Applying the Green and Ampt Loss Method

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Last Modified: 2023-11-02 06:00:11.986

HEC-HMS version 4.12-beta.1 was used to created this tutorial. You will need to use HEC-HMS version 4.12-beta.1, or newer, to open the project files.

Download the initial project files here:

Loss_Methods_v4.12_start.zip

Overview

In this tutorial you will apply the HEC-HMS Green and Ampt loss method to a modeling application. Initial parameter estimates will be estimated using GIS information and the model will be calibrated through trial and error.

Background

The Green and Ampt loss method was derived using a simplification of the comprehensive Richard's equation (1931) for unsteady water flow in soil.  Within this method, infiltration proceeds with so-called piston displacement, as shown below.

At the start of the simulation, the soil is assumed to be uniformly saturated and infinitely deep (Green & Ampt, 1911).  The method automatically accounts for ponding at the soil surface.  Similar to the Initial and Constant method, no means for extracting infiltrated water is included.  Therefore, this method should only be used for event simulation.  A layered version of the Green and Ampt method is also available within HEC-HMS that does include these processes but will not be used within this tutorial.

Parameters that are required to utilize this method within HEC-HMS include the initial moisture content or deficit [in/in or mm/mm], wetting front suction head [in or mm], saturated hydraulic conductivity [in/hr or mm/hr], and directly connected impervious area [percent].

Estimate Initial Parameter Values

A Note on Parameter Estimation

The values presented in this tutorial are meant as initial estimates.  This is the same for all sources of similar data including Engineer Manual 1110-2-1417 Flood-Runoff Analysis and the HEC-HMS Technical Reference Manual.  Regardless of the source, these initial estimates must be calibrated and validated.

Initial Moisture Content (or Initial Deficit)

The saturation of the soil layer at the start of the simulation can be defined as either an Initial Moisture Content or Initial Moisture Deficit.  In either case, this parameter is oftentimes defined with respect to the effective porosity of the soil layer.  As an example, if a soil layer with an effective porosity of 0.5 in³ / in³ is 80% saturated, its Initial Content would be 0.4 in³ / in³ (i.e. 0.5 in³ / in³ * 0.8).  Alternatively, the same soil layer could be defined using an Initial Deficit of 0.1 in³ / in³ (i.e. 0.5 in³ / in³ - 0.4 in³ / in³).  This parameter must be calibrated using observed data.

In order to estimate an appropriate Initial Moisture Content, two values must be estimated for the entire study area:

1. A representative effective porosity of the soil.
2. The soil moisture state of the soil at the beginning of the simulation.

Effective Porosity

To estimate a representative effective porosity throughout the study area, Gridded Soil Survey Geographic (gSSURGO) data for Pennsylvania was obtained from the U.S. Department of Agriculture's Geospatial Data Gateway.  Using GIS tools, surficial soil textures were then extracted from the gSSURGO data and are shown in Figure 2.  Also, the percent of the study area encompassed by each soil texture is shown in Table 1.

A tutorial describing how gSSURGO data can be obtained and formatted for use within HEC-HMS can be found here.

Table 1. Soil Textures Within the Study Area

These surficial soil textures can be used to estimate initial parameter values for nearly all loss methods within HEC-HMS, including the Green and Ampt loss method.  For instance, Rawls, Brakensiek, and Miller (1983) assembled data from thousands of soil samples located throughout the United States and related soil textures to various useful parameters.  The effective porosity of various soil textures is shown in Table 2.

Table 2. Soil Textures and Effective Porosity, reproduced from Rawls, Brakensiek, and Miller (1983)

Use the Mahoning Creek surficial soil textures map, Table 1, and Table 2 to answer the following questions.  

Question 1: What are the top five predominant soil textures throughout the study area?

Next, use Table 2 to answer the following question.  Also, (conservatively) assume that bedrock has an effective porosity of 0 in³ / in³.

Question 2: What is the effective porosity for the top five predominant soil textures found when answering Question 1?

Finally, estimate an average (i.e. representative) effective porosity for the study area using your answers to the previous two questions.  Don't spend too much time being overly precise; instead, quickly estimate the fraction of the study area that is encompassed by each predominant soil texture and multiply by the corresponding effective porosity.  Once you've done that for each of the five predominant soil textures, sum the values.

