Flood risks/Data uncertainties limitations

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Component is implemented in:
  • GLOFRIS model (version 3.0)
Components:
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Models/Databases
Key publications

Data, uncertainty and limitations

Data

Key external data used in the model are the digital elevation map, soil maps, initial land-use map, and a map of the global river network.

Uncertainties

The representativeness of the climate input is uncertain due to limited sampling length (generally 30 years or 100 years) and uncertainty in climate models. Thus, a multi-model ensemble of projections is highly recommended in preparing a scenario on future flood risk under a changed climate.

PCR-GLOBWB has uncertainties in its parameterisation of soils, vegetation, flood plain dimensions and roughness. These uncertainties are inherent to any hydrological model, and may be estimated using a multi-model ensemble built from runs with several hydrological models suitable for estimating flood hazards.

The downscaling algorithm is sensitive to the elevation model used and the choice of river and flood return periods (see also (Winsemius et al., 2012; Ward et al., 2013). This uncertainty is particularly relevant when computing the flood risk of low return period events. Under high return periods, the bank-full volume becomes relatively small compared to the total flood volume and thus less important. The uncertainty of the chosen bank-full volume relates to the following uncertainty. In areas with high protection standards (e.g., against 100-, 500- or 1000-year floods), the simulated time series are likely to be too short to establish a satisfying probability distribution of events. Thus, the applicability of the framework to date has been limited to areas with low protection standards. This is the case in most developing countries.

Limitations

Man-made interaction with river systems, such as the operation of dams and reservoirs, has not yet been taken into account. Instead, reservoirs are simulated as natural lakes with a free overflow. These could be included in future studies, but with the risk of incorrectly estimating reservoir operation during flood conditions. The impact of reservoir control could result in flood reduction provided adequate information is available to decide pre-releases. In many cases where such information is not available, the result may be larger floods due to unexpected water inflows. To date, reservoir management has not been considered in GLOFRIS.

The effect of levee breaches has not been included but can have large impacts on flood patterns. For example, during the Pakistan floods of 2009, large sections of a major embankment were destroyed by the floods, resulting in a completely different flood pattern than would be simulated by a model under the assumption of levee overtopping as the only flood mechanism. This type of flood mechanism requires a more interactive approach to mapping flood hazard that would allow for ‘what if’ scenarios on the schematisation of the elevation profile in a case study area. Such ‘what if’ scenarios are not suitable for a global approach as presented here.

Relatively simple stage-damage functions are used to estimate risks related to flood hazard and exposure. These functions vary greatly across the globe and may even represent the largest absolute uncertainty in our model results.

Flood risk is modelled as a function of flood hazard, exposure, and vulnerability, with vulnerability assumed to remain constant in time and space. However, future developments in resilience and adaptation measures may reduce vulnerability (e.g., due to increased awareness, other building methods or flood warning procedures).

Parts of Flood risks

  1. Introduction page
  2. Model description
  3. Policy issues
  4. Data, uncertainty and limitations
  5. Overview of references