Simulation Of Future Scenarios Research Articles

Future Projection of Water Resources of Ruzizi River Basin: What Are the Challenges for Management Strategy?

This study investigates the impact of climate change on hydrological dynamics in the Ruzizi River Basin (RRB) by leveraging a combination of observational historical data and downscaled climate model outputs. The primary objective is to evaluate changes in precipitation, temperature, and water balance components under different climate scenarios. We employed a multi-modal ensemble (MME) approach to enhance the accuracy of climate projections, integrating historical climate data spanning from 1950 to 2014 with downscaled projections for the SSP2-4.5 and SSP5-8.5 scenarios, covering future periods from 2040 to 2100. Our methodology involved calibrating and validating the SWAT model against observed hydrological data to ensure reliable simulations of future climate scenarios. The model’s performance was assessed using metrics such as R2, NSE, KGE, and PBIAS, which closely aligned with recommended standards. Results reveal a significant decline in mean annual precipitation, with reductions of up to 37.86% by mid-century under the SSP5-8.5 scenario. This decline is projected to lead to substantial reductions in surface runoff, evapotranspiration, and water yield, alongside a marked decrease in mean monthly stream flow, critically impacting agricultural, domestic, and ecological water needs. The study underscores the necessity of adaptive water resource management strategies to address these anticipated changes. Key recommendations include implementing a dynamic reservoir operation system, enhancing forecasting tools, and incorporating green infrastructure to maintain water quality, support ecosystem resilience, and ensure sustainable water use in the RRB. This research emphasizes the need for localized strategies to address climate-driven hydrological changes and protect future water resources.

Research Progress in Spatiotemporal Dynamic Simulation of LUCC

Land Use and Land Cover Change (LUCC) represents the interaction between human societies and the natural environment. Studies of LUCC simulation allow for the analysis of Land Use and Land Cover (LULC) patterns in a given region. Moreover, these studies enable the simulation of complex future LUCC scenarios by integrating multiple factors. Such studies can provide effective means for optimizing and making decisions about the future patterns of a region. This review conducted a literature search on geographic models and simulations in the Web of Science database. From the literature, we summarized the basic steps of spatiotemporal dynamic simulation of LUCC. The focus was on the current major models, analyzing their characteristics and limitations, and discussing their expanded applications in land use. This review reveals that current research still faces challenges such as data uncertainty, necessitating the advancement of more diverse data and new technologies. Future research can enhance the precision and applicability of studies by improving models and methods, integrating big data and multi-scale data, and employing multi-model coupling and various algorithmic experiments for comparison. This would support the advancement of land use spatiotemporal dynamic simulation research to higher levels.

Steady but model dependent Arctic amplification of the forced temperature response in 21st century CMIP6 projections

We examine sources of uncertainty in projections of Arctic amplification (AA) using the CMIP6 multi-model (MM) ensemble and single model initial-condition large ensembles of historical and future scenario simulations. In the CMIP6 MM mean, the annual mean AA ratio is steady at approximately 2.5, both in time and across scenarios, resulting in negligibly small scenario uncertainty in the magnitude of AA. Deviations from the steady value can be found at the low and high emission scenarios due to different root causes, with the latter being mostly evident in the summer and autumn seasons. Best estimates of model uncertainty are at least an order of magnitude larger than scenario uncertainty in CMIP6. The large ensembles reveal that irreducible internal variability has a similar magnitude to model uncertainty for most of the 21st century, except in the lowest emission scenario at the end of the 21st century when it could be twice as large.

Harnessing Digital Twins for Agriculture 5.0: A Comparative Analysis of 3D Point Cloud Tools

Digital twins are essential in Agriculture 5.0, providing an accurate digital representation of agricultural objects and processes, enabling data-driven decision-making, the simulation of future scenarios, and innovation for a more efficient and sustainable agriculture. The main objective of this article is to review and compare the main tools for the development of digital twins for Agriculture 5.0 applications using 3D point cloud models created from photogrammetry techniques. For this purpose, the most commonly used tools for the development of these 3D models are presented. As a methodological approach, a qualitative comparison of the main characteristics of these tools was carried out. Then, based on some images taken in an orange grove, a quality analysis of the 3D point cloud models obtained by each of the analyzed tools was carried out. We also obtained a synthetic quality index in order to have a way to categorize the different pieces of software. Finally, as a conclusion, we compared the performance of the different software tools and the point clouds obtained by considering objective metrics (from the 3D quality assessment) and qualitative metrics in the synthetic quality index. With this index, we found that OpenDroneMap was the best software in terms of quality-cost ratio. Also, the paper introduces the concept of Agriculture 6.0, exploring the integration of advancements from Agriculture 5.0 to envision the potential evolution of agricultural practices and technologies, considering their impact on social and economic aspects.

