Quantitative and qualitative modelisation of catchment hydrology … · Hydrologie & Hydraulique Agricole Génie Rural & Environnemental Quantitative and qualitative modelisation

Hydrologie & Hydraulique AgricoleGénie Rural & Environnemental

Quantitative and qualitative modelisation of catchment hydrology and at field scale

with « EPICgrid » (~ similar possibilities as AVSWAT, plus Deep Vadose Zone model)

(Modélisation hydrologique quantitative et qualitative des parcelles agricoles et des bassins versants)

(Summary of UHAGx-FUSAGx project as part of « Programme PIRENE », Région Wallonne,

coordination CEME-Ulg, and other projects)---

Ir. C. Sohier & Prof. S. Dautrebande, Ir Dr ----------------

(UHAGx-FUSAGx)Hydrologie et Hydraulique Agricole(Hydrology and Rural Engineering)

Génie Rural et Environnemental(Rural and Environnemental Engineering)

Faculté Universitaire des Sciences Agronomiques de Gembloux – Belgium(University of Gembloux)


EPIC-Grid (FUSAGx ) is part of an Pirene global model for the Meuse and Escaut

« EPIC-Grid » catchment and soils sub-model• Physically based model, without calibration• Distributed model at catchment or regional scale • Also field scale :EPICmod (adapted EPIC (Williams, 1995) for the walloon regio in

Belgium))• Quantity and quality (diffuse agricultural and dispersed domestic pollutions)• Soil-water-plant-climate• Soil and Subsoil (vadose zone) : unsaturated soils, rapid and slow saturated

flows, until basic groundwater• Daily (or hourly ) : in Floods-integrated model MOHICAN (for Sethy-Met-RW))• 30-40 years of simulation• (~ similar to AVSWAT, plus Deep Vadose Zone model)Groundwater sub-model quantity and quality : GEOMAC Ulg (Prof.

Dassargues) and UMH (Prof. Rorive)Surface water sub-model quality : CEME-Ulg (Prof. Smitz) , Ulb (Prof.

Verbank); quantity : HACH-Ulg (Prof. Pirotton),CEME-Ulg (Prof. Smitz) Integration of «Catchment », «Soil», «Surface» and «Groundwater»

models : CEME-Ulg (Prof. Smitz)


Transfers, lagsstocks, stock

variations

Exportations

Deep basic flow («very slow»)• Deep groundwatergeological zone

• Unsaturated and Variable saturated geological zone

• Soils & root zone

Slow hypodermic flow

Fast hypodermic flow

Overland flow(« very fast »)

Transfer ofDirect Runoff

River flow, transfers, lags

stocks, stock variations

Transfers, lags, stocks, stock

variations

Transfers, lags, stocks, stock

variations

• Land Use

• Surface water

HACH (Ulg) (surface water quantity) , ULB and CEME (Ulg) (surface water quality),

GEOMAC (Ulg) et Hydrogéologie (FPMs) (deep groundwater) UHAGx (FUSAGx) (soils, subsoils & catchment )

PIRENE : Catchment Integrated Modelisation (quantity-quality)

CEME (Ulg) (integration)


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Meteo data

Percolation

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Geology

Soils

GRId : 1 km²

Land use, crops growth,agricultural practices

Rivernetwork

RivernetworkFUSAGx EPIC-Grid

catchment, soil and vadose zone

modelisationTopography

In each grid : ponderation

of the different components


A. Hydric fluxes in EPIC-Grid (FUSAGx)

River

Each grid 1 km²Precipitations

Evaporation

Snow Transpiration

Percolation

Deep recharge

Lateral groundwater

Direct runoff(Surface and rapid lateralhypodermic flow)

Infiltration

Deep groundwater


EPIC-Grid Parameters (notably)

•Soils et Sub-Soils parameters :• Effective porosity• Field capacity• Saturated hydraulic horizontal KH

and vertical KV conductivities• …

No calibration,Except for ~ KV’s / KH’s


•

• EPIC-Grid Parameters for the vegetation (notably)• + than 40 crops, forest, grassland : partially adapted from EPIC (USA) data base, for the walloon regio

• Crop growth • height, LAI, yield, …• Agricultural practices• Fertilisations and pesticides•…


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Measured ( mm / d )

EPIc-grid Simulation + transfer ( mm / d )

Direct runoff component

Lateral flows component

Basic groundwater component

A.1. Validations (example) :• a) Catchment river flows

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• b) EPIC-Grid daily (or for hourly) flows simulation

EPIC-GRID : Observed and EPIC-GRID simulated daily discharges - 1995Lesse catchment at Daverdisse (302 sq. km)

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Observed discharges

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HA-FUSAG

y = 1.06xR2 = 0.95

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Observed daily discharges (mm/day)

