The Land-Potential Knowledge System (LandPKS): mobile apps and cloud computing for supporting land management decisions, inventory, monitoring and evaluation
A review of some ongoing activities to improve our local meteorological observation (in situ and remote) and forecast capabilities both here and abroad, with an extensive nod to three related USAID, USDA, and NOAA assisted Capacity Building Projects in Ethiopia and Kazakhstan.
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[00:00:00.000]Okay, well thank you very much, Dr. Tadesse,
[00:00:03.051]and really thank you all to the School of Natural Resources
[00:00:06.501]for having me here and sponsoring this activity.
[00:00:09.812]It's my pleasure to be here, I enjoyed immensely
[00:00:13.289]the drive from Grand Forks into Lincoln yesterday,
[00:00:15.879]beautiful day, almost too hot, anyway,
[00:00:20.428]but we're gonna talk about monitoring, forecasting
[00:00:22.379]weather and climate locally and globally,
[00:00:24.244]and there's a lot of big words in here,
[00:00:26.838]but it talks about data and information
[00:00:30.154]and getting the right data and information
[00:00:32.221]to the right places to inform the right people
[00:00:35.384]to make the right decisions, so with that,
[00:00:39.470]starting out here a little bit off the beaten trail,
[00:00:43.993]talk about modes of behavior in the physical
[00:00:45.876]and natural sciences, the term stationarity,
[00:00:49.154]transition, dynamism, chaos, and it's a little bit
[00:00:53.961]to explain where I'm coming from, I'm a meteorologist.
[00:00:56.858]In the National Weather Service,
[00:00:58.361]but often I'm dealing with people outside of meteorology
[00:01:01.361]in other related fields, and this term comes up quite a bit,
[00:01:05.749]stationarity or non-stationarity, as it is,
[00:01:07.796]and so I thought I'd pop this up here,
[00:01:10.045]statistical stationarity, statistical stationarity
[00:01:14.499]is when you have, again, a time series
[00:01:18.221]with that mean, with that variance,
[00:01:20.660]with those standard deviations
[00:01:22.263]that are all constant over time.
[00:01:26.628]So stationarity is the property of that underlying
[00:01:30.623]stochastic process, not necessarily observed data,
[00:01:35.188]and I bring that out, and I'll give you some examples here,
[00:01:38.990]so this, Lins in his paper points out,
[00:01:41.909]and it's derived from other work done previously,
[00:01:44.301]but there's a standard time sequence
[00:01:46.090]with a mean and with some variability around that, okay?
[00:01:51.058]Now with that, I've got underneath there
[00:01:54.309]the annual rainfall of Fargo, North Dakota.
[00:01:57.228]You see that as stationarity, is that a stationary
[00:02:00.281]time series or is it non-stationary?
[00:02:02.979]Well, it kind of depends on your point of view.
[00:02:07.218]So if we're looking at period of record,
[00:02:08.888]and you see that green line across there,
[00:02:10.541]that's the average across that whole period,
[00:02:12.793]if I were to look at 30-year normals,
[00:02:14.741]I would be looking at much different representation
[00:02:18.430]for the early part or the middle part
[00:02:20.299]or maybe the later part of that time sequence.
[00:02:24.421]So that's a problem when you start to look at
[00:02:26.528]sections of that data over time,
[00:02:32.062]and a lot of times, if that's annual rain fall,
[00:02:34.380]well what if you're designing something for a 30-year
[00:02:37.631]that would happen during, say, a dry period?
[00:02:40.834]And now you're going back into a wet period,
[00:02:44.167]and the red line on there, by the way,
[00:02:45.548]is a 10-year running mean of annual precipitation,
[00:02:48.804]but you, here in Lincoln and in Nebraska,
[00:02:52.355]have suffered a lot of the same problems I have
[00:02:54.368]with a high degree of variability,
[00:02:57.171]both diurn, day to day, week to week, month to month,
[00:03:00.375]year to year, in both temperature and precipitation.
[00:03:03.636]Some of the highest degree of variability
[00:03:04.913]in North America, so now again,
[00:03:09.000]with stationarity examples, you look at the top
[00:03:10.993]to the middle to the lower figure on there,
[00:03:13.576]they're all the same time series,
[00:03:15.315]they're just different chunks of that,
[00:03:17.133]so that's some work from Kostayanus and others,
[00:03:19.855]what Lins is citing in that, where if you look at the top,
[00:03:22.208]it looks like, well, kind of uniform
[00:03:24.634]and then there's this stochastic leap in the middle,
[00:03:26.109]and then in the lower, you've got all kinds of motion.
