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The large literature that aims to find evidence of climate migration delivers mixed findings. This meta-regression analysis i) summarizes direct links between adverse climatic events and migration, ii) maps patterns of climate migration, and iii) explains the variation in outcomes. Using a set of limited dependent variable models, we meta-analyze thus-far the most comprehensive sample of 3,625 estimates from 116 original studies and produce novel insights on climate migration. We find that extremely high temperatures and drying conditions increase migration. We do not find a significant effect of sudden-onset events. Climate migration is most likely to emerge due to contemporaneous events, to originate in rural areas and to take place in middle-income countries, internally, to cities. The likelihood to become trapped in affected areas is higher for women and in low-income countries, particularly in Africa. We uniquely quantify how pitfalls typical for the broader empirical climate impact literature affect climate migration findings. We also find evidence of different publication biases.
Climate change fundamentally transforms glaciated high-alpine regions, with well-known cryospheric and hydrological implications, such as accelerating glacier retreat, transiently increased runoff, longer snow-free periods and more frequent and intense summer rainstorms. These changes affect the availability and transport of sediments in high alpine areas by altering the interaction and intensity of different erosion processes and catchment properties.
Gaining insight into the future alterations in suspended sediment transport by high alpine streams is crucial, given its wide-ranging implications, e.g. for flood damage potential, flood hazard in downstream river reaches, hydropower production, riverine ecology and water quality. However, the current understanding of how climate change will impact suspended sediment dynamics in these high alpine regions is limited. For one, this is due to the scarcity of measurement time series that are long enough to e.g. infer trends. On the other hand, it is difficult – if not impossible – to develop process-based models, due to the complexity and multitude of processes involved in high alpine sediment dynamics. Therefore, knowledge has so far been confined to conceptual models (which do not facilitate deriving concrete timings or magnitudes for individual catchments) or qualitative estimates (‘higher export in warmer years’) that may not be able to capture decreases in sediment export. Recently, machine-learning approaches have gained in popularity for modeling sediment dynamics, since their black box nature tailors them to the problem at hand, i.e. relatively well-understood input and output data, linked by very complex processes.
Therefore, the overarching aim of this thesis is to estimate sediment export from the high alpine Ötztal valley in Tyrol, Austria, over decadal timescales in the past and future – i.e. timescales relevant to anthropogenic climate change. This is achieved by informing, extending, evaluating and applying a quantile regression forest (QRF) approach, i.e. a nonparametric, multivariate machine-learning technique based on random forest.
The first study included in this thesis aimed to understand present sediment dynamics, i.e. in the period with available measurements (up to 15 years). To inform the modeling setup for the two subsequent studies, this study identified the most important predictors, areas within the catchments and time periods. To that end, water and sediment yields from three nested gauges in the upper Ötztal, Vent, Sölden and Tumpen (98 to almost 800 km² catchment area, 930 to 3772 m a.s.l.) were analyzed for their distribution in space, their seasonality and spatial differences therein, and the relative importance of short-term events. The findings suggest that the areas situated above 2500 m a.s.l., containing glacier tongues and recently deglaciated areas, play a pivotal role in sediment generation across all sub-catchments. In contrast, precipitation events were relatively unimportant (on average, 21 % of annual sediment yield was associated to precipitation events). Thus, the second and third study focused on the Vent catchment and its sub-catchment above gauge Vernagt (11.4 and 98 km², 1891 to 3772 m a.s.l.), due to their higher share of areas above 2500 m. Additionally, they included discharge, precipitation and air temperature (as well as their antecedent conditions) as predictors.
The second study aimed to estimate sediment export since the 1960s/70s at gauges Vent and Vernagt. This was facilitated by the availability of long records of the predictors, discharge, precipitation and air temperature, and shorter records (four and 15 years) of turbidity-derived sediment concentrations at the two gauges. The third study aimed to estimate future sediment export until 2100, by applying the QRF models developed in the second study to pre-existing precipitation and temperature projections (EURO-CORDEX) and discharge projections (physically-based hydroclimatological and snow model AMUNDSEN) for the three representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.
The combined results of the second and third study show overall increasing sediment export in the past and decreasing export in the future. This suggests that peak sediment is underway or has already passed – unless precipitation changes unfold differently than represented in the projections or changes in the catchment erodibility prevail and override these trends. Despite the overall future decrease, very high sediment export is possible in response to precipitation events. This two-fold development has important implications for managing sediment, flood hazard and riverine ecology.