EPIC

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ID s

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isch

arge

s (m

m/d

ay)

Daily dischargesBissecting lineLinear regression

HA-FUSAGx


• c) Annual hydrological balances

•Annual runoff coefficients : EPIC-Grid : simulated versus measuredy = 1.02 x

R2 = 0.94

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Coefficients d'écoulement observés

Coe

ffici

ents

d'é

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ulés

Coefficients d'écoulement

Bissectrice des axes

Régression linéaire

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Coefficients d'écoulement simulés

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ents

d'é

coul

emen

t obs

ervé

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Coefficients d'écoulement

Bissectrice des axes

Régression linéaire

Gette

Lesse


A.2. Validations (example) : Soil moisture

Clayed-loamy soil (Famenne)

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D a te

Hum

idité

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umiq

ue (%

)

H um id i té vo lum iq ueo b s e rvé e (s t. 4 .1 )

H um id i té vo lum iq ues im ulé e p a r le m o d è leE P IC

Loamy soil – wheat (Hesbaye)

Simulation ( ) and measurements ( . )


A.3. Validations (example) : Crop growth

Height of wheat

EPICmod : Simulation ( ) and measurements ( . )

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8/09/95 28/10/95 17/12/95 5/02/96 26/03/96 15/05/96 4/07/96 23/08/96

mèt

res

" CHT "

Hteur moy

Hteur max

Hteur min

HMX = 0.9DLAP1 = 15.010DLAP2 = 55.80


A.4. Validation (example) : Soil TemperatuurEPICmod : Simulation ( ) and measurements ( )

Températures mensuelles du profil de sol (mesures et simulations sur sol nu à 10, 20 et 50 cm) moyennées de 1985 à 1989

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Simulation

Observation

UHAGx - FUSAGx


B. « Quality » fluxes EPIC -Grid

• Nitrates Cycle • Organic matter Cycle• Pesticides Cycle• Sediment yield (catchment's scale) • Agricultural diffuse erosion (field scale)• …

Evaporation Snow

PrecipitationTranspiration

Percolation

Deep recharge

Lateral flow

Direct runoffInfiltration


EPIC-Grid « Quality » Parameters

(No calibration)

• For soil and sub-soil

• Degradation, adsorption-desorption (pesticides)

• « Mobile-immobile » water (nitrates and pesticides)

•…


B.1. Validations : in root zone soil

Project SSTC - 2001 (FUSAgx-CRAGx)

EXEMPLE : N-NO3 Evolution in soils

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0 – 60 cm

(Validation)

(Validation)

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Nitra

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EPICmod Simulation ( ) and Measurement ( )

Project SSTC - 2001 (FUSAgx-CRAGx)

EXEMPLE : N-NO3 Evolution in soils

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(Validation)

(Validation)

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EPICmod Simulation ( ) and Measurement ( )


B.2. Validation : in subsoil, deep recharge (Programme-Action-Hesbaye: financement EU-RW-Aquawal,

(Étude CRAGx-Ulg (Prof. Monjoie) -FUSAGx))

EPICmaille Simulation of Deep Nitrates leaching near groundwater;Measurements near surface deep groudwater


B.3. Validation : Subsoil solution

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N-NO3 Concentration (mg/l) of soil and subsoil solutionD

epth

(met

ers)

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Concentration moyenne mesurée (Bovenistier, 98)courbe enveloppe des concentrations (min et max) mesurées 400 kg d'azote minéral appliqué pour les cultures maraîchères500 kg d'azote minéral appliqué pour les cultures maraîchères600 kg d'azote minéral appliqué pour les cultures maraîchères

Unité d'Hydraulique Agricole Forage à Bovenistier (Waremme) Mars 1998FUSAG Hesbaye

Project BOVENISTIER (collabor. With Prof. Monjoie Ulg) and CRAGx:Effect of agricultural practices on vegetable crops (field scale)Scenario Epic- maille :

if non vegetables crops

15 m

0

MeasurementsCRAgx (mean

and variability)

Effect of nitrates fertilisation on vegetables, 4 to 8 years before


C. Results of simulations

(Examples)


River Station Area Precipitation

(mm)

Simulatedactual

Evapotrans-ration --mm (mm)

