Clean water in lakes is essential for food production and drinking water, yet water quality of many lakes declines due to excessive nutrient input. Excessive nutrient input exerts high environmental pressure that ultimately causes lakes to shift from a healthy and economically productive clear water state into a blooming toxic algal soup. Attempts to reduce algal blooms have, so far, had mixed success. Importantly, adaptation and mitigation strategies in response to algal blooms close a feedback loop in which algal blooms are both caused by, and affect environmental pressure from nutrients. Essentially, mitigation strategies aim to reduce nutrient input and adaptation strategies aim to cope with algal blooms. To date, solutions typically focus on the effect of nutrients on algal blooms thereby ignoring how algal blooms trigger nutrient management. By neglecting part of the feedback, underlying causes of algal blooms, and their solutions are missed. Therefore, I will study the feedback loop between environmental pressure and ecosystem state to find optimal solutions to reduce algal blooms under four scenarios of mitigation and adaptation strategies. In step 1 I will develop a baseline for the effect of environmental pressure on lake ecosystem state. In step 2 I will develop a new model to optimize nutrient reductions given an available budget to study how the lake ecosystem state affects environmental pressure. In step 3 I will build the full feedback loop to identify optimal solutions for nutrient reductions for dynamic social-ecological systems. My Veni is novel in studying a feedback loop that combines ecological responses of algae in lakes with economic optimisation. This combination allows me to develop optimal solutions for algal blooms. These solutions will help to restore lakes to their former healthy and economically productive state thereby securing future support of lakes in food and drinking water supply.

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Ecosystem functioning and carbon or water cycling are affected strongly by climate. In turn, climate can experience change if vegetation changes, such that both negative feedbacks (climate change buffered by vegetation) and positive feedbacks (climate change enhances vegetation change and vice versa) can occur. Mechanisms acting on long timescales are fairly well understood and are being included into global climate models. At shorter time scales, such as seasonal (phenological) change or interannual variability, these feedbacks are not yet well understood. Examples include the effects of changing timing of leaf emergence, or extreme dry seasons in the Amazon, changing regional moisture transport and enhancing effects on vegetation. Seasonality of vegetation and interannual variability are also included in global climate models, but only in a simple, empirical way, mainly for temperature-induced phenology. Drought-induced phenology (e.g. Amazon dry season dependence) and interannual variability are generally not captured adequately in those models. In this project we will primarily investigate the magnitude of feedbacks between changed seasonal and interannual variability on one hand and climate on the other. In addition, we will contribute novel model components to represent especially drought-induced seasonal and interannual variability, starting from the hypothesis that productivity in one particular moment of time affects the carbon and water uptake capacities in subsequent periods, and that leaves are shedded if their productivity becoms negative, leading to variability in carbon productivity and water use over time. Preceding the model development we will test the sensitivity of existing coupled climate models to the representation of phenology. Towards the end of the project we will have the newly developed components coupled to the EC Earth system and evaluate them for three key regions: the Amazon, Europe and Siberia, where good and substantial data sets are available to validate. Finally we will use future climate projections in the coupled and decoupled model versions to assess changes in the potential strength and sign of feedbacks.

One of the major challenges of the 21st century is to manage the global water cycle in such a way that enough water will be available for both food production and the environment. The planetary boundary of human water consumption is likely to be reached in the near future. Currently, by far most water extracted by human is used by the agricultural sector. A combination of dietary change, population growth and climate change will have a large impact on water used for food production. There are, however, large uncertainties on how water demand and availability will change in the future. Two of the largest sources of uncertainties are land use change, and changes in water availability caused by a changing climate. Here, we propose to use an improved version of the LPJ-Image modelling framework to study the combined impacts of climate change and land use change. In addition, we propose that in order to better define the planetary boundaries on water use it is essential to define the water needed by the environment much more explicitly both in time and space. This will be done by developing a new environmental flow module within the LPJ-IMAGE modelling framework. The improved modelling framework will be used to test if both environmental needs and food demand can be met under a range of socio economic and climate change scenarios. Finally, we will evaluate different options which can prevent unsustainable water use. The options we will initially focus on are adapting land use, more efficient irrigation, and water prizing.

The main objective of the proposed study was to assess to what extent we could use satellite observations to detect the ecosystem-scale impact of the air pollutant ozone on plant photosynthesis. Current estimates of this impact are mainly based on laboratory experiments. Alternatively, we studied this by combining satellite observations with an European-scale air quality model. This included use of satellite observations of gases such as nitrogen dioxide that result in the formation of ozone. However, the study also revealed that apparently the quality of current ecosystem-scale vegetation products is not yet sufficient to allow detection of this ozone impact.