STATION-BASED OBSERVATIONS AND FLOOD RISK MODELLING OVER ITALY

ADRIANO FANTINI

PhD student at the University of Trieste and ICTP, Trieste, Italy

afantini@ictp.it

Aims

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

Models

  • ICTP RegCM and other Regional Climate Models
  • CHyM hydrological model
  • LISFLOOD-FP hydraulic model

Project overview

Participants

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

Methodology

Precipitation:

  • Observations
  • RCM output

Gridded netCDF:

  • River network
  • Discharges

hydrological model

For each RP, cell:

  • Gumbel distr
  • Hydrographs

Statistical RP analysis

LISFLOOD-FP model

For each RP, cell:

  • Flood extent
  • Flood depth

(multiple simulations)

  • RCM output
  • Discharges
  • Past floods

Validation for

Precipitation observations (from CETEMPS dataset)

  • Region: Italy
  • Total of ~3700 stations
  • Average of 1800 active stations: 1 station every 13x13km
  • Max 2600 at one time (2012): 1 station every 11x11 km
  • Min 500    at one time (2001): 1 station every 25x25 km
  • Time resolution: hourly data (!!!) in mm/h
  • ~2001-present, updating
  • No useful metadata

Elevation characteristics

MISSING TIMESTEPS

LOW STATION DENSITY

TIME

NUMBER OF STATIONS

Nespor and Sevruk, 1999

Macdonald and Pomeroy, 2008

Undercatch

>30% ?

IGNORE?

Common problems with

in-situ measurements

Temporal and spatial problems:

  • Short timescale
  • Missing periods
  • Low station density
  • Missing timesteps

Data quality problems:

  • Breaks and inhomogeneities
  • Manual measurement errors
  • Equipment errors and failures
  • Weather-related measurement errors

VERY FREQUENT OUTLIERS

The question is, as always:

HOW TO REMOVE OUTLIERS WITHOUT REMOVING HIGH PRECIPITATION EXTREMES?

FIRST-STAGE FILTERING

FLAGGING

FLAG CHECKING

CLEANED DATASET!

Possible 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
  • ...?

Rauthe et al. 2013; A Central European precipitation climatology–Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS)

Isotta et al. 2013; The climate of daily precipitation in the Alps: development and analysis of a high‐resolution grid dataset from pan‐Alpine rain‐gauge data

Perry et al., 2009; The generation of daily gridded datasets of temperature and rainfall for the UK

Hiebl et al., 2017; Daily precipitation grids for Austria since 1961—development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling

 

Possible 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
  • ...?

Rauthe et al. 2013; A Central European precipitation climatology–Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS)

Isotta et al. 2013; The climate of daily precipitation in the Alps: development and analysis of a high‐resolution grid dataset from pan‐Alpine rain‐gauge data

Perry et al., 2009; The generation of daily gridded datasets of temperature and rainfall for the UK

Hiebl et al., 2017; Daily precipitation grids for Austria since 1961—development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling

 

Possible flag-checking procedures

  • Visual comparison of maps/videos
  • Visual comparison of close timeseries
  • Comparison with daily datasets (EURO4M-APGD, E-OBS, ...)
  • Comparison with hourly datasets (PERSIANN, ...)
  • ...?

Rauthe et al. 2013; A Central European precipitation climatology–Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS)

Isotta et al. 2013; The climate of daily precipitation in the Alps: development and analysis of a high‐resolution grid dataset from pan‐Alpine rain‐gauge data

Perry et al., 2009; The generation of daily gridded datasets of temperature and rainfall for the UK

Hiebl et al., 2017; Daily precipitation grids for Austria since 1961—development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling

 

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%

FILTERED

ORIGINAL

REMOVED!

Monthly averages

However... flag checking madness: I still have ~60k flags to deal with!

At one flagged event per minute, that's 4 months of continuous, alienating work =

 

So I need either:

  • Better first-stage filtering
  • Faster flag checking

CAN YOU HELP?

pretty please

CETEMPS Hydrological Model

CHyM is a distributed (gridded) hydrological model from CETEMPS and University of L'Aquila.

  • Model working on all test domains
  • We can reproduce past results (Coppola et al. 2013) on our test domain (western Po basin)
  • HR DEMs working (at low model res)

CETEMPS Hydrological Model

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

CETEMPS Hydrological Model

CHyM-OP reproduced domains:

CETEMPS Hydrological Model

CHyM-OP reproduced domains:

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

LISFLOOD-FP input data

LISFLOOD-FP hydraulic model

Widely-used flood inundation model from the University of Bristol (Bates et al., 2010)

  • DEM
  • D4 River network
  • SDH

SDH:

observed

CHyM

Real stations

CHyM stations

NO FLOOD!

Thank you for listening!

Precipitation:

  • Observations
  • RCM output

Gridded netCDF:

  • River network
  • Discharges

hydrological model

For each RP, cell:

  • Gumbel distr
  • Hydrographs

Statistical RP analysis

LISFLOOD-FP model

For each RP, cell:

  • Flood extent
  • Flood depth

(multiple simulations)

  • RCM output
  • Discharges
  • Past floods

Validation for

?

afantini@ictp.it

Hydrological station-based observations and flood risk modelling over Italy

By odineidolon

Hydrological station-based observations and flood risk modelling over Italy

Presentation HyMeX workshop 2017, BARCELONA, 12+3min

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