Simulated Lateral

groundwater

SimulatedDeep

Groundwater recharge

Différence (%Precipitation) Simulations & measurements

Amblève Martinrive 1068 km² 1102.96 554.86 264.99 84.73 0.50 0.47 6

Berwinne Moelingen 129 km² 913.59 561.82 30.72 180.59 0.38 0.34 12

Bocq Yvoir 227 km² 912.70 528.01 137.39 108.54 0.43 0.34 26

Geer Kanne 465 km² 802.04 538.61 11.74 215.64 0.34 0.18 85

Hermetton Hastière 166 km² 875.88 543.42 149.34 58.63 0.39 0.37 5

Houille Felenne 114 km² 1146.70 574.71 245.81 142.41 0.50 0.51 -2

Hoyoux Modave 94 km² 925.04 542.50 112.42 135.73 0.42 0.31 33

Lesse Daverdisse 302 km² 1150.08 552.97 321.91 110.63 0.52 0.50 4

Lesse Gendron 1284 km² 1012.13 546.74 246.31 60.10 0.46 0.43 8

Mehaigne Moha 341 km² 768.73 539.10 29.34 168.31 0.31 0.25 23

Molignée Warnant 125 km² 890.99 526.14 132.51 91.61 0.41 0.40 4

Ourthe Mabompré 319 km² 1016.19 541.99 228.73 100.21 0.47 0.46 2

Ourthe Nisramont 738 km² 1028.05 548.06 249.22 86.34 0.47 0.50 -6

Ourthe Tabreux 1615 km² 992.06 538.69 224.46 68.36 0.46 0.43 6

Ourthe Sauheid 2910 km² 1024.09 542.45 232.06 74.66 0.47 0.47 0

Semois Membre 1230 km² 1230.75 552.75 326.66 109.61 0.55 0.56 -2

Vesdre Chaudfontaine 680 km² 1065.70 576.74 186.41 104.07 0.47 0.43 8

Viroin Treigne 554 km² 1002.64 563.61 200.78 97.59 0.44 0.46 -3

Catchment with water exportations (pumping, …)Differences < 4%

Differences < 10 %

Total river flow/ Precipitation

Simulation Measurements

Main type of groundwater

(mm)

Reminder : without calibration, except KV/KH

C.1. Simulated Hydrological balances of the Meuse sub-catchment’s


Unité d’Hydrologie et Hydraulique Agricole

Région wallonne - NO3 (mg/l) Concentration -Moy 1998-2000

Agricole diffus Agricole diffus

+domestic contribution NO3 (1)

Simulation EPICgridIn Rapid and slow

Hypodermical flows(local measurements)

Groundwater measurementsare or in deep or in hypodermic

Conc. NO3 (mg/l)

Unité d’Hydrologie et Hydraulique AgricoleUnité d’Hydrologie et Hydraulique Agricole

Région wallonne - NO3 (mg/l) Concentration -Moy 1998-2000

Diffuse agricultural NO3

+ domestic contribution NO3 (1)

Simulation EPICgrid

Conc. NO3 (mg/l)

C.2. Nitrates leaching simulations in vadose zone

RW Groundwater Atlas Diffuse agricultural NO3

Near Deep groundwater(Deep Groundwater recharge)

(1) Hypothesis : If 30% of houses are without collector


C.3. Eau d’Heure Catchment to Silenrieux :NO3 Concentration (mg/l) of leaching - Moy. 1998-2000

EPICgrid Simulationgroundwater recharge :

(diffuse agricultural only)

Atlas DGRNE-RWLocal in groundwater

Measurements(all sources of contamination)


Atlas DGRNE-RWLocal in groundwater

measurements(all sources of contamination)

EPICgrid SimulationVadose zone near deep groundwater

(Deep Groundwater recharge)

C.4. Atrazine Simulations(diffuse agricultural only)


C.5. Atrazine Simulations( for diffuse agricultural)

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EPIC-grid_PIRENE : Diffuse Atrazine Concentration in direct runoffGeer Catchment to Kanne (465 km²) - 2000

16/1

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Dire

ct R

unof

f (m

m/j)

Atr

azin

eC

once

ntra

tion

(µg/

l)

Atrazine Concentration (µg/l)

Direct runoff


C.6. Sediment yield (agricultural production)

fonction of …

• precipitation• overland flow • soil type • slope • lenght of slope • land use


C.6. a) Soil Erodibility (from Bollinne, 1971)


C.6. b) Sediment yield (T/ha.year)


D. Other developments or applications


Application : Cartography of the diffuse runoff risk


Application :Monthly Distribution of the actual Evapotranspiration (ETR)

(watershed scale)

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(mm

/moi

s)

ETR GeerETR Ourthe

Simulation EPIC-GRID - 1994

Vegetation growth

ETP (climate)

Geer watershed

Ourthe watershed

Effects of :

Soil moisture


Application :Assessment of effects of climatic changes

Annual flows production simulated by EPIC-GRID (example for one catchment)

-3 à +3%

-10 à -7% -3 à +8%

-26 à -19%

-34 à -19%

}0 à +3% 0 à +8%

-6 à +5%

}-5 à +3% +1 à +11%

-24 à -9%

}

CGCM1 ECHAM4 HadCM2

-20 à -8 %

Actual evapotranspirationQuantitative Low variations for all scenarios, but variations in seasonal distributions Lateral flowsImportant annual decrease (up to 26%) (except for CGCM1 scenario)Groundwater rechargeImportant annual decrease (up to 34%) (except for CGCM1 scenario)

