uncertainty analysis of nonpoint source pollution modeling5 input data uncertainty (1)changes in...
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An important implication for Soil and
Water Assessment Tool
Uncertainty analysis of nonpoint
source pollution modeling:
Professor Zhenyao Shen
2013 SWAT Conference
2013-07-17 Toulouse
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Methods of uncertainty analysis
Contents
Background
Sources of uncertainty
Implications
1
2
3
4
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Background
One of the greatest
contributors to the
water quality
degradation
• Total Maximum
Daily Load (TMDL)
• Water Framework
Directive (WFD)
•Essential tools to
develop watershed
programs
•SWAT, HSPF,
AnnGNPS etc.
NPS
pollution
Watershed
programs H/NPS models
The SWAT model accounts for
most of the key processes of NPS pollution
at basin scale.
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Uncertainty in NPS modeling
Meteorological processes
Geological processes
Hydrological processes
Ecological processes
Complexity of
watersheds
Natural randomness
Insufficient knowledge
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5
Input data uncertainty (1)Changes in natural conditions
(2)Limitations of measurement
(3)Lack of data
Structural uncertainty (1) The assumptions and simplification in the model
(2) Application of the model under conditions that are not quite consistent
with the model design
Parameter uncertainty
Parameters attained through empirical estimation and optimization of
observed data cannot ensure the precision and reliability of the predicted
results
Sources of uncertainty
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Model input
NPS
pollution
Soil
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Density of rain gauges
Intensively distributed rain gauges
are usually recommended
Single- and multi-gauge calibrations
exhibited no apparent differences
50ha→one well-located station
20km→the threshold distance
between stations
Watershed
characteristics
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Interpolation method
Variation of elevation is not
considered.
Centroid method
Those rain gauges far from the
centroids will be neglected.
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Interpolation method
Global interpolators with more precise
description of rainfall spatial variability for
large watersheds
Selection of an appropriate interpolator
The centroid method can provide adequate
accuracy in small watersheds
The Kriging method
The inverse distance
weighted method
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Interpolation method
Input uncertainty
Hydrologic modeling
NPS simulation
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Measurement errors
Rain measurement involves complicated processes
Complexity of the
environment Constrains of tools Lack of calibration
Perturbation methods
Measurement errors
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Digital elevation model
(DEM)
Land use-land cover
(LULC)
Soil type
Geographic Information System
(GIS)
Land characteristics
GIS data
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GIS data
Resolution
Model performance becomes better with the increase of resolution.
A level beyond which the simulation efficiency may hold steady exists.
was identified as a determining role in the selection of the
appropriate combination of resolutions.
SCS-CN
Threshold effect MUSLE
HRU
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Model parameter
Conceptual group Physical group
Measured
Estimated Calibration
Large number
Model structure
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Model parameter
Uncertainty of model outputs
Only a few parameters
significantly affected the uncertainty of the outputs
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Model parameter
Parameter range
Small adjustments may derive significant uncertainty especially
near the upper and lower limits of parameter range.
It is preferable to obtain a confidence range of each
parameter within which models can be well-calibrated.
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Model parameter
Equifinality
Different parameter groups may introduce the similar results
Model users should check if any information related to the
watershed characteristics and its underlying hydrologic
Processes.
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Model parameter
Probability distribution function (PDF)
Determining the PDF of each parameter is a critical step when
uncertainty analysis is conducted.
Sufficient number of simulation is required to satisfy the
convergence precision.
A proper sampling method is recommended.
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Model parameter
Targeted management
Uncertainty of NPS outputs displayed apparent variation among
different land use types.
Landform Physiognomy Underlying
surface
Anthropogenic
activities
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Model parameter
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Model parameter
Conservation practices
Proper land cover
Dry land
Nutrient management
Paddy
Grazing practices
Yellow earth
Vegetation density
Purple soil
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Model parameter
A greater uncertainty in the high-flow period
Multiple calibrations should be conducted at
different hydrological conditions
Temporal variation
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Model structure
Inaccurate description of
watershed system
Evapotranspiration
Flow routing
Snow accumulation
and melt
Ensemble prediction
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Methods of uncertainty analysis Method Critical considerations
System
nonlinearity
Correlation of
elements
Assumption of
PDFs
OTA √
SUFI-2 √ √ √
FOEA
MC √ √ √
GLUE √ √ √
Bayesian inference
√
√
Bootstrap
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Methods of uncertainty analysis
Easy to program; low computational requirements.
One factor at a time (OTA)
Semi-automated; all sources of uncertainty are accounted for.
Sequential Uncertainty Fitting, ver. 2 (SUFI-2)
Simple but with much hypothesis adopted.
First-order error analysis (FOEA)
Flexible; abundant simulation times are required to achieve reliable
prediction.
Monte Carlo
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Methods of uncertainty analysis
Huge sampling quantity; all sources of uncertainty are accounted for.
Strong dependence on the formulation of likelihood function.
Bayesian inference
High dependency on original samples; wide scope of application.
Bootstrap
Generalized Likelihood Uncertainty Estimation (GLUE)
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Implication
Other H/NPS models
sharing much similarity
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Implication
Input and structural uncertainty should be
paid more emphasis.
The interaction effect between these three
sources of uncertainty deserves more
attention.
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Acknowledgements
• National Science Foundation for Distinguished Young
Scholars (No. 51025933)
• National Science Foundation for Innovative Research
Group (No. 51121003)
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Email: [email protected]