The spatio‐temporal variability of annual precipitation and its local impact factors during 1724–2010 in Beijing, China

The Yellow River headwaters region (YRHR) contributes nearly 40% of total flow in the Yellow River basin, which is suffering from a serious water shortage problem. Investigation of the relationship between runoff and climate variables is important for understanding the variation trend of runoff in the YRHR under global climate change. Global and local climate variables, including the West Pacific subtropical high; northern hemisphere polar vortex (NH); Tibetan Plateau Index B (TPI‐B); southern oscillation index; sea surface temperature; and precipitation, evaporation, and temperature, were fully considered to explore the relationship with runoff at Jimai, Maqu, and Tangnaihai stations from 1956 to 2014. The results reveal that runoff had a decreasing trend, which will likely be maintained in the future, and there was a significant change in runoff around 1995 at all stations. Correlation analysis indicated that runoff was dominated by precipitation, NH, temperature, and TPI‐B, and a substantial correlation was observed with sea surface temperature and evaporation, but there was little correlation with West Pacific subtropical high and southern oscillation index. Furthermore, impacts of climate change on runoff variations were distinctly different at different temporal scales. Three dominant runoff periodicities were identified by a singular spectrum analysis‐multitaper method and continuous wavelet transform, that is, 1.0‐, 6.9‐, and 24.8‐year runoff periodicities. In addition, runoff was positively correlated with temperature at a 1‐year periodicity, negatively correlated with TPI‐B at a 6.9‐year periodicity, and positively correlated with NH at a 24.8‐year periodicity, that is, temperature, TPI‐B, and NH‐controlled runoff at annual, interannual, and interdecadal scales. Further, all analyses of the stations in the YRHR showed excellent consistency. The results will provide valuable information for water resource management in the YRHR.

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“…Therefore, an ordinary least‐squares regression on logarithms of each side was used to obtain the Hurst exponent:

log {(\frac{R}{S})}_{n} = log ((), C) + normalH log ((), n),

where H is the Hurst exponent (Krištoufek, ; Zhai et al, ).…”

Section: Methodsmentioning

confidence: 99%

Investigation of the relationship between runoff and atmospheric oscillations, sea surface temperature, and local‐scale climate variables in the Yellow River headwaters region

Chen

Wei

et al. 2018

Hydrological Processes

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“…Currently, many studies [35][36][37] have been devoted to the data to analyze the variation of the precipitation in the Beijing area. Given that the impact of EASM on the precipitation lags one month, the present study employed the sum amount of precipitation during June to September as the summer precipitation.…”

Section: Precipitation Datamentioning

confidence: 99%

“…The expanded urban land cover and increased aerosols of the Greater Beijing Metropolitan Area restrained the precipitation processes, leading to suppressed convection and rainfall over the northwest area, and enhanced convection and more precipitation in the southeast region [67,68]. Influenced by the topography, annual precipitation of the plain area of Beijing area is more than that of the mountainous area [35].…”

Section: Relationship Between Eamsi/nwpsh and The Precipitation At Inmentioning

confidence: 99%

“…The inter-annual and inter-decadal variations of precipitation in the Beijing area are apparent. The precipitation amount during the 21st century decreased by 100 mm compared to those during the 1990s [35]. Li et al [36,37] investigated the periodic variation of precipitation in the Beijing area at multi-timescales employing wavelet analysis.…”

Section: Introductionmentioning

confidence: 99%

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The Effects of Dominant Driving Forces on Summer Precipitation during Different Periods in Beijing

2017

Atmosphere

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Wavelet analysis methods (CWT, XWT, WTC) were employed to evaluate the impact of dominant climatic driving factors on summer precipitation in the Beijing area based on monthly precipitation data of Beijing ranging from 1880 to 2014. The two climatic driving factors, i.e., the East Asian summer monsoon (EASM) and the Northern Limit of Western Pacific Subtropical High (NWPSH) were considered in particular. The relationships between summer precipitation and EASM/NWPSH were also examined. The results revealed similar periods in low-frequency oscillation (76-95 years) and mid-range frequency oscillation (32-60 years) for the summer precipitation in the Beijing area and EASM/NWPSH. The summer precipitation correlated positively with the NWPSH and EASM, especially for periods of 43 years and 33 years, respectively. This indicates that summer precipitation during 1880-1960 and during the years after 1960 was significantly affected by NWPSH and EASM, respectively. Based on the periodic change of 33 years for both summer precipitation and EASM, heavy precipitation can be expected to occur again in Beijing at approximately 2026. Understanding the relationships between summer precipitation and climatic factors is of significant importance for precipitation predictions and water resource variations in the Beijing area.

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“…The grey correlation grade could be used to analyze the relationship between two data sequences, and the results could also be used to modify the domain impact factors for the groundwater level and water chemistry variation in groundwater [17,24,25].…”

Section: Grey Correlation Analysismentioning

confidence: 99%

Information Entropy Evolution for Groundwater Flow System: A Case Study of Artificial Recharge in Shijiazhuang City, China

2014

Entropy

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Abstract:The groundwater flow system is typical dissipative structure system, and its evolution can be described with system information entropies. The information entropies of groundwater in Shijiazhuang City had been calculated between 1960 and 2005, and the results show that the entropies have a decreasing trend throughout the research period, and they can be divided into our stages based on the groundwater flow system entropy variation as follows: entropy steady period (1960)(1961)(1962)(1963)(1964)(1965), entropy decreasing period (1965)(1966)(1967)(1968)(1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980), entropy increasing period (1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995) and secondary entropy decreasing period (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005); understanding the major and significant driving the pattern changing forces of groundwater levels is essential to groundwater management,. A new method of grey correlation analysis has been presented, and the results show that, the grey correlation grade between groundwater flow system information entropies and precipitation series is γ 01 = 0.749, the grey correlation grade between groundwater flow system information entropies and groundwater withdrawal series is γ 02 = 0.814, as the groundwater withdrawal is the main driving force of groundwater flow system entropy variation; based on the numerical simulation results, information entropy increased with artificial recharge, and a smaller recharge water volume would enhance the information entropy drastically, but then doubled water would not increase the information correspondingly, which could be useful to assess the health state of groundwater flow systems. OPEN ACCESSEntropy 2014, 16 4409

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The spatio‐temporal variability of annual precipitation and its local impact factors during 1724–2010 in Beijing, China

Cited by 37 publications

References 27 publications

Investigation of the relationship between runoff and atmospheric oscillations, sea surface temperature, and local‐scale climate variables in the Yellow River headwaters region

Investigation of the relationship between runoff and atmospheric oscillations, sea surface temperature, and local‐scale climate variables in the Yellow River headwaters region

The Effects of Dominant Driving Forces on Summer Precipitation during Different Periods in Beijing

Information Entropy Evolution for Groundwater Flow System: A Case Study of Artificial Recharge in Shijiazhuang City, China

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