scholarly journals Spaceborne Satellite for Snow Cover and Hydrological Characteristic of the Gilgit River Basin, Hindukush–Karakoram Mountains, Pakistan

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 531 ◽  
Author(s):  
Dostdar Hussain ◽  
Chung-Yen Kuo ◽  
Abdul Hameed ◽  
Kuo-Hsin Tseng ◽  
Bulbul Jan ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Runoff ◽  
Time Series Data ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Indus Basin ◽  
Series Data ◽  
Northwestern Part ◽  
Temperature And Precipitation ◽  
Mean Temperature

The Indus River, which flows through China, India, and Pakistan, is mainly fed by melting snow and glaciers that are spread across the Hindukush–Karakoram–Himalaya Mountains. The downstream population of the Indus Plain heavily relies on this water resource for drinking, irrigation, and hydropower generation. Therefore, its river runoff variability must be properly monitored. Gilgit Basin, the northwestern part of the Upper Indus Basin, is selected for studying cryosphere dynamics and its implications on river runoff. In this study, 8-day snow products (MOD10A2) of moderate resolution imaging spectroradiometer, from 2001 to 2015 are selected to access the snow-covered area (SCA) in the catchment. A non-parametric Mann–Kendall test and Sen’s slope are calculated to assess whether a significant trend exists in the SCA time series data. Then, data from ground observatories for 1995–2013 are analyzed to demonstrate annual and seasonal signals in air temperature and precipitation. Results indicate that the annual and seasonal mean of SCA show a non-significant decreasing trend, but the autumn season shows a statistically significant decreasing SCA with a slope of −198.36 km2/year. The annual mean temperature and precipitation show an increasing trend with highest values of slope 0.05 °C/year and 14.98 mm/year, respectively. Furthermore, Pearson correlation coefficients are calculated for the hydro-meteorological data to demonstrate any possible relationship. The SCA is affirmed to have a highly negative correlation with mean temperature and runoff. Meanwhile, SCA has a very weak relation with precipitation data. The Pearson correlation coefficient between SCA and runoff is −0.82, which confirms that the Gilgit River runoff largely depends on the melting of snow cover rather than direct precipitation. The study indicates that the SCA slightly decreased for the study period, which depicts a possible impact of global warming on this mountainous region.

scholarly journals Associations between Meteorological Factors and Reported Mumps Cases from 1999 to 2020 in Japan

Epidemiologia ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 162-178
Author(s):  
Keiji Mise ◽  
Ayako Sumi ◽  
Shintaro Takatsuka ◽  
Shin-ichi Toyoda
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Time Series Data ◽  
Least Squares Method ◽  
Meteorological Factors ◽  
Vaccine Coverage ◽  
Meteorological Data ◽  
Spectral Lines ◽  
Series Data ◽  
Mean Temperature

The present study investigated associations between epidemiological mumps patterns and meteorological factors in Japan. We used mumps surveillance data and meteorological data from all 47 prefectures of Japan from 1999 to 2020. A time-series analysis incorporating spectral analysis and the least-squares method was adopted. In all power spectral densities for the 47 prefectures, spectral lines were observed at frequency positions corresponding to 1-year and 6-month cycles. Optimum least-squares fitting (LSF) curves calculated with the 1-year and 6-month cycles explained the underlying variation in the mumps data. The LSF curves reproduced bimodal and unimodal cycles that are clearly observed in northern and southern Japan, respectively. In investigating factors associated with the seasonality of mumps epidemics, we defined the contribution ratios of a 1-year cycle (Q1) and 6-month cycle (Q2) as the contributions of amplitudes of 1-year and 6-month cycles, respectively, to the entire amplitude of the time series data. Q1 and Q2 were significantly correlated with annual mean temperature. The vaccine coverage rate of a measles–mumps–rubella vaccine might not have affected the 1-year and 6-month modes of the time series data. The results of the study suggest an association between mean temperature and mumps epidemics in Japan.

scholarly journals The Effect of Governance On Entrepreneurship: From All Income Economies Perspective

2021 ◽  
Author(s):  
Mekonnen Bogale Abegaz ◽  
Kenenisa Lemi Debela ◽  
Reta Megersa Hundie
Keyword(s):  
Time Series Data ◽  
Pearson Correlation ◽  
Control Variable ◽  
Political Stability ◽  
Series Data ◽  
Outcome Variable ◽  
Net Income ◽  
Linear Regression Models ◽  
Governance Indicators ◽  
Development Levels

