Analysis of Landscape Pattern Spatial Scale in Middle and Upper Reaches of Meijiang River Basin

Author(s):  
Yuchan Chen ◽  
Zhengdong Zhang ◽  
Chuanxun Yang ◽  
Yang Yang ◽  
Chen Zhang ◽  
...  
2020 ◽  
Vol 20 (3) ◽  
pp. 1046-1058
Author(s):  
Fan Gao ◽  
Bing He ◽  
Songsong Xue ◽  
Yizhen Li

Abstract Based on the Soil and Water Assessment Tool (SWAT) model, the monthly runoff processes of two land-use types in 2000 and 2015 were simulated in this paper. The relationship between runoff and landscape pattern was analyzed, and the spatial correlation between runoff and landscape pattern analyzed using the geographic weighted regression model combined with the change of landscape pattern in the study area from 2000 to 2015. The results show the following. (1) The SWAT model can simulate the monthly runoff processes in the catchment area of the Ulungur River Basin (URB) under different land-use types for 2000 and 2015, but the simulation effect in 2000 was found to be better than that in 2015. (2) From 2000 to 2015, the area of woodland and grassland decreased. Runoff was positively correlated with woodland, grassland, largest patch index, mean patch area (AREA_MN), and contagion index, and negatively correlated with others. This indicates that the landscape fragmentation of URB was aggravated in 2000–2015, the landscape balance was destroyed, and the ability of rainfall interception and water conservation was weakened. (3) Landscape pattern indicators of grassland had a negative spatial impact on URB runoff, and the northern region of URB was more severely affected in 2015 than in 2000. AREA_MN landscape pattern index had a positive impact on runoff in the northern part of URB, and the positive impact in northern URB in 2000 was better than that in 2015.


2019 ◽  
Vol 39 (22) ◽  
Author(s):  
吕乐婷 LÜ Leting ◽  
张杰 ZHANG Jie ◽  
彭秋志 PENG Qiuzhi ◽  
任斐鹏 REN Feipeng ◽  
江源 JIANG Yuan

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 990
Author(s):  
Yongfen Zhang ◽  
Nong Wang ◽  
Chongjun Tang ◽  
Shiqiang Zhang ◽  
Yuejun Song ◽  
...  

Landscape patterns are a result of the combined action of natural and social factors. Quantifying the relationships between landscape pattern changes, soil erosion, and sediment yield in river basins can provide regulators with a foundation for decision-making. Many studies have investigated how land-use changes and the resulting landscape patterns affect soil erosion in river basins. However, studies examining the effects of terrain, rainfall, soil erodibility, and vegetation cover factors on soil erosion and sediment yield from a landscape pattern perspective remain limited. In this paper, the upper Ganjiang Basin was used as the study area, and the amount of soil erosion and the amount of sediment yield in this basin were first simulated using a hydrological model. The simulated values were then validated. On this basis, new landscape metrics were established through the addition of factors from the revised universal soil loss equation to the land-use pattern. Five combinations of landscape metrics were chosen, and the interactions between the landscape metrics in each combination and their effects on soil erosion and sediment yield in the river basin were examined. The results showed that there were highly similar correlations between the area metrics, between the fragmentation metrics, between the spatial structure metrics, and between the evenness metrics across all the combinations, while the correlations between the shape metrics in Combination 1 (only land use in each year) differed notably from those in the other combinations. The new landscape indicator established based on Combination 4, which integrated the land-use pattern and the terrain, soil erodibility, and rainfall erosivity factors, were the most significantly correlated with the soil erosion and sediment yield of the river basin. Finally, partial least-squares regression models for the soil erosion and sediment yield of the river basin were established based on the five landscape metrics with the highest variable importance in projection scores selected from Combination 4. The results of this study provide a simple approach for quantitatively assessing soil erosion in other river basins for which detailed observation data are lacking.


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