drought monitoring with hydrological modelling - … · drought monitoring with hydrological...

1st Joint EARS/JRC International Drought Workshop, Ljubljana, 21.-25. September 2009 1

Drought Monitoring with Hydrological

Modelling

Stefan Niemeyer

IES - Institute for Environment and Sustainability

Ispra - Italy

http://ies.jrc.ec.europa.eu/

http://www.jrc.ec.europa.eu/


Why Modelling?

• (Hydrological) models simulations confirm/reject assumptions

of the (hydrological) system

• if proved to be valuable, the selected aspects of reality can be

reproduced in the chosen spatial and temporal resolution

• information without detailed observation / measurement of the

variables of interest!

• potential for future simulations, forecasting, etc.

• drawback: Calibration & Validation!


Hydrological Models

= representation of the hydrological cycle

• precipitation (rain / snow)

• partition on land surface into interception, direct runoff, andinfiltration

• (soil) evaporation, transpiration from vegetation

• soil water balance, soil moisture (vertical flow, lateral?)

• groundwater storage (and flow)

• runoff production (surface runoff, interflow, base flow, …)

• hydraulic routing in the river bed


Hydrological Models – spatial scale and resolution

• spatial reference is the river catchment area

• typically regional application (some 1000 km2)

• depending on model type, the region of study is divided into

smaller homogeneous areas

• polygons or regular grids

• grid spatial resolution typically from 100 m to 1-5 km


Hydrological Models – temporal scale and resolution

• simulation of days to years and decades

• temporal resolution depending on application:

• hourly to 6-12 hourly for flood modelling

• daily to monthly for water balance estimation


Hydrological Models – type of models:

• physically based:

• process oriented, highly parameterized

• statistical models:

• based on statistics rather than physics

• lumped models:

• low spatial differentiation, average values for large areas

• distributed models:

• high spatial differentiation, high resolution input data required


Hydrological versus Atmospheric Models

• atmospheric processes <> hydrological processes

• several levels in atmosphere <> surface meteorology only

• land surface, soil in one layer <> many vegetation and soil layers

• high temporal resolution (hours) <> typically daily temporal resolution

• low spatial resolution (50+km) <> typically 1km spatial resolution

• never local, regional, often global <> local / regional / catchment

• turbulent flux densities of latent heat <> evapotranspiration (mm)

But: Both models types extend increasingly into the other domain.

Future: coupled models!

• For Global Circulation Models (GCM) link to water balance models

• For Regional Climate Models (RCM) link to rainfall-runoff models

… requires collaboration of two traditionally separated scientific communities!


Hydrological Model at JRC – LISFLOOD

• a hydrological rainfall-runoff model

• capable of simulating the hydrological processes in a catchment

• developed by the FLOODS Action of IES/JRC

• specific objective to produce a tool for use in large and trans-

national catchments for a variety of applications, including:

• Flood forecasting

• Assessing the effects of river regulation measures

• Assessing the effects of land-use change

• Assessing the effects of climate change


Hydrological Model at JRC:

LISFLOOD – sketch of processes included in

The model


A physically based distributed rainfall-runoff model programmed in a dynamic GIS-language

• Rainfall/Snow partitioning

• Interception

• Evapotranspiration

• Leaf drainage

• Snow melt

• Soil water processes

• Groundwater flow

• River channel flow (kinematic and diffusion wave)

• Reservoir operations

• Retention storage / polders

• Lakes

• Dyke breaks (in prep)

LISFLOOD


LISFLOOD – (static) input data

CORINE

land cover

EU Soil

Data Base

CIS

Point Data

Cross sections, reservoirs, lakes, polders, …


data assimilation

LISFLOOD

model run

static data

Long-term

water balance

forecast ensembles

statistical

thresholds

flood alerts

forecast receptionand processing

ensembles

discharge

forecast

probability

critical valueexceedance

modelcalibration

synopticstations

observeddischarge

soil moisture, VI’s,snow cover, .. (RS)

data reception

and processing

downscalingupdating

forecasting

evaluation

medium-rangeweather forecasts

European Flood Alert System


Drought Products from LISFLOOD simulations at JRC

1. low flow estimates• applied to climate change scenarios

2. soil moisture estimates• soil moisture anomaly

• Soil moisture forecasts (medium-range)

