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Rank Correlation Methods
... A range of statistical methods can be employed to detect positive or negative trends in time series data. In the context of climate series, the Mann-Kendall test (Mann (1945) and Kendall (1975)) and Sen's slope (Sen (1968)) are commonly utilized. ...
... Subsequently, the climate indicators were subjected to trend tests using the nonparametric Mann-Kendall test (MANN, 1945;KENDALL, 1975), and the magnitude of these trends was determined using Sen's Slope estimator (SEN, 1968). A similar analysis was conducted for average temperature data and, warming stripes visualizations were created to facilitate comparison of temperature fluctuations across the quarters and years. ...
... The Mann-Kendall test (MANN, 1945;KENDALL, 1975) is a robust, sequential, non-parametric method used to determine whether a given data series has a statistically significant tendency to change its pattern of data behavior over time. As it is a non-parametric method, it does not require normal data distribution (YUE; PILON;CAVADIAS, 2002). ...
This study focuses on analyzing trends in extreme climate indices in 11 different regions of Brazil on a quarterly basis, from 1961 to 2019. The aim is to identify changes in climate trends over time, particularly in terms of increasing climatic extremes, such as maximum and minimum temperature. Statistical tests were used to determine the presence of trends and the value of the changes. This study reveals that in most cities exhibited the minimum temperature recorded tends to increase in at least two quarters per year, while only a few did not show significantly increasing trends in maximum temperature. In terms of extreme temperature indices, a few regions presented statistically significant trends, such as Belém and Cuiabá, which showed a reduction in the occurrence of cold nights and hot days, respectively, across all quarters. Significant increases in the percentage of hot days and hot nights, as well as in maximum, minimum, and average temperatures across different regions, were observed. Additionally, in some seasons of the year precipitation events changed, with an increase in the concentration of rain in short periods and in the number of consecutive days without precipitation. Resumo: Este estudo analisa trimestralmente as tendências dos índices climáticos extremos em 11 diferentes regiões do Brasil, de 1961 a 2019. O objetivo é identificar as mudanças nas tendências climáticas ao longo do tempo, em termos de aumento nos extremos climáticos, como temperatura máxima e mínima. Testes estatísticos foram usados para determinar a presença de tendências significativas nas variações dos parâmetros climáticos e quantificá-las. Este estudo revela que a maioria das cidades apresentou uma tendência de aumento do valor da temperatura mínima registrada em pelo menos dois trimestres por ano, enquanto apenas algumas não apresentaram tendências de aumento significativo na temperatura máxima. Em relação aos índices de temperaturas extremas, apenas algumas regiões apresentaram tendências estatisticamente significativas, como Belém e Cuiabá, que apresentaram redução na ocorrência de noites frias e dias quentes, respectivamente, em todos os trimestres. Foi observado aumento significativo na porcentagem de dias e noites quentes, bem como nas temperaturas máximas, mínimas e médias em diferentes regiões. Adicionalmente, algumas estações do ano apresentaram mudanças nos eventos de precipitação, com aumento da concentração de chuvas em períodos curtos e do número de dias consecutivos sem precipitação. Palavras Chave: Extremos climáticos; análise de tendências; séries temporais climáticas.
... Furthermore, the normalisation used in this index enables 71 comparison between areas with varying climatologies. To determine significant trends, the non-72 parametric Mann-Kendall test (MKT) was applied (Mann, 1945;Kendall, 1948 Kahya, 2006). To measure the 75 variation of trends, the non-parametric Sen's slope approach was used (Sen, 1968). ...
The objective of the authors is examines drought trends in Sicily over the past century. The analysis focuses on the nine provinces that comprise the region, The Standardized Precipitation Index (SPI) is used to identify pluviometric deficit at different time scales, including 3, 6, 12, and 24 months. Additionally, the Mann-Kendall test is applied to check if the SPI has a significant trend, especially if it is decreasing; as the SPI decreases, the pluviometric deficit increases.The statistical approach of the study confirms that in Sicily, meteorological and hydrological droughts are becoming more frequent. In addition, a rising trend of socio-economic droughts has been identified. It is therefore necessary to target mitigation and adaptation measures on the areas most vulnerable to drought in order to safeguard the agricultural sector and, consequently, a significant part of the region's productive activities. The methods used in this work could be applied to the management of water resources and the protection of the island's agricultural and economic sectors.
... Em amostras extensas, conforme descrito por Mann (1945) e Kendall (1975), estatística S assume uma distribuição aproximadamente normal, com média zero e variância σ² que pode ser expressa por: ...
As frentes frias desempenham um papel crucial no regime de precipitação no sudeste do Brasil, especialmente na região metropolitana de São Paulo (RMSP). A compreensão das tendências e variabilidades desses sistemas e da chuva associada é fundamental para avaliar impactos climáticos locais. Este estudo tem como objetivo analisar as características das frentes frias, a precipitação e seus extremos, contribuindo para melhorar a compreensão das tendências climáticas na região. Analisa ainda a habilidade da reanálise ERA-20C em capturar climatologia observada de frentes frias. Entre 1960-2010, uma média de 39,6 frentes frias por ano foram observadas na estação meteorológica localizada na RMSP. A ERA-20C mostrou resultados muito próximos com os obtidos das observações. Os extremos de precipitação foram identificados usando o percentil 95 da precipitação diária (em mm) de cada mês, considerando-se apenas os dias com precipitação acima de 0 mm. No período 1960-2010, este critério resultou em 486 eventos extremos de precipitação, dos quais 290 podem estar relacionados com a passagem de frentes frias na RMSP. Foram analisadas tendências para séries temporais de precipitação, frequência de frentes frias e precipitação extrema através do teste Mann-Kendall. Apenas a série temporal de precipitação anual acumulada apresentou taxa de crescimento linear significativa.
... This information is invaluable for policymakers, environmental scientists, and stakeholders who are actively involved in formulating strategies for sustainable resource management, particularly in the face of evolving and unpredictable climate patterns. As such, this study becomes an indispensable resource for those navigating the complex intersection of environmental science and policy implementation [11]. ...
The future development and sustainable management of water resources will be greatly influenced by the trend analysis of precipitation. This paper focuses on the analysis of rainfall trends in the Bundelkhand region, particularly in six districts covering the Ken basin. Mann-Kendall test was employed to identify potential trends, and the Sen’s slope estimator was utilized to quantify the magnitude of change during 1990 to 2020. Given that a significant portion of rainfall occurs during the southwest monsoon season, the study included both seasonal (monsoon) and annual analyses. The findings indicated a decreasing trend in precipitation for both the monsoon season and the annual scale, although many districts exhibited insignificant trends. The outcomes of this study offer valuable insights for agriculture, related sectors, and contribute to the food and energy security considerations for the six districts in the Bundelkhand region and regions with similar physiography.
... These methods detrend the data within a rolling window. In this study, a sensitivity analysis was conducted using Kendall's τ, a nonparametric statistic measuring the association between indicators and time, to identify the optimal size of the rolling window and bandwidth for the Gaussian filter (Bevan and Kendall 1971). Kendall's τ ranges from -1 to +1, where higher values indicate stronger trends, aiming to identify the detrending settings that best capture trends in the leading indicators. ...
