Enhancing Water Knowledge with New Data and Capacity

How can new skills and data help us?

enhancing water knowledge with new data and capacity

Water is essential for life, yet in situations knowledge about water dynamics is insufficient to meet societal needs. In many developing countries, for example, human capacity is often insufficient to set up, operate and refine a hydrological production chain (from monitoring to distribution of water information). As a consequence, societies are often taken by surprise by floods and droughts, incurring large costs for society, e.g. damaged infrastructure, disrupted transportation, cuts in energy supply, insurance claims, reduced food production, release of untreated sewage to water bodies, deterioration in public health, and loss of lives.

A city is another example of where water knowledge is insufficient. Monitoring systems in cities are e.g. often too sparse to capture the high spatio-temporal dynamics of precipitation. Small fast rain cells can quickly lead to severe flooding in both streets and sewers, incurring large costs for society (as described above). In addition, climate change projections indicate that intense rainfall will become more frequent in many areas, potentially enhancing societal costs further.
In this research field, we explore the potential to better understand and quantify water dynamics by utilizing new types of data and by developing human capacity. The ultimate aim is to apply this knowledge to better meet the societal needs for water information.  

New Data

A range of new data types for hydrology carry potential to enhance water quantification. 
One example is signal strength data from microwave links in telecommunication networks (Figure 1). The microwave links essentially transmit a GHz signal between two telecommunication tower locations through the lower atmosphere. Rain drops attenuates the signal, and by monitoring signal fluctuations rainfall intensity can be derived. This type of data could be particularly useful in developing countries (where telecommunication networks are both operational and widespread, unlike current monitoring networks) and in cities (providing more complete spatial coverage, higher temporal resolution, better ability to capture peak intensities, and redundancy for enhanced safety). To see microwave-based environmental monitoring in action, please navigate to our live visualisation demo.

Rainfall monitoring using telecommunication networks
Figure 1. Rainfall monitoring using telecommunication networks.

See our live demo here.

Another example is satellite-based altimetry measurements, which provide an opportunity to monitor water levels of lakes and large rivers (Figure 2). A key benefit of such measurements is their ability to monitor at many more locations than conventional field-based measurements, and the short latency until the measurements are available for further use. This in turn provides the opportunity to e.g. adjust operational hydrological models, and thus potentially improve hydrological forecasts. Please navigate here for more background and an example application in West Africa

Water level Niger river
Figure 2. Water level measurement by satellite altimetry (blue) and field gauges (green) in Mali. Source: Guillermo Grau, isardSAT (2018).

Often new data types are characterized by abundant data availability in time and/or space, but with lower accuracy of each single measurement compared with conventional in-situ data. This opens up several research and development questions, e.g.:

  • How accurately can water variables be quantified using e.g. telecommunication networks and satellite observations?
  • What is the most robust information content in different data types?
  • How can new data types be best combined with conventional data in order to make most use of their respective information content?
  • What is the added value of integrating new data types in a hydrological production chain?
  • In what way does our knowledge about hydrological processes and the water cycle change by utilizing new data types?

Capacity Development

Training Niger
Figure 3. Hands-on training on hydrological modelling in Niamey, Niger

See our operational hydrological forecasting and alert pilot system here

Skilled persons are a core foundation for generating and utilizing water knowledge. Expertise is needed within a variety of fields to set up, operate and refine all components in the hydrological production chain, to interpret the resulting data, and take action based on the knowledge gained. In this research field, we apply a hands-on, interactive, learning-by-doing approach to build capacity (Figure 3). Typically, we focus on a particular societal water challenge (e.g. floods) and raise capacity around that topic, guided by social science frameworks and agile development methods. In this co-learning process we not only learn about the water challenge, but also address a number of pertinent research and development questions, e.g.:

  • What skills are most needed?
  • Which components of the hydrological production chain are most/least developed?
  • What are the most appropriate methods to develop capacity and gather feedback in a given cultural context?
  • Which are the main hydrological processes causing the water challenge?  In what way does our understanding of the challenge change through the capacity development?
  • To what extent does the capacity development improve the preparedness and resilience of the society affected by the water challenge?

To learn more about one of our capacity development initiatives focussing on flood forecasting and alerts in West Africa, please visit www.fanfar.eu.

Selected publications 

Andersson J.C.M., Arheimer B., Traoré F., Gustafsson D., and Ali A. (2017). Process refinements improve a hydrological model concept applied to the Niger River basin. Hydrological Processes, 31, 4540–4554, doi.org/10.1002/hyp.11376 

Andersson J.C.M., Berg P., Hansryd J., Jacobsson A., Olsson J., and Wallin J .(2017), Microwave links improve operational rainfall monitoring in Gothenburg, Sweden. 15th International Conference on Environmental Science and Technology, 31st August – 2nd September 2017, Rhodes, Greece. Conference paper CEST2017_00249 in the CEST2017 Proceedings

Tollefson, J. 2017. Mobile-phone signals bolster street-level rain forecasts. Nature, 544, 146–147, dx.doi.org/10.1038/nature.2017.21799 

Magnusson J., Gustafsson D., Hüsler F., and Jonas, T. (2014), Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods, Water Resources Research, 50, 7816– 7835, doi.org/10.1002/2014WR015302

Olsson, J., & Josefsson, W. (2015). Skyfallsuppdraget – ett regeringsuppdrag till SMHI, SMHI Klimatologi rapport nr. 37, 45p. Norrköping, Sverige, SMHI - Sveriges Meteorologiska och Hydrologiska Institut