hydrological modelling with swat

1

Hydrological Modelling

with SWAT

Qianwen He, Nov. 2020

Lecture 2 – Model performance

evaluation and calibration

1. Model output visualization and evaluation

2. Calibration and uncertainty

3. Assignment

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Content – Lecture 2

SWAT – Soil and Water Assessment Tool, is a river basin, or

watershed scale model developed by Dr. Jeff Arnold for the USDA

Agricultural Research Service.

SWAT was developed to predict the impact of land management

practices on water, sediment and agricultural chemical yields in large

complex watersheds with varying soils, land use and management

conditions over long periods of time.

8 components: climate, hydrology, nutrients/pesticides, erosion, land

use/plant, management practices, channel processes, water bodies.

SWAT model introduction

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• Physically based

specific information about the catchment

Suitable for large catchments/basins, even continents

• Semi-distributed

HRU: hydrologic response unit (overlay of specific landuse, soil and slope)

Not lumped, not fully-distributed

Computationally efficient

• Long term impacts

Long-term effect (10~30 years)

Temporal scale: daily/monthly/annually

Not suitable for single event storm simulation

• Readily available datasets

SWAT model features

4

Hydrology component

Evapotranspiration

Precipitation

Root

zoneVadose

zone

Shallow

aquifer

Deep

aquifer

RevapPercolation Return Flow

Surface

Runoff

RechargeFlow Out

Plant Uptake

• Water balance

• Surface runoff

• Infiltration

• Evapotranspiration

• Soil water

• Groundwater

• Channel process

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Soil and water assessment tool theoretical report, 2009

Climate

Meteological dataSimulation period, as driving

force

Historical meteological dataWeather generator, to

generate missing data

Nutrient/Pesticide NitrogenPlant uptake, in-soil process,

erosion, in-stream processPhosphorous

Pesticide

ErosionOver land, instream, water

body sedimentMUSLE approach

Water bodies ReservoirWater balance

Pond/wetland

Land use/Plant growthGrowth cycle of plants based on heat unit theory

Management strategiesPlant growth cycle, time of fertilizer/pesticide, removal of

plant biomass

Other components in SWAT

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Digital Eelvation Model

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Input data overview – Robit watershed

(Satelite image from google earth)

Area:16.75 km2

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Input and output data overview

https://www.researchgate.net/figure/259527294_fig2_Fig-2-Overview-of-the-swat-model-model-inputoutput-parameters

Input data overview

Example Data Set: Robit Watershed, Lake Tana Basin

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Remark

Climate input, with measuring sites and time series

Location of the outlet

Observed discharge at basin outlet (m3/s)

Stream .shp

Landuse look up table, with corresponding SWAT

Landuse Code

Soil look up table, with corresponding data to link the

usersoil.xlsx

Soil database created by the user

Weather statistics for weather generator

SWAT model procedure

1. Watershed delineation

2. HRU analysis

3. Write Input tables

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2. HRU analysis


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2. HRU analysis


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2. HRU analysis


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Content

Part 1 – Model output visualization and evaluation

• Output database

• Results post-processing by QSWAT

• Evaluation statistics

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Read and visualize the output database

• Output tables

o Rch: e.g. discharge, sediment, water quality variables

(N,P),…

o Sub: e.g. water balance components in each subbasin

o Hru: e.g. water balance components each hru

o Sed, Wql,... Can be generated if selected

• Extract from database

o SQL

e.g. : to obtain the discharge of a certain rch at a certain year!

• Post-processing tool in QSWAT

o Static data

o Animation

o Time series plot

Part 1

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Model output database

Part 1

SWATOutput.mdb

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SQL used in the database

• Structured Qeury Language

• Extract from .sub

• Create Query Design sql view

SELECT * INTO sub197705

FROM sub WHERE YYYYMM = “197705” (OR/AND...)

• * means all the FIELDS from „sub“ (sub is the name of the

table), * can be replaced by any field name in table sub

• INTO is followed by the name of the table to be created

• WHERE is to extract the related data information

• „and“ and „or“ for

limiting conditions

Part 1

R library: “RODBC”

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QSWAT visualization

• Static data

o A single summary value

o A .shp file is created

• Animation

o Visualize the time series with

the spatial variation

• Plot

o Compare with observed data

o Compare between channels

Part 1

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QSWAT visualization – rch

• Static data

o Mean monthly FLOW_OUT in m3/s

Part 1

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QSWAT visualization – sub

• Static data

o Mean annual Potential

Evapotranspiration in mm

Part 1

21

QSWAT visualization – hru

• Static data

o Mean annual ET in mm

Part 1

22

Part 1


Nitrate from surface runoff Organic nitrogen from erosion

Nitrogen load to the stream

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• plot

o Monthly discharge from Feb., 1993 to Aug., 1997

Part 1

24

Evaluate model performance

• Graphical techniques

• Quantitative statistics: objective function

o Standard regression

• Pearson‘s correlation coefficient (r)

• Coefficient of determination (R2)

o Dimemsionless evaluation

• Nash-Sutcliffe efficiency (NSE)

o Error Index

• Percent bias (PBIAS)

Part 1

25

Pearson’s correlation coefficient

It is a measure of the linear correlation between the observed values and

the simulated values.

