And i’ve been revisiting some of my calibration techniques, and I’m curious how others approach this crucial step. In my recent project, I found that using a combination of streamflow data and rainfall-runoff models significantly enhanced predictive accuracy. What specific methods or tools are you finding effective in your calibration processes?
I’ve found that incorporating sensitivity analysis really helps in understanding how different parameters impact model outcomes. By tweaking a few key variables, you get a clearer picture of how error propagates in your predictions. Have you explored this approach much in your projects?
I’ve had success with using regional calibration datasets when fine-tuning my models. It allows for better parameterization based on local conditions. Just curious, have you considered including seasonality in your calibration, @daniel_smith72?