Divergent projections of future land use in the United States arising from different models and scenarios

Ecological forecasts of insect range dynamics: a broad range of taxa includes winners and losers under future climate

Species distribution models are the primary tools to project future species' distributions, but this complex task is influenced by data limitations and evolving best practices. The majority of the 53 studies we examined utilized correlative models and did not follow current best practices for validating retrospective or future environmental data layers. Despite this, a summary of results is largely unsurprising: shifts toward cooler regions, but otherwise mixed dynamics emphasizing winners and losers. Harmful insects were more likely to show positive outcomes compared with beneficial species. Our restricted ability to consider mechanisms complicates interpretation of any single study. To improve this area of modeling, more classic field and lab studies to uncover basic ecology and physiology are crucial.

Modeling of land use change under the recent climate projections of CMIP6: a case study of Indian river basin

The aim of the study was to investigate the land use change dynamics under CMIP6 projections using Land Change Modeler (LCM). The Global Sensitivity Analysis (GSA) techniques was applied to quantify the sensitivity of single parameter and combination of parameters. Land use and land cover (LULC) transitions of the baseline period (2006-2016) was assessed with a model performance accuracy of 80%. Receiver operating characteristic (ROC) analysis shows that the model has performed well for all the LULC classes except builtup land. Prediction under the SSP245 scenario depicts that areal extent of agricultural, forest, and snow, and glacier will decrease by the mid-century (2045). However, the grassland and barren land area will increase from the baseline period. A similar change pattern with a higher magnitude has also been predicted under SSP585 scenario. The CMIP6 forcing index considers socio-economic effects and LCM predicted an expansion in barren land which may be attributed to changes in cryosphere in the studied area.

Spatiotemporal change and prediction of land use in Manasi region based on deep learning

The Manasi region is located in an arid and semi-arid region with fragile ecology and scarce resources. The land use change prediction is important for the management and optimization of land resources. We utilized Sankey diagram, dynamic degree of land use, and landscape indices to explore the temporal and spatial variation of land use and integrated the LSTM and MLP algorithms to predict land use prediction. The MLP-LSTM prediction model retains the spatiotemporal information of land use data to the greatest extent and extracts the spatiotemporal variation characteristics of each grid through a training set. Results showed that (1) from 1990 to 2020, cropland, tree cover, water bodies, and urban areas in the Manasi region increased by 855.3465 km², 271.7136 km², 40.0104 km², and 109.2483 km², respectively, whereas grassland and bare land decreased by 677.7243 km² and 598.5945 km², respectively; (2) Kappa coefficients reflect the accuracy of the mode's predictions in terms of quantity. The Kappa coefficients of the land use data predicted by the MLP-LSTM, MLP-ANN, LR, and CA-Markov models were calculated to be 95.58%, 93.36%, 89.48%, and 85.35%, respectively. It can be found that the MLP-LSTM and MLP-ANN models obtain higher accuracy in most levels, while the CA-Markov model has the lowest accuracy. (3) The landscape indices can reflect the spatial configuration characteristics of landscape (land use types), and evaluating the prediction results of land use models using landscape indices can reflect the prediction accuracy of the models in terms of spatial features. The results indicate that the model predicted by MLP-LSTM model conforms to the development trend of land use from 1990 to 2020 in terms of spatial features. This gives a basis for the study of the Manasi region to formulate relevant land use development and rationally allocate land resources.

Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios

Sustainable development goals (SDGs) in the United Nations 2030 Agenda call for action by all nations to promote economic prosperity while protecting the planet. Projection of future land-use change under SDG scenarios is a new attempt to scientifically achieve the SDGs. Herein, we proposed four scenario assumptions based on the SDGs, including the sustainable economy (ECO), sustainable grain (GRA), sustainable environment (ENV), and reference (REF) scenarios. We forecasted land-use change along the Silk Road (resolution: 300 m) and compared the impacts of urban expansion and forest conversion on terrestrial carbon pools. There were significant differences in future land use change and carbon stocks, under the four SDG scenarios, by 2030. In the ENV scenario, the trend of decreasing forest land was mitigated, and forest carbon stocks in China increased by approximately 0.60% compared to 2020. In the GRA scenario, the decreasing rate of cultivated land area has slowed down. Cultivated land area in South and Southeast Asia only shows an increasing trend in the GRA scenario, while it shows a decreasing trend in other SDG scenarios. The ECO scenario showed highest carbon losses associated with increased urban expansion. The study enhances our understanding of how SDGs can contribute to mitigate future environmental degradation via accurate simulations that can be applied on a global scale.

