IMPACT OF LAND SURFACE VEGETATION CHANGE OVER THE LA PLATA BASIN ON THE REGIONAL CLIMATIC ENVIRONMENT: A STUDY USING CONVENTIONAL LAND-COVER/LAND-USE AND NEWLY DEVELOPED ECOSYSTEM FUNCTIONAL TYPES

Naturally occurring or human induced changes in land surface vegetation have been recognized as one of the important factors influencing climate change. The La Plata Basin in South America has experienced significant changes in structural land-cover/land-use types, and those changes can involve changes in the surface physical properties such as albedo and roughness length, evapotranspiration, infiltration, and water storage eventually affecting the development of precipita-tion and the hydroclimate of the basin. In this study, the Weather Research and Forecast (WRF) modeling system was employed to investigate the role of changing land surface conditions in the La Plata Basin. For this purpose, ensembles of seasonal simulations were prepared for a control case and two extreme land cover scenarios: the first one assumes an expansion of the agricultural activities and the second one assumes a "natural" vegetation cover where no croplands are present. An extreme anthropogenic land-cover change -simulating an extensive agricultural practice- implies that the northern part of the basin, where croplands replace forests and savannah, would experience an overall increase in albedo and reduced surface friction. The two changes lead to a reduction of sensible heat and surface temperature, and a somewhat higher evapotranspiration due to decreased stomatal resistance and stronger near-surface winds. The effect on sensible heat seems to dominate and leads to a reduction in convective instability. The stronger low level winds due to reduced friction also imply a larger amount of moisture advected out of the basin, and thus resulting in reduced moisture flux convergence (MFC) within the basin. The two effects, increased stability and reduced MFC, result in a reduction of precipitation. On the other hand, the southern part of the basin exhibits the opposite behavior, as crops would replace grasslands, resulting in reduced albedo, a slight increase of surface temperature and increased precipitation. Notably, the results are not strictly local, as advective processes tend to modify the circulation and precipitation patterns downstream over the South Atlantic Ocean. A newly developed land surface classification, so-called Ecosystem Functional Types (EFTs, systems that share homogeneous energy and mass exchanges with the atmosphere), is implemented in the WRF model to explore its usefulness in regional climate simulations of surface and atmospheric variables. Results show that use of the EFT data improves the climate simulation of 2-m temperature and precipitation, making EFTs a good alternative to land cover types in numerical climate models. An additional advantage of EFTs is that they can be calculated on a yearly basis, thus representing the interannual variability of the surface states. During dry years the 2-m temperature and 10-m wind are more sensitive to changes in EFTs, while during wet years the sensitivity is larger for the 2-m water vapor mixing ratio, convective available potential energy, vertically-integrated moisture fluxes and surface precipitation. This indicates that the impact of land-cover and land-use changes on the climate of the LPB is dependent not only on the wetness of the year, but also on the meteorological or climate variables. Comparisons with observations show that the simulated precipitation difference induced by EFT changes resembles the overall pattern of observed precipitation changes for those same years over the LPB. In the case of the 2-m temperature, the simulated changes due to EFT changes are similar to the observed changes in the eastern part and the southern part of the basin (especially in Uruguay), where t he strongest EFT changes occurred

Scenario analysis for the San Pedro River, analyzing hydrological consequences of a future environment.

Abstract. Studies of future management and policy options based on different assumptions provide a mechanism to examine possible outcomes and especially their likely benefits and consequences. The San Pedro River in Arizona and Sonora, Mexico is an area that has undergone rapid changes in land use and cover, and subsequently is facing keen environmental crises related to water resources. It is the location of a number of studies that have dealt with change analysis, watershed condition, and most recently, alternative futures analysis. The previous work has dealt primarily with resources of habitat, visual quality, and groundwater related to urban development patterns and preferences. In the present study, previously defined future scenarios, in the form of land-use/land-cover grids, were examined relative to their impact on surface-water conditions (e.g., surface runoff and sediment yield). These hydrological outputs were estimated for the baseline year of 2000 and predicted twenty years in the future as a demonstration of how new geographic information system-based hydrologic modeling tools can be used to evaluate the spatial impacts of urban growth patterns on surface-water hydrology

Development of scenarios for land cover, population density, impervious cover, and conservation in New Hampshire, 2010–2100

