Future Land Cover Changes Research Articles

Multi-Scenario land cover changes and carbon emissions prediction for peak carbon emissions in the Yellow River Basin, China

Research on future land cover changes and carbon emissions is essential for effective land resource management and developing feasible carbon mitigation strategies. This study focused on the Yellow River Basin and employed the Future Land Use Simulation (FLUS) and Autoregressive Integrated Moving Average (ARIMA) models to project future land cover and carbon emissions. Additionally, bivariate spatial autocorrelation was utilized to analyze the relationship between them. Key findings are as follows: 1) Historically, the Yellow River Basin has experienced an expansion in construction land, forests, grasslands, and water, while cropland and unused land have diminished. Notably, construction land displayed the most significant changes, whereas grasslands showed minimal modification. Looking ahead, both the ecological protection and inertial development scenarios exhibit consistent trends with historical patterns across the land type categories. In contrast, the economic priority development scenario forecasts an increase in construction land, cropland, and grasslands, indicating a distinct shift compared to the other scenarios. However, the ecological protection scenario proves to be more sustainable. 2) In the absence of intervention, the simulated carbon emissions from construction land throughout the basin display a linear increase across various scenarios, with provincial-level variations showing an increase from southwest to northeast. However, Henan and Sichuan are expected to experience slower reductions in carbon emissions, compared to other projections. There is a notable positive correlation between carbon emissions and the comprehensive index, indicating that regions with high emissions typically experience substantial land and economic development. 3) Energy consumption projections for 2030 and 2060 indicate that to align with China’s carbon goals, it is essential to reduce energy consumption and adjust the fossil to non-fossil fuel ratio to reduce carbon emissions. Substituting coal with clean energy and enhancing energy efficiency will be more effective for achieving low-carbon emission targets. In summary, this study provides significant guidance for China’s ecological conservation, low-carbon emission strategies, and global carbon emission control efforts.

Integrating Machine Learning, Land Cover, and Hydrological Modeling to Contribute Parameters for Climate Impacts on Water Resource Management

The purpose of this study is to establish basic policies for managing the impacts of climate change on water resources using the integration of machine learning and land cover modeling. We predicted future changes in land cover within the water management and assessed its vulnerability to climate change. After confirming this vulnerability, we considered measures to improve climate resilience and presented future water resource parameters. We reviewed the finances available to promote climate projects, noting the major river management funds. The future project will serve as a stepping stone to promote climate resilience projects addressing water resource challenges exacerbated by future climate change. The study examined the results of analyzing changes in land cover maps due to climate change and assessed vulnerability in water management areas until 2050. According to the analysis results, the regulations for our study areas were set lower than those for other water management zones, resulting in a high rate of urbanization. Therefore, the climate resilience project in the water management area should be implemented first, despite the need for a long-term view in adapting to climate change.

The Extent of Agricultural Land Cover Change: The Case of Bautista, Pangasinan

Urban Expansion in Bautista periodically alters its land cover, significantly diminishing agricultural land in favor of built-up areas. Therefore, a comprehensive investigation of past and future land cover changes is essential for sustainable development. The main objective of this research is to determine the extent of agricultural land cover change and its spatial pattern from 2018 to 2022 and in the year 2027 in Bautista, Pangasinan. This study employed a descriptive research approach integrating time series data, spatial analysis, and simulation tools to evaluate the dynamic changes in agricultural land cover. The extent of agricultural land cover change decreased from 3,480.95 hectares in 2018 to 3,417.04 hectares in 2022, primarily near road networks and within the identified urban barangays of the municipality. The change exhibited a clustered distribution, as per the summary report from the Average Nearest Neighbor using the Geographic Information System. In contrast, built-up land cover change expanded by 60.68 hectares from 2018 to 2022, following an increasing trend. Furthermore, the projected agricultural land cover change is expected to decrease continuously from 3,417.039 hectares in 2022 to 3,406.875 hectares in 2027. The results indicate a conversion of agricultural land to built-up areas between 2018 and 2022 and a further decrease in 2027, reflecting a shift from agriculture to man-made structures. This research underscores the importance of monitoring land conversions and provides a basis for sustainable solutions to address the growing demand for urbanization without compromising the future extent of agricultural land cover in Bautista, Pangasinan.

