applying and interpreting the swat sensitivity analysis and auto-calibration tools

Applying and Interpreting the SWAT Sensitivity Analysis and Auto-calibration Tools

byMike Van Liew

Dept. of Biological Systems EngineeringUniversity of Nebraska

Lincoln, NE

Heartland Regional Water Coordination Initiative

Available Auto-calibration tools in SWAT

--Auto-calibration tools created by Ann van Griensven (2005)

--Tools include: Sensitivity Analysis

Parasolmodel calibrationparameter uncertaintySunglasses

parameter uncertainty for calibration and and validation periods

Limitations of the ArcSWAT InterfaceAuto-calibration Tool

• The ArcSWAT Interface Sensitivity Analysis/Auto-Calibration and Uncertainty Tools only allow calibration at a single point within a watershed

• In some cases, a multi-point, regional approach to calibration is highly desirable, especially for large watersheds

Running the Sensitivity Analysis/Auto-calibration Tool in the Project Directory

• The ArcSWAT Interface provides a framework for constructing files that are necessary for performing sensitivity analysis or a multi-gage, multi-parameter calibration

• Some files employed in the Interface tools must be modified by hand to perform a multi-gage or multi-parameter calibration

• This can be accomplished by working in the project directory instead of the ArcSWAT Interface

Today’s Objectives:

• Learn how to create and modify the necessary files for running the sensitivity analysis and auto-calibration tools in a project directory for multi-gage, multi-constituent configurations

• Learn how to interpret the output files generated from the sensitivity analysis and auto-calibration tools

Parameter Sensitivity

• Challenge of determining which parameters to calibrate so that the model response mimics the actual field, subsurface, and channel conditions as closely as possible

• Calibration process becomes complex and computationally

extensive when the number of parameters in a model is substantial

• Sensitivity analysis can be helpful to identify and rank parameters that have significant impact on specific model outputs of interest

Sensitivity Analysis in SWAT

• helpful to model users in identifying parameters that are most influential in governing streamflow or water quality response

• allows model users to conduct two types of analyses:

--the first analysis may help to identify parameters that improve a particular process or characteristic of the model (assesses the impact of adjusting a parameter value on some measure of simulated output, such as average streamflow)

--second type of analysis uses measured data to provide an overall “goodness of fit” estimation between the modeled and the measured time series (identifies the parameters that are affected by the characteristics of the study watershed and those to which the given project is most sensitive)

Sensitivity Analysis

• Sensitivity analysis demonstrates the impact that change to an individual input parameter has on the model response

• Method in SWAT combines the Latin Hypercube (LH) and One-factor-At-a-Time (OAT) sampling

• LH = generates a distribution of plausible collections of parameter values from a multidimensional distribution

• During sensitivity analysis, SWAT runs (p+1)*m times, where p is the number of parameters being evaluated and m is the number of LH intervals or loops

• For each loop, a set of parameter values is selected such that a unique area of the parameter space is sampled

Sensitivity Analysis

• That set of parameter values is used to run a baseline simulation for that unique area

• Then, using one-at-a-time (OAT) sampling, a parameter is randomly selected, and its value is changed from the previous simulation by a user-defined percentage

• SWAT is run on the new parameter set, and then a different parameter is randomly selected and varied

• After all the parameters have been varied, the LH algorithm locates a new sampling area by changing all the parameters

Getting Started: Building Files to Conduct Sensitivity Analysis

• ArcSWAT Interface Sensitivity Analysis Tool Input and Output Windows

• Manually modify files in project directory that are written from the Interface

Sensitivity Input WindowAnalysis Location: Select from the SWAT simulation list a simulation for performing the sensitivity analysis

Subbasin: Select a subbasin within the project where observed data will be compared against simulated output

Sensitivity Input WindowHypercube intervals (Alpha_Bf): 10 intervals of0-0.1, 0.1-0.2 … 0.9-10.0

OAT change (Alpha_Bf):Changes by 5% x (1.0 -0.0) = 0.05Initial value of 0.13 becomes 0.08 or 0.18

Sensitivity Input Window

Select Parameters for conducting sensitivity analysis

Lower bound = 0.0Upper bound = 10.0

Adjust if necessary

Observed Data File Name

Variation Method:1) Replace by value2) Add to value3) Multiply by value (%)

Sensitivity Analysis Output WindowOutput Evaluation: Comparison variable(s)

Objective Function: Select optimization method

Write Input Files to Project Directory

Select Concentrations or Loads for Water Quality

Select Average Modeled Output (eg, streamflow)Or Percent of Time output is < a threshold value)

Main Output: Sensout.out

Input Data:

Objective and Response Functions

List of Parameters

Sample of Senspar.out file OAT = .05 Loops = 5run ALPHA_BF ESCO CH_K2 SOL_AWC GW_DELAY

0-1.0 0-1.0 0-150. 0-30

1 0.16 0.59 25.61 -0.99 8.76

2 0.16 0.59 25.61 -0.99 7.26

3 0.21 0.59 25.61 -0.99 7.26

4 0.21 0.59 25.61 -2.99 7.26

5 0.21 0.59 18.11 -2.99 7.26

6 0.21 0.64 18.11 -2.99 7.26

7 0.94 0.33 32.36 -13.83 19.30

8 0.94 0.33 39.86 -13.83 19.30

9 0.94 0.33 39.86 -13.83 20.80

10 0.99 0.33 39.86 -13.83 20.80

11 0.99 0.33 39.86 -11.83 20.80

12 0.99 0.38 39.86 -11.83 20.80

+ 20%

Main Output: Sensout.out

Parameter Ranking

Ranking of 16 Parameters for Mahantango Creek Watershed, PA

Ranking of 16 Parameters for Stevens Creek Watershed, PA

Mean Value Percent Difference in Objective Function Value with a 5% Change in Parameter Value for Stevens Creek Watershed, NE

