Ecosystem Services Model Research Articles

Spatiotemporal Assessment of Habitat Quality in Sicily, Italy

We measured the spatiotemporal dynamics of habitat quality (HQ) in Sicily in two different reference years, 2018 and 2050, assuming a business-as-usual scenario. To estimate HQ and related vulnerability, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Habitat Quality model and data on land use/land cover provided by the Esri Land Cover 2050 project. We also implemented a Coarse–Filter approach to validate the reliability of HQ measures and detect biodiversity hotspots that require priority conservation. Further, we used spatial statistic tools for identifying clusters or hotspot/coldspot areas and uncovering spatial autocorrelation in HQ values. Finally, we implemented a geographically weighted regression (GWR) model for explaining local variations in the effects on HQ estimates. The findings reveal that HQ in Sicily varies across space and time. The highest HQ values occur in protected areas and forests. In 2018, the average HQ value was higher than it was in 2050. On average, HQ decreased from 0.29 in 2018 to 0.25 in 2050. This slight decline was mainly due to an increase in crop and urbanized areas at the expense of forests, grasslands, and bare lands. We found the existence of a positive spatial autocorrelation in HQ, demonstrating that areas with higher or lower HQ tend to be clustered, and that clusters come into contact randomly more often in 2050 than in 2018, as the overall spatial autocorrelation moved from 0.28 in 2018 to 1.30 in 2050. The estimated GWR model revealed the sign and the significance effect of population density, compass exposure, average temperature, and patch richness on HQ at a local level, and that such effects vary either in space and time or in significance level. Across all variables, the spatial extent of significant effects intensifies, signaling stronger localized influences in 2050. The overall findings of the study provide useful insights for making informed decisions about conservation and land planning and management in Sicily.

Ecological risk assessment and response prediction caused by LUCC in the central Yunnan Province

With the acceleration of urbanization, unreasonable land use poses a serious threat to ecological security. However, there is still some space for improvement in the existing assessment of ecological risks (ERs) caused by land use/land cover change (LUCC). Therefore, this study takes the central Yunnan Province (CYP) as an example, and uses the Patch-Generating Land Use Simulation (PLUS) model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to simulate the land use/land cover (LULC) in different scenarios in the future, calculate the ecosystem services (ESs) from 2000 to 2020 and the next 20 years, as well as ERs of various types of ESs caused by LUCC. In addition, the Pearson correlation coefficient and geographically weighted regression (GWR) are used to analyze the trade-off and synergy relationships between various ecological risks, and the geographic detector method (GDM) is used to analyze their driving factors. The research results indicate that: (1) there have been significant changes in LULC in the CYP in the past 20 years. (2) There was significant spatial heterogeneity among various types of ESs in the CYP. (3) In the past 20 years, the distribution range of ERs has been relatively large, with significant spatial differences. (4) The ERs caused by LUCC in the next 20 years generally show a decreasing trend, and the ecological high-risk areas are mainly concentrated on construction land. (5) The ERs associated with ecosystem service types are mainly synergistic. ER is influenced by a combination of natural factors, socio-economic factors, and distance factors, with some factors having strong interactions. In short, the future ERs of CYP have decreased, and the ERs corresponding to various ecosystem services (ESs) interact with each other, causing a ripple effect. Finally, this study proposes policy recommendations to alleviate ERs in the CYP.

Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing

Analyzing the current trends and causes of carbon storage changes and accurately predicting future land use and carbon storage changes under different climate scenarios is crucial for regional land use decision-making and carbon management. This study focuses on Beijing as its study area and introduces a framework that combines the Markov model, the Patch-based Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess carbon storage at the sub-district level. This framework allows for a systematic analysis of land use and carbon storage spatiotemporal evolution in Beijing from 2000 to 2020, including the influence of driving factors on carbon storage. Moreover, it enables the simulation and prediction of land use and carbon storage changes in Beijing from 2025 to 2040 under various scenarios. The results show the following: (1) From 2000 to 2020, the overall land use change in Beijing showed a trend of “Significant decrease in cropland area; Forest increase gradually; Shrub and grassland area increase first and then decrease; Decrease and then increase in water; Impervious expands in a large scale”. (2) From 2000 to 2020, the carbon storage in Beijing showed a “decrease-increase” fluctuation, with an overall decrease of 1.3 Tg. In future carbon storage prediction, the ecological protection scenario will contribute to achieving the goals of carbon peak and carbon neutrality. (3) Among the various driving factors, slope has the strongest impact on the overall carbon storage in Beijing, followed by Human Activity Intensity (HAI) and Nighttime Light Data (NTL). In the analysis of carbon storage in the built-up areas, it was found that HAI and DEM (Digital Elevation Model) have the strongest effect, followed by NTL and Fractional Vegetation Cover (FVC). The findings from this study offer valuable insights for the sustainable advancement of ecological conservation and urban development in Beijing.

Assessment of Pluvial Flood Mitigation Ecosystem Service in a Riverside City Using the Integrated Valuation of Ecosystem Services and Tradeoffs Model for Ecological Corridor Mapping

Assessment of Pluvial Flood Mitigation Ecosystem Service in a Riverside City Using the Integrated Valuation of Ecosystem Services and Tradeoffs Model for Ecological Corridor Mapping

Carbon Storage Response to Land Use/Land Cover Changes and SSP-RCP Scenarios Simulation: A Case Study in Yunnan Province, China.

Changes in terrestrial ecosystem carbon storage (CS) affect the global carbon cycle, thereby influencing global climate change. Land use/land cover (LULC) shifts are key drivers of CS changes, making it crucial to predict their impact on CS for low-carbon development. Most studies model future LULC by adjusting change proportions, leading to overly subjective simulations. We integrated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, the Patch-generating Land Use Simulation (PLUS) model, and the Land Use Harmonization 2 (LUH2) dataset to simulate future LULC in Yunnan under different SSP-RCP scenarios of climate and economic development. Within the new PLUS-InVEST-LUH2 framework, we systematically analyzed LULC alterations and their effects on CS from 1980 to 2040. Results demonstrated that: (1) Forestland had the highest CS, whereas built-up land and water showed minimal levels. Western areas boast higher CS, while the east has lower. From 1980 to 2020, CS continuously decreased by 29.55 Tg. In the wake of population increase and economic advancement, the area of built-up land expanded by 2.75 times. Built-up land encroaches on other land categories and is a key cause of the reduction in CS. (2) From 2020 to 2040, mainly due to an increase in forestland, CS rose to 3934.65 Tg under the SSP1-2.6 scenario, whereas under the SSP2-4.5 scenario, primarily due to a reduction in forestland and grassland areas, CS declined to 3800.86 Tg. (3) Forestland is the primary contributor to CS, whereas the ongoing enlargement of built-up land is causing a sustained decline in CS. Scenario simulations indicate that future LULC changes under different scenarios will have a significant impact on CS in Yunnan. Under a green sustainable development pathway, Yunnan can exhibit significant carbon sink potential. Overall, this research offers a scientific reference for optimizing land management and sustainable development in Yunnan, aiding China's "double carbon" goals.

Integrating river transport processes and seasonal dynamics to assess watershed nitrogen export risk.

Excessive nitrogen exported to water bodies affects the balance of ecosystem and poses a threat to human health. Although the concept of water purification service helps quantify nitrogen export, the impact of river transport remains unclear. This study focused on nitrogen as a pollutant by utilizing the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess nitrogen export in the Dongting Lake Basin, taking into account both the processes of sub-basin nitrogen export and river transport. Additionally, the monthly variations within the year were further explored, together with the identification of the priority area of returning cropland to forest land. The results showed that in 2021, the total nitrogen load outside the Dongting Lake was 11.34×108 kg·year-1, with the sub-basins retaining a total of 9.02×108 kg·year-1, accounting for 79.57% of the total nitrogen load. Notably, the total nitrogen retention by the river made up 16.87% of the total nitrogen retention, up to 1.83×108 kg·year-1. In view of monthly variation, water purification service based on the entire process was higher in winter and late autumn, and lower in summer and early autumn. The priority areas for ecological project were mainly distributed around the Dongting Lake, achieving an improvement of 58.15% in water purification service with approximately 7.02% of the total returnable cropland. This study proposed a new approach of water purification service assessment through integrating river transport processes and seasonal dynamics, aiming to assess watershed nitrogen export risk more accurately.