Hydrological and Meteorological Variability in the Volga River Basin under Global Warming by 1.5 and 2 Degrees

The idea of the research to assess the impact of 1.5 °C and 2 °C global warming in the 21st century on the runoff formation in the Volga basin corresponds to the Paris agreement on climate change 2016 with the main goal to keep the global air temperature rise to below 2 °C relative to the pre-industrial level and to take measures to limit warming to 1.5 °C by the end of the 21st century. The purpose of this study was to obtain physically based results of changes in the water regime of the Volga basin rivers under global warming by 1.5 °C and 2 °C relative to pre-industrial values. The physical and mathematical model of runoff generation ECOMAG (ECOlogical Model for Applied Geophysics) was applied in calculations using data from global climate models (GCMs). The estimation of flow anomalies of the Volga River and its major tributaries showed a decrease in annual runoff by 10–11% relative to the period from 1970 to 1999. The largest relative decrease in runoff by 17–20% was noted for the Oka and Upper Volga rivers, while the Kama River had only a 1–5% decrease. The Volga winter runoff increased by 17% and 28% under global warming by 1.5 °C and 2 °C, respectively, and negative runoff anomalies during the spring flood and the summer–autumn period turned out to be in the range of 21 to 23%. Despite the increase in precipitation, the role of evaporation in the water balance of the Volga basin will only increase.

Spatio-Temporal Evolution and Future Simulation of Agricultural Land Use in Xiangxi, Central China

Researches on agricultural land use would help the stakeholders to make better decisions about agricultural resources. However, studies on agricultural land have been lacking. In this context, Xiangxi was chosen as a typical region, and five indicators (Kernel Density, change importance, etc.) and two models (gray forecasting model and GeoSoS-FLUS) were used, to explore the spatio-temporal evolution trends and simulate the future scenarios of agricultural land use. The results were as follows: (1) Xiangxi was dominated by agricultural land, and nearly 50% of total extent was forestry land. Extent of agricultural land decreased by about 56.89 km2 or 3.74% from 2000 to 2018; (2) The density of each agricultural land in the study area had considerable spatial heterogeneity, and showed a main trend of shrinkage, especially in the south regions; (3) In 2030, the spatial pattern and composition of agricultural land in Xiangxi will maintain the existing status, while both of the area and proportion of agricultural land will decline, with a loss of 241.34 km2 or 2.85% decrease from 2000. Nevertheless, the study believed that the slight shrinkage of the agricultural land in Xiangxi is in line with the objective law. At the same time, the study suggested to strengthen the scientific management and rational utilization of agricultural land, with emphasis on arable land and fishery land in the south, especially the administrative center.

Future Climate-Driven Runoff Change in the Large River Basins in Eastern Siberia and the Far East Using Process-Based Hydrological Models

The main goal of this study was to obtain new results on the physically based future hydrological consequences of climate change in the Amur, Lena, and Selenga River basins by using data from an ensemble of global climate (general circulation) models (GCMs) as boundary conditions in spatially distributed, process-based runoff formation models. This approach provides a basis for a more detailed comparison of the sensitivity of hydrological systems of neighboring large river basins in Eastern Siberia and the Far East. The greatest increases in annual flow are predicted for the Lena River under Representative Concentration Pathway (RCP) 2.6 and RCP 6.0 by the middle and end of the 21st century and for the Selenga River under RCP 6.0 by the end of the 21st century, while the Amur flow anomalies are close to zero. During the 21st century, the greatest relative changes in river flow are predicted for the spring flood, especially for the Lena and Selenga, under both scenarios. The summer–autumn and winter runoff of the Amur River has a negative change of up to 8% for the two RCPs, and, on the contrary, the anomalies are positive for the Lena and Selenga. Evaluating runoff variations between RCPs, we noted high summer–autumn and winter runoff changes for the Amur River under RCP 6.0 for the future period, a significant increase in anomalies of the spring and winter runoff of the Lena under RCP 6.0 by the end of the 21st century, and a greater prevalence of summer–autumn and winter runoff increase for the Selenga River under RCP 2.6 during the 21st century, but it is especially pronounced by its end.