Future changes in ecosystem services will depend heavily on changes in land cover and land use, which, in turn, are shaped by human activities. Given the challenges of predicting long-term changes in human behaviors and activities, scenarios provide a framework for simulating the long-term consequences of land-cover change on ecosystem function. As input for process-based models of terrestrial and aquatic ecosystem function, we developed scenarios for land cover, population density, and impervious cover for the state of New Hampshire for 2020–2100. Key drivers of change were identified through information gathered from six sources: historical trends, existing plans relating to New Hampshire’s land-cover future, surveys, existing population scenarios, key informant interviews with diverse stakeholders, and input from subject-matter experts. Scenarios were developed in parallel with information gathering, with details added iteratively as new questions emerged. The final scenarios span a continuum from spatially dispersed development with a low value placed on ecosystem services (Backyard Amenities) to concentrated development with a high value placed on ecosystem services (the Community Amenities family). The Community family includes two population scenarios (Large Community and Small Community), to be combined with two scenarios for land cover (Protection of Wildlands and Promotion of Local Food), producing combinations that bring the total number of scenarios to six. Between Backyard Amenities and Community Amenities is a scenario based on linear extrapolations of current trends (Linear Trends). Custom models were used to simulate decadal change in land cover, population density, and impervious cover. We present raster maps and proportion of impervious cover for HUC10 watersheds under each scenario and discuss the trade-offs of our translation and modeling approach within the context of contemporary scenario projects

Unintended Environmental Consequences of a Global Biofuels Program

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).Biofuels are being promoted as an important part of the global energy mix to meet the climate change challenge. The environmental costs of biofuels produced with current technologies at small scales have been studied, but little research has been done on the consequences of an aggressive global biofuels program with advanced technologies using cellulosic feedstocks. Here, with simulation modeling, we explore two scenarios for cellulosic biofuels production and find that both could contribute substantially to future global-scale energy needs, but with significant unintended environmental consequences. As the land supply is squeezed to make way for vast areas of biofuels crops, the global landscape is defined by either the clearing of large swathes of natural forest, or the intensification of agricultural operations worldwide. The greenhouse gas implications of land-use conversion differ substantially between the two scenarios, but in both, numerous biodiversity hotspots suffer from serious habitat loss. Cellulosic biofuels may yet serve as a crucial wedge in the solution to the climate change problem, but must be deployed with caution so as not to jeopardize biodiversity, compromise ecosystems services, or undermine climate policy.This study received funding from the MIT Joint Program on the Science and Policy of Global Change, which is supported by a onsortium of government, industry and foundation sponsors

Evaluation of buffer-radius modelling approaches used in forest conservation and planning

Spatial modelling approaches are increasingly being used to direct forest management and conservation planning at the landscape scale. A popular approach is the use of buffer-radius methods, which create buffers around distinct forest habitat patches to assess habitat connectivity within anthropogenic landscapes. However, the effectiveness and sensitivity of such methods have rarely been evaluated. In this study, Euclidean and least-cost buffer-radius approaches were used to predict functional ecological networks within the wooded landscape of the Isle of Wight (UK). To parameterize the models, a combination of empirical evidence and expert knowledge was used relating to the dispersal ability of a model species, the wood cricket (Nemobius sylvestris Bosc.). Three scenarios were developed to assess the influence of increasing the amount of spatial and species-specific input data on the model outcomes. This revealed that the level of habitat fragmentation for the model species is likely to be underestimated when few empirical data are available. Furthermore, the least-cost buffer approach outperformed simple Euclidean buffer in predicting presence and absence for the model species. Sensitivity analyses on model performance revealed high sensitivity of the models to variation in buffer distance (i.e. maximum dispersal distance) and permeability of common landscape features such as roads, watercourses, grassland and semi-natural habitat. This indicates that when data are lacking with which to parameterize buffer-radius models, the model outcomes need to be interpreted with caution. This study also showed that if sufficient empirical data are available, least-cost buffer approaches have the potential to be a valuable tool to assist forest managers in making informed decisions. However, least-cost approaches should always be used as an indicative rather than prescriptive management tool to support forest landscape conservation and planning

