Episode 13: Are floods and droughts really unpredictable?

Episode 13: Are floods and droughts really unpredictable?

Kevin Hyde, manager of the Montana Mesonet, discusses his views on predicting and mitigating the effects of flood and drought, how to build a robust weather network with high-quality data on a small budget, why setups should include other measurements such as soil moisture and NDVI, and the genius way he handles maintenance over such a large geographical area.


Kevin Hyde is the manager of the Montana Mesonet. Learn more about the Montana Mesonet project on their website.

Montana Mesonet website



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Hello, everybody, and welcome to We Measure the World, a podcast produced by scientists for scientists.


There’s four different measurements or four different considerations for drought. There’s meteorological drought, do we have a deficit in precipitation, weather, snow or rain? There’s hydrologic drought. How are our stream flows going? Do we have the water level in the reservoirs? What is our water supply looking like? Then there is terrestrial drought. And that is the piece that was missing in terms of understanding the drought of 2017. The piece that we were critically looking at this summer, terrestrial drought, how much water is within the soil moisture profile?


We Measure the World explores interesting environmental research trends, how scientists are solving research issues, and what tools are helping them better understand measurements across the entire soil plant atmosphere continuum. Thanks for joining us. Today’s guest is Kevin Hyde, who is the manager for the Montana Mesonet. And he’s here to talk to us about his work with that interesting and forward thinking Weather Network. So Kevin, thanks so much for being with us today.


It’s a pleasure. Thank you for having me.


So today, as I mentioned, we wanted to talk to you about the Montana Mesonet. But first, can you tell us just a little bit about your background, and then eventually how you came to be the Montana Mesonet manager,


I started out a kid in the woods, always playing with the water and learning the natural history, the environments I grew up in back East. I had a love of photography, which I then followed in college, and then for 15 years and career. But I kept coming back to environmental questions, and I wasn’t content, continuing with what I was doing with the camera. And I came to Montana back in 1999, and went to graduate school. I started out with a degree in geography as a physical geographer, and then went on for a PhD in forestry. And in the course of doing the forestry work, in my research, I’ve always been a field scientist. I love the depth of it and the analysis side, but I gotta be out in the field, I have to see things as they’re happening. I’ve got to get the data, I joke with people, and we start talking about why we make what choices in terms of equipment and measurements and all of that. And I’ll say, you know, I’ve got this funny habit of wanting to get evidence before I make a decision. And so I finished my degree, just back in 2013. Now my last degree, and had a wild and pretty wonderful postdoc down in Wyoming. There I had a chance for the first time to get my hands on some pretty intricate environmental measurement equipment and do some challenging work with geophysics. Came back to Montana. And my advisor said, Hey, I got this Mesonet thing that you want to do it. And I said, Sure. What is it? And that was back in 2016. And been building ever since.


So for the non meteorologists, non climatologists in our audience, can you just explain a little bit about what a Mesonet is: kind of its purposes, its uses?


I will also put it into context of my perspective as a scientist. I am a hydrologist. So I care about the whole continuum. And that strongly influenced how we built out the Mesonet. So a Mesonet is a high density environmental monitoring system, with an emphasis on meteorology, but with other variables as well. So when I say high density, if you look at if you’re familiar with the national weather service stations, they’re pretty widely spaced with the Mesonet. The idea is to pick up a fairly high resolution across broad landscapes of the metric you’re recording, so that you can get a particular with the metrics, measurements that vary quite a bit spatially. So we think about weather measurements. And we think of precipitation and temperature and relative humidity and wind speed are the common metrics for us to go for. Well, when you’re just dealing with the above ground metrics, that’s only giving you insights into the inputs into the system. And what our stations all have in common is that they also have soil moisture arrays going down to as deep as a meter. And so when we build out our high density, and include soil moisture, with the spatial variability of soil moisture, you need stations closer together. And so the Mesonet concept has emerged, really just in the last decade, the forefront of the development of the Mesonet was Oklahoma. They were the flagship if you will, they had the vision first. It was driven by the tornado alley they were in. They had to have high density measurements and on that now we’ve got a majority, I’m not even sure. It’s half of the states now we’re building out these higher density networks.


So you mentioned Oklahoma as the flagship down there. And Tornado Alley, Montana doesn’t really deal too much with tornadoes. So what are at least as much as Oklahoma? So what were the the main problems then that inspired this setup of the Montana Mesonet?


