Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 11

Trend Analysis Related Abstracts

11 Statistical Analysis of Rainfall Change over the Blue Nile Basin

Authors: Khaled Kheireldin, Mahmoud Roushdi, Hany Mustafa

Abstract:

Rainfall variability is an important feature of semi-arid climates. Climate change is very likely to increase the frequency, magnitude, and variability of extreme weather events such as droughts, floods, and storms. The Blue Nile Basin is facing extreme climate change-related events such as floods and droughts and its possible impacts on ecosystem, livelihood, agriculture, livestock, and biodiversity are expected. Rainfall variability is a threat to food production in the Blue Nile Basin countries. This study investigates the long-term variations and trends of seasonal and annual precipitation over the Blue Nile Basin for 102-year period (1901-2002). Six statistical trend analysis of precipitation was performed with nonparametric Mann-Kendall test and Sen's slope estimator. On the other hands, four statistical absolute homogeneity tests: Standard Normal Homogeneity Test, Buishand Range test, Pettitt test and the Von Neumann ratio test were applied to test the homogeneity of the rainfall data, using XLSTAT software, which results of p-valueless than alpha=0.05, were significant. The percentages of significant trends obtained for each parameter in the different seasons are presented. The study recommends adaptation strategies to be streamlined to relevant policies, enhancing local farmers’ adaptive capacity for facing future climate change effects.

Keywords: Climate Change, Trend Analysis, Mann-Kendall test, Blue Nile Basin

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10 A Ground Observation Based Climatology of Winter Fog: Study over the Indo-Gangetic Plains, India

Authors: Sanjay Kumar Srivastava, Anu Rani Sharma, Kamna Sachdeva

Abstract:

Every year, fog formation over the Indo-Gangetic Plains (IGPs) of Indian region during the winter months of December and January is believed to create numerous hazards, inconvenience, and economic loss to the inhabitants of this densely populated region of Indian subcontinent. The aim of the paper is to analyze the spatial and temporal variability of winter fog over IGPs. Long term ground observations of visibility and other meteorological parameters (1971-2010) have been analyzed to understand the formation of fog phenomena and its relevance during the peak winter months of January and December over IGP of India. In order to examine the temporal variability, time series and trend analysis were carried out by using the Mann-Kendall Statistical test. Trend analysis performed by using the Mann-Kendall test, accepts the alternate hypothesis with 95% confidence level indicating that there exists a trend. Kendall tau’s statistics showed that there exists a positive correlation between time series and fog frequency. Further, the Theil and Sen’s median slope estimate showed that the magnitude of trend is positive. Magnitude is higher during January compared to December for the entire IGP except in December when it is high over the western IGP. Decade wise time series analysis revealed that there has been continuous increase in fog days. The net overall increase of 99 % was observed over IGP in last four decades. Diurnal variability and average daily persistence were computed by using descriptive statistical techniques. Geo-statistical analysis of fog was carried out to understand the spatial variability of fog. Geo-statistical analysis of fog revealed that IGP is a high fog prone zone with fog occurrence frequency of more than 66% days during the study period. Diurnal variability indicates the peak occurrence of fog is between 06:00 and 10:00 local time and average daily fog persistence extends to 5 to 7 hours during the peak winter season. The results would offer a new perspective to take proactive measures in reducing the irreparable damage that could be caused due to changing trends of fog.

Keywords: Climatology, Trend Analysis, visibility, Mann-Kendall test, spatial variability, fog, temporal variability

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9 Mapping Crime against Women in India: Spatio-Temporal Analysis, 2001-2012

Authors: Ritvik Chauhan, Vijay Kumar Baraik

Abstract:

Women are most vulnerable to crime despite occupying central position in shaping a society as the first teacher of children. In India too, having equal rights and constitutional safeguards, the incidences of crime against them are large and grave. In this context of crime against women, especially rape has been increasing over time. This paper explores the spatial and temporal aspects of crime against women in India with special reference to rape. It also examines the crime against women with its spatial, socio-economic and demographic associates using related data obtained from the National Crime Records Bureau India, Indian Census and other government sources of the Government of India. The simple statistical, choropleth mapping and other cartographic representation methods have been used to see the crime rates, spatio-temporal patterns of crime, and association of crime with its correlates.  The major findings are visible spatial variations across the country and are also in the rising trends in terms of incidence and rates over the reference period. The study also indicates that the geographical associations are somewhat observed. However, selected indicators of socio-economic factors seem to have no significant bearing on crime against women at this level.