Question 3: What is an average (i.e. representative) effective porosity for the study area?

Soil Moisture

In order to estimate the amount of moisture present within the soil throughout the study area, the antecedent conditions at the beginning of September 2018 must be investigated.  Daily average humidity, daily precipitation accumulation, daily average temperature, and daily average wind speed for August 2018 are shown below.

Use the figure above and the following information to answer Question 4.  Assume that the soil within the study area:

1. Was completely saturated by rainfall on August 22, 2018 and

2. Requires approximately 14 days with no rain to completely dry under normal, summer conditions

Question 4: Estimate an initial content (in³ / in³) for the study area at the beginning of September 2018.

Wetting Front Suction Head

The wetting front suction head (referred to as Suction in HEC-HMS) describes the attraction of water within the void spaces of the soil column.  Like effective porosity, this parameter can also be estimated using surficial soil textures.  The wetting front suction head of various soil textures is shown in Table 3.

Table 3. Soil Textures and Wetting Front Suction Head, reproduced from Rawls, Brakensiek, and Miller (1983)

Use the Mahoning Creek surficial soil textures map, Table 1, Table 3, and your answers to the previous questions to answer the following question.  

Question 5: What is an average (i.e. representative) wetting front suction head for the study area?

Saturated Hydraulic Conductivity

The saturated hydraulic conductivity (referred to as Conductivity in HEC-HMS) defines the rate at which surface water will enter the soil column when completely saturated.  This parameter can also be estimated using surficial soil textures.  The saturated hydraulic conductivity of various soil textures is shown in Table 4.

Table 4. Soil Textures and Saturated Hydraulic Conductivity, reproduced from Rawls, Brakensiek, and Miller (1983)

Use the Mahoning Creek surficial soil textures map, Table 1, Table 4, and your answers to the previous questions to answer the following question.  

Question 6: What is an average (i.e. representative) saturated hydraulic conductivity for the study area?

Directly Connected Impervious Area

Directly connected impervious areas are surfaces where runoff is conveyed directly to a waterway or stormwater collection system.  These surfaces differ from disconnected impervious areas where runoff encounters permeable areas which may infiltrate some (or all) of the runoff prior to reaching a waterway or stormwater collection system.  Within HEC-HMS, no loss calculations are carried out on the percentage of the subbasin that is specified as impervious area; all precipitation that falls on that portion of the subbasin becomes excess precipitation and subject to direct runoff.  Within this tutorial, a directly connected impervious area percentage of 0 will be assumed.

Modify the Existing HEC-HMS Project

Now that you've estimated initial parameters, begin modifying the existing HEC-HMS project.

1. Open the Punxsutawney project and then open the Mahoning Creek Basin Model.
2. Select the Mahoning Creek subbasin element.
3. On the Subbasin tab, change the Loss Method from None to Green and Ampt.
4. Select the Loss tab to open the Green and Ampt Component Editor.
5. Enter the Initial Content, Saturated Content, Suction, Conductivity, and Impervious percentage that were estimated in the previous section.  The Green and Ampt Component Editor should resemble the figure below.
   
   
6. Create a new simulation run by clicking Compute | Create Compute | Simulation Run.
   
   
   1. Name the new simulation run "Sept2018_GreenAmpt". Select Next>.
   2. Select the Mahoning Creek basin model. Select Next>.
   3. Select the MRMS meteorologic model. Select Next>.
   4. Select the September 2018 control specifications. Click the Finish button.
7. Select the Sept2018_InitialConstsimulation run from the Compute toolbar.
   
8. Press the Compute All Elements button,  , to run the simulation.
9. Select the Mahoning Creek subbasin element and click the the Result Graph button, , and the Summary Table button, .
   
   1. Leave the summary table and plot open so you can see results change as you modify the loss method parameters. The observed streamflow is represented by the black dotted line and the model's computed streamflow is represented by the solid blue line. 
   2. Notice that there is much less computed runoff than the observed flow.  This is confirmed within the Summary Table which shows large differences in observed and computed runoff volumes and peak flows, and poor statistical metrics.
      