Well, I want to backtrack just a second you said tornadoes, we are beginning to see tornadoes, which is just reinforcing our need to have this high density system out there. So when we went to build out the Montana Mesonet, the lead was Mike as the climatologist, my supervisor, Kelsey, Jen-So. And the vision there was to really capture the spatial variability of a very large state, very low population density, and high degree of spatial variability in the terrain, everything from the flattest of plains to some rugged mountains in western Montana. The idea was to build the network with the combined metrics of the meteorological metrics, as well as the soil moisture metrics. So we could get at these deeper hydrologic questions, not just how much water came into the system, but How deep did it go? And what were the consequences of it coming out? How long did it stay? When did it leave? And so Kelsey’s vision was, again to create basically Hydromat, where we’re getting the readings that are essential for us to understand how plants grow, how much water is available for plants, I think that may have been one of the most important driving problems, recognizing that vegetation growth is a manifestation of all the inputs. The primary objective was to support Montana agriculture, and support the management of Montana’s forests, and also to provide the information necessary to understand how our environments were changing under changing climate conditions, how the vegetation was changing, and how the cascade of effects of vegetation change.


So in your concern, or at least a concern for those involved with the hydrology, or at least the system there in Montana. You mentioned climate change is impacting Montana. It’s impacting everywhere. We’re dealing with droughts, especially mega droughts here in western North America. Can you tell us a little bit about how drought then is affecting Montana and how maybe this Montana Mesonet can help mitigate some of the effects of drought?


Mitigation is certainly a goal. Monitoring drought has traditionally been looking at precipitation and not paying attention again to what’s going on below the surface. We certainly went through a rough year of drought this past summer, as I was out there talking to ranchers and farmers. There was a lot of pain out there, a tremendous amount of pain. People that had been on land for multiple generations. And I would ask him, How does this compare?” They referred back to, I believe, 1982. And their memory was the last deep drought. And they said, it seems bad or worse, people were selling off cattle early. They were having to not just sell off the cattle they were going to they were raising for that season, but they started to have to sell off their breeding stock. In many cases, we had one farmer I worked with early in the season, where we just put a new meter base station in this year, who had 100% loss of his crops this last year, to talk about the relationship between the measurements and the significance of those measurements to understand drought. I’ll go back to the flat drought of 2017. flash drought, no one saw it coming. It wasn’t anticipated. snow packs were reasonably normal melt off occurred a little bit earlier in the season. That’s something that’s been happening through the west now for the last 20 years anyways, but it looked like a kind of normal going into a normal growing season. And then it was a deep, harsh drought, there were several factors that were missed. The wind speeds picked up earlier than expected and the temperature began to increase earlier than expected. But the metric that was strongly missed was the soil moisture measurement. That’s our reserve, that’s our water reservoir. And as evapotranspiration increases with increased wind and temperature, there was minimal monitoring of soil moisture profiles, almost none. And in the retrospective analysis that was done that was identified as one of the most crucial data needs. We’ve got to monitor the soil reservoir. And so as we build out the system, we have over 100 stations, now all of them have soil moisture profiles. And with it we’re starting to come up with new high enough density where we’re able to show the patterns of water deficiency in the soil throughout the state. We’ll put this in the broader context of how it is we measure and monitor drought. There’s four different measurements or four different considerations for drought. There’s meteorological drought, do we have a deficit in precipitation, whether it’s snow or rain, there’s hydrologic drought. How are our stream flows going? Do we have the water level in the reservoirs? What is our water supply looking like? Then there is terrestrial drought. And that is the piece that was missing in terms of understanding the drought of 2017. The piece that we were critically looking at this summer, terrestrial drought, how much water is within the soil moisture profile? The fourth is the social drought. How is the deficit of water affecting our culture, our climate, our lifestyles, mental health, emotional health, I saw depression out in the field. So as we think about these four different types of drought, meteorological drought, we can see it pretty quickly. We’re chronically measuring it, we’ve been doing that as cultures for quite some time. Again, hydrologic drought, you can see it, you can measure it pretty quickly, you can kind of say, well, we can look at the relationship between precipitation or snowpack and what kind of river flow and hydrologic drought changes a little bit more slowly than meteorological. Drought, the drought, terrestrial drought changes, can start changing fast at the surface, certainly. But as you get deeper and deeper in the soil profile, your reservoir dries, and that is one of the biggest changes we’re seeing. Now, we have the data and a long enough period of record where we’re starting to have enough years of data, we can look at it and go, Ah, yeah, that’s a trend. It’s not significant, statistically over time. But that’s a different part of the conversation, it goes to the high density network. With a high density monitoring network, we have more frequent measurements, while our Mesonet is relatively young, statistically speaking, we also have the benefit of longer term measurements from other stations that have been out there longer. Not very many of those stations, but other networks, federal networks in particular that have been out there. And this is where artificial intelligence comes in. Conventional wisdom is bent, if you don’t have 25 years worth of data, you can’t use the data, you can’t say anything about your measurements by, frankly, 20th century statistical knowledge and practice. And then yeah, our data are too young to use. But this is where artificial intelligence comes in. You take those older data streams, we have a high enough density of stations. Now, we’re re-coming up with, really important understanding where we can say this has been the data trend from the existing stations, we then join into that stream of data strands from the existing stations, all the other measurements we’re making on our current Mesonet. And then even a year’s worth of data starts becoming relevant.


