Don Pierson

My research deals with the interactions between climate, watershed hydrology, and lake ecosystems. In particular how these interactons are changing as a result of global warming To better evaluate such interactions I make use of automated lake monitoring systems, and Lake modeling simulations. I work both at the Department of Limnology in Uppsala and the Erken Laboratory in Norrtälje.

Overview

My research involves quantifying the effects of environmental variability on lakes that result in variations in thermal structure, light climate, nutrient inputs, and ultimately lake ecology. I am interested in the coupling of lakes to their watersheds and how variations in climate affects this. For example, how climate simultaneously influences the watershed processes that affect the seasonality of streamflow and the timing and pattern of lake thermal structure that affect the transport of the materials entering from the watershed. Ongoing climate change influences all of these processes, and much of my research is involved in evaluating the effects of climate change on lakes. Computer models are important tools in evaluating climate change and I use models that simulate both watershed and lake processes in my research. I have worked on some of the first projects to evaluate the effects of climate change on European lakes, and at present I am one of the coordinators of the lake sector of the Inter-sectorial Impact Model Intercomparison Project (https://www.isimip.org/). When not working at Uppsala University I have been the section chief for Water Quality modeling for the New York City Water Supply. There, building on our experience in Europe, we initiated the NYC Climate Change Integrated Modeling Project one of the first long term evaluations of the effects of climate change on a major water supply in the United States. https://www.wucaonline.org/assets/pdf/pubs-modeling.pdf)

To better understand climate-lake interactions I have developed an expertise in automated lake monitoring systems. Starting in 1986 as a PhD student I began working with the monitoring program at Lake Erken – automating collection of lake meteorological data and developing systems to measure lake water temperature at high temporal and vertical resolution (http://130.238.87.115:8080/Erken4/index.html). As a consequence, Lake Erken now has one of the longest records of such data in Europe. I now work part time at the Erken Laboratory (http://www.ieg.uu.se/erken-laboratory/) developing and expanding automated monitoring systems and making these data more widely available through the Swedish Infrastructure for Ecosystem Science – SITES network (http://www.fieldsites.se/). I have also been a long-term participant in the Global Lakes Ecological Observatory – GLEON network (http://www.gleon.org/) and the Networking Lake Observatories in Europe Network (https://www.dkit.ie/networking-lake-observatories-europe)

As we measure at higher frequency and in a continuous and consistent manner with the help of automated systems we are becoming more and more aware of the fact that change to aquatic systems is not gradual, but instead is the consequence of episodic events. These events occur along a continuum in magnitude from wind gusts to hurricanes, but have the common characteristic of having an effect that persists longer than the event itself. We are presently examining the long-term records from Erken and other sites in Europe to gain a better understanding of the effects of episodic events on the thermal structure and ecology of lakes.

In the past I have led research projects that developed remote sensing methods that could be applied to inland water bodies. Present work developing lake modeling and lake water quality forecasting methods in the projects described below are aimed at both providing short and long term forecasts of lake water quality that can support water management decisions. As a consequence of this experience, and that of working for the New York City water supply, I have a knowledge and appreciation of applied limnological research to support the management of water resources. I am currently involved in three European Union Funded Projects described below, and I am also a participant in the Marie Sklodowska-Curie Innovative Training Networks European Joint doctorate project MANTEL, which will train a group of 12 PhD students in a consortium of 8 Universities and 14 additional partner organizations. I jointly supervise two PhD students, who will be using lake modeling methods to evaluate the effects of episodic events.

Current Projects

BLOOWATER - Supporting tools for the integrated management of drinking water reservoirs contaminated by Cyanobacteria and cyanotoxins

The presence of toxins from blooms of cyanobacteria in drinking water reservoirs and recreational freshwater bodies may represent serious health risks for the human population. Furthermore, ongoing and future changes in lake thermal structure that lead to warmer water temperatures and more stable thermal stratification are expected to enhance cyanobacteria blooms and reduce the effectiveness of traditional methods for controlling the frequency and severity of these blooms. The BLOOWATER project will develop a comprehensive suite of tools that can be used by water managers to better anticipate the occurrence of cyanobacteria blooms and to better treat drinking water when contaminated by cyanotoxins. Our role in BLOOWATER will be to develop and test forecasting methods that can be used to predict the probability of cyanobacteria blooms. These will be integrated into a comprehensive monitoring and decision support system that will help water mangers prepare for enhanced water treatment. Two methods of bloom forecasting will be tested. We will use deterministic models that predict lake hydrodynamics and biogeochemistry and include algorithms that predict cyanobacteria functional group biomass. We will test if future prediction of this functional group biomass can be a reliable indicator of the onset of cyanobacteria blooms. Secondly, we will test if different Machine Learning methods can better predict the probability of cyanobacteria blooms. The algal community dynamics that determine the patterns of succession favoring cyanobacteria are far more complex than those that can be represented in simple models that simulate only a few functional groups of phytoplankton. These models do however provide highly detailed information on the temporal and vertical variations in water temperature, mixing rates, light levels and nutrient concentrations. We will test to see if combining these detailed physio-chemical data with machine learning methods will lead to hybrid models with improve predictive capacity.

