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flood_extremes() fits the generalized extreme value
distribution to annual maximum rainfall, both stationary and with a
linear trend in location, and reports a likelihood-ratio test for
changing extremes together with design return levels. A dependency-free
maximum-likelihood engine is used by default; extRemes is
used when installed and requested.
flood_scenario() turns design return levels into a
present-day or climate-adjusted event by three methods:
"delta" (change-factor scaling, the default),
"trend" (projecting the fitted location trend forward) and
"cmip6" (reserved for downscaled projections).
roughness() assigns Manning’s roughness by three
methods: "constant", "landcover" (lookup from
a table, defaulting to floodflow_lc_roughness, and
user-overridable) and "ndvi" (a monotonic empirical
function of vegetation index). Works on vectors and, where
terra is installed, on rasters.
flood_runoff() converts rainfall into a discharge
series. Potential evapotranspiration is computed by the Oudin formula
(pet_oudin()) from temperature and latitude. GR4J via
airGR is used when installed; otherwise a mass-conserving
conceptual fallback with routing lag runs.
flood_route() provides a five-method routing ladder
that turns discharge into a routed hydrograph and a water depth:
"manning-normal", "kinematic",
"diffusive", "muskingum-cunge" (default) and
"dynamic". Depth comes from Manning’s equation; hydrograph
routing uses the numerically stable Muskingum-Cunge family, varying its
diffusion across the ladder.
flood_hydraulics() derives the time-and-motion
family from routed flow: peak velocity, time of concentration by the
Kirpich, Kerby, combined Kerby-Kirpich and velocity methods
(tc_kirpich(), tc_kerby()), channel travel
time, and an event-relative time-to-peak.
flood_uncertainty() applies Generalized Likelihood
Uncertainty Estimation (GLUE): it samples roughness and width from
priors, weights each run by its agreement with an observed depth, and
returns a predictive depth band and inverse parameter estimates.
Equifinality among parameters is reported rather than hidden.
flood_vulnerability() combines hazard, exposure and
vulnerability into a normalised risk index (Risk = Hazard x Exposure x
Vulnerability), identifying hotspots where deep flooding meets dense,
susceptible population.
flood_surrogate() trains a fast emulator (random
forest via ranger, or a log-linear fallback) of the depth
model for rapid what-if exploration.
flood_route() gains an optional hand
argument: supply a Height Above Nearest Drainage surface (or a DEM) and
it produces a spatial inundation-depth raster,
depth_raster, which flood_map(layer = "depth")
draws as a real inundation map.
flood_map() renders a chosen layer (depth, risk,
velocity, uncertainty) as an interactive map via tmap or
leaflet when available, and always returns a tidy value
summary so the pipeline works without mapping engines.
flood_project object that carries data and
results through the pipeline, with print() and
summary() methods and an is_flood_project()
predicate.Imports (only stats and utils),
all modelling engines under Suggests with graceful guards,
MIT license, testthat (edition 3) test suite, and
continuous integration.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.