Changes in precipitation and extreme precipitation in a warming environment in China

Sun, Jun; Ao, Juan

doi:10.1007/s11434-012-5542-z

Cited by 112 publications

(50 citation statements)

References 16 publications

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“…More than 80 check dams were destroyed during the flooding event (Chen et al, 2005), which aggravated considerably the flood magnitude and offset the effect of other SCP on reduction of flooding. Finally, numerous studies reported that the rainfall extremes have increased around the world (Alexander et al, 2006;Allan and Soden, 2008;Liu et al, 2009;Sun and Ao, 2013). Extreme precipitation events are likely to increase in the YRB according to multiple GCMs projections (Xu et al, 2009b;Wu et al, 2015).…”

Section: Possible Future Changes In Flood In the Yrbmentioning

confidence: 99%

Investigation of changes in the annual maximum flood in the Yellow River basin, China

Bai

Liu

Liang

et al. 2016

Quaternary International

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a b s t r a c tFlooding in the Yellow River basin (YRB) has changed significantly over the last few decades because of climate change and human activities. Determining how the flooding changed and identifying the main driving factors of flood change is crucial to flood risk assessment and water resources planning. However, few studies have been conducted, especially in the whole YRB. To fill this gap, we investigate the spatial and temporal change of the annual maximum flood (AMAXF) in the YRB using observed data from 32 key hydrological stations (including 15 mainstream stations and 17 tributary stations). The ManneKendall test combined with trend index method is used to evaluate the trend in precipitation and AMAXF. The trend results indicate the AMAXF over the whole basin is dominated by decreasing trends: 72% of the stations exhibit significant decreasing trends (at 0.1 significance level) and 22% of the stations show no significant decreasing trends in the AMAXF. Both flood trends and abrupt change time exhibit obvious regional differences: the flood decreases is more pronounced in the midstream basins than in the headwater of the basin; the abrupt changes mainly occurred in the early 1990s for the upper reaches, and in the late 1990s for the middle reaches. To investigate the causes of flood change, the trends of precipitation extremes are analyzed in relation to the trends of peak floods. The analysis reveals that the decreasing precipitation extremes only results in the AMAXF reduction in the upstream basin. The decreasing AMAXF in the midstream and downstream of the basin are mainly attributable to the impacts of human activities (mainly including dam construction and soil conservation practices). In general, anthropogenic impacts play an increasingly important role in the AMAXF changes in the YRB. According to the possible changes in forcing factors, the AMAXF over the whole basin is expected to further decrease in the near future. However, there are still large flood risks in the tributary basins due to the collapse of check dams caused by extreme storms.

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Section: Possible Future Changes In Flood In the Yrbmentioning

confidence: 99%

Investigation of changes in the annual maximum flood in the Yellow River basin, China

Bai

Liu

Liang

et al. 2016

Quaternary International

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“…A major increase of the water vapor in Xinjiang happened in the middle of the 1980s. This regional wetting trend has been ascribed to the effect of significant climate warming in middle and high latitudes during the same period (Dai et al 2007;Sun and Ao 2013). Such a wetting trend supports the increasing trend in the extreme part of precipitation, contributing to some PP of SEPA (R95pTOT) over the region.…”

Section: B Sources Of Potential Predictabilitymentioning

confidence: 59%

Potential Predictability of Seasonal Extreme Precipitation Accumulation in China

Wei

Zeng

Jones

2017

Journal of Hydrometeorology

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The potential predictability of seasonal extreme precipitation accumulation (SEPA) across mainland China is evaluated, based on daily precipitation observations during 1960-2013 at 675 stations. The potential predictability value (PPV) of SEPA is calculated for each station by decomposing the observed SEPA variance into a part associated with stochastic daily rainfall variability and another part associated with longer-timescale climate processes. A Markov chain model is constructed for each station and a Monte Carlo simulation is applied to estimate the stochastic part of the variance. The results suggest that there are more potentially predictable regions for summer than for the other seasons, especially over southern China, the Yangtze River valley, the north China plain, and northwestern China. There are also regions of large PPVs in southern China for autumn and winter and in northwestern China for spring. The SEPA series for the regions of large PPVs are deemed not entirely stochastic, either with long-term trends (e.g., increasing trends in inland northwestern China) or significant correlation with well-known large-scale climate processes (e.g., East Asian winter monsoon for southern China in winter and El Niño for the Yangtze River valley in summer). This fact not only verifies the claim that the regions have potential predictability but also facilitates predictive studies of the regional extreme precipitation associated with large-scale climate processes.

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“…1d), China precipitation is heaviest (> 200 mm in winter, Fig. 1f) and has significantly increased since the 1960s, and several prolonged ice storm events have caused severe damage in recent decades (Zhai et al 2005;Sun and Ao 2013;Li et al 2015; also Fig. 3).…”

Section: Introductionmentioning

confidence: 99%

Tropical Indian Ocean warming contributions to China winter climate trends since 1960

Yao

Liu

et al. 2018

Clim Dyn

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This study investigates observed and modeled contributions of global sea surface temperature (SST) to China winter climate trends in 1960-2014, including increased precipitation, warming through about 1997, and cooling since then. Observations and Atmospheric Model Intercomparison Project (AMIP) simulations with prescribed historical SST and sea ice show that tropical Indian Ocean (TIO) warming and increasing rainfall causes diabatic heating that generates a tropospheric wave train with anticyclonic 500-hPa height anomaly centers in the TIO or equatorial western Pacific (TIWP) and northeastern Eurasia (EA) and a cyclonic anomaly over China, referred to as the TIWP-EA wave train. The cyclonic anomaly causes Indochina moisture convergence and southwesterly moist flow that enhances South China precipitation, while the northern anticyclone enhances cold surges, sometimes causing severe ice storms. AMIP simulations show a 1960-1997 China cooling trend by simulating increasing instead of decreasing Arctic 500-hPa heights that move the northern anticyclone into Siberia, but enlarge the cyclonic anomaly so it still simulates realistic China precipitation trend patterns. A separate idealized TIO SST warming simulation simulates the TIWP-EA feature more realistically with correct precipitation patterns and supports the TIWP-EA teleconnection as the primary mechanism for long-term increasing precipitation in South China since 1960. Coupled Model Intercomparison Project (CMIP) experiments simulate a reduced TIO SST warming trend and weak precipitation trends, so the TIWP-EA feature is absent and strong drying is simulated in South China for 1960-1997. These simulations highlight the need for accurately modeled SST to correctly attribute regional climate trends.

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Changes in precipitation and extreme precipitation in a warming environment in China

Cited by 112 publications

References 16 publications

Investigation of changes in the annual maximum flood in the Yellow River basin, China

Investigation of changes in the annual maximum flood in the Yellow River basin, China

Potential Predictability of Seasonal Extreme Precipitation Accumulation in China

Tropical Indian Ocean warming contributions to China winter climate trends since 1960

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