Generating early warning signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such early warning signal systems are still limited by the lack of data and uncertainties associated with predicting complex systems such as ecosystems. This research aims to investigate the feasibility of developing an early warning system capable of identifying an upcoming critical transition within mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land cover. To identify early warning signals, indicators such as autocorrelation (acf(1)), standard deviation (SD), and skewness (SK) are applied. The findings indicate a signal of reduced resilience by a significant increase in NDVI statistical metrics (acf(1): 0.50, SD: 0.9). Although MNDWI showed significant early warning signals in Qeshm Island (acf(1); 0.86, SD: 0.90), it provided a false alarm in the reference study site. MVWR failed to generate early warning signals of reduced resilience (acf(1); -0.100, SD: -0.07, SK: -0.21). Land-use land-cover change may explain reduced resilience in the forests. This study not only emphasizes the potential of remote sensing in monitoring the state of mangrove forests but also contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts aimed at safeguarding mangroves. This is the first report of the successful application of remote sensing in generating early warning signals of reduced resilience within mangrove forests in the Middle East.
... Here, we use data from five surface stations and twelve points of the NCEP/NCAR reanalysis database (Kistler et al. 2001) on the west (75° W), east (45° W), and over the AP (60° W) to investigate extremes of air surface temperature and wind components over these different climatic regions. Thus, the aim of this study is firstly to verify trends and eventual changes in the time series, applying non-parametric statistical analyses of Mann (1945), Kendall (1975), Sen (1968), and Pettitt (1979) to verify trends and eventual changes in the time series. In addition, extreme value statistics is applied to better understand the extremes of the data based on the tails. ...
The west side of the Antarctic Peninsula (AP) has shown great variability since the middle of the last century characterized by warming mainly because of the oceanic and atmospheric effects such as the disintegration of floating ice and the strength of westerly winds. Here, we used two climatic databases (reanalysis from 1979 to 2020 and surface stations from 1947 to 2020) to investigate trends in extreme air temperatures and wind components in the oceanic region between 55° S and 70° S in the west (75° W) and in the east sector (45° W) and over the AP (60° W). Non-parametric statistical trend tests and extreme value approaches are used. A set of annual, monthly, and seasonal series are fitted. The extremal index is applied to measure the degree of independence of monthly excesses over a threshold considered extreme events. Increasing trends are verified in the annual and monthly temperature and wind series. The greatest trends are observed for seasonal series from reanalysis without change-point in summer and winter. Decreasing trends are observed for maximum temperature in summer and positive trends mainly for the westerly winds over the AP. But in winter, the maximum temperature shows an increasing trend also over the AP. Most of reanalysis seasonal minimum temperature and wind components as well as maximum and minimum temperatures from stations present increasing trends with change-point but tend to stability after the breakpoints. The generalized distribution (GEV) is used to fit temperatures and westerly wind between South America (SA) and north of the AP. The 100-year return levels exceed the maximum value of the maximum temperature in Esperanza and maximum westerly winds at several grid points. Pareto and Poison distributions are applied for the maximum temperatures from stations and the 100-year return levels are not exceeded. Our findings show significant positive trends for monthly wind components near the SA in the region of the westerly winds whose changes in position influence directly the SAM, which modifies the atmospheric patterns in the South Hemisphere (SH). A predominance of seasonal warming is identified, which may impact the climate with consequences not only locally but also in other regions.
... To explore this, we have analyzed the trends in seasonal SST anomalies and the magnitude of vertically integrated mean water vapor transport (IVT) during the period from 1980 to 2015, as shown in Fig. 10. The Mann-Kendal-Theil-Sen trend analysis is used to assess the strength and significance of trends [19][20][21] . The significant increase in SST, up to 0.5 °C per decade, over the entire tropical South Asian region during 1980-2015 can be clearly observed. ...
While the spatio-temporal characteristics of Indian summer monsoon precipitation and its extreme spells have been extensively studied, the northeast monsoon, which occurs from October to December (i.e., post-monsoon season) and affects the southern peninsula of India, has not received as much attention. In light of this, the present study explores the spatio-temporal characteristics of precipitation during the northeast monsoon, with a particular emphasis on widespread extreme precipitation events and their associated large-scale synoptic systems, using recent ensemble of high-resolution regional climate models (RCMs) simulations and the Indian monsoon data assimilation and analysis (IMDAA) reanalysis. The study reveals that both models tend to underestimate the intensity and frequency of observed precipitation events, although their skills in reproducing the observed spatial patterns of both mean and extreme precipitation are quite high (r > 0.75). A substantial increase in widespread extreme precipitation events (nearly twofold), along with a 30% rise in precipitation intensity, has been observed in the recent decade compared to the 1980s, and models demonstrate a similar directional change but tend to underestimate the magnitude of observed precipitation. This increase appears to be linked to the rapid warming of the Indian Ocean, which, in turn, increases the water vapor in the atmosphere, ultimately supplying more moisture to the southeastern peninsular India. On the other hand, observed discrepancies in replicating some of the reported widespread impactful extreme precipitation events in the years 2007 and 2015 over the southern India region underscore the need for caution when interpreting model simulations. Low-pressure systems, such as troughs, associated with cyclonic circulations originating from the Bay of Bengal, have been identified as the primary sources of moisture fueling heavy precipitation during these events. Cluster analysis highlights varying synoptic patterns within the general framework, emphasizing the need for a more nuanced approach in simulating and forecasting extreme precipitation events. Overall, this study underscores the importance of enhancing modeling capabilities to better understand and prepare for the growing challenges posed by extreme precipitation events.
... A detailed explanation of the methodology used in the estimation of CDHW characteristics is provided in Mukherjee and Mishra (2021). In this study, trends in CDHW characteristics were calculated based on Kendall's tau and the Sen slope estimator (Sen, 1968), whereas the statistical significance of the trends were tested using the Mann Kendall Trend test at 95% confidence level (Bevan & Kendall, 1971;Mann, 1945). ...
Over the past few decades, South Asia (SA) has experienced an upsurge in the frequency of severe monsoonal compound drought and heatwave (CDHW) occurrences. Climate models that identify land‐atmosphere coupling as a major contributing factor for this exacerbation and anticipate an increase in the intensity and frequency of CDHW occurrences in future also represent this. For the first time, this study investigated the future evolution of monsoonal CDHW events based on new generations of the CMIP6 and population products by applying a multivariate framework. Specifically, this study explored the impacts of natural climate variability and future land‐atmosphere coupling on the monsoonal CDHW event risks and their bivariate return periods for two future time‐periods and emission scenarios across SA and its subregions. The odds of CDHW occurrences were then examined using a logistic regression model and its association with the natural and anthropogenic drivers was determined. The results indicate that the monsoonal CDHWs occurrence is anticipated to increase substantially during the late twenty‐first century (2056–2090). The 50‐year CDHW events might increase by two‐fold across most of SA by the mid‐21st century under the high emission scenario. We find that the co‐occurring dry and warm conditions rapidly strengthens with soil moisture and temperature coupling and are further exacerbated by land‐atmospheric feedback loops. Our findings show that persistent dry spells contribute significantly to heatwave events, emphasizing regional exposure to changing climates.
... where n is the length of the sequence, x i and x j are the observations at i and j time respectively. When the data value has independent identically distribution ,S is the normally distributed approximately, and the variance is given by the following formula (Kendall 1948;Mann 1945): ...