Abstract The purpose of this study is to analyze the effect of governance indicators on Entrepreneurship. Explanatory research design with Pearson correlation and multiple linear regression models were applied. Five-year World Bank data (2014–2018) of 126 countries from all economic development levels were used. Worldwide governance indicators considered are voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and corruption control. Gross net income was taken as a control variable. To measure entrepreneurship, the number of formally registered limited liability businesses as a percentage of the working-age population, was used. To make highly skewed time series data of dependent variable (entrepreneurship) closer to normal, logarithmic transformation was made and heteroscedasticity of residuals was checked. The finding of Pearson correlation shows that there are strong and significant correlations(r > 0.466, p < 0.01) between predictors and the outcome variable and among predictor variables. Regression analysis was computed after two highly collinear variables were dropped from the model using the VIF test. The study found that the remaining four independent variables and the control variable predict 71.5% of the variance in the outcome variable. Except for voice and accountability, all predictors have their own statistically significant influence on entrepreneurship. Thus, working on each predictor up to the standard application can bring incremental changes in new business formation and entry. The researchers believe that this study is of significant interest to policymakers, program developers, entrepreneurs, analysis, and supporters since it provides useful insight on how governance indicators influence entrepreneurship.

scholarly journals An improved framework to predict river flow time series data

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7183 ◽  
Author(s):  
Hafiza Mamona Nazir ◽  
Ijaz Hussain ◽  
Ishfaq Ahmad ◽  
Muhammad Faisal ◽  
Ibrahim M. Almanjahie
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
River Flow ◽  
Time Series Data ◽  
Flow Time ◽  
Indus Basin ◽  
Series Data ◽  
Percentage Error ◽  
Empirical Bayesian ◽  
Noise Characteristics

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.

scholarly journals Estimating the weight of a snow cover using only meteorological factors

1993 ◽  
Vol 18 ◽  
pp. 190-192
Author(s):  
Kenji Shinojima ◽  
Hiroshi Harada
Keyword(s):  
Snow Cover ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Moving Average ◽  
Data Acquisition System ◽  
Degree Days ◽  
Forest Product Research Institute ◽  
Temperature And Precipitation ◽  
Using Data

We compute the weight of the snow cover as a function of the daily quantity of precipitation and daily melting using only data from the Automated Meteorological Data Acquisition System (AMeDAS), which is used widely in Japan. The correlation between long-term measurements and meteorological data in AMeDAS factors was computed by statistical methods from the Forestry and Forest Product Research Institute, Tokamachi Experiment Station, in Niigata Prefecture, using data for 11 winter seasons (1977–87). The daily quantity of melting is expressed with a three-day moving average of degree days. The coefficient of correlation between the daily groups of each value of the 1323 days during the 11 winter seasons was 0.986 with a standard deviation of ±590 Ν m−2. Thus, if air temperature and precipitation can be obtained for an area, the weight of the snow cover can be estimated with confidence.

scholarly journals Estimating the weight of a snow cover using only meteorological factors

1993 ◽  
Vol 18 ◽  
pp. 190-192
Author(s):  
Kenji Shinojima ◽  
Hiroshi Harada
Keyword(s):  
Snow Cover ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Moving Average ◽  
Data Acquisition System ◽  
Degree Days ◽  
Forest Product Research Institute ◽  
Temperature And Precipitation ◽  
Using Data

We compute the weight of the snow cover as a function of the daily quantity of precipitation and daily melting using only data from the Automated Meteorological Data Acquisition System (AMeDAS), which is used widely in Japan. The correlation between long-term measurements and meteorological data in AMeDAS factors was computed by statistical methods from the Forestry and Forest Product Research Institute, Tokamachi Experiment Station, in Niigata Prefecture, using data for 11 winter seasons (1977–87).The daily quantity of melting is expressed with a three-day moving average of degree days. The coefficient of correlation between the daily groups of each value of the 1323 days during the 11 winter seasons was 0.986 with a standard deviation of ±590 Ν m−2. Thus, if air temperature and precipitation can be obtained for an area, the weight of the snow cover can be estimated with confidence.

