Multi-Scenario Land Use and Carbon Storage Assessment in the Yellow River Delta Under Climate Change and Resource Development
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area Overview
2.2. Data Sources and Preprocessing
3. Research Methodology
3.1. Comprehensive Prediction of Seawater Inundation Risk
3.1.1. Active Flooding Algorithm
3.1.2. SBAS-InSAR (Small Baseline Subset-InSAR) LS Monitoring
3.2. PLUS Model for LULCC Simulation
3.2.1. Model Principle
3.2.2. Parameter Setting
- 1.
- Neighborhood weights
- 2.
- Transfer cost matrix
3.3. InVEST Model for Carbon Storage Assessment
3.4. Multi-Scenario Setting
- (1)
- NDS (Natural Development Scenario): This scenario is based on the SSP2–45 pathway, representing a moderate socio-economic development trajectory and emissions model. It assumes that social and economic policies remain stable, with no active interventions to influence future land use changes. LULCC is expected to follow the trends and characteristics observed from 2000 to 2010 (Table S3). The future LULC structure is forecasted based on historical transition probabilities, without any restrictions on land type conversion. Under this scenario, the YRD will maintain its current rate of LS, and SLR is projected to reach 15.90 cm by 2030 and 20.46 cm by 2060.
- (2)
- EPS (Ecological Protection Scenario): This scenario is based on the SSP1–26 pathway, which represents a sustainable, green development trajectory characterized by low levels of socio-economic development and emissions. Aligned with the national “dual carbon” targets and the Paris Agreement, this scenario emphasizes the limitation of carbon emissions. The principle of ecological priority is central to this scenario, imposing strict restrictions on the conversion of woodlands and water bodies (including rivers, lakes, reservoirs and ponds, tidal flats, and wetlands) into construction lands, cultivated land (paddy fields, drylands), or unused lands. However, other land types may be converted to woodlands, grasslands, or water bodies (Table S4). In this scenario, ecological nodes are also protected. As a result, the LS rate is expected to decrease to 50% of its current rate, and SLR is projected to reach 13.74 cm by 2030 and 16.68 cm by 2060 under this climate model.
- (3)
- EGS (Economic Growth Scenario): This scenario is based on the SSP5–85 pathway, representing a model of the highest socio-economic development and emissions. In this scenario, the primary focus is on regional economic growth and infrastructure development, with no policy interventions to limit land use changes. Given the current trends in land use transitions and urban expansion, the likelihood of converting other land types into construction lands is increased, while the reverse conversion of construction lands is heavily restricted (Table S5). Under this scenario, the LS rate in the study area is expected to rise to 150% of its current level. The impact of carbon emissions on SLR will be most pronounced, with a projected SLR of 15.51 cm by 2030 and 22.41 cm by 2060.
4. Results and Discussion
4.1. LULCC Characteristics from 1990 to 2020
4.1.1. Spatiotemporal Evolution of LULC
4.1.2. LULCC and Transfer Characteristics
4.2. Carbon Storage Changes from 1990 to 2020
4.2.1. Spatial and Temporal Distribution of Carbon Storage
4.2.2. Response of Carbon Storage to LULCC
4.3. Multi-Scenario Prediction of LULC and Carbon Storage Based on Seawater Inundation Model
4.3.1. Multi-Scenario Seawater Inundation Prediction
4.3.2. Temporal and Spatial Changes of LULC and Carbon Storage Under Multiple Scenarios