   3. To better match the observed runoff, the Green and Ampt loss method parameters must be calibrated.

Calibrate the Model

Calibrating a model to afford better agreement between computed and observed runoff oftentimes requires simultaneous changes to more than just one process (e.g. calibrate both loss and baseflow at the same time).  However, within this tutorial, only the Green and Ampt loss method parameters will be modified.

1. Begin by modifying the loss parameters to approximately match the initiation of runoff.
   1. Change the Conductivity to 0.05 in/hr and rerun the simulation.
   2. The precipitation (dark blue) and precipitation loss (red) hyetographs for the Mahoning Creek subbasin element are plotted in the figure below.  Notice that the precipitation volume from the start of the simulation through approximately 09Sep2018 12:00 is used to satisfy the different between the initial content and the saturated content (approximately 1 inch).  Afterwards, precipitation is infiltrated at a rate that decreases towards an asymptotic limit equal to the saturated hydraulic conductivity.  When using a time step of 15 minutes, the effective saturated hydraulic conductivity is 0.0125 inches / 15 minutes (i.e. 0.05 in/hr * 15 / 60).
      
      
   3. When looking at the Result Graph and Summary Table for the Mahoning Creek subbasin element, the computed hydrograph begins to rise at approximately the same time as the observed hydrograph, as shown below.  However, the computed runoff volume (3.5 inches) is still less than the observed runoff volume (4.4 inches) so further modifications are required.
      
2. Continue modifying the loss parameters to approximately match the observed runoff volume.
   1. Change the Suction to 7.5 inches and rerun the simulation.
   2. As shown below, more precipitation is infiltrated than before.  However, the asymptotic limit (i.e. saturated hydraulic conductivity) is still the same.  Thus, the initially high rate at which precipitation is infiltrated decreases MORE QUICKLY than it did when the wetting front suction head was set to 5.6 inches.  This demonstrates the effect that changes in wetting front suction head have on infiltration when using the Green and Ampt method: as wetting front suction head increases, so does the rate at which infiltration approaches the saturated hydraulic conductivity (and vice versa).
      
   3. Change the Conductivity to 0.025 in/hr and rerun the simulation.
   4. As shown below, the minimum infiltration rate has decreased.  But, the amount of precipitation loss prior to runoff initiation has also decreased.  When using the Green and Ampt loss method, changes to saturated hydraulic conductivity will likely result in the greatest change to the resultant runoff hydrograph.  Ultimately, more runoff is produced than before (computed = 4.88 inches vs observed = 4.4 inches).  Also, the computed peak flow rate more closely matches the observed peak flow rate.  However, the shape of the computed runoff hydrograph doesn't match the observed hydrograph shape on September 9th and 10th; in particular, the computed runoff is much greater than the observed runoff within those two days.  Thus, additional modifications are necessary.
      
   5. To reduce the amount of runoff on September 9th and 10th, decrease the initial content to 0.05 in³ / in³ and rerun the simulation.
   6. Notice that the computed runoff volume is now better approximating the observed runoff volume (4.69 vs 4.4 inches), as shown below.
      
3. Continue adjusting the Green and Ampt loss method parameters in an attempt to simultaneously best match the peak flow rate, runoff volume, and hydrograph shape.  Record your RMSE Std Dev, Percent Bias, and Nash-Sutcliffe statistical metrics as you adjust model parameters.
   1. At this point, it should be apparent that simultaneously matching peak flow rate, runoff volume, and hydrograph shape can be difficult.
   2. For instance, the "double peak" runoff response within the observed results between September 9th - 11th is nearly impossible to recreate when using a single subbasin.  This type of "signature" suggests two discrete pulses of runoff.  To better match this response, two or more subbasins should be delineated.

Summary Questions

Question 7. What were the final Green and Ampt loss method parameters and corresponding RMSE Std Dev, Percent Bias, and Nash-Sutcliffe statistical metrics for the calibrated model?

Question 8. Is it possible to reduce the computed runoff response on September 17th - 18th while not adversely affecting the computed runoff response on September 9th - 12th when using the Green and Ampt loss method?  If not, why?

Download the final project files here:

Green_and_Ampt_4.12_finish.zip

Continue to Applying the Deficit and Constant Loss Method