This thesis shows that QRF can be a very useful tool to model sediment export in high-alpine areas. Several validations in the second study showed good performance of QRF and its superiority to traditional sediment rating curves – especially in periods that contained high sediment export events, which points to its ability to deal with threshold effects. A technical limitation of QRF is the inability to extrapolate beyond the range of values represented in the training data. We assessed the number and severity of such out-of-observation-range (OOOR) days in both studies, which showed that there were few OOOR days in the second study and that uncertainties associated with OOOR days were small before 2070 in the third study. As the pre-processed data and model code have been made publically available, future studies can easily test further approaches or apply QRF to further catchments.
The need to develop sustainable resource management strategies for semi-arid and arid rangelands is acute as non-adapted grazing strategies lead to irreversible environmental problems such as desertification and associated loss of economic support to society. In such vulnerable ecosystems, successful implementation of sustainable management strategies depends on well-founded under-standing of processes at different scales that underlay the complex system dynamic. There is ample evidence that, in contrast to traditional sectoral approaches, only interdisciplinary research does work for resolving problems in conservation and natural resource management. In this thesis I combined a range of modeling approaches that integrate different disciplines and spatial scales in order to contribute to basic guidelines for sustainable management of semi-arid and arid range-lands. Since water availability and livestock management are seen as most potent determinants for the dynamics of semi-arid and arid ecosystems I focused on (i) the interaction of ecological and hydro-logical processes and (ii) the effect of farming strategies. First, I developed a grid-based and small-scaled model simulating vegetation dynamics and inter-linked hydrological processes. The simulation results suggest that ecohydrological interactions gain importance in rangelands with ascending slope where vegetation cover serves to obstruct run-off and decreases evaporation from the soil. Disturbances like overgrazing influence these positive feedback mechanisms by affecting vegetation cover and composition. In the second part, I present a modeling approach that has the power to transfer and integrate ecological information from the small scale vegetation model to the landscape scale, most relevant for the conservation of biodiversity and sustainable management of natural resources. I combined techniques of stochastic modeling with remotely sensed data and GIS to investigate to which ex-tent spatial interactions, like the movement of surface water by run-off in water limited environments, affect ecosystem functioning at the landscape scale. My simulation experiments show that overgrazing decreases the number of vegetation patches that act as hydrological sinks and run-off increases. The results of both simulation models implicate that different vegetation types should not only be regarded as provider of forage production but also as regulator of ecosystem functioning. Vegetation patches with good cover of perennial vegetation are capable to catch and conserve surface run-off from degraded surrounding areas. Therefore, downstream out of the simulated system is prevented and efficient use of water resources is guaranteed at all times. This consequence also applies to commercial rotational grazing strategies for semi-arid and arid rangelands with ascending slope where non-degraded paddocks act as hydrological sinks. Finally, by the help of an integrated ecological-economic modeling approach, I analyzed the relevance of farmers’ ecological knowledge for longterm functioning of semi-arid and arid grazing systems under current and future climatic conditions. The modeling approach consists of an ecological and an economic module and combines relevant processes on either level. Again, vegetation dynamics and forage productivity is derived by the small-scaled vegetation model. I showed that sustainable management of semi-arid and arid rangelands relies strongly on the farmers’ knowledge on how the ecosystem works. Furthermore, my simulation results indicate that the projected lower annual rainfall due to climate change in combination with non-adapted grazing strategies adds an additional layer of risk to these ecosystems that are already prone to land degradation. All simulation models focus on the most essential factors and ignore specific details. Therefore, even though all simulation models are parameterized for a specific dwarf shrub savanna in arid southern Namibia, the conclusions drawn are applicable for semi-arid and arid rangelands in general.
High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25° × 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987–2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume—particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987–1997, to much more positive trends across large regions of HMA during the periods 1997–2007 and 2007–2016. We also find that changes in high percentile monthly snow-water volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances.
Climate change of anthropogenic origin is affecting Earth’s biodiversity and therefore ecosystems and their services. High latitude ecosystems are even more impacted than the rest of Northern Hemisphere because of the amplified polar warming. Still, it is challenging to predict the dynamics of high latitude ecosystems because of complex interaction between abiotic and biotic components. As the past is the key to the future, the interpretation of past ecological changes to better understand ongoing processes is possible. In the Quaternary, the Pleistocene experienced several glacial and interglacial stages that affected past ecosystems. During the last Glacial, the Pleistocene steppe-tundra was covering most of unglaciated northern hemisphere and disappeared in parallel to the megafauna’s extinction at the transition to the Holocene (~11,700 years ago). The origin of the steppe-tundra decline is not well understood and knowledge on the mechanisms, which caused shifts in past communities and ecosystems, is of high priority as they are likely comparable to those affecting modern ecosystems. Lake or permafrost core sediments can be retrieved to investigate past biodiversity at transitions between glacial and interglacial stages. Siberia and Beringia were the origin of dispersal of the steppe-tundra, which make investigation this area of high priority. Until recently, macrofossils and pollen were the most common approaches. They are designed to reconstruct past composition changes but have limit and biases. Since the end of the 20th century, sedimentary ancient DNA (sedaDNA) can also be investigated. My main objectives were, by using sedaDNA approaches to provide scientific evidence of compositional and diversity changes in the Northern Hemisphere ecosystems at the transition between Quaternary glacial and interglacial stages.
