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Applying and Interpreting the SWAT Sensitivity Analysis and Auto-calibration Tools by Mike Van Liew Dept. of Biological Systems Engineering University.

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Presentation on theme: "Applying and Interpreting the SWAT Sensitivity Analysis and Auto-calibration Tools by Mike Van Liew Dept. of Biological Systems Engineering University."— Presentation transcript:

1 Applying and Interpreting the SWAT Sensitivity Analysis and Auto-calibration Tools by Mike Van Liew Dept. of Biological Systems Engineering University of Nebraska Lincoln, NE Heartland Regional Water Coordination Initiative

2 Available Auto-calibration tools in SWAT --Auto-calibration tools created by Ann van Griensven (2005) --Tools include: Sensitivity Analysis Sensitivity Analysis Parasol Parasol model calibration parameter uncertainty Sunglasses parameter uncertainty for calibration and parameter uncertainty for calibration and and validation periods and validation periods

3 Limitations of the ArcSWAT Interface Auto-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

4 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

5 Todays 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

6 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

7 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)

8 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

9 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

10 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

11 Sensitivity Input Window Analysis 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

12 Sensitivity Input Window Hypercube intervals (Alpha_Bf): 10 intervals of 0-0.1, … OAT change (Alpha_Bf): Changes by 5% x ( ) = 0.05 Initial value of 0.13 becomes 0.08 or 0.18

13 Sensitivity Input Window Select Parameters for conducting sensitivity analysis Lower bound = 0.0 Upper bound = 10.0 Adjust if necessary Observed Data File Name Variation Method: 1)Replace by value 2)Add to value 3)Multiply by value (%)

14 Sensitivity Analysis Output Window Output 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)

15 Main Output: Sensout.out Input Data: Objective and Response Functions List of Parameters

16 Sample of Senspar.out file OAT =.05 Loops = 5 runALPHA_BFESCOCH_K2SOL_AWCGW_DELAY %

17 Main Output: Sensout.out Parameter Ranking

18 Ranking of 16 Parameters for Mahantango Creek Watershed, PA

19 Ranking of 16 Parameters for Stevens Creek Watershed, PA

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

21 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

22 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

23 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

24 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

25 Limitations of the ArcSWAT Interface Auto-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

26 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

27 Auto-calibration Input Window Analysis 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

28 Auto-calibration Input Window Optimization Settings MAXN = Maximum number of trials before optimization is terminated NGS = Number of complexes IPROB = sets the threshold for ParaSol: 1 = 90% CI 2 = 95% CI 3 = 97.5% CI Calibration Method: ParaSol or ParaSol with Uncertainty Analysis Observed Data File Name

29 Auto-c alibration Input Window: Observed Daily Record for Streamflow Year of observed record Observed Daily Streamflow in cms Julien day of observed record

30 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

31 Auto-calibration Input Window Select Parameters for calibration Adjust initial lower and upper bounds, if necessary (note: minimum lower bound for SURLAG = 0.5)

32 Auto-calibration input files: Changepar Parasolin MAXN IPROB NGS

33 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

34 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

35 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

36 Auto-calibration Output Window Output 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

37 Auto-calibration input file: fig Autocal Command Code and Observed Data Files for 2 Gage Locations

38 Auto-calibration input file: Filecio ICLB =AutoCalibration Default = 0 Sensitivity = 1 Optimization = 2 Optimization with uncertainty = 3 Bestpar = 4 NYSKIP = Warm-up Number of years simulated

39 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

40 Auto-calibration output file: Parasolout Parameter Uncertainty Ranges Calibration

41 Auto-calibration output file: goodpar and bestpar Calibration Parameter listings

42 Auto-calibration output file: Autocal Parameter Uncertainty Ranges Calibration Monthly Streamflow Monthly Sediment Load

43 Measured versus Simulated Streamflow with Parasol Uncertainty CI

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