Coefficient: -1 ≤ r ≤ 1

Performance rating for recommended statistics

High correlation ±0.5 < r <= ±1.00

Medium correlation ±0.3 < r <= ±0.5

Low correlation ±0.1 < r <= ±0.3

No correlation r = 0

Perfect positive/negative

linear relationshipr = 1/r = -1

𝑟 =σ𝑖=1𝑛 (𝑌𝑖

𝑜𝑏𝑠 − 𝑌𝑚𝑒𝑎𝑛𝑜𝑏𝑠 )(𝑌𝑖

𝑠𝑖𝑚 − 𝑌𝑚𝑒𝑎𝑛𝑠𝑖𝑚 )

σ𝑖=1𝑛 (𝑌𝑖

𝑜𝑏𝑠 − 𝑌𝑚𝑒𝑎𝑛𝑜𝑏𝑠 ))2 σ𝑖=1

𝑛 (𝑌𝑖𝑠𝑖𝑚 − 𝑌𝑚𝑒𝑎𝑛

𝑠𝑖𝑚 ))2

Moriasi, (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations

Part 1

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Pearson’s correlation coefficient

r is the person‘s coefficient, r=0.8

R2 is the coefficient of determination, the proportion of the variance in

measured data is acceptable

• 0 ≤ R2 ≤ 1

• R2 ≥ 0.5 is acceptable

Part 1

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Nash-Sutcliffe efficiency (NSE)

It is a normalized statistic that determines the relative magnitude of the

residual variance compared to the measured data variance (Nash and

Sutcliffe, 1970). NSE indicates how well the plot of the observed versus

simulated data fits the 1:1 line.

𝑁𝑆𝐸 = 1 −σ𝑖=1𝑛 (𝑌𝑖

𝑜𝑏𝑠 − 𝑌𝑖𝑠𝑖𝑚)2


𝑜𝑏𝑠 − 𝑌𝑚𝑒𝑎𝑛𝑜𝑏𝑠 )2 𝑌𝑚𝑒𝑎𝑛

𝑜𝑏𝑠 is the mean of observed data

Performance rating for recommended statistics for a monthly time step

Very good 0.75 < NSE <= 1.00

Good 0.65 < NSE <= 0.75

Satisfactory 0.50 < NSE <= 0.65

Unsatisfactory NSE <= 0.50

Mean observed value is a

better predictor than simulated

value

NSE <= 0.0


Part 1

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Percent bias (PBIAS)

It measures the average tendency of the simulated data to be larger or

smaller than their observed counterparts. (Gupta et al., 1999). It is the

deviation of data being evaluated, expressed as a percentage.

𝑃𝐵𝐼𝐴𝑆 =σ𝑖=1𝑛 (𝑌𝑖

𝑜𝑏𝑠 − 𝑌𝑖𝑠𝑖𝑚) × 100


𝑜𝑏𝑠)

Performance rating for recommended statistics for a monthly time step

Streamflow N, P

Very good PBIAS < ±10 PBIAS < ±25

Good ±10 <= PBIAS < ±15 ±25 <= PBIAS < ±40

Satisfactory ±15 <= PBIAS < ±25 ±40 <= PBIAS < ±70

Unsatisfactory PBIAS >= ±25 PBIAS >= ±70


Part 1

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PBIAS and NSE

Part 1

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Content

Part 2 – Calibration and uncertainty

• Verification, calibration and validation

• Sensitivity analysis and uncertainty analysis

• Manual calibration and auto-calibration

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Model verification, calibration and validation

• Model verification

o Confirming the model is correctly implemented

• Model calibration

Refining the parameter values to make the simulated variable to fit the

observed one. e.g. ParA: [10, 20] ParA: 15

o calibrate or not: evaluate model performance

o which parameters to calibrate: sensitivity analysis

• Model validation

Testing the fitting model to verify its accuracy and estimation of its range

of applicability1998 20082005

calibration validation

Part 2

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Modelling procedure

Part 2

Validation

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Sensitivity analysis

Estimate the rate of change in the output of a model with respect to

changes in model inputs.