Navigating Urban Sustainability: Urban Planning and the Predictive Analysis of Busan’s Green Area Dynamics Using the CA-ANN Model

While numerous studies have employed deep learning and high-resolution remote sensing to predict future land use and land cover (LULC) changes, no study has integrated these predictive tools with the current urban planning context to find a potential issues for sustainability. This study addresses this gap by examining the planning context of Busan Metropolitan City (BMC) and analyzing the paradoxical objectives within the city’s 2040 Master Plan and the subordinate 2030 Busan Master Plan for Parks and Greenbelts. Although the plans advocate for increased green areas to enhance urban sustainability and social wellbeing, they simultaneously support policies that may lead to a reduction in these areas due to urban development. Using the CA-ANN model in the MOLUSCE plugin, a deep learning-based LULC change analysis, we forecast further urban expansion and continued shrinkage of natural green areas. During 1980–2010, Busan Metropolitan City (BMC) underwent high-speed urban expansion, wherein the urbanized areas almost doubled and agricultural lands and green areas, including forests and grassland, reduced considerably. Forecasts for the years 2010–2040 show continued further expansion of urban areas at the expense of areas for agriculture and green areas, including forest and grasslands. Given the master plans, these highlight a critical tension between urban growth and sustainability. Despite the push for more green spaces, the replacement of natural landscapes with artificial parks and green areas may threaten long-term sustainability. In view of these apparently conflicting goals, the urban planning framework for BMC would have to take up increasingly stronger conservation policies and adaptive planning practices that consider environmental preservation on a par with economic development in the light of the planning context and trajectory of urbanization.

Impacts of Forest Management‐Induced Productivity Changes on Future Land Use and Land Cover Change

AbstractAnthropogenic land use and land cover change (LULCC) is projected to continue in the future. However, the influence of forest management on forest productivity change and subsequent LULCC projections remains under‐investigated. This study explored the impacts of forest management‐induced change in forest productivity on LULCC throughout the 21st century. Specifically, we developed a framework to softly couple the Global Change Analysis Model and Global Timber Model to consider forest management‐induced forest productivity change and projected future LULCC across the five Shared Socioeconomic Pathways (SSPs). We found future increases in forest management intensity overall drive the increase of forest productivity. The forest management‐induced forest productivity change shows diverse responses across all SSPs, with a global increase from 2015 to 2100 ranging from 3.9% (SSP3) to 8.8% (SSP1). This further leads to an overall decrease in the total area with a change of land use types, with the largest decrease under SSP1 (−7.5%) and the smallest decrease under SSP3 (−0.7%) in 2100. Among land use types, considering forest management‐induced change significantly reduces the expansion of managed forest and also reduces the loss of natural land in 2100 across SSPs. This suggests that ignoring forest management‐induced forest productivity change underestimates the efficiency of wood production, overestimates the managed forest expansion required to meet the future demand, and consequently, potentially introduces uncertainties into relevant analyses, for example, carbon cycle and biodiversity. Thus, we advocate to better account for the impacts of forest management in future LULCC projections.

Modelling future land use land cover changes and their impacts on urban heat island intensity in Guangzhou, China

During rapid urbanization in developing countries, changes in land use and land cover (LULC) can significantly alter urban land surface temperatures (LST), exacerbating the urban heat island (UHI) effect and degrading the outdoor environment. In this study, taking Guangzhou, China, as an example, we used Landsat series satellite data from 1992 to 2022, classified the LULC of the study area by the Support Vector Machine (SVM) method, estimated the LST of the area by the mono-window algorithm, and classified the LST of the study area into five UHI intensity classes based on the normalized values of the LST, and explored the influence of the LULC on the distribution of the UHI intensity. The CA-ANN (cellular automata-artificial neural network) model in QGIS software was employed to forecast the distribution of LULC and UHI intensity in Guangzhou for 2032. The findings reveal a strong correlation between UHI intensity and LULC, with water bodies and vegetation primarily exhibiting low and sub-low temperatures, while urban areas exhibit sub-high and high temperatures. The prediction results show that, according to the current development trend, compared with 1992, the water body and vegetation cover in 2032 will decrease by 46.97% and 34.24%, the building land will increase by 263.71%, and the sub-high and high temperature areas will increase by 127.76% and 375.92%. By analysing the spatial and temporal changes in LULC and its relationship with the distribution of UHI intensity during urbanization, this study assists government administrations and urban planners in devising sensible urban development strategies and implementing effective measures to plan LULC rationally. This approach aims to mitigate the impacts of the urban heat island and foster sustainable urbanization.

