a b s t r a c tKeywords: Yellow River water discharge sediment load climate change human activity Water discharge and sediment load have changed continuously during the last half century in the Yellow River basin, China. In the present paper, data from 7 river gauging stations and 175 meteorological stations are analyzed in order to estimate quantitatively the contributions of human activities and climate change to hydrological response. Coefficients of water discharge (C w ) and sediment load (C s ) are calculated for the baseline period of 1950s-1960s according to the correlations between the respective hydrological series and regional precipitation. Consequently, the natural water discharge and natural sediment load time series are reconstructed from 1960s-2008. Inter-annual impacts are then separated from the impacts of human activities and climate change on the hydrological response of different regions of the Yellow River basin. It is found that human activities have the greatest influence on changes to the hydrological series of water discharge and sediment load, no matter whether the effect is negative or positive. Moreover, the impact of human activities is considerably greater on water discharge than sediment load. During 1970-2008, climate change and human activities respectively contribute 17% and 83% to the reduction in water discharge, and 14% and 86% to the reduction in sediment yield in the Upper reaches of Yellow River basin; The corresponding relative contributions in the Middle reaches are 71% and 29% to reductions in water discharge, and 48% and 52% to reductions in sediment load. Moreover, it is observed that the impacts of human activities on the whole basin are enhanced with time. In the 2000s, the impact of human activities exceeds that of climate change in the 2000s, with human activities directly responsible for 55% and 54% of the reductions in water discharge and sediment load in the whole basin.
Vegetation significantly influences human health in the Yellow River basin and the plant cover is vulnerable to people. Typical types of erosion in the Yellow River basin include that caused by water, wind and freeze-thaw. In this paper, vegetation cover change from 1982 to 2006 was studied for a number of different erosion regions. The Global Inventory Monitoring and Modeling Studies Normalized Difference Vegetation Index (GIMMS NDVI) data were employed, while climatic data were also used for analysis of other influencing factors. It was shown that: (1) generally the vegetation cover in different erosion regions displayed similar increasing trends; (2) spatially the vegetation cover was highest in the water erosion region, the second highest was in the freeze-thaw region and the lowest in the wind erosion region; and (3) vegetation cover in the Yellow River basin is influenced by climate factors, especially by temperature. In water erosion regions, the temporal change of vegetation cover seemed complicated by comprehensive climatic and human influences. In wind erosion regions, the vegetation cover had close relations to precipitation. In freeze-thaw erosion regions, the vegetation cover was primarily altered by temperature. In all the three erosion regions, significant change of the vegetation cover occurred from 2000 just after the 'Grain for Green' (GFG) programme was implemented throughout China.
The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (B cont (0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the fieldobserved distribution of R. bieti. B cont (0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (−0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.
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