Cloud properties and their projected changes in CMIP models with low to high climate sensitivity

Abstract. Since the release of the first Coupled Model Intercomparison Project version 6 (CMIP6) simulations, one of the most discussed topics is the higher effective climate sensitivity (ECS) of some of the models, resulting in an increased range of ECS values in CMIP6 compared to previous CMIP phases. An important contribution to ECS is the cloud climate feedback. Although climate models have continuously been developed and improved over the last few decades, a realistic representation of clouds remains challenging. Clouds contribute to the large uncertainties in modeled ECS, as projected changes in cloud properties and cloud feedbacks also depend on the simulated present-day fields. In this study, we investigate the representation of both cloud physical and radiative properties from a total of 51 CMIP5 and CMIP6 models. ECS is used as a simple metric to group the models, as the sensitivity of the physical cloud properties to warming is closely related to cloud feedbacks, which in turn are known to have a large contribution to ECS. Projected changes in the cloud properties in future scenario simulations are analyzed by the ECS group. In order to help with interpreting the projected changes, model results from historical simulations are also analyzed. The results show that differences in the net cloud radiative effect as a reaction to warming among the three model groups are driven by changes in a range of cloud regimes rather than individual regions. In polar regions, high-ECS models show a weaker increase in the net cooling effect of clouds, due to warming, than the low-ECS models. At the same time, high-ECS models show a decrease in the net cooling effect of clouds over the tropical ocean and the subtropical stratocumulus regions, whereas low-ECS models show either little change or even an increase in the cooling effect. Over the Southern Ocean, the low-ECS models show a higher sensitivity of the net cloud radiative effect to warming than the high-ECS models.

Spatiotemporal variability in human thermal comfort perception in open-air spaces: application to the state of Minas Gerais, Brazil.

The objective of this study was to propose bioclimatic zoning to classify human thermal comfort and discomfort in the state of Minas Gerais, Brazil; both historical and future scenarios are considered. Thus, historical series (1961 to 2017) of the effective temperature index as a function of the wind (ETW) were obtained as a function of the monthly average values of the minimum, mean, and maximum dry-bulb air temperatures (tdb,min, tdb,mean, and tdb,max, respectively), in addition to the mean relative humidity ([Formula: see text], %) and mean wind speed ([Formula: see text], m s -1). The data were obtained from 34 weather stations and subjected to trend analysis by using the nonparametric Mann-Kendall test, thus enabling the simulation of future scenarios (for 2028 and 2038). Then, to define the thermal ranges of the bioclimatic zoning, maps of ETWmin, ETWmean, and ETWmax were created from geostatistical analysis. Overall, the results show warming trends for the upcoming years in Minas Gerais municipalities. All climatic seasons showed an increase in the frequency of new classifications in the upper adjacent classes, which indicates climate warming. Therefore, when considering future scenarios for the autumn and winter seasons, attention should be given to changes in predicted thermal sensation, especially in the Central Minas Gerais, Belo Horizonte Metropolitan, South/Southwest Minas, Campo das Vertentes, and Zona da Mata.

Controls and significance of priming effects in lake sediments

AbstractWarming and eutrophication influence carbon (C) processing in sediments, with implications for the global greenhouse‐gas budget. Temperature effects on sedimentary C loss are well understood, but the mechanism of change in turnover through priming with labile organic matter (OM) is not. Evaluating changes in the magnitude of priming as a function of warming, eutrophication, and OM stoichiometry, we incubated sediments with 13C‐labeled fresh organic matter (FOM, algal/cyanobacterial) and simulated future climate scenarios (+4°C and +8°C). We investigated FOM‐induced production of CH4 and microbial community changes. C loss was primed by up to 17% in dominantly allochthonous sediments (ranging from 5% to 17%), compared to up to 6% in autochthonous sediments (−9% to 6%), suggesting that refractory OM is more susceptible to priming. The magnitude of priming was dependent on sediment OM stoichiometry (C/N ratio), the ratio of fresh labile OM to microbial biomass (FOM/MB), and temperature. Priming was strongest at 4°C when FOM/MB was below 50%. Addition of FOM was associated with activation and growth of bacterial decomposers, including for example, Firmicutes, Bacteroidetes, or Fibrobacteres, known for their potential to degrade insoluble and complex structural biopolymers. Using sedimentary C/N > 15 as a threshold, we show that in up to 35% of global lakes, sedimentation is dominated by allochthonous rather than autochthonous material. We then provide first‐order estimates showing that, upon increase in phytoplankton biomass in these lakes, priming‐enabled degradation of recalcitrant OM will release up to 2.1 Tg C annually, which would otherwise be buried for geological times.

