RegCM for the estimation of flood risk maps: providing driving data for the CHyM hydrological model

Adriano Fantini

30 May 2018

IX ICTP Workshop on the Theory and Use of Regional Climate Models

Aims

  • Flood risk mapping over Italy
  • scientific, reliable approach
  • future projections

Models

  • ICTP RegCM
  • CHyM hydrological model
  • CA2D hydraulic model

Project overview

Participants

  • Erika Coppola
  • Adriano Fantini
  • Filippo Giorgi
  • Rita Nogherotto
  • Francesca Raffaele
  • Marco Verdecchia

Methodology

Precipitation:

  • Observations
  • RCM output

Gridded netCDF:

  • River network
  • Discharges

hydrological model

For each RP, cell:

  • Gumbel distr
  • Hydrographs

Statistical RP analysis

hydraulic model

For each RP, cell:

  • Flood extent
  • Flood depth

(multiple simulations)

  • RCM output
  • Discharges
  • Past floods

Validation for

RegCM

1.0 - Observations

We have access to several Italian observational datasets provided by the University of L'Aquila for:

  • temperature
  • precipitation
  • water level
  • discharge

~2000-2017

only!

1.1 - Observations

Some examples...

Outliers

1.2 - Observations

FIRST-STAGE FILTERING

FLAGGING

FLAG CHECKING

CLEANED DATASET!

1.3 - Observations

First-stage filtering procedures

  • Removal of extreme values > 200 mm/h
  • Removal of isolated > 100 mm/h reports
  • Removal of complete months with > 1800 mm (2.5 mm/h)
  • Removal of continuous identical values

1.4 - Observations

Flagging procedures

  • Mean + n*SD threshold
  • Median + n*IQR threshold
  • Peaks in the values distribution
  • Isolated dry/wet event flagging
  • Low correlation of close stations

Results so far...

(by applying first-stage filtering procedures only)

Before After Diff
Total valid values 250M 243M -2.6%
Total flags 324468 58008 -82.1%
pr > Mean + 20SD  3240 2538 -21.7%
pr > Median + 20IQR  49753 22519 -54.7%
pr > 100 mm/h  221822 711 -99.6%
Other softer flags 49646 32240 -35.1%

1.5 - Observations

FILTERED

ORIGINAL

1.6 - Observations

1.7 - Observations

2.0 - Regional Climate Models

How to reproduce 200-years floods with just ~15 years of precipitation data?

Statistical analysis helps, but a longer time period is required

RegCM as driving data for the CHyM hydrological model

EURO-CORDEX Simulations (thanks James Ciarlo`!):

  • RegCM 4.6.1 (ERA-Interim driven) 1979-2016
  • RegCM 4.6.1 (HadGEM driven) 1971-2100 (WIP)

2.1 - Regional Climate Models

First, a basic validation of the data:

  • 4 regions
  • 2000-2016
  • precipitation only

Available datasets:

  • RegCM-ERA-In (12km)
  • RegCM-HadGEM (12km)
  • Italian OBS data (~12km)
  • E-OBS (~25km)
  • EURO4M-APGD (~5km)
  • CHIRPS (~5km)
  • ...

2.2 - Regional Climate Models

2.3 - Regional Climate Models

E-OBS

REGCM

HR-OBS

2.4 - Regional Climate Models

3.0 - Cetemps Hydrological Model

CHyM Is a distributed (gridded) hydrological model. Peculiarities:

  • Can build DEM from various sources, smoothing by cellula automata algorithms
  • Can use several kind of inputs, such as station observations, gridded model data, etc.
  • Designed to work on any domain
  • NetCDF output
  • Maintained at the ICTP by Fabio Di Sante

3.1 - Cetemps Hydrological Model

3.1 - Cetemps Hydrological Model

Creating the river network is sometimes easier said than done...

Real Po river

CHyM simulation 1

CHyM simulation 2

3.2 - Cetemps Hydrological Model

Sanity check:

can we reproduce river basins?

3.2 - Cetemps Hydrological Model

Sanity check: skill of the model driven with the observations

3.6 - Cetemps Hydrological Model

Sanity check: how does the model assimilate RegCM's precipitation?

3.5 - Cetemps Hydrological Model

How does the model perform if driven with RegCM, compared with if driven with observations? (Liguria example)

CHyM (RegCM-driven)

CHyM (OBS-driven)

3.6 - Cetemps Hydrological Model

3.6 - Cetemps Hydrological Model

How does the model perform if driven with RegCM, compared with if driven with observations? (Central-South Italy example)

3.6 - Cetemps Hydrological Model

3.7 - Cetemps Hydrological Model

4 - Statistics

How to estimate hundred-years floods with only a few (~20) years worth of data?

The methodology is taken from Maione et al., 2003

Annual maxima

Gumbel extreme value distribution

Fit parameters

SDH: "Typical" flood timing curve for each river cell

CA2D input data

5.0 - Hydraulic model

  • DEM
  • River network
  • SDH

Thanks for your attention!

Precipitation:

  • Observations
  • RCM output

Gridded netCDF:

  • River network
  • Discharges

hydrological model

For each RP, cell:

  • Gumbel distr
  • Hydrographs

Statistical RP analysis

For each RP, cell:

  • Flood extent
  • Flood depth

(multiple simulations)

  • RCM output
  • Discharges
  • Past floods

Validation for

CA2D model

afantini@ictp.it

RegCM for the estimation of flood risk maps: providing driving data for the CHyM hydrological model

By odineidolon

RegCM for the estimation of flood risk maps: providing driving data for the CHyM hydrological model

Presentation for the Ninth ICTP Workshop on the Theory and Use of Regional Climate Models, 20-25 min

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