Landscape Horoscopes - the science of prediction

Prediction of the cumulative effects of development on a landscape is a relatively new and still evolving discipline. Brad Stelfox is an adjunct professor at the University of Alberta and one of the best-known Canadians working in the field. He is particularly known for his work in northern Alberta.

Dr. Stelfox was recently interviewed by Northern Perspectives editor, Clive Tesar.

Why do we need cumulative effects modeling?

Photo: Shelagh Montgomery

Humans tend to evaluate what's going on around them in their concept of appropriate time and space, and that's often a period of a year, and an area of a square kilometre or two. Over those kind of time periods and spatial scales, landscape often doesn't change too quickly. Maybe it only changes one or two percent every year or two, so we don't detect it as a significant or major change. But when we back up and start looking at things over longer periods of time and in larger areas, especially over the course of generations, we begin to see a lot of change in the landscape. This affects its ability to provide us with certain services, such as clean water, clean air, and viable populations.

So we may see a land use arrive that provides a significant suite of benefits-jobs, income, tax base-these things are all viewed as being good, and they are very good, they're wonderful assets, but often we don't appreciate the consequences that come with those land uses because of this inappropriate evaluation of space and time.

Cumulative effects, I think, is about adopting tools and understandings to help people-resource managers, politicians, the public in general-to project their understanding of what will happen to their landscape, and what benefits and liabilities will be experienced over ten years, over thirty years, over fifty years.

Throughout history, people have attempted to predict what will happen in the future. How do current methods of modeling effects help our understanding?

There are various methods, but as soon as you take any model that predicts the future you are speculating, you are not dealing with fact, you are dealing with speculation. However, history is empirical, as long as we've done a reasonably good job of recording it. Whether it be the temperature on a certain day, the size of a community, the size of a caribou herd. So one approach is to understand the historical development of an area, and then use that information to project into the future. If the past tells us something about the future, and we project it at a similar rate, where does this landscape go? That's one approach that I've used in certain circumstances. For instance, the Alberta population has been growing at, say, 2.6 percent over the past twenty years, so projecting growth of 2.6 percent for the future might be reasonable.

The other way that we use models is where we say let's not deal with history. We know where the landscape is today, we know the landscape is influenced by things like the energy sector and forestry and agriculture, and expanding settlements. We go to people who know those industries. If we're talking about the energy sector, for instance, we'd go to the Canadian Association of Petroleum Producers, if we're talking about the forestry sector, we'd go to the Alberta Forest Products Association, and maybe the provincial government agency that regulates forestry and say, "To the best of your ability, what do you think is going to happen?" and then all we do is take our models, and grow what they think will happen and explore the consequences of that.

So we're not telling anyone, "this is how it will be," we're saying, "This is a reasonable scenario given the people we've talked to, and if this scenario unfolds, this is what's going to happen in terms of jobs, in terms of tax base, this is what's going to happen in terms of roads, these settlements will expand at this rate, and this is what will happen to populations of various species."

So there are two basic approaches, looking at historical patterns and projecting into the future, and what I call a land-use sector approach, where you go to the experts and say, "To the best of your ability, what do you think will happen in the future?"

You have developed your own model for predicting effects- how does that work?

The model that I've developed over the last decade is ALCES or A Landscape Cumulative Effects Simulator. It recognizes that a landscape is not uniform but is made up of lots of little parts of different landscapes, shrubs, grasses, several different forest types, fens and bogs, six different tundra types, three different types of human community, pipelines, seismic lines, well-sites, cut blocks. Landscape is made up of a whole lot of different things, and therefore the model has to able to track the number, area, and length of each of those things on the landscape. ALCES tracks about two or three hundred cover types.

So you have that as your basis; what do you put on top of it?

When we have a study area, we turn to the Geographic Information Systems people and say, "Tell us what the landscape looks like today." So I work with these people, they say, "The study area is this size and this is its composition -so much forest, so much grassland, so many cutlines et cetera."