The Detection of Past and Future Land Use and Land Cover Change in Ugam Chatkal National Park, Uzbekistan, Using CA-Markov and Random Forest Machine Learning Algorithms

This comprehensive study investigates land use and land cover (LULC) changes in Ugam Chatkal Na-tional Park, Uzbekistan, over a 30-year period from 1993 to 2022 with Landsat satellite images. Utili-zing advanced CA-Markov and Random Forest machine learning algorithms, it meticulously analyzes historical data to understand past trends and projects future LULC changes. According to remote sen-sing analysis of the past, our findings show the sharp decline of glacier land cover from 2105 km2 to 1334 km2 in the Ugam Chatkal National Park, replaced by tree cover (from 327 km2 in 1993 to 450 km2 in 2022), rangelands (1259 km2 in 1993 to 1355 km2 in 2020), and rocks (from 834 km2 in 1993 to 1390 km2 in 2022). Agriculture, water and bare land witnessed some fluctuations but did not change significantly. At the same time, the region experienced some urbanization, raising the urban area from 50 km2 in 1993 to 90 km2 after 29 years. The article suggests three possible scenarios for the future of the region: “hard”, “soft” and “bad” scenarios. Land cover change predictions are done in TerrSet software with the CA-Markov model for four decades: 2035, 2045,2055 and 2065. Hard and soft sce-narios predict similar patterns for the future: a decline in glacier cover and a rise of tree cover, rock and rangelands, with a slight increase in agriculture and urban classes. Whereas “bad” scenario, which incorporates rapid urbanization and agricultural expansion for the study area, forecasts a climb of the urban area until 415 km2 (8% of the territory) until 2065, and 286 km2 for agriculture.

Response of Streamflow to Future Land Use and Cover Change and Climate Change in the Source Region of the Yellow River

This study utilizes meteorological and leaf area index (LAI) data for three shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5) from four general circulation models (GCMs) of the sixth climate model intercomparison project (CMIP6) spanning from 2015 to 2099. Employing calibrated data and incorporating future land use data under three SSPs, the distributed hydrology soil vegetation model (DHSVM) is employed to simulate streamflow in the source region of the Yellow River (SRYR). The research aims to elucidate variations in streamflow across different future scenarios and to estimate extreme streamflow events and temporal distribution changes under future land use and cover change (LUCC) and climate change scenarios. The main conclusions are as follows: The grassland status in the SRYR will significantly improve from 2020 to 2099, with noticeable increases in temperature, precipitation, and longwave radiation, alongside a pronounced decrease in wind speed. The probability of flooding events increases in the future, although the magnitude of the increase diminishes over time. Both LUCC and climate change contribute to an increase in the multi-year average streamflow in the region, with respective increments of 48.8%, 24.5%, and 18.9% under SSP1–2.6, SSP2–4.5, and SSP5–8.5. Notably, the fluctuation in streamflow is most pronounced under SSP5–8.5. In SSP1–2.6, the increase in streamflow during the near future (2020–2059) exceeds that of the distant future (2059–2099). Seasonal variations in streamflow intensify across most scenarios, leading to a more uneven distribution of streamflow throughout the year and an extension of the flood season.