scholarly journals Climatic Trends in Different Bioclimatic Zones in the Chitwan Annapurna Landscape, Nepal

Climate ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 136
Author(s):  
Dol Raj Luitel ◽  
Pramod K. Jha ◽  
Mohan Siwakoti ◽  
Madan Lall Shrestha ◽  
Rangaswamy Munniappan
Keyword(s):  
Climate Change ◽  
Time Series Data ◽  
Lapse Rate ◽  
Series Data ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Average Annual Temperature ◽  
Bioclimatic Zone ◽  
The Impact ◽  
Precipitation Trends

The Chitwan Annapurna Landscape (CHAL) is the central part of the Himalayas and covers all bioclimatic zones with major endemism of flora, unique agro-biodiversity, environmental, cultural and socio-economic importance. Not much is known about temperature and precipitation trends along the different bioclimatic zones nor how changes in these parameters might impact the whole natural process, including biodiversity and ecosystems, in the CHAL. Analysis of daily temperature and precipitation time series data (1970–2019) was carried out in seven bioclimatic zones extending from lowland Terai to the higher Himalayas. The non-parametric Mann-Kendall test was applied to determine the trends, which were quantified by Sen’s slope. Annual and decade interval average temperature, precipitation trends, and lapse rate were analyzed in each bioclimatic zone. In the seven bioclimatic zones, precipitation showed a mixed pattern of decreasing and increasing trends (four bioclimatic zones showed a decreasing and three bioclimatic zones an increasing trend). Precipitation did not show any particular trend at decade intervals but the pattern of rainfall decreases after 2000AD. The average annual temperature at different bioclimatic zones clearly indicates that temperature at higher elevations is increasing significantly more than at lower elevations. In lower tropical bioclimatic zone (LTBZ), upper tropical bioclimatic zone (UTBZ), lower subtropical bioclimatic zone (LSBZ), upper subtropical bioclimatic zone (USBZ), and temperate bioclimatic zone (TBZ), the average temperature increased by 0.022, 0.030, 0.036, 0.042 and 0.051 °C/year, respectively. The decade level temperature scenario revealed that the hottest decade was from 1999–2009 and average decade level increases of temperature at different bioclimatic zones ranges from 0.2 to 0.27 °C /decade. The average temperature and precipitation was found clearly different from one bioclimatic zone to other. This is the first time that bioclimatic zone level precipitation and temperature trends have been analyzed for the CHAL. The rate of additional temperature rise at higher altitudes compared to lower elevations meets the requirements to mitigate climate change in different bioclimatic zones in a different ways. This information would be fundamental to safeguarding vulnerable communities, ecosystem and relevant climate-sensitive sectors from the impact of climate change through formulation of sector-wise climate change adaptation strategies and improving the livelihood of rural communities.

scholarly journals Climate Change Made Major Contributions to Soil Water Storage Decline in the Southwestern US during 2003–2014

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1947
Author(s):  
Jianzhao Liu ◽  
Liping Gao ◽  
Fenghui Yuan ◽  
Yuedong Guo ◽  
Xiaofeng Xu
Keyword(s):  
Climate Change ◽  
Soil Water ◽  
Water Resource Management ◽  
Time Series Data ◽  
Meteorological Data ◽  
Water Storage ◽  
Climate Change Impacts ◽  
Soil Water Storage ◽  
Series Data ◽  
Atmospheric Water

Soil water shortage is a critical issue for the Southwest US (SWUS), the typical arid region that has experienced severe droughts over the past decades, primarily caused by climate change. However, it is still not quantitatively understood how soil water storage in the SWUS is affected by climate change. We integrated the time-series data of water storage and evapotranspiration derived from satellite data, societal water consumption, and meteorological data to quantify soil water storage changes and their climate change impacts across the SWUS from 2003 to 2014. The water storage decline was found across the entire SWUS, with a significant reduction in 98.5% of the study area during the study period. The largest water storage decline occurred in the southeastern portion, while only a slight decline occurred in the western and southwestern portions of the SWUS. Net atmospheric water input could explain 38% of the interannual variation of water storage variation. The climate-change-induced decreases in net atmospheric water input predominately controlled the water storage decline in 60% of the SWUS (primarily in Texas, Eastern New Mexico, Eastern Arizona, and Oklahoma) and made a partial contribution in approximately 17% of the region (Central and Western SWUS). Climate change, primarily as precipitation reduction, made major contributions to the soil water storage decline in the SWUS. This study infers that water resource management must consider the climate change impacts over time and across space in the SWUS.

