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Qiaohong Sun and Chiyuan Miao
Open access
Qiaohong Sun, Chiyuan Miao, and Qingyun Duan

Abstract

This study focuses on changing trends in the spatial variance and annual distribution of precipitation across mainland China during the period 1957–2014. The influence on precipitation of temperature, the East Asian summer monsoon (EASM), and related atmospheric circulation variables are examined to explore the underlying mechanisms driving the changes in precipitation. Statistically significant downward trends in the number of wet days were observed in humid regions. Large parts of southeastern China featured high temporal inequality of rainfall over the course of a year, with extreme precipitation events contributing a relatively large percentage of the total annual precipitation. Arid regions generally showed statistically significant upward trends in the number of wet days and in the fraction of extreme precipitation but a decrease in the temporal inequality. These spatial heterogeneities indicate that extreme precipitation became more widespread across mainland China. Temperature dominated the long-term changes in precipitation indices over large regions of mainland China, except in the Jianghuai region, where the weakening EASM induced greater precipitation and a more uneven annual distribution of precipitation. The effects of temperature on precipitation were region dependent and varied with precipitation intensity. This contributed to the overall decrease in the spatial variance of extreme precipitation and the increase in the temporal inequality of precipitation over eastern China. However, the EASM was more important for the interannual variability of precipitation indices over the west of northwestern China, the Yanghuai region, and some grids in southern China. The EASM exerted a zonal influence on precipitation variability through the modulation of water vapor patterns, wind fields, and convection activities.

Open access
Chiyuan Miao, Qiaohong Sun, Dongxian Kong, and Qingyun Duan
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Chiyuan Miao, Hamed Ashouri, Kuo-Lin Hsu, Soroosh Sorooshian, and Qingyun Duan

Abstract

This study evaluates the performance of a newly developed daily precipitation climate data record, called Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), in capturing the behavior of daily extreme precipitation events in China during the period of 1983–2006. Different extreme precipitation indices, in the three categories of percentile, absolute threshold, and maximum indices, are studied and compared with the same indices from the East Asia (EA) ground-based gridded daily precipitation dataset. The results show that PERSIANN-CDR depicts similar precipitation behavior as the ground-based EA product in terms of capturing the spatial and temporal patterns of daily precipitation extremes, particularly in the eastern China monsoon region, where the intensity and frequency of heavy rainfall events are very high. However, the agreement between the datasets in dry regions such as the Tibetan Plateau in the west and the Taklamakan Desert in the northwest is not strong. An important factor that may have influenced the results is that the ground-based stations from which EA gridded data were produced are very sparse. In the station-rich regions in eastern China, the performance of PERSIANN-CDR is significant. PERSIANN-CDR slightly underestimates the values of extreme heavy precipitation.

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Jiaojiao Gou, Chiyuan Miao, Luis Samaniego, Mu Xiao, Jingwen Wu, and Xiaoying Guo

Abstract

Reliable, spatiotemporally continuous runoff records are necessary for identifying climate change impacts and planning effective water management strategies. Existing Chinese runoff data to date have been produced from sparse, poor-quality gauge measurements at different time scales. We have developed a new, quality-controlled gridded runoff dataset, the China Natural Runoff Dataset version 1.0 (CNRD v1.0), which provides daily, monthly, and annual 0.25° runoff estimates for the period 1961–2018 over China. CNRD v1.0 was generated using the Variable Infiltration Capacity (VIC) model. A comprehensive parameter uncertainty analysis framework incorporating parameter sensitivity analysis, optimization, and regionalization with 200 natural or near-natural gauge catchments was used to train the VIC model. Overall, the results show well-calibrated parameters for most gauged catchments except arid and semiarid areas, and the skill scores present high values for all catchments. For the pseudo-/test-ungauged catchments, the model parameters estimated by the multiscale parameter regionalization technique offer the best regionalization solution. CNRD v1.0 is the first free public dataset of gridded natural runoff estimated using a comprehensive model parameter uncertainty analysis framework for China. These results indicate that CNRD v1.0 has high potential for application to long-term hydrological and climate studies in China and to improve international runoff databases for global-scale studies.