Strengths of the Automated Approach to Calibration in SWAT

• Manual calibration of a dozen or more parameters that govern streamflow can be a very time consuming and frustrating process

• The auto-calibration procedure in SWAT provides a powerful, labor-saving tool that can be used to substantially reduce the frustration and uncertainty often associated with manual calibration

• The Parasol with Uncertainty Analysis tool in SWAT provides optimal parameter values that are determined through an optimization search. It also provides an indication of how sensitive a parameter is to being precisely calibrated, based upon the user supplied input range

Shuffled Complex Evolution Algorithm (SCE-UA)

• calibration procedure based on a Shuffled Complex Evolution Algorithm (SCE-UA) and a single objective function

• In a first step, the SCE-UA selects an initial population of parameters by random sampling throughout the feasible parameter space for “p” parameters to be optimized, based on given parameter ranges

• The population is partitioned into several communities (complexes), each consisting of “2p+1” points

Shuffled Complex Evolution Algorithm (SCE-UA)

• Each community is made to evolve based on a statistical “reproduction process” that uses the simplex method, an algorithm that evaluates the objective function in a systematic way with regard to the progress of the search in previous iterations

• At periodic stages in the evolution, the entire population is shuffled and points are reassigned to communities to ensure information sharing

• As the search progresses, the entire population tends to converge toward the neighborhood of global optimization, provided the initial population size is sufficiently large

Shuffled Complex Evolution Algorithm (SCE-UA)

Initialize

No

Shuffle

Evolve

Assess

End

Replace Parents by Offspring

Generate Offspring

Select Parents

Repeat x times to

generate x offspring

Repeat y times to

generate y offspring

Yes

Limitations of the ArcSWAT InterfaceAuto-calibration Tool

• The ArcSWAT Interface Sensitivity Analysis/Auto-Calibration and Uncertainty Tools only allow calibration at a single point within a watershed

• In some cases, a multi-point, regional approach to calibration is highly desirable, especially for large watersheds

Building Files to Conduct Auto-calibration

• ArcSWAT Interface Auto-calibration Tool Input and Output Windows

• Manually modify files in project directory that are written from the Interface

Auto-calibration Input WindowAnalysis Location: Select from the SWAT simulation list a simulation for performing the calibration

Subbasin: Select a subbasin within the project where observed data will be compared against simulated output

Auto-calibration Input WindowOptimization Settings

MAXN = Maximum number of trials before optimization is terminated

NGS = Number of complexes

IPROB = sets the threshold for ParaSol:1 = 90% CI2 = 95% CI3 = 97.5% CI

Calibration Method:ParaSol or ParaSol with Uncertainty Analysis

Observed Data File Name

Auto-calibration Input Window: Observed Daily Record for Streamflow

Year of observed record

Observed Daily Streamflow in cms

Julien day of observed record

Input Window: Observed Monthly Record for Streamflow and Sediment

Observed Monthly Sediment Load (tons/day)

Observed Monthly Streamflow (cms)

Month of observed record

Year of observed record

Auto-calibration Input WindowSelect Parameters for calibration

Adjust initial lower and upper bounds, if necessary

(note: minimum lower bound for SURLAG = 0.5)

Auto-calibration input files:

ChangeparParasolin

MAXN

IPROB

NGS

Auto-calibration input: Multigage Changepar file is created by combining two or more changepar files that are specific for certain subbasins or HRUs in the project

Upper Gage

Lower Gage

Auto-calibration input: Multigage Changepar file is created by combining two or more changepar files that are specific for certain subbasins or HRUs in the project

For parameters that vary by HRU, select All Land Uses, Soils, and Slopes for Subbasins that are relevant to a particular gage

For parameters that vary by Subbasin, select All Subbasins that are relevant to a particular gage

Auto-calibration input: Multigage Changepar file is created by combining two or more changepar files that are specific for certain subbasins or HRUs in the project

Subbasins for gage 2

Subbasins for gage 1

HRUs for gage2

HRUs for gage 1

Auto-calibration Output WindowOutput Evaluation: Select parameter to be calibrated

Objective Function: Select optimization method

Write Input Files to Project Directory

Select Concentrations or Loads for Water Quality Calibration

Auto-calibration input file: fig

Autocal Command Code and Observed Data Files for 2 Gage Locations

Auto-calibration input file: Filecio

ICLB =AutoCalibrationDefault = 0Sensitivity = 1Optimization = 2Optimization with uncertainty = 3Bestpar = 4

NYSKIP = Warm-up

Number of yearssimulated

Auto-calibration input file: Objmet

Code number for Autocalfile in .fig

Concentration or load

Objective function method

Given weight for objective function

Code number for calibration variable

Auto-calibration output file: Parasolout

Parameter Uncertainty

Ranges

Calibration

Auto-calibration output file: goodpar and bestpar

CalibrationParameter listings

Auto-calibration output file: Autocal

Parameter Uncertainty

RangesCalibrationMonthly Streamflow

Monthly Sediment Load

Measured versus Simulated Streamflow with Parasol Uncertainty CI