A DIFFERENT SOIL CONCEPTUALIZATION FOR THE TOPKAPI MODEL APPLICATION WITHIN THE DMIP 2

12/14/201012/14/2010 AGU Fall Meeting, San FranciscoAGU Fall Meeting, San Francisco

Gabriele Coccia(1), Cinzia Mazzetti(2), Enrique A. Ortiz(3) and

Ezio Todini(1)

(1)University of Bologna, Bologna, Italy(2)ProGea Srl, Bologna, Italy

(3)HidroGaia, Paterna (Valencia), Spain

A DIFFERENT SOIL CONCEPTUALIZATION

FOR THE TOPKAPI MODEL APPLICATION

WITHIN THE DMIP 2

12/14/201012/14/2010 AGU Fall Meeting, San FranciscoAGU Fall Meeting, San Francisco 22

TOPKAPI MODEL: A DISTRIBUTED AND PHYSICALLY BASED TOPKAPI MODEL: A DISTRIBUTED AND PHYSICALLY BASED

HYDROLOGIC MODELHYDROLOGIC MODELMeteorological Data:

Precipitation, Air TemperatureDEM Soil Type Maps Land Use Maps

Sub-Surface Flow Overland Flow

Discharge


( )

( )

Θ=

=+Θ

−

αβ∂∂

∂∂

ϑϑ~

tan

~

Lkq

px

q

tL

s

rsContinuity equation

Dynamic equation

( ) s

ss

sus

uo

s VX

XCQQpX

t

V αα2

2 −++=∂∂

Non-linear reservoir equation for SOIL component

( )( ) ααϑϑ

βL

LkC

rs

ss

−=

tan

Soil type map

(pedology)

SOIL COMPONENTSOIL COMPONENT

Ground surface

Qs

Qs

Qop

Soil parameters

rϑ

sϑ

= residual soil moisture content

= saturated soil moisture content

= thickness of the surface soil layer [m]

= horizontal saturated hydraulic conductivity [ms-1]

= parameter which depends on the soil characteristics

L

sk

sα


RESULTSRESULTS

EVALUATION INDEXES

% BIAS 20.348

Rmod

0.84

NASH-SUTCLIFF Coeff 0.85

The basin behavior is well reproduced and the evaluation indexes

are good but…

Simulated discharge

Observed discharge

Simulated discharge

Observed discharge


RESULTSRESULTS

…the soil saturation process is well reproduced?

During the wetting up period the subsurface contribute to the

flow is too large and during the wet period it is too small

Even if the soil

moisture is high

Steep Recession

CurvesHigh base flow

Even if the soil

moisture is low

Simulated discharge

Observed discharge

Soil Moisture


A DIFFERENT SOIL CONCEPTUALIZATIONA DIFFERENT SOIL CONCEPTUALIZATION

Superficial Soil Layer:

Depth ≈ 0.5 m ≈ 1.5 ft

High conductivity

Deeper Soil Layer:

Depth ≈ 1.2 m ≈ 4 ft

Low conductivity

Two non-linear reservoir are

solved:

•Precipitation infiltrates directly in

the deeper layer

•The sub-surface flow is generated

by both layers and afterwards it

flows into the downstream cell

respective layer

•The excess flow from the deeper

layer goes into the more superficial

one

•The superficial flow is generated

by the excess flow of the more

superficial layer


Superficial Soil Layer:

Depth ≈ 0.5 m ≈ 1.5 ft

High conductivity

Deeper Soil Layer:

Depth ≈ 1.2 m ≈ 4 ft

Low conductivity

The wet period is regulated by the

more superficial layer, which has a

high conductivity and allows

reproducing a slower recession

process

The wetting up period is regulated

by the deeper layer, which stores

most of the precipitation and

generates a small flow due to the

low conductivity

A DIFFERENT SOIL CONCEPTUALIZATIONA DIFFERENT SOIL CONCEPTUALIZATION


RESULTSRESULTS

The base flow during

the wetting up period

is strongly reduced

Maybe a Hortonian infiltration

mechanism is being missed by the

model?

