HS
Hydrological Sciences

Talking hydrology: an interview with Jana von Freyberg on her work in the Rietholzbach catchment

Talking hydrology: an interview with Jana von Freyberg on her work in the Rietholzbach catchment

This is the first post of “Talking hydrology”, an interview-based series of posts in the HS Blog that present the experience and personal views of hydrologists and people interested in talking about hydrology. The “Talking hydrology” series is edited by the team of the HS blog together with Leonie Kiewiet (University of Zurich, Switzerland).

Here, we will talk about experimental hydrology, which focuses on hydrologists who work in different experimental catchments across Europe and beyond.

We interviewed Jana von Freyberg, a post doc at ETH Zurich (Switzerland). She talks about her experience in experimental hydrology in the Rietholzbach catchment in Switzerland, where she carried out her PhD work.

 

1- When were you introduced to experimental hydrology? What was the most exciting memory that you have?

I made my first hydrological and hydrochemical measurements in the field during my undergraduate studies at TU Berlin during a 1-week excursion in Bavaria, Germany, led by Traugott Scheytt. The goal was to compare the flow regimes and water quality of two streams that originate from very different geological systems. This initial field work experience was an exciting introduction to experimental hydrology and hydrochemistry since I conducted every step (planning, sample collection, analysis, data interpretation) myself in a short amount of time. It was also a great experience to work together in a group and to gradually improve our field-work skills. Only after this excursion did I realize how much can be learned about the subsurface properties of a catchment by studying the flow regime and hydrochemistry of streams.

 

2- During the PhD you carried out most of your research in the Rietholzbach catchment, which is a long-term monitored catchment in Switzerland. What were the main objectives of the research at the beginning of the hydrological monitoring?

In most mountainous headwater catchments, recharge, storage and discharge of groundwater strongly control streamflow regimes and stream water quality; however, experimental data in these regions are sparse and hydrogeologic processes are often ignored or simplified in conceptual catchment models. Therefore, in my PhD project, I studied the dominant drivers of groundwater recharge (i.e., climatic forcing and landscape properties) and the responses driven by groundwater discharge (i.e., streamflow generation and solute transport) in the Rietholzbach catchment. For this, I installed a spatially-distributed groundwater table monitoring network, conducted geophysical measurements and performed repeated sampling campaigns. In addition, I benefited greatly from the existing research infrastructure and I am thankful to the group of Prof. Sonia Seneviratne for sharing data they collected in the Rietholzbach catchment.

 

3- What are the current objectives of the research in Rietholzbach? How have these objectives evolved in the years?

Hydrological research in the Rietholzbach catchment began in 19761, when it was equipped with three streamflow gauging stations, a meteorological station and a large weighting lysimeter. At this time, the general objective of the research program was to provide an experimental database for studying hydrological processes in a nearly pristine environment. Specifically, fundamental research was carried out on quantifying and simulating preferential water flow through soil macropores at the plot scale. At the catchment scale, several numerical (e.g., DIFGA, PREVAH, WaSIM) and conceptual models (e.g., J.W. Kirchner’s simple dynamical systems approach) have been developed to characterize the flow and transport pathways of water through the landscape.

Since 2007, the research group of Prof. Sonia Seneviratne (Land-Climate Dynamics, Institute for Atmospheric and Climate Science) at ETH Zurich has been maintaining the site. Their focus is on soil moisture and evapotranspiration measurements and they also investigate mechanisms leading to climate extremes such as droughts and heatwaves.

In addition, several researchers, including myself, perform related research at the site. These projects focus on the temporal changes in stream networks (Gianluca Botter from University of Padova in collaboration with Mario Schirmer from Eawag), as well as on the role of landscape and climatic properties on stream water ages2.

 

4- What kind of advantages and challenges can be encountered while collecting data for a PhD or a post doc project?

To me, data collection in the field is always a unique learning experience that cannot be replaced with reviewing literature or modelling. Because I was responsible for data collection and data post-processing during my PhD, I knew exactly where the data came from and which data I could trust. While being in the field, I have experienced the catchment landscape, the weather, the hydrological features (such as springs, river channels, wetlands), and thus gathered “soft data” that are useful for performing the most informative measurements and for developing a conceptual idea of how the catchment functions.
I have also learned how to plan and conduct field campaigns that often involved several people. Today, I heavily rely on these skills for my postdoctoral projects that involve field work, for planning Master’s thesis projects, as well as for writing grant applications in which I need to provide detailed information about the proposed sampling design.

In my opinion, the biggest challenge of field work is the time needed for organizing and conducting sampling campaigns, troubleshooting, and for collecting enough data that permit robust scientific analyses. Unforeseen things happen (such as mice biting through the cables of my soil moisture probes) that require quick reactions and some technical skills to avoid data loss. I further spend quite some time with data post-processing and database management. In addition, the costs for installations, the monitoring equipment, as well as laboratory analyses of water samples can be quite high.