As the most important city in China, Beijing has experienced an economic soar, large-scale population growth and eco-environment changes in recent 20 years.Evaluating climate and human-induced vegetation changes could reveal the relationship of vegetation-climate-human activities and provide important insights for the coordination of economic growth and environmental protection. Based on a long-term MODIS vegetation index dataset, meteorological data (temperature, precipitation) and impervious surface data, the Theil-Sen regression and the Mann-Kendall method are used to estimate vegetation change trend in this study and the residual analysis is utilized to distinguish the impacts of climate factors and human activities on vegetation restoration and degradation from 2000 to 2019 in Beijing. Our results show that the increasing vegetation areas account for 80.2\% of Beijing. The restoration of vegetation is concentrated in the urban core area and mountainous area, while the degradation of vegetation is mainly concentrated in the suburbs. In recent years, the vegetation in most mountainous areas has changed from restoration to significant restoration, indicating that the growth of mountain vegetation has continued to restore. We also found that in the process of urban expansion, vegetation browning occurred in 53.1\% of the urban built-up area, while vegetation greening occurred in the rest part. We concluded that precipitation is the main climatic factor affecting the growth of vegetation in Beijing mountain areas through correlation analysis. Human activities have significantly promoted the vegetation growing in the northern mountainous area thanks to the establishment of environmental protection areas. The negative correlation between vegetation and the impervious surface tends to gradually expand outwards, which is consistent with the trend of urban expansion. The positive correlation region remains stable, but the positive correlation is gradually enhanced. The response of vegetation to urbanization demonstrated a high degree of spatial heterogeneity. These findings indicated that human activities played an increasingly important role in influencing vegetation changes in Beijing.
... Prior to applying metric-based models, the data needs to undergo detrending and smoothing indicators and time, to identify the optimal size of the rolling window and bandwidth for the 224 Gaussian filter (Bevan and Kendall 1971). Kendall's τ ranges from -1 to +1, where higher 225 values indicate stronger trends, aiming to identify the detrending settings that best capture 226 trends in the leading indicators. ...
Detecting abrupt transitions in ecosystems, known as regime shifts, holds immense implications for conservation and management endeavors. This research aims to investigate the feasibility of developing an early warning system capable of identifying an upcoming critical transition within Mangrove Forest ecosystems. Employing a fusion of remote sensing analysis, time series analysis, and the critical slowing down theory, Mangrove Forests' state change was explored across two distinct study sites. One site has been adversely affected by disturbances stemming from land use and land cover changes, while the other serves as an unaffected reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, quantifying three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). Furthermore, temporal alterations in land-use and land cover are scrutinized using Landsat data from 1996, 2002, 2008, and 2014. To identify early warning signals of critical transitions, indicators such as autocorrelation, skewness, and standard deviation are applied. The results show the robust capabilities of remote sensing in generating early warning signals of critical transition in Mangrove Forests. NDVI outperformed MVWR and MNDWI as ecosystem state indicators. This study not only highlights the potential of remote in identifying the approaching regime shifts in Mangrove Forest ecosystems but also adds knowledge on ecosystem dynamics. This is the first report of the successful application of remote sensing in generating early warning signals for imminent critical transitions within Mangrove forests in the Middle East.
... These methods detrend the data within a rolling window. In this study, a sensitivity analysis was conducted using Kendall's τ, a nonparametric statistic measuring the association between indicators and time, to identify the optimal size of the rolling window and bandwidth for the Gaussian filter (Bevan and Kendall 1971). Kendall's τ ranges from -1 to +1, where higher values indicate stronger trends, aiming to identify the detrending settings that best capture trends in the leading indicators. ...
Detecting abrupt transitions in ecosystems, known as regime shifts, holds immense implications for conservation and management endeavors. This research aims to investigate the feasibility of developing an early warning system capable of identifying an upcoming critical transition within Mangrove Forest ecosystems. Employing a fusion of remote sensing analysis, time series analysis, and the critical slowing down theory, Mangrove Forests' state change was explored across two distinct study sites. One site has been adversely affected by disturbances stemming from land use and land cover changes, while the other serves as an unaffected reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, quantifying three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). Furthermore, temporal alterations in land-use and land cover are scrutinized using Landsat data from 1996, 2002, 2008, and 2014. To identify early warning signals of critical transitions, indicators such as autocorrelation, skewness, and standard deviation are applied. The results show the robust capabilities of remote sensing in generating early warning signals of critical transition in Mangrove Forests. NDVI outperformed MVWR and MNDWI as ecosystem state indicators. This study not only highlights the potential of remote in identifying the approaching regime shifts in Mangrove Forest ecosystems but also adds knowledge on ecosystem dynamics. This is the first report of the successful application of remote sensing in generating early warning signals for imminent critical transitions within Mangrove forests in the Middle East.
... Since we understand ICL as a process of metaoptimization, we also compare the attention to training tokens for ICL and finetuning with the Kendall rank correlation coefficient (Kendall, 1948 of the last query token in the l-th attention layer, which is summed across attention heads. For finetuning, we first record all the attention queries Q ′ (l,h) ∈ R d ′ ×N of the training tokens, and then use the inner product between them and the attention query q (l,h) ∈ R d ′ of the last token in the query example as the finetuning attention weights to the training tokens: m ...
Despite the research on the responses of grassland vegetation to climate change and topography has advance worldwide, the large-scale importance of these parameters to grassland vegetation greening in arid regions across environmental gradients is unclear. To address this, in this study, we applied MODIS Normalized Difference Vegetation Index (NDVI) data and trend analysis methods to measure the spatial–temporal variation in grassland vegetation greening in central Eurasia. Multiple regression models and hierarchical partitioning were used to quantify the importance of climate [annual precipitation (AP), annual mean temperature (AMT), relative humidity (RH)] and topography [elevation (ELE), aspect (ASP), topographic position index (TPI)] to the NDVI. The results showed that there was a significant increasing trend in the NDVI of meadows, but not other grassland types, from 2000 to 2021 (3.3 × 10⁻³/year, p < 0.05). Additionally, the responses of the NDVI to climate and topography in deserts were positively correlated with RH, AP, and ELE. Meanwhile, the dependence of NDVI on climate and topography decreased with increasing RH. Under conditions of escalating AMT and AP, RH and ELE independently contributed to explaining the NDVI. However, RH may be the key determinant of long-term NDVI stabilization in arid grassland. These findings underscore the significance of vegetation–climate–topography feedback and can inform the development of more comprehensive and effective climate mitigation and adaptation strategies.
mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.
Biodiversity reserves are a crucial in-situ method to conserve biodiversity hotspots as they are sensitive to climate change. The Nanda Devi Biosphere Reserve (NDBR) in the western Himalayas is enriched with diverse endemic flora and fauna and endorses the second-highest mountain peak in the world. However, in the recent decade, this region has potentially warmed at an alarming rate. With 36 temperature and precipitation indices from high-resolution 40-year data from ERA5 reanalysis and CHIRPS, this paper assesses the state of warming and extreme climatic events. Apart from the indices, Landsat (NASA/USGS, USA) and QuickSCAT (ISRO, India) were utilized to assess the region’s response to climate change. An increase of 0.73ºC in the last decade for minimum, 0.26°C for maximum temperatures was observed, with the highest anomaly of 1.7°C in 2016. The reserve’s vegetation pattern has changed with the vegetative region’s dispersal towards the north and higher elevations. In the year 2000, the area without any vegetation covered 79% of the total area, which declined to a mere 23.8% in the year 2020, equivalent to a 70% decline in the area. Similarly, the area with very dense region covered only 0.02% of the total area in the year 2000, and in the year 2020, it increased to 109%. Snow cover seems to be worst affected in the region with dense snow cover declining maximum by 2020. From coverage of 12.3% of the total area of the reserves, it was reduced to a mere 0.02%, showing a decline of nearly 100% in the region. Our findings show that although protected areas are meant to be resilient to external anthropogenic intrusions, they are highly susceptible to the intrinsic forces of induced climate change. We suggest that reserve managers enable robust measures to identify the distribution of vulnerable species and introduce new methods to preserve the pristine hotspot region.
