Land Use And Land Cover Scenarios Research Articles

Investigating the Impact of Recent and Future Urbanization on Flooding in an Indian River Catchment

Socioeconomic growth in India has caused massive infrastructure development which has resulted in extensive damage to the natural environment. A consequence of this urbanization has been extensive monsoon flooding in many locations within the country. The impact of recent land use and land cover (LULC) change because of urbanization and a series of future LULC scenarios is assessed for the Meenachil river basin in central Kerala, India. This catchment flows into the Kuttanad administrative area, which has the country’s lowest elevation, an increasing population, and currently suffers from regular flooding. Hydrological modeling using SHETRAN and hydraulic modeling using HEC-RAS predicts that an extreme event will produce a 105% rise in flood depth in 2100 compared to 2005. A scenario that incorporates Nature-based Solutions suggests the rise in flood depth could be reduced by 44%. A catchment response for future development is needed but is hindered by different administrative boundaries within the river basins that flow into the Kuttanad administrative area, and so this study concludes by providing regional-scale planning recommendations that integrate hydrologic components.

Modeling Environmental Vulnerability for 2050 Considering Different Scenarios in the Doce River Basin, Brazil

Understanding climate change and land use impacts is crucial for mitigating environmental degradation. This study assesses the environmental vulnerability of the Doce River Basin for 2050, considering future climate change and land use and land cover (LULC) scenarios. Factors including slope, elevation, relief dissection, precipitation, temperature, pedology, geology, urban distance, road distance, and LULC were evaluated using multicriteria analysis. Regional climate models Eta-HadGEM2-ES and Eta-MIROC5 under RCP 4.5 and RCP 8.5 emission scenarios were employed. The Land Change Modeler tool simulated 2050 LULC changes and hypothetical reforestation of legal reserve (RL) areas. Combining two climate and two LULC scenarios resulted in four future vulnerability scenarios. Projections indicate an over 300 mm reduction in average annual precipitation and an up to 2 °C temperature increase from 2020 to 2050. Scenario 4 (RCP 8.5 and LULC for 2050 with reforested RLs) showed the greatest basin area in the lowest vulnerability classes, while scenario 3 (RCP 4.5 and LULC for 2050) exhibited more high-vulnerability areas. Despite the projected relative improvement in environmental vulnerability by 2050 due to reduced rainfall, the complexity of associated relationships must be considered. These results contribute to mitigating environmental damage and adapting to future climatic conditions in the Doce River Basin.

A Bayesian analysis of adaptation of mountain grassland production to global change

Abstract In mountains, grasslands managed for livestock production sustain local economies, culture and identity. However, their future fodder production is highly uncertain under climate change: While an extended growing season may be beneficial, more frequent and intense summer droughts could also reduce fodder quantity and quality. Land use and land cover (LULC) changes are another major driver of grassland biomass production, but combined effects of future land use transitions and climate change are rarely quantified. We modelled combined climate and LULC scenarios for grassland production of the Maurienne Valley (French Alps) by 2085. We built a Bayesian Belief Network (BBN) from long‐term grassland production monitoring data complemented with expert knowledge. We assessed the potential of two candidate adaptations, intensification as an incremental solution and silvopastoralism as a transformative solution to compensate combined impacts of two climate scenarios and three land use change scenarios. Total biomass production was far more sensitive to LULC than to climate scenarios. Production losses were largest under the conservation LULC scenario (−28% on average between 2020 and 2085), followed by the tourism development scenario (−7%) and the business‐as‐usual scenario (+3%). Climate change under representative concentration pathways (RCP) 8.5 altered the seasonality of production by increasing potential production from May to July while decreasing summer regrowth. Synthesis and applications: Changes in LULC are more decisive for global biomass production than climate change. However, under the most extreme climate change scenario (RCP8.5), the seasonal shift in production and increased interannual variability threaten the current grass‐based protected designation of origin (PDO) production system. Only the intensification adaptation solution showed significant gains in total biomass production. Still, the silvopastoralism would require less investment compared to the intensification and have a similar efficiency when assessing the gains of biomass by the surface concerned with adaptation solutions. Along with decreased total annual production due to decreasing grassland area compounded by more extreme climate change, the seasonal shift in production and increased interannual variability threaten the current grass‐based PDO production system. Further Bayesian modelling co‐developed with local stakeholders and experts could greatly contribute to adaptation planning of the regional production system.

