were assessed. Mann-Kendall test and Sen's slope estimator at 8 out of 10 (80%) in the considered posts showed the presence of a positive trend in changes in the average monthly values of streamflow. At the same time, meteorological data sets at 19 of 34 (56%) observation stations have a positive trend of changes in the average monthly precipitation and temperature.

Spatiotemporal dynamics of ecosystem services in response to climate variability in Maze National Park and its environs, southwestern Ethiopia.

Climate variability is one of the major factors affecting the supply of ecosystem services and the well-being of people who rely on them. Despite the substantial effects of climate variability on ecosystem goods and services, empirical researches on these effects are generally lacking. Thus, this study examines the spatiotemporal impacts of climate variability on selected ecosystem services in Maze National Park and its surroundings, in southwestern Ethiopia. We conducted climate trend and variability analysis by using the Mann-Kendall (MK) trend test, Sen's slope estimator, and innovative trend analysis (ITA). Relationships among ecosystem services and climate variables were evaluated using Pearson's correlation coefficient (r), while partial correlation was used to evaluate the relationship among key ecosystem services and potential evapotranspiration (PET). The MK tests show a decreasing trend for both mean annual and main rainy season rainfall, with Sen's slope (β) = -0.721 and β = -0.1.23, respectively. Whereas, the ITA method depicted a significant increase in the second rainy season rainfall (Slope(s) = 1.487), and the mean annual (s = 0.042), maximum (s = 0.024), and minimum (s = 0.060) temperature. Spatial correlations revealed significant positive relationships between ecosystem services and the mean annual rainfall and Normalized Difference Vegetation Index (NDVI), while negative correlations with the mean annual temperature. Additionally, temporal correlations highlighted positive relationships among key ecosystem services and the main rainy season rainfall. The maximum and minimum temperatures and ecosystem services were negatively correlated; whereas, there was strong negative correlations between annual (r = -0.929), main rainy season (r = -0.990), and second rainy season (r = -0.814) PET and food production. Thus, understanding the spatiotemporal variability of climate and the resulting impacts on ecosystem services helps decision-makers design ecosystem conservation and restoration strategies to increase the potential of the ecosystems to adapt to and mitigate the impacts of climate variability.

Monitoring and evaluation of vegetation restoration in the Ebinur Lake Wetland National Nature Reserve under lockdown protection.

For a long time, human activities have been prohibited in ecologically protected areas in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). The implementation of total closure is one of the main methods for ecological protection. For arid zones, there is a lack of in-depth research on whether this measure contributes to ecological restoration in the reserve. The Normalized Difference Vegetation Index (NDVI) is considered to be the best indicator for ecological monitoring and has a key role to play in assessing the ecological impacts of total closure. In this study, we used Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to select optimal data and utilized Sen slope estimation, Mann-Kendall statistical tests, and the geographical detector model to quantitatively analyze the normalized difference vegetation index (NDVI) dynamics and its driving factors. Results were as follows: (1) The vegetation distribution of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) had obvious spatial heterogeneity, showing low distribution in the middle and high distribution in the surroundings. The correlation coefficients of Landsat-8 and MODIS, Sentinel-2 and MODIS, and Sentinel-2 and Landsat-8 were 0.952, 0.842, and 0.861, respectively. The NDVI calculated from MODIS remote sensing data was higher than the value calculated by Landsat-8 and Sentinel-2 remote sensing images, and Landsat-8 remote sensing data were the most suitable data. (2) NDVI indicated more degraded areas on the whole, but the ecological recovery was obvious in the localized areas where anthropogenic closure was implemented. The ecological environment change was the result of the joint action of man and nature. Man-made intervention will change the local ecological environment, but the overall ecological environment change was still dominated by natural environmental factors. (3) Factors affecting the distribution of NDVI in descending order were as follows: precipitation > evapotranspiration > land use type > elevation > vegetation type > soil type > soil erosion > slope > temperature > slope direction. Precipitation was the main driver of vegetation change in ELWNNR. The synergistic effect of the factors showed two-factor enhancement and nonlinear enhancement, and the combined effect of the driving factors would increase the influence on NDVI.