[00:03:30.414]And so he points out that if the changes in the red line
[00:03:32.713]are known, if you know that there is some function
[00:03:36.643]that is responsible for that change,
[00:03:38.308]well, then you've got non-stationarity,
[00:03:40.121]and you can go with that description,
[00:03:42.302]but if not, if that's random processes and functions
[00:03:45.964]in there, well, then you've just got
[00:03:48.010]a high degree of variability, and it's a stationary
[00:03:50.732]type of thing, but the takeaway from this
[00:03:54.441]is that stationarity does not mean static or unchanging,
[00:03:59.221]just as non-stationarity in the dataset
[00:04:01.276]does not mean change or a trend.
[00:04:06.317]So that's a little subtlety in the data.
[00:04:08.997]So I prefer to stick to this word, transition,
[00:04:11.717]a little easier to handle, but transition,
[00:04:13.552]changing from one state or stage to another,
[00:04:17.481]so a known example given in Lins' work and others,
[00:04:20.371]is that a known nonstationarity in data
[00:04:23.026]and especially in hydrology is the effects
[00:04:25.032]of urbanization on the magnitude of flooding.
[00:04:27.978]So for another words, if you increase
[00:04:29.941]the complexity and the runoff in a system,
[00:04:32.360]you're gonna change the response rate
[00:04:35.269]and the flood rate in that, so you could double
[00:04:37.519]the magnitude of a 100-year flood peak
[00:04:39.311]or increase the frequency of that
[00:04:41.951]so it's no longer 100-year but 50-year, 5. it.
[00:04:45.286]Now a suspected nonstationarity is one
[00:04:47.679]that I'm just citing from some IPCC literature
[00:04:50.432]that is oh, where we might see an increase
[00:04:53.236]in precipitation going from one in 20-year annual
[00:04:57.541]return rates to maybe one in five or one in 15.
[00:05:00.717]So you'll see that type of terminology
[00:05:02.374]expressed quite a bit in climate change,
[00:05:05.182]climate variability literature,
[00:05:06.758]and it's, like, well, is that a nonstationarity, truly,
[00:05:10.033]or is that just something else reflected in the data?
[00:05:13.322]And so Lins would say that, lacking an accurate
[00:05:16.363]physical understanding and stochistical evidence,
[00:05:19.033]you know, you can't be sure.
[00:05:23.898]So I'll look at this, and this is a transition, maybe,
[00:05:27.276]and that's North Dakota average annual temperatures.
[00:05:31.812]So there you see the annual skew in there,
[00:05:36.379]and you see the line which is showing the trend,
[00:05:38.377]the 2014 was our fifth warmest year on record,
[00:05:41.725]our trend over the last decade
[00:05:43.830]and throughout the last century
[00:05:45.214]is roughly a quarter of a degree per decade
[00:05:47.687]increase in temperature, steepest trend in the United States
[00:05:52.826]Ooh, is that climate change?
[00:05:57.516]Oh, I've heard already some of the speakers
[00:05:58.886]I've talked about think similar,
[00:06:01.313]and I remember a question here
[00:06:02.921]a few weeks back where somebody must have asked,
[00:06:04.995]oh, but what accounts for that change in temperature?
[00:06:08.731]Do we know?
[00:06:10.944]Well, I'll give you a hint, the greatest degree of change
[00:06:12.980]is not daytime temperatures, it's nighttime,
[00:06:15.263]and it's winter season, so then we can look at
[00:06:19.487]whether or not that's humidity in the air,
[00:06:23.663]which is affecting the low level temperature
[00:06:25.364]or the surface temperature in the nighttime temperature.
[00:06:27.975]Another thing we're looking on in this graph
[00:06:30.410]and some of the discussion with that
[00:06:31.737]with Dr. Adnan Akyuu, our state climatologist,
[00:06:34.060]in North Dakota, some of that other
[00:06:36.286]might be something we've noticed
[00:06:37.818]which has to do with traffic and volume.
