Systems Analysis, Integrated Assessment and Modelling
In SIAM, we develop and apply models and formal techniques in order to understand, demonstrate, and predict the behavior of natural, technical, social and economical systems that pertain to water and other natural resources. Read more
Chaparro Pedraza, P. C., Matthews, B., de Meester, L., & Dakos, V. (2021). Adaptive evolution can both prevent ecosystem collapse and delay ecosystem recovery. American Naturalist, 198(6) (13 pp.). doi:10.1086/716929, Institutional Repository
There is growing concern about the dire socioeco-logical consequences of abrupt transitions between alternative ecosystem states in response to environmental changes. At the same time, environmental change can trigger evolutionary responses that could stabilize or destabilize ecosystem dynamics. However, we know little about how coupled ecological and evolutionary processes affect the risk of transition between alternative ecosystem states. Using shallow lakes as a model ecosystem, we investigate how trait evolution of a key species affects ecosystem resilience under environmental stress. We find that adaptive evolution of macrophytes can increase ecosystem resilience by shifting the critical threshold, which marks the transition from a clear-water state to a turbid-water state to a higher level of environmental stress. However, following the transition, adaptation to the turbid-water state can delay the ecosystem recovery back to the clear-water state. This implies that restoration could be more effective when implemented early enough after a transition occurs and before organisms adapt to the alternative state. Our findings provide new insights into how to prevent and mitigate the occurrence of regime shifts in ecosystems and highlight the need to understand ecosystem responses to environmental change in the context of coupled ecological and evolutionary processes.
Study region: Northeast China Study focus: Northeast China is an important region for industry and agriculture in China. In this region, investigations are lacking on the spatial distribution of snow melt contributions to the spring maximum runoff/discharge, and no studies have compared the spring and summer potential flood risks. Here, for the first time in Northeast China, we investigated the spatial distribution of snow melt contributions to spring maximum runoff/discharge and compared the spring and summer potential flood risks in terms of their spatial distributions, crop production and economic exposures. New hydrological insights for the region: We find that snow contributes approximately three-fourths of spring maximum floods from 1982 to 2011. On average, potential economic exposures to the summer and spring floods represent 3.9% and 0.4% of total GDP, respectively. Potentially exposed production of maize, rice and soybean to summer floods accounts for approximately 2.8% of the total, and potentially exposed wheat production to the spring floods accounts for 0.3% of the total. GDP growth amplifies increasing trends of potential economic exposure, while changes in potential crop production exposure are dominated by flood variations. This study is unique in that snow melt contributions to the spring maximum floods are quantified and that potential GDP and crop production exposure risks to spring and summer floods are quantified and compared for the first time in Northeast China.
Ashraf Vaghefi, S., Muccione, V., van Ginkel, K. C. H., & Haasnoot, M. (2021). Using decision making under deep uncertainty (DMDU) approaches to support climate change adaptation of Swiss Ski Resorts. Environmental Science and Policy, 126, 65-78. doi:10.1016/j.envsci.2021.09.005, Institutional Repository
Climate change threatens winter tourism in the Alps severely, and ski resorts are struggling to cope under uncertain climate change. We aim to identify under what conditions physical and economic tipping points for ski resorts may occur under changing climate in six Swiss ski resorts representing low, medium, and high elevation in the Alps. We use exploratory modeling (EMA) to assess climate change impacts on ski resorts under a range of futures adaptation options: (1) snowmaking and (2) diversifying the ski resorts' activities throughout the year. High-resolution climate projections (CH2018) were used to represent climate uncertainty. To improve the coverage of the uncertainty space and account for the climate models' intra-annual variability, we produced new climate realizations using resampling techniques. We demonstrate the importance of five factors, namely climate scenarios (RCPs), intra-annual climate variability, snow processes model, and two adaptation options, in ski resorts survival under a wide range of future scenarios. In six ski resorts, strong but highly variable decreases in the future number of days with good snow conditions for skiing (GSD) are projected. However, despite the different characteristics of the resorts, responses are similar and a shrunk of up to 31, 50, and 62 days in skiing season (Dec-April) is projected for the near-future (2020–2050), mid-future (2050–2080), and far-future (2070–2100), respectively. Similarly, in all cases, the number of days with good conditions for snowmaking (GDSM) will reduce up to 30, 50, and 74 days in the skiing season in the near-, mid-, and far-future horizons, respectively. We indicate that all ski resorts will face a reduction of up to 13%, 33%, and 51% of their reference period (1981–2010) revenue from winter skiing activities in the near-, mid-, and far-future horizons. Based on the outcomes of the EMA, we identify Dynamic Adaptive Policy Pathways (DAPP) and determine the adaptation options that ski resorts could implement to avoid tipping points in the future. We highlight the advantages of adaptive planning in a first of its kind application of DMDU techniques to winter tourism. We specify the possible adaptation options ranging from "low revenue diversification and moderate snowmaking" to "high revenue diversification and large snowmaking" and demonstrate when an adaptation action fails and a change to a new plan is needed. By the end of the century, we show that only ski resorts with ski lines above 1800–2000 m elevation will survive regardless of the climate scenarios. Our approach to decision-making is highly flexible and can easily be extended to other ski resorts and account for additional adaptation options.
