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

Liu, Yongqiang; Long, Hualou; Li, Tingting; Tu, Shuangshuang

doi:10.1016/j.landusepol.2015.04.023

Cited by 116 publications

(48 citation statements)

References 52 publications

(46 reference statements)

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“…The land use transfer matrix in different periods was calculated by the Markov model, through which the land use transfer direction and areas in the study area in 35 years were disclosed. The calculation formula is shown in reference [55]. According to the calculated land use transfer matrix, proportions of net losses and net gains of land use types at different stages were calculated [55]:…”

Section: Land Use Transfer Analysismentioning

confidence: 99%

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Research on Land Use Changes and Ecological Risk Assessment in Yongjiang River Basin in Zhejiang Province, China

Tian

Gong

et al. 2019

Sustainability

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Studying land use changes and ecological risk assessment in Yongjiang River Basin in Zhejiang Province, China, provides theoretical references for optimal configuration of land resources and maintaining stability of ecosystems. Given impacts of land use changes on landscape patterns in the Yongjiang River Basin, ecological risk assessment indexes were constructed and used to analyze temporal and spatial variation characteristics of ecological risk within different periods. Results show that (1) the construction land area was increased quickly, while the cultivated area decreased sharply. A prominent characteristic of land use changes was manifested by transforming cultivated area and forestland into construction land. The utilized degree of the land increased continuously. Spatially, the land utilized degree in northern regions was higher than that in southern regions and the degree in eastern regions was higher than that in western regions. (2) The ecological risk in the Yongjiang River Basin was intensified and the area of high ecological risk was expanded by 893.96 km2. Regions with low and relatively low ecological risks concentrated in western and southern regions of the Basin, whereas regions with high ecological risks were mainly in northern and eastern regions. Landscapes in cities and towns at a high economic development level are highly sensitive to human activities. (3) Transformation of ecological risk is complicated. Land area with the ecological risk changing from a low level to a high level was 4.15 times that with the ecological risk changing from a high level to a low level. There were 15 transformation directions among different ecological risk regions.

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Section: Land Use Transfer Analysismentioning

confidence: 99%

“…The calculation formula is shown in reference [55]. According to the calculated land use transfer matrix, proportions of net losses and net gains of land use types at different stages were calculated [55]:…”

Section: Land Use Transfer Analysismentioning

confidence: 99%

Research on Land Use Changes and Ecological Risk Assessment in Yongjiang River Basin in Zhejiang Province, China

Tian

Gong

et al. 2019

Sustainability

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“…The identification of spatial distribution and variation characteristics of surface runoff is of great practical significance for surface runoff management. Assessment of the impacts of land use/cover change (LUCC) and rainfall change on surface runoff has received increased attention in recent years [13][14][15][16][17][18][19][20]. Numerous studies assessing the effect of land use change, climate change, and urbanization expansion on surface runoff have been carried out worldwide with computer models such as Soil and Water Assessment Tool (SWAT), MIKE System Hydrological European (MIKE-SHE), Hydrological Land Use Change (HYLUC), Long-Term Hydrology Impact Assessment (L-THIA), and Storm Water Management Model (SWMM)) [17,[21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Impacts of Land Use/Cover Change and Rainfall Change on Surface Runoff in China

Chen

Liu

et al. 2019

Sustainability

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Assessment of the impacts of land use/cover change (LUCC) and rainfall change on surface runoff depth can help provide an understanding of the temporal trend of variation of surface runoff and assist in urban construction planning. This study evaluated the impacts of LUCC and rainfall change on surface runoff depth by adopting the well-known Soil Conservation Service-Curve Number (SCS-CN) method and the widely used Long-Term Hydrologic Impact Assessment (L-THIA) model. National hydrologic soil group map of China was generated based on a conversion from soil texture classification system. The CN values were adjusted based on the land use/cover types and soil properties in China. The L-THIA model was configured by using the adjusted CN values and then applied nationally in China. Results show that nationwide rainfall changes and LUCC from 2005 to 2010 had little impact on the distribution of surface runoff, and the high values of runoff depth were mainly located in the middle and lower reaches of the Yangtze River. Nationally, the average annual runoff depths in 2005, 2010 and 2015 were 78 mm, 83 mm and 90 mm, respectively. For the 2015 land use data, rainfall change caused the variation of surface runoff depth ranging from −203 mm to 476 mm in different regions. LUCC from 2005 to 2015 did not cause obvious change of surface runoff depth, but expansion of developed land led to runoff depth increases ranging from 0 mm to 570 mm and 0 mm to 742 mm from 2005 to 2010 and 2010 to 2015, respectively. Potential solutions to urban land use change and surface runoff control were also analyzed.

