Evaluation of 3D soil hydraulic data using the SWAT model on the Zala and Kapos catchments,

Hungary

Pres.:Zsolt Jolankai1

Contributors: Mate Kardos1, Zsolt Kozma1

Laszlo Pasztor2, Brigitta Szabo2,3

1. Budapest University of Technology and Economics

2.Research Institute of Soil Sciences and Agricultural Chemistry

3.Georgikon Faculty, University of Pannonia

SWAT conference 2019, Vienna

Fri, 19-July,2019MTA ATK TAKI

OUTLINE

• Objectives

• Case study areas

• Soil databases

• Way to create soil input files

• Modelling with SWAT

• Results

• COnclusions

Objective of the study

Improve soil information for hidrological simulations and for otherpurposes

Assess the quality of the soil hydraulic database quality throughhydrological modelling

ZalaCca. 1500 km2

KaposCca. 3300 km2

Case study areas

National HydroDEM 50x50m

Zala catchment - Elevation and soil texture

National HydroDEM50x50m

Kapos catchment - Elevation and soil texture

Main principles of the methodology

• Use Soil Hydrologycal Groups instead of texture classes to generate HRU-s

• Load spatial information into the model input files (to further improve spatialinformation)

• Compare model results with a coarser European scale database to highlight thedifferences based on soil information solely

• See if it is possible to calibrate model without adjusting soil parameters (UsingSUFI2 algorithm for calibration)

• Support recharge estimation with 1D soil profile water flux calculation

Soil data – Two database (for Zala pilot)

• 250 by 250 m horizontal resolution

• 6 layers (2 meters)

• Used data for the model: • Available water content (EU-SH)

• Saturated hydraulic conductivity (EU-SH)

• Clay,silt,sand content (Soilgrids)

• Bulk density (Soilgrids)

• Maximum rooting depth(Soilgrids)

• WRB Taxonomic classes (Soilgrids)

• Organic carbon content (Soilgrids)

National DoSoRemi (DSR) databaseEU-SoilHydrogrids (EU-SH) database andISRIC Soilgrids database

• 100 by 100 m horizontal resolution

• 3 layers (30-60-90 cm) + 1

• Used data for the model: • Available water content (AWC)

• Saturated hydraulic conductivity (Ksat)

• Clay,silt,sand content

• Bulk density

• Maximum rooting depth

• Soil Hydrological Groups

• Organic carbon content

https://eusoilhydrogrids.rissac.hu/250.php

https://www.soilgrids.org

http://dosoremi.hu/table.html

Soil data – Two database (for Kapos pilot)

• 250 by 250 m horizontal resolution

• 6 layers 2m

• Used data for the model: • Available water content (EU-SH)

• Saturated hydraulic conductivity (EU-SH)

• Clay,silt,sand content (Soilgrids)

• Bulk density (Soilgrids)

• Maximum rooting depth(Soilgrids)

• WRB Taxonomic classes (Soilgrids)

• Organic carbon content (Soilgrids)

HWSD databaseEU-SoilHydrogrids (EU-SH) database andISRIC Soilgrids database

• 250 by 250 m horizontal resolution

• 6 layers 2m

• Used data for the model: • Available water content

• Saturated hydraulic conductivity

• Clay,silt,sand content

• Bulk density

• Maximum rooting depth

• WRB taxonomic classes

• Organic carbon content

https://eusoilhydrogrids.rissac.hu/250.php

https://www.soilgrids.org

http://dosoremi.hu/table.html

HUN-PTF(9 classes)

RFK(8 classes)

EU-Soil HydroGrids(13 classes)

Unsupervised classificationof soil hydraulic maps for thecatchment of Lake Balaton

Ksatmin: 0.49 cm/daymax: 12.88 cm/daymean:1.7 cm/daystd.dev: 0.66 cm/day

DoSoRemi 100 m

SoilHydrogrids 250 m

Ksatmin: 0.61 cm/daymax: 66.2 cm/daymean:36 cm/daystd.dev: 14.7 cm/day

AWC mm H2O

DoSoRemi 100 m

SoilHydrogrids 250 m

AWCmin: 0.07 mmH2Omax: 0.19 cm/day

AWCmin: 0.16 mmH2Omax: 0.2 cm/day

Soil data – Soil Hydrogical Units (SHG)

• The soil hydrologic groups were determined by clustering the relevantsoil hydraulic properties, which are• Water content at saturation

