Biogeophysical climate impacts of forest management in Switzerland

Forests influence climate through biogeochemical and biogeophysical processes. Biogeochemical processes include greenhouse gas (GHG) exchange as well as emissions of other chemical compounds such as biogenic volatile organic compounds, which can act as aerosol precursors. The biogeophysical effect, on the other hand, refer to the alteration of land properties such as albedo, evapotranspiration and surface roughness. The climate impacts of land use activities such as forestry are routinely monitored in terms of GHG emissions under the United Nations Framework Convention on Climate Change. The associated biogeophysical impacts, however, are not accounted for as part of this framework despite the growing awareness that these effects matter regionally and should therefore be considered in the decision-making process. In this report, we synthetizes the current state of knowledge concerning the biogeophysical effect of forestry activities with a special focus on Switzerland. Beside reviewing the existing literature we also present new results for Switzerland based on observation-driven estimates as well as process-based modelling

The role of urban trees in reducing land surface temperatures in European cities

Urban trees influence temperatures in cities. However, their effectiveness at mitigating urban heat in different climatic contexts and in comparison to treeless urban green spaces has not yet been sufficiently explored. Here, we use high-resolution satellite land surface temperatures (LSTs) and land-cover data from 293 European cities to infer the potential of urban trees to reduce LSTs. We show that urban trees exhibit lower temperatures than urban fabric across most European cities in summer and during hot extremes. Compared to continuous urban fabric, LSTs observed for urban trees are on average 0-4 K lower in Southern European regions and 8-12 K lower in Central Europe. Treeless urban green spaces are overall less effective in reducing LSTs, and their cooling effect is approximately 2-4 times lower than the cooling induced by urban trees. By revealing continental-scale patterns in the effect of trees and treeless green spaces on urban LST our results highlight the importance of considering and further investigating the climate-dependent effectiveness of heat mitigation measures in cities

Biomass heat storage dampens diurnal temperature variations in forests

Observational evidence suggests that compared to non-forested areas, forests have a cooling effect on daytime land surface temperature (LST) and a warming effect on nighttime LST in many regions of the world, thus implying that forests dampen the diurnal temperature range. This feature is not captured by current climate models. Using the Community Land Model 5.0 (CLM5.0), we show that this diurnal behavior can be captured when accounting for biomass heat storage (BHS). The nighttime release of energy absorbed by the vegetation biomass during the day increases both nighttime LST and ambient air temperature in forested regions by more than 1 K. The daytime cooling is weaker than the nighttime warming effect, because the energy uptake by the biomass is compensated by a reduction in the turbulent heat fluxes during day. This diurnal asymmetry of the temperature response to BHS leads to a warming of daily mean temperatures, which is amplified during boreal summer warm extremes. Compared to MODIS, CLM5.0 overestimates the diurnal LST range over forested areas. The inclusion of BHS reduces this bias due to its dampening effect on diurnal LST variations. Further, BHS attenuates the negative bias in the nighttime LST difference of forest minus grassland and cropland, when compared to MODIS observations. These results indicate that it is essential to consider BHS when examining the influence of forests on diurnal temperature variations. BHS should thus be included in land surface models used to assess the climatic consequences of land use changes such as deforestation or afforestation

Do Electric Vehicles Mitigate Urban Heat? The Case of a Tropical City

On top of their well known positive impact on air quality and CO2 emissions, electric vehicles generate less exhaust heat compared to traditional vehicles thanks to their high engine efficiency. As such, electric vehicles have the potential to mitigate the excessive heat in urban areas—a problem which has been exacerbated due to urbanisation and climate change. Still, the heat mitigation potential of electric vehicles has not been fully understood. Here, we combine high-resolution traffic heat emission inventories with an urban climate model to simulate the impact of the fleet electrification to the near-surface air temperature in the tropical city of Singapore. We show that a full replacement of traditional internal combustion engine vehicles with electric vehicles reduces the near-surface air temperature by up to 0.6°C. The heat mitigation potential is highest during the morning traffic peak and over areas with the largest traffic density. Interestingly, the reduction in exhaust heat emissions due to the fleet electrification during the evening traffic peak hardly leads to a reduction of near-surface air-temperatures, which is attributed to the different atmospheric conditions during morning and evening. This study presents a new quantification of the city-wide impact of electric vehicles on the air temperature in a tropical urban area. The results may support policy-makers toward designing holistic solutions to address the challenge of climate change adaptation and mitigation in cities.Peer Reviewe

Sprawl or compactness? How urban form influences urban surface temperatures in Europe

The surface of cities is often warmer than the surface of their surroundings. This phenomenon is known as the surface urban heat island (SUHI) effect and has several adverse implications. Studies have shown that the SUHI effect tends to be weaker if urban form is characterized by sprawl or polycentrism. These findings suggest that urban heat could be mitigated if a city is less compact. By analyzing high-resolution remote-sensing land surface temperature (LST) and land-cover data for 293 European cities, this study shows that — contrary to many previous findings — sprawling or polycentric urban forms do not necessarily lead to a decrease of LSTs over urban areas. In southern European cities, sprawl could even lead to the warming of urban areas during specific daytimes, highlighting the importance of considering environmental and regional contexts when determining the role of urban form in heat mitigation. It is also crucial to consider the predominant type of land cover surrounding a city since sprawl into forested areas could have a very different effect than sprawl into agricultural areas. These results illustrate the complexity of urban form related heat mitigation and that policy- and decision-makers have to consider local and regional contexts when steering urban form.ISSN:2590-252

