scholarly journals Spatio-Temporal Trends and Variability of Temperature Over Bale Zone, Ethiopia

2019 ◽  
Vol 9 (2) ◽  
pp. 903-916
Keyword(s):  
Minimum Temperature ◽  
Temporal Trends ◽  
Temperature Series ◽  
Spatiotemporal Variability ◽  
Maximum Temperature ◽  
Data Series ◽  
Parametric Test ◽  
Temperature Trends ◽  
Increasing Trend ◽  
Bale Zone

Understanding of temperature trends and their spatiotemporal variability has great significances on making deep insight for planners, managers, professionals and decision makers of water resources and agriculture. Therefore, this research was set with aim to analyze spatiotemporal variability of temperature and their time series trends over Bale Zone. Statistical analysis: Parametric test with regression analysis on the anomalies like deviation from mean and Non-parametric test with Mann-Kendall test together with Sen’s Slope Estimator & Zs statistics has been used for estimation of trends of a historical data series of monthly, seasonal and annually maximum and minimum temperature of selected meteorological stations in Bale Zone. Both tests relatively shows same results for monthly, seasonal and yearly temperature series. The coefficient of variation (CV) was used for variability analysis. Arc GIS 9.3 software was also used to investigate the spatial variability temperature (minimum and maximum) for the period under review. These methodology has shown a significant increasing and decreasing trends at 95% confidence level for certain time scale temperature series: temperature trends (i.e the mean maximum temperature series) showed a significant increasing trend in Robe (Annual, Spring, February, March, April, May, July, and October), Ginir (February, July, September, and December).Mean minimum temperature series showed a substantial increasing trend in Robe (May, July, September, and November) and Hunte (September). It is also observed that Mean seasonal and annually minimum temperature of the stations have shown higher variability than those mean seasonal and annual maximum temperature of the stations.

scholarly journals The Fingerprint of Climate Change and Urbanization in South Korea

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 273 ◽  
Author(s):  
Won-Ho Nam ◽  
Guillermo Baigorria ◽  
Eun-Mi Hong ◽  
Taegon Kim ◽  
Yong-Sang Choi ◽  
...  
Keyword(s):  
Climate Change ◽  
South Korea ◽  
Urban Areas ◽  
Temporal Trends ◽  
Weather Data ◽  
Maximum Temperature ◽  
Island Effect ◽  
Temperature Trends ◽  
Agricultural Areas ◽  
Precipitation And Temperature

Understanding long-term changes in precipitation and temperature patterns is important in the detection and characterization of climate change, as is understanding the implications of climate change when performing impact assessments. This study uses a statistically robust methodology to quantify long-, medium- and short-term changes for evaluating the degree to which climate change and urbanization have caused temporal changes in precipitation and temperature in South Korea. We sought to identify a fingerprint of changes in precipitation and temperature based on statistically significant differences at multiple-timescales. This study evaluates historical weather data during a 40-year period (1973–2012) and from 54 weather stations. Our results demonstrate that between 1993–2012, minimum and maximum temperature trends in the vicinity of urban and agricultural areas are significantly different from the two previous decades (1973–1992). The results for precipitation amounts show significant differences in urban areas. These results indicate that the climate in urbanized areas has been affected by both the heat island effect and global warming-caused climate change. The increase in the number of rainfall events in agricultural areas is highly significant, although the temporal trends for precipitation amounts showed no significant differences. Overall, the impacts of climate change and urbanization in South Korea have not been continuous over time and have been expressed locally and regionally in terms of precipitation and temperature changes.

scholarly journals Temperature trends in Fiji: a clear signal of climate change

2013 ◽  
Vol 31 (1) ◽  
pp. 27 ◽  
Author(s):  
Ravind Kumar ◽  
Mark Stephens ◽  
Tony Weir
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Pacific Islands ◽  
Extreme Values ◽  
Maximum Temperature ◽  
Temperature Trends ◽  
Clear Signal ◽  
Rate Of Increase ◽  
Significant Difference ◽  
Using Data

This paper analyses trends in temperature in Fiji, using data from more stations (10) and longer periods (52-78 years) than previous studies. All the stations analysed show a statistically significant trend in both maximum and minimum temperature, with increases ranging from 0.08 to 0.23°C per decade. More recent temperatures show a higher rate of increase, particularly in maximum temperature (0.18 to 0.69°C per decade from 1989 to 2008). This clear signal of climate change is consistent with that found in previous studies of temperatures in Fiji and other Pacific Islands. Trends in extreme values show an even stronger signal of climate change than that for mean temperatures. Our preliminary analysis of daily maxima at 6 stations indicates that for 4 of them (Suva, Labasa, Vunisea and Rotuma) there has been a tripling in the number of days per year with temperature >32°C between 1970 and 2008. The correlations between annual mean maximum (minimum) temperature and year are mostly strong: for about half the stations the correlation coefficient exceeds 60% over 50+ years. Trends do not vary systematically with location of station. At all 7 stations for which both trends are available there is no statistically significant difference between the trends in maximum and minimum temperatures.