Assessing the Influence of Land Use and Land Cover (LULC) Changes on Hydrological Response of the Timah-Tasoh Reservoir

Changing patterns in land use and land cover (LULC) is potentially due to development pressure. Due to the increase in LULC change, it alters natural ecosystems and changing the composition of the natural ecosystem services. This study provides evidence to support the hypothesis that LULC can influence both inflow and discharge. The objectives of the study were to measure the impact of LULC change on inflow of the Timah-Tasoh reservoir. The study area land cover was reclassified into five categories which included agriculture, built-up areas, forest, vacant land, and water bodies. Based on the effect of LULC on hydrological response, it is observed that the catchment is not sensitive to changes in LULC. Two mains of LULC scenarios were used including deforestation and afforestation. With an increase in CN to 89.26 (deforestation), the inflow rate increases by 2% (342.20 m3/s) compared to the present inflow rate. Meanwhile, the inflow rate will decrease to 0.21% (334.80 m3/s) if the CN value is 80.02 (afforestation). Thus, by using this study’s results, a better method and parameters compatible with another hydrological modelling can be developed for future study.

Impact of rainfed agriculture on spatio-temporal patterns of water balance and the interaction between groundwater and surface water in sub-humid plains

The expansion of rainfed agriculture, especially soybean cultivation in sub-humid plains, alters water balance and the exchange between groundwater-surface water (GW-SW). However, to date, there are no studies that analyze how these anthropic disturbances affect hydrological connectivity in these systems, especially the GW-SW interactions. The objective of this study is to analyze how the increase in rainfed agriculture affects the spatio-temporal patterns of the water balance and the GW-SW interaction. For this analysis, a coupled GW-SW flow model was implemented under land use and land cover (LULC) scenarios, to quantify the spatio-temporal dynamics for different components of water balance and GW-SW interactions for the upper creek basin of Del Azul. A simulation was carried out for a period of 13 years (2003–2015) on a daily scale and it was contrasted through three multitemporal LULC maps. The results point that substitution of natural pastures, the reduction of winter crops and the decrease of crop rotation, due to the increase of soybean monoculture in the basin under study, modifies the water balance, especially the annual rates of surface runoff and soil moisture which may increase between 3.5 and 9.4 % and between 1.4 and 4.4 % respectively, thus increasing the annual streamflows between 2.6 and 6.8 % and the groundwater heads between 0.2 and 0.6 m. This leads to changes in the hetereogeneity of the GW-SW interaction, a reduction between 0.3 and 3 % is observed in the discharge from the Pampeano aquifer to the Del Azul stream, while the recharge rates from the Del Azul stream to the Pampeano aquifer increase between 2 and 17.8 %. The application of the SWAT-MODFLOW model under LULC scenarios, improves the prediction of the regional hydrologic connectivity on sub-humid plains, because the hydrological processes occurring in the surface and non-saturated zone are governed by shallow groundwater dynamics.

Assessing the response of non-point source nitrogen pollution to land use change based on SWAT model

