Step 4: Dynamic Canopy Method for the Long-Term Post-Wildfire Hydrology Model

This tutorial walks you through configuring and calibrating an HEC-HMS model for long-term post-wildfire hydrology simulations.

Software Version

HEC-HMS version 4.13 was used to develop this tutorial.

Project Files

Download the Flow Duration Curve file

Flow_Duration_Curves.xlsx

Incorporation of the Dynamic Canopy Method

In Step 3, we identified the need to represent post-fire recovery processes—such as dynamic soil-loss and canopy-recovery modules—to capture long-term landscape evolution. Therefore, in Step 3, the Pak & Lee Dynamic Surface Method was incorporated in this model. In Step 4, our second option is to incorporate the Dynamic Canopy Method. Wildfires often decimate canopy cover, eliminating interception and transpiration pathways and thereby amplifying runoff and erosion. As vegetation regenerates over years, canopy storage capacity, leaf-area index, and transpiration rates gradually recover—behavior that fixed-canopy models cannot capture. Our dynamic canopy-recovery approach overcomes this by updating evapotranspiration monthly using reference evaporation rates and time-series crop coefficients, enabling the model to reproduce both the immediate post-fire runoff surge and the slower, multi-year rebound in interception and evapotranspiration.

We implemented and recalibrated this approach in HEC-HMS for the Arroyo Seco watershed, fitting the January 18–19, 2010 event and then extending the simulation through February 14, 2019.

In Step 4, we will

• review the calibrated input parameters and the corresponding results of the Dynamic Canopy method simulations,

• compare the two event model calibration runs with and without the Dynamic Canopy method,

• quantify the improvement gained by the Dynamic Canopy method, and

• identify opportunities to further refine the model for long-term post-wildfire hydrology.

Compare two calibrated results with and without Dynamic Canopy method for the Long-Term Post-Wildfire hydrology Model

Select the Basin Models folder to expand the Watershed Explorer.
Select LGA_CUH_LR_Dys_R and LGA_CUH_LR_Dys_R_C Basin Models and review all its methods and input parameters.
Run the LGA_CUH_LR_Dys_R_Event and LGA_CUH_LR_DyS_R_C_Event simulations, calibrated to the January 18–19, 2010 event for with and without Dynamic Surface method.
Examine the simulation results, then answer the questions below.

Question 1:
How was the Dynamic Canopy method implemented in the long‐term post‐wildfire hydrology analysis—updating monthly evapotranspiration with reference evaporation rates and time‐series crop coefficients—to capture both the immediate post‐fire runoff surge and the multi‐year rebound in interception and evapotranspiration?
No single method perfectly captures post‐wildfire canopy recovery, since wildfires strip away interception and transpiration pathways and vegetation regrows at rates that depend on climate (temperature, precipitation) and plant community. Accurately representing this complexity demands a flexible, data‐informed modeling approach. In our study, we employ HEC-HMS’s Dynamic Canopy feature to simulate gradual canopy restoration after fire by updating key canopy parameters over time.
- Use the Monthly Average option for Evapotranspiration method in Meteorologic Models.
- Prepare a Time-Series Data for Crop Coefficient Gages as a yearly canopy recovery index to apply into Monthly Average Evapotranspiration rate.
- Using this approach, HEC-HMS dynamically adjusts evapotranspiration over time to simulate gradual post-fire canopy recovery, as shown below.
Question 2:
What are the results of the calibration?
- Computed peak discharge (value, date, and time)
- Total runoff volume
- Nash–Sutcliffe Efficiency
Which model—LGA_CUH_LR_Dys_R_Event or LGA_CUH_LR_DyS_R_C_Event—shows better calibration according to these metrics, and is it robust enough to run continuously for nine years?
compared to USGS Arroyo Seco gage observations, both the layered and standard Green-Ampt models exhibit a strong match.
- Computed peak discharge for LGA_CUH_LR_DyS_R_Event: 4,229 cfs on 18 Jan 2010 at 22:45
- Computed peak discharge for LGA_CUH_LR_DyS_R_C_Event: 3759 cfs on 18 Jan 2010 at 22:45
  Observed peak discharge: 4,230 cfs on 18 Jan 2010 at 22:30
- Total runoff volume: 1.04 in (LGA_CUH_LR_DyS_R_Event) vs. 0.99 in (LGA_CUH_LR_DyS_R_C_Event) vs. 1.04 in (observed)
- Nash–Sutcliffe efficiency: 0.863 (LGA_CUH_LR_DyS_R_Event) vs. 0.819 (LGA_CUH_LR_DyS_R_C_Event)
Both models deliver similarly high‐quality event‐based calibration. Although LGA_CUH_LR_DyS_R_Event posts a slightly higher Nash–Sutcliffe efficiency (0.863 vs. 0.819) including more accurate peak discharge and runoff volume, we can say that LGA_CUH_LR_DyS_R_C_Event model was incorporated with the Dynamic Canopy to simulate the dynamic evapotranspiration over time after fire. Next, we’ll compare their performance over a nine-year continuous simulation.
Calibration results without the Dynamic Canopy method (LGA_CUH_LR_DyS__R_Event)