scholarly journals Temporal Trends and Spatial Patterns of Temperature and Its Extremes over the Beijing-Tianjin Sand Source Region (1960–2014), China

2018 ◽  
Vol 2018 ◽  
pp. 1-26
Author(s):  
Wei Wei ◽  
Baitian Wang ◽  
Kebin Zhang ◽  
Zhongjie Shi ◽  
Genbatu Ge ◽  
...  
Keyword(s):  
Source Region ◽  
Temporal Trends ◽  
Extreme Temperature ◽  
Spatiotemporal Variability ◽  
Mountain Area ◽  
Increasing Trend ◽  
Cold Tolerant ◽  
Temperature Indices ◽  
Slope Estimator ◽  
Sand Source

In order to examine temperature changes and extremes in the Beijing-Tianjin Sand Source Region (BTSSR), ten extreme temperature indices were selected, categorized, and calculated spanning the period 1960–2014, and the spatiotemporal variability and trends of temperature and extremes on multitimescales in the BTSSR were investigated using the Mann-Kendall (M-K) test, Sen’s slope estimator, and linear regression. Results show that mean temperatures have increased and extreme temperature events have become more frequent. Annual temperature has recorded a significant increasing trend over the BTSSR, in which 51 stations exhibited significant increasing trends (p<0.05); winter temperature recorded the most significant increasing trend in the northwest subregion. All extreme temperature indices showed warming trends at most stations; a higher warming slope in extreme temperature mainly occurred along the northeast border and northwest border and in the central-southern mountain area. As extreme low temperature events decrease, vegetation damage due to freezing temperatures will reduce and low cold-tolerant plants may expand their distribution range northward to revegetate barren areas in the BTSSR. However, in water-limited areas of the BTSSR, increasing temperatures in the growing season may exacerbate stress associated with plants relying on precipitation due to higher temperatures combining with decreasing precipitation.

Homogeneity Evaluation of Historical Rainfall and Temperature Series in Mato Grosso

2019 ◽  
pp. 1-7
Author(s):  
Elizangela Selma da Silva ◽  
José Holanda Campelo Júnior ◽  
Francisco De Almeida Lobo ◽  
Ricardo Santos Silva Amorim
Keyword(s):  
Trend Analysis ◽  
Minimum Temperature ◽  
Statistical Tests ◽  
Kendall Test ◽  
Maximum Temperature ◽  
Data Series ◽  
Positive Trend ◽  
Mato Grosso ◽  
Temperature Climate ◽  
Precipitation And Temperature

The homogeneity investigation of a series can be performed through several nonparametric statistical tests, which serve to detect artificial changes or non-homogeneities in climatic variables. The objective of this work was to evaluate two methodologies to verify the homogeneity of the historical climatological series of precipitation and temperature in Mato Grosso state. The series homogeneity evaluation was performed using the following non-parametric tests: Wald-Wolfowitz (for series with one or no interruption), Kruskal-Wallis (for series with two or more interruptions), and Mann-Kendall (for time series trend analysis). The results of the precipitation series homogeneity analysis from the National Waters Agency stations, analyzed by the Kruskal-Wallis and Wald-Wolfowitz tests, presented 61.54% of homogeneous stations, being well distributed throughout Mato Grosso state, whereas those of the trend analysis allowed to identify that 87.57% of the rainfall-gauging stations showed a concentrated positive trend, mainly in the rainy season. Out of the conventional stations of the National Institute of Meteorology of Mato Grosso, seven were homogeneous for the precipitation variable, five for maximum temperature and four stations were homogeneous for minimum temperature. For the trend analysis in the 11 stations, positive trends of random nature were observed, suggesting increasing alterations in the analyzed variables. Therefore, the trend analysis performed by the Mann-Kendall test in the precipitation, and maximum and minimum temperature climate series, indicated that several data series showed increasing trends, suggesting a possible increase in precipitation and temperature values over the years. The results of the Kruskal-Wallis and Wald-Wolfowitz tests for homogeneity presented more than 87% of homogeneous stations.

scholarly journals Analysis of hydrometeorological variables over the transboundary Komadugu-Yobe basin, West Africa