In recent years, with the growth of the population and the continuous expansion of agricultural land, non-point source (NPS) pollution has gradually become the primary cause of deteriorating water quality in the aquatic environment. Compared to point source pollution, NPS pollution is more diffuse, complex in its mechanisms, and challenging to pinpoint its sources. This study utilized the SWAT-Land Use Update Tool (SWAT-LUT) to dynamically update multi-year land use and land cover (LULC) data into the SWAT model to investigate the differences in nitrogen pollution sources in Jincheng City under different LULC scenarios. Two models were constructed in this study: SWAT-UNI, which utilized static 1997 LULC data, and SWAT-MULTI, which incorporated dynamic LULC data from 1997 to 2022. During the calibration period, SWAT achieved R2 and NSE values exceeding 0.82 for the daily streamflow simulation results, and these values remained above 0.76 during the validation period. Additionally, the Patch-generating Land Use Simulation (PLUS) model was employed to forecast the land use evolution in Jincheng City from 2022 to 2032 to explore the future response of NPS nitrogen pollution. From 1997 to 2022, significant changes were observed in agricultural land, forested land, and grassland areas within Jincheng City. Agricultural land and forested land increased by 3.29% and 4.71% of the total area of Jincheng City, respectively, while grassland decreased by 10.4%. In the prediction of land use evolution from 2022 to 2032, the evolutionary trends remained similar to previous patterns, albeit with a slightly decelerated pace. Simulation results indicated that the top three sources of nitrogen pollution in Jincheng City's water bodies in 1997 were atmospheric deposition (39.8%), nitrogen fertilizer application (29.8%), and soil nitrogen reservoirs (21.4%). With the continuous expansion of agricultural land, nitrogen pollution from nitrogen fertilizer application accounted for 35.6% of the TN (Total Nitrogen) load in water bodies in 2022, surpassing atmospheric deposition to become the dominant factor. The contribution of soil nitrogen reservoirs to nitrogen pollution in water bodies within Jincheng City showed a continuous upward trend over the twenty-five years, resulting in a total nitrogen load of 565.1 tons in 2022, ranking second and becoming a crucial aspect in pollution control efforts. Regarding seasonal distribution, the crop growing season (March to September) was identified as the critical period for controlling nitrogen pollution from nitrogen fertilizer application, while the autumn and winter seasons were crucial for controlling nitrogen pollution from atmospheric deposition and soil nitrogen reservoirs. The predictive results for future NPS nitrogen pollution indicate a continual increase in annual TN inflow into the river from nitrogen fertilizer application and soil nitrogen reservoirs, reaching 1841.6 tons in 2032, accounting for 65.2% of the total inflow. This research contributes to supporting decision-making for NPS pollution control measures in Jincheng City.

Using multilayer perceptron and similarity-weighted machine learning algorithms to reconstruct the past: A case study of the agricultural expansion on grasslands in the Uruguayan savannas.

Changes in land use and land cover (LULC) have significant implications for biodiversity, ecosystem functioning, and deforestation. Modeling LULC changes is crucial to understanding anthropogenic impacts on environmental conservation and ecosystem services. Although previous studies have focused on predicting future changes, there is a growing need to determine past scenarios using new assessment tools. This study proposes a methodology for LULC past scenario generation based on transition analysis. Aiming to hindcast LULC scenario in 1970 based on the transition analysis of the past 35 years (from 1985 to 2020), two machine learning algorithms, multilayer perceptron (MLP) and similarity weighted (SimWeight), were employed to determine the driver variables most related to conversions in LULC and to simulate the past. The study focused on the Aristida spp. grasslands in the Uruguayan savannas, where native grasslands have been extensively converted to agricultural areas. Land use and land cover data from the MapBiomas project were integrated with spatial variables such as altimetry, slope, pedology, and linear distances from rivers, roads, urban areas, agriculture, forest, forestry, and native grasslands. The accuracy of the predicted maps was assessed through stratified random sampling of reference images from the Multispectral Scanner (MSS) sensor. The results demonstrate a reduction of approximately 659 934 ha of native grasslands in the study area between 1985 and 2020, directly proportional to the increase in cultivable areas. The MLP algorithm exhibited moderate performance, with notable errors in classifying agriculture and grassland areas. In contrast, the SimWeight algorithm displayed better accuracy, particularly in distinguishing grassland and agriculture classes. The modeled map using SimWeight accurately represented the transitions between grassland and agriculture with a high level of agreement. By modeling the 1970s scenario using the SimWeight model, it was estimated that the Aristida spp. grasslands experienced a substantial reduction in grassland coverage, ranging from 9982.31 to 10 022.32 km2 between 1970 and 2020. This represents a range of 60.8%-61.07% of the total grassland area in 1970. These findings provide valuable insights into the driving factors behind land use change in the Aristida spp. grasslands and offer useful information for land management, conservation, and sustainable development in the region. The study's main contribution lies in the hindcasting of past LULC scenarios, utilizing a tool used primarily for forecasting future scenarios. Integr Environ Assess Manag 2024;20:1140-1155. © 2023 SETAC.