Electronic tagging and tracking aquatic animals to understand a world increasingly shaped by a changing climate and extreme weather events

Despite great promise for understanding the impacts and extent of climate change and extreme weather events on aquatic animals, their species, and ecological communities, it is surprising that electronic tagging and tracking tools, like biotelemetry and biologging, have not been extensively used to understand climate change or develop and evaluate potential interventions that may help adapt to its impacts. In this review, we provide an overview of methodologies and study designs that leverage available electronic tracking tools to investigate aspects of climate change and extreme weather events in aquatic ecosystems. Key interventions to protect aquatic life from the impacts of climate change, including habitat restoration, protected areas, conservation translocations, mitigations against interactive effects of climate change, and simulation of future scenarios, can all be greatly facilitated by using electronic tagging and tracking. We anticipate that adopting animal tracking to identify phenotypes, species, or ecosystems that are vulnerable or resilient to climate change will help in applying management interventions such as fisheries management, habitat restoration, invasive species control, or enhancement measures that prevent extinction and strengthen the resilience of communities against the most damaging effects of climate change. Given the scalability and increasing accessibility of animal tracking tools for researchers, tracking individual organisms will hopefully also facilitate research into effective solutions and interventions against the most extreme and acute impacts on species, populations, and ecosystems.

Adapting a process-oriented cold hardiness model to conifers

Plant phenology has been and continues to be impacted by climate change. Process-oriented modeling of phenology reveals biological characteristics through interpretation of model results and parameter values. This paper aims to implement a cold hardiness model using historical Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) data, determine if model results improve with data clustering by seed source or growing location, and interpret model results into biological meaning. These interpretations have applications for reforestation and studies of plant phenological response to climate change.Cold hardiness data were compiled through literature review. A dynamic process-oriented cold hardiness model driven by thermal time based on daily temperatures was applied to multiple data clustering scenarios. Model results were analyzed for goodness of fit to determine model error, efficiency, and bias. A sensitivity analysis was performed to determine parameter sensitivity, and further validate the model calibration.Data clustering by seed source improved fit compared to clustering by growing location or no clustering, when applied to the full dataset. Using only temperature inputs, model results had low error when data modeled were similar. Results show that for cold hardiness acclimation a linear growing degree function with a threshold of 10 °C was adequate across testing data, as was an upper limit cold hardiness temperature of –3 °C.Interpretation of model results show that both acclimation to growing sites and seed source genetics impact cold hardiness response, though clustering by seed source improved model performance. This model could be applied to mitigate cold related risks to seedlings during production and establishment, and serve as a template for predicting phenological responses in simulated future climate scenarios.

Future Scenarios For Land use and Coverage in the Morro do Chapéu State Park/Bahia/Brazil

Objective: The present study area is a conservation unit that faces environmental conflicts associated with deforestation, hence the objective of this article is to evaluate changes in land use and land cover between the years 1985 and 2020, in the region currently covered by the Park State of Morro do Chapéu/BA and its surroundings, in addition to predicting future scenarios. Method: Images from the Mapbiomas project from 1985 and 2020 were used, and from the neural network method, using the MOLUSCE plugin, it was possible to simulate a land use and land cover model for the year 2040. Results and conclusion: The results showed little significant changes, practically imperceptible, with the most affected areas being those occupied by natural vegetation and agricultural classes. Research implications: The Morro de Chapéu State Park is home to numerous floristic and faunal species, in addition to its physical, biological and environmental complexity, therefore predicting changes in the dynamics of land use and cover is the main factor for conserving this wealth. Originality/value: This study contributes to the implementation of policies aimed at preserving this conservation unit and at the same time incorporates an unprecedented element with the simulations of future scenarios.

Optimizing marine macrophyte capacity to locally ameliorate ocean acidification under variable light and flow regimes: Insights from an experimental approach.