Assessing decentralized groundwater governance performance in the lower Mekong region: The case of Khon Kaen province, Thailand

Decentralized groundwater management has been implemented in various forms in the Lower Mekong Region (LMR) aiming to improve the sustainability and equity of groundwater (GW) management. However, fragmented implementation and unclear delegation of responsibilities have hindered the success of these approaches. The Royal Thai government has been a pioneer in introducing comprehensive groundwater policies and has implemented formal measures for GW management for over five decades, including the decentralized GW management model introduced in 2011. This study examined the performance of GW governance (GWG) in the LMR through the case of Khon Kaen province, Thailand, and using the OECD water framework with contextualized indicators. Sen's slope estimation and the Mann-Kendal test were also utilized to look into the patterns of groundwater extraction data in the study area from 2000 to 2019 in relation to the policy timeline analysis.The findings revealed a significant increase in GW extraction for business usage after the implementation of decentralization, driven by urbanization, and economic and tourist development. The research also found that the rise of new water actors with divergent interests, marginalization, the absence of non-governmental actors, and a lack of scientific and technical capacities prevent the effective GWG. However, the study discovered that the majority of GWG principles are favorably related to one another, with regulatory frameworks, defined roles and duties, and coherent policy being closely associated with most GWG principles. On the other hand, trade-offs across sectors and users were seen to have a detrimental effect on other GWG principles. The findings contribute to evolving the GWG Theory of Change (TOC), centering on GW users. This challenges the conventional idea of addressing water shortage solely through increased infrastructure, emphasizes institutional pluralism and scientific evidence for market-based mechanisms, and addresses GW use disparities.

Trend detection and change point analysis of inflows in Karuppanadhi and Gundar dams of Chittar River Basin, Tamil Nadu, India

Abstract Gaining insights into the patterns of hydroclimatic elements such as inflows to dams is essential to adeptly strategize and oversee water resource planning. For this study, we focused on Karuppanadhi and Gundar dams within the Chittar River Basin in Tamil Nadu. We employed various time series trend assessment methods – Mann–Kendall (MK), modified Mann–Kendall (MMK), Sen slope estimator, and innovative trend analysis (ITA). Furthermore, we conducted change point detection analysis using homogeneity tests, namely Pettitt's test, standard normal homogeneity test (SNHT), and Buishand test. The analysis was carried out across three-time scales: monthly, seasonal, and annual for 30 years (1991–2020) span. Results revealed declining trends significantly across all three timescales for the Karuppanadhi dam. Whereas for the Gundar dam, notable trends included increased patterns in January, March, December, and the winter season, while other months/seasons showed statistically decreasing trends. ITA exhibited greater sensitivity in identifying trends at monthly and seasonal scales, indicating its superior trend detection over MK and MMK methods. Change point analysis of trends identified a rising trend in August post-2017 for both dams. Other change points indicated decreasing inflow trends thereafter. Analysing trends and change points in dam inflows aid decisions on water resources management.

Block-level long-term rainfall variability using trend analysis in a state of central India

Abstract Rainfall is the key weather element which regulates the hydrological cycle, availability of water resources and crop production. In this study, spatial and temporal variability of rainfall has been investigated on seasonal and annual time scales of the 149 blocks of Chhattisgarh State using 120 years (1901–2020) of rainfall data. Non-parametric, and Theil and Sen's slope estimator were used to identify possible trends and ascertain the variability in the magnitude. The results revealed that there exists a well-marked spatial variability in rainfall over Chhattisgarh on annual and seasonal time scales. Out of 149 blocks, a significant negative rainfall was noticed in 105 blocks. Annual rainfall showed a significant positive trend in a few blocks like Bhopalpattnam, Bijapur, Usur, and Konta. A similar pattern of trend was noticed in the monsoon season. The results of the study demand the urgent need to formulate policies and strategies for water resource management and planning. The blocks which showed the positive rainfall trends can be identified to intensify the cultivation of more water-requiring crops based on the suitability for that region. The findings of this study can be used as valuable information for crop planning, policy-making and preparation of contingency plans.

