Drought monitoring is a key topic in environmental monitoring and assessment although there is still a need to determine the correlation between drought monitoring indices and remote sensing products. We analyzed the correlation between the self-calibrating Palmer Drought Severity Index (sc_PDSI), the Standardized Precipitation Index and the Standardized Precipitation Evapotranspiration Index (SPEI) and terrestrial water storage monitored through the Gravity Recovery and Climate Experiment (GRACE) on a monthly timescale from 2002 to 2015 in China. As a consequence of anomalies in the soil water budget, the highly significant correlation between the sc_PDSI and the GRACE satellite-observed terrestrial water storage suggested that these two datasets are the most suitable for use in monitoring droughts. In comparing the three drought indices, the sc_PDSI was introduced as a means of drought monitoring in the Yangtze, Pearl, Huaihe, Southeast and Songhua River Basins, whereas the SPEI was found to be more applicable to other major river basins, such as the Inland River Basin. These diverse spatial behaviors are caused by the differences between the hydrological droughts characterized by these three drought indices. 相似文献
Concentrations of suspended solids in lakes can affect the latter’s primary productivity and reflect changes in sediment deposition. Determining the temporal and spatial distribution of suspended solid concentrations has important significance in lake water environmental management; this is particularly urgent for Poyang Lake, the largest freshwater lake in China. In this study, suspended solid concentration inversion models for Poyang Lake were created using a semi-empirical method with regression analysis between continuously measured suspended solid concentration data and multi-band moderate-resolution imaging spectroradiometer images for spring, summer, autumn, and winter from 2009 to 2012. The coefficient of determination (R2) is from 0.6 to 0.9 and the average relative error for the accuracy verification was between 10 and 30%. The seasonal distributions of suspended solid concentrations in Poyang Lake from 2000 to 2013 were then obtained using optimal reversal models. The results showed that the seasonal variation in suspended solid concentrations had a “W” shape in which high spring and autumn and low summer and winter values. The suspended solid concentrations increased annually from 2000 to 2013 and were mainly distributed in the northern and central portions of the lake, with lower values along the shorelines. Further analysis indicated that the large difference in water level between the wet and dry seasons is an important factor in explaining these seasonal variations. Moreover, the suspended solid concentrations were poorly correlated with water temperature and chlorophyll-a concentration but more highly correlated with the deferred chlorophyll-a concentration. 相似文献
Identifying and analyzing the urban–rural differences of social vulnerability to natural hazards is imperative to ensure that urbanization develops in a way that lessens the impacts of disasters and generate building resilient livelihoods in China. Using data from the 2000 and 2010 population censuses, this study conducted an assessment of the social vulnerability index (SVI) by applying the projection pursuit cluster model. The temporal and spatial changes of social vulnerability in urban and rural areas were then examined during China’s rapid urbanization period. An index of urban–rural differences in social vulnerability (SVID) was derived, and the global and local Moran’s I of the SVID were calculated to assess the spatial variation and association between the urban and rural SVI. In order to fully determine the impacts of urbanization in relation to social vulnerability, a spatial autoregressive model and Bivariate Moran’s I between urbanization and SVI were both calculated. The urban and rural SVI both displayed a steadily decreasing trend from 2000 to 2010, although the urban SVI was always larger than the rural SVI in the same year. In 17.5% of the prefectures, the rural SVI was larger than the urban SVI in 2000, but was smaller than the urban SVI in 2010. About 12.6% of the urban areas in the prefectures became less vulnerable than rural areas over the study period, while in more than 51.73% of the prefectures the urban–rural SVI gap decreased over the same period. The SVID values in all prefectures had a significantly positive spatial autocorrelation and spatial clusters were apparent. Over time, social vulnerability to natural hazards at the prefecture-level displayed a gathering–scattering pattern across China. Though a regional variation of social vulnerability developed during China’s rapid urbanization, the overall trend was for a steady reduction in social vulnerability in both urban and rural areas.