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Qiaohong Sun, Chiyuan Miao, Amir AghaKouchak, Iman Mallakpour, Duoying Ji, and Qingyun Duan
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Qiaohong Sun, Chiyuan Miao, Amir AghaKouchak, Iman Mallakpour, Duoying Ji, and Qingyun Duan

Abstract

Predicting the changes in teleconnection patterns and related hydroclimate extremes can provide vital information necessary to adapt to the effects of the El Niño–Southern Oscillation (ENSO). This study uses the outputs of global climate models to assess the changes in ENSO-related dry/wet patterns and the frequency of severe dry/wet events. The results show anomalous precipitation responding asymmetrically to La Niña and El Niño, indicating the teleconnections may not simply be strengthened. A “dry to drier, wet to wetter” annual anomalous precipitation pattern was projected during La Niña phases in some regions, with drier conditions over southern North America, southern South America, and southern central Asia, and wetter conditions in Southeast Asia and Australia. These results are robust, with agreement from the 26 models and from a subset of 8 models selected for their good performance in capturing observed patterns. However, we did not observe a similar strengthening of anomalous precipitation during future El Niño phases, for which the uncertainties in the projected influences are large. Under the RCP4.5 emissions scenario, 45 river basins under El Niño conditions and 39 river basins under La Niña conditions were predicted to experience an increase in the frequency of severe dry events; similarly, 59 river basins under El Niño conditions and 61 river basins under La Niña conditions were predicted to have an increase in the frequency of severe wet events, suggesting a likely increase in the risk of floods. Our results highlight the implications of changes in ENSO patterns for natural hazards, disaster management, and engineering infrastructure.

Free access
Phu Nguyen, Andrea Thorstensen, Soroosh Sorooshian, Qian Zhu, Hoang Tran, Hamed Ashouri, Chiyuan Miao, KuoLin Hsu, and Xiaogang Gao

Abstract

The purpose of this study is to use the PERSIANN–Climate Data Record (PERSIANN-CDR) dataset to evaluate the ability of 32 CMIP5 models in capturing the behavior of daily extreme precipitation estimates globally. The daily long-term historical global PERSIANN-CDR allows for a global investigation of eight precipitation indices that is unattainable with other datasets. Quantitative comparisons against CPC daily gauge; GPCP One-Degree Daily (GPCP1DD); and TRMM 3B42, version 7 (3B42V7), datasets show the credibility of PERSIANN-CDR to be used as the reference data for global evaluation of CMIP5 models. This work uniquely defines different study regions by partitioning global land areas into 25 groups based on continent and climate zone type. Results show that model performance in warm temperate and equatorial regions in capturing daily extreme precipitation behavior is largely mixed in terms of index RMSE and correlation, suggesting that these regions may benefit from weighted model averaging schemes or model selection as opposed to simple model averaging. The three driest climate regions (snow, polar, and arid) exhibit high correlations and low RMSE values when compared against PERSIANN-CDR estimates, with the exceptions of the cold regions showing an inability to capture the 95th and 99th percentile annual total precipitation characteristics. A comprehensive assessment of each model’s performance in each continent–climate zone defined group is provided as a guide for both model developers to target regions and processes that are not yet fully captured in certain climate types, and for climate model output users to be able to select the models and/or the study areas that may best fit their applications of interest.

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Yang Lang, Aizhong Ye, Wei Gong, Chiyuan Miao, Zhenhua Di, Jing Xu, Yu Liu, Lifeng Luo, and Qingyun Duan

Abstract

Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July–September (JAS)] to late autumn [October–December (OND)] and from winter [December–February (DJF)] to spring [March–May (MAM)]. The skill of CFSv2 precipitation forecasts is low during summer [June–August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging.

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