Using Multi-Objective Optimization for Improving the Sustainability of Urban Development

Urbanization is a global phenomenon observed at unprecedented pace and scale. It is a threat to many ecosystem services and yet it also offers the possibility to shape a sustainable future. The United Nations have defined 11 goals that are intended to make cities and communities sustainable. Reaching these goals is a wicked challenge for decision-makers because the goals are often conflicting and there are many uncertainties and complexities that need to be considered when addressing them. In order to help decision makers to deal with a wicked challenge, to anticipate and understand the consequences of their actions or even to help them develop a vision for the future, it is often necessary to employ simulation models. A wide variety of modelling and simulation approaches dealing with urban development is available. However, many of them are not very suitable to deal with the wickedness of the problem of reaching the sustainable urban development goals. In particular, they may struggle to deal with the many conflicting objectives involved. Using a multi-objective optimization approach specifically offers the possibility to account for conflicting objectives. It offers potential solutions to problems that are too complex for humans to solve, and it can be used to derive optimal solutions comprising a vision for a sustainable future. Thus, multi-objective optimization has some interesting properties that could be exploited and used to complement the existing set of modeling approaches used to support decision-making related to sustainable urban development. However, so far the possible applications of multi-objective optimization have rarely been demonstrated to support decision-makers dealing with the wicked challenge of improving the sustainability of urban development. The aim of this thesis is not only to offer support to decision-makers dealing with the sustainability of urban development, but to demonstrate how multi-objective optimization can be used to support decision-makers for a larger scope of problems related to land-use change and management. The latter is supposed to promote the use of multi-objective optimization in land-use system science. A further step in promoting multi-objective optimization in land-use system science is to present effective algorithms to solve spatially explicit multi-objective optimization problems, which is a prerequisite for applying multi-objective optimization. I exemplify the use of multi-objective optimization by addressing two goals that are related to sustainable urban development. These two goals (i.e. objectives) are to maximize compact urban development and to minimize the loss of fertile agricultural soils in Swiss municipalities. In chapter 2, the use of multi-objective optimization will be promoted by showing that there are efficient ways of solving spatially explicit optimization problems. As a strategy for solving the optimization problem I use a so-called genetic algorithm, which is a popular approach when dealing with two objectives. Promoting a wider use of multi-objective optimization approaches, by applying them to answer new research questions and showing possible new applications is achieved in chapters 3, 4 and 5 of this thesis. In more detail, in chapter 2 I demonstrate how genetic algorithms (GAs) can be modified in order to solve complex multi-objective optimization problems involving spatial relationships. I used and adapted, the so-called NSGA-II (Non-dominated Sorting Genetic Algorithm – II), for solving the multi-objective problem of minimizing the loss of fertile agricultural soils (i.e., agricultural productivity) due to urban growth and at the same time to maximize compact (i.e. contiguous) urban development. I compared existing modifications of GAs from literature and modifications developed by myself. In order to account for the spatial relationships, I found that it was crucial to include knowledge about how compact urban patterns evolve. In contrast, objectives such as agricultural productivity that do not involve any spatial dependencies (i.e. no neighborhood-relationships are involved) do not need to be dealt with in a special way. This knowledge provides a guideline for future researchers on how to solve multi-objective optimization problems involving spatial relationships. In chapter 3, I show that it is possible to efficiently reduce the loss of agricultural productivity by steering the pattern of urban growth. In a first analysis, I reveal that fertile soils are often found in the vicinity of existing urban areas. This leads to a potential trade-off, as compact (i.e., contiguous) urban development can thus lead to a high loss of fertile soils. In order to protect as much agricultural productivity (i.e., fertile soils) as possible, decision makers can either resign from having compact urban development or can strive for solutions (i.e. urban patterns) that were obtained in an optimization process. Although using optimization is a difficult and costly approach, my results show that this approach can be used in an efficient manner. Firstly, by comparing simulations of Business As Usual (BAU) urban expansion and urban patterns obtained by multi-objective optimization, I was able to show that there exist some regions for which there is a large difference between BAU and optimal solutions (methodical details on how the BAU urban expansion was modelled can be found in Appendix 1.). In order to aid decision makers, I derived a simple rule stating that in regions where high urban growth is anticipated (i.e. many agricultural areas will be converted into urban) the difference between BAU and optimal solutions is large and policy-makers should focus their efforts on steering the pattern of urban development in these regions. Secondly, there are areas of agricultural land that can be converted into urban without losing compactness or the most fertile soils. This knowledge could help policy-makers and planners prioritize areas that can safely be converted to urban without destroying more fertile soils than necessary or without losing more compactness than necessary. In chapter 4, I use multi-objective optimization in order to simulate zoning decisions of urban planners and I exemplify how planning paradigms (in the form of constraints) can be an obstacle in reaching optimal solutions. Although there are many objectives that urban planners need to account for, I am assuming that they focus mainly on reducing the loss of fertile soils and promoting compact urban growth. The planning paradigms that I consider is that zoning is carried out at a local level (i.e. at municipality level) and that a predefined amount of urban zones needs to be created in each municipality. My results show that planning at a local level can be a constraint in reaching more optimal solutions and that cooperation at higher levels is required for an improved protection of agricultural productivity. While the latter is not surprising, I further show that using multi-objective optimization can elucidate whether and how municipalities should cooperate. The two ways in which municipalities can cooperate are (1) that they adapt their preferences in collaboration with other municipalities and (2) that they make agreements on the amounts of urban growth that is permitted in each municipality. The first option for collaboration can be useful if, e.g., two municipalities adapt their preferences, because in one of them a slightly higher loss of compactness can lead to a large gain in agricultural productivity, while in the other one a small increase in the loss of agricultural productivity can lead to a large gain in compactness The second option for collaboration is more effective than the first option, i.e., leading to a stronger reduction of loss of agricultural productivity. However, the second option may require a stronger institutional framework than the first one and may thus also be related to disadvantages. In chapter 5, I use multi-objective optimization to simulate consecutive planning periods in order to come up with recommendations on the length of planning horizons. Again I am relying on the example of allocating urban zones in such a way that compactness of the derived urban patterns is maximal and the loss of agricultural productivity is minimal. In a toy experiment, I prove that due to the non-linear combinatorial nature of the problem of optimally allocating urban zones, short planning horizons may lead to non-optimal solutions. However, in a real-world situation I show that a short planning horizon does not necessarily lead to non-optimal patterns. While these results are interesting, the approach described in chapter 5 is especially valuable for demonstrating a methodology that could be enhanced in order to study in more detail the adequate length of planning horizons depending on the objectives involved and the characteristics of the planning perimeter. In summary, this thesis shows that multi-objective optimization can complement current urban- and land-use modelling approaches by answering novel questions or questions that remained unanswered so far. The methodological advances presented in chapter 2, 3, 4 and 5 will help future researches to understand whether multi-objective optimization may be an appropriate approach to address their research questions. Pursuing the same purpose, i.e., facilitating researchers with a better understanding on the possibilities and limitations of multi-objective optimization, chapter 6 discusses a potential taxonomy of optimization approaches for urban and land-use modelling. The same chapter concludes with possible future research directions that build upon results presented in this thesis and with some recommendations concerning sustainable urban development