Evaluation and Prediction of Land-Use Changes using the CA_Markov Model

AbstractThe purpose of this study was to model and predict temporal and spatial patterns of land-use change in the Zayandehrud basin. In this research, the CA-Markov prediction model was used to simulate and predict land-use change. First, the land-use changes from 1996 to 2018 were studied and then the future changes for 2030 and 2050 were simulated. Afterward, the future land-use scenarios were designed. The model was validated by comparing the simulated map of 2018 with the real map, and the kappa coefficient of 94 % was utilized to evaluate the model. Based on the results, the Built-up land-use was altered from 13016 hectares in 1996 to 154194 hectares in 2050. This outcome necessitates the management of the future development of the city. Furthermore, the amount of agricultural land was varied from 177067 hectares in 1996 to 40,000 hectares in 2050. Among all the changes, agricultural lands attracted the most attention and concerns. The results indicated the land-use changes in the form of urban areas and reducing area of agricultural lands. Such alterations were taken place in two distinct stages: urban lands have been developing since 2013, with a direct impact on the reduction of vegetation due to the conversion of agricultural lands into other land-uses. The dynamic trend of changes has also been confirmed and intensified since 1996. In 2018, a significant area of agricultural lands was converted into urban and industrial areas. In addition, the agricultural and orchard lands were 74057 hectares in 2018 and can be reduced to 40,000 hectares by 2050. It revealed 34057 hectares lost as compared to the agricultural and orchard lands in 2018. The present study depicts that the expansion of urban and industrial activities and reducing the level of agricultural land in the region requires more attention and care in land management. Extended Abstract:Introduction: Land use/Land cover (LULC) change is one of the main issues of sustainable development. To provide a rational science for regional planning decisions and sustainable development, land use pattern prediction models based on past preliminary information can be used to construct future scenarios of land-use changes. Modeling and predicting land use changes provide an interesting perspective for applications in planning units such as river basins and make it an effective tool for analyzing the causal dynamics of the future landscape under different scenarios. Land-use models are considered a powerful tool for understanding the spatio-temporal pattern of land-use changes, such as the Markov chain, cellular automation, and hybrid models based on these methods, which are widely used to simulate the spatial and temporal dimensions of land use. In the present study, land use changes prediction was performed using a combined model of cellular automation and the Markov chain (CA-Markov) to simulate temporal and spatial land-use patterns. The present study tries to predict land-use changes in the Zayandehrood river basin. The Zayandehrud basin is currently facing major environmental problems (such as water resources scarcity, population growth, urban development, and agricultural land degradation). Therefore, it is essential to evaluate land-use changes for this sensitive basin. In particular, the objectives of the research include two stages: 1) patial modeling of land-use change, and 2) predicting spatio-temporal patterns of land-use changes in the Zayandehrud river basin.Therefore, in the present research, land-use changes from 1996 to 2018 were investigated and future changes for 2030 and 2050 were simulated. Methodology: In this research, land-use changes modeling was performed in three time periods 1996 to 2013 (17-year period), 2013 to 2018 (5-year period), and 2018 to 2030 and 2050. The purpose of modeling is to determine the capabilities of the Markov chain model and integrate it with cellular automation to detect land-use changes. The images were classified into 4 classes: agriculture and gardens, built-up (urban areas, airport, and road), industrial towns, and other land uses (abandoned lands and fallow, rangeland, water areas). Finally, land-use changes modeling was performed in the period 1996 to 2050 (54-year period). The steps of the research method are as follows:Step 1: Pre-processing of satellite images: Radiometric correction was applied to the images. Next, the images were processed using the FLAASH module in ENVI5.3 software to reduce atmospheric interference. Then, by synthesizing the name and wavelength of the bands, image storage, mosaic, and mask clipping, a preprocessed remote sensing image was generated. Finally, the preprocessed remote sensing image was obtained.Step 2: Processing satellite images: Types of land use images in the area in ENVI 5.3 were extracted using visual interpretation and supervised classification methods. Land use classification algorithms were used to estimate the three main land-use classes (agriculture, urban, and industrial development). The principal component analysis method was performed on the images and was identified agricultural by high resolution. Land use classification for 1996, 2013, and 2018 was done with a classification approach based on the decision tree. To classify the images, maximum likelihood methods, artificial neural networks, and support vector machines were used. The final classification was performed using decision tree analysis. Finally, prediction of land-use changes was performed on images by performing the CA_Markov analysis in TerrSet software.Step 3: Post-processing of satellite images: Using Google Earth and cross-tab analysis, TerrSet software evaluated the accuracy of classifying land-use thematic maps. Using the existing database, a validation process was performed to ensure the accuracy of the model in predicting land-use changes for the forecasted 2018 map. The accuracy of the simulated model of land-use change in 2018 was validated and then compared with the actual map of the same year. The validation process was performed by generating the kappa coefficient. Discussion: In this study, land-use changes in the Zayandehrood basin were identified and investigated. The results showed that land-use changes are in the form of urban development and reduction of agricultural land use. Such changes have occurred in two distinct stages. First, urban land expansion has prevailed since 2013, with a direct impact on declining vegetation as a result of the conversion of agricultural land to other land uses. The dynamic trend of changes has also been confirmed and intensified since 1996. Because in 2018, a significant area of agricultural lands was converted into urban and industrial areas. Future scenarios based on the CA-Markov model provide valuable information about future land use and land cover changes in the study area. This study can identify land-use changes in different periods and depict the increase or decrease of important land uses in the region. According to the study of Motlagh et al. (2020), land-use changes were studied based on three possible scenarios (i.e. the current trend of land use growth, conservation of agricultural lands, and urban development forecast). Future scenarios for 2030 and 2050 estimate that there will be a significant reduction in vegetation and agricultural lands and orchards and continued urban and industrial development in areas along the Zayandehrood basin. Expansion of the agricultural sector along with the conservation of natural resources is not only one of the most important challenges of sustainable development in the Zayandehrud basin but is also essential for future strategic land use plans. Compilation of instructions for sustainable agricultural development can be a way to strike a balance between nature conservation and economic development in the region. Conclusion: In summary, this study demonstrates how the proposed CA-Markov model is used to better simulate land use complex and dynamic changes over time. Of all the land-use changes, the most worrying is the situation in the region for agricultural lands. If the current trend of land use continues, we estimate that by 2050, its area will be halved, and such changes in the landscape will undoubtedly change the entire ecosystem of the basin, emphasizing that the negative effects on the vegetation of the basin have a direct impact on the economic sector of the region because maintaining the quality of the environment of the Zayandehrood river basin is essential for ecotourism. Therefore, the management and planning of the basin are highly recommended to preserve its unique ecosystem, as well as to protect the vegetation in the area. The methods and results of this study will be useful for policymakers and urban planners for precise planning of the region to be able to manage the city using farms and conserving water resources and urban infrastructure development planning for environmentally sustainable development. Keywords: Land-Use Changes, Cellular Automation, the Markov Chain, Zayandehrud River Basin. References- Asgarian, A., Soffianian, A., Pourmanafi, S., & Bagheri, M. (2018). Evaluating the spatial effectiveness of alternative urban growth scenarios inprotecting cropland resources: A case of mixed agricultural-urbanized landscape in central Iran. Journal of Sustainable Cities and Society, 43, 197-207.- Assaf, C., Adamsa, C., Ferreira, F. F., & Françac, H. (2021). 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Research priorities in land use and land-cover change for the Earth System and Integrated Assessment Modelling