You talked about the importance and the impact of droughts there in Montana and elsewhere, not only to the environment, but also the human impact as well. Was it easy to get buy in for this project, for this kind of setup? Or to fundraise a large scaled statewide Mesonet like this?


Funding was the challenge. Our initial seed for doing it was a statewide funding initiative from the governor’s office. And our first seed money was $75,000. And that’s what we started with colleagues over at MSU, who saw what we were doing, they said, “Wow, well, yeah, I can fund a couple of stations here and a couple of stations there”. The big change came for us when the BLM wanted to work with us, and they committed what they committed to fund us to install 21 stations for them. That all sudden doubled our network, and wasn’t all of the sudden, but it was like there was like, “Oh, you have traction!” I mean, you’re a group. The first year we had eight stations in, I doubled it to 16 at the end of the next year, then doubled again. And then we got a partnership with the Montana Department of Ag. serendipitously they were monitoring their pesticide monitoring wells using Meter equipment. So they were fully compatible and they wanted to top off their stations with the same technology that we’re using on ours from Meter. There was a momentum that came over time. And there’s still a resistance to directly fund, we’re getting more people coming out of the woodwork. Now we just got some foundation funding to put three stations down in The Paradise Valley. So the visibility is there. The problem with building networks; meteorological data, soil moisture, data, whatever is that the general public is used to proceeding it is free. Well, no, it’s not. It’s paid for by taxpayer dollars. So there’s been a challenge. I think if we were on a direct fundraising route now, to continue to build out what we’re calling AgriMet with the Meter equipment. And if you’d like we can go to the distinction between AgriMet and otherwise. Now, with the deep, deep drought of this last year, a lot of people are paying attention now. Right up to the governor’s office, and we may get official state support, it seems. That’s that’s a may, most importantly now we have momentum and then there’s the question of that second project. The new project through the Corps of Engineers, that has totally transformed what we’re doing and how we’ll go about continuing what we do.


Can you elaborate a little bit about that about your collaboration there with the Army Corps of Engineers? I know that they’re they’re working on; is it an upper Missouri River Watershed project? And how does that all connect with the Montana Mesonet?