Moving towards integrating soil into spatial planning: No net loss of soil-based ecosystem services

Degradation of ecosystems and the related loss of ecosystem services have called for new policies to achieve no net loss (NNL) of or even net gain between detrimental environmental impacts and restoration or preservation measures. While biodiversity offsetting has a long tradition, soils have rarely been considered in the accounting. Considering the crucial role of soil for ecosystem functioning and biodiversity and the increasing pressure on soil resources, we investigate how a NNL strategy building on a soil-based ecosystem services index can help steer sustainable spatial development. An ecosystem services’ soil quality index allows to explicitly address the interests of a broad range of stakeholder on soil uses. Using a market-driven spatial planning instrument based on a land price fee linked to the soil quality index, we demonstrate how soil quality loss and related ecosystem services could be reduced by up to 60% compared to current practice in a case study in Switzerland. More importantly, the suggested instrument allows to account for the spatial variability of the supply of the ecosystem services and the diversity of stakeholder demands for various soil qualities. We close with a discussion on the consequences of implementing a soil-based NNL strategy for spatial development and its generic application for steering settlement development.ISSN:0301-4797ISSN:1095-863

How spatial policies can leverage energy transitions − Finding Pareto-optimal solutions for wind turbine locations with evolutionary multi-objective optimization

Energy production from the power of wind is being increasingly promoted around the world to reach climate and energy targets and gain independence from fossil fuels. The identification of possible sites for on-shore wind energy production faces multiple challenges. National wind energy strategies cite various physical, ecological, social and economic constraints in defining wind turbine locations. Furthermore, the acceptance of new infrastructure is highly dependent on regional and local conditions.Consequently, the design of policy guidelines that simultaneously consider various constraints and goals in a spatially explicit manner is highly challenging. To tackle this challenge, we demonstrate how a state-of-the-art evolutionary optimization algorithm can inform policy-makers in leveraging various planning policies to optimize wind energy production. We investigate the spatial and non-spatial effects of different policies considering multiple planning targets and constraints. Moreover, we analyze trade-offs between different wind turbine planning targets. Based on the results, we outline several policy implications to support the identification of development areas for wind turbines in Switzerland. The proposed optimization method enables us to better understand the national planning horizon within a regional context and vice versa.ISSN:1462-9011ISSN:1873-641

Participatory multi-objective optimization for planning dense and green cities

ISSN:0964-0568ISSN:1360-055