Assessing the impacts of Grain-for-Green Programme on ecosystem services in Jinghe River basin, China

Xu, Chao; Jiang, Yanan; Su, Zhenhui; Liu, Yujun; Lyu, Jingyu

doi:10.1016/j.ecolind.2022.108757

Cited by 28 publications

(23 citation statements)

References 35 publications

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“…This study reveals a declining trend in the mean values of hydrological and sediment connectivity in the Jinghe River Basin from 2005 to 2020, registering a decrease from −5.47 to −5.58 and −9.74 to −10.05, respectively. This result is consistent with that of Xu et al (2022), who discovered a year‐on‐year reduction in soil loss from the Jinghe River from 2000 to 2015. A sustained multiyear decline in connectivity is a reality, and it lowers the risk of soil erosion (Tian et al, 2023), although the influencing factors of this change are diverse.…”

Section: Discussionsupporting

confidence: 93%

“…The Loess Plateau's characteristics are particularly reflected in the Jinghe River Basin in two significant ways: (a) The Jinghe River Basin is located in the middle of the Loess Plateau, with various topographic features such as hills, gullies, plains, and mountains. (b) The basin has withstood severe soil erosion over many years and presently boasts well‐recovered vegetation, making it a typical basin of the Loess Plateau (Xu & Pan, 2022; Xu et al, 2022). Therefore, many researchers have explored the Jinghe River Basin and radiated it to the whole Loess Plateau in terms of the runoff, sediment, and vegetation (Lyu et al, 2023; Zhao et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Elucidating the response mechanisms of hydrological and sediment connectivity to the river network structure, vegetation, and topographic features in the Jinghe River Basin

Liu,

Wu,

Guo

et al. 2024

Land Degrad Dev

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The management of basin water and soil resources greatly benefits from investigating the spatial changes and primary determinants of hydrological and sediment connectivity from perspectives of topographic features, vegetation characteristics, structural features of river networks. However, quantifying the effects of influencing factors and their interactions on connectivity is still a challenge in the field of studies of surface processes. To address this challenge, we applied the geographical detector model (GDM) and random forest (RF) to quantify the relative importance and explanatory power of topographic factors, vegetation features, and river network structure on the hydrological connectivity and sediment connectivity and to clarify the interaction mechanism of various factors in the Jinghe River Basin. Results indicate that from 2005 to 2020, the mean value of hydrological connectivity (ICH) witnessed a decreasing trend from −5.47 to −5.58. Similarly, the mean value of sediment connectivity (ICS) declined over the same period from −9.74 to −10.05, with high values registered in valleys and low values in plains. The river density (D) and hydrological weight factor (normalized SCS‐CN) exhibited a greater spatial explanatory power on ICH than other parameters, reaching 0.149 and 0.182, respectively. The vegetation factor (0.299) and sediment weight factor (0.410) manifested considerably more influence over ICS. Spatial integration between relevant elements can enhance our understanding of basin connectivity. Interactions between vegetation, topography, and river network structure provide more explanatory power compared to interactions among same‐type features. Ultimately, this study offers a theoretical template for understanding the management of soil and water resources in the Jinghe River Basin, as well as the spatial variability in soil erosion.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Elucidating the response mechanisms of hydrological and sediment connectivity to the river network structure, vegetation, and topographic features in the Jinghe River Basin

Liu,

Wu,

Guo

et al. 2024

Land Degrad Dev

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“…Unlike most current studies that directly use a single classification, such as natural breakpoints for data discretization studies [13,62], this study used OPGD to implement a combination of methods for optimal data discretization (Figure 12). Based on the highest q-value parameter combination (Table S3), the quantile method was used to optimally classify elevation, temperature, GDP, the density of other public facilities services, distance from major roads, distance from national roads, distance from railroads, and distance from water systems.…”

Section: Drivers Of the Spatial Variation In Carbon Storagementioning

confidence: 99%

“…The InVEST model (integrated valuation of ecosystem services and tradeoffs), jointly developed by Stanford University, WWF, and The Nature Conservancy, has the advantages of an open-source nature and low-input data volume. This model is used in watersheds [12,13], urban clusters [14], regions [15,16], high-vegetation-density areas [17], and sea islands [18]. It has been widely used.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation and Quantitative Attribution of Carbon Storage Based on Multiple Satellite Data and a Coupled Model for Jinan City, China