Spatial Configuration and Sustainable Conservation of Ecotourism Resources in the Dabie Mountains, Eastern China, Using an Ecosystem Services Model

Given the growing interest in sustainable tourism and the need to preserve ecological integrity, understanding how tourism resource development can coexist with biodiversity conservation is crucial, especially in ecologically sensitive regions. The Dabie Mountain region, with its rich biodiversity and cultural heritage, is a prime area for exploring the balance between tourism and environmental protection. Our study examined the spatial configuration of ecotourism resources in the Dabie Mountains, focusing on the role of ecosystem services, biodiversity hotspots, and traditional villages. Using the InVEST model, kernel density analysis, and a geographic detector, we found that the study area primarily consisted of lower-habitat-quality regions. Approximately 48,914.05 km2 (96.82% of the total area) was classified as low-quality habitat, presenting potential risks to biodiversity and ecosystem services in these regions. Higher habitat quality was observed in the eastern and southern regions, particularly in Yue Xi County, which exhibited superior biodiversity and habitat conditions compared to other administrative areas. Class V habitats, covering 39.89 km2 (31.69% of Class V area), were located in areas with greater terrain relief and proximity to water bodies, which contribute to higher biodiversity. This study identified a potential ecotourism development space of 649.79 km2, with a spatial distribution aligned with natural landscape features that support biodiversity conservation. Yue Xi and Jinzhai together accounted for 49.84% of the total ecotourism development space. Our study emphasizes the need to balance biodiversity conservation with tourism development, ensuring that ecotourism goals are met while preserving ecological integrity and cultural heritage. These findings provide a strong foundation for future research on biodiversity conservation in tourism development and offer insights for achieving sustainable ecological management in the region.

Urban ecosystem services, ecological security patterns and ecological resilience in coastal cities: The impact of land reclamation in Macao SAR.

Land reclamation from the sea is a common practice to create territorial space and accommodate urbanisation in coastal cities. However, previous studies did not adequately examine the changes in ecosystem service values, the spatial transformation of the ecological network and the ecological resilience at an urban scale in the context of land reclamation. This study uses Macao SAR, a fully urbanised city with two-thirds of its land reclaimed from the sea, as a case study. We quantify urban ecosystem services (UES) values and model the spatial transformation of the ecological security patterns (ESPs) throughout the city's land reclamation history from 2000 to 2020. Subsequently, we evaluate the resilience of the ESPs' components and their relationships with urbanisation by using spatial element elasticity indices and regression analysis. Results show that urban land expanded by 27% through land reclamation, increasing the value of UES by 13%. The growth rates of different categories of UES vary significantly. ESPs extended over the newly reclaimed and urbanised lands, but the resilience levels of ecological sources, corridors and nodes decreased. There is a strong positive correlation between UES values and urbanisation, and a strong negative correlation between ecological resilience and urbanisation. This study recommends five specific environmental spatial planning and management strategies to address the expansion and integration of ESPs, aiming not only to provide ecological services but also to enhance ecological resilience in coastal cities.

Assessment of dynamic water yield using multi scenario of LULC in Cisadane Watershed West Java, Indonesia

Uncontrolled economic development often leads to land degradation, a decline in ecosystem services, and negative impacts on community welfare. This study employs water yield (WY) modeling as a method for environmental management, aiming to provide a comprehensive understanding of the relationship between Land Use Land Cover (LULC), Land Use Intensity (LUI), and WY to support sustainable natural resource management in the Cisadane Watershed, Indonesia. The objectives include: (1) analyzing changes in WY for 2010, 2015, and 2021; (2) predicting WY for 2030 and 2050 under two scenarios—Business as Usual (BAU) and Protected Forest Area (PFA); (3) assessing the impacts of LULC and climate change on WY; and (4) exploring the relationship between LUI and WY. The Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model calculates actual and predicted WY conditions, while the Coupling Coordination Degree (CCD) analyzes the LULC-WY relationship. Results indicate that the annual WY in 2021 was 215.8 × 108 m³, reflecting a 30.42% increase from 2010. Predictions show an increasing trend in WY under both scenarios for 2030 and 2050 with different magnitudes. Rainfall contributes 88.99% more dominantly to WY than LULC. Additionally, around 50% of districts exhibited unbalanced coordination between LUI and WY in 2010 and 2020. This study reveals the importance of ESs in sustainable watershed management amidst increasing demand for natural resources due to population growth.

Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern Iran.

This study aimed to integrate game theory and deep learning algorithms with the InVEST Ecosystem Services Model (IESM) for Sediment Retention (SR) modeling in the Kasilian watershed, Iran. The Kasilian watershed is characterized by multiple sub-watersheds, which vary in their environmental conditions and SR potential, with a total of 19 sub-watersheds. The research was carried out in four phases: mapping SR using the IESM, implementing the Fallback bargaining algorithm based on game theory, applying deep learning algorithms (CNN, LSTM, RNN), and performing statistical analysis for optimal model selection. Based on the results, the analysis of geo-environmental criteria indicated that sub-watersheds with poor conditions regarding rain erosivity, soil erodibility, LS, elevation, and land use faced greater challenges in SR. Utilizing the Fallback bargaining algorithm for sub-watershed prioritization revealed that sub-watershed 5 emerged as having the highest SR potential due to high rain erosivity and a significant LS factor. Spatial SR mapping via game theory algorithm demonstrated that northern sub-watersheds in the Kasilian watershed had greater SR potential. Deep learning algorithms were also utilized for SR distribution modeling, where the RNN model was deemed optimal, yielding error statistics of MAE: 0.05, MSE: 0.04, R2: 0.79, RMSE: 0.20, and AUC: 0.97. The SR distribution patterns demonstrated that RNN and LSTM algorithms exhibited similar classification outcomes, differing from those of the CNN algorithm. The prioritization of sub-watersheds using various approaches revealed that the Fallback bargaining algorithm showed a 47% similarity with the InVEST model results. In contrast, deep learning models such as CNN, LSTM, and ARANN exhibited 84%, 79%, and 79% similarity, respectively. These findings supported SR zonation maps, reinforcing that deep learning models outperformed the game theory algorithm. The Alpha Diversity Indices (ADI) confirmed that the outputs from the LSTM and RNN models showed identical changes across all indices. Minimal variations in the other approaches suggested that all five methods yielded similar results based on diversity indices (including Taxa, Dominance, Simpson, and Equitability), indicating no significant differences among them when compared to the InVEST model in sediment modeling.

Economic valuation of forest conservation: insight from a current systematic literature review

Forest conservation is vital for biodiversity, climate regulation and livelihoods. However, valuing its economic benefits is challenging. This review assesses and synthesizes current methodologies and findings on the economic valuation of forest conservation, focusing on non-market benefits and diverse valuation approaches. The study was reviewed from 2018 to 2023 using Scopus, Web of Science (WoS), and EconBiz, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Analyzing 37 articles, three themes were identified: Community and Socioeconomic Aspects (15 articles), Economic Valuation Techniques (16 articles), and Policy Implications (6 articles). Contingent valuation and choice modelling were found to be predominant, especially in tropical forests of developing countries, where higher WTP values than other regions highlight their importance in supporting conservation efforts. Though challenges such as data disparities and limitations persist, recent advancements, such as the use of remote sensing and ecosystem service modelling, have improved the precision of forest conservation valuations. Standardized methodologies and better data integration are required to improve the accuracy and comparability of economic valuations, ensuring more reliable data for policy-making and resource allocation in forest conservation. This review aids researchers, policymakers, and practitioners in enhancing forest conservation strategies through robust economic valuation.