Understanding the responses of water yield (WY) to climate change and vegetation greening is crucial for water resources management. However, quantifying the feedback relationships among climate, vegetation, and WY remains challenging. In this study, we developed a system feedback assessment framework based on Integrated Valuation of Ecosystem Services and Tradeoff (InVEST), gravity center model, and structural equation model to reveal these relationships. The framework involves identifying land cover transfer projects, assessing spatio-temporal variations in climate, vegetation, and WY, and quantifying their system feedback relationships, which was applied in the Yellow River Basin (YRB) with significant climate change and vegetation greening. The results demonstrated that precipitation, Normalized Difference Vegetation Index (NDVI), and WY showed significant increasing trends (p < 0.05) at rates of 2.92 mm/a, 0.004 /a and 0.71 mm/a, respectively, during 1991–2020. The areas of significant increase in precipitation, NDVI, and WY were primarily concentrated in the source and midstream of the YRB. The gravity centre of WY moved southwest from 1991 to 2000 and then northeast from 2000 to 2020 in the YRB. Precipitation changes and ecological restoration were identified as key driving factors affecting the variation of WY. Our findings highlight the possible negative effects of ecological restoration on WY variation and provide important information for developing adaptation strategies for land use management in the YRB or other regions.
Anthropogenic-induced climate change triggered increased extreme precipitation events, posing a significant threat to vulnerable regions like Pakistan. However, the lack of reliable long-term in-situ records hampers the monitoring of climatic extremes in the country. This study assessed the skill of four daily gridded precipitation datasets (APHRODITE, CHIRPS, CPC, and PGF) in tracking changes in precipitation extremes from 1985 to 2016 at 42 meteorological stations across Pakistan. This study employed Sen slope estimator in determining the change and the Mann-Kendall (MK) and its modified version (mMK) to test the significance of the changes. Spatial analysis of trends based on in-situ and gridded datasets revealed substantial increases in most precipitation extremes. However, there were large variations in the skill of gridded products in estimating precipitation extremes. The APHRODITE and PGF overestimated or underestimated trend significance, respectively. The CPC outperformed the others, exhibiting an RMSE of 3.96, Pbias of 18.5, NSE of 0.23, md 0.53, and a correlation coefficient of 0.6 in estimating changes at a 95% confidence level. Moreover, CPC also demonstrated superior performance in estimating trends at a 99% confidence level. It also performed best in estimating mMK trend significance at a 99% confidence level. Therefore, the study recommends CPC for tracking extreme precipitation trends in the region.
Groundwater is essential for obtaining freshwater and plays a crucial role in promoting sustainable development in agriculture, industry, and the socioeconomic status of an area. However, due to the extensive use of groundwater, many areas in India are facing a decline in their groundwater levels. Groundwater level time series analysis aids in detecting trends, analyzing behavior, and determining the causes of water level decline. We considered four seasons for identifying the trends in groundwater levels i.e. Post-Monsoon Rabi, Monsoon, Pre-Monsoon and Post-Monsoon Kharif seasons. Using Mann–Kendall test, analysis of trends for Southern India which consists of 8 states was performed. Data on the piezometric level of 181 observation wells from 1996 to 2018 has been taken into consideration. The significant declining trend was observed in 47 observation wells in monsoon season, 61 wells in post-monsoon Rabi, 27 wells in post-monsoon Kharif and 45 wells in pre-monsoon from the results. Out of all wells only one well exhibited increasing trend over 8 states. The outcomes of the study are going to help the groundwater development authorities in the respective states for resource management, water supply planning, groundwater recharge management, environmental protection and policy formulation and regulation.
Reservoirs play a crucial role in regulating water availability and enhancing water security. Here, we develop NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) based Global Water Reservoir (GWR) product, consisting of measurements of reservoir area, elevation, storage, evaporation rate, and evaporation loss for 164 large global reservoirs. The dataset is available at 8-day and monthly temporal resolutions. Since the Moderate Resolution Imaging Spectroradiometer (MODIS) is close to the end of its life, we further evaluated the consistency between MODIS and VIIRS-based GWR to ensure continuity to the 20+ year MODIS GWR product. Independent assessment of VIIRS reservoir storage (8-day) retrievals against in-situ measurements shows an average of R² = 0.84, RMSE = 0.47 km³, and NRMSE = 16.45%. The evaporation rate has an average of R² = 0.56, RMSE = 1.32 mm/day, and NRMSE = 28.14%. Furthermore, results show good consistency (R² ≥ 0.90) between the VIIRS and MODIS-based product components, confirming that long-term data continuity can be achieved. This dataset can provide valuable insights for long-term trend analysis, hydrological modeling, and understanding hydroclimatic extremes in the context of reservoirs.
Agriculture is highly dependent on environmental, climate and weather conditions and on extreme weather events leading to natural disasters. Such events are more and more frequent in Italy, and in the last decades huge public investments were dedicated to risk management policies in agriculture. In order to set an adequate weather-related risk assessment, a robust analysis of the hazard is needed, which requires an agro-meteorological approach to detect the potential impacts of weather extremes on agricultural activities. With the aim of assessing the effectiveness of the current risk management policy in catching the main hazards, specific agro-meteorological indices were applied to highlight occurrence, trends, and spatial patterns of extreme events. The analysis was based on reanalysis datasets and focused on a study area in Southern Italy (Campania region) during the 1981–2021 period. The findings are reported in terms of maps and statistics aggregated at administrative unit level (5 provinces) and show a general intensification of weather extremes in the last decades, both in frequency and intensity of the events. The main indications refer to growth rates of heavy precipitation, potentially leading to flood, locally exceeding 3–4 mm/year, an increasing number of months with severe/extreme droughts, mainly concentrated during the growing season. An upward trend was also observed for days with extreme maximum temperatures, which already exceeded or approached 50% between June and September in the 1981–2021 period in most areas. Maximum temperatures above 35 °C are becoming more frequent and in the inner areas they were reached in 10 days in the 2021 summer quarter. On the other hand, no significant trends were detected for late frosts. In terms of policy implications, the results seem to suggest that some extreme weather events can no longer be considered as exceptional at the present time and in a trend perspective, making them less suitable to be addressed through the risk management tools based almost exclusively on the strategy of transferring risks (insurances and more recent mutual funds) both for farmers and for the allocation of public resources. Therefore, the need is underlined for improving the design of the risk management policies to increase farms’ resilience and adaptation to climate change. Moreover, the study highlights the information potential of agro-meteorological indices in supporting evidence-based decision making in agriculture.
The average annual precipitation values in the Cheliff-Zahrez basin range from 80 to 600 mm/an, indicating a semi-arid climate. This work revolves around a new drought exceedance probability index (DEPI), a standardized precipitation index (SPI) and effective drought index (EDI) in different time scales (3, 6, 9, 12 and 24) derived from monthly precipitation series stretching from September 1970 to August 2015. The latter aims to analyze the performance, similarities and differences between the most used drought indices such as SPI and EDI and to compare their results with those obtained using DEPI. The results indicated that the majority of selected stations tend to a decline in annual rainfall, with a dominant break in series between 1970 and 1980. The Mann–Kendall test result showed that the monthly rainfall trend had significantly decreased in the majority of studied stations. Furthermore, the SPI and EDI series showed persistent monthly drought conditions from January 1970 to March 2010 with an extreme drought peak of − 3. However, the DEPI index showed monthly drought events (< 0.5) of different classes beginning from March 1980 to December 2010. In the study area, the coefficient of determination explained a high variation (> 80%) between SPI and EDI at the time scale of 24 months. At the 9- and 12-month scale, the coefficient of determination showed a variance greater than 50% over the entire basin. On the other hand, the coefficients of determination results between SPI and EDI for the time scale of 3 to 6 months are characterized by a gradient from the northeast to southwest or from 10 to 90%. The stations of the Coastline 2 basin have revealed that the relationship between SPI and DEPI as well as EDI and DEPI is not significant, being allowed between 10 and 30% on time scales of 3, 6, 9, 12 and 24 months. The results of this work and information on the severity and persistence of droughts will be essential to enable managers to adopt an integrated and sustainable management of scarce resources and aim at minimizing agricultural production losses in the region.