Keywords: Society, Trend Analysis, crime against women, crime mapping

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8 Trend Analysis of Annual Total Precipitation Data in Konya

Authors: Naci Büyükkaracığan

Abstract:

Hydroclimatic observation values ​​are used in the planning of the project of water resources. Climate variables are the first of the values ​​used in planning projects. At the same time, the climate system is a complex and interactive system involving the atmosphere, land surfaces, snow and bubbles, the oceans and other water structures. The amount and distribution of precipitation, which is an important climate parameter, is a limiting environmental factor for dispersed living things. Trend analysis is applied to the detection of the presence of a pattern or trend in the data set. Many trends work in different parts of the world are usually made for the determination of climate change. The detection and attribution of past trends and variability in climatic variables is essential for explaining potential future alteration resulting from anthropogenic activities. Parametric and non-parametric tests are used for determining the trends in climatic variables. In this study, trend tests were applied to annual total precipitation data obtained in period of 1972 and 2012, in the Konya Basin. Non-parametric trend tests, (Sen’s T, Spearman’s Rho, Mann-Kendal, Sen’s T trend, Wald-Wolfowitz) and parametric test (mean square) were applied to annual total precipitations of 15 stations for trend analysis. The linear slopes (change per unit time) of trends are calculated by using a non-parametric estimator developed by Sen. The beginning of trends is determined by using the Mann-Kendall rank correlation test. In addition, homogeneities in precipitation trends are tested by using a method developed by Van Belle and Hughes. As a result of tests, negative linear slopes were found in annual total precipitations in Konya.

Keywords: Precipitation, Trend Analysis, Konya, hydroclimatology

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7 Long Term Changes of Water Quality in Latvia

Authors: Maris Klavins, Valery Rodinov

Abstract:

The aim of this study was to analyze long term changes of surface water quality in Latvia, spatial variability of water chemical composition, possible impacts of different pollution sources as well as to analyze the measures to protect national water resources - river basin management. Within this study, the concentrations of major water ingredients and microelements in major rivers and lakes of Latvia have been determined. Metal concentrations in river and lake waters were compared with water chemical composition. The mean concentrations of trace metals in inland waters of Latvia are appreciably lower than the estimated world averages for river waters and close to or lower than background values, unless regional impacts determined by local geochemistry. This may be explained by a comparatively lower level of anthropogenic load. In the same time in several places, direct anthropogenic impacts are evident, regarding influences of point sources both transboundary transport impacts. Also, different processes related to pollution of surface waters in Latvia have been analyzed. At first the analysis of changes and composition of pollutant emissions in Latvia has been realized, and the obtained results were compared with actual composition of atmospheric precipitation and their changes in time.

Keywords: Pollution, Water Quality, Human impact, Trend Analysis

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6 Spatio-Temporal Variability and Trends in Frost-Free Season Parameters in Finland: Influence of Climate Teleconnections

Authors: Masoud Irannezhad, Sirpa Rasmus, Saghar Ahmadian, Deliang Chen, Bjorn Klove

Abstract:

