Skip to main content

A modern way of automate calibration of a hydrologic model

Calibration of hydrologic model can be tedious, that is why we spent great efforts to automate this process. And sometimes we need some tool that is universal, reusable, so that we don't have to re-invent the wheel again and again.

Today I want to introduce a very effective framework to conduct a hydrologic model calibration. I call it framework because you can apply this method to any model and use any of your preferred language in some steps.

Here is the framework:
Let me explain what is going on:

  1. PEST generate new parameter file based on a simple template;
  2. PEST call Python interface to start model simulation;
  3. Python interface translates parameter file to model input files;
  4. Python interface launches SWAT simulation;
  5. Python interface extracts results; and
  6. PEST analyzes result and updates parameters.

A few highlights here:
  1. This is an example for a SWAT model, and you can change it to any model you are calibrating;
  2. I used Python, but you can also use any other language such as C/C++ or even bash;
  3. I used PEST, this is the only requirement you cannot find many good alternatives.
  4. You can launch as many SWAT simulations as you want in parallel manner.
Here are some more details:
PEST is a model-independent parameter estimation package. You are welcome to check its user manual to obtain its mathematical background. One of the highlights is that it supports both local and global optimization.

PEST is very flexible when it interacts with model I/O, however, it is still not convenient enough. For example, if the model produces binary result instead of text-based, it won't work well.

If you only use PEST without the interface, you will have something like this:

The differences and difficulties are:
  1. You will have to prepare 10-100 times more template files depending on the model complexity;
  2. You will have to read the results directly using PEST in a much more complicated way;
  3. You may have trouble with slave directory forwarding because no all model I/O are friendly designed.

That is why I introduced Python as an interface between model simulation and PEST. Python can be considered as an agent to prepare simulation and extract final results for PEST simulation. It can also speed up your final analysis if the optimization is finished!

If you have noticed, this method is also cross platform ready and it support parallel computing/calibration.

Let me know if you have any question.


Popular posts from this blog

Spatial datasets operations: mask raster using region of interest

Climate change related studies usually involve spatial datasets extraction from a larger domain.
In this article, I will briefly discuss some potential issues and solutions.

In the most common scenario, we need to extract a raster file using a polygon based shapefile. And I will focus as an example.

In a typical desktop application such as ArcMap or ENVI, this is usually done with a tool called clip or extract using mask or ROI.

Before any analysis can be done, it is the best practice to project all datasets into the same projection.

If you are lucky enough, you may find that the polygon you will use actually matches up with the raster grid perfectly. But it rarely happens unless you created the shapefile using "fishnet" or other approaches.

What if luck is not with you? The algorithm within these tool usually will make the best estimate of the value based on the location. The nearest re-sample, but not limited to, will be used to calculate the value. But what about the outp…

Numerical simulation: ode/pde solver and spin-up

For Earth Science model development, I inevitably have to deal with ODE and PDE equations. I also have come across some discussion related to this topic, i.e.,

In an attempt to answer this question, as well as redefine the problem I am dealing with, I decided to organize some materials to illustrate our current state on this topic.

Models are essentially equations. In Earth Science, these equations are usually ODE or PDE. So I want to discuss this from a mathematical perspective.

Ideally, we want to solve these ODE/PDE with initial condition (IC) and boundary condition (BC) using various numerical methods.

Because of the nature of geology, everything is similar to its neighbors. So we can construct a system of equations which may have multiple equation for each single grid cell. Now we have an array of equation…

Lessons I have learnt during E3SM development

I have been involved with the E3SM development since I joined PNNL as a postdoc. Over the course of time, I have learnt a lot from the E3SM model. I also found many issues within the model, which reflects lots of similar struggles in the lifespan of software engineering.

Here I list a few major ones that we all dislike but they are around in almost every project we have worked on.

Excessive usage of existing framework even it is not meant to Working in a large project means that you should NOT re-invent the wheels if they are already there. But more often, developers tend to use existing data types and functions even when they were not designed to do so. The reason is simple: it is easier to use existing ones than to create new ones. For example, in E3SM, there was not a data type to transfer data between river and land. Instead, developers use the data type designed for atmosphere and land to do the job. While it is ok to do so, it added unnecessary confusion for future development a…