Then what the model does is have a series of land-use 'engines' in it that allow the various land uses that are transforming the landscape to start working. So we have agricultural, human settlement, mining, transportation, energy, forestry engines, and the model eats information from each of these sectors after we've consulted with the specialists. We ask what it is that people are planning to do. In an energy play we would ask if they're going to drill wells, how many and where, if there are going to be seismic lines, how big they are and how long the landscape takes to recover. So the model has the ability to incorporate this information, and 'grow' each footprint on the land each year, keep track of how old it is, and what the rate of reclamation could be.

It's like a massive accounting tool. Think of it like a bank account, it tells you of your deposits and debits, and what the shape of your account, your landscape, will be like at any point in the future.

The Bathurst caribou herd is of particular value to people in the region. Photo: Shelagh Montgomery

What does this tell a community? Say a small community in the Slave Geological Province, how does this model help them?

Let's look at a diamond mine. Someone comes into that community and says, "You've got some kimberlite pipes on your land, we have commercial grade diamonds, we'd like to extract them. We will bring to your community lots of benefits. We'll hire your people, we're going to provide revenue, a tax base, a change in lifestyle that will be very beneficial." Those things all sound good, and they are fundamentally good things. Without exception, all land use, in my opinion, causes benefits. And all land use, without exception, causes liabilities. You can't have one without the other. The challenge for the community is to understand twenty or fifty years into the future what are the suite of benefits and liabilities. If you've got diamond mines coming in, they have to be dug from the ground-that will leave pits, there's going to be water use, roads, a workforce, electricity.

We can keep track of all of these land uses and grow them, so that a local politician can say, "If we let this land use unfold the way it's proposed, this is where we'll be in terms of employment and taxes, our road density is going to go from 0.2 per square kilometre to 0.75, our human population will grow by this much in year twenty-five, and our caribou populations are changing because of human changes to the landscape." This information, even though the future hasn't unfolded yet, is now available to the resource manager.

So in theory they're making more informed decisions today about things that will happen tomorrow.

What degree of certainty do you attach to your predictions?

"Without exception, all land use, in my opinion, causes benefits. And all land use, without exception, causes liabilities. You can't have one without the other. The challenge for the community is to understand twenty or fifty years into the future what are the suite of benefits and liabilities." Dr. Brad Stelfox, University of Alberta

Like all models, the accuracy of the output is determined by the quality of the input. You have to work hard to get good information into them. It's wrong to view these models as saying this is the way it will be in such and such a year. The appropriate way to use them is to say, we have a particular opportunity in front of us, if we have good information defining how these land uses will unfold, we can use models like ALCES to tell us about the relative change and direction of change. Although it gives you exact numbers each year, the probability of it giving you the true number for any given year is very low, but the relative nature of the trend can be very high and very predictive. So you have to use it not as the predictor of an exact future, but to assist you in understanding the relative risk of various scenarios you might explore.

How does your model handle prediction of unknown events, such as the likelihood that a road to a known mine will lead to other unknown developments along the route?

Let's look at something that confronts both the NWT and Yukon, a possible pipeline. There's a lot of people working on these projects saying, "If we put the pipelines here, this is what the landscape will look like." People can learn a certain amount from those approaches. But in reality a good chunk of especially the NWT is underlain by hydrocarbon deposits. As soon as someone builds a large transmission pipeline the ability to tie in is very great. That's where the true cumulative effects are likely to be felt; it's not just a single linear feature that's moving gas off the delta down into the States, but all of the feeder lines and associated activity that's going to go along parallel to it.

The model has the ability to say, if this pipeline's in this region and if there are hydrocarbon deposits in the ground, then we would anticipate that a certain amount of welldrilling will occur. But again, this is where you go back to the industry, if you get good candid players around the table, they can start to tell you what is likely to happen as the result of building infrastructure. _