Analyzing effects of climate change on streamflow in a glacier mountain catchment using an ARMA model

Liu, Youcun; Wu, Jing Cynthia; Liu, Yan; Hu, Bill X.; Hao, Yonghong; Huo, Xueli; Fan, Ye; Yeh, Tian‐Chyi Jim; Wang, Zhong Liang

doi:10.1016/j.quaint.2014.10.001

Cited by 22 publications

(17 citation statements)

References 36 publications

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“…Huang Huiping [18][19][20] and other scholars have even pointed out that China's annual average reference evapotranspiration underwent an overall reduction over the past 50 years. In short, due to the harsh climatic conditions and fragile ecological environment in northern China [21], especially the northwestern region, numerous studies on surface water have been successfully conducted in the northwestern region. However, the research is often overlooked in terms on agricultural climate resources and water balance particularly in the Jiangnan hilly areas in the humid climate zone due to the so-called "excellent climatic conditions and good ecological environment."…”

Section: Introductionmentioning

confidence: 99%

Characteristics and Drivers of Reference Evapotranspiration in Hilly Regions in Southern China

Liu

Chen

et al. 2019

Water

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This paper has adopted related meteorological data collected by 69 meteorological stations between 1951 and 2013 to analyze changes and drivers of reference evapotranspiration (ET0) in the hilly regions located in southern China. Results show that: (1) ET0 in southern China’s hilly regions reaches its maximum in summer and its minimum in winter, and that the annual ET0 shows an increasing trend. ET0 happened abrupt change due to the impact of abrupt meteorological variables changes, and the significant year of mutation were 1953, 1964 and 2008. Most abrupt changes of ET0 in meteorological stations occurred in the 1950s and 1960s. (2) The low value of ET0 was mainly captured in high-altitude areas. Spatially, the ET0 in the east was higher than that in the west. With the exception of a handful of stations, the trend coefficients of ET0 were all positive, exhibiting a gradual rise. Changes in ET0 in the east were much more sensitive than that in the west. Since ET0 was affected by the cyclical changes in relative humidity, short-period oscillations were observed in all these changes. (3) In general, the ET0 was negatively correlated with relative humidity, and positively correlated with temperature and sunshine percentage. ET0 is most sensitive to changes in average temperature, with a sensitivity coefficient of 1.136. ET0 showed positive sensitivity to average temperature and sunshine hours, which were notable in the northeastern, and uniform in the spatial. ET0 showed negatively sensitivity to relative humidity, and the absolute value of sensitivity coefficient in the northwestern is smaller. The highest contribution to ET0 is the average temperature (6.873%), and the total contribution of the four meteorological variables to the change of ET0 is 7.842%. The contribution of average temperature, relative humidity, and sunshine hours to ET0 is higher in the northern and eastern, northern, northern and eastern areas, respectively. Climate indexes (Western Pacific Index (WP), Southern Oscillation Index (SOI), Tropical Northern Atlantic Index (TNA), and El Niño-Southern Oscillation (ENSO)) were correlated with the ET0. In addition, the ET0 and altitude, as well as the latitude and longitude were also correlated with each other.

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Section: Introductionmentioning

confidence: 99%

Characteristics and Drivers of Reference Evapotranspiration in Hilly Regions in Southern China

Liu

Chen

et al. 2019

Water

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“…Recently, ARMA model has been widely used for prediction purposes of various time series applications such as the works conducted in (Liu et al, 2015;Ratnam et al, 2015;Liu and Shao, 2016;Gankevich and Degtyarev, 2018;Karimpour et al, 2017) For instance, Liu et al (2015) employed the ARMA model to analyze the impact of climate change (air temperature and precipitation) on the stream-flow that originated from mountain glaciers Urumqi River basin area, China. To generate the proper model, they collected their time series of the monthly air temperature and precipitation over a period of 48 years.…”

Section: Related Workmentioning

confidence: 99%

Forecasting the Number of Monthly Active Facebook and Twitter Worldwide Users Using ARMA Model

Al‐Haija¹,

Mao²,

Nasr³

2019

Journal of Computer Science

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In this study, an Auto-Regressive Moving Average (ARMA) Model with optimal order has been developed to estimate and forecast the short term future numbers of the monthly active Facebook and Twitter worldwide users. In order to pickup the optimal estimation order, we analyzed the model order vs. the corresponding model error in terms of final prediction error. The simulation results showed that the optimal model order to estimate the given Facebook and Twitter time series are ARMA[5, 5] and ARMA[3, 3], respectively, since they correspond to the minimum acceptable prediction error values. Besides, the optimal models recorded a high-level of estimation accuracy with fit percents of 98.8% and 96.5% for Facebook and Twitter time series, respectively. Eventually, the developed framework can be used accurately to estimate the spectrum for any linear time series.

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“…A comparison between the S&P 500 and London Stock Exchange shows that both markets are efficient and are financially stable during periods of boom and bust. In 2015, Liu et al [3] used an autoregressivemoving-average model (arma) to quantitatively analyse the influence of the air temperature and the precipitation on the streamflow that originated from mountain glaciers. The arma model was subsequently applied to analyse the transformed time series.…”

Section: E164mentioning

confidence: 99%

Based on the PCA-ARIMA-BP hybrid model of stock price prediction research

Luo

Wang

2017

ANZIAMJ

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A pca-arima-bp hybrid model is proposed to study the Shanghai Composite Index. The model is based on principle component analysis (pca), autoregressive integrated moving average model (arima), and backward propagation (bp) neural network. We use data mining methods to select data. bp neural network, pca-bp model and pcaarima-bp hybrid model prediction results are compared. The results show that the pca-arima-bp hybrid model can effectively improve the prediction precision. This can guide investors to avoid risks and improve benefit.

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Analyzing effects of climate change on streamflow in a glacier mountain catchment using an ARMA model

Cited by 22 publications

References 36 publications

Characteristics and Drivers of Reference Evapotranspiration in Hilly Regions in Southern China

Characteristics and Drivers of Reference Evapotranspiration in Hilly Regions in Southern China

Forecasting the Number of Monthly Active Facebook and Twitter Worldwide Users Using ARMA Model

Based on the PCA-ARIMA-BP hybrid model of stock price prediction research

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