In this thesis, I provide snapshots of entire ancient ecosystems and describe compositional changes between Quaternary glacial and interglacial stages, and confirm the vegetation composition and the spatial and temporal boundaries of the Pleistocene steppe-tundra. I identify a general loss of plant diversity with extinction events happening in parallel of megafauna’ extinction. I demonstrate how loss of biotic resilience led to the collapse of a previously well-established system and discuss my results in regards to the ongoing climate change. With further work to constrain biases and limits, sedaDNA can be used in parallel or even replace the more established macrofossils and pollen approaches as my results support the robustness and potential of sedaDNA to answer new palaeoecological questions such as plant diversity changes, loss and provide snapshots of entire ancient biota.
In the wake of 21st century, humanity witnessed a phenomenal raise of urban agglomerations as powerhouses for innovation and socioeconomic growth. Driving much of national (and in few instances even global) economy, such a gargantuan raise of cities is also accompanied by subsequent increase in energy, resource consumption and waste generation. Much of anthropogenic transformation of Earth's environment in terms of environmental pollution at local level to planetary scale in the form of climate change is currently taking place in cities. Projected to be crucibles for entire humanity by the end of this century, the ultimate fate of humanity predominantly lies in the hands of technological innovation, urbanites' attitudes towards energy/resource consumption and development pathways undertaken by current and future cities. Considering the unparalleled energy, resource consumption and emissions currently attributed to global cities, this thesis addresses these issues from an efficiency point of view. More specifically, this thesis addresses the influence of population size, density, economic geography and technology in improving urban greenhouse gas (GHG) emission efficiency and identifies the factors leading to improved eco-efficiency in cities. In order to investigate the in uence of these factors in improving emission and resource efficiency in cities, a multitude of freely available datasets were coupled with some novel methodologies and analytical approaches in this thesis.
Merging the well-established Kaya Identity to the recently developed urban scaling laws, an Urban Kaya Relation is developed to identify whether large cities are more emission efficient and the intrinsic factors leading to such (in)efficiency. Applying Urban Kaya Relation to a global dataset of 61 cities in 12 countries, this thesis identifed that large cities in developed regions of the world will bring emission efficiency gains because of the better technologies implemented in these cities to produce and utilize energy consumption while the opposite is the case for cities in developing regions. Large cities in developing countries are less efficient mainly because of their affluence and lack of efficient technologies. Apart from the in uence of population size on emission efficiency, this thesis identified the crucial role played by population density in improving building and on-road transport sector related emission efficiency in cities. This is achieved by applying the City Clustering Algorithm (CCA) on two different gridded land use datasets and a standard emission inventory to attribute these sectoral emissions to all inhabited settlements in the USA. Results show that doubling the population density would entail a reduction in the total CO2 emissions in buildings and on-road sectors typically by at least 42 %. Irrespective of their population size and density, cities are often blamed for their intensive resource consumption that threatens not only local but also global sustainability. This thesis merged the concept of urban metabolism with benchmarking and identified cities which are eco-efficient. These cities enable better socioeconomic conditions while being less burden to the environment. Three environmental burden indicators (annual average NO2 concentration, per capita waste generation and water consumption) and two socioeconomic indicators (GDP per capita and employment ratio) for 88 most populous European cities are considered in this study. Using two different non-parametric ranking methods namely regression residual ranking and Data Envelopment Analysis (DEA), eco-efficient cities and their determining factors are identified. This in-depth analysis revealed that mature cities with well-established economic structures such as Munich, Stockholm and Oslo are eco-efficient. Further, correlations between objective eco-efficiency ranking with each of the indicator rankings and the ranking of urbanites' subjective perception about quality of life are analyzed. This analysis revealed that urbanites' perception about quality of life is not merely confined to the socioeconomic well-being but rather to their combination with lower environmental burden.