Optimization of ecological network to improve water conservation services in the Nianchu River Basin

Against the backdrop of water resource depletion in the Qinghai-Tibet Plateau, water conservation services (WCS) have emerged as critical ecosystem services warranting attention. Adjusting the ecological network (EN) structure and optimizing topological relationships among landscape elements are instrumental in facilitating ecological processes, constituting an effective approach for enhancing WCS. We employed the Integrated Valuation of Ecosystem Services and Tradeoffs model to simulate the WCS value in the Nianchu River Basin and used the Minimum Cumulative Resistance model in combination with the Linkage Mapper to construct an EN. Through topological analysis based on complex network theory, we examined the EN topology and identified ecological pinch points and barriers by applying circuit theory. According to the correlation between EN topology and WCS, and taking into account local vegetation restoration potential, we ultimately optimized the EN to enhance the WCS value. The results revealed a positive correlation between the WCS value and betweenness centrality (r = 0.86, p < 0.01) of forest patches, as well as with the clustering coefficient of grassland patches (r = 0.44, p < 0.05). Following the optimization of key areas with restoration potential, we enhanced the EN structure by adding six new patches, eliminating four redundant corridors, and establishing 11 new corridors. The optimized EN demonstrated enhanced stability in robustness tests, indicated by a reduced slope of edge recovery against malicious attacks (from −0.0224 to −0.0199) and an increase in the WCS value of 630,311.6 m³. This study highlights the importance of landscape spatial structure in enhancing ecosystem services, providing valuable insights for sustainable water resource management in the ecologically sensitive regions of the Qinghai-Tibet Plateau.

Assessing the Interaction Impacts of Multi-Scenario Land Use and Landscape Pattern on Water Ecosystem Services in the Greater Bay Area by Multi-Model Coupling

Water ecosystem services (WESs) are intrinsically associated with the livelihood of urban residents and are frequently disrupted by human activities. Land use and landscape patterns are key driving factors of alterations in WESs. However, existing research primarily quantifies single-factor influences and often overlooks the interactions between these factors. This study addresses this gap by employing a multi-model coupling approach, integrating the Patch-generating Land Use Simulation (PLUS), Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and Geographical Detector (GD) models alongside various indicators to analyse the evolution of land use, landscape patterns and WESs in the Greater Bay Area from 2000 to 2020, and to simulate spatio-temporal change patterns in different scenarios from 2030 to 2050. Additionally, this study examines the multi-factorial interactions between land use, landscape patterns, and WESs. The results indicate that (1) urbanisation steadily increased, leading to intensified landscape fragmentation, and water yield (WY) and total phosphorus (TP) consistently increased, while total nitrogen (TN) in water gradually decreased; (2) urban areas exerted the most significant impact on WY in the Greater Bay Area while Patch density (PD) had a stronger influence on WY, and Shannon’s diversity index (SHDI) had the most pronounced effect on TN and TP; (3) the interaction between any two land-use types or landscape indices exerted a greater impact on WESs compared with the impact of individual factors alone. The interaction between urban areas and cropland substantially influenced WY (q¯ = 0.634) and most strongly affected TN and TP in water (q¯ = 0.74 and 0.73, respectively). SHDI and PD had the most significant impact on WY in the economic development scenario (q¯ = 0.19) and exhibited the greatest influence on the TN and TP levels in the ecological priority scenario (q¯ = 0.12 and 0.15, respectively). Our findings can provide theoretical and technical support for the integrated scientific planning of regional water ecosystems and the development of comprehensive land use policies in the future.

Resource review: Assessing, mapping and modeling of mangrove ecosystem services in the Asia-Pacific region

Resource review: Assessing, mapping and modeling of mangrove ecosystem services in the Asia-Pacific region

Combined effects of urbanization and climate variability on water and carbon balances in a rice paddy-dominated basin in southern China