Calibration results with the Dynamic Canopy method (LGA_CUH_LR_DyS_R_Event)
Run the LGA_CUH_LR_DyS_R_C simulation (17Jan2020 - 14Feb1019) using the Dynamic Canopy method.
Examine the simulation results, then answer the questions below.

Question 3:
What are the results for 9-year simulation?
- Computed peak discharge (value, date, and time)
- Total runoff volume
- Nash–Sutcliffe Efficiency
Do these metrics show that the layered G&A + Pak & Lee infiltration limit dynamic surface model with dynamic canopy recovery (LGA_CUH_LR_DyS_R_C) outperforms the version without dynamic canopy (LGA_CUH_LR_DyS_R), and is it robust enough for a nine-year continuous simulation?
Both layered G&A + Pak & Lee infiltration limit dynamic surface model with and without dynamic canopy method—were evaluated against USGS Arroyo Seco gage data. The version with dynamic canopy method delivers substantial improvement, particularly in the later years of the simulation, and closely matches the observed flows.
Computed peak discharge for with Dynamic Canopy: 3759 cfs on 18 Jan 2010 at 22:45
Computed peak discharge for without Dynamic Canopy: 4,229 cfs on 18 Jan 2010 at 22:45
Observed peak discharge: 4,620 cfs on 06 Feb 2010 at 13:45
Total runoff volume: 45.97 in (with Dynamic Canopy) vs. 31.36 in (without Dynamic Canopy) vs. 42.45 in (observed)
Nash–Sutcliffe efficiency: 0.565 (with Dynamic Canopy) vs. 0.552 (without Dynamic Canopy)
These metrics reveal that the dynamic canopy model delivers a higher Nash–Sutcliffe efficiency and more accurate runoff volumes, while the without dynamic canopy version better reproduces peak discharge. However, based solely on this comparison, it remains unclear which approach is best suited for a nine-year continuous post-fire simulation. Therefore, we need to more effort for evaluating the results.
New Results for the the layered G&A + Pak & Lee infiltration limit dynamic surface model with dynamic canopy recovery (LGA_CUH_LR_DyS_R_C)
Previous Results for the the layered G&A + Pak & Lee infiltration limit dynamic surface model without dynamic canopy recovery (LGA_CUH_LR_DyS_R)
Question 4:
What strategies can we employ to improve the model and ensure reliable long-term post-wildfire hydrology estimates?
We’ve already enhanced the long-term post-wildfire hydrology model using the tools currently available in HEC-HMS. I’d now like to propose setting aside additional time to evaluate the results via flow-duration curves. The curves reveal two key insights:
• With only the dynamic surface model (gray), simulated flows align well with low-range conditions (20–70% exceedance) but diverge elsewhere.
• Introducing dynamic canopy treatment on top of that surface (blue) dramatically improves overall efficiency. It closely matches high- and mid-range flows (0–10% exceedance) yet still falls short for the low-range band.
To improve the low-range flows in the dynamic-canopy scenario and dynamic-surface (blue), we need to account for slow, deep groundwater discharge. Based on our review, I recommend the following enhancements for the long-term post-wildfire hydrology simulations:
- Upgrade the baseflow scheme from a two-layer to a three-layer linear reservoir to captures delayed groundwater release.
These refinements will strengthen low-flow performance and overall model fidelity.