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“…They also have been seriously considered as alternative water supplies in high population density states, cities, district and towns during dry periods because of water shortage issues and supplies from main reservoir not being sufficient to meet water demands (Ali, Saadon, Abd Rahman, & Khalid, ; Xue, Yin, & Xie, ). The increasing concern is they are facing numerous threats attributable to rapid urbanization, subsequent increased human activities, significant changes in land‐use patterns, high population growth and, more recently, climate change impacts (Ding, Chen, Cheng, & Wang, ; Erol & Randhir, ; Liu, Long, Li, & Tu, ; Zessner et al., ; Zhu, Zhang, & Tong, ).…”

Section: Introductionmentioning

confidence: 99%

“…It must be highlighted that continuous, long‐term monitoring of lake water quality with standard sets of parameter during both dry and wet season is compulsory (Glavan, Miličić, & Pintar, ; Knuppe & Meissner, ; Sage, Bonhomme, Al Ali, & Gromaire, ). In this regard, long‐term consistent monitoring can provide trend information and sufficient data to be analysed in order to help detect dominant pollution sources, whether point or non‐point sources, assess the quantity of nutrients arising from catchment activities, identify the prominent stressors (or drivers), determine the magnitude of watershed activity impacts on water quality status and identify which land‐use types may profoundly affect a lake (Agalanu, ; Behmel, Damour, Ludwig, & Rodriguez, ; Dunalska et al., ; Leon‐Munoz et al., ; Liu et al., ).…”

Section: Introductionmentioning

confidence: 99%

Development of spatial similarity‐based modelling to improve integrated lake water quality management in Malaysia

et al. 2018

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This study describes similarity‐based modelling techniques used to develop a spatially based water quality prediction model to facilitate sustainable lake water quality management. The lentic nature of lakes allows them to slowly absorb pollutants over a long period of time with readily noticeable signs, causing symptoms to appear only when the water quality has significantly degraded, meaning the risk of improper water quality management can be very high. Thus, failure to establish sustainable planning at the watershed scale was found to be a major threat of water quality degradation, from extemporary approaches often practised in lake management. Accordingly, the developed model is tailored for lakes facing moderate to serious water quality data limitation. The geodatabase integrates the identified driving factors of physical, social and water quality with significant influences on the status of lake water quality. A 1 km buffer radius with percentages of built‐up area, population, lake surface area and rainfall is measured. Calculation of a water quality index was then quantified on the basis of a similarity‐modelling technique. Lake Putrajaya was chosen as a control point for developing the indicator index. A total of 93 recreational lakes within Selangor and Kuala Lumpur were selected as modelling points. The results of this study indicated the similarity technique of spatial modelling is sufficiently reliable to be applied as an early assessment indicator. From the 93 lakes in this study, none feel in the category of either “bad” or “excellent,” with the majority being in class 3 (medium water quality status) and only four considered as having a good water quality condition. The balance of 35 lakes was considered to exhibit poor water quality. The model output is an indicator index, providing classification guidelines for the water quality status of the assessed lake as an early assessment tools.

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

Cited by 116 publications

References 52 publications

Research on Land Use Changes and Ecological Risk Assessment in Yongjiang River Basin in Zhejiang Province, China

Research on Land Use Changes and Ecological Risk Assessment in Yongjiang River Basin in Zhejiang Province, China

Assessment of the Impacts of Land Use/Cover Change and Rainfall Change on Surface Runoff in China

Development of spatial similarity‐based modelling to improve integrated lake water quality management in Malaysia

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