Lu,

Xue,

Zhang

et al. 2023

Remote Sensing

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Rapidly predicting and revealing the spatiotemporal characteristics and driving factors of land-use changes in carbon storage within megacities under different scenarios is crucial to achieving sustainable development. In this study, Jinan City (JNC) is taken as the study area, and the Markov-FLUS-InVEST model is utilized to predict and analyze the spatiotemporal variation in carbon storage in 2030 under three scenarios, namely, the natural development scenario (S1), the ecological conservation scenario (S2), and the economic development scenario (S3). The drivers of carbon storage changes were identified using an optimal parameter-based geographic detection (OPGD) model. The findings indicate that (1) land use from 2010 to 2018 shows a trend of continuous expansion of construction land and reduction in arable land. (2) The main types of carbon pools were cropland, forest, and grassland, accounting for more than 96% of the total amount. Carbon storage showed a decreasing trend from 2010 to 2018, and the main type of carbon pool that decreased was cropland. The center of gravity of carbon storage increases and decreases was located in the southern Lixia District, and the center of gravity of increase and decrease moved to the southwest by 3057.48 m and 1478.57 m, respectively. (3) From 2018 to 2030, the reductions in carbon stocks were 3.20 × 106 t (S1), 2.60 × 106 t (S2), and 4.26 × 106 t (S3), and the carbon release was about 9 times (S1), 4 times (S2), and 10 times (S3) that of the carbon sink. (4) The contribution of slope (A2) ∩ nighttime light index (B6) and elevation (A1) ∩ nighttime light index (B6) to the regional heterogeneity of carbon stocks was the largest among the interaction drivers. To sum up, this study deepens the simulation of spatial and temporal dynamics of carbon storage under land-use changes in megacities and the related driving mechanism, which can provide the basis for scientific decision-making for cities to conduct territorial spatial planning and ecological protection and restoration.

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“…Climate variation, accompanied by temperature increments and extreme precipitation, have fundamentally affected the state of water resources (Capo et al, 2018;Li and Wang, 2021;Ouyang, 2021). Additionally, human activity has greatly changed the properties of water yield and consumption through underlying surface construction (Hu et al, 2021;Li et al, 2022a;Xu et al, 2022). In the context of climate warming and intensified human activity, it is critical to analyze the spatio-temporal evolution of the water yield coefficient and identify driving factors in development of watershed and water-land resources.…”

Section: Introductionmentioning

confidence: 99%

Evolution and attribution of the water yield coefficient in the Yiluo river basin

Hou

Yan

Qin

et al. 2022

Front. Environ. Sci.

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Our aim in this research was to detect historical and future water yield coefficient evolution and attribution. Based on the calibrated and validated water yield coefficient model in the Yiluo River Basin, the coefficient for the years 2000–2020 was simulated, along with the future projection for 2030–2050 under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). The spatio-temporal evolution of historical and future water yield coefficients was then analyzed. Moreover, the geographical detector model was used to detect the impacts of climate, land use, and terrain factors on the water yield coefficient. The results showed that the water yield coefficient increased by 8.53% from 2000 to 2020, with the coefficient of farmland increasing by 10.47% and that of forestland decreasing by 8.93%. The coefficient was highest under the SSP370 scenario and the lowest under the SSP585 scenario in projections for 2030–2050. Compared to 2000–2020, the coefficients of the two scenarios increased by 12.2% and 2.0%, respectively. Consequently, under the SSP370 and SSP585 scenarios, the coefficient of farmland increased by 13.2% and 2.7%, and that of the forestland decreased by 0.9% and 14.6%, respectively. Driving factors detection indicated that land use types had the strongest explanatory power affecting the water yield coefficient; the explanatory value reached 26.5% in 2000–2020 and will exceed 29.5% in 2030–2050. In addition, the interaction between any two factors was stronger than a single factor. This research provides scientific support for the precise management of watershed and water-land resources.

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Assessing the impacts of Grain-for-Green Programme on ecosystem services in Jinghe River basin, China

Cited by 28 publications

References 35 publications

Elucidating the response mechanisms of hydrological and sediment connectivity to the river network structure, vegetation, and topographic features in the Jinghe River Basin

Elucidating the response mechanisms of hydrological and sediment connectivity to the river network structure, vegetation, and topographic features in the Jinghe River Basin

Spatiotemporal Variation and Quantitative Attribution of Carbon Storage Based on Multiple Satellite Data and a Coupled Model for Jinan City, China

Evolution and attribution of the water yield coefficient in the Yiluo river basin

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