 

5- What kind of advice would you give to early career scientists approaching field work?

Take your time before you start data collection. An important advice to all early-stage PhD students is to spend some days in your research catchment to familiarize yourself with the landscape and the existing infrastructure (accessibility, private wells, irrigation, etc.) and to talk to the local residents so that they get to know you and your project. Once you start data collection you are more or less bound to your sampling sites and you might be limited in exploring other (maybe more interesting) parts of your study catchment.

Don’t give up too easily. The first installations or measurement campaigns are never perfect and you will learn as you proceed with the data collection. I have found it useful to talk to other field hydrologists about their approaches and to participate in workshops and summer schools (e.g., Catchment Science Summer School3) to learn how field work is done in other catchments.

Field work is always more fun if not done alone. Having a PhD colleague who is involved in your project would be optimal to share ideas, conduct field campaigns and discuss data. Alternatively, you could (co-) supervise a BSc or MSc thesis project in which the student collects and analyses a subset of your data.

It is very important to set a deadline for data collection. It seems tempting to collect more data during some last big storm event or to do one more tracer test, however, it is very likely that those data that you have collected in the last years may already be the most representative for your site.

Learn how to set-up and to manage a database. Because I used a variety of temporal high-resolution data sets from different sampling locations, I used a relational database which allowed for efficient querying and plotting of the data. This was particularly useful when I needed to retrieve specific data sets for colleagues that have used these data for model calibration. Organizing your data in a relational database further allows data to be easily publicly available after the end of your PhD. Thus, your data will potentially be of great use for site-to-site comparison studies, model testing and for sparking new research ideas.

 

6- Have you ever wondered how experimental hydrology could evolve in the next few years? What kind of hypotheses should be tested? And what kind of groundbreaking instrumentation or methodologies are needed to improve our knowledge of hydrological processes by experimental data?

The heavy isotopes of water (18O and 2H) have been used extensively over the last decades as environmental tracers, and we have learned a lot about the age composition of the streamflow hydrograph during storm events. I am excited about new technological developments that combine membrane systems with field-deployable isotope analysers, with which we can continuously monitor short-term isotopic variations not only in stream water and precipitation, but also in soils and tree xylem. These new methods provide important information about water fluxes between these water pools that otherwise remain missing if measurements are made only occasionally and at some specific locations, such as at the catchment outlet.

Secondly, ‘wireless sensor networks’ now allow for spatially-distributed, continuous measurements of numerous hydro-climatic variables. Because environmental sensors and loggers have become cheaper, smaller and more energy-efficient over the last years, we now can install many of these sensors across much large catchment areas. Digital wireless data communication networks allow for transmitting these sensor data directly to a local data storage unit or a cloud service so that data collection will be less time consuming and risky. Wireless sensor networks will prove to be very useful in high-altitude, snow-dominated catchments to quantify spatio-temporal changes in snow storage, as well as in forested landscapes to monitor subsurface water-vegetation interaction in response to climatic variability.

Lastly, while generating complex and large data sets with distributed sensor networks might be a primarily technical challenge, the statistical analyses of these data requires advanced programming skills. Thus, experimental catchment hydrologists that use wireless sensor networks for data collection may benefit from collaborating closely with data scientists who are experienced in building pipelines for the analysis of very large and noisy semi-structured data sets.

 

Thank you, Jana, for your time and insights in experimental catchment hydrology!
For more info, the reader can visit the Rietholzbach catchment website or contact Jana von Freyberg.

 

References and web links
1 Seneviratne S.I., Lehner I., Gurtz J., Teuling A.J., Lang H., Moser U., Grebner D., Menzel L., Schroff K., Vitvar T., Zappa M., 2012. Swiss prealpine Rietholzbach research catchment and lysimeter: 32 year time series and 2003 drought event. Water Resources Research, 48, W06526. DOI:10.1029/2011WR011749
2 von Freyberg J., Allen S.T., Seeger S., Weiler M., Kirchner J.W., 2018. Sensitivity of young water fractions to hydro-climatic forcing and landscape properties across 22 Swiss catchments. Hydrology and Earth System Sciences, 22, 3841-3861. DOI:10.5194/hess-22-3841-2018
3 Catchment Science Summer School

Giulia Zuecco
Giulia Zuecco is an assistant professor at University of Padova (Italy). Her main research interests are on the application of tracers (stable water isotopes and major ions) to track water through forested catchments, the hydrology of Alpine catchments dominated by snowmelt and glacier melt and the quantification of subsurface hydrologic connectivity. Giulia works currently on a research project on “Ecohydrological dynamics and water pathways in forested catchments”.

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>

*