Uncertainty analysis of hydrological processes based on ARMA-GARCH model

Wang, Hongrui; Gao, Xiong; Qian, Longxia; Shen, Yu

doi:10.1007/s11431-012-4909-3

Cited by 19 publications

(8 citation statements)

References 17 publications

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“…The Ljung-Box test has been used to assess the absence of auto-correlation in MSPI wh and MSSI wh time series, at a significance level of 0.05 (Ljung and Box, 1978;Hipel and McLeod, 1996). (Wang et al, 2012).…”

Section: Whitening Mspi and Mssimentioning

confidence: 99%

Composite Agrometeorological Drought Index Accounting for Seasonality and Autocorrelation

et al. 2018

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Whitening Mspi and Mssimentioning

confidence: 99%

Composite Agrometeorological Drought Index Accounting for Seasonality and Autocorrelation

et al. 2018

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“…() introduced the ARMA‐GARCH model in hydrology to fit a daily stream flow series from the upper Yellow River, China. ARMA‐GARCH was also used to address the uncertainty of hydrology (Elek and Markus, ; Laux et al ., ; Wang et al ., ). However, the ARMA‐GARCH model cannot fit the sequence with a nonstationary mean; thus, the autoregressive integrated moving average (ARIMA) model with GARCH disturbances (i.e.…”

Section: Introductionmentioning

confidence: 97%

The role of anthropogenic activities in karst spring discharge volatility

Yin

Hao

et al. 2014

Hydrological Processes

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Abstract:The traditional hydrological time series methods tend to focus on the mean of whichever variable is analysed but neglect its timevarying variance (i.e. assuming the variance remains constant). The variances of hydrological time series vary with time under anthropogenic influence. There is evidence that extensive well drilling and groundwater pumping can intercept groundwater runoff and consequently induce spring discharge volatility or variance varying with time (i.e. heteroskedasticity). To investigate the time-varying variance or heteroskedasticity of spring discharge, this paper presents a seasonal autoregressive integrated moving average with general autoregressive conditional heteroskedasticity (SARIMA-GARCH) model, whose the SARIMA model is used to estimate the mean of hydrological time series, and the GARCH model estimates its time-varying variance. The SARIMA-GARCH model was then applied to the Xin'an Springs Basin, China, where extensive groundwater development has occurred since 1978 (e.g. the average annual groundwater pumping rates were less than 0. To identify whether human activities or natural stressors caused the heteroskedasticity of Xin'an Springs discharge, we segmented the spring discharge sequence into two periods: a predevelopment stage (i.e. 1956-1977) and a developed stage (i.e. 1978-2012), and set up the SARIMA-GARCH model for the two stages, respectively. By comparing the models, we detected the role of human activities in spring discharge volatility. The results showed that human activities caused the heteroskedasticity of the Xin'an Spring discharge. The predicted Xin'an Springs discharge by the SARIMA-GARCH model showed that the mean monthly spring discharge is predicted to continue to decline to 0.93 m

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“…In the aforementioned literature, only a few studies (Fathian, Mehdizadeh, et al., 2019; Pandey et al., 2019; H. Wang et al., 2012; Zha et al., 2020) have explored the out‐of‐sample forecasting performance of the SETAR and GARCH models. However, the in‐sample modeling strengths of time series models are not guaranteed to provide better out‐of‐sample forecasts (Boero & Marrocu, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…In the aforementioned literature, only a few studies (Fathian, Mehdizadeh, et al, 2019;Pandey et al, 2019;H. Wang et al, 2012;Zha et al, 2020) have explored the out-of-sample forecasting performance of the SETAR and GARCH models.…”

Section: Introductionmentioning

confidence: 99%

Point and Interval Forecasting of Groundwater Depth Using Nonlinear Models

Guo

Song

2021

Water Resources Research

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This study focuses on the development of a new model that can be used not only to forecast the groundwater depth but also to quantify the uncertainty of forecasts. Therefore, we built a hybrid of Self‐Exciting Threshold Autoregressive‐Generalized Autoregressive Conditional Heteroscedasticity model, namely SETAR‐GARCH model to obtain point and interval forecasts of groundwater depth. In addition, single SETAR model and a hybrid of SETAR and Kernel Density Estimation model, referred as SETAR‐KDE, were compared. Two average monthly groundwater depth of Xi'an and Baoji cities in Shaanxi, China, were used to evaluate the models. The study results demonstrate that the SETAR‐GARCH model achieves satisfactory point and interval forecasting performance and reduces the uncertainty of interval forecasting. Furthermore, we found and proved a significant finding that the GARCH model does not have any influence on the point forecasting, but it can achieve superior performance of interval forecasting by combined with SETAR model. This study may provide a new idea for nonstationary and nonlinear groundwater depth prediction and further provide a reference for the design and management of water systems in the changing environment.

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Uncertainty analysis of hydrological processes based on ARMA-GARCH model

Cited by 19 publications

References 17 publications

Composite Agrometeorological Drought Index Accounting for Seasonality and Autocorrelation

Composite Agrometeorological Drought Index Accounting for Seasonality and Autocorrelation

The role of anthropogenic activities in karst spring discharge volatility

Point and Interval Forecasting of Groundwater Depth Using Nonlinear Models

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