The seasonal design flood (SDF) plays an important role in guiding water resources management and floodwater utilization. To estimate the SDF, studies have established the joint distribution of flood peak (i.e., annual maximum daily discharge) and timing (i.e., occurrence date) and assumed a monotonic dependence structure (MDS) between these two flood elements within the bivariate framework. However, the MDS assumption cannot fully represent the flood seasonality dynamics, which might affect the reliable assessments of the bivariate flood risks. To solve this issue, this study proposes a mixed-copula function to model a nonmonotonic dependence structure (NMDS) between flood peak and timing, and then derives the SDFs under nonstationary conditions by using the equivalent reliability method. As the NMDS can characterize the turnaround relationship between peak discharge and timing, the NMDS performs better at modeling observed (1951–2019) flood peaks than the traditional MDS, as reported by a case study in the Wujiang River Basin, China. The results suggest that, within the flood season from May to October, the NMDS-based estimation framework produces higher SDFs in June and July but smaller SDFs in the remaining months than the annual design flood. This allows for reducing the flood control storage capacity to improve floodwater utilization downstream without increasing flood risk. After further investigating the potential effects of a changing environment on peak discharges in the future (2020–2099), it is expected that reservoir regulation might play a more important role in flattening the seasonal fluctuations in floods. These results provide rich information as references for flood risk management and floodwater resources utilization under a changing environment.
Purpose
Anemia in cancer should be diagnosed and treated according to guideline recommendations. The implementation of ESMO and German guidelines and their effect on anemia correction was analyzed.
Methods
This retrospective epidemiological study, representative for Germany, analyzed data on anemia management of cancer patients with anemia ≥ grade 2. The Guideline Adherence Score (GLAD) for diagnosis (GLAD-D) and therapy (GLAD-T) was defined as follows: 2 points for complete, 1 point for partial, 0 point for no adherence.
Results
Data were analyzed for 1046 patients. Hb levels at diagnosis of anemia were 8–10 g/dL in 899 (85.9%) patients, 7–8 g/dL in 92 (8.7%), and < 7 g/dL (5.0%) in 52. Transferrin saturation was determined in 19% of patients. Four hundred fifty-six patients received RBC (43.6%), 198 (18.9%) iron replacement, 106 (10.1%) ESA, and 60 (5.7%) vitamin B12 replacement. 60.6% of patients receiving iron replacement were treated intravenously and 39.4% were treated orally. Two hundred eighty-eight (36.6%) of 785 patients receiving transfusions had no guideline-directed indication. GLAD-D was 2 in 310 patients (29.6%), 1 in 168 (16.1%), and 0 in 568 (54.3%). GLAD-T was 2 in 270 patients (25.8%), 1 in 320 patients (30.6%), and 0 in 456 patients (43.6%). Higher GLAD-D significantly correlated with higher GLAD-T (τB = 0.176, p < 0.001). GLAD-T 2 was significantly associated with greater Hb increase than GLAD-T 0/1 (p < 0.001) at 28 days (10.2 vs. 9.7 g/dL) and at 2 months (10.4 vs. 9.9 g/dL).
Conclusions
Anemia assessment is inadequate, transfusion rates too high, and iron and ESA therapy too infrequent.
Trial registration
ClinicalTrials.gov, NCT05190263, date: 2022–01-13.
Spatial and temporal distributions and influencing factors of extreme precipitation are important bases for coping with future climate change. The spatiotemporal variability and affecting factors of extreme precipitation indices (EPIs) in east of northwest China (ENW) during 1961–2015 were investigated using a series of approaches such as modified Mann–Kendall trend test, Hurst exponent, ensemble empirical mode decomposition (EEMD), and geodetector model. The results showed that CDD and CWD decreased significantly (P < 0.01), with rates of 1.4 days/decade and 0.07 days/decade, respectively. EPIs in ENW exhibited an obvious heterogeneity. CDD gradually increased from the southeast to the northwest. The remaining EPIs generally showed the opposite trend. Geodetector results demonstrated that large-scale circulation factors had a significant impact on EPIs in ENW. The influence of large-scale climate factors on EPIs was concentrated in nonlinear enhancement, and Nino3.4 and SO were the dominant driving factors that played a major role in the variability of EPIs. The results of this study provided a reference for ENW and other arid and semi-arid regions to cope with extreme climates and develop corresponding strategies.
The spatial and temporal dynamics of daily ultraviolet index (UVI) for a period of 18 years (2004–2022) over the Indian state of Kerala were statistically characterised in the study. The UVI measurements used for the study were derived from the ultraviolet-B (UVB) irradiance measured by the Ozone Monitoring Instrument (OMI) of the AURA satellite and classified into different severity levels for analysis. Basic statistics of daily, monthly and seasonal UVI as well as Mann-Kendall (MK) statistical trend characteristics and the rate of change of daily UVI using Theil-Sen’s slope test were also evaluated. A higher variability of UVI characteristics was observed in the Kerala region, and more than 79% of the measurements fell into the categories of very high and extreme UVI values, which suggests the need of implementation of appropriate measures to reduce health risks. Although the UVI measured during the study period shows a slight decrease, most of the data show a seasonal variation with undulating low and peak values. Higher UVI are observed during the months of March, April and September. The region also has higher UVI during the southwest monsoon (SWM) and summer seasons. Although Kerala region as a single whole unit, UVI show a non-significant decreasing trend (-0.83), the MK test revealed the increasing and decreasing trends of UVI ranging from -1.96 to 0.41 facilitated the delineation of areas (domains) where UVI are increasing or decreasing. The domain of UVI increase occupies the central and southern (S) parts, and the domains of decrease cover the northern (N) and S parts of the Kerala region. The rate of change of daily UVI in domain of increase and decrease shows an average rate of 0.34 × 10−5 day−1 and −2 × 10−5 day−1, respectively. The parameters (rainfall, air temperature, cloud optical depth (COD) and solar zenith angle (SZA)) that affect the strength of UV rays reaching the surface indicate that a cloud-free atmosphere or low thickness clouds prevails in the Kerala region. Overall, the study results indicate the need for regular monitoring of UVI in the study area and also suggest appropriate campaigns to disseminate information and precautions for prolonged UVI exposure to reduce the adverse health effects, since the study area has a high population density.
The densely populated coast of the Gulf of Guinea (GoG) is experiencing escalating erosion from climate change (CC). To assess Longshore Sediment Transport (LST) changes in the past and future, an empirical formula was used to model the GoG’s LST. Nearshore wave conditions, crucial for LST estimation, were derived by projecting significant wave height, mean wave period, and mean wave direction from deep water to the breaker zone. These wave conditions were used as input for LST computation. Analysing three time slices: Past (1979–2005), mid-century (2026–2045), and end-century (2081–2100) under RCPs 4.5 and 8.5, results indicate continuous increase in LST in most locations, with local and seasonal variations. LST magnitudes range from 104 to 105 m3y−1 in the GoG, with the Cameroonian coast having the least. This aligns with the rising wave energy from CC. LST directions are predominantly eastward, though some locations, like off Lagos and Cameroon-Gabon coasts, show westward trend, correlating with coastal orientation influenced by southward wave directions. RCP 8.5 projections suggest mid-century increase, intensifying by end of century, with yearly rates of change around 103 m3y−1 and future LST changes up to 105 m3y−1. This situation implies accelerated sediment removal, heightening erosion.