The urgent need to remediate ocean acidification has brought attention to the ability of marine macrophytes (seagrasses and seaweeds) to take up carbon dioxide (CO2) and locally raise seawater pH via primary production. This physiological process may represent a powerful ocean acidification mitigation tool in coastal areas. However, highly variable nearshore environmental conditions pose uncertainty in the extent of the amelioration effect. We developed experiments in aquaria to address two interconnected goals. First, we explored the individual capacities of four species of marine macrophytes (Ulva lactuca, Zostera marina, Fucus vesiculosus and Saccharina latissima) to ameliorate seawater acidity in experimentally elevated pCO2. Second, we used the most responsive species (i.e., S. latissima) to assess the effects of high and low water residence time on the amelioration of seawater acidity in ambient and simulated future scenarios of climate change across a gradient of irradiance. We measured changes in dissolved oxygen, pH, and total alkalinity, and derived resultant changes to dissolved inorganic carbon (DIC) and calcium carbonate saturation state (Ω). While all species increased productivity under elevated CO2, S. latissima was able to remove DIC and alter pH and Ω more substantially as CO2 increased. Additionally, the amelioration of seawater acidity by S. latissima was optimized under high irradiance and high residence time. However, the influence of water residence time was insignificant under future scenarios. Finally, we applied predictive models as a function of macrophyte biomass, irradiance, and residence time conditions in ambient and future climatic scenarios to allow projections at the ecosystem level. This research contributes to understanding the biological and physical drivers of the coastal CO2 system.

Constraining the First Year of Ice‐Free Arctic: Importance of Regional Perspective

AbstractThe ice‐free Arctic in summer not only symbolizes human‐induced climate change but also highlights the need to critically consider climate change adaptation policies. To constrain projections when the Arctic Ocean will first become ice‐free, studies have typically combined the historical observations of the Arctic sea ice area (SIA) with future emission scenario simulations. However, these studies primarily relied on the historical climatology and trend of the Arctic SIA, without considering regional variations. In this study, we analyze September SIA projections using the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations, while considering Arctic sub‐regions. Additionally, we assess the impact of incorporating sub‐region September SIA when constraining the first year of ice‐free Arctic in September. CMIP6 models generally overestimate the historical September SIA decreasing trend in the central Arctic, whereas they underestimate this trend in the surrounding shelf seas. The central Arctic, where the region expected to retain sea ice for the longest period in the future, holds particular significance for projecting when the Arctic will first become ice‐free. Consequently, when we employ the historical trend of September SIA in the central Arctic as a constraint, observationally‐constrained projections suggest a delay of 12 years (2056) for the first ice‐free September compared to raw/unconstrained projections (2044 in model average) under a high‐emission scenario. These findings underscore the importance of considering model biases in central Arctic SIA when constraining projections of the first year of ice‐free Arctic, which may occur later than previously projected in many studies.

Spatio-temporal change analysis and prediction of land use and land cover changes using CA-ANN model.

The spatial and temporal representation of land use and land cover (LULC) changes helps to understand the interactions between natural habitats and other areas and to plan for sustainability. Research on the models used to determine the spatio-temporal change of LULC and simulation of possible future scenarios provides a perspective for future planning and development strategies. Landsat 5 TM for 1990, Landsat 7 ETM + for 2006, and Landsat 8 OLI for 2022 satellite imageries were used to estimate spatial and temporal variations of transition potentials and future LULC simulation. Independent variables (DEM, slope, and distances to roads and buildings) and the cellular automata-artificial neural network (CA-ANN) model integrated in the MOLUSCE plugin of QGIS were used. The CA-ANN model was used to predict the LULC maps for 2038 and 2054, and the results suggest that artificial surfaces will continue to increase. The Düzce City center's artificial surfaces grew by 100% between 1990 and 2022, from 16.04 to 33.10 km2, and are projected to be 41.13 km2 and 50.32 km2 in 2038 and 2054, respectively. Artificial surfaces, which covered 20% of the study area in 1990, are estimated to cover 64.07% in 2054. If this trend continues, most of the 1st-class agricultural lands may be lost. The study's results can assist local governments in their land management strategies and aid them in planning for the future. The results suggest that policies are necessary to control the expansion of artificial surfaces, ensuring a balanced distribution of land use.