Local and regional climate trends and variabilities in Ethiopia: Implications for climate change adaptations

Ethiopia is experiencing considerable impact of climate change and variability in the last five decades. Analyzing climate trends and variability is essential to develop effective adaptation strategies, particularly for countries vulnerable to climate change. This study analyzed trends and variabilities of climate (rainfall, maximum temperature (Tmax), and minimum temperature (Tmin)) at local and regional scales in Ethiopia. The local analysis was carried out considering each meteorological station, while the regional analyses were based on agro-ecological zones (AEZs). This study used observations from 47 rainfall and 37 temperature stations obtained from the Ethiopian Meteorological Institute (EMI) for the period of 1986 to 2020. The Modified Mann-Kendall (MMK) trend test and Theil Sen's slope estimator were used to analyze the trends and magnitudes of change, respectively, in rainfall as well as temperature. The coefficient of variation (CV) and standardized anomaly index (SAI) were also employed to evaluate rainfall and temperature variabilities. The local level analysis revealed that Bega (dry season), Kiremt (main rainy season), and annual rainfall showed increasing trend, albeit no significant, in most stations, but the rainfall in Belg (small rainy) season showed a non-significant decreasing trend. The regional levels analysis also indicated an increasing trend of Bega, Kiremt, and annual rainfall in most AEZs, while Belg rainfall showed a decreasing trend in the greater number of AEZs. The result of both local and regional levels of analysis discerned a spatially and temporally more homogeneous warming trend. Both Tmax and Tmin revealed an increasing trend in annual and seasonal scales at most meteorological stations. Likewise, an increase was recorded for mean Tmax and Tmin in entire/most AEZs. The observed trends and variabilities of rainfall and temperature have several implications for climate change adaptations. For example, the decrease in Belg rainfall in most AEZs would have a negative impact on areas that heavily depend on Belg season's rainfall for crop production. Some climate adaptation options include identifying short maturing crop varieties, soil moisture conservation, and supplemental irrigation of crops using harvested water during the main rainy season. Conversely, since the first three months of Bega season (October to December) are crop harvest season in most parts of Ethiopia, the increase in Bega rainfall would increase crop harvest loss, and hence, early planting date and identifying short maturing crops during the main rainy season are some climate adaptation strategies. Because of the increase in temperature, water demand for irrigation during Bega season will increase due to increased evapotranspiration. On the other hand, the increase in Kiremt rainfall can be harvested and used for supplemental irrigation during Bega as well as the small rainy season, particularly for early planting. In view of these findings, it is imperative to develop and implement effective climate-smart agricultural strategies specific to each agro-ecological zone (AEZ) to adapt to rainfall and temperature changes and variabilities.

Spatiotemporal &amp; Geostatistical Modelling of Groundwater Level Depth Over Haryana-Punjab, India

The present study focuses on the Haryana and Punjab regions of the Indus basin, India. The study aims to analyze the long-term spatial-temporal changes in the groundwater level from 1996 to 2019. The modelling study involves a twofold objective. First, the ordinary kriging method estimates and evaluates spatial and temporal variations in groundwater level depth (surface-to-water level). Second, the study applies a pixel-based Mann-Kendall trend analysis. The point kriging cross-validation (PKCV) results are optimum, acceptable, and supports the unbiasedness hypothesis of kriging. The trend, significant or not, is determined by the Mann-Kendall test, while Sen's slope estimator determines the slope magnitude of the trend. Results revealed a significantly (at 99% and 95% confidence interval) high-rate depletion zone of groundwater level from 1996–2019 in the southwestern-central region to the western-northern area for all four seasons. The average rate of groundwater level in these zones declined from 40.36 cm/yr. to 37.42 cm/yr. It was observed from trend modelling in the monsoon season for 2014-2019 (Six-year window) that the net per cent area of groundwater level for the study area in the high- and low-rate depletion zone was hiked by 0.90% and 2.17%, respectively.

