ERA5 is the latest fifth-generation reanalysis global atmosphere dataset from the European Centre for Medium-Range Weather Forecasts, replacing ERA-Interim as the next generation of representative satellite-observational data on the global scale. ERA5 data have been evaluated and applied in different regions, but the performances are inconsistent. Meanwhile, there are few precise evaluations of ERA5 precipitation data over long time series have been performed in Chinese mainland. This study evaluates the temporal-spatial performance of ERA5 precipitation data from 1979 to 2018 based on gridded-ground meteorological station observational data across China. The results showed that ERA5 data could capture the annual and seasonal patterns of observed precipitation in China well, with correlation coefficient values ranging from 0.796 to 0.945, but ERA5 slightly overestimated precipitation in the summer. Nonetheless, the results also showed that the accuracy of the precipitation products was strongly correlated with topographic distribution and climatic divisions. The performance of ERA5 shows spatial inherently across China that the highest correlation coefficient values locate in eastern, Northwestern and North China and the lowest biases locate in Southeast China. This study provides a reliable data assessment of the ERA5 data and precipitation trend analyses in China. The results provide accuracy references for the further use of precipitation satellite data for hydrological calculations and climate numerical simulations.
In recent years, the streamflow of the Laohahe Basin in China showed a dramatic decrease during the rainy season as a result of climate change and/or human activities. The objective of this work was to document significant streamflow changes caused by land use and land cover (LULC) changes and to quantify the impacts of the observed changes in Laohahe Basin. In the study area, the observed streamflow has been influenced by LULC changes, dams, and irrigation from rivers, industry, livestock and human consumption. Most importantly, the growth of population and gross domestic product (GDP) accompanied by the growth in industrial and agricultural activities, which led to LULC changes with increased residential land and cropland and decreased grassland since 2000s. Statistical methods and Variable Infiltration Capacity (VIC) hydrological model were used to estimate the effects of climate change and LULC changes on streamflow and evaportranspiration (ET). First, the streamflow data of the study area were divided into three sub-periods according to the Pettitt test. The hydrological process was then simulated by VIC model from 1964 to 2009. Furthermore, we compared the simulated results based on land use scenarios in 1989, 1999 and 2007, respectively for exploring the effect of LULC changes on the spatio-temporal distribution of streamflow and ET in the Laohahe Basin. The results suggest that, accompanied with climate change, the LULC changes and human water consumption appeared to be the most likely factors contributing to the significant reduction in streamflow in the Laohahe Basin by 64% from1999 to 2009. Keywords: hydrological response; land use and land cover changes; streamflow; evapotranspiration; semi-arid region Citation: XiaoLi YANG, LiLiang REN, Yi LIU, DongLai JIAO, ShanHu JIANG. 2014. Hydrological response to land use and land cover changes in a sub-watershed of West Liaohe
Global climate models (GCMs) are state-of-the-art tools for understanding climate change and predicting the future. However, little research has been reported on the latest NEX-GDDP-CMIP6 product in China. The purpose of this study was to evaluate the simulated performance and drought capture utility of the NEX-GDDP-CMIP6 over China. First, the simulation skills of the 16 GCMs in NEX-GDDP-CMIP6 were evaluated by the ‘DISO’ (Distance between Indices of Simulation and Observation), a big data evaluation method. Second, the DISO framework for drought identification was constructed by coupling the correlation coefficient (CC), false alarm rate (FAR) and probability of detection (POD). Then, it was combined with the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evaporation Index (SPEI) to evaluate the drought detection capability of NEX-GDDP-CMIP6. The result shows that (1) NEX-GDDP-CMIP6 can reproduce the spatial distribution pattern of historical precipitation and temperature, which performs well in simulating warming trends but fails to capture precipitation's fluctuation characteristics; (2) The best-performing model in precipitation is ACCESS-CM2 (DISO 1.630) and in temperature is CESM2 (DISO 3.246); (3) The multi-mode ensembles (16MME) perform better than the best single model, indicating that a multi-model ensemble can effectively reduce the uncertainty inherent in models. (4) The SPEI calculated by 16MME identifies drought well in arid, while the SPI is recommended for other climate classifications in China.
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