Wilderness areas in a changing landscape: changes in land use, land cover, and climate

Wilderness areas are not immune to changes in land use, land cover, and/or climate. Future changes will intensify the balancing act of maintaining ecological conditions and untrammeled character within wilderness areas. We assessed the quantitative and spatial changes in land use, land cover, and climate predicted to occur in and around wilderness areas by (1) quantifying projected changes in land use and land cover around wilderness areas; (2) evaluating if public lands surrounding wilderness areas can buffer future land-use change; (3) quantifying future climate conditions in and around wilderness areas; and (4) identifying wilderness areas expected to experience the most change in land use, land cover, and climate. We used projections of land use (four variables), land cover (five variables), and climate (nine variables) to assess changes for 707 wilderness areas in the contiguous United States by mid-21st century under two scenarios (medium-low and high). We ranked all wilderness areas relative to each other by summing and ranking decile values for each land use, land cover, and climate variable and calculating a multivariate metric of future change. All wilderness areas were projected to experience some level of change by mid-century. The greatest land-use changes were associated with increases in agriculture, clear cutting, and developed land, while the greatest land cover changes were observed for grassland, forest, and shrubland. In 51.6% and 73.8% of wilderness areas, core area of natural vegetation surrounding wilderness was projected to decrease for the medium-low and high scenarios, respectfully. Presence of public land did not mitigate the influence of land-use change around wilderness areas. Geographically, projected changes occurred throughout the contiguous U.S., with areas in the northeast and upper Midwest projected to have the greatest land-use and climate change and the southwestern U.S. projected to undergo the greatest land cover and climate change. Our results provide insights into potential future threats to wilderness areas and the challenges associated with wilderness stewardship and climate adaptation. Despite the high degree of protection and remoteness of wilderness areas, effective management and preservation of these lands must consider future changes in land use, land cover, and climate.

Projected losses of ecosystem services in the US disproportionately affect non-white and lower-income populations

Addressing how ecosystem services (ES) are distributed among groups of people is critical for making conservation and environmental policy-making more equitable. Here, we evaluate the distribution and equity of changes in ES benefits across demographic and socioeconomic groups in the United States (US) between 2020 and 2100. Specifically, we use land cover and population projections to model potential shifts in the supply, demand, and benefits of the following ES: provision of clean air, protection against a vector-borne disease (West Nile virus), and crop pollination. Across the US, changes in ES benefits are unevenly distributed among socioeconomic and demographic groups and among rural and urban communities, but are relatively uniform across geographic regions. In general, non-white, lower-income, and urban populations disproportionately bear the burden of declines in ES benefits. This is largely driven by the conversion of forests and wetlands to cropland and urban land cover in counties where these populations are expected to grow. In these locations, targeted land use policy interventions are required to avoid exacerbating inequalities already present in the US.

Projecting effects of land use change on human well-being through changes in ecosystem services

Changing patterns of land use, temperature, and precipitation are expected to impact ecosystem services, including water quality and quantity, buffering of extreme events, soil quality, and biodiversity. Scenario analyses that link such impacts on ecosystem services to human well-being may be valuable in anticipating potential consequences of change that are meaningful to people living in a community. Ecosystem services provide numerous benefits to community well-being, including living standards, health, cultural fulfillment, education, and connection to nature. Yet assessments of impacts of ecosystem services on human well-being have largely focused on human health or monetary benefits (e.g. market values). This study applies a human well-being modelling framework to demonstrate the potential impacts of alternative land use scenarios on multi-faceted components of human well-being through changes in ecosystem services (i.e., ecological benefits functions). The modelling framework quantitatively defines these relationships in a way that can be used to project the influence of ecosystem service flows on indicators of human well-being, alongside social service flows and economic service flows. Land use changes are linked to changing indicators of ecosystem services through the application of ecological production functions. The approach is demonstrated for two future land use scenarios in a Florida watershed, representing different degrees of population growth and environmental resource protection. Increasing rates of land development were almost universally associated with declines in ecosystem services indicators and associated indicators of well-being, as natural ecosystems were replaced by impervious surfaces that depleted the ability of ecosystems to buffer air pollutants, provide habitat for biodiversity, and retain rainwater. Scenarios with increases in indicators of ecosystem services, however, did not necessarily translate into increases in indicators of well-being, due to covarying changes in social and economic services indicators. The approach is broadly transferable to other communities or decision scenarios and serves to illustrate the potential impacts of changing land use on ecosystem services and human well-being.