Spatiotemporal Trends of Extreme Temperature Events Along the Qinghai‐Tibet Plateau Transportation Corridor From 1981 to 2019 Based on Estimated Near‐Surface Air Temperature

AbstractThe intensification of global warming leads to the increased frequency of extreme temperature events. Many studies have reported on numerous regions facing extreme hot and cold temperatures to some degree. In this study, the daily maximum, minimum and mean near‐surface air temperatures were estimated to explore the detailed spatial trends of extreme temperature events along the Qinghai‐Tibet Plateau Transportation Corridor (QTPTC). Sixteen extreme temperature indices were used to represent extreme temperature events, and the Mann‐Kendall trend test and Sen's slope estimation method were employed to explore the spatiotemporal trends of extreme temperatures along the QTPTC during 1981–2019. The possible factors that affect the trends of extreme temperature events were also discussed, indicating that: (a) There was an exacerbation of extreme hot events, while the prevalence of extreme cold events weakened along the QTPTC. This was in accordance with the trend of global warming. (b) The trends of extreme temperature indices based on daily minimum temperature were stronger than those based on daily maximum temperature in most areas, except for the eastern region. Furthermore, as altitude increased, the amplitude of warming intensified. (c) Unlike the trend in most areas, the intensified warming trend occurred in the low‐altitude areas in the eastern region where there are more human activities and rapid urbanization, and the daily maximum temperature trend was more severe there. The results can provide references for policymakers to formulate corresponding adaptation strategies, addressing the impacts of extreme temperature events on transportation facilities with significant elevation differences.

Ecological risk assessment of grassland vegetation change based on Bayesian model in Xilin Gol League, China

As an essential ecological barrier in the north of China, the grassland degradation situation in the Xilin Gol League of Inner Mongolia has remained severe in recent years. Based on data spanning from 2010 to 2022, this study utilizes a set of ten indicators, encompassing altitude, temperature, precipitation, wind speed, FVC, LAI, FRAR, NPP, ET, and above-ground biomass, to construct a Bayesian model for assessing ecological risks associated with vegetation change in the Xilin Gol League. In the case of the most crucial parameter, above-ground biomass, we integrate spectral bands, vegetation indices, and ground-truth data, employing the Random Forest algorithm to estimate the above-ground biomass in the Xilin Gol League. The study further applies the Mann-Kendall trend test and Sen's slope estimation to analyze changes in this parameter and employs it as an indicator for assessing grassland degradation. The results showed that (1) Overall, the risk level decreases gradually from the west to the east and aligns with the grassland types of the Xilin Gol League. (2) In terms of spatial and temporal distribution, medium and high-risk areas are primarily located in the western region of the Xilin Gol League, showing a distinct westward shift from 2010 to 2022. (3) Areas with higher grassland degradation risk are characterized by lower altitudes, higher temperatures, less precipitation, lower vegetation coverage, and reduced potential evapotranspiration. (4) Under the pressures of both climate warming and vegetation change, there has been a significant shift in grassland degradation risk levels in the Xilin Gol League. The application of the Bayesian model effectively reveals intricate causal relationships among various variables, such as climate, terrain, and vegetation dynamics. This study could provide guidance for the prevention and control of grassland degradation in the Xilin Gol League.

Trend analysis of rainfall from 1981-2022 over Zambia

There is insufficient paucity of information on trends in long-term monthly and decadal rainfall in Zambia. This study assessed the monthly and decadal trends in rainfall over the agro-ecological regions (AERs) and Zambia from 1981 to 2022. The Mann-Kendall test statistic was used at 5 % significant level to compute trends in rainfall at monthly and decadal time step on CHIRPS v2 at 0.05° resolution. R/RStudio Sen's slope estimator was used to give the magnitude of the observed trends. The monthly rainfall time series trend over Zambia ranges from −0.04 to 0.03. The decadal trend analysis of rainfall at annual and monthly time step exhibits a decreasing/increasing trend with Sen's slope between −49.27 and 71.26 mm. Decadal trend at annual time step in AERIII, AERIIa, AERIIa and AERI exhibits a Sen's slope of −44.11 to 62.48 mm, −15.29 to 41.58 mm, −6.08 and 71.26 mm, and 2.20–64.86 mm, respectively. The decadal trend at monthly time step in AERIII, AERIIa, AERIIa and AERI exhibits a Sen's slope of −132.08 to −3.15 mm, −123.39 to −8.57 mm, −73.08 to −15.17 mm, and −80.02 to −5.21 mm, respectively. Decrease in rainfall is expected to affect agriculture, energy, water resources, sanitation and socio-economic aspects. Rainfall pattern shows spatio-temporal variability over Zambia. The results provide valuable input into the National Adaptation Plan and also useful for strategic planning purposes in water resources management under a changing climate. It is evident that spatio-temporal time steps utilized in this study provides new insights of rainfall trends at seasonal, monthly, and annual and decadal time steps.

