Chapter 17 Modeling in hydrology (16 pts)
17.1 Note: You are given the code but will need to update it to be current (i.e., data and predictions run until the end of the last water year).
17.2 Background
17.2.1 Identifying the question
The Predict the Peak Fundraiser is run by the Montana Freshwater Partners, a nonprofit that preserves and restores streams and wetlands throughout the sate of Montana. The task is simple: predict the peak flow of the Yellowstone, Sun, or Gallatin River and win big! We’ll build a model to predict peak flows on each of those rivers, and students can opt in to submitting their modeled results if they so choose.
17.2.2 Modeling approaches
Empirical Models are based on empirical analysis of observed inputs (e.g., rainfall) or outputs (ET, discharge). These simple models may not be transferable to other watersheds. Also, they may not reveal much about the physical processes influencing runoff. Therefore, these types of models may not be valid if the study area experiences land use or climate change.
Conceptual Models describe processes with simple mathematical equations. For example, we might use a simple linear equation to interpolate precipitation inputs over a watershed with a high elevation gradient using precipitation measurements from two points (high and low). This represents the basic relationship between precipitation and elevation, but does not capture all features that affect precipitation patterns (e.g. aspect, prevailing winds). The combined impact of these factors is probably negligible compared to the substantial amount of data required to accurately model them.
Physically Based Models These models offer deep insights into the processes governing runoff generation by relying on fundamental physical equations like mass conservation. However, they come with drawbacks. Their implementation often demands complex numerical solving methods and a significant volume of input data. Without empirical data to validate these techniques, there is a risk of introducing substantial uncertainty into our models, reducing their reliability and effectiveness.
When modeling watersheds, we often use a mix of empirical, conceptual, and physically based models. The choice of model type depends on factors like the data we have, the time or computing resources we can allocate, and how we plan to use the model.