Reliability and accuracy of soil moisture datasets are essential for understanding changes in regional climate such as precipitation and temperature. Soil moisture datasets from the Essential Climate Variable (ECV), the Coupled Model Intercomparison Project Phase 5 (CMIP5), the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), the Global Land Data Assimilation System (GLDAS), and reanalysis products are widely used. These datasets generated by different techniques are compared in a common framework over China in this study. The comparison focuses on four aspects: spatial pattern, temporal correlation, long-term trend, and the relationships with precipitation and the Normalized Difference Vegetation Index (NDVI). The results indicate that all soil moisture datasets reach a good agreement on the spatial patterns of wet and dry soil. These patterns are also consistent with that of precipitation. However, there are considerable discrepancies in the absolute values of soil moisture among these datasets. In terms of unbiased Root-Mean-Square Difference (unRMSE, i.e., removing the differences in absolute values), all modeled datasets obtain performances comparable with ECV observations. Our results also suggest that a multi-model ensemble of soil moisture datasets can improve the representation of soil moisture conditions. The optimal dataset from which the wetting/drying trends in soil moisture have the highest consistency in terms of changes in precipitation and NDVI varies by season. Specifically, in spring, CMIP5 in northwest China shows that the trends in soil moisture are consistent with the changes in precipitation and NDVI. In summer, ECV presents the most identical performance compared to the changes in precipitation and NDVI. In autumn, GLDAS and Reanalysis have better performance in south China and parts of north China. In winter, GLDAS performs the best in the east of south China, followed by the Reanalysis dataset. These discrepancies among the datasets present various changes in different regions, which should be well noted and discussed before use.
Water quality evaluation is fundamental for water resources management. In this study, a water quality index (WQI) was constructed to evaluate water quality in an estuary region. First, principal component analysis and the Bartlett method were used to select more important water quality parameters from multivariables. Second, quality curves and weights of selected parameters were assigned, and then WQI scores were calculated. The WQI method was applied to the Eastern Pearl River Delta in China as a case study. Results showed that water quality in the upstream area and the coastal region was better than in the central delta, with an average WQI of 72, 55 and 14, respectively. Results further revealed that water quality in the coastal region was more variable (the standard variation of WQIs is near 20) due to more rapid changes in hydrologic features, while water quality in the inland area was more stable (the standard variation is around 10). Comparison between the WQI and fuzzy evaluation methods indicated the reliability of the WQI method. This WQI method can evaluate water quality in the estuarine delta area well, and statistical techniques used in this paper can be applied in different geographical areas considering their specific characteristics.
Disasters caused by extreme precipitation under global warming are anticipated to have a strong negative impact on urban construction and social security. In this study, daily grid precipitation datasets of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) for the period 1961–2018 were extracted to explore the temporal and spatial characteristics of extreme precipitation by using regression analysis, moving average and kriging interpolation. The frequency and intensity indices showed an increasing trend, whereas a decreasing trend was found for the persistence indices, which indicates that GBA tends to slowly become wetter. The mean values of extreme precipitation indices (EPIs) in GBA generally increased from west to east and from north to south. Except for the indices of consecutive wet days and consecutive dry days, other EPIs showed an upward trend in most regions, especially in coastal cities where floods are more likely to occur. Principal component analysis and regression analysis showed that the correlations between the EPIs mostly passed the 0.05 significance test, which suggests that they had a good indicator of extreme precipitation in GBA. This study provides a theoretical basis for extreme precipitation disaster prevention and control within the urban agglomerations of the GBA.
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