In summary, the findings of this dissertation has three general conclusions for improving emission and ecological efficiency in cities. Firstly, large cities in emerging nations face a huge challenge with respect to improving their emission efficiency. The task in front of these cities is threefold: (1) deploying efficient technologies for the generation of electricity and improvement of public transportation to unlock their leap frogging potential, (2) addressing the issue of energy poverty and (3) ensuring that these cities do not develop similar energy consumption patterns with infrastructure lock-in behavior similar to those of cities in developed regions. Secondly, the on-going urban sprawl as a global phenomenon will decrease the emission efficiency within the building and transportation sector. Therefore, local policy makers should identify adequate fiscal and land use policies to curb urban sprawl. Lastly, since mature cities with well-established economic structures are more eco-efficient and urbanites' perception re ects its combination with decreasing environmental burden; there is a need to adopt and implement strategies which enable socioeconomic growth in cities whilst decreasing their environment burden.
Cities to the rescue?
(2017)
Despite the proliferation and promise of subnational climate initiatives, the institutional architecture of transnational municipal networks (TMNs) is not well understood. With a view to close this research gap, the article empirically assesses the assumption that TMNs are a viable substitute for ambitious international action under the United Nations Framework Convention on Climate Change (UNFCCC). It addresses the aggregate phenomenon in terms of geographical distribution, central players, mitigation ambition and monitoring provisions. Examining thirteen networks, it finds that membership in TMNs is skewed toward Europe and North America while countries from the Global South are underrepresented; that only a minority of networks commit to quantified emission reductions and that these are not more ambitious than Parties to the UNFCCC; and finally that the monitoring provisions are fairly limited. In sum, the article shows that transnational municipal networks are not (yet) the representative, ambitious and transparent player they are thought to be.
The work is designed to investigate the impacts and sensitivity of climate change on water resources, droughts and hydropower production in Malawi, the South-Eastern region which is highly vulnerable to climate change. It is observed that rainfall is decreasing and temperature is increasing which calls for the understanding of what these changes may impact the water resources, drought occurrences and hydropower generation in the region. The study is conducted in the Greater Lake Malawi Basin (Lake Malawi and Shire River Basins) and is divided into three projects. The first study is assessing the variability and trends of both meteorological and hydrological droughts from 1970-2013 in Lake Malawi and Shire River basins using the standardized precipitation index (SPI) and standardized precipitation and evaporation Index (SPEI) for meteorological droughts and the lake level change index (LLCI) for hydrological droughts. And later the relationship of the meteorological and hydrological droughts is established.
While the second study extends the drought analysis into the future by examining the potential future meteorological water balance and associated drought characteristics such as the drought intensity (DI), drought months (DM), and drought events (DE) in the Greater Lake Malawi Basin. The sensitivity of drought to changes of rainfall and temperature is also assessed using the scenario-neutral approach. The climate change projections from 20 Coordinated Regional Climate Downscaling Experiment (CORDEX) models for Africa based on two scenarios (RCP4.5 and RCP8.5) for the periods 2021–2050 and 2071–2100 are used. The study also investigates the effect of bias-correction (i.e., empirical quantile mapping) on the ability of the climate model ensemble in reproducing observed drought characteristics as compared to raw climate projections.
The sensitivity of key hydrologic variables and hydropower generation to climate change in Lake Malawi and Shire River basins is assessed in third study. The study adapts the mesoscale Hydrological Model (mHM) which is applied separately in the Upper Lake Malawi and Shire River basins. A particular Lake Malawi model, which focuses on reservoir routing and lake water balance, has been developed and is interlinked between the two basins. Similar to second study, the scenario-neutral approach is also applied to determine the sensitivity of climate change on water resources more particularly Lake Malawi level and Shire River flow which later helps to estimate the hydropower production susceptibility.
Results suggest that meteorological droughts are increasing due to a decrease in precipitation which is exacerbated by an increase in temperature (potential evapotranspiration). The hydrological system of Lake Malawi seems to have a >24-month memory towards meteorological conditions since the 36-months SPEI can predict hydrological droughts ten-months in advance. The study has found the critical lake level that would trigger hydrological drought to be 474.1 m.a.s.l.
Despite the differences in the internal structures and uncertainties that exist among the climate models, they all agree on an increase of meteorological droughts in the future in terms of higher DI and longer events (DM). DI is projected to increase between +25% and +50% during 2021-2050 and between +131% and +388% during 2071-2100. This translates into +3 to +5, and +7 to +8 more drought months per year during both periods, respectively. With longer lasting drought events, DE is decreasing. Projected droughts based on RCP8.5 are 1.7 times more severe than droughts based on RCP4.5.