A comprehensive analysis and future projection of land use and land cover dynamics in a fast-growing city: A case study of Sekondi-Takoradi metropolis, Ghana

Sekondi-Takoradi is the most urbanized metropolis in the Western region of Ghana. Over the past two decades, it has experienced rapid shifts in both population density and land cover. Unfortunately, these transformations have significantly altered the natural landscape and expanded urban coverage. Previous studies have not established enough evidence on land use and land cover changes in the metropolis. For this reason, this study examined the existence, rate, and spatial distribution of land use and land cover as well as the future changes in land cover in the metropolis. Landsat imagery spanning from 1991, 2009, 2016, to 2023 provided the basis for evaluating the spatiotemporal dynamics of land use and cover within the metropolis. The Random Forest Classification algorithm was employed to categorize the images into three distinct classes: water, vegetation, and built-up areas. Analysis revealed a rapid growth in built-up areas by 63.08 km² (32.91 %), accompanied by a decrease in vegetation and water coverage by 60.99 km² (31.82 %) and 2.08 km² (1.09 %) respectively. This highlights the accelerating urbanization trend, emphasizing the critical need for vigilant monitoring and controlled urban expansion to mitigate potential adverse effects. Projections for 2030 indicate further changes with water areas decreasing to 1.21 km² (0.63 %), vegetation diminishing to 95.31 km² (49.73 %), and built-up areas expanding to 95.14 km² (49.64 %). This signifies a significant shift towards built-up areas, altering the metropolis' land cover composition and potentially impeding the achievement of Sustainable Development Goal 11. Understanding these changes in land use and land cover holds significant value for policymakers, enabling them to effectively monitor and safeguard natural resources.

Advanced CMD predictor screening approach coupled with cellular automata-artificial neural network algorithm for efficient land use-land cover change prediction

The prediction of future land use and land cover (LULC) change is essential for natural resource management and the development of environmental monitoring strategies, especially for a non-resilient system. Thus, a novel LULC prediction model was proposed by coupling a hybrid predictor screening approach with the Cellular Automata coupled Artificial Neural Network (CA-ANN) approach. The hybrid predictor screener was formed by integrating the outcomes of Correlation Coefficient (CC), Mutual Information (MI), and Decision Tree (DT) methods and termed as the CMD technique. The CA-ANN model was trained by LULC maps of 1995 and 2005 and tested by the map of 2020 for these four sets of variables (i.e., CC, MI, DT, and CMD). The results revealed that the CMD predictors were the most influential variables for LULC prediction, followed by the DT-based variables. In contrast, the CC-based predictors were found to be the least effective, followed by the MI variables. Thus, the CMD-CA-ANN model was established as the benchmark model in this study. The study showed that elevation and distance from roads were more critical variables for LULC prediction compared to slope and aspect parameters. The proposed model demonstrated that by 2080, there would be an increase in the area of built-up land (5.79%), cropland (4.35%), shrubland (1.62%), and water bodies (0.3%) and a decrease in the area of forested land (1.34%) and barren land (10%) in the study area. The research has the potential to significantly contribute to the identification and management of significant environmental changes.

Modeling the impact of historical and future land use land cover changes on the hydrological response of an Ethiopian watershed

Modeling the impact of historical and future land use land cover changes on the hydrological response of an Ethiopian watershed

Are human-induced changes good or bad to dynamic landscape connectivity?

Land managers must find a compromise between rapidly changing landscapes and biodiversity conservation through ecological networks. Estimating ecological networks is a key approach to enhance or maintain functional connectivity by identifying the nodes and links of a graph, which represent habitats and their corresponding functional corridors, respectively. To understand the current state of biodiversity, it is necessary to consider dynamic landscape connectivity while relying on relevant land cover maps. Although a current land cover map is relatively easy to produce using existing data, this is challenging for past landscapes. Here we investigated the impact of changes in landscape connectivity in an urban landscape at a fine scale on the habitat availability of two bird species: the tree pipit Anthus trivialis and the short-toed treecreeper Certhia brachydactyla. These species, exhibiting different niche ecologies, have shown contrasting population trends at a medium-term scale. The occurrences of C. brachydactyla were better correlated with resistance values that maximise the use of corridors, whereas the occurrences of A. trivialis better fitted with intermediate resistance values. The statistical approach indirectly highlighted relevant information about the ecology the capacity of both species to use urban habitats. Landscape connectivity increased for both species over the 24-year study period and may have implications for local abundances, which could explain, at the national scale, the increase in populations of C. brachydactyla, but not the decrease in populations of A. trivialis. Thus, more attention must be paid on rural habitats and their associated species that are more impacted by human activities, but efforts could also be achieved in urban areas especially for highly corridor-dependent species. Studying dynamic landscape connectivity at a fine scale is essential for estimating past and future land cover changes and their associated impacts on ecological networks, to better reconcile human and biodiversity concerns in land management.