Lightning strikes present a significant risk in India, particularly in densely populated and agrarian states like West Bengal. However, there has been limited research on the patterns and trend of these hazards. The present study investigated the spatiotemporal changes in lightning flashes and their fatalities in West Bengal using high-resolution Tropical Rainfall Measuring Mission Lightning Imaging Sensor (TRMM LIS) data and statistical data for the period between 2000 and 2020. The Mann–Kendall and Sen tests and cluster analysis approach were employed to estimate district- and physiographic division-wise variations in lightning occurrence and trends. Additionally, lightning fatality data for the study area spanning from 2000 to 2020 were analyzed using Mann–Kendall and Sen tests, supplemented by cluster analysis. The geospatial results indicate that districts in the south have a lower frequency of lightning flashes, while districts in the north have a higher frequency. Furthermore, the number of flashes in the region increased from 2000 to 2012 and has since displayed a downward trend since 2018. The study also revealed a steady rise in the average number of flashes from March to July. Two prominent peaks were observed: one in May and another in September. Additionally, afternoon and evening peaks in lightning activity were noted. Significant decreasing trends in lightning activity were observed in the Jalpaiguri and Murshidabad districts between 2000 and 2020. The seasonal lightning flash rate density pattern showed that the peak values are recorded during the pre-monsoon months (March–May). The temporal analysis of lightning deaths showed an upward trend from 2000 to 2020. The study concludes that lightning-induced fatalities are a significant concern in West Bengal and suggests mitigative actions.
Graphical abstract
The quantity and quality of river flow is one of the factors that are related to the health of humans and living organisms, and it is strongly affected by watershed activities, which are naturally or artificially caused by hu-man activities. In this regard, it is necessary to evaluate the effects of human activities on river flow regime to optimally use of water resources. Therefore, in the present study, the temporal changes in the flow health of water outflow from Neor Wetland has been assessed in pre and post construction of Neor Dam in (1978-1987) and (1987-2019) periods. Therefore, temporal changes in the flow health of Neor river flow were evaluated using Flow Health software. The results indicated that the value of the Flow Health index was equal to 0.77 in the period before the construction of the Neor Dam. While in the period after the construction of the dam, the value of the Flow Health index has been decreased (0.74). Among the nine investigated indicators, the indica-tors of persistently very low and lowest monthly flow had not changed in the study period. In addition, the index of flood flow interval has the most deviation from the natural condition. On the other hand, the results showed that the precipitation in the study area had a non-significant increasing trend, while the flow discharge had no-significant decreasing trend. The results of the present research can be used in interpretating the ef-fects of storage dam on the flow variability and hydrological river flow health.
Chapter 13 is the first of two chapters describing connections, equivalencies, and relationships relating to fourfold contingency tables. First, Pearson’s mean-square contingency coefficient, \(\phi ^{2}\), and tetrachoric correlation coefficient, \(r_{\text{tet}}\), are described. Second, Yule’s Q and Y measures are described, and the connections linking Q and Y are established. Third, the odds ratio is described, and the connections linking the odds ratio with Yule’s Q and Yule’s Y statistics are detailed. Fourth, Goodman and Kruskal’s \(t_{a}\) and \(t_{b}\) asymmetric measures of association are presented, and the connections linking \(t_{a}\), \(t_{b}\), \(\chi ^{2}\), Pearson’s \(\phi ^{2}\), and Pearson’s \(r_{xy}^{2}\) are described. Fifth, Somers’ \(d_{yx}\) and \(d_{xy}\) asymmetric measures are described, the connections linking \(d_{yx}\) and \(d_{xy}\) with simple percentage differences are established, and the connections with the corresponding unstandardized regression coefficients are detailed. Sixth, Kendall’s \(\tau _{b}\) measure of ordinal association is described, and the connections among \(\tau _{b}\), \(\chi ^{2}\), Pearson’s \(\phi \), and Pearson’s \(r_{xy}\) are delineated. Finally, the connections linking Pearson’s \(\phi ^{2}\) and Cohen’s \(\kappa \) are established.
Kolkata, the capital of West Bengal, has been witnessing the incident of land subsidence for many years. The main objective of this study is to estimate the land subsidence of the KMC area by excessive withdrawal of groundwater using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique. The advantage of this technique is that it estimates the line of sight displacement very precisely from persistent or permanent scatterer (PS) points. A series of Sentinel datasets are used to perform this analysis. It is observed that the velocity of subsidence is significantly high in some parts of the city. Areas like Gardenrich, Thakurpukur, Behala, Nayabad, Mukundapur, Science City, Dumdum, Paikpara, Cossipore, Central, Barabazr and Topsia are showing a concerning amount of subsidence. The maximum decline of the piezometric head is about 7.25 m in this period. The comparison between land subsidence-affected areas and piezometric level change in those areas confirms the reason for subsidence in those regions. So, groundwater extraction should be restricted with proper planning and policy implementation in all parts of the city to manage the problem of land subsidence.
With the escalating global impacts of climate change, the prevalence of drought has become a pressing concern. Effective routine observation and analysis of drought have gained paramount importance. Future drought policies are adversely affected due to the inability to accurately reflect the drought changes caused by spatial differences in analysis studies. A new approach is required. Presenting a novel perspective, this study focuses on a comprehensive temporal and spatial analysis of drought occurrences in Turkey. Employing the 12-month standardized precipitation index (SPI) calculated from data spanning 1990 to 2020, the study undertook rigorous geographic information system (GIS)-based spatial processing. After the areal sizes were calculated, drought trends were revealed by applying both Mann–Kendall (MK) and innovative trend analysis (ITA) tests on percentile areas. The assessment encompassed five distinct categories, with individual trend analyses conducted for each category. Significance levels of 1%, 5%, and 10% were applied in both trend tests. The evaluation of province-level and regional-level drought trends was conducted following the generation of province-based areal trend maps. The findings revealed a declining trend within the humid categories along the Black Sea and Mediterranean coastal regions, and the inland provinces of Eastern Anatolia. Additionally, an areal change was detected from relatively more humid or drier classes to near normal classes. This study’s outcomes enhance long-term drought assessment and monitoring at national and regional scales. This new approach in drought analysis, determined according to Turkey’s conditions, can be used in future drought planning by eliminating spatial differences in the international sense.
In metabolomic studies, metabolite concentrations and their associative relationships are usually quantified and interpreted in term of correlation indexes like the Pearson’s and Spearman’s correlation coefficients. The aim of such analysis is usually to obtain information about the underlying metabolic network and its characteristics. However, there is often no direct relationship between the patterns of metabolite associations and the structure of the metabolic networks regulating metabolite concentrations. The first part of the chapter reviews the basic biochemical mechanisms from which the patterns of correlations observed in metabolomic data arise with a focus on their interpretation. The second part reviews the most common measures of association (Pearson’s, Spearman’s, and Kendall’s correlation coefficient and mutual information) and the statistical assumption underlying their applicability. Robust methods are briefly discussed, and a component model for the comprehensive analysis of correlation matrices is also presented.
