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  • Author or Editor: Wei Chen x
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Rucong Yu, Haoming Chen, and Wei Sun

Abstract

In this study, a regional rainfall event (RRE) is defined by observed rainfall at multiple, well-distributed stations in a given area. Meanwhile, a regional rainfall coefficient (RRC), which could be used to classify local rain (LR) and regional rain (RR) in the given area, is defined to quantify the spatiotemporal variation of rainfall events. As a key parameter describing the spread of rainfall, RRC, together with duration and intensity, presents an effort to explore more complete spatiotemporal organization and evolution of RREs. Preliminary analyses of RREs over the Beijing plain reveal new, interesting characteristics of rainfall. The RRC of RRE increases with longer duration and stronger intensity. Most of the RREs with maximum peak rainfall intensity below 2 mm h−1 or duration shorter than 3 h have RRC less than 0.4, indicating that these events are not uniformly spread over the region. Thus, they are reasonably classified into LR. RREs with RRC above 0.5 could be classified into RR, which usually lasts longer than 4 h and has primary peak rainfall occurring from 1700 to 0600 LST. For most of the intense long-duration RR, evolutions of RRC and rainfall intensity are not consistent. The RRC reaches a maximum a few hours after the peak intensity was reached. The results of this study enrich the understanding of rainfall processes and provide new insight into understanding and quantifying the space–time characteristics of rainfall. These findings have great potential to further evaluate cloud and precipitation physics as well as their parameterizations in numerical models.

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Jiangfeng Wei, Robert E. Dickinson, and Haishan Chen

Abstract

This study examines a lagged soil moisture–precipitation (S–P) correlation for 24 yr of boreal summer (1979–2002) from the 40-yr ECMWF Re-Analysis (ERA-40), the NCEP–Department of Energy (DOE) reanalysis 2 (R-2), the North American Regional Reanalysis (NARR), 10 yr (1986–95) of data from phase 2 of the Global Soil Wetness Project (GSWP-2), and two 24-yr model simulations with the NCAR Community Atmosphere Model version 3.1 (CAM3). The different datasets and model simulations all show a similar negative-dominant S–P correlation pattern with wet areas having more significantly negative correlations than the dry areas. The experiments with CAM3 show that this correlation pattern is not caused by the soil moisture feedback. Rather, the combined effect of the precipitation variability and the memory of soil moisture is the main reason for this correlation pattern. Theoretical analysis confirms this conclusion and shows that the correlation pattern is related to both the precipitation spectrum and the time scale of soil moisture retention. This study suggests that the attribution of lagged correlations of precipitation with soil moisture or related variables should be done cautiously.

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Rui Wang, Xin Yan, Zhenguo Niu, and Wei Chen

Abstract

Water surface temperature is a direct indication of climate change. However, it is not clear how China’s inland waters have responded to climate change in the past using a consistent method on a national scale. In this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2015 to study the temporal and spatial variation characteristics of water surface temperature in China using the wavelet transform method. The results showed the following: 1) the freezing date of China inland water has shown a significant delaying trend during the past 16 years with an average rate of −1.5 days yr−1; 2) the shift of the 0°C isotherm position of surface water across China has clear seasonal changes, which first moved eastward about 25° and northward about 15°, and then gradually moved back after the year 2009; 3) during the past 16 years, the 0°C isotherm of China’s surface water has gradually moved north by about 0.09° in the latitude direction and east by about 1° in the longitude direction; and 4) the interannual variation of water surface temperature in 17 lakes of China showed a similar fluctuation trend that increased before 2010, and then decreased. The El Niño and La Niña around 2010 could have impacts on the turning point of the annual variation of water surface temperature. This study validated the response of China’s inland surface water to global climate change and improved the understanding of the wetland environment’s response to climate change.

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Wen Wang, Wei Cui, Xiaoju Wang, and Xi Chen

Abstract

The Global Land Data Assimilation System (GLDAS) is an important data source for global water cycle research. Using ground-based measurements over continental China, the monthly scale forcing data (precipitation and air temperature) during 1979–2010 and model outputs (runoff, water storage, and evapotranspiration) during 2002–10 of GLDAS models [focusing on GLDAS, version 1 (GLDAS-1)/Noah and GLDAS, version 2 (GLDAS-2)/Noah] are evaluated. Results show that GLDAS-1 has serious discontinuity issues in its forcing data, with large precipitation errors in 1996 and large temperature errors during 2000–05. While the bias correction of the GLDAS-2 precipitation data greatly improves temporal continuity and reduces the biases, it makes GLDAS-2 precipitation less correlated with observed precipitation and makes it have larger mean absolute errors than GLDAS-1 precipitation for most months over the year. GLDAS-2 temperature data are superior to GLDAS-1 temperature data temporally and spatially. The results also show that the change rates of terrestrial water storage (TWS) data by GLDAS and the Gravity Recovery and Climate Experiment (GRACE) do not match well in most areas of China, and both GLDAS-1 and GLDAS-2 are not very capable of capturing the seasonal variation in monthly TWS change observed by GRACE. Runoff is underestimated in the exorheic basins over China, and runoff simulations of GLDAS-2 are much more accurate than those of GLDAS-1 for two of the three major river basins of China investigated in this study. Evapotranspiration is overestimated in the exorheic basins in China by both GLDAS-1 and GLDAS-2, whereas the overestimation of evapotranspiration by GLDAS-2 is less than that by GLDAS-1.

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Wei Li, Jie Chen, Lu Li, Hua Chen, Bingyi Liu, Chong-Yu Xu, and Xiangquan Li

Abstract

Subseasonal to seasonal (S2S) weather forecasting has made significant advances and several products have been made available. However, to date few studies utilize these products to extend the hydrological forecast time range. This study evaluates S2S precipitation from eight model ensembles in the hydrological simulation of extreme events at the catchment scale. A superior bias correction method is used to correct the bias of S2S precipitation for hydrological forecasts, and the results are compared with direct bias correction of hydrological forecasts using raw precipitation forecasts as input. The study shows that the S2S models can skillfully forecast daily precipitation within a lead time of 11 days. The S2S precipitation data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Korea Meteorological Administration (KMA), and United Kingdom’s Met Office (UKMO) models present lower mean error than that of other models and have higher correlation coefficients with observations. Precipitation data from the ECMWF, KMA, and UKMO models also perform better than that of other models in simulating multiple-day precipitation processes. The bias correction method effectively reduces the mean error of daily S2S precipitation for all models while also improving the correlation with observations. Moreover, this study found that the bias correction procedure can apply to either precipitation or streamflow simulations for improving the hydrological forecasts, even though the degree of improvement is dependent on the hydrological variables. Overall, S2S precipitation has a potential to be applied for hydrological forecasts, and a superior bias correction method can increase the forecasts’ reliability, although further studies are still needed to confirm its effect.

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