Simulation of Nonstationary Spring Discharge Using Time Series Models
Y. Zha and
Y. Hao ()
Additional contact information
Y. Liu: Tianjin Normal University
B. Wang: Texas A&M University
H. Zhan: Texas A&M University
Y. Fan: Tianjin Normal University
Y. Zha: Wuhan University
Y. Hao: Texas A&M University
Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 15, No 10, 4875-4890
Abstract We present a detailed analysis and comparison of two time series models, i.e., ARIMA and ARIMA-GARCH, to simulate the discharge of a karst spring (Niangziguan Springs (NS) complex) in the northern China. Statistical tests for the residuals are applied to examine the reasonability of the models. Statistically, both models are reasonably good to simulate the mean value of the discharge of the NS complex. The statistical test shows that the residual discharge data have conditional time-varying variance and volatility clustering, known as heteroscedasticity of the data. Calibration test shows that the ARIMA-GARCH model gives a varying confidence interval, which can more effectively capture the heteroscedasticity of the data, comparing with a constant confidence interval in the ARIMA model. In the validation and application process, we applied two approaches to simulate the discharge data: (1) fixed models, and (2) evolving models. The confidence interval width monotonically increases in both fixed models, and the fixed ARIMA-GARCH model has faster increasing confidence interval width than the fixed ARIMA model. This suggests that the fixed time series models are only suitable for short-term prediction. However, we found that this drawback can be compensated by updating the model once new data become available. Our evolving models show more reasonable confidence interval width for both models. In addition, the application shows that the ARIMA-GARCH model is very sensitive to the data fluctuation. We also found the evolving ARIMA-GARCH model was able to return to the narrow confidence interval width once the fluctuation diminished. Hence, we conclude that the ARIMA-GARCH model is more suitable for the sequences with strong heteroscedasticity.
Keywords: ARIMA model; ARIMA-GARCH model; Heteroscedasticity; Karst spring; Niangziguan Springs (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s11269-017-1783-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:31:y:2017:i:15:d:10.1007_s11269-017-1783-6
Ordering information: This journal article can be ordered from
Access Statistics for this article
Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris
More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla ().