Stream temperature modeling in mountainous environments

Stream temperature is one of the key variables affecting the habitat suitability of numerous aquatic species. Over the past decades, research efforts on this topic have concentrated on low-land rivers of North-America, whereas mountainous environments have received much less attention - above all in Europe. The present thesis introduces two new models for stream temperature prediction in Alpine watersheds. Both are tested over selected catchments in Switzerland, a mountainous country which presents the advantage of possessing a dense network of automatic stream temperature measurement stations. The first model is specifically designed to provide stream temperature estimates in ungauged catchments, so as to compensate for the scarcity of temperature measurement sites in mountainous environments. Its design is based on a new statistical approach. As opposed to standard statistical models, which are common to many disciplines, the present one aims at incorporating some of the physics controlling stream temperature in its own structure. Its formulation is derived from an analytical solution to the equation describing the energy balance of an entire stream network. Some terms of this solution cannot be readily determined based on data available at the regional scale; they are approximated using standard statistical techniques. The resulting model is statistical in nature, but includes elements of thermodynamic principles. Its accuracy is shown to be similar to the one of a standard statistical model, its root mean square error being 1.3°C at the monthly time scale. In virtue of its physical basis, the model can be used to investigate into more detail the factors controlling stream temperature at the regional scale, as shown through a simple example. The second model is intended to provide deterministic stream temperature predictions, to be used for example in climate change studies. It builds upon an existing physically-based model, which has been entirely written anew in order to clarify its structure and ease future developments. Conceived as an add-on to the spatially distributed snow model Alpine3D, it simulates the flows of both water and energy within the catchment, based on a semi-distributed approach. Some components of the model can be represented using various alternatives; for example, three different techniques are available to simulate the temperature of subsurface runoff. This flexibility allows the model to be tuned to the specific needs of each user, but also permits a more thorough assessment of the simulation uncertainty by comparing the predictions of the various alternatives. Evaluation of the model in a high alpine watershed indicates that hourly mean discharge is reproduced with a Nash-Sutcliffe efficiency (NSE) of 0.82, and hourly mean stream temperature with a NSE of 0.78. Both models are shown to contribute to a better understanding of stream temperature dynamics in Alpine environments. Future work involves further research on the structure of the statistical model, as well as the application of the deterministic model within the framework of climate change studies.

Thermo-Economic Optimisation of Solar Tower Thermal Power Plants

James Spelling

Amidst a backdrop of rapidly increasing world-wide electricity demand, solar thermal power generation shows great potential for supplying the electricity needs of numerous countries in the Sun-belt regions of the world. In these regions, the absence of significant biomass, hydrological or geothermal reserves makes solar thermal power the most promising solution for meeting that most fundamental of demands: energy. Amidst the different options available for harnessing the Sun’s power, solar thermal technologies have been shown capable of producing electricity at the most economically viable rates [32]. However, all currently existing solar thermal power plants have been based around the use of Rankine, or steam turbine, cycles which are limited in the efficiencies they can achieve. In order to make better use of the Sun’s energy, higher efficiency cycles should be used. With their high concentration ratios, solar thermal power tower systems are amongst the best options for making use of the Sun’s potential. As the energy delivery temperature of central receiver systems is higher, they harness the solar radiation at a higher exergy level [4]. This higher temperature also opens the road to the use of more advanced thermodynamic cycles. Hybrid fossil-fuel – solar systems are also imaginable [13], as the energy delivery temperature is compatible with standard gas turbine cycles. Hybrid systems can help mitigate the requirements of thermal energy storage, as well as reducing the perceived risk when investing in new solar technology, which stimulates research and development, as well as providing employment. Recent developments in the field of high temperature volumetric receivers [10] along with rock- based packed-bed storage systems have opened up an interesting possibility. High temperature receivers allow the use of higher-efficiency combined-cycle setups, whereas packed-bed units offer the possibility of cheap storage. Larger storage volumes allow pure-solar systems to extend their power production into the night, making hybrid options less attractive. This is compounded by the common practice of guaranteeing a preferred tariff for electricity sales from pure-solar sources With this in mind, a complete dynamic model of a power plant based on the pure-solar concept has been elaborated. The performance of the setup is evaluated over a range of days, using solar insolation profiles obtained from satellite data. In order to examine different design options and their impact on the performance of the power plant, a multi-objective, thermo-economic optimisation of both the power plant superstructure and operating conditions was performed using the new, dynamic models. By means of an evolutionary algorithm [17], a family of Pareto-optimal points were obtained, representing the trade-off between increased power plant efficiency, and lower levelised energy costs. Through use of optimisation, it was shown that exergetic efficiencies in the region of 21-27% can be achieved, for relatively low power outputs of between 3 and 11 MWe. These configurations correspond with levelised electricity costs in the region of 14-21 UScts/kWhe. In most regions, the use of solar power is encouraged by the guarantee of a preferential tariff for electricity sales from renewable sources. Sale of electricity at a price of around 24 UScts/kWhe [32] should therefore be imaginable, ensuring the economic viability of solar thermal power tower systems. It can be concluded that, when properly designed, solar thermal power plants based on combined cycles are both economically and thermodynamically promising. By raising the efficiency of the plant, the size of the solar collector field is diminished, increasing the otherwise low energy densities of solar power systems. By reducing the levelised electricity costs, solar thermal power plants become more economically viable, accelerating their construction and thus, hopefully, reducing our dependence on fossil-fuels.