Contribution of multi-objective land use optimization to carbon neutrality: A case study of Northwest China

Land use and land cover (LULC) patterns fundamentally influence the carbon cycle of terrestrial ecosystems, and rational optimization of LULC patterns can contribute to achieving carbon neutrality goals. This study constructs a multi-objective LULC optimization coupled model by integrating the objective of carbon neutrality into the LULC optimization process, with Northwest China serving as the study case. This model integrated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with the Intergovernmental Panel on Climate Change (IPCC) inventory methodology to assess the spatiotemporal variations in carbon storage and emissions in Northwest China between 2000 and 2020, which aimed to elucidate the mechanisms through which changes in LULC affect carbon neutrality. Additionally, through the amalgamation of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Patch-Generating Land Use Simulation (PLUS) model, optimized the quantitative structure and spatial layout of four LULC scenarios for Northwest China in 2060: natural development (ND), low carbon emissions (CE), high carbon storage (CS), and carbon neutrality (CN), projected the evolving pathways of carbon storage and emissions within these scenarios while examining the values of different LULC scenarios and their respective contributions to carbon neutrality. The study's findings underscore that the substantial expansion of built-up land development areas and farmland from 2000 to 2020 has resulted in notable carbon storage and emissions increases. Moreover, changes in carbon emissions and carbon storage show significant spatial consistency with changes in LULC patterns. In contrast to the ND scenario, the LULC patterns within the CE, CS, and CN scenarios exhibit higher LULC values and contribute more substantially to carbon neutrality. Remarkably, among these scenarios, the CN scenario stands out for achieving the maximum contribution to carbon neutrality and LULC value, thereby establishing it as the preeminent LULC pattern for the Northwest China region. As the study concludes, it recommended that future developmental strategies in Northwest China prioritize curtailing the expansion of built-up areas while bolstering the conservation of vital ecological zones such as forests and grasslands. This study addresses the inherent tension between regional economic progress and environmental preservation, offering a foundation of scientific insights for effectively managing LULC patterns and attaining carbon neutrality goals.

Analysis of past and future urban growth on a regional scale using remote sensing and machine learning

This research investigates Land Use and Land Cover (LULC) changes in the Porto Alegre Metropolitan Region (RMPA). A 30-year historical analysis using Landsat satellite imagery was made and used to develop LULC scenarios for the next 20 years using a Multilayer Perceptrons (MLP) model through an Artificial Neural Network (ANN). These maps analyze the urban area’s expansion over the years and project their potential development in the future. This research considered several critical factors influencing urban growth, including shaded relief, slope, distances from main roadways, railway stations, urban centers, and the state capital, Porto Alegre. These spatial variables were incorporated into the model’s learning processes to generate future urbanization scenarios. The LULC historical maps precision showed excellent performance with a Kappa index greater than 88% for the studied years. The results indicate that the urbanization class witnessed an increase of 236.78 km2 between 1990 and 2020. Additionally, it was observed that the primary concentration of urbanized areas since 1990 has predominantly occurred around Porto Alegre and Canoas. Lastly, the future forecasts for LULC changes in 2030 and 2040 indicate that the urban area of the RMPA is projected to reach 1,137.48 km2 and 1,283.62 km2, respectively. In conclusion, based on the observed urban perimeter in 2020, future projections indicate that urban areas are expected to increase by more than 443.29 km2 by 2040. The combination of remote sensing data and Geographic Information System (GIS) enables the monitoring and modeling the metropolitan area expansion. The findings provide valuable insights for policymakers to develop more informed and conscientious urban plans, as well as enhance management techniques for urban development.

Climate-altered Precipitation is more Important than Land Use when Modeling Ecosystem Services Associated with Surficial Processes.

