DES 606 : Watershed Modeling with HEC-HMS

DES 606 : Watershed Modeling with HEC-HMS Module 13 Theodore G. Cleveland, Ph.D., P.E 30 Sep 11

Parameter Estimation • Parameter estimation in HEC-HMS refers to the specification of various values in different hydrologic elements. • Methods of practical value are trial-and-error (using external initial estimates) and optimization-type techniques.

Parameter Estimation • This module focuses on automated methods (Chapter 13) in HEC-HMS • Estimating certain parameters does not make sense • Area, Length: Usually quite measurable • Other parameters will need some estimation • Time of concentration, Ksat

Calibration • Model calibration is an estimation process • Observed responses are compared to model output. • Parameters are adjusted to make the model output agree with the observed responses. • Requires that observations exist! • Calibration/Estimation is a “process”

Estimation Process • Consider a HEC-HMS model. • It could have dozens of sub-basins, multiple routing elements, etc. • Each of these elements has multiple descriptive and process parameters. • These need to be estimated. • Initial Estimates • Whether using the automated tools or trial-and-error initial values are needed.

Initial Estimates • Make the initial estimates using various hydrology and hydraulic tools already presented. • Rules-of-thumb are appropriate for making initial estimates. • Then the calibration process refines these estimates.

Merit Function • HEC-HMS calls this the objective function. • It measures “distance” between the observed and simulated response. • Sum of Squared Errors (a common error function) • Sum of Absolute Errors (also common) • HEC-HMS has several other merit functions. • For trial-and-error adjustments, a graph of simulated and observed results is probably adequate.

Illustrative Example • To get an idea of the concepts involved, a single sub-basin with a reservoir is examined. • This is the EX4 case. • To create the example HEC-HMS was opened and a new project was created. • Then the simulation from EX4 was imported into the new project, and the data localized. • Then the new project is saved – this project will be the one we use for automated parameter adjustments.

Automated Parameter Estimation • Here is the initial situation. • We will intentionally change the lag time to produce a poor “fit” then explore using the automated parameter estimation tool to recover the estimate.

Initial Estimates • Suppose the figure to the right represents our “best” estimates by conventional means. • Table look-up, equations to estimate Tc, etc. • We have observations as indicated by the black-dots.

Building an Optimization Trial • Automatic parameter adjustment is called an optimization trial. • We will create a trial, then specify how it is to function.

Create Optimization Trial • Select “create optimization trial” • Prompted to name the trial • Use the default this example (Trial 1)

Which Base Simulation? • Select a simulation (that contains observations) • In this example only get one choice, but if one had several open projects would need to choose.

Select Observation Location • Next select where the observations are available. • In this example we have two choices, but the observation set is at the inlet to the reservoir, in our case the outlet of the sub-basin.

Optimization Trials Icon • Notice the icon “change” that indicates we are starting optimization trials.

Compute “Trials” • Next we switch to the “compute” tab in the upper left pane of the GUI. • Select optimization and Trial 1

Optimization Specifications • Optimization Trial specifications: • Description • RunID • Minimization Method • Gradient • Simplex (Nelder-Mead) • Tolerance and iteration count.

Add Parameters • Next we “right-click” the Trial 1 to select parameters to add to the automated adjustments.

Set Parameter Initial Values • Parameter 1 selected • Choose element to adjust (in this case Sub-Basin1) • Choose what to adjust (in this case Lag) • Can specify initial values (default is values in originating simulation)

Run the Optimization Trial • Run the Trial in the same way as a regular simulation. • Trials are run independently, the original simulation is unchanged

Examine the Results • Results tab, then select various summaries.

Flow Comparison Result • Especially useful result is the flow comparison chart. • Perfect 1:1 agreement (like shown) is indicative of fabricated observations (which is indeed true in the example).

Automated Adjustment – Multiple Parameters • The real help with automated adjustments comes when there are multiple parameters to adjust. • To continue with the example, suppose both the timing value and Ksat are initially poor.

Multiple Parameters • Now we will instruct the optimization trial to consider two parameters at the same time. • Need to add a second parameter, and select its initial value. • Certainly improved from the original model

Accepting the Results • The automated adjustments are kept separate from the base model until the analyst actually changes the base model inputs. • HEC-HMS does not automatically change the base model – protects against unanticipated values creeping into the base simulation. • Suggest that once values are accepted as the “calibrated” model – a duplicate model be created called “Calibrated Model of …” and the original base model be kept in a separate project as documentation of the process.

Summary • Discussed automated parameter adjustments to calibrate a model. • Demonstrated with a simple case. • Suggested that once adjusted parameters are “accepted” a separate model be built to preserve the initial thinking and to document that an optimization process occurred.

DES 606 : Watershed Modeling with HEC-HMS