Evaluating Water Balance Variables under Land Use and Climate Projections in the Upper Choctawhatchee River Watershed, in Southeast US

Changes in water balance variables are essential in planning and management. Two major factors affecting these variables are climate change and land use change. Few researches have been done to investigate the combined effect of the land use change and climate change using projections. In this study the hydrological processes in Upper Choctawhatchee River Watershed were modeled using the Soil and Water Assessment Tool (SWAT) to investigate the impacts of climate and land use change. We integrated land use projection based in the Shared Socioeconomic Pathways with future climate data to study the combined effects on Hydrological response of the watershed. Future rainfall and temperature, for two time periods, were obtained using General Climate Models to provide SWAT with the climatic forcing in order to project water balance variables. The simulation was carried out under two radiative forcing pathways of RCP4.5 and RCP6.0. Land use change focused on urbanization dominated the climate changes. Impacts on water balance variables differed seasonally. Results showed surface runoff experienced major changes under both emissions scenarios in some months up to 5 times increase. Among the water balance variables, evapotranspiration (ET) as the least dominant pathway for water loss showed the modest changes with the largest decrease during fall and summer. Projection indicated more frequent extreme behavior regarding water balance during midcentury. Discharge was estimated to increase through the year and the highest changes were projected during summer and fall with 186.3% increase in November under RCP6.0. Relying on rainfall for farming along with reduced agricultural landuse (11.8%) and increased urban area (47%) and population growth would likely make the water use efficiency critical. The model demonstrated satisfactory performance, capturing the hydrologic parameters. It thus can be used for further modelling of water quality to determine the sustainable conservation practices and extreme weather events such as hurricane and tropical storms.

Global projections of future urban land expansion under shared socioeconomic pathways

Despite its small land coverage, urban land and its expansion have exhibited profound impacts on global environments. Here, we present the scenario projections of global urban land expansion under the framework of the shared socioeconomic pathways (SSPs). Our projections feature a fine spatial resolution of 1 km to preserve spatial details. The projections reveal that although global urban land continues to expand rapidly before the 2040s, China and many other Asian countries are expected to encounter substantial pressure from urban population decline after the 2050s. Approximately 50-63% of the newly expanded urban land is expected to occur on current croplands. Global crop production will decline by approximately 1-4%, corresponding to the annual food needs for a certain crop of 122-1389 million people. These findings stress the importance of governing urban land development as a key measure to mitigate its negative impacts on food production.

Projected urban growth in the southeastern USA puts small streams at risk

Future land-use development has the potential to profoundly affect the health of aquatic ecosystems in the coming decades. We developed regression models predicting the loss of sensitive fish (R² = 0.39) and macroinvertebrate (R² = 0.64) taxa as a function of urban and agricultural land uses and applied them to projected urbanization of the rapidly urbanizing Piedmont ecoregion of the southeastern USA for 2030 and 2060. The regression models are based on a 2014 investigation of water quality and ecology of 75 wadeable streams across the region. Based on these projections, stream kilometers experiencing >50% loss of sensitive fish and invertebrate taxa will nearly quadruple to 19,500 and 38,950 km by 2060 (16 and 32% of small stream kilometers in the region), respectively. Uncertainty was assessed using the 20 and 80% probability of urbanization for the land-use projection model and using the 95% confidence intervals for the regression models. Adverse effects on stream health were linked to elevated concentrations of contaminants and nutrients, low dissolved oxygen, and streamflow alteration, all associated with urbanization. The results of this analysis provide a warning of potential risks from future urbanization and perhaps some guidance on how those risks might be mitigated.