Ecosystem services (ESs) associated with surficial processes may change according to shifts in land use, land cover, and climate parameters. Estimating these shifts can be important for land development planning, as urbanization alters soil processes that can manifest legacy effects. We employed the InVEST suite of models for sediment retention, nutrient delivery, and carbon storage to postulate how these ESs will change in the Upstate of South Carolina under future precipitation and land use and land cover (LULC) scenarios. We used the average precipitation from 1981-2010 and WorldClim precipitation projections for 2021-2040 and 2041-2060 to embody climatic precipitation shifts. For our LULC scenarios, we used 2011 and 2016 NLCD landscapes, then projected future LULC to hypothesize four future scenarios. We found that for the ES models that included both precipitation and LULC as inputs, precipitation dictated ES delivery far more heavily than land use or land cover. LULC scenarios produced consistent changes in ES delivery for all models except sediment export. Phosphorus and sediment exports increased between 2011 and 2016 due to LULC change, while nitrogen export stayed the same and carbon storage decreased. Land development that prioritizes forest cover will cause the least change in ESs, but allowing for continued forest loss to low-density development will have the most intense implications for ESs. Prioritization of land uses that preserve ESs associated with surficial processes will be critical to the longevity of agriculture and ecosystem integrity in this rapidly developing region. Land development planners should integrate consideration of ESs associated with surficial processes into future regional planning.

Land-Use Change Effects on Soil Erosion: The Case of Roman “Via Herculia” (Southern Italy)—Combining Historical Maps, Aerial Images and Soil Erosion Model

Land use and land cover (LULC) strongly influence soil erosion/sediment yield, and predicting changes in soil erosion is an important management strategy. Tracing the Earth’s past also helps us better understand the future evolution of the landscape, but research using modern mapping capabilities is hampered by the scarcity of historical landscape information. To fill the data gap and provide an example of how historical maps might be used in land-use change research, we combined an old paper map based on the IT Military Topographical Institute (ITM), aerial photos, and orthophotos to derive land-use history and landscape dynamics at fine scales for a segment of the Roman route “Via Herculia” located in the north-western sector of the Basilicata Region, Italy. Three LULC scenarios were then analysed to represent land use in 1870, 1974, and 2013. Starting from such scenarios, we applied a soil erosion model (Unit Stream Power Eosion and Deposition—USPED) to understand how land-use change over time has modified the areas subject to erosion and deposition. The results show an increase in erosion (from 17% to 20% of the total area) and sediment deposition (from 15% to 19%) over the period 1870–1974. In contrast, over the period of 1974–2013, the results show a decrease in gross erosion (from 20% to 14% of the total area) and sediment deposition (from 19% to 13%).

Updated isoprene and terpene emission factors for the Interactive BVOC (iBVOC) emission scheme in the United Kingdom Earth System Model (UKESM1.0)

Abstract. Biogenic volatile organic compounds (BVOCs) influence atmospheric composition and climate, and their emissions are affected by changes in land use and land cover (LULC). Current Earth system models calculate BVOC emissions using parameterisations involving surface temperature, photosynthetic activity, CO2 and vegetation type and use emission factors (EFs) to represent the influence of vegetation on BVOC emissions. We present new EFs for the Interactive BVOC Emission Scheme (iBVOC) used in the United Kingdom Earth System Model (UKESM), based on those used by the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.1 scheme. Our new EFs provide an alternative to the current EFs used in iBVOC, which are derived from older versions of MEGAN and the Organizing Carbon and Hydrology in Dynamic Ecosystem (ORCHIDEE) emission scheme. We show that current EFs used by iBVOC result in an overestimation of isoprene emissions from grasses, particularly C4 grasses, due to an oversimplification that incorporates the EF of shrubs (high isoprene emitters) into the EF for C3 and C4 grasses (low isoprene emitters). The current approach in iBVOCs assumes that C4 grasses are responsible for 40 % of total simulated isoprene emissions in the present day, which is much higher than other estimates of ∼ 0.3 %–10 %. Our new isoprene EFs substantially reduce the amount of isoprene emitted by C4 grasslands, in line with observational studies and other modelling approaches, while also improving the emissions from other known sources, such as tropical broadleaf trees. Similar results are found from the change to the terpene EF. With the new EFs, total global isoprene and terpene emissions are within the range suggested by the literature. While the existing model biases in the isoprene column are slightly exacerbated with the new EFs, other drivers of this bias are also noted. The disaggregation of shrub and grass EFs provides a more faithful description of the contribution of different vegetation types to BVOC emissions, which is critical for understanding BVOC emissions in the pre-industrial and under different future LULC scenarios, such as those involving wide-scale reforestation or deforestation. Our work highlights the importance of using updated and accurate EFs to improve the representation of BVOC emissions in Earth system models and provides a foundation for further improvements in this area.