It’s a very good question. It’s the upper “Missouri River Basin Project”. It was followed by the major floods of 2011. And then again in 2019. In 2011, there was record flooding and record damage downstream. Well, actually in Montana as well. And 2019, It’s a matter of public record that the flooding compromised Strategic Command, south of Omaha, and strategic command is the basis of our nuclear deterrence and defense that got the first flood got Congress riled up. The second flood said, really nailed it down. And they came to the Corps and said, “Why did you miss these floods?” and the Corps simply said, “We don’t have enough data”. We don’t understand the essential need for a couple of metrics of precipitation and soil moisture, which were very clear in their mind. They knew that they needed to have a dense network to deal with. And in this era of political division, the senators were not divided at all. They just did, they’re the Ag states; Wyoming, Montana, North and South Dakota and Nebraska. Those are the states involved in the project. They just said, “Yeah, this has got to get done”. And so there was no hesitancy in Congress to go ahead and fund it. In fact, with this new infrastructure bill that just passed, there’s additional funding for this continued expansion of the Corps of Engineers stations. It changed, it changed our technology, it changed the complexity we are compelled to work with now, but it doesn’t change the need for the Agri Met. What we’re calling the meter base station, the Argi Met is every bit compliant with the guidelines of a US American Society of Agricultural and Biological engineers, Met Agri Met. Agri Met, in that it provides the necessary metrics to support crop production. For that purpose, these Meter stations are quite extraordinary. There’s a new technology that, with low energy demand, can measure the snow water equivalent to how much water is in the snow, using GPS signals. But the Corps of Engineer stations have what we think of as the conventional weighing precipitation gauge. We have cameras on those stations, we have snow depth sensors on the stations. So it’s an order of magnitude more expensive to build these stations. And of course, there’s all the complexities that come with it. But we can also now report every five minutes. We have a full local solar power system that allows us to do things we can’t do with the Agri Met. But also because they’re more expensive, they take longer to put in. I mean they’re more complex, they take longer to put in, and we can’t put them close together. So that’s why the two systems we now have two systems that we integrate, we call the Meter base system, the Agri Met and the Corps of Engineers system, the Hydro Met.


Can you go into a bit more detail about your setups, what you measure and why you do it that way? You’ve mentioned trying to measure meteorological, hydrological terrestrial drought situations. You’ve talked about temperature, wind speed, relative humidity, can you give a bit more detail into just how everything is set up in those various weather stations.


So the Agri Met stations, the core of the AgriMet stations is the self contained units of Meter EM 60 G’s and we’re transferring over to the ZL6 now as that new technology is there, and as some of the stations start to age out, that’s the core, the communication and all the data collection and the brain if you will the system. All the stations have the Atmos the multi environmental environmental metric. So we’ve we’re getting solar radiation, liquid precipitation, wind speed and direction RH temperature, lightning counts, which we found to be actually quite useful. And then every station has soil moisture probes there are either four, three or four soil on moisture probes, we started putting them in an imperial units, so 4 inches, 8 inches, 20 inches and 36 If we have the fourth one in there. Now, why the caveat on the fourth one? Well, because the BLM also wants to, they commissioned us to measure vegetation response. So we use the NDVI sensor pairs to close that loop. So we can see you know, what’s coming in to go and what is the vegetation response relative to those other metrics. So that’s the basic setup. That is the setup of our, our Meter systems, our Agri Met systems, and all the measurements are made to the environmental atmospheric measurements are all made at eight feet, top of a post. So we switch over to the Corps of Engineers station, it is running off of Campbell Scientific technology. So we can do a lot more with unique programming, and more frequent metric reporting and everything. The sensor configuration is our wind monitor is up at ten meters, three, three feet. And that’s gonna give us that synoptic view of the of the wind. And we have our HT arch tab. There’s a snow depth sensor there. And we have the blue barometer, ring, precipitation gauge, then we have a soil moisture pit. In that case, now we add a fifth sensor. And all stations do have five, these are metrics. So they’re going 510 2050 and 100 centimeters at all stations, getting that extra resolution near the surface. And that’s the core of the network. We have an over SPECT, solar power system, I did that deliberately. So we have overhead moving forward, pretty robust. But again, we needed that build out, we want to put in other instruments, we’re already talking. Putting up particulate matter, we want to add that we want to add the net radiometer, we really want to know not just the water balance, but the energy balance. But so that’s what we can do. That’s what these new systems can provide us.


I think that’s pretty amazing. Just everything that can go into that. And that there’s more, there’s more that we can that we can look for that we can track as well. Another thing that’s unique about the Montana Mesonet is the way that you are marrying the field observations with laboratory characterizations. Can you describe that lab work and kind of the value that it adds to the overall project?


Absolutely. So at every station, when we put in a soil moisture pit, we collect three controlled volume soil samples, the two inch cylinder samples. And so every depth we’ve got, with the three samples, we bring them back to the lab, we run a water retention curve, you’re determining how tightly the water is bound to the soil at any given level of volumetric water content. And that’s necessary to find out the theoretical wilting point in each level of reading. And to really help us understand how much water the soil can hold, how tightly it’s bound, just what is the potential for plant withdrawal based on how tightly it’s bound to the soil? And then you can invert that data. And what does that mean? That means that if you’ve got that pressure, you have it where you have a plot that shows the pressure at which water is bound to the soil at any given level of volumetric water content. So then as we move forward in a probe says it’s 20% Biometric water, then you can say, well, as you see on that curve 20% corresponds to this particular pressure. And then how close are you to the wilting point? So for irrigators, that might be the information they need to know, hey, when do I add water and maybe how much these are future applications, but these are being worked on now. And they’re also being done in conjunction with a researcher who’s doing his PhD asking questions for the Department of Natural Resources and conservation. That’s one part of the soil analysis, the water retention curve. And the other part of it is the particle size distribution. So we can really understand the water retention curve in the context of the soil texture. And again, just improve our understanding of how much of the water that goes in is available at any given time.


At several points in our discussion, you’ve talked about spatial variability. I mean, we’re dealing with a huge area almost 150,000 square miles. You have a wide variety of you know, biomes and geography, range land and the high plains moving up into forested, riverine, montane environments. How do you work in or deal with all of that kind of variability within your dataset?


Answering that question, well starts with how we were required to start building out the system, versus how we where are we are now, initially, we had to be opportunistic, who could fund what and then given who would fund what and where they were, we would then place the station at that site in an area that will we felt best represented the site. So that opportunism has done well for us as the density has increased in the stations. When we started working with the BLM. What we did there is I took all of the lands under their jurisdiction, and then I did an unsupervised classification. What does that mean in the geographic information system, digital mapping, I took all their lands, and I then put them in the context of three basic metrics, that would give us a really good idea of some of the fundamental behavior of water across the jurisdictions. So I looked at the historic water deficit and the gap between precipitation and evaporative demand. I looked at that data, I looked at Sand content in the soil. And that gave me a good idea of what the water holding capacity might be. And the third thing I looked at is relative greenness. So how the green has changed over time, and by putting those three together, we were able to classify the BLM lands into seven different classes. And I then vetted that back through the BLM managers, the people that really know the land. And they said, Yeah, that’s credible, we believe it. And so then we were able to start to say, Okay, we’re gonna place stations here and here, and make sure that we’re representative across the landscape at the BLM jurisdiction before the corp project opened up. And as we got more latitude, and we realized, well, we need to start being that much more deliberative, in where everything goes, we began to be able to go, well, we need to consider a different location, do you have another place for us to work, so we’re better we can better represent where you are. And again, I put that information back into a digital system. With the Corps of Engineers project, they have constructed a grid of roughly 500 square miles per cell. That gives us a spacing matrix. And also, now we’re working with that much more resolved soil characteristic layers, we’re able to go okay. Now in this cell, we want to represent this type of terrain, these types of soil conditions. So I actually work now with a web based mapping system. Where I can look and say, Oh, well, I’m supposed to in this cell, we want to emphasize these particular characteristics. So that’s going to make sure that we’re doing the best we can to represent those terrains. Even as we do that, it’s also helping refine our understanding where more Agri-met systems need to be installed, when we can once again add more stations to the system. We’ve matured to the point now where we can be that much more deliberative and how we do it.


How does this project impact weather prediction? It seems that you’re trying to, in some ways, create an early warning system for various in Montana, is that the future path of this project?


It’s not the end goal. It’s one of the objectives. The end goal is to provide a range of the most relevant decision support tools that can help for land management. The data now are directly influencing how we assess drought and this is real time, how will you assess drought. So we have again, Kelsey state climatologist, our assistant state climatologist, excuse me, Zach, the two of them are, been sitting on a governance, drought and water supply committee brought in by the Department of Natural Resources and conservation, to help inform how we can better understand the progress of drought. And also, we’ve received funding from NOAA and a program called night as the National Integrated Drought Information System. So again, Kelsey, and Zach are being commissioned to work directly again, with the governor’s office and with the regional drought analysts to understand how to refine the National Drought, monitor and improve its applications and its reliability, frankly, and also to improve the public’s trust in the system. So this is where the data are being directly used. Right now, there’s an integration of satellite data with the ground data where and when you take if you got the satellite data, and you’ve got now the density of ground points that we have to work with, you can begin to interpolate between those points in a way that’s much more reliable and has higher confidence intervals. You can trust it better. It’s going to be statistically more robust, and your uncertainties are going way down. So that’s one of the most important first applications. The Montana Grain Growers Association has taken a real interest in what we’re doing. We’ve introduced the soil moisture continuous layer of the state. And they’re going, Yeah, that’s the data we needed. That’s what we need. Now, how do we get it? And how do we interpret it? And how do we use it? That’s where we are right now, we’re learning more and more about the needs of the managers and the producers, private and public. And as we know, those needs better we can they’ll Okay, we have these data here that needs now how to best create a tool we have data we didn’t have before in general. So moisture monitoring is just its nascent, the whole emergence of a soil moisture monitoring network for the nation is emerging. We’re one of the first states, if not the first, to have such a high density of deep soil moisture arrays, typically, when other locales put their Mesonet networks in, they would put in one or two soil sensors, because they kind of were asked to do it. And the understanding wasn’t there in the sample. And it was meteorology, not hydrometeorology.