• Water content at wilting point

• Water content at field capacity

• Saturated hydraulic conductivity

• These data have been available in three depth at pilot scale:• 0-30 cm

• 30-60 cm

• 60-90 cm

• The clustering method of H2O K-means was used for the purpose, number of clusters (k) was varied, and 9 cluster was found the best

Soil data –SoilHydrogicalUnits (SHG)clustering

SHG- 9 classes

WRB Soil TaxonomicUnits(STU)

Szalafő

PET (SWAT)

Zero bottom flux

Precipitation9

m

GWL(simulated)

10

m

Runoff allowed

Mualem-van Genuchten soil parameters from the 3D database

Soil moisture profile

(simulated)

Hydrus 1D

Ad hoc 1d soil column approach – check recharge

Nearly no recharge!

0

1

2

3

4

5

6

7

8

9

100

0.25

0.5

0.75

1

1.25

1.5

1.75

2

January July January July January July January July January July January

Pre

cip

itat

ion

[cm

/day

]

Po

ten

tial

eva

po

tran

spir

atio

n [c

m/d

ay]

Version - SWAT 2012 Rev627

Simulation period:

• 10 years of simulation (1994-2003) (wet and dry periods are included )

• 3 years warm up period

Kapos pilot

• 10 years of simulation (1994-2003)

• 4 years warm up period prior to that

SWAT model set up

• Manual calibration• One at a time calibration on subcatchments (daily steps)

Autocalibration

• SUFI 2 optimisation algorithm

• Objective function: NSE

• Dynamic base flow separation is included

• Global sensitivity is also based on SUFI2 scheme

• Global sensitivity of parameters – several runs with different parametersets

Calibration procedure

Land use data

• CORINE 2000 data - level 3

• 18 land cover classes for the Zala pilot Land useSWAT code

Area (ha) %Wat.Area

Agricultural Land-Row Crops AGRR 59103 40.03Forest-Deciduous FRSD 28367 19.21Forest-Evergreen FRSE 15891 10.76Agricultural Land-Generic AGRL 10406 7.05Pasture PAST 8213 5.56Residential-Low Density URLD 5625 3.81Forest-Mixed FRST 4629 3.14Range-Brush RNGB 4374 2.96Range-Grasses RNGE 3748 2.54Vineyard GRAP 2486 1.68Industrial UIDU 1567 1.06Wetlands-Mixed WETL 1155 0.78Orchard ORCD 1115 0.76Water WATR 424 0.29Transportation UTRN 238 0.16Residential URBN 199 0.13Olives OLIV 53 0.04Residential-High Density URHD 49 0.03

Kapos catchment data

• CORINE 2006 data - level 3

• 11 classes for the Kapos catchment

Corine code SWAT code

area_km

2 area_%

211 AGRR 1812.28 56%

311 FRSD 560.95 17%

312 FRSE 251.46 8%

242 AGRL 141.33 4%

231 PAST 108.11 3%

112 URLD 92.66 3%

324 RNGB 79.15 2%

321 RNGE 55.67 2%

121 UIDU 51.57 2%

512 WATR 36.35 1%

411 WETL 33.89 1%

total 3223.42 100%

HRU definition – Zala pilot

Model 1 – 250 m

• WRB texture classes

• Corine land cover

• 6 slope classes

• 4400 HRU’s total

• 5-5-5 % threshold

• 1050 HRU after thresholdapplication

Model 2 – 100 m

• Soil Hydological Groups

• Corine land cover

• 6 slope classes

• 6700 HRU’s total

• 5-5-5 % threshold

• 1756 HRU after thresholdapplication

HRU definition – Kapos pilot

Model 1 – 250 m

• HWSD classes-Soilgrids

• Corine land cover

• 3 slope classes

• 442 HRU after thresholdapplication

• 57 subbasins

Model 2 – 250 m

• HWSD (6 classes retained)

• Corine land cover

• 3 slope classes

• 442 HRU after thresholdapplication

• 57 subbasins

• 11 Precipitation gauge time series (daily values)

• 3 wind station time series

• 3 relative humidity time series

• 3 temperature gauge time series

• 1 solar radiation time series

• 5 river gages

Hydrometeorology– Zala pilot

Model results – comparison of default simulations

• Step 1: SWAT Model with both soil data was run with no modelparameter adjustments