5. Conclusions
- (1)
- From 1990 to 2020, land use in the YRD showed a clear trend of coastal expansion and production-oriented transformation, with grasslands, wetlands, and unused lands extensively converted into drylands, culture areas, construction lands, and salt pans. The most intensive changes occurred between 2000 and 2010, reflecting rapid urbanization and industrial restructuring. Despite significant reductions in ecological land, 84.08% of drylands remained stable, underscoring their dominance. These patterns highlight the persistent trade-off between economic development and ecological preservation, emphasizing the need for more balanced land management strategies;
- (2)
- From 1990 to 2020, carbon storage in the YRD declined overall, with pronounced spatial heterogeneity. Low-carbon densities were concentrated in coastal areas dominated by construction lands, tidal flats, salt pans, and culture areas, while inland grasslands, paddy fields, and woodlands served as key carbon sinks. The region experienced a net carbon loss of 2.22 × 106 t, largely driven by the conversion of high-carbon ecosystems to low-carbon land uses. Carbon sink areas accounted for only 10.78% of the landscape, highlighting the profound impact of LULCC on carbon dynamics and the urgent need for targeted land management and ecological restoration to enhance long-term sequestration;
- (3)
- By integrating IPCC AR6 climate scenarios (SSP1–26, SSP2–45, SSP5–85) with LS monitoring data, this study conducted a multi-scenario assessment of seawater inundation risk in the YRD under future SLR-LS coupling. The results revealed significant spatial heterogeneity in LS, with low-lying areas experiencing severe subsidence facing the highest inundation risk. From the EPS to the EGS, inundation progressively expanded into zones with intensified LS. By 2030 and 2060, under high-emission and development scenarios, carbon-rich ecosystems such as wetlands and grasslands are projected to face compounded threats from both inundation and accelerated development, leading to substantial carbon loss and ecological degradation. These findings highlight the urgent need to regulate resource extraction and enhance coastal protection planning to mitigate future risks.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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---|---|---|---|---|
Remote sensing | Land use | 1990, 2000, 2010, 2020 | https://www.resdc.cn/, accessed on 25 September 2024 | Raster |
Sentinel-1A | 2020–2024 | https://dataspace.copernicus.eu/, accessed on 24 September 2024 | ||
Landsat 8 OLI_TIRS | 2021 | https://www.gscloud.cn/home, accessed on 24 September 2024 | ||
Natural environment | DEM | 2020 | https://www.nasa.gov/, accessed on 24 September 2024 | Raster |
Slope | Obtained by DEM calculation | |||
Average annual temperature | https://www.resdc.cn/, accessed on 25 September 2024 | |||
Average annual precipitation | ||||
Soil type | Unknown | |||
Socio-economic | Population density | 2020 | https://www.resdc.cn/, accessed on 25 September 2024 | Raster |
GDP | ||||
Location conditions | Distance to protected area | 2020 | https://www.webmap.cn/, accessed on 26 September 2024 | Vector |
Distance to water | ||||
Distance to city | ||||
Distance to railway | ||||
Distance to highway | ||||
Distance to national highway | ||||
Distance to provincial highway | ||||
Distance to county highway | ||||
Others | SLR | 2030, 2060 | [46] | Statistics |
Carbon density | 2010 | https://www.nesdc.org.cn/, accessed on 27 September 2024 |