Mechanistic interaction between climate variables rainfall and temperature on surface water quality and water treatment costs at the Barekese Headworks, Ghana: A time series analysis and water quality index modelling approach

Extreme rainfall and temperatures are climate variables that threaten global water supplies and surface water quality (SWQ). To understand how rainfall and temperature interact with surface water quality and water treatment costs, this study, unlike previous ones, uses time series analysis (TSA) and water quality index (WQI) modelling to fill significant research gaps. The study uses data from the Barekese Water Treatment Headworks, Ghana. Water quality data from 2000 to 2019 for the Barekese Headworks were obtained from the Ghana Water Company Limited. Rainfall data for the catchment was obtained from the Ghana Meteorological Agency. The Mann-Kendall statistical test for trend and the Canadian Council of Ministers of the Environment (CCME) water quality index was applied to data sets. The Mann-Kendall trend test showed no significant change in annual temperatures. An increasing trend for annual rainfall was observed, but this was not statistically significant (Z = 0.21). Sen's slope estimator (Q) showed that rainfall increases at 3.03 mm annually. pH correlated negatively with rainfall (r = - 0.15). Correlations were observed between rainfall and temperature and dissolved oxygen (DO), turbidity, Total Dissolved Solids (TDS), Nitrate (NO3−), Phosphate (PO43−), and Manganese (Mn). Rainfall was observed to increase the cost of liming, coagulation, and disinfection. A 20.26% deterioration in SWQ was observed from 2009 to 2019. The SWQ over the period under study and according to the CCME water quality index was 80% marginal, 10% fair and 10% poor. The findings reveal that the concurrent use of TSA and WQI modelling can help elucidate how rainfall and temperature interact with SWQ and water treatment costs. It further contributes knowledge to attaining the Africa Union Agenda 2063 on climate resilience and the Sustainable Development Goals (SDG) 6 and 6.3 on universal water access and quality improvement.

Variability and trend detection of water discharge over the past 60 years in the Vietnamese Hieu River Basin

Abstract This paper presents an in-depth analysis of the variability and trend detection of water discharge in the Hieu River Basin over the period from 1/1/1961 to 31/12/2020. Firstly, the wavelet analysis is implemented to investigate the flow variability at different time scales. Using time series of daily flow at Quy Chau and Nghia Khanh, the periodicities of flow are examined, showing that periodic oscillations in flow mainly occurred at 1 year, from 2 to 4 years, and from 4 to 8 years. Secondly, discharge variability is investigated, revealing an increase from January to September (excluding June) and a decrease from October to December. At Quy Chau, flow increased seasonally and annually by 0.19 and 0.06 m3/s, respectively, while its values at Nghia Khanh decreased of up to −0.50 m3/s. Thirdly, temporal trends of flow are assessed using Mann–Kendall and Sen's slope estimator, and sequential Mann–Kendall test. Results show flow decreases during 1961–1969, 1973–1976, and 1996–2020 and increases in 1969–1973 and 1976–1996. Temporal patterns of flow at Quy Chau and Nghia Khanh demonstrated synchronization across different time scales. Relationship between flow and rainfall is discussed to investigate insights into their relationship.

Assessing the performance of parametric and non‐parametric tests for trend detection in partial duration time series

AbstractThe 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.