• Aim

o Determine parameters that requires more accurate values

o Model the behaviour and the capability of the system

• One-at-a-time sensitivity analysis

• Global sensitivity analysis: random sampling methods

o Lantin Hypercube sampling: an efficient implementation of Monte

Carlo scheme

Part 2

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Sensitivity analysis

Part 2

Parameter value change Parameter value change

NS

E

NS

E

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One-at-a-time sensitivity analysis

• Repeatedly vary one parameter at a time while holding the others

fixed.

• Local sensitivity analysis

o It only addresses sensitivity relative to the point estimates chosen and

not for the entire parameter distribution

• Disadvantages:

o Without regarding to the combined varability resulting from

considering all input parameters simultaneously

o Low efficiency

parameters number a; number of possible values b ba

• When P2 is around x1 value,

P1 is more sensitive

• When P2 is at x2, P1 is less

sensitive

Part 2

ParB

0 1

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Global Sensitivity analysis

– Random sampling method

• Global sensitivity analysis, auto-

calibration

• Select representiative parameter sets

from all possible combinations (e.g. ba)

• Latin Hypercube Sampling

o Parameters have a uniform distribution

o Random parameter distributions are

divided into N equal probablility

intervals.

o Simulations should implement ≥ K+1 (K

is the number of paramters varied)

• Evaluate the relative impact on the model

and rank the parameters

ParA

-3 3

2 parameters, 3 simulations

ParA= -2, 0, or 2

ParB=0.15, 0.45, or 0.8

Simulation ParA ParB

1 0 0.15

2 -2 0.8

3 2 0.45

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Uncertainty analysis

• Aim

o Estimate the uncertainty of

change in the output of a model

with respect to changes in model

inputs.

• Propogation of the uncertainties

in the parameters leads to

uncertainties in the model output

variables

Part 2

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Source of uncertainties

• Input data uncertainty

o Precipitation is by far the largest source of uncertainty in

hydrologic modelling (wind, spatial variation)

• Model structural uncertainty

o Inability to truly present physical processes in model equations

• Model paramter uncertainty

o Not only one parameter set, but many parameter sets can

generate good performance

o Empirical values

• Output data uncertainty is an aggregation of all

uncertainty sources

Part 2

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Uncertainty analysis

Parameter uncertainty

Monthly discharge [m3/s] Monthly NO3 load [kg]

r__SOL_BD().sol -0.2 0.2

r__USLE_K().sol -0.2 0.2

v__BC3.swq 0.2 0.4

v__ERORGN.hru 0 5

Part 2

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Manuel calibration

• Trial and Error process

• Time-consuming

Part 2

41

Auto-calibration

Update each HRU with

the parameter value \TxtInOut\

SWAT.exe

Part 2

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Auto-calibration using SWAT-CUP

Software developed to enable auto-calibration for the SWAT model • Different auto-calibration and uncertainty analysis approaches

• e.g. SUFI-2 (Sequential Uncertainty Fitting, ver.2)

o Robit Watershed

o Measured discharge: Jan. 1993 – Dec. 1997

o Simulation period: Jan. 1993 – Dec. 1997

o Output variable for calibration: discharge at SUB1 outlet

o Before calibration: NSE=0.55, satisfactory

Part 2

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• Parameter selection


Parameter Description Process

OV_N Manning’s n value for overland flow Runoff

ESCO Soil evaporation compensation factor Soil

DEP_IMP Depth to the impervious layer for

modeling perched water tables [mm]

Soil

DIS_STREAM Average distance to the stream [m] Runoff

CN2 SCS curve number for moisture condition

II

Runoff

SOL_AWC Available water capacity of the soil layer

[mm/mm]

Soil

GWQMN Threshold depth of water in the shallow

aquifer required for return flow to occur

[mm]

Groundwater

GW_DELAY Groundwater delay [days] Groundwater

CH_N2 Manning’s n value for the main channel Channel

ALPHA_BF Baseflow alpha factor Baseflow

500 simulations

Part 2

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45


Part 2

• Tim Davie (2008): Fundamentals of Hydrology, Second Edition;

• Chong-yu Xu (2002): Textbook of Hydrologic Models; www.soil.tu-

bs.de/lehre/Master.Unsicherheiten/2012/Lit/Hydrology_textbook.pdf

• Axel Bronstert (2005): Coupled models for the hydrological cycle. Integrating

atmosphere, biosphere and pedosphere

• swat.tamu.edu

• SWAT2009 Theoretical Documentation http://swat.tamu.edu/documentation/

• SWAT2009 Input/Output File Documentation http://swat.tamu.edu/documentation/

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Reference

http://www.soil.tu-bs.de/lehre/Master.Unsicherheiten/2012/Lit/Hydrology_textbook.pdf

http://swat.tamu.edu/documentation/

http://swat.tamu.edu/documentation/

hydrological modelling with swat

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