Evaluation of Runoff Responses to Land Use Changes and Land Cover Changes in the Upper Huaihe River Basin, China

2012 ◽  
Vol 17 (7) ◽  
pp. 800-806 ◽  
Author(s):  
Qu Simin ◽  
Bao Weimin ◽  
Shi Peng ◽  
Yu Zhongbo ◽  
Li Peng ◽  
...  
2020 ◽  
Vol 79 (15) ◽  
Author(s):  
Qiongfang Li ◽  
Guobin Lu ◽  
Xingye Han ◽  
Zhengmo Zhou ◽  
Tianshan Zeng ◽  
...  

2017 ◽  
Vol 27 (1) ◽  
pp. 13-24 ◽  
Author(s):  
Fang Wang ◽  
Quansheng Ge ◽  
Qibiao Yu ◽  
Huaxin Wang ◽  
Xinliang Xu

2015 ◽  
Vol 737 ◽  
pp. 728-731 ◽  
Author(s):  
Yuan Yuan Han ◽  
Tao Cai

In this study, Soil and Water Assessment Tool (SWAT) model was used to simulate land-use change effects on water quantity in the upper Huaihe river basin above the Xixian hydrological controlling station with a catchment area of 10,190 km2 by the use of three-phase (1980s、1990s、2000s) land-use maps, soil type map (1:200000), 1980 to 2008 daily time series of rainfall from the upper Huaihe river basin. On the basis of the simulated time series of daily runoff, land-use change effects on spatio-temporal change patterns of runoff coefficients and runoff modules were investigated. The results revealed that under the same condition of soil texture and terrain slope the advantage for runoff generation and the sensitivity of rainfall-runoff relationship to rainfall descended by farmland, paddy field, woodland.The outputs could provide important references for soil and water conservation and river health protection in the upper stream of Huaihe river.


2020 ◽  
Vol 12 (4) ◽  
pp. 1570 ◽  
Author(s):  
Mads Christensen ◽  
Jamal Jokar Arsanjani

The United Nations 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDG’s) presents a roadmap and a concerted platform of action towards achieving sustainable and inclusive development, leaving no one behind, while preventing environmental degradation and loss of natural resources. However, population growth, increased urbanisation, deforestation, and rapid economic development has decidedly modified the surface of the earth, resulting in dramatic land cover changes, which continue to cause significant degradation of environmental attributes. In order to reshape policies and management frameworks conforming to the objectives of the SDG’s, it is paramount to understand the driving mechanisms of land use changes and determine future patterns of change. This study aims to assess and quantify future land cover changes in Virunga National Park in the Democratic Republic of the Congo by simulating a future landscape for the SDG target year of 2030 in order to provide evidence to support data-driven decision-making processes conforming to the requirements of the SDG’s. The study follows six sequential steps: (a) creation of three land cover maps from 2010, 2015 and 2019 derived from satellite images; (b) land change analysis by cross-tabulation of land cover maps; (c) submodel creation and identification of explanatory variables and dataset creation for each variable; (d) calculation of transition potentials of major transitions within the case study area using machine learning algorithms; (e) change quantification and prediction using Markov chain analysis; and (f) prediction of a 2030 land cover. The model was successfully able to simulate future land cover and land use changes and the dynamics conclude that agricultural expansion and urban development is expected to significantly reduce Virunga’s forest and open land areas in the next 11 years. Accessibility in terms of landscape topography and proximity to existing human activities are concluded to be primary drivers of these changes. Drawing on these conclusions, the discussion provides recommendations and reflections on how the predicted future land cover changes can be used to support and underpin policy frameworks towards achieving the SDG’s and the 2030 Agenda for Sustainable Development.


2012 ◽  
Vol 12 ◽  
pp. 906-916 ◽  
Author(s):  
Zhang Qingqing ◽  
Xu Hailiang ◽  
Fu Jingyi ◽  
Yu Pujia ◽  
Zhang Peng

2019 ◽  
Author(s):  
Vasilică-DănuÈ› Horodnic ◽  
◽  
PetruÈ›-Ionel Bistricean ◽  
Dumitru Mihăilă ◽  
Vasile Efros

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