Land Use Change Detection and Prediction in Upper Siem Reap River, Cambodia

Siem Reap River has played a crucial role in maintaining the Angkor temple complex and livelihood of the people in the basin since the 12th century. Land use in this watershed has changed considerably over the last few decades, which is thought to have had an influence on river. This study was carried out as part of assessing the land use and climate change on hydrology of the upper Siem Reap River. The objective was to reconstruct patterns of annual deforestation from 1988 to 2018 and to explore scenarios of land use 40 and 80 years into the future. A supervised maximum likelihood classification was applied to investigate forest cover change in the last three decades. Multi-layer perceptron neural network-Markov chain (MLPNN-MC) was used to forecast land use and land cover (LULC) change for the years 2058 and 2098. The results show that there has been a significantly decreasing trend in forest cover at the rate 1.22% over the last three decades, and there would be a continuous upward trend of deforestation and downward trend of forest cover in the future. This study emphasizes the impacts of land use change on water supply for the Angkor temple complex (World Heritage Site) and the surrounding population.

Predicting combined effects of land use and climate change on river and stream salinity

Agricultural, industrial and urban development have all contributed to increased salinity in streams and rivers, but the likely effects of future development and climate change are unknown. I developed two empirical models to estimate how these combined effects might affect salinity by the end of this century (measured as electrical conductivity, EC). The first model predicts natural background from static (e.g. geology and soils) and dynamic (i.e. climate and vegetation) environmental factors and explained 78% of the variation in EC. I then compared the estimated background EC with current measurements at 2001 sites chosen probabilistically from all conterminous USA streams. EC was more than 50% greater at 34% of these sites. The second model predicts deviation of EC from background as a function of human land use and environmental factors and explained 60% of the variation in alteration from background. I then predicted the effects of climate and land use change on EC at the end of the century by replacing dynamic variables with published projections of future conditions based on the A2 emissions scenario. By the end of the century, the median EC is predicted to increase from 0.319 mS cm^-1 to 0.524 mS cm^-1 with over 50% of streams having greater than 50% increases in EC and 35% more than doubling their EC. Most of the change is related to increases in human land use, with climate change accounting for only 12% of the increase. In extreme cases, increased salinity may make water unsuitable for human use, but widespread moderate increases are likely a greater threat to stream ecosystems owing to the elimination of low EC habitats.This article is part of the theme issue 'Salt in freshwaters: causes, ecological consequences and future prospects'.

Forest loss in New England: A projection of recent trends

New England has lost more than 350,000 ha of forest cover since 1985, marking a reversal of a two-hundred-year trend of forest expansion. We a cellular land-cover change model to project a continuation of recent trends (1990–2010) in forest loss across six New England states from 2010 to 2060. Recent trends were estimated using a continuous change detection algorithm applied to twenty years of Landsat images. We addressed three questions: (1) What would be the consequences of a continuation of the recent trends in terms of changes to New England's forest cover mosaic? (2) What social and biophysical attributes are most strongly associated with recent trends in forest loss, and how do these vary geographically? (3) How sensitive are projections of forest loss to the reference period—i.e. how do projections based on the period spanning 1990-to-2000 differ from 2000-to-2010, or from the full period, 1990-to-2010? Over the full reference period, 8201 ha yr-1 and 468 ha yr-1 of forest were lost to low- and high-density development, respectively. Forest loss was concentrated in suburban areas, particularly near Boston. Of the variables considered, 'distance to developed land' was the strongest predictor of forest loss. The next most important predictor varied geographically: 'distance to roads' ranked second in the more developed regions in the south and 'population density' ranked second in the less developed north. The importance and geographical variation in predictor variables were relatively stable between reference periods. In contrast, there was 55% more forest loss during the 1990-to-2000 reference period compared to the 2000-to-2010 period, highlighting the importance of understanding the variation in reference periods when projecting land cover change. The projection of recent trends is an important baseline scenario with implications for the management of forest ecosystems and the services they provide.