I think that’s amazing. And I guess, with all of this going on, how do you feel that the Montana Mesonet project has gone so far as it got, as well as you hoped? Are there other things that you still are hoping to accomplish going forward? What are your feelings Overall, about how things have gone?


We’ve got a very robust system now, and there’s so much more we want to do with it, and I want to do with it, we’re gonna get this built out, and it’s gonna be a lot of pressure to get it done in a relatively short period of time. Okay, bring it on, I see where we’re going. And we’re going to have an incredibly robust network, within the next five years, what I dream up and where I want to see it go, even as we get the core in there, and we maintain and sustain the aggregates, side of things, you gotta recognize that upper Missouri River Basin only goes to the Continental Divide, and that leaves a third of our state out of out of the loop, I want to see that balance, I want to see us not forget the western part of the state, there’s only so much we can do as we grow. But I really want to see us do more, I want to see us grow our relationships with Montana Extension Service, work through them and other channels to be able to fill in with more of the Agri-Met stations so we can get those critical metrics that we still need to get. And even when I say that I think about all this, this technology is emerging. So maybe 10 or 15 years from now I will be able to fly over the airplane and drop all these little bots. And they’ll do all the measurements for us. I mean, we could get there. And I think that’s it. It’s fun to say that but I think about who would have thought, who would have thought we were able to launch a rocket and shoot William Shatner into the upper atmosphere and recover both capsules, and they can land on bare earth, you know. So I think technology’s changing. That said, it’s really gratifying to see what we’ve been able to accomplish so far.


So along those lines, what are some of the positive impacts that you’ve seen from the Mesonet that you’re particularly proud of?


I think understanding soil water, I think that’s one of the most important accomplishments, we’ve we’ve seen, the building out our own interfaces, building out our relationships across the state across so many populations, so many constituents, and to see their curiosity and to really greet their buy in, it’s easy to ask people that even have the most skeptical thinking about climate change, it’s easy to ask them, would you allow us to use a piece of your land with the station and he has kind of a big footprint? And we’re gonna do this or I’m gonna do that. Then he goes, Yeah, sure. So that’s really gratifying the number of hits that we get on the website to see how more and more people are relying on these data I hear in the field, anecdotally, that’s what they’re doing with it, whether it’s whether it’s to know what kind of code to put on that day, and to have some of my favorite partners, Kevin such and such is going on, you know, I get these texts and and just see that they’re paying attention in that way. That’s really pretty, hugely gratifying.


And have you run into any problems or roadblocks along the way? And how did you get past those?


I think there was a stage where we didn’t know how we were going to sustain the network. Because, again, it goes back to that comment I made earlier that the perception is that these data are free. And how do you deal with that? It’s resolving now, but in part, it’s because we built something that has caught people’s attention. And we do have paid subscribers, we had to have paid partners who at least contributed to carrying the nut on their station. So those things we’ve overcome, it was bold for us to go with one of these all in one type systems. And boy, we caught a lot of flack for that. It’s been, oh, you’re not real, because you don’t have, you know, 10s of 1000s of dollars in your equipment. And it really goes back to again, going back to when we consider the challenge of getting the data we needed 20-30 and 50 years ago, we have to build out these monitoring stations as fast as possible. And when you consider Montana, Montana is a very large landmass, very low population density, we have some of the greatest social needs in the country, when it comes to taking care of people and dealing with really extraordinary social problems. I am fine that the legislature hasn’t given us any money. And I come to this from a business background anyways, where I ran my own business, and I believe in an entrepreneurial spirit. But that said, we had a limited budget. And so why did we go with an all in one system right up front? Because we did some critical comparisons, we looked very carefully at these all in one systems, we found out the accuracy and precision of these systems would give us all the metrics we needed reliably to do the agricultural calculations and to do it provide the weather service with some essentially data that was necessary to fill gaps. Then we looked at the cost. And we said, Well, yeah, this makes sense. We could do a lot fast. I can put an aggregate station in by myself in about six hours, I put three in, in September, in one week. And there’s like, you get them out there. They’re very low maintenance. And it’s just like, you know, you go out, you do your firmware updates. And it’s just, it’s manageable, for I was the one bench and one man show for a while. The point being that when it came to talking with individuals that are wanting to build out their own systems, it really does matter how much money you have in the context of the state you’re working in. And I make no apologies, different technologies for different reasons. We needed the data a long time ago, we can’t wait until we have the perfect budget. And really there’s no sacrifice being made there.