2019 ◽  
Vol 11 (4) ◽  
pp. 1339-1354 ◽  
Author(s):  
O. E. Adeyeri ◽  
P. Laux ◽  
A. E. Lawin ◽  
S. O. Ige ◽  
H. Kunstmann
Keyword(s):  
Time Scales ◽  
Minimum Temperature ◽  
River Discharge ◽  
Kendall Test ◽  
Mitigation Measures ◽  
Maximum Temperature ◽  
Spatiotemporal Trends ◽  
Increasing Trend ◽  
Temperature Ranges ◽  
Positive Correlations

Abstract Spatiotemporal trends in daily observed precipitation, river discharge, maximum and minimum temperature data were investigated between 1971 and 2013 in the Komadugu-Yobe basin. Significant change points in time series are corrected using Adapted Caussinus-Mestre Algorithm for homogenizing Networks of Temperature series algorithm. Mann–Kendall test and Sen's slope are used to estimate the trend and its magnitude at dry, wet and annual season time scales, respectively. Preliminary results show an increasing trend of the observed variables. There is a latitudinal increase (decrease) in the basin temperature (precipitation) from lower to higher latitudes. The minimum temperature (0.05 °C/year) increases faster than the maximum temperature (0.03 °C/year). Overall, the percentage changes in minimum temperature range between 3 and 10% while that of maximum temperature ranges between 1 and 3%. Due to precipitation dependence on regional characteristics, the highest percentage change was recorded in precipitation with values between −5 and 97%. In all time scales, river discharge and precipitation have strong positive correlations while the correlation between river discharge and temperature is negative. It is imperative to advocate and support positive developmental practices as well as establishing necessary mitigation measures to cope with the effects of climate in the basin.

scholarly journals Observed Trends and Variability of Temperature and Precipitation and Their Global Teleconnections in the Upper Indus Basin, Hindukush-Karakoram-Himalaya

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 973
Author(s):  
Azfar Hussain ◽  
Jianhua Cao ◽  
Ishtiaq Hussain ◽  
Saira Begum ◽  
Mobeen Akhtar ◽  
...  
Keyword(s):  
Climate Change ◽  
Scientific Basis ◽  
Indus Basin ◽  
Spatiotemporal Variability ◽  
Maximum Temperature ◽  
Local Climate ◽  
Upper Indus Basin ◽  
Temperature And Precipitation ◽  
Increasing Trend ◽  
Increasing Trends

Having an extreme topography and heterogeneous climate, the Upper Indus Basin (UIB) is more likely to be affected by climate change and it is a crucial area for climatological studies. Based on the monthly minimum temperature (Tmin), maximum temperature (Tmax) and precipitation from nine meteorological stations, the spatiotemporal variability of temperature and precipitation were analyzed on monthly, seasonal, and annual scales. Results show a widespread significant increasing trend of 0.14 °C/decade for Tmax, but a significant decreasing trend of −0.08 °C/decade for Tmin annually, during 1955–2016 for the UIB. Seasonally, warming in Tmax is stronger in winter and spring, while the cooling in Tmin is greater in summer and autumn. Results of seasonal Tmax indicate increasing trends in winter, spring and autumn at rates of 0.38, 0.35 and 0.05 °C/decade, respectively, while decreasing in summer with −0.14 °C/decade. Moreover, seasonal Tmin results indicate increasing trends in winter and spring at rates of 0.09 and 0.08 °C/decade, respectively, while decreasing significantly in summer and autumn at rates of −0.21 and −0.22 °C/decade respectively for the whole the UIB. Precipitation exhibits an increasing trend of 2.74 mm/decade annually, while, increasing in winter, summer and autumn at rates of 1.18, 2.06 and 0.62 mm/decade respectively. The warming in Tmax and an increase in precipitation have been more distinct since the mid-1990s, while the cooling in Tmin is observed in the UIB since the mid-1980s. Warming in the middle and higher altitude (1500–2800 m and >2800 m) are much stronger, and the increase is more obvious in regions with elevation >2800 m. The wavelet analysis illustrated sporadic inter-annual covariance of seasonal Tmax, Tmin and precipitation with ENSO, NAO, IOD and PDO in the UIB. The periodicities were usually constant over short timescales and discontinuous over longer timescales. This study offers a better understanding of the local climate characteristics and provides a scientific basis for government policymakers.

