Hydrological Modelling with SWAT
Transcript of Hydrological Modelling with SWAT
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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
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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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SWAT model procedure
1. Watershed delineation
2. HRU analysis
3. Write Input tables
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SWAT model procedure
1. Watershed delineation
2. HRU analysis
3. Write Input tables
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SWAT model procedure
1. Watershed delineation
2. HRU analysis
3. Write Input tables
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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
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QSWAT visualization – hru
• Static data
o Mean annual ET in mm
Part 1
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Part 1
QSWAT visualization – hru
Nitrate from surface runoff Organic nitrogen from erosion
Nitrogen load to the stream
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QSWAT visualization – hru
• plot
o Monthly discharge from Feb., 1993 to Aug., 1997
Part 1
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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
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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
σ𝑖=1𝑛 (𝑌𝑖
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
Moriasi, (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations
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
σ𝑖=1𝑛 (𝑌𝑖
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
Moriasi, (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations
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
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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
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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
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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
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• Parameter selection
Auto-calibration using SWAT-CUP
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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Auto-calibration using SWAT-CUP
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Auto-calibration using SWAT-CUP
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