Urban sprawl prediction in Batna City, Eastern Algeria, Using the SLEUTH model

As a developing country, Algeria has experienced urban extensions, often poorly managed, which have generated numerous social, urban, and economic problems. In this context, this work aims to present an integrated approach combining spatial modeling, geographic remote sensing, and geographic information system to plan sustainable urban growth and mitigate the aforementioned issues. This work introduces urban sprawl simulation using the SLEUTH model, based on the cellular automata method. SLEUTH, implemented with a open-source code, facilates the simulation and prediction of urban sprawl. Applied to the Batna metropolitan area, SLEUTH is calibrated using four chronological series of data extracted from satellite images spanning from 1986 to 2020, with approximately 2,000 hectares transformed into urban land, representing an increase of about 180%. Future scenarios simulations were conducted for a 50-year period up to 2072, revealing two growth stages of urbanized areas. Beyond 2048, population density experiences a constant increase, marking the point at which Batna city reaches its urbanization limits. These findings highlight the necessity for urban planners to prepare an appropriate urban policy within a suitable timeframe. The integration of the SLEUTH model into the decision-making process is recommended to enhance urban policy management.

Monitoring and Modeling of Spatio-Temporal Urban Expansion and Land Use/Land-Cover Change in Mountain Landscape: A Case Study of Dalat City, Vietnam

The lack of ability to control human activities led to changes of land use/land cover (LULC) in Dalat City where rapid urbanization and the demand to expand agricultural land have resulted in dramatic forest reductions. This study assessed the rate and extent of LULC changes over the past 12 years and simulated future scenarios in Dalat City, Lam Dong Province, Vietnam by using an integrated model of Markov chain and logistics regression. Three land-use maps used to analyze land-use change were extracted from satellite images in 2010, 2016, and 2022 by classification approach. The outcome of this process indicates a significant increase in agricultural and built-up land of 48.22 km2 and 9.36 km2, respectively; a decrease in forest land of 55.61 km2, and a minor change in water bodies and bare land in the 2010-2022 period. Prediction maps of land-use change in 2028 and 2034 are generated after the model is validated by comparing the actual map with the prediction map of LULC in 2022 using Kappa statistics. Transition of forest area to other land use types, especially land for expansion of built-up and agricultural land is the crucial trend of land-use change in the future according to the forecast model. Compared to 2022, forest area in 2034 will decrease by 60.65 km2 while built-up and agricultural land will increase by 14.07 km2 and 43.61 km2, respectively. The research results provide valuable information as a foundation for land-use policy planning and local urban development to ensure sustainable development objectives.

A decrease in rockfall probability under climate change conditions in Germany

Abstract. The effect of climate change on rockfalls in the German low mountain regions is investigated following two different approaches. The first approach uses a logistic regression model that describes the combined effect of precipitation, freeze–thaw cycles, and fissure water on rockfall probability. The climate change signal for the past 6 decades is analysed by applying the model to meteorological observations. The possible effect of climate change until the end of the century is explored by applying the statistical model to the output of a multi-model ensemble of 23 regional climate scenario simulations. It is found that the number of days per year exhibiting an above-average probability for rockfalls has mostly been decreasing during the last few decades. Statistical significance is, however, present at only a few sites. A robust and statistically significant decrease can be seen in the Representative Concentration Pathway (RCP) climate scenario 8.5 (RCP8.5) simulations for Germany and neighbouring regions, locally falling below −10 % when comparing the last 30 years of the 20th century to the last 30 years of the 21st century. The most important factor determining the projected decrease in rockfall probability is a reduction in the number of freeze–thaw cycles expected under future climate conditions. For the second approach four large-scale meteorological patterns that are associated with enhanced rockfall probability are identified from reanalysis data. The frequency of all four patterns exhibits a seasonal cycle that maximises in the cold half of the year (winter and spring). Trends in the number of days that can be assigned to these patterns are determined both in meteorological reanalysis data and in climate simulations. In the reanalysis no statistically significant trend is found. For the future scenario simulations all climate models show a statistically significant decrease in the number of rockfall-promoting weather situations.

Seismic Fragility Curves for Reinforced Concrete Dual System Buildings

A seismic fragility curve is a visual representation that illustrates the likelihood of a structure surpassing a particular damage or performance limit state caused by an earthquake with a specific intensity or ground motion level. This curve is typically generated using probabilistic seismic hazard analysis and structural reliability analysis methods. It is based on statistical models that rely on past earthquake data and simulations of future earthquake scenarios to predict the structure or system’s behavior under seismic forces. In this study, the seismic performance of 30 stories of 95 m height dual system reinforced concrete buildings located in Erbil is evaluated by analyzing three distinct ground motions. A non-linear platform is used to simulate and analyze data, followed by the generation of seismic inter-story drift fragility curves using Incremental Dynamic Analysis. The buildings’ seismic structural performance is assessed based on five different performance levels, including operational phase, immediate occupancy, damage control, life safety, and collapse prevention (CP). Each level is associated with different levels of damage and corresponding degrees of functionality and safety. The fragility curves show that the building has a 50% chance of achieving or exceeding the (CP) level with highly intense ground vibrations with peak ground acceleration = 1.6 g. In addition, these curves can be beneficial in creating future local design codes and provide significant support in evaluating the seismic performance of existing buildings.