Exploring the Impact of Future Land Uses on Flood Risks and Ecosystem Services, With Limited Data: Coupling a Cellular Automata Markov (CAM) Model, With Hydraulic and Spatial Valuation Models

Land use changes can majorly affect many parameters that are directly or indirectly interlinked to various human-environmental systems, including hydrological processes and flood risks. The knowledge of future land cover changes is crucial for better managing human-environmental interactions and addressing potential environmental challenges, such as floods. In this work, the impact of future land cover changes in flood inundation is assessed, using a case study in northeast Indiana, US. A Cellular Automata Markov (CAM) model is applied, combining Geographic Information Systems (GIS) and Python, to predict land changes and provide future land cover maps, along with statistical validation measures. The land use map outputs are then used in a HEC-RAS hydraulic model, to test the different flooding impacts under a design storm, using the rain-on-grid routine. The results indicate that even slightly more urbanized and deforested areas can increase the potential flood extent. Furthermore, the impacts of these forecasted land cover changes are quantified in monetary terms, based on a spatial Ecosystem Services Valuation (ESV) model. The findings indicate that as certain land uses (mainly wetlands, followed by forests) give their place to build-up areas, barren land, or even agricultural lands, the ‘lost’ value due can reach 1.5 million USD in 2051. The novelty of this study lies in int integrated character, combining for the first time to our knowledge land cover forecast with hydrologic-hydraulic modelling and spatial ESV, showing thus the future changes, risks, and potential economic losses, respectively. This application uses the minimum necessary input data to perform the analyses, and all data and codes are publicly available, contributing thus to the transferability and reproducibility of the approach.

LAND USE AND LAND COVER SIMULATION BASED ON INTEGRATION OF ARTIFICIAL NEURAL NETWORKS WITH CELLULAR AUTOMATA-MARKOV CHAIN MODELS APPLIED TO EL-FAYOUM GOVERNORATE

Abstract. The paper aims to evaluate the effectiveness of the multi-layer perceptron-Markov chain analysis (MLP-MCA) integrated method in predicting future Land Use and Land Cover (LULC) change scenarios in Fayoum due to rapid urbanization. The study employed machine learning algorithms for image classification using Google Earth Engine (GEE) for classification techniques to derive LULC maps from Landsat imagery taken in 2001, 2011, and 2021. The 2001 and 2011 LULC maps were used to predict the LULC scenario for 2021 using MLP-MCA, and the predicted result was validated against the observed 2021 LULC map using Area under the curve (AUC) that was derived from the Receiver Operating Characteristics (ROC). Subsequently, the study predicted future LULC changes for 2031 using two sub-models; sub- Agri and sub-built. The results show that a rapid growth in both built and agricultural area. The findings of this study highlight the potential of the MLP-MCA method in predicting future LULC changes due to urbanization.