It is also found that an annual temperature increase of 1°C decreases mean lake level and outflow by 0.3 m and 17%, respectively, signifying the importance of intensified evaporation for Lake Malawi’s water budget. Meanwhile, a +5% (-5%) deviation in annual rainfall changes mean lake level by +0.7 m (-0.6 m). The combined effects of temperature increase and rainfall decrease result in significantly lower flows on Shire River. The hydrological river regime may change from perennial to seasonal with the combination of annual temperature increase and precipitation decrease beyond 1.5°C (3.5°C) and -20% (-15%). The study further projects a reduction in annual hydropower production between 1% (RCP8.5) and 2.5% (RCP4.5) during 2021–2050 and between 5% (RCP4.5) and 24% (RCP8.5) during 2071–2100.
The findings are later linked to global policies more particularly the United Nations Framework Convention on Climate Change (UNFCCC)’s Paris Agreement and the United Nations (UN)’s Sustainable Development Goals (SDGs), and how the failure to adhere the restriction of temperature increase below the global limit of 1.5°C will affect drought and the water resources in Malawi consequently impact the hydropower production. As a result, the achievement of most of the SDGs will be compromised.
The results show that it is of great importance that a further development of hydro energy on the Shire River should take into account the effects of climate change. The information generation is important for decision making more especially supporting the climate action required to fight against climate change. The frequency of extreme climate events due to climate change has reached the climate emergency as saving lives and livelihoods require urgent action.
During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007–2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash–Sutcliffe efficiency of 0.72 and a Kling–Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century.
The energy sector is both affected by climate change and a key sector for climate protection measures. Energy security is the backbone of our modern society and guarantees the functioning of most critical infrastructure. Thus, decision makers and energy suppliers of different countries should be familiar with the factors that increase or decrease the susceptibility of their electricity sector to climate change. Susceptibility means socioeconomic and structural characteristics of the electricity sector that affect the demand for and supply of electricity under climate change. Moreover, the relevant stakeholders are supposed to know whether the given national energy and climate targets are feasible and what needs to be done in order to meet these targets. In this regard, a focus should be on the residential building sector as it is one of the largest energy consumers and therefore emitters of anthropogenic CO 2 worldwide.
This dissertation addresses the first aspect, namely the susceptibility of the electricity sector, by developing a ranked index which allows for quantitative comparison of the electricity sector susceptibility of 21 European countries based on 14 influencing factors. Such a ranking has not been completed to date. We applied a sensitivity analysis to test the relative effect of each influencing factor on the susceptibility index ranking. We also discuss reasons for the ranking position and thus the susceptibility of selected countries. The second objective, namely the impact of climate change on the energy demand of buildings, is tackled by means of a new model with which the heating and cooling energy demand of residential buildings can be estimated. We exemplarily applied the model to Germany and the Netherlands. It considers projections of future changes in population, climate and the insulation standards of buildings, whereas most of the existing studies only take into account fewer than three different factors that influence the future energy demand of buildings. Furthermore, we developed a comprehensive retrofitting algorithm with which the total residential building stock can be modeled for the first time for each year in the past and future.
The study confirms that there is no correlation between the geographical location of a country and its position in the electricity sector susceptibility ranking. Moreover, we found no pronounced pattern of susceptibility influencing factors between countries that ranked higher or lower in the index. We illustrate that Luxembourg, Greece, Slovakia and Italy are the countries with the highest electricity sector susceptibility. The electricity sectors of Norway, the Czech Republic, Portugal and Denmark were found to be least susceptible to climate change. Knowledge about the most important factors for the poor and good ranking positions of these countries is crucial for finding adequate adaptation measures to reduce the susceptibility of the electricity sector. Therefore, these factors are described within this study.
We show that the heating energy demand of residential buildings will strongly decrease in both Germany and the Netherlands in the future. The analysis for the Netherlands focused on the regional level and a finer temporal resolution which revealed strong variations in the future heating energy demand changes by province and by month. In the German study, we additionally investigated the future cooling energy demand and could demonstrate that it will only slightly increase up to the middle of this century. Thus, increases in the cooling energy demand are not expected to offset reductions in heating energy demand. The main factor for substantial heating energy demand reductions is the retrofitting of buildings. We are the first to show that the given German and Dutch energy and climate targets in the building sector can only be met if the annual retrofitting rates are substantially increased. The current rate of only about 1 % of the total building stock per year is insufficient for reaching a nearly zero-energy demand of all residential buildings by the middle of this century. To reach this target, it would need to be at least tripled. To sum up, this thesis emphasizes that country-specific characteristics are decisive for the electricity sector susceptibility of European countries. It also shows for different scenarios how much energy is needed in the future to heat and cool residential buildings. With this information, existing climate mitigation and adaptation measures can be justified or new actions encouraged.