100 Years of Land-Use and Land-Cover Data: What Has Been the Effect of Spatial Planning in Coastal Land-Use and Land-Cover Change?

The Sustainable Development Goals require us to rethink spatial planning policies’ effectiveness. This article proposes a reproducible method for assessing the effect of past planning practices and simulating future land-use and land-cover (LULC) changes with a Cellular Automata model. The originality of our approach is to systematically compare observed changes in LULC with the planning rules in force over almost a century of evolution. A quasi-exhaustive database was constructed at a very fine spatial resolution for the municipality of Portimão (Southern Portugal), including the location and changes of LULC categories, and the planning rules of the corresponding time period on nine dates between 1947 and 2018. The quantified measurement of the actual effect of planning rules enables us to identify other determinants of the evolution. Findings reveal that the policies established by the local government—which aimed to foster well-planned comprehensive urban areas—were not as effective as intended. The quantified discrepancies between planning recommendations and observed evolution help to simulate which LULC scenarios could be designed to reach the expected result in future planning policies. Our assessment method could be applied in other urban and tourist regions where land artificialization exerts strong pressure on the environment.

Identifying the impact of land use land cover change on streamflow and nitrate load following modeling approach: a case study in the upstream Dong Nai River basin, Vietnam.

Tri An Reservoir is a vital source of water for agriculture, industry, hydropower, and public usage in Southern Vietnam. Due to human activities, water eutrophication has become a serious problem in recent decades. This study investigated for the first time the impact of land use and land cover (LULC) change on streamflow and nitrate load from the upstream Dong Nai River basin, which is the largest watershed of the reservoir. The study utilized several LULC scenarios, including LULC 2000, 2010, and 2020. The SWAT model was applied to model the watershed during the period 1997-2009. Results showed that the hydrological model performed satisfactorily based on the Nash-Sutcliffe efficiency (NSE) coefficient, the root mean square error observations standard deviation ratio (RSR), and the percent bias (PBIAS). The average simulated values of monthly streamflow and nitrate load were 453.7, 450.0, 446.7 m3/s and 17,699.43, 17,869.13, 17,590.81 tonnes for the LULC 2000, 2010, and 2020 scenarios, respectively. There were no significant differences in streamflow and nitrate load at the basin level under the different LULC scenarios. However, when looking at the subbasin level, there were differences in nitrate load among the scenarios. This suggests that the impacts of LULC on nitrate load may be more pronounced at smaller scales. Overall, our finding underscores the importance of modeling techniques in predicting the impacts of LULC change on streamflow and water quality, which can ultimately aid in the sustainable management of water resources.

Investigating impact of CORDEX-based predicted climatic and LCM-based LULC scenarios on hydrologic response of a semi-gauged Indian catchment.