Downscaling future land cover scenarios for freshwater fish distribution models under climate change

The decreasing freshwater biodiversity trend can be attributed to anthropogenic impacts in terms of climate and land cover change. For targeted conservation efforts, mapping and understanding the distribution of freshwater organisms consists of an important knowledge gap. Spatial modelling approaches offer valuable insights into present-day biodiversity patterns and potential future trajectories, however methodological constraints still hamper the applicability of addressing future climate and land cover change concurrently in one modelling workflow. Compared to climate-only projections, spatially explicit and high-resolution land cover projections have seen less attention, and the lack of such data challenges modelling efforts to predict the possible future effects of land cover change especially on freshwater organisms. Here we demonstrate a workflow where we downscale future land cover projection data from the Shared Socioeconomic Pathway (SSP) scenarios for South America at 1 km2 spatial resolution, to then predict the future habitat suitability patterns of the Colombian fish fauna. Specifically, we show how the land cover data can be converted from plain numbers into a spatially explicit representation for multiple SSP scenarios and at high spatial resolution, employing freshwater-specific downscaling aspects when spatially allocating the land cover category grid cells, and how it can be fitted into an ensemble species distribution modelling approach of 1209 fish species. Our toolbox consists of a suite of open-source tools, including Dinamica EGO, R, GRASS GIS and GDAL, and we provide the code and necessary steps to reproduce the workflow for other study areas. We highlight the feasibility of the downscaling, but also underline the potential challenges regarding the spatial scale and the size of the spatial units of analysis.

Predicting the future land use and land cover changes for Saroor Nagar Watershed, Telangana, India, using open-source GIS.

The dynamics of land use and land cover are profoundly affected by the growth, mobility, and demand of people. Thematic maps of land use and land cover (LULC) help planners account for conservation, concurrent uses, and land-use compressions by providing a reference for analysis, resource management, and prediction. The purpose of this research is to identify the transition of land-use changes in the Saroor Nagar Watershed between 2008 and 2014 using the Modules for Land Use Change Evaluation (MOLUSCE) plugin (MLP-ANN) model and to forecast and establish potential land-use changes for the years 2020 and 2026. To predict how these factors affected LULC from 2008 to 2014, MLP-ANN was trained with maps of DEM, slope, distance from the road, and distance to a waterbody. The projected and accurate LULC maps for 2020 have a Kappa value of 0.70 and a correctness percentage of 81.8%, indicating a high degree of accuracy. Changes in LULC are then predicted for the year 2026 using MLP-ANN, which shows a 17.4% increase in built-up area at the expense of vegetation and barren land. The results contribute to the development of sustainable plans for land use and resource management.

Estimating future streamflow under climate and land use change conditions in northeastern Hokkaido, Japan

Study regionAbashiri River, northeastern Hokkaido, Japan. Study focusIn response to the growing concern over likely future climate and land use land cover (LULC) changes, a better understanding of the consequent streamflow changes is needed in northeastern Hokkaido, Japan. The present study focuses on the Abashiri River Basin (ARB), northeastern Hokkaido, and estimates the future local streamflow and its change in quantity, seasonality, flow regime, and spatial distribution under various possible impacts of climate and LULC conditions. The estimation is produced using the Soil and Water Assessment Tool (SWAT), a hydrological model suitable for managemental analysis. Future conditions are applied as alterations to the initial model inputs. New hydrological insights for the regionPossible impact of climate change leads to significant changes in streamflow quantity and flow pattern around the snowmelt season, while negative and insignificant change in streamflow is expected in response to possible LULC change. The results highlight a concerning future for local agricultural water use. Consequently, practical recommendations for local water resources management are advised, focusing on precautionary measures and development insights. Compared to existing research in this region, the present study provides a comprehensive estimation of the possible future streamflow changes on a finer spatiotemporal scale. Further application in the future to help the local water resources management is expected.

Future land use land cover changes in El-Fayoum governorate: a simulation study using satellite data and CA-Markov model