scholarly journals A Statistical Model for Predicting Neutropenic Fever

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 5258-5258
Author(s):  
Ariel Nelson ◽  
Dan Eastwood ◽  
Tao Wang ◽  
Karen Carlson ◽  
Laura C Michaelis ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Febrile Neutropenia ◽  
Predictive Value ◽  
Minimum Temperature ◽  
Temperature Data ◽  
Temperature Series ◽  
Series Data ◽  
Maximum Temperature ◽  
Current Definition ◽  
Single Temperature

Abstract Background Febrile neutropenia (FN) is a common occurrence associated with chemotherapy regimens used in patients (pts) with acute myelogenous leukemia (AML). Febrile neutropenia is presently defined as a single temperature of ≥38.3°C (101°F) or a temperature of ≥38.0°C (100.4°F) for >1 hour in a patient with an absolute neutrophil count < 500/mm3. Due to the potential for life threatening infections, fever in a patient with neutropenia is considered an oncologic emergency. Initiating appropriate antibiotic therapy as soon as possible in these patients leads to better outcomes. However, to our knowledge, there is no evidence that supports the current definition of neutropenic fever. Aim To identify a temperature pattern that is predictive of the subsequent development of febrile neutropenia in neutropenic pts with AML Methods After obtaining IRB approval we retrospectively obtained demographic and temperature data from hospitalized patients with AML undergoing induction therapy who were admitted to our institution between 12/8/2012 and 12/7/2013. Temperature data was recorded at intervals per physician order and nursing discretion during admission. We identified fever as a single temperature ≥38.3°C (101°F) or consecutive temperatures recorded 1 hour apart ≥38.06°C (100.5°F). Data was processed using SAS data programming to create and summarize this pilot temperature series data. Data for 68 patients containing 137 fever events was divided into 203 segments: a series was considered to end at the time of fever (or end of data) and a new series for the same patient began 24 hours after a preceding fever. Plots were created showing temperature over time leading up to fever (end of series). Our data consists of unequal interval time series data and does not lend itself to the usual methods of statistical ROC analysis. An ROC-like analysis to estimate sensitivity and specificity of a maximum temperature that predicts for a subsequent episode of FN was performed. Temperature data was subset into 7 time intervals: a pre-fever interval spanning 4 to 28 hours preceding fever or series end, and 6 non-fever intervals, each 24 hours long and spanning the period from 48 to 192 hours before fever or series end, for a total of 854 data windows. Statistics on each patient series within each interval were used as variables in predicting fever onset in logistic regression analysis. The variables included were maximum temperature within 24 hours, minimum temperature within 24 hours, average of positive increases between subsequent measurements, and largest 24 hour increase. Statistical analysis consisted of a generalized linear model with logit link (logistic regression) predicting fever at least 4 hours before onset, and used generalized estimating equations to adjust for correlated temperature measures within patient. Results Of the 68 patients identified, 47% were male, 53% were female with a mean age of 56.3 ± 15.1 years. Our fever curve plots suggest that there is an increase in average temperature at least 24 hours before the onset of fever in those patients that will go on to develop a fever by current definition (Figure 1). A prediction score including, maximum temperature within 24 hours, minimum temperature within 24 hours, average of positive increases between subsequent measurements, and largest 24 hour increase was able to predict 86.1% of oncoming FN events 4 to 28 hours before onset and reject 67.4% of non-FN events. This rule has a negative predictive value of 96.2% and a positive predictive value of 33.7%. Discussion Our analysis demonstrates the feasibility of using temperature series data for early prediction of FN. A more comprehensive analysis is planned and is expected to result in higher sensitivities. If subsequent analysis proves to be significant this data may be used to develop future prospective clinical studies to evaluate new fever criteria and may alter our current definition and management of pts with FN. Figure 1. 4-days of temperature series data preceding onset of fever or end of series if no fever. The dark lines are LOESS smoothed average temperatures for series ending in fever (dash) or non-fever (solid). Figure 1. 4-days of temperature series data preceding onset of fever or end of series if no fever. The dark lines are LOESS smoothed average temperatures for series ending in fever (dash) or non-fever (solid). Disclosures No relevant conflicts of interest to declare.

scholarly journals Spatial and Temporal Variations of Temperature in the Punjab Province, Pakistan

2021 ◽  
Vol 12 (1) ◽  
pp. 67-75
Author(s):  
Sohail Abbas ◽  
Safdar Ali Shirazi ◽  
Nausheen Mazhar ◽  
Kashif Mahmood ◽  
Ashfak Ahmad Khan
Keyword(s):  
Climate Change ◽  
Temperature Change ◽  
Minimum Temperature ◽  
Temporal Variations ◽  
Maximum Temperature ◽  
Punjab Province ◽  
Mean Temperature ◽  
Increasing Trend ◽  
Slope Estimator ◽  
Level Of Significance