2009

Ecohydrological and Metacommunity Studies of Proliferative Kidney Disease Spread in Freshwater Salmonid Fish

Luca Carraro

Proliferative kidney disease (PKD) is a high-mortality pathology affecting freshwater salmonid populations in Europe and North America. Its causative agent, the myxozoan Tetracapsuloides bryosalmonae, has a complex life-cycle exploiting freshwater bryozoans (mainly Fredericella sultana) and salmonids as hosts. PKD has recently increased in incidence and severity, causing remarkable declines in fish catches. In addition, environmental change is feared to cause PKD outbreaks in regions at higher latitude and altitude, as warmer water temperatures exacerbate disease development and fish mortality. In this perspective, this Thesis develops an integrated approach, involving field and modelling work, for the prediction of the incidence of PKD in river basins. In particular, the Thesis develops a novel spatially-explicit model of PKD epidemiology in riverine host metacommunities. The model, summarizing the current knowledge on disease transmission modes and parasite's life-cycle, accounts for both local population and disease dynamics of bryozoans and fish, as well as hydrodynamic dispersion of parasite spores and hosts along the river network. Model validation was attained through an integrated study of PKD in a prealpine Swiss river, where data on fish abundance, disease prevalence, concentration of primary hosts' and parasite's DNA in environmental samples (eDNA) and water temperatures were gathered at multiple locations within the catchment. In this context, a new method for predicting the spatial distribution of bryozoan density based on eDNA samples was developed. As water temperature is crucial to PKD severity, a deterministic, spatially-explicit water temperature model was formulated and tested on the case-study basin. The model, based on water and energy budgets at the reach scale, considers the effects of the spatial heterogeneity in environmental drivers, allowing the evaluation of gradients of daily mean water temperature along the river network. Stability and sensitivity analyses performed on the local epidemiological model proved that the introduction of T. bryosalmonae in a disease-free community is very likely to trigger a PKD outbreak. Simulation experiments conducted on synthetic river network replicas showed that network connectivity engenders high PKD prevalence at downstream sites, while the speed of invasion fronts in disease-free environments is amplified by climate change. Moreover, patchily located bryozoan hotspots proved able to sustain the infection at the whole river scale. Validation on the case study confirmed the reliability of the epidemiological model, and identified the prediction of bryozoan distribution as a key direction for future research. In this regard, the developed eDNA transport model possibly opens avenues for the modeling of species distributions in freshwater ecosystems, also beyond the case of PKD. The spatially-explicit temperature model was shown to outperform traditional models based on local heat budgets. Such a tool is instrumental to several ecohydrological applications, from the identification of river reaches at highest PKD-related risk, to the assessment of habitat suitability of fish or other freshwater species. Overall, this Thesis bridges the fields of ecology, epidemiology, hydrology and mathematical modelling in order to produce an integrated study of PKD, in the perspective of grasping the factors allowing disease persistence and devising mitigation strategies.

EPFL2018

Thermo-Economic Optimisation of Solar Tower Thermal Power Plants

James Spelling

2009

Ecohydrological and Metacommunity Studies of Proliferative Kidney Disease Spread in Freshwater Salmonid Fish

Luca Carraro

EPFL2018