Abstract Urbanization is known to elevate storm runoff, but how it influences carbon cycle and ecosystem productivity through altering the evapotranspiration (ET) process is less clear. We examined the combined effects of urbanization including change in impervious surface area (ISA) and climate variability on the water and carbon balances of the Qinhuai River Basin (QRB) over 2001–2018. QRB represents a typical rice paddy-dominated region that experienced rapid urbanization in southern China. We improved a monthly scale water supply stress index ecosystem service model by integrating local eddy flux measurements and high-resolution remote sensing data. We found a significant downward trend in both ET (−4.6 mm yr−1, p &lt; 0.05) and gross primary productivity (GPP) (−10.4 gC m−2 yr−1, p &lt; 0.05) but a significant upward trend in water yield (Q) (+28.6 mm yr−1, p &lt; 0.05). These ecosystem function changes coincided with a 96% increase in urban areas, 1.9-fold increase in ISA, and a 37% reduction in rice paddy fields. The mean annual watershed GPP decreased from 1048 gC m−2 to 998 gC m−2 while the annual Q increased from 284 mm to 669 mm from 2001 to 2018. Scenario modeling experiments suggested that the negative impacts of loss of rice paddy fields and increase in ISA on ET and GPP overwhelmed the positive impacts of climate warming. The reduction in GPP and increase in Q were largely attributed to the increases in ISA, not necessarily due to changes in land use types (e.g. urban area). The expansion of urban area, increase in ISA and reduction in leaf area index, and increase in precipitation explained the increase in Q. Our research offers insight about the interactions of carbon and water cycles through the critical ET processes under a changing climate and land surface characteristics at a watershed level. Our modeling tool and analysis provides land managers and policy makers information for designing effective ‘Urban Nature-based Solutions’ to mitigate the negative environmental effects of urbanization on carbon and water resources.

Significant negative impact of human activities on carbon storage in the Yellow River Delta over the past three decades

With the increasing intensification of human activities, significant changes in land use and land cover (LULC) have posed a severe threat to the carbon storage capacity of wetland ecosystems. A deep understanding of this impact is crucial for protecting regional ecosystems and promoting sustainable development. This study utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the human activity intensity (HAI) index to conduct detailed grid analysis and global analysis of carbon storage through creating fishnet system and explored the complex relationship between carbon storage and HAI in the Yellow River Delta (YRD), China. The results indicated that over the past 30 years, natural wetlands such as meadow wetlands and salt marshes in the study area had undergone significant degradation due to escalating human activities, while artificial wetlands and non-wetland areas expanded. Concurrently, the total regional carbon storage had declined by 2.08 Tg, representing a significant drop of 8.22 % in the YRD from 1990 to 2020. Among them, dry land, as the primary land type, served as the most crucial carbon pool. Additionally, the human activity intensity of land surface (HAILS) increased significantly, with a growth rate of 37.27 %. HAI mapping revealed a continuous expansion of areas with high HAI. In contrast, the Yellow River Delta National Nature Reserve (YRDNNR) maintained relatively low HAI. Correlation analysis further showed the significant negative correlation (p < 0.01) between carbon storage and HAI, with r values of grid analysis ranging from −0.1395 to −0.0334, while that for global analysis was −0.9643, respectively. This reflected the spatial heterogeneity and agglomeration effects of data analysis across different scales. This study provides valuable insights for achieving the “dual carbon” goals and supporting the conservation and management of wetland ecosystems.

Integrated spatial prioritization of urban nature-based solutions for climate adaptation, mitigation, and justice

ABSTRACT In densely populated cities, people confront multiple impacts of climate change, including stormwater runoff, flooding, heat waves, and health issues. To address these impacts, nature-based solutions (NbS) have gained significant attention in recent years due to their potential to contribute to sustainability, resilience, and ecosystem services. Nevertheless, more research is required to understand the wider potential of NbS and to identify priority areas for their deployment. This study developed an analytical approach and implemented it in the Tehran Metropolitan Area (TMA) by considering factors related to NbS co-benefits. The approach included spatial prioritization, spatial correlation analysis, and suitability analysis. It combines a Geographic Information System, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, Zonation, Best-Worst Method, Bivariate Local Indicator of Spatial Autocorrelation analysis (BiLISA), a Fuzzy Inference System, and Boolean overlay analysis. The results include maps showing priority areas for different types of NbS and spatial co-benefits and mismatches. High-priority areas are located in disadvantaged areas. In total, 105.73 km2 (17% of the TMA) was designated as highly prioritized. We show that various NbS interventions are applicable, with green roofs and rainwater harvesting being the most feasible. This study can assist decision-makers in optimizing NbS to deliver maximum co-benefits.

