Compound flood hazard at Lake Como, Italy, is driven by temporal clustering of rainfall events

Published in Earth & Environment
Compound flood hazard at Lake Como, Italy, is driven by temporal clustering of rainfall events
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When we think about floods, we have often in mind the following classification:

  • river floods, occurring when water overspills the river banks;

  • pluvial floods, caused by rainfall which cannot be absorbed fast enough by ground or drainage systems;

  • coastal floods, due to the combination of rainfall and storm surge.

Common classification of floods

Another typology is lake floods, related to the oscillation of the water level causing inundation of neighboring settlements. It may be hard to believe that these events may last for months. During the 2011 flooding of Lake Champlain, caused by the co-occurrence of warm temperatures, record precipitation, and rapid melting of near-record snowpack, the lake level remained above the minor flood stage a full sixty-seven days (ILCRRSB 2019).

Long durations have important consequences on the damage of buildings and infrastructures, but also on agriculture, preventing crops from reaching maturity. The lake water quality decreases and the aquatic vegetation may deteriorate due to lower water transparency, higher water depth and turbidity. Health issues like mold exposure or the transmission of waterborne diseases are also affected.

These considerations make emerge the complexity of this natural hazard, which is likely to require a combination of factors to occur and which may cause cascading impacts. In this regard, the concept of compound events, proposed for the first time by the Intergovernmental Panel on Climate Change (IPCC) in 2012 (Seneviratne et al. 2012), may be useful to investigate lake floods. Compound events are defined as the combination of multiple drivers and/or hazards that contributes to societal or environmental risk and they are classified in four groups (Zscheischler et al. 2020):

  • multivariate compound events: the extreme impact is due to multiple drivers/hazards in the same area;

  • preconditioned compound events: the presence of a preconditioning variable increases the resulting impact;

  • spatially compounding events: the extreme impact is due to multiple drivers/hazards in spatially connected locations;

  • temporally compounding events: the extreme impact is due the temporal succession of hazards/drivers.

Example of variables and connections involved in the occurrence of lake floods

The scheme of our investigation follows some general steps, that can be adopted also for other type of compound events:

  1. Selection of potential drivers of the hazard: for example, rainfall and/or snowmelt;

  2. Identification of connections between drivers: rainfall can occur clustered in time (i.e., with multiple events in a small-time window), it may affect all or only some of the basins drained by the Lake, or it may occur during periods of high discharge in the outflow stream thus preventing water outflow;

  3. Association of drivers to hazards: causal inference or statistical techniques can be used to check if the selected drivers are significant triggers of the investigated hazard;

  4. Identification of connections between hazards: some examples are listed below:

    • two or more hazards triggered by the same cause e.g., consecutive lake floods triggered by the same rainfall cluster;

    • a first hazard alters the likelihood of a second hazard e.g., the occurrence of a flood increases the lake water level which is still high when the trigger of the second flood occurs;

    • the occurrence of one hazard changes the vulnerability of the exposed elements that are then affected by a second hazard e.g., the second flood occurs when there is still water in the town, therefore prolonging the duration of the exposure of buildings to water.

  5. Attribution of synoptic conditions to hazards: are there some weather circulations or teleconnections which increase hazard likelihood?

After these premises, let’s came back to our case study: Lake Como (Northern Italy). This lake has a branched shape in its southern part and one of the branches has no outlet. Here, the town of Como, subjected to frequent flooding from the lake, is located. Historical flooding affecting the town are well documented. The first reported event dates back in 1255 and it was triggered by an earthquake. Mining historical documentation, we found pictures of people sailing the inundated town square or walking on wooden footbridges, that gave us a clearer idea of the implications for people living in this area.

Despite good historical records, we did not exploit this information, due to the lack of meteorological data and we only focused on the period 1980 – 2020. Using precipitation and snowmelt data, we assessed how much the two triggers contributed to the water level increase that resulted in the flooding of the town. In case of rainfall, we distinguished between events that were part of a temporal clustering and events sparse and distant in time.

What we discovered is that in 70% of cases, floods were preceded by a temporal clustering of rainfall. This was also the predominant trigger of the seven most severe floods. To a lesser extent, and mainly during spring, floods were driven by isolated rainfall events that occurred when the water level was already high due to previous rainfall and/or melting. Moving to hazard connections, we sometimes observed that, after the occurrence of a flood, the water level had no time to lower enough before the occurrence of new rainfall events, triggering therefore a second flood. We considered the two floods to be part of a cluster of floods.

Different modes of occurrence of lake floods

The last step was the investigation of the meteorological conditions associated with the temporal clustering of rainfall triggering lake floods. What we found was that the most severe events were associated to a persistent cyclonic circulation over northern Italy, resulting in a sustained moisture advection from North Atlantic Ocean. In addition, we found that the same meteorological conditions triggering severe floods, triggered also other natural hazards in the basin, like the Cortenova-Bindo or the Val Pola landslides.

This work shows the complexity of lake floods, and it provides insights on their mechanisms and connections for the case of an Italian alpine basin, hoping this may be of help in the study of other lakes or similar typologies of natural hazards.

References

ILCRRSB (International Lake Champlain-Richelieu River Study Board). The causes and impacts of past floods in the lake Champlain-Richelieu river basin: Historical information on flooding. Tech. Rep. https://ijc.org/en/lcrr/causes-and-impacts-past-floods-lake-champlain-richelieu-river-basin-historical-information (2019).

Seneviratne, S. et al. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) (eds Field, C.) 109–230 (Cambridge University Press, 2012).

Zscheischler, J. et al. A typology of compound weather and climate events. Nat. Rev. Earth Environ. 1, 333–347 (2020).

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