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Guixing Chen, Ruoyu Lan, Wenxin Zeng, He Pan, and Weibiao Li

Abstract

The complex features of rainfall diurnal cycles at the south China coast are examined using hourly rain gauge data and satellite products (CMORPH and TRMM 3B42) during 1998–2014. It is shown that morning rainfall is pronounced near the coasts and windward mountains, with high rainfall in the summer monsoon season, while afternoon rainfall is dominant on land, and nocturnal rainfall occurs at northern inland sites. Both satellite products report less morning rainfall and more afternoon rainfall than the rain gauge data, and they also miss the midnight rainfall minimum. These errors are mainly attributable to an underestimation of morning moderate and intense rains at coasts and an overestimation of afternoon–evening light rains on land. With a correction of the systematic bias, satellite products faithfully resolve the spatial patterns of normalized rainfall diurnal cycles related to land–sea contrast and terrains, suggesting an improved data application for regional climate studies. In particular, they are comparable to the rain gauge data in showing the linear reduction of morning rainfall from coasts to inland regions. TRMM is marginally better than CMORPH in revealing the overall features of diurnal cycles, while higher-resolution CMORPH captures more local details. All three datasets also present that morning rainfall decreases from May–June to July–August, especially on land; it exhibits pronounced interannual variations and a decadal increase in 1998–2008 at coasts. Such long-term variations of morning rainfall are induced by the coastal convergence and mountain liftings of monsoon shear flow interacting with land breeze, which is mainly regulated by monsoon southwesterly winds in the northern part of the South China Sea.

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Xiaoduo Pan, Xin Li, Kun Yang, Jie He, Yanlin Zhang, and Xujun Han

Abstract

Development of an accurate precipitation dataset is of primary importance for regional hydrological process studies and water resources management. Here, four regional precipitation products are evaluated for the Heihe River basin (HRB): 1) a spatially and temporally disaggregated Climate Prediction Center Merged Analysis of Precipitation (CMAP) at 0.25° spatial resolution (DCMAP); 2) a fusion product obtained by merging China Meteorological Administration station data and Tropical Rainfall Measuring Mission precipitation data at 0.1° spatial resolution supported by the Institute of Tibetan Plateau Research (ITP), Chinese Academy of Sciences (ITP-F); 3) a disaggregated CMAP downscaled by a statistical meteorological model tool at 1-km spatial resolution (DCMAP–MicroMet); and 4) a Weather Research and Forecasting (WRF) Model simulation with 5-km resolution (WRF-P). The validation metrics include spatial pattern, temporal pattern, error analysis with respect to observation data, and precipitation event verification indicators. The results indicate that 1) precipitation from the DCMAP product may not be suitable for water cycle studies at the watershed scale because of its coarser spatial resolution and 2) ITP-F, WRF-P, and DCMAP–MicroMet precipitation products generally show similar spatial–temporal patterns in HRB but have varying performances between different subbasins.

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Xiaogang He, Ming Pan, Zhongwang Wei, Eric F. Wood, and Justin Sheffield

Abstract

Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to lack of long-term data records, standardized methods for event identification, geographical inconsistencies, and data uncertainties. To tackle these challenges, this article presents the development of the first Global Drought and Flood Catalogue (GDFC) for 1950–2016 by merging the latest in situ and remote sensing datasets with state-of-the-art land surface and hydrodynamic modeling to provide a continuous and consistent estimate of the terrestrial water cycle and its extremes. This GDFC also includes an unprecedented level of detailed analysis of drought and large-scale flood events using univariate and multivariate risk assessment frameworks, which incorporates regional spatial–temporal characteristics (i.e., duration, spatial extent, severity) and global hazard maps for different return periods. This Catalogue forms a basis for analyzing the changing risk of droughts and floods and can underscore national and international climate change assessments and provide a key reference for climate change studies and climate model evaluations. It also contributes to the growing interests in multivariate and compounding risk analysis.

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Xiaoli Yang, Xiaohan Yu, Yuqian Wang, Xiaogang He, Ming Pan, Mengru Zhang, Yi Liu, Liliang Ren, and Justin Sheffield

Abstract

A multimodel ensemble of general circulation models (GCM) is a popular approach to assess hydrological impacts of climate change at local, regional, and global scales. The traditional multimodel ensemble approach has not considered different uncertainties across GCMs, which can be evaluated from the comparisons of simulations against observations. This study developed a comprehensive index to generate an optimal ensemble for two main climate fields (precipitation and temperature) for the studies of hydrological impacts of climate change over China. The index is established on the skill score of each bias-corrected model and different multimodel combinations using the outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Results show that the optimal ensemble of the nine selected models accurately captures the characteristics of spatial–temporal variabilities of precipitation and temperature over China. We discussed the uncertainty of subset ensembles of ranking models and optimal ensemble based on historical performance. We found that the optimal subset ensemble of nine models has relative smaller uncertainties compared with other subsets. Our proposed framework to postprocess the multimodel ensemble data has a wide range of applications for climate change assessment and impact studies.

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Fan Yang, Qing He, Jianping Huang, Mamtimin Ali, Xinghua Yang, Wen Huo, Chenglong Zhou, Xinchun Liu, Wenshou Wei, Caixia Cui, Minzhong Wang, Hongjun Li, Lianmei Yang, Hongsheng Zhang, Yuzhi Liu, Xinqian Zheng, Honglin Pan, Lili Jin, Han Zou, Libo Zhou, Yongqiang Liu, Jiantao Zhang, Lu Meng, Yu Wang, Xiaolin Qin, Yongjun Yao, Houyong Liu, Fumin Xue, and Wei Zheng

CAPSULE

The Desert Environment and Climate Observation Network (DECON) could promote collaborative research on desert dust-storms, boundary-layer and land-atmosphere interactions to better understand the status and role of the Taklimakan desert.

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