The present study aims at documenting the impact of different climate and land use change scenarios on runoff in the Kangsabati River basin. While the study relies on India Meteorological Department (IMD), National Oceanic and Atmospheric Administration's Physical Sciences Laboratory (NOAA-PSL), and a multi-model ensemble of six driving models from Coordinated Regional Downscaling Experiment-Regional Climate Models (CORDEX RCM) for climate data input, it depends on IDRISI Selva's Land Change Modeller (LCM) and Soil and Water Assessment Tool (SWAT) model to generate projected land use land change maps and simulate its streamflow response, respectively. A total of four land use and land cover (LULC) scenarios, representing four projected land use change, were modelled across three climatic scenarios, called Representative Concentration Pathways (RCPs). With runoff being predominantly impacted more by climate change than LULC, volumetric runoff is expected to be 12-46% higher than the baseline period of 1982-2017. Conversely, while surface runoff is expected to decrease by 4-28% in lower parts of the basin, it will increase by 2-39% in the rest of it, depending on the subtle alterations in land use and climatic variability.

Impact of LULC on debris flow using linear aggression model from Gilgit to Khunjerab with emphasis on urban sprawl.

In this research, the impact of land use and land cover (LULC) on debris flow was evaluated in the Gilgit to Khunjerab region. Two events have been done: (i) LULC stimulations for 2026 and 2030 using the MOLUSCE plugin and (ii) debris flow susceptibility mapping using linear aggression model. The evaluation of LULC on debris flow susceptibility is based on two scenarios: (i) existing (2010, 2014, 2018, 2022) LULC scenarios and (ii) stimulated (2026, 2030) LULC scenarios. The linear aggression model has 16 contributing factors to developing the debris flow susceptibility mapping. The main contributing components in debris flow susceptibility mapping are slope and LCCS. According to the linear aggressiveness model, debris flow susceptibility grows as the LULC changes, and the high susceptibility zones' share increases. For the current years 2010, 2014, 2018, and 2022, as well as the stimulated years 2026 and 2030, the model had high success rates (> 90.0%) and prediction rates (> 85.0%). The findings backed up prior research and suggested that the impact of LULC will grow in the future.

Assessment of Future Land Use/Land Cover Scenarios on the Hydrology of a Coastal Basin in South-Central Chile

Land use and land cover (LULC) change is one of the clearest representations of the global environmental change phenomenon at various spatial and temporal scales. Chile is worldwide recognized to have areas dedicated to non-native forest plantations that specifically in coastal range show high environmental and economic deterioration, questioning the sustainability of the forestry industry. Currently, there are no studies in Chile that reveal the real effects of the LULC change on the water balance at basin or sub-basin scales associated with future scenarios, which might contribute to territorial decision-making and reveal the real magnitude of the effects of these dynamics. In this study, in order to study LULC dynamics in a coastal basin in South-Central Chile, we assessed and analyzed the effects of future LULC change scenarios on the hydrological processes by generating future synthetic land cover maps from Landsat (Landsat 5 TM and Landsat 8 OLI) image datasets. The hydrological model Soil Water Assessment Tool (SWAT) was calibrated and validated, using hydroclimatic time series, to simulate discharges and other hydrological components over those future LULC scenarios. The LULC future scenarios were projected using combined Markov chain analysis (CA–Markov) and cellular automata algorithms for the near (2025), middle (2035) and far (2045) future. The results revealed that the effects on the different components of the water balance of the basin are not as significant except in the soil water transfer in percolation (increase 72.4%) and groundwater flow (increase 72.5%). This trend was especially observed in sub-basins with non-native forest plantations that dominated land cover in the year 2035, in which an increase of 43.6% in percolation and groundwater flows resulted in increased aquifer recharge and water storage, mainly offset by a decrease of 27% in the evapotranspiration. This work demonstrates the importance of evaluating the impacts of the dynamics of LULC on the hydrological response of a coastal basin, and also on how the land use governance and policy are closely linked to that of water resources.

The impacts of LULC and climate change scenarios on the hydrology and sediment yield of Rib watershed, Ethiopia.

