Drainage Modelling in the 21st Century: "What we now know about what drives a successful model," explains Dr. Charles Rowney

January 26, 2014 - Posted in in 2014, Leading Change, Look At Rainfall Differently, Uncategorised

Note to Reader:

The Voice of Experience

Periodically, the Water Balance Model Partnership holds a Partners Forum. These gatherings provide an opportunity for local governments to learn from each other, and reflect on what has been accomplished through alignment and collaboration.

In April 2011, Metro Vancouver hosted the Water Balance Model Partners Forum at its offices in the City of Burnaby. Dr. Charles Rowney, the Scientific Authority for the Water Balance Model and creator of the QUALHYMO calculation engine, reported out on the implications of computing technology decisions.

In providing context for the strategy behind development of the Water Balance Model, Dr. Rowney’s theme was: “The Voice of Experience – What we now know about what drives a successful model”.

What are the impediments to success?

“I do a lot of talking around North America and elsewhere about models and model requirements. As a result, I have been able to distil a synthesis of the opinions of several hundred people from all around the world who have done a lot of modelling. Within this group are individuals who I consider to be the premier people in their field. When we discussed the question…what are the major issues?…seven themes emerged,” stated Dr. Rowney. He identified the seven as follows in order of priority:

1. Meeting Data Needs
2. Inadequate Problem Formulation
3. Time / Money
4. State of Practice
5. Understanding
6. Questionable Need
7. Forecast Condition

“The number one point of pain is meeting data needs. We have all heard the stories about a model such as HSPF with 30 or 40 parameters to adjust, and the best curve-fitting engine in the world, but we can’t find the data. We can’t make it work.”

The Uncertainty Cascade

Charles Rowney introduced his BC audience to a synthesis that he has coined as the Uncertainty Cascade. This mind-map comprises eleven steps that cascade down from a theory to interpretation of results:

1. Theory
2. Conceptual Model
3. Mathematical Model
4. Solution Algorithm
5. Code
6. Adjusted Algorithm
7. Executable
8. Site Representation
9. Calibration
10. Case Representation
11. Interpretation

His key message was that that there is a preoccupation with theory, but the heavy lifting takes place in the last four steps. “We need to keep our focus on SOLUTIONS on the ground,” he repeatedly emphasized.

What We Now Know About What Drives a Successful Model

“If you take a look at what we are dealing with, we start off with a theory, we develop a conceptual model of that theory and how things work. Next, we come up with a mathematical model that describes that concept, and we have a solution algorithm of some sort. We write a bunch of computer code, we adjust that because the code never really does what we want it to, and we come up with an executable,” explained Dr. Rowney.

“Then we start to represent the site and start putting all our data together. We calibrate and adjust our model with the data. Then we start to think about how we will look at our future case. And finally we start to interpret our results.”

“What I find interesting is that a lot of the discussion and arguments are about the theory and model. You will hear people make these kinds of statements: I have a model that does this or does that; I can do a pipe this big or that big; I can do this kind of thing, I can do that kind of thing. But when you start to think about how all this fits together, it becomes clear that all the heavy lifting is down at the other end.”

“The real problems and solutions come together when you look at the site and the data you have to represent what you have. How do you compare the future condition that is very undefined with a calibrated tool that is very well defined? There is much that we do that has a place and purpose BUT sometimes is questionable.”

“One of the outcomes that we are really trying to push for is the ability to interpret results, and the ability to represent the cases that we are actually trying to solve. We all need to place our emphasis on the data that we have available and the things that we can do to represent this reality. And after that, we need to pick our tools and solutions simply because they will solve that process.”

Focus on Solutions

“In summary, what we have learned is that we really need to look at things from the point of view of the solution. As we have been working on the Water Balance Model, we have been orienting it to THE SOLUTION. We are keeping it as simple as possible, but no simpler. The tool has to be consistent, inexpensive, and workable with limited data. It has to fit the local context, and it has to be able to evolve as we learn.

“What is it that we really want to solve? Where are we driving this? We are trying to come up with a solution. Once we have figured out the solution that we need, we need to come up with tools that do that and no more and no less.”

“We have ample horsepower to pick just about any theory we want and put it inside the Water Balance tool. But what we really need to focus on is what are the solutions that are really necessary.”

Bridge Between Scales of Need

“There are two levels of thinking swirling around in this room today. At one level is the broad scale of planning where we look at how and where we might go tomorrow – for example, how do we view the watershed and what we might do to protect receiving waters. And at the other end, the need to eventually put something on or in the ground.”

“We need to bridge those two kinds of needs. With the Water Balance Model, we have a tool in a platform that is designed to do just that. As we go forward with model development, we need to know more and more about that polarity. At one end, it is about where are we going to take this tool. At the other end, lot by lot by lot, it is about how we put things in the ground to ensure they work.”

“What we have learned is that we really need to take a look at this from the point of view of the solution. So, as we have been working on this Water Balance Model tool, we have been orienting it to THE SOLUTION.”

“We only go as complicated as is necessary. We strive to make the tool as simple as possible, but no simpler. It has to be consistent, cheap and workable with limited data. It has to fit the local context; and it has to evolve because we are not at the end point today. The Water Balance Model will continue to grow and adapt over time,” concluded Dr. Rowney.