Land use transitions and their effects on water environment in Huang-Huai-Hai Plain, China

This paper analyzes the spatio-temporal dynamic patterns of land use in Huang-Huai-Hai Plain, one of the China's most important grain production bases experiencing rapid urban-rural transformation development, using high-resolution Landsat TM (Thematic Mapper) data and series data of water environmental quality monitoring in 2000 and 2010, and related socio-economic data from government departments. After assessing the change of water environmental quality of Huang-Huai-Hai Plain during 2000-2010, three spatial econometric regression models including Spatial Lag Model (SLM), Spatial Error Model (SEM) and ordinary least squares (OLS) are used to explore the correlationships between land use transitions and water environmental changes. The outcomes indicated that, during the research period, land use pattern changes in the study area were characterized by the loss of large quantities of farmland and the increase of construction land and water body. On the whole, the water environment in the study area was obviously improved, but there also existed partial deterioration as evidenced by the increase of monitoring sections with water environmental quality below Class V. In general, the increases of grassland, forested land and water body have positive effects on water environmental quality while farmland and construction land have negative effects on that. The authors argue that construction land use and farmland use have almost equal negative effects on water environmental quality, and managing agricultural non-point source pollution is pivotal to improve local water environmental quality. Finally, some of the major implications for managing the land and water resources in the plain areas of China as well as other developing countries undergoing rapid urban-rural transformation development are discussed in the aspects of ecological farming practices, rural construction land management, land use planning and urban planning. (C) 2015 Elsevier Ltd. All rights reserved.