You’re dealing with what is it over 100 stations all throughout Montana? How do you deal with the general upkeep that could be, you know, potentially a logistical nightmare?


It’s getting more complicated, of course, with increasing stations, but it’s still when you look at the Agri-Met. In particular, it’s easy to monitor the systems. I’ve worked with my programmer to have data feeds that I can call up on my screen. So I have several different ways when I log in each day, I can look and there’s a quick panel that says health red or green. And then I can start digging in. So that part, you get a pretty good early warning. And then on a pretty regular basis, we’d go through and comb and just do a visual of all the metrics through the web based service, you can see what’s going on. And when you get out there in the field, it’s not complicated to do the updates, and most critically, because of the simplicity of the system. I can call somebody and say, Hey, would you go out and push the reset? I think it just got hung up. No problem. We had one of our older loggers, end of life, I think was why it failed, which is reasonable that’s been out there for five years and some more extreme environments. And all I had to do was send him a new logger period, he went out and slipped on a couple of zip ties. It was just easy to get it done. We had one of the Atmos units go out, I sent it off to our partner CRO agency, he was able to put the Atmos up and get it lined up and leveled and plug it back in. So this is critical to being able to maintain all those stations. Because it’s just not complicated. And it’s not uncommon at all for me to call somebody and say hey, when you get a chance, or could you check it out. Another important part of it is the partnership we have with the Montana Department of Ag. We just help each other.


Any other details you’d like to add about the future of the Montana Mesonet project?


Now it’s about developing both the physical infrastructure and expanding the IT infrastructure. So we really would go into a major leap in our growth so it’s really And then integrating that with the application development and working with our partners. So it’s a new day, a new level of sophistication that’s going to be demanded of all of us a new level of basic planning, and which is kind of a nice problem to have that we’ve arrived at this point, then as I consider the next couple of decades of my life, I think about what I want this to look like, what kind of legacy I want leave behind.


Great, where can folks in our audience if they want to learn more about the Montana Mesonet? Where can they go to find out more information about that?


Montana climate Office website, address is climate.umt.edu. And you’ll see our homepage with a launching pad for the launch point for the Mesonet data. We have a Drought Monitor, that is the product of the work being done to improve the National Drought Monitor into more spatially explicit monitoring. And there are other resources on that page as well. As we think about the challenges of building and sustaining these systems, how do we keep paying for them? Whose date is this? And how do we make best use of the data, we have made a choice to make our data public or not trying to monetize it. I mean, we’re being funded in part by the BLM. That’s public domain data. But we’ve also decided that we could try to protect the data, we could try to put up walls or you could try to prevent scraping. You know, a lot of people spend time doing this, and they’re smarter than we are. We want the public to benefit from all this effort. And the headache of trying to monetize is I just said, you know, it’s just we’re not gonna go there. We are a public service agency. And yes, we all have challenges in figuring out how to pay for this stuff. But it’s a discussion, and it’s an essential discussion. And I hope that it’s a discussion that all the folks involved in Mesonet buildouts can continue to have constructively as we figured out really how to put these data to the best use for the public interest.


All right, our time’s up for today. Thank you again, Kevin. We do really appreciate you taking the time to talk with us today. And it’s definitely been a very interesting conversation here. If you in the audience have any questions about this topic or want to hear more, feel free to contact us at metergroup.com or reach out to us on Twitter @meter_env. And you can also view the full transcript from today in the podcast description. So that’s all for now. Stay safe, and we’ll catch you next time on We Measure the World

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