Sustainable management of water for human uses and maintaining river health requires reliable information about the future availability of water resources. We quantified the separate and combined impacts of climate and land cover changes on runoff for the historical record and for modelled future scenarios in the upper Han River and Luan River, supply and demand zones respectively of the middle route of the South to North Water Transfer Project in China, the world's largest inter-basin water transfer project. We used a precipitation-runoff model, averaged multiple climate model predictions combined with three emissions scenarios, a combined CA-Markov model to predict land cover change, and a range of statistical tests. Comparing baseline with 2050: climate change would cause an average reduction in runoff of up to 15 % in the upper Han River and up to 9 % in the Luan River catchment; a scenario involving increased forest cover would reduce runoff by up to 0.19 % in the upper Han River and up to 35 % in the Luan River; a scenario involving increased grass cover would increase runoff by up to 0.42 % in the upper Han River and up to 20 % in the Luan River. In the lower Luan River, the mean annual flow after 1998 fell to only 17 % of that of the baseline period, posing a serious threat to river health. This was explained largely by extraction of surface water and groundwater, rather than climate and land use change.Water Resour Manage
Many coastal regions in China are confronted with pressing problems of scarce land resources and heavy population. Over the past 30 years, considerable parts of coastal tidelands have been enclosed and reclaimed for agricultural land uses. To assess, plan, and implement large-scale reclamation programs, up-to-date and reliable information concerning the nature, areal extent, and physical and chemical characteristics of coastal saline lands is essential. This paper reports a remote sensing approach to detecting coastal saline land uses in Shangyu City, China, by using multi-temporal Landsat images. First, with the aid of resolution-sharpened Landsat-7 ETM+ images and their enhanced linear features, a visual interpretation is applied to extract individual dikes. Based on time series images and local government records, a spatial zoning procedure is then used to define six sub-zones with different historical years of reclamation. It shows that a total of 15,668 ha of coastal saline lands were enclosed and reclaimed from 1969 to 1996. Second, a modified land-use classification system for the study area is prescribed, and both unsupervised and supervised classifiers are performed for land-use classifications of grouped sub-zones. Information obtained from the spatial zoning, Tasseled Cap transformation and Normalized Difference Vegetation Index, is also utilized to facilitate the supervised classification process. Finally, a detailed land-use map is produced, with an overall classification accuracy of 77.8%. Results show that dominant agricultural land uses of sub-zones are changed with historical reclamation years, from saline lands with wildgrass (very recently reclaimed) to aqua-farm ponds, to cotton fields, and to paddy fields and orchards (very early reclaimed). This transform process is primarily affected by soil salinities, and according to a soil survey an electrical conductivity of saturation extract decreased from 7.3 ds/m in the saline land reclaimed in 1996 to below 2 ds/m in the land reclaimed before 1969. The study concludes that multi-temporal remotely sensed images are important and effective data sources for monitoring the rapid changes of coastal land uses.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.