The quality of drinking water supply can be controlled through the assessment of source water status and continuous monitoring of treated water quality. Water disinfection is a vital component of water treatment. This study was conducted to assess the quality of water (source and treated) from Erelu waterworks that supplies potable water to Oyo town and its environs in Southwest part of Nigeria. The aim is to establish the quality of water supplied to the consumers. Six routine monitoring parameters (RMPs): pH, colour, total hardness (TH), total alkalinity (TA), flocculation and chlorine residual (CR) data were collected over a 3-year period (2018–2020) for quality status assessment of raw water and water treated by the Water Corporation of Oyo State, Nigeria. Conventional water treatment methods involving aeration, coagulation, flocculation, sedimentation, filtration, and disinfection were used on raw water samples. The RMPs data were analyzed using descriptive statistics and Analysis of variance (ANOVA). Three different regression models (linear, semi-log and double log) were used for the raw water data to generate models for the prediction of flocculation. The raw (untreated) water was soft to moderately hard with relatively high colour values that exceed the WHO permissible limit (2011). However, all the assessed RMPs values for the disinfected water lie below the WHO guideline limits for drinking purpose. The highest flocculation values of 100–120 mg/l Al2(SO4)3 were recorded from April to July of 2019. Colour showed positive correlation with TA (r = 0.357) and flocculation (r = 0.569) (significant at p < 0.01). Total alkalinity showed moderate positive correlation with flocculation (r = 0.584; p < 0.01). The pH, TA and TH concentrations of the reservoir water were all below the WHO guideline limits except colour (> 15 Hu). The quality of disinfected treated water indicates that levels of pH, colour, TH and TA lie within the WHO acceptable limits for safe drinking water (WHO (2017): Guidelines for drinking water quality. 4th edn. Incorporating the first addendum: WHO guidelines approved by the guidelines review committee. World Health Organization, Geneva.). The mean CR of treated water was > 0.5 mg/l at water treatment plant (WTP) and concurs with the WHO guideline that higher levels should be close to the disinfection point and highly likely to maintain the desirable limit (0.2–0.5 mg/l Cl) by the time it gets to the point of use. The regression analyses showed that semi-log model performs better than both linear and double-log models for predicting flocculation with highest value of R² = 69.5% and significant F-value (17.631). The RMPs obtained showed that water treatment practices at the Waterworks are effective. A further study to monitor the levels of nutrient load, chlorination-by-products and coliform count in disinfected water should be done to maintain transparency of treated water quality.
In present study, the variability in wintertime maximum (Tmax) and minimum (Tmin) temperature patterns over India using observed and deep learning techniques have been assessed. The analysis has been caried out for the period 1979–2018 during the months from November to February. The month of February depicted strongest variability in Tmax and Tmin over Northwest India (NWI) with significant + ve trend for upper half of the country. Wintertime temperature variability was seen to be dominant in the Indo-Gangetic plain area covering some parts of NWI and Northeast India (NEI) for Tmax and Tmin. Also, a gradual increase in the spatial coverage, engulfing majority of South Peninsular India (SPI) and Central India (CI) of the rising Diurnal Temperature Range (DTR) was found from November to January. Decreasing DTR was observed only for January extending along Indo-Gangetic plains. The model Random Forest (RF) performed quite well relative to Long Short-Term Memory model (LSTM) in predicting the winter temperatures (especially for Tmax) during all the considered months. The RF made a robust Tmax forecast during NDJF over all India (RMSE – 0.51, MAPE – 1.4). However, its performance is not up to the mark during the month of February over NEI (RMSE – 1.63, MAPE – 4.5). The maximum fluctuating patterns of temperature have been found during the month of February. The study emphasizes on algorithm-based approaches to study the temperature, so that better understanding could be developed for the meteorological sub-divisions over India.
The Horn of Africa (HOA) is frequently plagued by severe droughts, significantly affecting food security, particularly in less developed and rapidly population-growing regions. This study aims to provide crucial insights into the long-term spatiotemporal patterns of drought and their impact on croplands in the HOA. The findings will contribute to developing effective early warning systems to mitigate drought-related risks, ensure sustainable water and food production and alleviate the consequences of droughts in the region. To achieve this, the study estimates the standardised precipitation evapotranspiration index (SPEI) for different time scales (3, 6 and 12 months). Over the previous 120 years (1901-2020), it examines the geographical and temporal trends of drought frequency, intensity, length, affected areas, and the extent of agricultural land impacted. The results reveal that moderate droughts of varying duration occur approximately once every 5 years throughout most of the HOA, while severe droughts occur once every 10 to 15 years. On the contrary, extreme droughts are predominantly observed in the eastern part of the HOA. The study also highlights a gradual decline in atmospheric water availability in the region. There has been increased rainfall variability in recent decades compared to the early twentieth century, leading to a rise in droughts. The analysis indicates a significant increase in 3-month droughts, particularly in the eastern part of the HOA, which historically has been more susceptible to droughts. Before 1940, HOA did not experience severe 3-month droughts, but they have become more common throughout the region since 1981. Similar trends have been observed for droughts of 6 and 12 months. Furthermore, the frequency of croplands affected by droughts of different durations has nearly doubled between 1981 and 2020 compared to 1901-1940. These findings shed light on the increasing risk of drought in the HOA and emphasise the need for proactive measures to address the challenges posed by droughts.
Climate change is projected to threaten groundwater resources in many regions, but projections are highly uncertain. Quantifying the historic impact potentially allows for understanding of hydrologic changes and increases confidence in the predictions. In this study, the responses of karst discharge to historic and future climatic changes are quantified at Blautopf Spring in southern Germany, which is one of the largest karst springs in central Europe and belongs to a regional aquifer system relevant to the freshwater supply of millions of people. Statistical approaches are first adopted to quantify the hydrodynamic characteristics of the karst system and to analyse the historic time series (1952–2021) of climate variables and discharge. A reservoir model is then calibrated and evaluated with the observed discharge and used to simulate changes with three future climate-change scenarios. Results show that changes in the annual mean and annual low discharge were not significant, but the annual peak discharge shifted to a low state (<13.6 m3 s−1) from 1988 onwards due to decreasing precipitation, increasing air temperature, and less intense peak snowmelt. The peak discharge may decrease by 50% in this century according to the projections of all climate-change scenarios. Despite there being no significant historic changes, the base flow is projected to decrease by 35–55% by 2100 due to increasing evapotranspiration. These findings show the prolonged impact of climate change and variability on the floods and droughts at the springs in central Europe, and may imply water scarcity risks at similar climatic and geologic settings worldwide.
The annual, seasonal, and monthly trends of air temperatures were analyzed for thirteen urban and five rural meteorological stations in Kazakhstan for the 1963–2020 period. The non-parametric Mann–Kendall (M–K) rank correlation and Sen’s slope estimator methods and the parametric least-squares linear regression (LSLR) were used to determine whether there were positive or negative statistically significant trends in mean, average maximum, and minimum air temperature time series along with diurnal temperature ranges (DTRs), and temperature differences between five large and small cities. In addition, Kazakhstan’s annual and seasonal air temperature series were analyzed in terms of autocorrelation (serial correlation) coefficients. Coefficients of variations indicated that mean annual temperature variability is the highest in northern cities. Results of the M–K trend test indicated that the highest and lowest increases in the mean air temperatures were observed in spring and autumn, respectively. The magnitudes of the significant increasing trends in annual air temperature ranged between 0.23 °C/decade at Karagandy and 0.54 °C/decade at Kyzylorda. Annual and seasonal diurnal temperature ranges (DTRs) reveal insignificantly decreasing and increasing trends at most of the stations characterized by urbanization. According to the results of both M–K and LSLR tests, annual and winter air temperature differences of some station pairs tend to significantly increase, which may mean that the differences in the calculated temperature range between large and small cities might have widened significantly. However, significance test for the calculated autocorrelation coefficients of the annual and seasonal air temperature data showed that most of the series clearly appear as a low-frequency variability on the significantly increased long-term averages.