The recharge and content of groundwater and surface water reservoirs decrease with possible problems of human and animals drinking


Application : Assessment of effects of climatic changes

Monthly flows production and hydrological balances , as simulated by EPIC-GRID (example)

C G C M 1

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JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Moy

mm

/moi

s

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2 0 7 0 - 2 0 9 9

H A D C M 2

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/moi

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/moi

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Lateral flows* Increase for some winter months with possible increase of river flows * Decrease for summer months. This decrease coupled with lower

groundwater recharge will induce lower flows in the river with possible problems for river population, aquaculture, ...


Application Simulations on 30 years ; mean and variability of Nitrates quantity in 1,5 m soil tickness and leaching concentrations at 1,5 m depth; wheat in rotations with other crops

Rotation BFE

Rotation BFPF , wheat,after potatoes

y = 0.99x + 22.88R2 = 0.76

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0 20 40 60 80 100 120teneur en azote (kgN-NO3/ha) sur le profil de sol 0 -1.5 m, 5 jours après récolte

du froment (20/8)

conc

entra

tion

annu

elle

en

nitra

te d

es e

aux

de

less

ivag

e (m

gNO

3/l)

à 1.

2 m

, du

20/8

au

19/8

après froment (BFE)

droite de régressionfroment (BFE)

teneur

concentra tion

B F E

y = 1.03x + 56.52R2 = 0.56 y = 1.07x + 30.19

R2 = 0.82

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teneur en azote (kgN-NO3/ha) sur le profil de sol 0 -1.5 m, 5 jours après récolte du froment (20/8)

conc

entra

tion

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elle

en

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te d

es e

aux

de

less

ivag

e (m

gNO

3/l)

à 1.

2 m

, du

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au

19/8

froment après betterave, précédent pdt (BFPF)

droite de régression froment

après betterave (BFPF)

teneur

concentration

teneur

concentration

B F FP

Rotation BFE , Wheat


0%25%

50%75%

100%

- 0 N

- 30 N

- 60 N

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5

10

15

20

25

30

35

40

NO

3 C

once

ntra

tion

in le

achi

ng a

t dep

th

1.5

m

(mg

NO

3/l)

% green interculture

Reduction of subsequent crop

NO3 leaching

Application : Green interculture Scénarios – Hesbaye in RW –Mean on 30 years (Same climate as 1971-2000)

Study for RW and GIREA-UCl


Application :Real time prevision of floods

« DECIDE-flood Belgium »-Project (ESA-EU and Sethy-MET-RW financering), with Matra (Fr)

VIGILANCE PHASE (Prevention) PRE-ALERT & ALERT PHASES (Emergency)

Real timeEPIC-GRID

Model

Hydro-MeteoData

Daily Simulation

SAR PRI Image

Soil Moisture

Validation

Soil Moisture(+Potential runoff parameter

+Soil temperature) HYDROMAXModel

Initi

alis

atio

n

Hydrological/HydraulicModel

Hydro-MeteoData

RTFlood Map Forecast

RT RiverFlow Forecast

HYDROMAXModel

Hydrological/HydraulicModel Flood Assessment

Map

ASSESSMENT PHASE

Validation

VulnerabilityMap

PotentialDamage Assessment

Map

SAR PRI Image

RAMSESIm. Server

RA

MSE

SIm

. Ser

ver

VulnerabilityMap

RT PotentialDamage Map Forecast

Accurate DEMGIS Data

GIS Data


ESA-DECIDE-Flood in Belgium

0

5

10

15

20

25

30

1/01

/00

8/01

/00

15/0

1/00

22/0

1/00

29/0

1/00

5/02

/00

12/0

2/00

19/0

2/00

26/0

2/00

4/03

/00

11/0

3/00

18/0

3/00

25/0

3/00

1/04

/00

8/04

/00

15/0

4/00

22/0

4/00

29/0

4/00

6/05

/00

13/0

5/00

20/0

5/00

27/0

5/00

Rai

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oil M

oist

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emp.

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Pote

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SAR Soil Moisture Track151 (%Vol. - 10cm)SAR Soil Moisture Track380 (%Vol. - 10cm)Average Rainfall (mm)Soil Moisture (%Vol. - 10cm)Soil Temperature (°C - 10cm)Potential Runoff Parameter (CN) ]EPIC-grid simulations

]Radar satellite

Application : Validation » of radar satellite catchment soil moisture by EPIC-grid simulations

Documents

Quantitative and qualitative modelisation of catchment hydrology … · Hydrologie & Hydraulique Agricole Génie Rural & Environnemental Quantitative and qualitative modelisation