Does non-stationarity of extreme precipitation exist in the Poyang Lake Basin of China?

Lei, Xiangyong; Gao, Lu; Ma, Miaomiao; Wei, Jianhui; Xu, Ligang; Wang, Lan; Lin, Hui

doi:10.1016/j.ejrh.2021.100920

Cited by 12 publications

(13 citation statements)

References 43 publications

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“…Yue and Wang (2002) proposed "prewhitening" before the MK test to eliminate the effect of the serial correlation in trend detection so that the trend of series has improved credibility. Many scholars (Burn and Hag Elnur, 2002;Mirdashtvan and Mohseni Saravi, 2020;Lei et al, 2021a;Shin et al, 2021) have used the prewhitening Mann-Kendall (PWMK) trend test method to describe the trend variation of relative humidity, surface temperature, and other hydrological and meteorological variables in various climatic zones and argued that the trend of prewhitening correction has greater accuracy than the MK. This study used the PWMK to calculate the trend of extreme temperature indices in ERA5 data and station data.…”

Section: Trend Analysismentioning

confidence: 99%

“…Climate change has resulted in the strengthening of extreme weather and climate events in every region worldwide. Many scholars have observed significant changes in extreme cold and warm weather at global or regional scales (Zhang et al, 2019;Sheridan et al, 2020;Lei et al, 2021a). Extreme high-temperature events can cause heatwaves and droughts, which directly affect human survival, health, and crop growth (Alexander et al, 2006;Donat et al, 2014;Jiang et al, 2019;Zhan et al, 2020;Watts et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

Lei²,

Chen³

et al. 2022

Front. Environ. Sci.

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The fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts (ERA5) is the latest reanalysis product. However, the reliability of ERA5 to capture extreme temperatures is still unclear over China. Hence, based on conventional meteorological station data, a new criterion (DISO) was used to validate the ERA5 capturing extreme temperature indices derived from the Expert Team on Climate Change Detection and Indices (ETCCDI) across the six subregions of China on different timescales. The conclusions are as follows: the original daily temperatures (mean temperature, maximum temperature, and minimum temperature) can be well reproduced by ERA5 reanalysis over China. ERA5 tends to exhibit more misdetection for the duration of extreme temperature events than extreme temperature intensity and frequency. In addition, ERA5 performed best in the summer and worst in the winter, respectively. The trend of absolute indices (e.g., TXx and TNx), percentile-based indices (e.g., TX90p, TX10p, TN90p, and TN10p), and duration indices (e.g., WSDI, CSDI, and GSL) can be captured by ERA5, but ERA5 failed to capture the tendency of the diurnal temperature range (DTR) over China. Spatially, ERA5 performs well in southeastern China. However, it remains challenging to accurately recreate the extreme temperature events in the Tibetan Plateau. The elevation difference between the station and ERA5 grid point contributes to the main bias of reanalysis temperatures. The accuracy of ERA5 decreases with the increase in elevation discrepancy.

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Section: Trend Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

Lei²,

Chen³

et al. 2022

Front. Environ. Sci.

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“…For example, many studies defined an extreme precipitation event when the daily precipitation exceeded the 90th or 95th percentile of a long-term precipitation series (e.g., [22,23]). Some studies preferred to use the series of climate indices provided by the Expert Team on Climate Change Detection Monitoring and Indices (ETCCDMI) (e.g., [24]). The present study selected the typical extreme precipitation event that leads to a serious flood disaster.…”

Section: Discussionmentioning

confidence: 99%

Simulation of an Extreme Precipitation Event Using Ensemble-Based WRF Model in the Southeastern Coastal Region of China