Evaluation and Optimization Model of Green Space Ecosystem Services in Urbanization Process

Evaluation and Optimization Model of Green Space Ecosystem Services in Urbanization Process

Quantifying the Effect of Land Use and Land Cover Changes on Spatial-Temporal Dynamics of Water in Hanjiang River Basin

As a vital part of the geo-environment and water cycle, ecosystem health and human development are dependent on water resources. Water supply and demand are influenced significantly by land use and cover change (LUCC) which shapes the surface ecosystems by altering their structure and function. Under future climate change scenarios, LUCC may greatly impact regional water balance, yet the impact is still not well understood. Therefore, examining the spatial relationship between LUCC and water yield services is crucial for optimizing land resources and informing sustainable development policies. In this study, we focused on the Hanjiang River Basin and used the patch-generating land use simulation (PLUS) model, coupled with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, to assess water yield services under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. For the first time, we considered the impact of future changes in socio-economic and water use indicators on water demand using correction factors and ARIMA projections. The relationship between water supply and demand was explored using this approach, and LUCC’s effects on this balance are also discussed. Results indicate that: (1) The patterns of LUCC are similar for the three scenarios from 2030 to 2050, with varying levels of decrease for cropland and significant growth of built-up areas, with increases of 6.77% to 19.65% (SSP119), 7.66% to 22.65% (SSP245), and 15.88% to 46.69% (SSP585), respectively, in the three scenarios relative to 2020; (2) The future supply and demand trends for the three scenarios of produced water services are similar, and the overall supply and demand risks are all on a downward trend. Water demand continues to decline, and by 2050, the water demand of the 3 scenarios will decrease by 96.275×108t, 81.210×108t, and 84.13×108t relative to 2020, respectively; while supply decreases from 2030 to 2040 and rises from 2040 to 2050; (3) Both water supply and demand distributions exhibit spatial correlation, and the distribution of hotspots is similar. The water supply and demand are well-matched, with an overall supply-demand ratio greater than 1.5; (4) LUCC can either increase or decrease water yield. Built-up land provides more water supply compared to other land types, while forest land has the lowest average water supply. Limiting land use type conversions can enhance the water supply.

Mapping Biodiversity Conservation Priorities for Protected Areas for Spatial Optimization: A Case Study in the Songnen Plain, China.

The decline in biodiversity poses a serious threat to natural ecosystems and has become one of the most pressing global environmental issues. Establishing conservation priorities for protected areas (PAs) is one of the most direct and effective biodiversity conservation measures. However, conservation gaps arise as a result of existing problems in spatial layout of PAs, including overlapping protection scopes, artificial fragmentation of natural ecological regions, as well as "over-protection" and "over-exploitation." To optimize the spatial layout of PAs and improve the efficiency of biodiversity conservation, we employed the Habitat Quality module of the Integrated Assessment of Ecosystem Services and Tradeoffs (InVEST) model and the Maximum Entropy (MaxEnt) model to assess the PAs in the Songnen Plain, China. The combined model (MaxEnt-InVEST) revealed that the conservation priorities for PAs in the Songnen Plain occupied a total area of 14,764.14 km2 (10.24% of the total area of the Songnen Plain). The conservation priorities outside PAs occupied a total area of 7858.45 km2 (5.45% of the total area of the Songnen Plain) and were primarily distributed in the northeastern, central, and southwestern regions of the Songnen Plain. This indicated that existing PAs did not offer adequate protection for local biodiversity. The consistency of our combined modeling framework was 72.11%, which enabled a more accurate assessment of biodiversity hotspots and respects the land uses of the Songnen Plain. In addition, the modeling framework successfully created maps of conservation gaps of biodiversity hotspots based on actual species distribution data and considers current land uses. Our study was aimed at optimizing the spatial conservation efficiency of the Songnen Plain by assessing the conservation gaps in the Songnen Plain. It could provide a reference for the future development of a PA system centering on national parks.