This study aims to monitor the changes in land use land cover (LULC) in El-Fayoum governorate over time (past, present, and future) to provide current information for stakeholders involved in land use planning. The study utilized Landsat satellite images and applied the Support Vector Machine algorithm using ArcGIS Pro 2.8.3 to classify the images into four major LULC classes: water, desert, built-up, and agricultural. To evaluate the accuracy of the LULC maps, the study used kappa statistical parameters, which ranged from 0.91 to 0.94, indicating acceptable results for further analysis. To predict spatio-temporal LULC changes, the study considered biophysical and socioeconomic factors such as distance to canals, distance to roads, distance to urban areas, a digital elevation model, and slope. A combination of Multi-Criteria Evaluation, a Fuzzy Membership Function, and the Analytic Hierarchy Process were employed to develop a land cover suitability map. The Hybrid CA-Markov model of the IDRISI-TerrSet software was used to simulate LULC changes, and the accuracy of the simulation was validated using 2020 imagery data. The values gained from the kappa indices for agreement (standard) = 0.9006, kappa for lack of information (no) = 0.916, and kappa for location at grid cell level (location) = 0.9572 demonstrate that the results of the simulation of the LULC changes were deemed satisfactory. The future scenarios modeled in LULC indicate a significant change in the LULC classes over time, specifically for 2030. The change rates of agriculture, desert, built-up, and water areas in El-Fayoum in 2030 compared to 2020 are estimated to be 9.68%, − 17.58%, 133.62%, and 6.06%, respectively. These findings establish both past and future LULC trends and provide crucial data useful for planning and sustainable land use management.

Assessing the Influence of Land Cover and Climate Change Impacts on Runoff Patterns Using CA-ANN Model and CMIP6 Data

Dhaka city is experiencing rapid land cover changes, and the effects of climate change are highly visible. Investigating their combined influence on runoff patterns is vital for sustainable urban planning and water resources management. In this work, multi-date land cover classification was performed using a random forest (RF) algorithm. To validate accuracy of land cover classification, an assessment was conducted by employing kappa coefficient, which ranged from 85 to 96%, indicating a high agreement between classified images and the reference dataset. Future land cover changes were forecasted with cellular automata-artificial neural network (CA-ANN) model. Further, soil conservation service -curve number (SCS-CN) rainfall-runoff model combined with CMIP6 climate data was employed to assess how changes in land cover impact runoff within Dhaka metropolitan development plan (DMDP) area. Over the study period (2020–2100), substantial transformations of land cover were observed, i.e., built-up areas expanded to 1146.47 km2 at the end of 2100, while agricultural areas and bare land diminished considerably. Consequently, monsoon runoff increased from 350.14 to 368.24 mm, indicating elevated hydrological responses. These findings emphasized an intricate interplay between urban dynamics and climatic shifts in shaping runoff patterns, underscoring urgency of incorporating these factors into urban planning strategies for sustainable water resources management in a rapidly growing city such as Dhaka.

Uncertain Spatial Pattern of Future Land Use and Land Cover Change and Its Impacts on Terrestrial Carbon Cycle Over the Arctic–Boreal Region of North America

AbstractLand use and land cover change (LULCC) represents a key process of human‐Earth system interaction and has profound impacts on terrestrial ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regional LULCC projections by integrated assessment models, such as the Global Change Analysis Model (GCAM). The uncertainty associated with the different spatial downscaling methods and its impacts on the subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on their impacts on the carbon cycle over the Arctic‐Boreal Vulnerability Experiment (ABoVE) domain, where extensive LULCC is expected. Specifically, we used the Future Land Use Simulation model (FLUS) and the Demeter model to generate 0.25° gridded LULCC data (i.e., LULCCFLUS and LULCCDemeter, respectively) with the same input of regional LULCC projections from GCAM, under both the low (i.e., SSP126) and high (i.e., SSP585) greenhouse gas emission scenarios. The two sets of downscaled LULCC were used to drive the Community Land Model version 5 and prognostically simulate the terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial‐temporal differences between LULCCFLUS and LULCCDemeter, and the spatial distributions of the needleleaf evergreen boreal tree, broadleaf deciduous boreal tree, broadleaf deciduous boreal shrub, and C3 arctic grass are particularly different under both SSP126 and SSP585. Additionally, the spatiotemporal differences are larger under SSP126 than SSP585, due to more intensive LULCC under SSP126 than SSP585 from GCAM projection. The differences in LULCC further lead to large discrepancies in the spatial patterns of projected gross primary productivity, ecosystem respiration, and net ecosystem exchange, which represent more than 79% of the contributions of future LULCC in 2100. Additionally, the difference in carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by the spatial downscaling process in future LULCC projections and carbon cycle simulations.