et al. 2022

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Extreme weather events have increased significantly in the past decades due to global warming. As a robust forecast and monitoring tool of extreme weather events, regional climate models have been widely applied on local scales. This study presented a simulation of an extreme precipitation event in the Southeastern Coastal Region of China (SEC), where floods, typhoons, and mountain torrents occur frequently using the Weather Research and Forecast model (WRF) driven by GEFS (The Global Ensemble Forecast System) ensemble members (one control run and 20 ensemble members) from 01 UTC 14 June to 18 UTC 16 June 2010. The observations of hourly precipitation records from 68 meteorological stations in the SEC were applied to validate the WRF ensemble simulations with respect to 3-hourly cumulative precipitation (3hP), 6-hourly cumulative precipitation (6hP) and total cumulative precipitation (TCP). The results showed that all WRF 20 ensemble outputs could capture the extreme precipitation events fairly well with the Pearson correlation coefficient ranging from 0.01 to 0.82 and 0.16 to 0.89 for 3 and 6hP, respectively. The normalized root mean square error was comparable between the control run and 20 ensembles for 3hP (0.67 vs. 0.63) and 6hP (0.51 vs. 0.53). In general, WRF underestimated the observations for TCP. The control run (En00) modeled 28.1% less precipitation, while the 20 ensembles modeled 3.9% to 55.5% less precipitation than observations. The ensemble member 12 (En12) showed the best TCP simulation with the smallest bias. The average of 20 ensembles simulated 31.7% less precipitation than observations. The total precipitation was not captured by WRF with a significant bias that ranged from −203.1 to 112.3 mm. The storm centers were generally not captured by WRF in this case study. WRF ensembles underestimated the observation in the central Fujian Province while overestimated in the northern and southern Fujian Province. Although the average of ensembles can reduce the uncertainty to a certain extent, the individual ensemble (e.g., En12) may be more reliable on local scales.

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“…The PLB which plays a crucial role in food security, water conservation, flood storage, climate regulation and ecological protection. However, some previous studies revealed that the risk of climate extremes and the frequency of droughts and floods in the PLB increased in the last 20 years, which has resulted in water resources problems and ecological problems under the background of global warming (Shankman et al, 2006;Guo et al, 2008;Lei et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, the climate change studies in the PLB were mainly focused on simulating precipitation or extreme precipitation changes, and the effects of climate change on the basin runoff, evapotranspiration or meteorological drought (Li and Hu, 2019;Lei et al, 2021a), since only a few studies concentrate on predicting future climate change. Thus, quantitative and scientific prediction of the future climate change of the PLB under different global warming targets is projected by the multi-model ensemble (MME) based on 17 CMIP6, which provides scientific guidance for water resources management, agricultural activity planning, flood and drought disaster prevention and ecological environment management of the PLB.…”

Section: Introductionmentioning

confidence: 99%

Projection of future climate change in the Poyang Lake Basin of China under the global warming of 1.5–3°C

Zhang

Xu²,

Hu³

et al. 2022

Front. Environ. Sci.

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This study projected the future climate changes in the Poyang Lake Basin (PLB) of China under various global warming targets (1.5-3 °C), based on the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) and 4 statistical downscaling methods, including Quantile Mapping (QM), Daily Translation (DT), Delta, and Local Intensity Scaling (LOCI). The RMSE, R 2 and KGE indicators were used to evaluate the competency of the aforementioned methods applied to daily precipitation (Pre), daily mean temperature (Tas), daily maximum temperature (Tasmax), and daily minimum temperature (Tasmin). The global warming of 1.5, 2 and 3 °C will occur around 2040, from 2045 to 2080 and around 2075, respectively, for the emission scenarios of SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The results demonstrated that under the 1.5, 2 and 3 °C global warming targets, the projected annual precipitation declined by 14.82, 11.92 and 8.91% relative to the reference period (1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005), respectively. The Tas increased significantly by 0.43, 0.94 and 1.92 °C and the Tasmax increased by 0.58, 1.11 and 2.09 °C. The Tasmin decreased by 0.29 °C under the 1.5 °C warming target, while it increased by 0.19 and 1.18 °C under the 2 and 3 °C warming targets. The spatial distributions of future annual precipitation in the PLB were relative consistent. However, the regional variability was significant, which the southern and eastern regions experienced more precipitation than the northern and western regions. The south-central part of the Ganjiang basin was the high-value area while the northeastern part was the low-value area. The Tas, Tasmax and Tasmin had a consistent spatial variation characteristic that the high latitude areas were warmer than the low latitude areas, and the western regions were warmer than the central and eastern regions while the northeastern regions were cooler than the remaining regions.

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Does non-stationarity of extreme precipitation exist in the Poyang Lake Basin of China?

Cited by 12 publications

References 43 publications

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

Simulation of an Extreme Precipitation Event Using Ensemble-Based WRF Model in the Southeastern Coastal Region of China

Projection of future climate change in the Poyang Lake Basin of China under the global warming of 1.5–3°C

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