Impacts of LUCC and climate change on runoff in Lancang River Basin

Xiaodong, 窦小东 Dou; Wei, 黄玮 Huang; Qi, 易琦 Yi; Xiaozhou, 刘晓舟 Liu; Huiting, 左慧婷 Zuo; Meng, 李蒙 Li; Zhongliang, 李忠良 Li

doi:10.5846/stxb201811302610

Cited by 4 publications

(5 citation statements)

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“…The spatial distribution of precipitation decreases from south-east to north-west. The average annual precipitation in the eastern part of the basin is more than 500 mm, while in the western part it is around 250 mm (Dou et al, 2019). Sparse population and low exploitation of water resources.…”

Section: Study Area and Datamentioning

confidence: 99%

“…The Lancang River (LR) originates in the northeastern Tanggula Mountains in Qinghai Province, China. During the past four decades, precipitation in the LR basin has shown a significant downward trend due to climate change (Li et al, 2017;Dou et al, 2019;Wang et al, 2022b). The agricultural water consumption of the LR is basically stable, and the water consumption for industry and domestic use is gradually increasing (Gao et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Attribution analysis of multi-temporal scale changes of streamflow in the source area of Lancang River with seasonal scale Budyko model

Liu,

Chen,

Zhang

et al. 2023

Front. Ecol. Evol.

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Under the influence of climate change and human activities, the intra-annual distribution characteristics of streamflow have changed, directly affecting the exploitation of water resources and the health of ecosystems. The trend-free pre-whitening Mann-Kendall (TFPW-MK) test method, concentration degree and concentration period, and Bernaola-Galvan (BG) segmentation algorithm were applied to analyze variation trend, intra-annual distribution characteristics, and abrupt year of streamflow. Then, the monthly water storage and monthly actual evaporation of the source area of the Lancang River (SALR) were calculated by the monthly ABCD model. Finally, the contributions of different factors to runoff variability at multiple time scales were quantified using the seasonal-scale Budyko hypothesis approach. The results showed that: (1) The runoff revealed a significant upward trend on the annual scale. Runoff exhibited a significant upward trend in January, October and November, and runoff in other months and seasons exhibited an insignificant upward trend. (2) The intra-annual distribution characteristics of runoff in the SALR showed an obvious “Single-peak type“ distribution, reaching a maximum in July and August. (3) The year of sudden change in streamflow was 2008. (4) The contribution of climate change and human activities to the annual runoff change was 83.3% and 16.7%, respectively. The degree of influence of climate change on runoff change was ranked as spring (96.8%), autumn (85.3%), winter (82.2%) and summer (58.2%). The order of impact of human activity on runoff change was summer (41.8%), winter (17.8%), autumn (14.7%), spring (3.2%).

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Section: Study Area and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attribution analysis of multi-temporal scale changes of streamflow in the source area of Lancang River with seasonal scale Budyko model

Liu,

Chen,

Zhang

et al. 2023

Front. Ecol. Evol.

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“…Land use / cover change (LUCC) is central to human development and utilization of the natural environment (Shiyao et al, 2022). Landscape pattern change affects the comprehensive geographical factors such as climate (Ying et al, 2022, Xiao-lei et al, 2022, Limin et al, 2022, soil (Jiang et al, 2015, Zahn andErolu, 2019) and water (Bi et al, 2018, Xiaodong et al, 2019, Zhang and Shao, 2020. Additionally, the value of regional ecosystem services are affected (Meng andDong, 2019, Zhou et al, 2019) which threatens the regional ecological environment health and improves the regional risk (Yuxin et al, 2021, Kaisheng andXuejun, 2022).…”

Section: Introductionmentioning

confidence: 99%

Based on ecological risk assessment of landscape from the perspective of Saihanba landscape pattern changes

Kang,

Yang,

Qing

et al. 2024

Preprint

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Examining the Saihanba Mechanical Forest Farm, this study utilized landsat remote sensing data from 1987, 1997, 2001, 2013, and 2020 to interpret land use from the vector machine method, and to decipher evolving land use patterns over the last four decades. Grounded in landscape ecology theory, an innovative evaluation index for landscape ecological risk was introduced, leading to the delineation of 382 ecological risk evaluation units based on the degree of global landscape risk. Employing landscape pattern indices and a method around spatial autocorrelation, we analyzed the spatial and temporal distribution characteristics and spatial correlation patterns of landscape ecological risk across five distinct periods. Geostatistical approaches were used to explore the driving factors of landscape risk. Results indicate significant shifts in land use types since 1987, notably with a evident increase in forest landscape predominantly at the expense of grassland and sandy land. Over the 1987 to 2020 period, landscape risk demonstrated a gradual decline, with mid-high and high-risk areas clustering in specific locales, while the broader region predominantly featured low landscape risk. The landscape ecological risks in each period of the study area showed a positive spatial correlation and tended to gather in space. Since its establishment in 1962, the landscape changes caused by human factors such as afforestation projects and scientific forest management methods have reduced the ecological risk of the landscape. A comprehensive exploration using geographic detectors identified nine ecological driving factors, with soil type emerging as the principal risk determinant, and its synergistic interaction with precipitation and other factors surpassing the individual factor effects.

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“…Land use/cover change (LUCC) is central to human development and utilization of the natural environment [1]. Landscape pattern change affects comprehensive geographical factors such as climate [2][3][4], soil [5,6] and water [7][8][9]. Additionally, the value of regional ecosystem services are affected [10,11] which threatens the regional ecological environment health and increases the regional risk [12,13].…”

Section: Introductionmentioning

confidence: 99%

Landscape Ecological Risk Assessment of Saihanba under the Change in Forest Landscape Pattern

Kang,

Yang,

Qing

et al. 2024

Forests

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Examining the Saihanba Mechanical Forest Farm, this study utilized Landsat remote sensing data from 1987, 1997, 2001, 2013, and 2020 to interpret land use from the Support Vector Machine (SVM) method, and to decipher evolving land use patterns over the last four decades. Grounded in landscape ecology theory, an innovative evaluation index for landscape ecological risk was introduced, leading to the delineation of 382 ecological risk evaluation units. Employing landscape pattern indices and a method of spatial autocorrelation, we analyzed the spatial and temporal distribution characteristics and spatial correlation patterns of landscape ecological risk across five distinct periods. Geostatistical approaches were used to explore the driving factors of landscape risk. The results indicate that since 1987, there have been significant changes in land use types, especially in forest landscapes, their proportion increasing from 23.19% to 74.55%. In 1987, the proportion of high-risk areas was 72.30%, but in 2020, high-risk areas had significantly decreased and clustered in specific locations. The landscape ecological risks in each period of the study area showed a positive spatial correlation and tended to gather in space. After comprehensive exploration using a geographic detector, we found that landscape type, temperature, and vegetation coverage are the main risk factors. Among them, landscape type has the greatest impact on the landscape and works together with slope, aspect, and precipitation. In forest farm management, only the adaptation and adjustment of single factors are often paid attention to, while the compound effects of multiple factors are ignored. The results of this study bring important reference value to the operation and development of forest farms.

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Impacts of LUCC and climate change on runoff in Lancang River Basin

Cited by 4 publications

References 0 publications

Attribution analysis of multi-temporal scale changes of streamflow in the source area of Lancang River with seasonal scale Budyko model

Attribution analysis of multi-temporal scale changes of streamflow in the source area of Lancang River with seasonal scale Budyko model

Based on ecological risk assessment of landscape from the perspective of Saihanba landscape pattern changes

Landscape Ecological Risk Assessment of Saihanba under the Change in Forest Landscape Pattern

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