Fig. 4. Conceptual representation of changes through time in drivers of
river floods. Global warming, and associated changes in temperature and
precipitation, often overlaps with changes in the hydrology, such as
land-cover change, construction of levees and large-scale irrigation.
Global temperature data are from the National Centers for Environmental
Information of NOAA. Flood occurrence data is hypothetical. The bottom
box illustrates the questions pertinent to a multi-driver framework for
conditional attribution.
To judge which hydrological changes should be included, it is necessary
to know the hydrological history of the basin (Fig. 4). Different
relevant hydrological changes have likely occurred at different times,
and can be very old, as in the case of the Netherlands, where water
management has a famously long history (Hoeksema, 2007). Restricting the
analysis to the period coinciding with most of climate change, i.e., the
last century, makes the problem more tractable, and seems more relevant
from the perspective of flood risk management. In the following, we
review information on land-cover change and on other human
interventions.
3.2.2 Land-cover change
For flood attribution, land-cover (land-use) maps need to be available
for the present and also for past periods. Ideally, to ascertain whether
changes overlap with climate change, maps should cover ca. the last
century. Other relevant aspects are accuracy, resolution and the number
of land-cover classes that are differentiated. Fine resolution is
especially useful for detailed hydrodynamic modelling in urban contexts,
whereas for hydrological modelling of large basins, resolution can be
coarser. The priority should be to access any local data curated by
regional institutions, which will typically be finer, more accurate, and
may extend further back in time. Should this not be available,
potentially useful global and continental datasets are:
- Global Land Survey. Global maps curated by the US Geological Survey,
for years 1975, 1990, 2000, 2005, 2010, at 30 m of resolution.
Available at
https://www.usgs.gov/landsat-missions/global-land-survey-gls.
- Global Land Cover and Land Use Change (Potapov et al., 2022). Global
maps from satellite imagery, for period 2000-2020, at 30 m of
resolution. https://glad.umd.edu/dataset/GLCLUC2020.
- Climate Change Initiative Land Cover V2. Global maps curated by the
European Space Agency, from each year between 1992 and 2015, at 300 m
of resolution. https://www.esa-landcover-cci.org/.
- Land-Use Harmonization (Hurtt et al., 2020). Global modelled maps for
period 850-2100, at 0.25° of resolution. https://luh.umd.edu/.
- Corine Land Cover. Maps over Europe for years 1990, 2000, 2006, 2012,
and 2018, at 100 m of resolution.
https://land.copernicus.eu/pan-european/corine-land-cover.
- LUCAS LUC V1.1 (Hoffmann, 2022). Annual maps for Europe from 1950 to
2100, at 0.1° of resolution, based on multiple datasets and methods.
If different datasets have comparable merits, including multiple
datasets in the modelling could enable quantifying the uncertainty
relative to the land-cover. When land-cover changes affect large areas
of the river basin, they should be included in the hydrological
modelling step. When land-cover changes affect the urban areas adjacent
to the flood, it may be appropriate to include them in the hydrodynamic
modelling step. Most distributed hydrological models have internal
representation of land use for processes as evapotranspiration, canopy
interception, infiltration, irrigation (e.g., Horton et al., 2022);
however, the associated parametrizations are not evident and are
model-dependent. Another aspect that should be considered is whether the
land-cover changes had implications for soil properties. Deforestation,
for example, is known to cause loss of soil, especially on steep
terrain. If this is the case, soil changes should be included in the
modelling, either using direct available information, or recurring to
assumptions.
3.2.3 Other human interventions on
hydrology
Flood attribution requires information on human hydrological
interventions: their key features relevant to the modelling, and the
timing of their realisation. Similarly, progressive changes to the
channel geometry and channel bed elevation as a result of fluvial
aggradation or incision need to be assessed. As with land-cover, often
the best information should be accessed in collaboration with local
authorities. However, there are a few global datasets that can function
as alternatives:
- Global Dam Watch. This can be used to assess the presence of dams and
reservoirs in the basin upstream of the study area, and the date of
construction thereof. http://globaldamwatch.org.
- Global Water Watch (Donchyts et al., 2022). This contains time series
of surface area for 71,000 reservoirs.
https://www.globalwaterwatch.earth/.
- Historical Irrigation Database (Siebert et al., 2015). This contains
information on which global areas are equipped for irrigation, in
time-series covering 1900-2005, at 5’of resolution.
https://aquaknow.jrc.ec.europa.eu/en/content/historical-irrigation-dataset-hid.
- OpenDELvE (Nienhuis et al., 2022). This contains maps, locations and
metadata of known levees over global river deltas.
https://www.opendelve.eu/.
These datasets might still miss data for data-poor regions. It could be
therefore valuable to try to assess the presence and extent of
interventions indirectly, using more general datasets that have recently
emerged. For example, HydroATLAS (Linke et al., 2019) contains
information on hydrological, climatological, environmental and
anthropogenic characteristics of river basins and segments, at 15” of
resolution; the database of free-flowing rivers (Grill et al., 2019)
contains information about human pressure on river segments, notably on:
degree of fragmentation and of regulation by dams; on urban areas,
enabling assumptions on the presence of levees and river confinement
structures.
The hydrological and hydrodynamic modelling will then need to adopt
methods to represent these interventions in the simulations, often by
‘burning’ them into the elevation map (Wing et al., 2019), and drawing
from the large experience documented in the literature (e.g., Remo et
al., 2018; Zhao et al., 2016).