Identifying the temperature change at a regional level is one of the essential parameters to determine the intensity of climate change. The current investigation provides an examination of changing trends of temperature in the Punjab province from 1970 to 2019. Sen's slope estimator method is applied to monthly data of mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin) to calculate the rate of temperature change. Statistical methods were used to find out the level of significance in terms of negative or positive trends to examine the variability among various weather observatories. Moreover, predicted values have also been observed for a detailed analysis of temperature variability and trends. Significant and pronounced changes in the mean temperature (T mean) are distinguished all over the Punjab regions with an increasing trend from North to South Punjab. In the case of maximum temperature (Tmax), a faster rate of rising in temperature is observed over the Southern and Western regions of Punjab. In contrast, the minimum temperature (Tmin) shows an increasing trend in Central Punjab. The findings provide detailed insight to policymakers for the planning of mitigating efforts and adaptation strategies in response to climate change.

scholarly journals ASSESSMENT OF LONG TERM CLIMATIC VARIABILITY OF UTTARAKHAND

2018 ◽  
Vol 1 (4) ◽  
Author(s):  
Sujeet Kumar ◽  
Shakti Suryavanshi
Keyword(s):  
Minimum Temperature ◽  
Monsoon Season ◽  
Climatic Variability ◽  
Kendall Test ◽  
Summer Season ◽  
Climatic Variables ◽  
Maximum Temperature ◽  
Mountain Areas ◽  
Mk Test ◽  
Increasing Trend

A trend analysis was performed for historic (1901-2002) climatic variables (Rainfall, Maximum Temperature and Minimum Temperature) of Uttarakhand State located in Northern India. In the serially independent climatic variables, Mann-Kendall test (MK test) was applied to the original sample data. However, in the serially correlated series, prewhitening is utilized before employing the MK test. The results of this study indicated a declining trend of rainfall in monsoon season for seven out of thirteen districts of Uttarakhand state. However, an increasing trend was observed in Haridwar and Udhamsingh Nagar districts for summer season rainfall. For maximum and minimum temperature, a few districts exhibited a declining trend in monsoon season whereas many districts exhibited an increasing trend in winter and summer season. Mountain dominated areas (as Uttarakhand state) are specific ecosystems, distinguished by their diversity, sensitivity and intricacy. Thus the variability of rainfall and temperature has a severe and rapid impact on mountainous ecosystems. Nevertheless, mountains have significant impacts on hydrology, which may further threaten populations living in the mountain areas as well as in adjacent, lowland regions.

scholarly journals Trend Analysis of Precipitation and Temperature over Different Districts of Karnataka: An Aid to Climate Change Detection and Cropping System Option

2020 ◽  
pp. 15-25
Author(s):  
S. Sridhara ◽  
Pradeep Gopakkali ◽  
R. Nandini
Keyword(s):  
Minimum Temperature ◽  
Monsoon Season ◽  
Cropping System ◽  
Weather Conditions ◽  
Kendall Test ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Land Preparation ◽  
Mk Test ◽  
Increasing Trend

Aims: To know the rainfall and temperature trend for all the districts of Karnataka state to develop suitable coping mechanisms for changing weather conditions during the cropping season. Study Design: The available daily data of rainfall (1971-2011) and minimum and maximum temperature (1971-2007) for each district was collected from NICRA-ICAR website. A non-parametric model such as the Mann-Kendall (MK) test complemented with Sen’s slope estimator was used to determine the magnitude of the trend. Place and Duration of Study: The rainfall data of 41 years (1971-2011) and temperature data of 37 years (1971-2007) was collected for all 27 districts of Karnataka. Methodology: Basic statistics related to rainfall like mean, standard deviation (SD), the coefficient of variation (CV) and the percentage contribution to annual rainfall were computed for monthly and season-wise. Mann-Kendall test was used to detect trend for rainfall as well as temperature. Results: An increasing trend in rainfall during winter, monsoon and annual basis for all most all the districts of Karnataka and decreasing trend of rainfall during pre and post-monsoon season was noticed. An early cessation of rainfall during September month in all most all the districts of Karnataka was observed. Similarly, monthly mean, maximum and the minimum temperature had shown an increasing trend over the past 37 years for all the districts of Karnataka. Conclusion: The more variation in rainfall during the pre-monsoon season was observed, which is more important for land preparation and other operations. The increasing trend of maximum and minimum temperature throughout the year may often cause a reduction in crop yield. It is necessary to change crops with its short duration varieties in order to avoid late season drought.

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