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

Wenying Zeng, Songbai Song, Yan Kang, Xuan Gao and Rui Ma
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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

Sustainability, 2022, vol. 14, issue 12, 1-12

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.

Keywords: TVP-SV-VAR model; Markov chain Monte Carlo; simultaneous impact; lag impact; Linjiacun hydrological station (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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