• Poor fit

Average Surface runoff mm– DoSoRemi 100m

Average Surface runoff– EU-SH&Soilgrids

Lateral runoff – DoSoRemi 100m

Lateral runoff – EU-SH&Soilgrids

Groundwater runoff –DoSoRemi 100m

Groundwater runoff –EU-SH&Soilgrids

Water yield (mm)– DoSoRemi 100m

Water yield (mm)– EU-SH&Soilgrids

Lateral runoff – HSWD nocal

Lateral runoff – HSWD cal

Lateral runoff – WRB-SHG nocal

Lateral runoff – WRB-SHG cal

Comparison of default simulations (no calibration) - yearly budget for the catchment

Zala pilot EU-SH DoSORemiSURQ mm 68.06 44.77LATQ mm 41.24 7.32GWQ mm 13.52 39.51WYIELD mm 123.98 94.59PCP mm 608.70 608.70Roff coeff. 0.19 0.16BFI 0.41 0.48LATQ/GWQ 3.05 0.19

Kapos pilot HWSD Soilgrids+EU_SHGSURQ mm 99.16 128.9LATQ mm 13.7 24.9GWQ mm 99.95 31.2WYIELD mm 212.6 187.2PCP mm 689 689Roff coeff. 0.3 0.27BFI 0.5 0.3LATQ/GWQ 0.14 0.8

Model results – comparison of default simulations

• Step 1: SWAT Model with both soil data was run with no modelparameter adjustments

• Step 2: Model calibration experiences

Results

• Hargraves evapotranspiration method prooved to fit te best on multiple subcatch.

• NSE of 0.4 is achieved for calibration on subcatcments

• Including soil parameters in the calibration improved the calibration results

• Global sensitivity suggest Ksat, AWC and ZMX as important soil parameters forcalibration• GW_delay, (CN2), ESCO, CANMX

• Best calibration results suggest low CN2 values (40-60), high gw_delay vaues(400-600), ESCO values btw 0.8-0.9

• Vertical differentiation of soil parameters improved calibration (No successfullcalibration was achieved without it)

• Lateral flow seems to dominate the runoff. Startification of soil is necessarry

Model results – calibration on Zala pilot

• Example on vertical soil parameter sensitivity

(Subcatchment of Zala)

Model results – calibration on Zala pilot

Best calibration result on Sub 38 on Zala

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

01/01/1997 07/20/1997 02/05/1998 08/24/1998 03/12/1999 09/28/1999 04/15/2000 11/01/2000 05/20/2001 12/06/2001

Measured Calculated

Model results – calibration on both catchments

Zala Sub 38 w/o soil

Zala Sub 38 w soil

Kapos Model 1 w soil

Kapos Model 2 w soil

NSE 0.12 0.39 0.45 0.46

PBIAS 31.3 19.3 0.4 0.48

R2 0.27 0.5 0.61 0.48

VOL_FR 1.46 1.24 0.91 1.17

Comparison of calibrated simulations- yearly budget for the catchment

Zala Sub38 DSR uncal. DSR cal

SURQ mm 68.06 0

LATQ mm 41.24 15

GWQ mm 13.52 25

WYIELD mm 123.98 44

PCP mm 608.70 613

Roff coeff. 0.19 0.07

BFI 0.41 0.91

LATQ/GWQ 3.05 0.60

Kapos pilot HWSD Soilgrids+EU_SHG

SURQ mm 13.8 5.4

LATQ mm 60.6 50

GWQ mm 0.05 27

WYIELD mm 82.3 99.4

PCP mm 689 689

Roff coeff. 0.13 0.14

BFI 0.7 0.77

LATQ/GWQ 1140 1.84

Conclusions

• Model water balance component outputs are spatially sensitive to soil data

• Stratification of the soil information is important to successful calibration

• Soil depth is an important parameter.

• Current soil data information is not sufficiently accurate to support successfulhydrological model setup without further calibration.

Reference

The research has been supported by the Hungarian National Research, Development and Innovation Office (NRDI) under grants KH124765, KH126725, the János Bolyai Research Scholarship of the Hungarian Academy of Sciences and the MTA Cloud infrastructure (https://cloud.mta.hu/).

Tóth, B; Szatmári, G; Takács, K; Laborczi, A; Makó, A; Rajkai, K; Pásztor, L:Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatisticsHYDROLOGY AND EARTH SYSTEM SCIENCES (2019)

Acknowledgements

SAV

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ATE

When: 25-26 June 2020

Where: Budapest University of Technology and Economics SAV

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Theme: Solids in River Basins – Soil and particle bound pollutants, with focus on Modelling, Monitoring and Management.

RIVER BASINS 2020 Conference

www.riverbasins.kit.edu