Variability and changes in thermal conditions play a crucial role in functioning of human society, particularly over cold climate regions like Finland. Accordingly, the frost-free season (FFS) parameters in terms of start (FFSS), end (FFSE) and length (FFSL) have substantial effects not only on natural environment (e.g. flora and fauna), but also on human requirements (e.g. agriculture, forestry and energy generation). Applying the 0°C threshold of minimum temperature (Tmin), the FFS was defined as the period between the last spring frost as FFSS and the first fall frost as FFSE. For this study, gridded (10 x 10 km2) daily minimum temperature datasets throughout Finland during 1961-2011 was used to investigate recent spatio-temporal variations and trends in frost-free season (FFS) parameters and their relationships with the well-known large-scale climate teleconnections (CTs). The FFS in Finland naturally increases from north (~60 days) to south (~190 days), in association with earlier FFSS (~24 April) and later FFSE (~30 October). Statistically significant (p<0.05) trends in FFSL were all positive (increasing) ranged between 0 and 13.5 (days/decade) and mainly observed in the east, upper west, centre and upper north of Finland. Such lengthening trends in FFS were attributable to both earlier FFSS and later FFSE mostly over central and upper northern Finland, while only to later FFSE in eastern and upper western parts. Variations in both FFSL and FFSS were significantly associated with the Polar (POL) pattern over northern Finland, while with the East Atlantic (EA) pattern over eastern and upper western areas. However, the POL and Scandinavia (SCA) patterns were most influential CTs for FFSE variability over northern Finland.

Keywords: Trend Analysis, Finland, climate teleconnections, frost-free season

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5 Application of Seasonal Autoregressive Integrated Moving Average Model for Forecasting Monthly Flows in Waterval River, South Africa

Authors: Megersa Olumana Dinka, Kassahun Birhanu Tadesse

Abstract:

Reliable future river flow information is basic for planning and management of any river systems. For data scarce river system having only a river flow records like the Waterval River, a univariate time series models are appropriate for river flow forecasting. In this study, a univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied for forecasting Waterval River flow using GRETL statistical software. Mean monthly river flows from 1960 to 2016 were used for modeling. Different unit root tests and Mann-Kendall trend analysis were performed to test the stationarity of the observed flow time series. The time series was differenced to remove the seasonality. Using the correlogram of seasonally differenced time series, different SARIMA models were identified, their parameters were estimated, and diagnostic check-up of model forecasts was performed using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AIc) and Hannan-Quinn (HQc) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 was selected as the best model for Waterval River flow forecasting. Therefore, this model can be used to generate future river information for water resources development and management in Waterval River system. SARIMA model can also be used for forecasting other similar univariate time series with seasonality characteristics.

Keywords: Validation, Trend Analysis, white noise, heteroscedasticity, stationarity test

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4 Long-Term Trends of Sea Level and Sea Surface Temperature in the Mediterranean Sea

Authors: Bayoumy Mohamed, Khaled Alam El-Din

Abstract:

In the present study, 24 years of gridded sea level anomalies (SLA) from satellite altimetry and sea surface temperature (SST) from advanced very-high-resolution radiometer (AVHRR) daily data (1993-2016) are used. These data have been used to investigate the sea level rising and warming rates of SST, and their spatial distribution in the Mediterranean Sea. The results revealed that there is a significant sea level rise in the Mediterranean Sea of 2.86 ± 0.45 mm/year together with a significant warming of 0.037 ± 0.007 °C/year. The high spatial correlation between sea level and SST variations suggests that at least part of the sea level change reported during the period of study was due to heating of surface layers. This indicated that the steric effect had a significant influence on sea level change in the Mediterranean Sea.

Keywords: Trend Analysis, Mediterranean Sea, altimetry, AVHRR, sea level and SST changes

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3 Detection of Trends and Break Points in Climatic Indices: The Case of Umbria Region in Italy

Authors: R. Morbidelli, C. Saltalippi, A. Flammini

Abstract:

The increase of air surface temperature at global scale is a fact, with values around 0.85 ºC since the late nineteen century, as well as a significant change in main features of rainfall regime. Nevertheless, the detected climatic changes are not equally distributed all over the world, but exhibit specific characteristics in different regions. Therefore, studying the evolution of climatic indices in different geographical areas with a prefixed standard approach becomes very useful in order to analyze the existence of climatic trend and compare results. In this work, a methodology to investigate the climatic change and its effects on a wide set of climatic indices is proposed and applied at regional scale in the case study of a Mediterranean area, Umbria region in Italy. From data of the available temperature stations, nine temperature indices have been obtained and the existence of trends has been checked by applying the non-parametric Mann-Kendall test, while the non-parametric Pettitt test and the parametric Standard Normal Homogeneity Test (SNHT) have been applied to detect the presence of break points. In addition, aimed to characterize the rainfall regime, data from 11 rainfall stations have been used and a trend analysis has been performed on cumulative annual rainfall depth, daily rainfall, rainy days, and dry periods length. The results show a general increase in any temperature indices, even if with a trend pattern dependent of indices and stations, and a general decrease of cumulative annual rainfall and average daily rainfall, with a time rainfall distribution over the year different from the past.