The paper aims is to present a multi-criteria decision-making algorithm for solving decision-making problems with the utilization of the C-IFSs (circular intuitionistic fuzzy sets) features. In it, the uncertainties present in the data are handled with the help of C-IFSs in which we considers the circular rating of each object within a certain radius. Later on, we propose a novel algebraic framework for C-IFSs based on Archimedean t-norm operations, including addition, multiplication, subtraction, and division. These operations enable the aggregation of preferences from multiple experts into a single ranking. Also, we propose an extended EDAS (Evaluation Based on Distance from Average Solution) method, which utilizes weighted aggregation operators and defuzzification techniques to rank alternatives. To validate our approach, we provide a numerical example and compare the results with existing methods. Additionally, we discuss the time complexity of the algorithm. The proposed methodology offers decision-makers the flexibility to analyze the influence of different ratings on the final decision and select suitable parameters.
Land surface all-wave net radiation (Rn) is crucial in determining Earth's climate by contributing to the surface radiation budget. This study evaluated seven satellite and three reanalysis long-term land surface Rn products under different spatial scales, spatial and temporal variations, and different conditions. The results showed that during 2000-2018, Global Land Surface Satellite Product (GLASS)-Moderate Resolution Imaging Spectroradiometer (MODIS) performed the best (RMSE=25.54 Wm⁻², bias=−1.26 Wm⁻²), followed by ERA5 (the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis) (RMSE=32.17 Wm⁻², bias=−4.88 Wm⁻²) and GLASS-AVHRR (Advanced Very-High-Resolution Radiometer) (RMSE=33.10 Wm⁻², bias=4.03 Wm⁻²). During 1983-2018, GLASS-AVHRR and ERA5 ranked top and performed similarly, with RMSE values of 31.70 and 33.08 Wm⁻² and biases of −4.56 and 3.48 Wm⁻², respectively. The averaged multi-annual mean Rn over the global land surface of satellite products was higher than that of reanalysis products by about 10∼30 Wm⁻². These products differed remarkably in long-term trends variations, particularly pre-2000, but no significant trends were observed. Discrepancies were more frequent in satellite data, while reanalysis products showed smoother variations. Large discrepancies were found in regions with high latitudes, reflectance, and elevation which could be attributed to input radiative components, meteorological variables (e.g., cloud properties, aerosol optical thickness), and applicability of the algorithms used. While further research is needed for detailed insights.
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.
Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical informationfor improved water resource planning. However, the spatiotemporal changing behavior of rainfallis much less understood in a tropical monsoon‑dominated country like Bangladesh. To this end,this research aims to analyze spatiotemporal variations in rainfall for the period 1980–2020 overBangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trendanalysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years’ rainfallchanges nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inversedistance weighting model was adopted within the ArcGIS environment. Results show that meanannual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann–Kendalltrend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthlyrainfall. More than 80% of stations face a declining trend from November to March and August. Thereis a declining trend for seasonal rainfall at 82% of stations during the pre‑monsoon, 75% duringthe monsoon, and 100% during the post‑monsoon. A significant decline trend was identified in thenorth‑center during the pre‑monsoon, the northern part during the monsoon, and the southern andnorthwestern portions during the post‑monsoon season. Predicted rainfall by MLP till 2030 suggeststhat there will be little rain from November to February, and the maximum fluctuating rainfall willoccur in 2025 and 2027–2029. The ECMWF ERA5 reanalysis data findings suggested that changingrainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitationrates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortageof water resources and the anticipated rise in water demand, the study’s findings have someimplications for managing water resources in Bangladesh.
Drought depends upon many factors such as lack of precipitation, sunshine duration, and altitude. In this study, future rainfall trend pattern was studied, and an attempt was made to quantify the amount of rainfall deficit to cause drought occurrence and corresponding required water for the recovery of future drought. The non-standardized anomaly index (AI) was used to quantify the water deficit for future drought event, which is based on CMIP 6 climatic model generated rainfall data under the four different shared socioeconomic pathways’ (SSPs) scenarios (SSP 1–2.6, SSP 2–4.5, SSP 3–7.0, and SSP 5–8.5) for the chosen study area (the upper Tapti River basin in central India). Our study found out that the future rainfall pattern would be an increasing trend for all the SSPs scenarios up to the end of the twenty-first century. Particularly, SSP 5–8.5 scenario projected increasing rainfall pattern and more number of drought events than other SSPs. In specific season, the future monsoon season showed more number of drought events. The water required for drought recovery was found increased from SSP 1–2.6 to SSP 5–8.5 scenario. The results presented in this study are helpful to adopt drought management activity in response to changing rainfall pattern under climate change aspect.
In the present study, the significance of the quality indicators of “Fosuk” type driving masses used for the monolithic construction of the walls of the steel foundry ladles used in steel production was determined by rank correlation method. Six quality indicators of driving masses were studied: linear changes, open porosity, compressive strength, beginning of deformation, 4% deformation and 40% deformation. The three most significant indicators of driving masses “Fosuk” type have been determined. A multi-criteria optimization was performed, using a genetic optimization algorithm, the set of fifty Pareto-optimal solutions (Pareto-front) were found in the two-dimensional space of the control parameters within their range limits. A multi-criteria analysis was done by using fractional-rational generalized functions of usefulness in order to determine the optimal content of clay substances (Al2O3) and the temperature of heat treatment of the inner lining of the steel foundry ladles.KeywordsDriving massesQuality indicatorsMulti-criteria optimizationPareto-optimal solutionsFractional-rational generalized functions of usefulness
Climate change studies of locations with comparable Köppen–Geiger climate classifications have received little attention. The current study investigated trends in extreme temperature (Tmax and Tmin [maximum and minimum temperatures], DTR [diurnal temperature range], and TR20 [tropical nights]) and precipitation indices (R10, R20, and R30; [annual count of days when rainfall ≥ 10, 20, and 30 mm, respectively], and Prcptot; [annual total precipitation 1 mm]) in three locations: Port Harcourt (PH) in Nigeria, Thane (TH) in India, and Laoag City (LC) in the Philippines under similar Köppen–Geiger climate classification. In this study, the distributional assumptions of the linear model were applied for trend estimation and hypothesis testing. It was hypothesized that changes in climate extreme indices will be symmetrical in the three locations; however, results revealed that trends in Tmax were asymmetrical in all locations. In TH and LC, Tmin demonstrated symmetry and strong overlap in trend confidence interval. TR20 and DTR demonstrated symmetry and substantial overlap in trend confidence intervals in all locations. Precipitation indices exhibited starkly different patterns, with no agreement in trend confidence intervals found across locations. Our findings revealed significant wetting trends in TH and LC, as well as non-significant warming and drop in Tmax, respectively. PH is significantly drying with significant warming in Tmax. All locations revealed significant warming in Tmin and TR20, as well as drop in DTR; however, the DTR trend in TH is not significant. The effects of changes in climate extreme indices presented in this study may help to explain the regional heterogeneity in agricultural productivity. Our findings revealed that climate change along the lines of similar Köppen–Geiger classification is slightly congruent. Furthermore, there are signs of probable location-specific climate change impacts, which would necessitate local mitigation and adaptation activities.
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