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Response of Runoff to Meteorological Factors Based on Time-Varying Parameter Vector Autoregressive Model with Stochastic Volatility in Arid and Semi-Arid Area of Weihe River Basin

Author

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  • Wenying Zeng

    (College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China)

  • Songbai Song

    (College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China)

  • Yan Kang

    (College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China)

  • Xuan Gao

    (College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China)

  • Rui Ma

    (College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China)

Abstract

This study explores the response characteristics of runoff to the variability of meteorological factors. A modified vector autoregressive (VAR) model is proposed by combining time-varying parameters (TVP) and stochastic volatility (SV). Markov chain Monte Carlo (MCMC) is used to estimate parameters. The TVP-SV-VAR model of daily runoff response to the variability of meteorological factors is established and applied to the daily runoff series from the Linjiacun hydrological station, Shaanxi Province, China. It is found that the posterior estimates of the stochastic volatility of the four variables fluctuate significantly with time, and the variance fluctuations of runoff and precipitation have strong synchronicity. The simultaneous impact of precipitation and evaporation on the pulse of runoff is close to 0. Runoff has a positive impulse response to precipitation, which decreases as the lag time increases, and a negative impulse response to temperature and evaporation with fluctuation. The response speed is precipitation > evaporation > temperature. The TVP-SV-VAR model avoids the hypothesis of homoscedasticity of variance and allows the variance to be randomly variable, which significantly improves the analysis performance. It provides theoretical support for the study of runoff response and water resource management under the conditions of climate change.

Suggested Citation

  • Wenying Zeng & Songbai Song & Yan Kang & Xuan Gao & Rui Ma, 2022. "Response of Runoff to Meteorological Factors Based on Time-Varying Parameter Vector Autoregressive Model with Stochastic Volatility in Arid and Semi-Arid Area of Weihe River Basin," Sustainability, MDPI, vol. 14(12), pages 1-12, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:6989-:d:833577
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    1. Jebabli, Ikram & Arouri, Mohamed & Teulon, Frédéric, 2014. "On the effects of world stock market and oil price shocks on food prices: An empirical investigation based on TVP-VAR models with stochastic volatility," Energy Economics, Elsevier, vol. 45(C), pages 66-98.
    2. Kastner, Gregor, 2019. "Sparse Bayesian time-varying covariance estimation in many dimensions," Journal of Econometrics, Elsevier, vol. 210(1), pages 98-115.
    3. Luo, Chunling & Shen, Lijuan & Xu, Ancha, 2022. "Modelling and estimation of system reliability under dynamic operating environments and lifetime ordering constraints," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    4. Boini Narsimlu & Ashvin Gosain & Baghu Chahar, 2013. "Assessment of Future Climate Change Impacts on Water Resources of Upper Sind River Basin, India Using SWAT Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3647-3662, August.
    5. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    6. Robert N. McCauley & Patrick McGuire & Vladyslav Sushko, 2015. "Global dollar credit: links to US monetary policy and leverage," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 30(82), pages 187-229.
    7. Jouchi Nakajima, 2011. "Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 29, pages 107-142, November.
    8. Eric Eisenstat & Rodney W. Strachan, 2016. "Modelling Inflation Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(5), pages 805-820, August.
    9. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    10. Wang, Pingping & Tang, Yincai & Joo Bae, Suk & He, Yong, 2018. "Bayesian analysis of two-phase degradation data based on change-point Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 244-256.
    11. Abdus Samad Azad & Rajalingam Sokkalingam & Hanita Daud & Sajal Kumar Adhikary & Hifsa Khurshid & Siti Nur Athirah Mazlan & Muhammad Babar Ali Rabbani, 2022. "Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study," Sustainability, MDPI, vol. 14(3), pages 1-20, February.
    12. Kramer, Walter, 1998. "Fractional integration and the augmented Dickey-Fuller Test," Economics Letters, Elsevier, vol. 61(3), pages 269-272, December.
    13. Hengxu Jin & Xiaoping Rui & Xiaoyan Li, 2022. "Analysing the Performance of Four Hydrological Models in a Chinese Arid and Semi-Arid Catchment," Sustainability, MDPI, vol. 14(6), pages 1-15, March.
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    1. Jinping Zhang & Dong Wang & Yuhao Wang & Honglin Xiao & Muxiang Zeng, 2023. "Runoff Prediction Under Extreme Precipitation and Corresponding Meteorological Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3377-3394, July.

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