Keywords: Climatic Change, temperature, Trend Analysis, rainfall regime

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2 Spatiotemporal Variability in Rainfall Trends over Sinai Peninsula Using Nonparametric Methods and Discrete Wavelet Transforms

Authors: Mosaad Khadr

Abstract:

Knowledge of the temporal and spatial variability of rainfall trends has been of great concern for efficient water resource planning, management. In this study annual, seasonal and monthly rainfall trends over the Sinai Peninsula were analyzed by using absolute homogeneity tests, nonparametric Mann–Kendall (MK) test and Sen’s slope estimator methods. The homogeneity of rainfall time-series was examined using four absolute homogeneity tests namely, the Pettitt test, standard normal homogeneity test, Buishand range test, and von Neumann ratio test. Further, the sequential change in the trend of annual and seasonal rainfalls is conducted using sequential MK (SQMK) method. Then the trend analysis based on discrete wavelet transform technique (DWT) in conjunction with SQMK method is performed. The spatial patterns of the detected rainfall trends were investigated using a geostatistical and deterministic spatial interpolation technique. The results achieved from the Mann–Kendall test to the data series (using the 5% significance level) highlighted that rainfall was generally decreasing in January, February, March, November, December, wet season, and annual rainfall. A significant decreasing trend in the winter and annual rainfall with significant levels were inferred based on the Mann-Kendall rank statistics and linear trend. Further, the discrete wavelet transform (DWT) analysis reveal that in general, intra- and inter-annual events (up to 4 years) are more influential in affecting the observed trends. The nature of the trend captured by both methods is similar for all of the cases. On the basis of spatial trend analysis, significant rainfall decreases were also noted in the investigated stations. Overall, significant downward trends in winter and annual rainfall over the Sinai Peninsula was observed during the study period.

Keywords: Trend Analysis, Rainfall, discrete wavelet transform, Sinai Peninsula, Mann–Kendall test

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1 Multi-Indicator Evaluation of Agricultural Drought Trends in Ethiopia: Implications for Dry Land Agriculture and Food Security

Authors: Dawd Ahmed, Venkatesh Uddameri

Abstract:

Agriculture in Ethiopia is the main economic sector influenced by agricultural drought. A simultaneous assessment of drought trends using multiple drought indicators is useful for drought planning and management. Intra-season and seasonal drought trends in Ethiopia were studied using a suite of drought indicators. Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), and Z-index for long-rainy, dry, and short-rainy seasons are used to identify drought-causing mechanisms. The Statistical software package R version 3.5.2 was used for data extraction and data analyses. Trend analysis indicated shifts in late-season long-rainy season precipitation into dry in the southwest and south-central portions of Ethiopia. Droughts during the dry season (October–January) were largely temperature controlled. Short-term temperature-controlled hydrologic processes exacerbated rainfall deficits during the short rainy season (February–May) and highlight the importance of temperature- and hydrology-induced soil dryness on the production of short-season crops such as tef. Droughts during the long-rainy season (June–September) were largely driven by precipitation declines arising from the narrowing of the intertropical convergence zone (ITCZ). Increased dryness during long-rainy season had severe consequences on the production of corn and sorghum. PDSI was an aggressive indicator of seasonal droughts suggesting the low natural resilience to combat the effects of slow-acting, moisture-depleting hydrologic processes. The lack of irrigation systems in the nation limits the ability to combat droughts and improve agricultural resilience. There is an urgent need to monitor soil moisture (a key agro-hydrologic variable) to better quantify the impacts of meteorological droughts on agricultural systems in Ethiopia.

Keywords: Climate Change, Food Security, Trend Analysis, droughts, Ethiopia, autocorrelation, SPI, SPEI, palmer z-index, PDSI

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