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Dashan Wang, Xianwei Wang, Lin Liu, Dagang Wang, and Zhenzhong Zeng

Abstract

Urban areas demonstrate great influence on precipitation, yet the spatial clustering features of precipitation are still unclear over urban areas. This study quantitatively examines the spatial clustering of precipitation intensity in 130 urban-affected regions over mainland China during 2008–15 using a high-resolution merged precipitation product. Results show that the spatial heterogeneity patterns display diverse distribution and vary with precipitation intensity and urban sizes. Extreme and heavy precipitation has higher spatial heterogeneity than light precipitation over the urban-affected regions of grade 1 cities, and their mean Moran’s I are 0.49, 0.47, and 0.37 for the intensity percentiles of ≥95%, 75%–95%, and <75%, respectively. The urban signatures in the spatial clustering of precipitation extremes are observed in 37 cities (28%), mainly occurring in the Haihe River basin, the Yangtze River basin, and the Pearl River basin. The spatial clustering patterns of precipitation extremes are affected by the local dominant synoptic conditions, such as the heavy storms of convective precipitation in Beijing (Moran’s I = 0.47) and the cold frontal system in the Pearl River delta (Moran’s I = 0.78), resulting in large regional variability. The role of urban environments for the spatial clustering is more evident in wetter conditions [e.g., relative humidity (RH) > 75% over Beijing and RH > 85% over the Pearl River delta] and warmer conditions (T > 25°C over Beijing and T > 28°C over the Pearl River delta). This study highlights the urban modification on the spatial clustering of some precipitation extremes, and calls for precautions and adaptation strategies to mitigate the adverse effect of the highly clustered extreme rainfall events.

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Chuanguo Yang, Zhaohui Lin, Zhongbo Yu, Zhenchun Hao, and Shaofeng Liu

Abstract

A hydrologic model coupled with a land surface model is applied to simulate the hydrologic processes in the Huaihe River basin, China. Parameters of the land surface model are interpolated from global soil and vegetation datasets. The characteristics of the basin are derived from digital elevation models (DEMs) and a national geological survey atlas using newly developed algorithms. The NCEP–NCAR reanalysis dataset and observed precipitation data are used as meteorological inputs for simulating the hydrologic processes in the basin. The coupled model is first calibrated and validated by using observed streamflow over the period of 1980–87. A long-term continuous simulation is then carried out for 1980–2003, forced with observed rainfall data. Results show that the model behavior is reasonable for flood years, whereas streamflows are sometimes overestimated for dry years since the 1990s when water withdrawal increased substantially because of the growing industrial activities and the development of water projects. Observed streamflow and water withdrawal data showed that human activities have obviously affected the surface rainfall–runoff process, especially in dry years. Two methods are proposed to study the human dimension in the hydrologic cycle. One method is to reconstruct the natural streamflow series using local volumes of withdrawals. The simulated results are more consistent with the reconstructed hydrograph than the initially observed hydrograph. The other method is to integrate a designated module into the coupled model system to represent the effect of human activities. This method can significantly improve the model performance in terms of streamflow simulation.

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Zhangkang Shu, Jianyun Zhang, Junliang Jin, Lin Wang, Guoqing Wang, Jie Wang, Zhouliang Sun, Ji Liu, Yanli Liu, Ruimin He, Cuishan Liu, and Zhenxin Bao

Abstract

We evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–2018 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.

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Xiaolei Wang, Yi Luo, Lin Sun, Chansheng He, Yiqing Zhang, and Shiyin Liu

Abstract

Runoff in the Amu Darya River (ADR) in central Asia has been declining steadily since the 1950s. The reasons for this decline are ambiguous, requiring a complete analysis of glaciohydrological processes across the entire data-scarce source region. In this study, grid databases of precipitation from the Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) and temperature from Princeton’s Global Meteorological Forcing Dataset (PGMFD) are used to force the distributed, glacier-enhanced Soil and Water Assessment Tool (SWAT) model to simulate glaciohydrological processes for 1951–2007 so as to determine long-term streamflow changes and the primary driving factors in the source region of the ADR. The study suggests that the database was a suitable proxy for temperature and precipitation forcing in simulating glaciohydrological processes in the data-scarce alpine catchment region. The estimated annual streamflow of 72.6 km3 in the upper ADR had a decreasing trend for the period from 1951 to 2007. Change in precipitation, rather than in temperature, dominated the decline in streamflow in either the tributaries or mainstream of the ADR. The streamflow decreased by 15.5% because of the decline in precipitation but only increased by 0.2% as a result of the increase in temperature. Thus, warming temperature had much less effect than declining precipitation on streamflow decline in the ADR in central Asia in 1951–2007.

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Shaobo Sun, Baozhang Chen, Quanqin Shao, Jing Chen, Jiyuan Liu, Xue-jun Zhang, Huifang Zhang, and Xiaofeng Lin

Abstract

Land surface models (LSMs) are useful tools to estimate land evapotranspiration at a grid scale and for long-term applications. Here, the Community Land Model, version 4.0 (CLM4.0); Dynamic Land Model (DLM); and Variable Infiltration Capacity model (VIC) were driven with observation-based forcing datasets, and a multiple-LSM ensemble-averaged evapotranspiration (ET) product (LSMs-ET) was developed and its spatial–temporal variations were analyzed for the China landmass over the period 1979–2012. Evaluations against measurements from nine flux towers at site scale and surface water budget–based ET at regional scale showed that the LSMs-ET had good performance in most areas of China’s landmass. The intercomparisons between the ET estimates and the independent ET products from remote sensing and upscaling methods suggested that there were fairly consistent patterns between each dataset. The LSMs-ET produced a mean annual ET of 351.24 ± 10.7 mm yr−1 over 1979–2012, and its spatial–temporal variation analyses showed that (i) there was an overall significant ET increasing trend, with a value of 0.72 mm yr−1 (p < 0.01), and (ii) 36.01% of Chinese land had significant increasing trends, ranging from 1 to 9 mm yr−1, while only 6.41% of the area showed significant decreasing trends, ranging from −6.28 to −0.08 mm yr−1. Analyses of ET variations in each climate region clearly showed that the Tibetan Plateau areas were the main contributors to the overall increasing ET trends of China.

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