Future scenarios in the former oil capital: coastal flooding and social vulnerability in Macaé, RJ.

The accelerated urban growth in Macaé had important consequences on socio-spatial organization, especially about housing spaces that became increasingly difficult to be accessed by the low-income population. The most devalued lands, such as mangroves and floodplains, were occupied by the low-income population. The proposal highlighted in this project focuses directly on the problem of rising sea levels and flooding in the urban space of Macaé, which is of social interest. A simulation of future scenarios with sea level rise above the current one, allowing the identification of areas flooded by marine transgression on a time scale of 100years (for the year 2100). For this, the rate was chosen for the simulation: the greenhouse gas scenario RCP8.5, as given in IPCC's Fifth Assessment Report (AR5) of 2014. A radiative forcing that corresponds to more than 700ppm CO2-eq, but less than 1500ppm, the projected increase is 1m to more than 3m (medium confidence) and more than 3m (medium confidence). This assessment is based on the average confidence in the contribution from thermal expansion and low confidence in the modeled contribution modeled contribution of the ice sheets. Therefore, the climate change-induced global mean sea level rise is caused by thermal expansion of ocean water and ocean mass gain, the latter being mainly due to a decrease in land ice mass. The estimated sea-level rise used for the projection of this study is 2.15, as proposed by Grinsted et al. in 2009.

Construction and optimization of ecological networks based on future scenario simulation in Qianshan City, Anhui Province.

The rapid urbanization adversely affects landscape pattern of mountainous cities. It is of great practical significance to explore the spatial and temporal evolution of ecological networks in mountainous areas to achieve regional ecological security. Taking Qianshan City in Anhui Province as an example, based on the land use data in 2012 and 2020, we simulated the land use situation in 2036 with PLUS model, and constructed the ecological networks in 2020 and 2036, respectively. We further analyzed its spatial and temporal evolution characteristics and explored the optimization path of ecological network. The results showed that the scale of various land use types in Qianshan City would change little from 2020 to 2036. The construction land would be centered on the built-up area, expanding in a point and block shape to the surrounding area. The ecological space would be continuously squeezed and encroached. The overall complexity and connectivity of ecological networks in Qianshan City would increase. The number and area of ecological source sites would increase, expanding spatially to the southwest and northeast. The overall density and number of ecological corridors would increase, with a lack of ecological corridor connections in the east-west direction, which need urgent improvement. The stability of ecological networks could be improved through three major measures, including protection and restoration of source sites, optimization and cultivation of corridors, and zoning control.

Evolution of Urban Ecosystem Service Value and a Scenario Analysis Based on Land Utilization Changes: A Case Study of Hangzhou, China

Simulating the change in ecosystem service values (ESVs) caused by land use changes in metropolitan areas under multiple scenarios is of great significance to ensure regional ecological security and sustainable urban development. This study assessed the variations in land use and ESV in the main urban area of Hangzhou, China, from 2000 to 2020. A total of four future land use scenarios for 2030 were created using the cellular automata–Markov prediction model and ESVs were calculated for four future scenarios. The results are as follows: (1) Arable land and construction land were the most drastic types of land use changes in the main urban area of Hangzhou during the study period. From 2000 to 2020, construction land in the main urban area of Hangzhou expanded rapidly, with an increase of 46,916.82 hm2, while the cultivated land area decreased significantly by 38,396.43 hm2. (2) The ESV in the study area continuously declined from 2000 to 2020, with high-value ecosystem service areas predominantly found in forest areas and water areas. (3) The values of ecosystem services under the simulated future scenarios of natural development, rapid development, planned development, and ecological development were CNY 15.053, 14.525, 16.690, and 16.799 billion, respectively. The planned development and ecological development scenarios effectively ensure a high ESV. The results indicated that balancing various types of land use is essential to guarantee ecological security in urban development. Moreover, urban development and construction should be undertaken in areas with a low ESV. This forecasting study can serve as a key reference for policy makers regarding the urban landscape sustainability of Hangzhou City. The integrated simulation method of land use and ESV proposed in this study can shed light on the urban spatial layout and spatial regulation in urban land use planning.