Watershed-scale hydrology and soil erosion are the main environmental components that are greatly affected by environmental perturbations such as climate and land use and land cover (LULC) changes. The purpose of this study is to assess the impacts of scenario-based LULC change and climate change on hydrology and sediment at the watershed scale in Rib watershed, Ethiopia, using the empirical land-use change model, dynamic conversion of land use and its effects (Dyna-CLUE), and soil and water assessment tool (SWAT). Regional climate model (RCM) with Special Report on Emission Scenarios (SRES) and Representative Concentration Pathway (RCP) outputs were bias-corrected and future climate from 2025 to 2099 was analyzed to assess climate changes. Analysis of the LULC change indicated that there has been a high increase in cultivated land at the expense of mixed forest and shrublands and a low and gradual increase in plantation and urban lands in the historical periods (1984-2016) and in the predictions (2016-2049). In general, the predicted climate change indicated that there will be a decrease in precipitation in all of the SRES and RCP scenarios except in the Bega (dry) season and an increase in temperature in all of the scenarios. The impact analysis indicated that there might be an increase in runoff, evapotranspiration (ET), sediment yield, and a decrease in lateral flow, groundwater flow, and water yield. The changing climate and LULC result in an increase in soil erosion and changes in surface and groundwater flow, which might have an impact on reducing crop yield, the main source of livelihood in the area. Therefore, short- and long-term watershed-scale resource management activities have to be designed and implemented to minimize erosion and increase groundwater recharge.

Investigating Relationships between Runoff–Erosion Processes and Land Use and Land Cover Using Remote Sensing Multiple Gridded Datasets

Climate variability, land use and land cover changes (LULCC) have a considerable impact on runoff–erosion processes. This study analyzed the relationships between climate variability and spatiotemporal LULCC on runoff–erosion processes in different scenarios of land use and land cover (LULC) for the Almas River basin, located in the Cerrado biome in Brazil. Landsat images from 1991, 2006, and 2017 were used to analyze changes and the LULC scenarios. Two simulations based on the Soil and Water Assessment Tool (SWAT) were compared: (1) default application using the standard model database (SWATd), and (2) application using remote sensing multiple gridded datasets (albedo and leaf area index) downloaded using the Google Earth Engine (SWATrs). In addition, the SWAT model was applied to analyze the impacts of streamflow and erosion in two hypothetical scenarios of LULC. The first scenario was the optimistic scenario (OS), which represents the sustainable use and preservation of natural vegetation, emphasizing the recovery of permanent preservation areas close to watercourses, hilltops, and mountains, based on the Brazilian forest code. The second scenario was the pessimistic scenario (PS), which presents increased deforestation and expansion of farming activities. The results of the LULC changes show that between 1991 and 2017, the area occupied by agriculture and livestock increased by 75.38%. These results confirmed an increase in the sugarcane plantation and the number of cattle in the basin. The SWAT results showed that the difference between the simulated streamflow for the PS was 26.42%, compared with the OS. The sediment yield average estimation in the PS was 0.035 ton/ha/year, whereas in the OS, it was 0.025 ton/ha/year (i.e., a decrease of 21.88%). The results demonstrated that the basin has a greater predisposition for increased streamflow and sediment yield due to the LULC changes. In addition, measures to contain the increase in agriculture should be analyzed by regional managers to reduce soil erosion in this biome.

Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil

The simulation and analysis of future land use and land cover—LULC scenarios using artificial neural networks (ANN)—has been applied in the last 25 years, producing information for environmental and territorial policy making and implementation. LULC changes have impacts on many levels, e.g., climate change, biodiversity and ecosystem services, soil quality, which, in turn, have implications for the landscape. Therefore, it is fundamental that planning is informed by scientific evidence. The objective of this work was to develop a geographic model to identify the main patterns of LULC transitions between the years 2000 and 2018, to simulate a baseline scenario for the year 2036, and to assess the effectiveness of the Chapecó River ecological corridor (an area created by State Decree No. 2.957/2010), regarding the recovery and conservation of forest remnants and natural fields. The results indicate that the forest remnants have tended to recover their area, systematically replacing silviculture areas. However, natural fields (grassland) are expected to disappear in the near future if proper measures are not taken to protect this ecosystem. If the current agricultural advance pattern is maintained, only 0.5% of natural fields will remain in the ecological corridor by 2036. This LULC trend exposes the low effectiveness of the ecological corridor (EC) in protecting and restoring this vital ecosystem.