Until the deeper soil (green line)

is not saturated, the more

superficial layer (red line) does

not contribute to the base flow

Old Simulated discharge

Observed discharge

New Simulated discharge


Observed discharge



RESULTSRESULTS

Recession curves

better reproducedRecession curves

better reproduced

Some lost events can

now be simulated

During the wet period, the deeper layer

(green line) is always almost saturated and the

more superficial layer (red line) regulates the flow


Observed discharge



RESULTSRESULTS

Recession

curve better

reproduced

Another clue let think the soil saturation process is more

realistic: the snow melting

Snow melting


Observed discharge


PERCENT BIAS


RESULTSRESULTSNASH-SUTCLIFFE

MODIFIED CORRELLATION COEFFICIENT

The BIAS is reduced to less than

half of its previous value

The N-S coefficient has been

incremented from 0.85 to 0.89

The MODIFIED CORRELATION

COEFFICIENT has been

incremented from 0.84 to 0.92


PROBABILISTIC FORECASTSPROBABILISTIC FORECASTS

The BIAS is still quite high, is it possible to further reduce it?

In the last years in operational applications, such as flood

forecasting system, flood warning management or water

resource management, another concept is taking place among

hydrologists:

THE PREDICTIVE UNCERTAINTY

Namely, the probability distribution of the future real event

conditional upon all the knowledge and information available

up to the present (Krzysztofowicz, 1999), usually embodied in a

model forecast.

Krzysztofowicz, R.: Water Resour. Res., 35, 2739-2750, 1999.


PROBABILISTIC FORECASTSPROBABILISTIC FORECASTS

Following the ideas of Krzysztofowicz , the Department of Earth and

Geo-Environmental Sciences of Bologna University developed a

BAYESIAN PROCESSOR for assessing the PREDICTIVE UNCERTAINTY

From:

Historical series of observed and forecasted values

Using:

An inferential bayesian process

Allows:

Combining many models forecasts

Assessing the predictive uncertainty

Reducing the BIAS

G. Coccia and E. Todini: HESSD, 7, 9219-9270, 2010


RESULTSRESULTS

PERCENT BIAS

NASH-SUTCLIFF Coeff. MODIFIED CORRELLATION Coeff.


PREDICTIVE UNCERTAINTY ASSESSMENTPREDICTIVE UNCERTAINTY ASSESSMENT


01/10/1995 30/09/200231/05/1997 31/01/1998

ANN: CALIBRATION VERIFIC. VALIDATION

01/10/1995 30/09/200231/05/1997 01/05/2000

MCP: VALIDATION CALIBRATION

1st PHASE OF DMIP2: BARON FORK RIVER AT ELDON, OK, USA

TOPKAPI MODEL

H0

EVAPOTRANSPIRATION

UNDERGROUND

LOSSES

X5H4

T4

D4

RAINFALL

EXCEDENT

INFILTRATION

PERCOLATION

T1

X4

X3

X2 Hu

H2

T3

H3

D3

D2

T2

H1

Y0

SNOWMELT

X1

X0

D1

Y1

BASE FLOW

Y4

INTERFLOW

DIRECT

RUNOFF

Y3

Y2

T0

PRECIPITATION

SNOW

TETIS MODEL

ANN MODEL

MODELS COMBINATIONMODELS COMBINATION

Developed by: Department of Hydraulic and Environmental Engineering,

Polithecnical University of Valencia, Prof. F. Francés et al.


MODELS COMBINATIONMODELS COMBINATION

PERCENT BIAS MOD. CORRELLATION COEFF. NASH-SUTCLIFF Coeff.


CONCLUSIONSCONCLUSIONS

The distributed and physically based model TOPKAPI is able

to reproduce with good accuracy the different hydrological

processes

The 2 LAYERS SOIL STRUCTURE allows better reproducing the

BASE FLOW during the wetting up period and the RECESSION

CURVES during the wet period

Even if more tests are required, the introduction of the deeper

soil layer makes more realistic the soil infiltration and the sub-

surface flow processes, taking in account the higher

conductivity of the more superficial layer due to the lower soil

compactness


CONCLUSIONSCONCLUSIONS

Improvements in forecasting models are important for reducing

the uncertainty on the forecasted event…

…anyway…

…the perfect model does not exist, but many different models

are available (data driven, lumped, physically based…).

Hence, the probabilistic approach can be a better way to

improve forecasts, thank to:

Reduction of the BIAS

Model combination

Predictive uncertainty assessment

12/14/201012/14/2010 AGU Fall Meeting, San FranciscoAGU Fall Meeting, San Francisco

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A DIFFERENT SOIL CONCEPTUALIZATION FOR THE TOPKAPI MODEL APPLICATION WITHIN THE DMIP 2