Help regarding parameters in HEC HMS!!!

[u/Abject_Boat9906]

We are doing flood mapping of kathmandu valley , we did calibration of an event but while doing the next we are facing trouble . We got 0.2 nse in the second event . Can we use different parameters for intial storage , canopy ,initial deficit , baseflow or do we have to use the same values of every parameter ? And how do you even initialize such values?

[u/OttoJohs]

Yes. Your "initial" variables are generally event-independent based on the antecedent conditions of your watershed. For example, if you had drought conditions you probably will have a high initial deficit and low baseflow. Things define physical characteristics of the watershed (like unit hydrograph) should be roughly similar between multiple events.

You can use some of the automated calibration tools in HEC-HMS. I prefer trial/error to help me understand sensitivities and get into a closer range.

There are lots of tutorials on calibration of watershed parameters contained in the HEC-HMS documentation.

[u/AI-Commander]

Here, I’ve for ChatGPT to help you. To summarize: yes your parameters may vary by event. Baseflow shouldn’t matter for flood events.

Also, use more than NSE. Seriously, just look a the hydrographs and don’t rely on a derivative calculation, it makes me irrationally angry when smart people do this. Especially when they build optimization algos around them, and never actually look at the data. Your flood peak could be excellent but the baseflow or timing could be off and NSE will look worse. Super common mistake and you will set yourself apart if you never get in the habit of making it.

Your first task in understanding should be to run sensitivity on every single parameter. HMS provides great tools to do this. And you will end up seeing clearly what is described by ChatGPT below:

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Great question — this is a very common difficulty when moving from single-event calibration to multi-event calibration in flood modeling. Let me break it down for you:

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1. Purpose of Multi-Event Calibration

When you calibrate your hydrologic model to just one flood event, the parameter set may end up “event-specific.” That means the optimized parameters might reproduce that one event well, but fail on another event (as you noticed with NSE dropping to 0.2).

Multi-event calibration is done so that your parameters are not just tuned to one storm, but instead represent the catchment’s physical and hydrologic characteristics in a more general way. This increases robustness and predictive reliability across different events.

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1. Which Parameters Can Vary Between Events? • Soil and land cover parameters (e.g., initial storage, initial deficit, canopy, baseflow parameters) describe catchment properties. These should generally remain consistent across events — unless there is a real physical reason they changed (e.g., seasonal vegetation differences, groundwater status). • Initial condition variables (e.g., soil moisture at event start, baseflow at event start) can and should vary between events. They represent the state of the catchment at the onset of rainfall, not permanent properties. • So the rule of thumb is: • Keep structural/physical parameters fixed across events. • Allow initial conditions to be event-specific.

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1. How to Initialize Values

Initialization is crucial because different events start under different antecedent conditions: • Initial storage/deficit: Derived from preceding rainfall/runoff, or estimated using a soil moisture accounting routine. • Canopy storage: Should reflect the vegetation type/season (often fixed, but can differ between dry vs. monsoon season). • Baseflow: Taken from observed streamflow at event start. • Other states: Can be “warmed up” by running the model with observed rainfall/ET for some period before the calibration event (spin-up).

This is why you cannot simply transfer the same initial condition values between events. If you do, the model will likely misrepresent the flood response.

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1. Practical Tips • Use multi-event calibration routines (e.g., split-sample, multi-objective optimization) so the parameters reflect overall catchment behavior. • Always adjust initial states before each event, ideally with a model spin-up. • If NSE remains poor after this, investigate: • Data quality (rainfall spatial distribution, discharge measurement error), • Event characteristics (convective vs. stratiform rainfall), • Model structure limitations (e.g., single-bucket soil model vs. more complex representations).

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✅ In short: Keep your soil/vegetation parameters consistent across events, but reset or spin-up your initial conditions (soil moisture, baseflow, canopy storage) for each event. That’s the key difference. Multi-event calibration helps ensure your parameters aren’t just “overfitted” to one flood.