Common:• 5 km spatial resolution

• Daily time step


Low flow estimates from LISFLOOD

• model driven by high-resolution regional climate simulations

• two periods of each 30 years: end of previous & end of this century

• scenario derived from greenhouse gas emission scenario

A2 of IPCC

• HIRHAM RCM of PRUDENCE project

• 12 km spatial resolution


Low flow estimates from LISFLOOD

• simulation of time series of daily discharge

• 7-days moving average, construction of flow duration curve

• derivation of annual minimum flow for 30 years

and both model runs

• block maxima and partial duration series to obtain

minimum flows and flow deficits

scientific publication: Feyen & Dankers (2009) J. Geophys. Res., 114, D17116, 2009


LISFLOOD calibration

• 8 parameters controlling:

• infiltration, snowmelt, overland and river flow, residence times in the soil and subsurface reservoirs

• estimated in 231 catchments by calibrating the model against

historical records of river discharge

• at least 4 years between 1995 and 2002

• For catchments without discharge measurements simple regionalization techniques (regional averages)

were applied to obtain the parameters


Location of the discharge

gauging stations used in the

calibration of the hydrological

model

(indicated by the small gray

circles) and for validation of

the climate driven simulations

(indicated by the

larger white circles). Feyen &

Dankers (2009).


Change in the estimated minimum flow in the scenario run relative to the control run

Feyen & Dankers (2009)


Soil moisture estimates from LISFLOOD

• available at EDO


1.8 2.2 2.6 3.0 3.4 3.8 4.2 4.6 5.0

very wet very dry

Daily Soil Moisture Map


-4 -3 -2 -1 0 1 2 3 4

wetter normal drier

Daily Soil Moisture

Anomaly


-4 -3 -2 -1 0 1 2 3 4

wetter normal drier

Forecasted Soil

Moisture Anomaly


Regional soil moisture information


Validation:

• Comparison with independent products on top-soil moisture:• Global Soil Moisture Archive derived from ESA ERS

scatterometer data• Provided by Vienna University of Technology (W. Wagner)

• 1992 – 2000

• daily (Surface wetness) or decadal (Soil Water Index) resolution

• ca. 50 km spatial resolution


Global Soil Moisture Archivehttp://www.ipf.tuwien.ac.at/radar/ers-scat/home.htm


ERS -1 and -2 satellites, Scatterometer radar

OrbitType: Near-circular, polar, Sun-synchronous Altitude: 782 to 785 km Inclination: 98.52 deg. Period: About 100 minutes Orbits per day: 14.3 Repeat cycle: 3-day, 35-day and 176-day

Frequency: 5.3 GHz (C-band) Bandwidth: 15.55 MHz Polarisation: Linear Vertical Spatial resolution ~ 45 kmSwath width ~ 500 kmSwath stand-off 200 km to right of sub-satellite trackLocalisation accuracy ± 5 km Radiometric resolution 6 %


Topsoil (5 cm) moisture obtained by means of change detection algorithms. Daily values

Soil Water index obtained by means of an antecedent moisture model. One value every ten days. The T value has been determined by calibration on ground data

Soil moisture retrieval


Comparison between LISFLOOD and ERS/SCAT derived soil moisture

estimates:

Correlation and RMSE

Dependency from base information

Variogram analysis

Scaling behaviour


10 20 30 40 50 60

20

40

60

80

1 2 3 4 51

2

3

4

5

pF LF

pF

ER

S

1993 1994 1995 1996 1997 1998 1999 2000

0

10

20

30

40

50

60

70

80

year

P(m

m)

1993 1994 1995 1996 1997 1998 1999 20001.5

2

2.5

3

3.5

4

4.5

5

5.5

year

pF

corr. coeff.=0.26214 - RMSE=0.66542 - Average rainfall 356


1 2 3 4 51

2

3

4

5month 1

pF ERS

pF

LF

1 2 3 4 51

2

3

4

5month 2

pF ERSpF

LF

1 2 3 4 51

2

3

4

5month 3

pF ERS

pF

LF

1 2 3 4 51

2

3

4

5month 4

pF ERS

pF

LF

1 2 3 4 51

2

3

4

5month 5

pF ERS

pF

LF

1 2 3 4 51

2

3

4

5month 6

pF ERS

pF

LF

1 2 3 4 51

2

3

4

5month 7

pF ERS

pF

LF

1 2 3 4 51

2

3

4

5month 8

pF ERS

pF

LF

1 2 3 4 51

2

3

4

5month 9

pF ERS

pF

LF

1 2 3 4 51

2

3

4

5month 10

pF ERS

pF

LF

1 2 3 4 51

2

3

4

5month 11

pF ERS

pF

LF

1 2 3 4 51

2

3

4

5month 12

pF ERS

pF

LF


Legend

rmse

Value

1

0

∑ −=

N

xxN

RMSE2

21 )(1

RMSE and R calculated in the temporal domain

RMSE has an average of 0.45, and a standard deviation of 0.16.Given the soil texture features, a 0.05 m3/m3 error ranges between 0.38 and 1.03 pF units at wilting level and between 0.23 and 0.60 pF units at field capacity.