scholarly journals Deep learning classification of temporal data in ecology

2020 ◽  
Author(s):  
César Capinha ◽  
Ana Ceia-Hasse ◽  
Andrew M. Kramer ◽  
Christiaan Meijer
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Meteorological Data ◽  
Species Distribution Modelling ◽  
Series Data ◽  
Temporal Data ◽  
Climate Time Series ◽  
Biological Entities

AbstractTemporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach transforms the temporal data into static predictors of the classes. However, recent deep learning techniques can perform the classification using raw time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We present a general approach for time series classification that considers multiple deep learning algorithms and illustrate it with three case studies: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications, proving its potential for wide applicability across subfields of ecology. We recommend deep learning as an alternative to techniques requiring the transformation of time series data.

scholarly journals STUDY OF MAOWUSU SANDY LAND VEGETATION COVERAGE CHANGE BASED ON MODIS NDVI

2018 ◽  
Vol XLII-3 ◽  
pp. 2127-2133
Author(s):  
Q. Ye ◽  
H. Liu ◽  
Y. Lin ◽  
R. Han
Keyword(s):  
Vegetation Cover ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Linear Regression Analysis ◽  
Growth Period ◽  
Vegetation Coverage ◽  
Sandy Land ◽  
Vegetation Growth ◽  
Modis Ndvi ◽  
Temperature And Precipitation

This paper selected 2006-2016 MODIS NDVI data with a spatial resolution of 500m and time resolution of 16d, got the 11 years’ time series NDVI data of Maowusu sandy land through mosaicking, projection transformation, cutting process in batch. Analysed the spatial and temporal distribution and variation characteristics of vegetation cover in year, season and month time scales by maximum value composite, and unary linear regression analysis. Then, we combined the meteorological data of 33 sites around the sandy area, analysed the response characteristics of vegetation cover change to temperature and precipitation through Pearson correlation coefficient. Studies have shown that: (1) The NDVI value has a stable increase trend, which rate is 0.0075&amp;thinsp;/&amp;thinsp;a. (2) The vegetation growth have significantly difference in four seasons, the NDVI value of summer&amp;thinsp;&amp;gt;&amp;thinsp;autumn&amp;thinsp;&amp;gt;&amp;thinsp;spring&amp;thinsp;&amp;gt;&amp;thinsp;winter. (3) The NDVI value change trend is conformed to the gauss normal distribution in a year, and it comes to be largest in August, its green season is in April, and yellow season is in the middle of November, the growth period is about 220&amp;thinsp;d. (4) The vegetation has a decreasing trend from the southeast to the northwest, most part is slightly improved, and Etuokeqianqi improved significantly. (5) The correlation indexes of annual NDVI with temperature and precipitation are &amp;minus;0.2178 and 0.6309, the vegetation growth is mainly affected by precipitation. In this study, a complete vegetation cover analysis and evaluation model for sandy land is established. It has important guiding significance for the sand ecological environment protection.

scholarly journals HUBUNGAN TINGKAT PENDAPATAN DENGAN TINGKAT KONSUMSI MASYARAKAT DI KECAMATAN BONTONOMPO KABUPATEN GOWA

2013 ◽  
Vol 9 (1) ◽  
Author(s):  
Sanusi Am Sanusi Am ◽  
Ansar Ansar
Keyword(s):  
Time Series ◽  
Significant Relationship ◽  
Time Series Data ◽  
Pearson Correlation ◽  
Income Level ◽  
Analytical Tool ◽  
Central Bureau ◽  
Series Data ◽  
Public Consumption ◽  
The Relationship

The purpose of this study is to explain the relationship between the level of income with the level of public consumption in District Bontonompo Gowa District. This research uses time series data obtained from Central Bureau of Statistics (BPS). The analytical tool used is Pearson correlation formula with the help of SPSS For Windows Release 16. The results concluded that the income level has a significant relationship to the level of public income in District Bontonompo Gowa Regency. It is expected that the Gowa Regency government can pursue programs that can encourage the creation of more and more diverse employment so that the communities of each bias can earn a decent income and meet their consumption needs

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