WARREDOC International Winter School on Data Rich Hydrology 2019

The "Data Rich Hydrology" Winter School 2019 took place in beautiful Colombella, Perugia (Italy). It was jointly organised by the Water Resources Research and Documentation Center (WARREDOC) and UNESCO World Water Assessment Program (WWAP). The WARREDOC was established at the Università per Stranieri di Perugia (UNISTRAPG) since 1985 - developing research, advanced training and scientific communication in the field of water, environment and disaster risk management.

http://warredoc.unistrapg.it/en/events/2019-winter-school/

The days were organised into a serious of lectures and lab sessions and food and accommodation were provided all at the location of the Villa Colombella, that was an extraordinary experience. We were completely immersed in this place students and lecturers altogether.

The program encompassed mainly lectures of absolute high scientific standard and well presented by the experienced and well-known lecturers.

- The Era of Data Rich Hydrology, 1st keynote lecture, by Prof. Rafael L. Bras

Prof. Bras is one of the forefathers of hydrology (Google Scholar). He gave us a history lesson of conceptual, numerical and later computational hydrology and modelling of catchments. He concluded with the outlook of what we as young hydrologists should keep striving towards to improve understanding and modelling of the hydrological cycle.

- The WWDR and SDG 6 Synthesis Report, 2nd keynote lecture, by Prof. Stefan Uhlenbrook, also head of UNESCO WWAP

- Remote sensing and data assimilation in hydrology by Prof. Fabio Castelli

- Hydrologic modelling in a data rich world by Prof. Prof Riccardo Rigon

- Citizen science and big data in hydrology by Prof. Fernando Nardi

- Beyond traditional extreme value theory: lessons learned from rainfall and hurricane intensity by Prof. Marco Marani

More topics got covered by further renowned professors, researchers and practitioners in hydrological and hydraulic modelling:

- Groundwater hydrology and hydrological process mechanics
- The water-food-energy nexus
- Modelling scaling properties of precipitation fields
- Hydrologic measurements and novel observation technologies
- Drones in Hydrology (lecture & hands on)
- Hydrological risk assessment: Return period and probability of failure
- Advances in the space-time analysis of rainfall extremes
- Data poor vs. data rich cases for flood hazard (lecture & hands on)
- Distributed Data quality and urban flood modelling uncertainty
- Stream flow measurements: ground and satellite observations
- Remote sensing data and tools to foster inland water monitoring and flood modeling

I also had the pleasure to get interviewed by research fellow and PhD student Francisco Pena, who does a radio show on Disaster Risk Reduction. We had a great chat about our ideas and views on the topics and lectures during this Winter School on Hydrology and did some brainstorming:

http://www.radiophonica.com/podcast/13941 (link to the radio show)

If you like to check out Francisco's pages: https://www.linkedin.com/in/franciscope%C3%B1a/ (LinkedIn) and and https://twitter.com/FebronioPena (Twitter)

Starting to play with spatial-temporal data

Thinking  the big picture is a different thing to actually implement it :-) Well, who doesn't know that. Having some hydrological time-series (groundwater levels) in place in a sensor observation service (SOS), the hydrogeological all-in-one-wonder-portal is going to get a glance of the next level :-p
The last weeks I started to play around with the R environment for statistical computing and visualization (The R Project). 52°North developed a neat R toolkit to access and digest SOS time-series - sos4R.

It is well documented and pretty easy to connect to a SOS server, and query observations. So for the fun of it and to demonstrate the general feasibilty, I quickly queried the groundwater levels of the Horowhenua area in New Zealand, where I got some sample data (courtesy by the regional council).

With the R sos4R, fields and akima packages from the CRAN R packages archive I (quite coarsely) interpolated the groundwater surfaces for the years 1991-2009 and put the images together as an animated gif (meters above mean sea level over time).

Discussion

I am aware of the total uselessness of this particualr way presenting :-) No years, the scale changes slightly, and the exact spatial extent and north orientiation are not reliable :-p

Nevertheless, for just playing around, this was a motivating simple first shot to easily visualise changes over time.

I would like to play around with the gstat and spacetime R package, integrate a more sophisticated script as a 52°North WPS process and have those things happening automagically in the interwebz.