WATExR - Integration of climate seasonal prediction and ecosystem impact modelling for an efficient adaptation of water resources management to increasing climate extreme events. https://watexr.eu/

The WATExR project has the goal of evaluating the potential to produce water quality forecasts, over seasonal time scales. These forecasts will be integrated into a QGIS-based interface to ensure efficient decision making and adaptation of water resources management to an increased frequency of climate extreme events. This at the cutting edge of what is possible with present climate projections, but is also potentially of great importance for water utilities, providing a longer window of opportunity to enact measures to manage water quality and water use. Our role in this project is to continue to develop and improve the lake water quality models and methods that are used for forecasting, and to also support WATExR participation in the ISIMIP project. At Lake Erken we are developing a QGIS interface that allows student to run lake model simulations of Erken and other lakes that are simulated as part of the ISIMIP.

ISIMIP Inter Sectorial Impact Model Intercomparision project (https://www.isimip.org/)

ISIMIP offers a framework for consistently projecting the impacts of climate change across different sectors and spatial scales. I am one of the co-coordinators of the lake sector in the ISIMIP, where a large group of lake modelers have been using 7 different lake models to simulate lake conditions over four different scenarios developed from four different global climate models. Simulations have been carried out at 62 lake sites where high quality data is available to calibrate and verify model simulation under historical conditions, and at a global scale using generic lake models applied to 17 500 grid cells that occur over land. Present simulations focus on future changes in lake thermal structure. Plans for future simulations include cross sectorial simulations that include hydrology and agriculture and simulate future changes in lake biogeochemisty.

MANTEL - Management of Climatic Extreme Events in Lakes Reservoirs for the Protection of Ecosystem Services

Environmental perturbations to lakes and reservoirs occur largely as episodic events. These range from relatively short mixing events to storms and heat waves. While the driving events occur along a continuum of frequency and magnitude, their effect is generally longer lasting than the events themselves. In addition, the more extreme weather events are now becoming increasingly frequent, a trend that has been linked to directional climate change and is projected to continue in the coming decades. Understanding the impact of these short-lived pressures requires monitoring that captures the event (hours–days) and the ensuing impact, that can last for months or even years. Only recently has automated high frequency monitoring (HFM) of lakes been adopted throughout Europe. This Training Network will investigate the effects of the most extreme events, and the cumulative effects of lower magnitude events, using HFM, while at the same time training a cohort of doctoral students in state-of-the art technology, data analysis and modelling. The aim of the MANTEL project is to expose students to new methods of water quality monitoring and data evaluation that capture the effects of episodic climatic events, thus ensuring that future water management strategies can explicitly account for their effects.

Past Projects

PROGNOS - Predicting In-Lake Responses to Change Using Near Real Time Models (http://www.waterjpi.eu/joint-calls/joint-call-2015-waterworks-2014/prognos)

Lakes and reservoirs are under continuous pressure from urbanization and agricultural intensification, and from changes in climate, including an increasing occurrence of extreme climatic events. These pressures can reduce water quality by promoting the occurrence of nuisance algal blooms and higher levels of dissolved organic carbon (DOC), two issues that can substantially increase the costs for water treatment. To monitor such changes in water quality, automated high frequency (HF) monitoring systems are increasingly being adopted for lake and reservoir management across Europe. These HF data are mostly used to provide near real time (NRT) information on the present lake state. An even more valuable tool for water management, however, would be to use HF data to run computer models that forecast the probability of a change in lake state in the coming weeks. This will potentially provide a greater window of opportunity over which to make water quality management decisions, and will increase the value of HF monitoring data, ensuring that their potential to guide water quality management is fully realized. I was the consortium coordinator of the PROGNOS project which was funded by the European Union Water JPI program.The project developed, demonstrated and disseminated forecast based adaptive solutions to improve the management of nuisance algal blooms and the production disinfection by-products from DOC. Models that simulate lake biogeochemistry phytoplankton and DOC concentrations were developed and improved. New methods of model calibration were developed, and the methodology to couple models to weather forecasts in order to simulate future lake conditions was, tested and demonstrated. The Project has produced 13 peer reviewed publications, 6 publicly available data sets that document the data, models and methods used in PROGNOS, and has led to the development of an independent company that specialize in adaptive water management and water quality forecasting systems.