This special issue has highlighted recent and innovative methods and results that integrate observations and modelling analyses of regional to global aspect of biophysical and biogeochemical interactions of land-cover change with the climate system. Both the Earth System and the Integrated Assessment modeling communities recognize the importance of an accurate representation of land use and land-cover change to understand and quantify the interactions and feedbacks with the climate and socio-economic systems, respectively. To date, cooperation between these communities has been limited. Based on common interests, this work discusses research priorities in representing land use and land-cover change for improved collaboration across modelling, observing and measurement communities. Major research topics in land use and land-cover change are those that help us better understand (1) the interaction of land use and land cover with the climate system (e.g. carbon cycle feedbacks), (2) the provision of goods and ecosystem services by terrestrial (natural and anthropogenic) land-cover types (e.g. food production), (3) land use and management decisions and (4) opportunities and limitations for managing climate change (for both mitigation and adaptation strategies

Biophysical suitability, economic pressure and land-cover change: a global probabilistic approach and insights for REDD+

There has been a concerted effort by the international scientific community to understand the multiple causes and patterns of land-cover change to support sustainable land management. Here, we examined biophysical suitability, and a novel integrated index of “Economic Pressure on Land” (EPL) to explain land cover in the year 2000, and estimated the likelihood of future land-cover change through 2050, including protected area effectiveness. Biophysical suitability and EPL explained almost half of the global pattern of land cover (R 2 = 0.45), increasing to almost two-thirds in areas where a long-term equilibrium is likely to have been reached (e.g. R 2 = 0.64 in Europe). We identify a high likelihood of future land-cover change in vast areas with relatively lower current and past deforestation (e.g. the Congo Basin). Further, we simulated emissions arising from a “business as usual” and two reducing emissions from deforestation and forest degradation (REDD) scenarios by incorporating data on biomass carbon. As our model incorporates all biome types, it highlights a crucial aspect of the ongoing REDD + debate: if restricted to forests, “cross-biome leakage” would severely reduce REDD + effectiveness for climate change mitigation. If forests were protected from deforestation yet without measures to tackle the drivers of land-cover change, REDD + would only reduce 30 % of total emissions from land-cover change. Fifty-five percent of emissions reductions from forests would be compensated by increased emissions in other biomes. These results suggest that, although REDD + remains a very promising mitigation tool, implementation of complementary measures to reduce land demand is necessary to prevent this leakage