Land Use Type | Aboveground Carbon Density | Underground Carbon Density | Soil Carbon Density | Dead Organic Carbon Density |
---|---|---|---|---|
PF | 8.5 | 5 | 25.5 | 0.3 |
DR | 10 | 3.3 | 17.8 | 0.3 |
WO | 34.2 | 7.4 | 19.2 | 2.8 |
GR | 14.3 | 6.2 | 15.7 | 1.5 |
RC | 1.25 | 0.4 | 20 | 0 |
LA | 1.25 | 0.4 | 25 | 0 |
RP | 0.8 | 0.4 | 27 | 0 |
BO | 1.5 | 0.5 | 15 | 0 |
WE | 2 | 0.6 | 16.6 | 0.1 |
TF | 0 | 0 | 16 | 0 |
SP | 0 | 0 | 12.8 | 0 |
CA | 0.5 | 0.1 | 12 | 0 |
CL | 0 | 0 | 14.4 | 0 |
UL | 0 | 0 | 17.7 | 0 |
Land Use Type | PF | DR | WO | GR | RC | LA | RP | BO | WE | TF | SP | CA | CL | UL |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PF | 22.89 | 114.19 | 0 | 0.01 | 0.03 | 0.04 | 4.91 | 0 | 0 | 0 | 1.42 | 0.01 | 38.47 | 0 |
DR | 67.62 | 1933.87 | 0.01 | 9.41 | 3.26 | 0.03 | 62.34 | 0.25 | 14.16 | 0.03 | 6.95 | 24.89 | 177.09 | 0 |
WO | 0.15 | 10.58 | 0.47 | 0 | 0.09 | 0 | 0.16 | 0.03 | 0 | 0 | 0.27 | 0 | 2.12 | 0 |
GR | 8.19 | 385.72 | 8.06 | 73.17 | 3.25 | 0 | 73.99 | 0.45 | 110.86 | 133.68 | 81.10 | 172.37 | 118.46 | 0.07 |
RC | 0.06 | 8.48 | 0.04 | 0.55 | 85.40 | 0 | 0.08 | 14.86 | 0.08 | 11.02 | 0.02 | 0.54 | 1.08 | 0 |
LA | 0 | 0.66 | 0 | 0.34 | 0 | 1.10 | 0.83 | 0 | 0.11 | 0 | 0 | 0.15 | 0.45 | 0 |
RP | 8.28 | 74.29 | 0 | 0.55 | 0.25 | 0 | 110.72 | 1.16 | 10.52 | 0.97 | 39.14 | 130.73 | 35.33 | 0 |
BO | 0 | 8.32 | 0 | 0 | 1.37 | 0 | 0.35 | 4.89 | 0.06 | 0.20 | 0.04 | 0.01 | 0.05 | 0 |
WE | 14.45 | 260.39 | 0 | 16.52 | 1.11 | 0 | 15.96 | 3.91 | 41.01 | 58.01 | 69.11 | 182.82 | 100.16 | 1.20 |
TF | 0 | 0 | 0 | 5.74 | 2.55 | 0 | 0.26 | 11.92 | 0.02 | 313.36 | 0.33 | 7.90 | 1.78 | 0.27 |
SP | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
CA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
CL | 0.96 | 89.77 | 0 | 4.10 | 0.22 | 0.03 | 3.32 | 0.03 | 8.08 | 12.05 | 94.59 | 2.70 | 441.50 | 0 |
UL | 0.04 | 22.43 | 0 | 0.25 | 0.09 | 0 | 10.89 | 11.57 | 0 | 34.35 | 13.90 | 52.18 | 55.78 | 0.60 |
Scenario Simulation | EPS | NDS | EGS | |||
---|---|---|---|---|---|---|
Year | 2030 | 2060 | 2030 | 2060 | 2030 | 2060 |
Height of SLR (cm) | 13.74 | 16.68 | 15.90 | 20.46 | 15.51 | 22.41 |
Inundation area (km2) | 68.93 | 478.78 | 95.30 | 571.80 | 428.90 | 590.52 |
Land area (km2) | 6227.64 | 5817.80 | 6201.27 | 5724.78 | 5867.67 | 5706.05 |
Lost carbon storage (×106 t) | 0.17 | 1.35 | 0.24 | 1.58 | 1.23 | 1.73 |
Remaining carbon storage (×106 t) | 14.43 | 13.26 | 14.34 | 12.88 | 14.24 | 13.73 |
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Wang, Z.; Liu, X.; Zhang, S.; Meng, X.; Zhang, H.; Guo, X. Multi-Scenario Land Use and Carbon Storage Assessment in the Yellow River Delta Under Climate Change and Resource Development. Remote Sens. 2025, 17, 1603. https://doi.org/10.3390/rs17091603
Wang Z, Liu X, Zhang S, Meng X, Zhang H, Guo X. Multi-Scenario Land Use and Carbon Storage Assessment in the Yellow River Delta Under Climate Change and Resource Development. Remote Sensing. 2025; 17(9):1603. https://doi.org/10.3390/rs17091603
Chicago/Turabian StyleWang, Zekun, Xiaolei Liu, Shaopeng Zhang, Xiangshuai Meng, Hongjun Zhang, and Xingsen Guo. 2025. "Multi-Scenario Land Use and Carbon Storage Assessment in the Yellow River Delta Under Climate Change and Resource Development" Remote Sensing 17, no. 9: 1603. https://doi.org/10.3390/rs17091603
APA StyleWang, Z., Liu, X., Zhang, S., Meng, X., Zhang, H., & Guo, X. (2025). Multi-Scenario Land Use and Carbon Storage Assessment in the Yellow River Delta Under Climate Change and Resource Development. Remote Sensing, 17(9), 1603. https://doi.org/10.3390/rs17091603