Adelisardou, F., Zhao, W., Chow, R., Mederly, P., Minkina, T., & Schou, J. S. (2021). Spatiotemporal change detection of carbon storage and sequestration in an arid ecosystem by integrating Google Earth Engine and InVEST (the Jiroft plain, Iran). International Journal of Environmental Science and Technology. doi:10.1007/s13762-021-03676-6, Institutional Repository
Our study uses regional-scale maps to quantify carbon storage and sequestration from different land use types to evaluate the effects of future land use scenarios. We developed an integrated modeling approach to assess the spatiotemporal impacts of land use/cover change (LUCC) on the provision and value of the carbon storage and sequestration during the historical period (2000-2019) and predicted scenarios (2019-2046) in the Jiroft plain, Iran. We integrated several analytic tools for our analysis, which was comprised of Google Earth Engine (GEE), Cellular Automata Markov Chain (CA-MC) model, Intensity Analysis (IAA), and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Our results demonstrate that: (1) agriculture and urban expansion led to a considerable decrease in carbon storage, mainly due to rapid deforestation from 2000-2019; (2) if the historical trend continues under the business as usual (BAU) scenario, it will lead to considerable social costs due to the loss of stored carbon in the plain (2,624,113 Mg) with an annual average sequestration loss of -475,547 Mg; (3) the downward carbon sequestration trend could potentially be reversed by employing the environmentally sound planning (ESP) scenario that is estimated to save 3,705,491 Mg in carbon storage, with annual average sequestration gain of + 605,830 Mg. The design scenarios provide a useful guide for policymakers and local governments to help understand the potential outcomes of the various development strategies, which will ultimately lead to more effective ecosystem management.
Qi, W., Feng, L., Yang, H., Zhu, X., Liu, Y., & Liu, J. (2021). Weakening flood, intensifying hydrological drought severity and decreasing drought probability in Northeast China. Journal of Hydrology: Regional Studies, 38, 100941 (12 pp.). doi:10.1016/j.ejrh.2021.100941, Institutional Repository
Study region: Northeast China Study focus: Northeast China is one of the largest agricultural regions in the world and a strategically important granary for China. Information on hydrological extremes is crucially important for water management. Yet, comprehensive studies on hydrological extremes, both extreme flood and drought events, based on hydrological gauge comprehensive and large coverage observations are still lacking in Northeast China. This study investigated observed hydrological extremes with 124 hydrological gauges in the region ranging from the 1950s to present for the first time. New hydrological insights for the region: We find that flood extremes, mean discharge and standardized discharge are decreasing in over 80% of the gauges, and no gauges show significantly increasing flood extremes. The findings suggest that sustainable water management strategies should be employed to meet increasing water demand for long-term development. In addition, we find that flood peak discharge and flood volume are significantly correlated in all the gauges, motivating flood risk studies considering multiple flood variables together. Further, we quantify probability changes of severe and extreme hydrological droughts, and find that the probability of drought events is decreasing in most of the gauges, suggesting that the number of drought events is reducing. Data availability: Discharge data are from the hydrology bureau, and can be found by contacting Songliao River Water Resources Commission, Ministry of Water Resources, the People’s Republic of China (http://www.mwr.gov.cn/english/).
Eawag intends to further develop artificial intelligence methods to enable their increasing use in water research. One current application is the monitoring of plankton communities in lakes. With the help of machine learning...
Eawag intends to further develop artificial intelligence methods to enable their increasing use in water research. One current application is the monitoring of plankton communities in lakes. With the help of machine learning methods, it has been possible to implement an automatic classification of the microorganisms.
Bridging the gap between data science and mechanistic modelling to gain knowledge about community assembly
Heterogeneous data platform for operational modeling and forecasting of Swiss lakes in collaboration with the Swiss Data Science Center.
Deep Neural Networks (DNNs) have shown empirical performance but they are still nevertheless a black-box function modeling data
Scalable Bayesian inference framework for uncertainty quantification in stochastic models using thousands of processors in parallel at the Swiss Supercomputing Center and ETH Zurich.
Mechanistic modelling of the macroinvertebrate community composition in rivers.
We compare invasions in aquatic and terrestrial ecosystems primarily at large (national) spatial scales and among several higher-level taxa (insects, molluscs, crustaceans, all major vertebrate classes, and plants).
We use machine learning methods to predict the effects of chemicals on aquatic species.
Development of a semi-distributed hydrological model with a “flexible” approach. Testing and comparing of different model structures to combine modeling and experimenting into a learning process.