Legend

corrcoeff

Value

1

-1

),cov(),cov(

),cov(),(

2211

2121

xxxx

xxxxR

⋅=

∑ −−=

N

xxN

xx ))((1

),cov( 221121 µµ

R has an average of 0.48 and a standard deviation of 0.30.



0 0.5 1 1.5-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Corr

el. c

oeff

.

RMSE

1

2

3

4

5

6

Legend

1

2

3

4

5

6


R has an average of 0.48 and a standard deviation of 0.30.

RMSE has an average of 0.45, and a standard deviation of 0.16.

Given the soil texture features, a 0.05 m3/m3 error ranges between 0.38 and 1.03 pF units at wilting level and between 0.23 and 0.60 pF units at field capacity.

The ERS/SCAT derived and the LISFLOOD modelled soil suction have a good agreement over large regions, with almost 90% of the area having a positive R and 66% having RMSE<0.5.

The two datasets show large differences in the Alpine region, in eastern Spain, in northern Scandinavia and on the Carpathian mountains.




estimates:



Variogram analysis

Scaling behaviour


POTENTIAL ERROR SOURCES

SOIL FEATURESDEPTHTEXTUREMETEOROLOGICAL INPUTSPOINT DATAINTERPOLATION PROCEDURES

LISFLOOD MODELSOIL MOISTURE PROCESSESFROZEN SOILSSNOWERS-SWICHANGE DETECTIONMISSING AND FROZEN SAMPLES

Explained variance Predictor

RMSE R

Elevation 8% 8%

1st layer soil depth 18% 0%

Average annual rainfall 11% 0%

Annual rainfall coefficient of variation 4% 1%

% of missing samples 15% 10%

% of snow/ice covered samples 20% 26%



estimates:



Variogram analysis

Scaling behaviour


5 km

5 km

50 100 150 200

20

40

60

80

100

120

140

160

180

200

1.5

2

2.5

3

3.5

4

4.5

50 km

2 4 6 8 10 12 14 16 18 20

2

4

6

8

10

12

14

16

18

20

1.5

2

2.5

3

3.5

4

4.5

LISFLOOD ERS/SCAT


50 km

50 km

2 4 6 8 10 12 14 16 18 20

2

4

6

8

10

12

14

16

18

20

1.5

2

2.5

3

3.5

4

4.5

50 km

2 4 6 8 10 12 14 16 18 20

2

4

6

8

10

12

14

16

18

20

1.5

2

2.5

3

3.5

4

4.5

LISFLOOD ERS/SCAT


200 km

200 km

1 2 3 4 5

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

1.5

2

2.5

3

3.5

4

4.5

200 km

1 2 3 4 5

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

1.5

2

2.5

3

3.5

4

4.5

LISFLOOD ERS/SCAT


The ERS SWI derived and the LISFLOOD modelled soil suction have a good agreement over large regions, with almost 90% of the area having a positive R and 66% having RMSE<0.5.

The two datasets show large differences in the Alpine region, in eastern Spain, in northern Scandinavia and on the Carpathian mountains.

Final remarks


Outlook

• implement “best-calibrated” version of LISFLOOD with focus on

soil moisture estimates

• look for alternative land surface schemes that include processes

missing in LISFLOOD, but relevant for droughts (e.g. capillary rise)

• first pilot study on comparison of LS models:

• changing requirements, now also towards global applications

• additional drought information derived e.g. from ET estimates (EF?)

• resulting in the recommendation of the Community Land Model CLM3.0

(http://www.cgd.ucar.edu/tss/clm/)

• increasing availability of RS products on soil moisture

• ERS/Metop (Eumetsat SAF), SMOS (ESA 2009), SMAP (NASA 2014)

• further comparison and verification, data assimilation?


… thank you!

drought monitoring with hydrological modelling - … · drought monitoring with hydrological...

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