[00:06:40.749]And you look at just the amount of traffic
[00:06:42.489]on interstate and road corridors
[00:06:45.683]and how that movement of air in the wintertime
[00:06:48.903]prevents air from stabilizing, so what are some
[00:06:54.103]of our hottest and coldest temperatures,
[00:06:56.706]when do they occur, our coldest temperatures occur
[00:06:59.580]when you have a stably stratified layer,
[00:07:02.283]you have a deep thermal inversion of the surface.
[00:07:05.703]Our hottest temperatures occur when we have a very
[00:07:08.505]super-adiabatic relatively calm air at the surface
[00:07:13.005]and with that, again, very dependent on those conditions
[00:07:17.507]very near the surface in that stable stratification.
[00:07:21.625]What happens if you have a lot of traffic in an area?
[00:07:24.268]Like an urban corridor or an interstate corridor
[00:07:26.818]or any other area around here,
[00:07:29.718]you start to mix that air, and you prevent
[00:07:31.835]some of that stratification.
[00:07:34.896]Does that show up in the data?
[00:07:36.435]So there's a lot of possibilities.
[00:07:39.928]So dynamism, that's where I come from.
[00:07:42.139]As a meteorologist, I'm usually dealing with
[00:07:46.212]atmospheric motions, atmospheric forcings,
[00:07:50.398]and I'm used to seeing things changing,
[00:07:54.987]so the weather tomorrow I do not expect
[00:07:56.938]to be the same as today or the same as yesterday
[00:07:59.884]or next week to be different, and why,
[00:08:01.970]because of those day to day, hour to hour changes
[00:08:05.599]that are due to weather, which are very dynamic
[00:08:08.722]and interacting, but they also cascade through systems,
[00:08:13.235]so anyway, I'm not going to go into
[00:08:15.096]all the meteorological equations,
[00:08:16.588]temperature, pressure, humidity,
[00:08:19.060]and then the wind direction and speed
[00:08:20.913]and X, Y, and Z components, our six unknowns,
[00:08:23.229]our six equations, and looking at that over time,
[00:08:25.843]very dynamic, very non-linear, typically
[00:08:29.249]hydrostatically balanced, though real significant and fun
[00:08:32.419]weather occurs in those non-hydrostatic environments
[00:08:37.306]and generally non-chaotic, but as I said,
[00:08:39.963]you can have energy cascading from scale to scale
[00:08:43.442]within that, and so at what point does,
[00:08:47.163]could chaos or that singularity affect your modeling system?
[00:08:54.962]But in the meantime, that all comes together,
[00:08:56.342]meteorology, hydrology, soil sciences,
[00:08:58.812]so you're in a wonderful School of Natural Resources
[00:09:01.023]where you can bring some of those together,
[00:09:02.830]but there's, February of 2011 from the first
[00:09:07.520]through the end of, going into May,
[00:09:09.768]and there's the descending blue line is snowpack melting,
[00:09:14.365]the multiple colored lines across there
[00:09:16.246]are different depths and soil temperature,
[00:09:19.321]so the near surface layers being below freezing,
[00:09:22.167]the deeper ones being above, and then at some point
[00:09:24.058]in the middle there, the snow has melted
[00:09:27.191]and the ground's starting to thaw.
[00:09:29.649]You're starting to see the inversion
[00:09:31.041]in those temperatures where now your
[00:09:32.605]near surface layers are thawing,
[00:09:34.507]and then that deep dark black line in there
[00:09:36.833]is a riverine response, a hydrograph, river flow.
[00:09:41.424]And so I have to deal in that environment,
[00:09:43.053]trying to figure out how those are going to come together.
[00:09:46.132]So weather, climate, hydrology, soils, land use,
[00:09:48.538]drainage, surface drainage, drain tiles,
[00:09:52.593]and in the measurement, within those systems,
[00:09:54.742]the spatial and temporal spacing
[00:10:00.914]of data and information that's in there
[00:10:02.693]and then how that data is dealt with,
[00:10:04.950]so that's a fun world, does that lead to chaos?
[00:10:10.597]Here's a question for you, does the butterfly
[00:10:13.459]flapping its wings off the coast of South America
[00:10:15.853]actually cause a tornado in central Nebraska?
[00:10:19.973]Raise your hand if you say yes.
[00:10:26.258]Maybe, oh, come on, raise your hand if you agree no.
[00:10:35.125]Many of you are non-committed,
[00:10:36.870]but if you're reading the popular press
[00:10:38.811]and even some science magazines,
[00:10:40.772]you will say, oh, that's true.
[00:10:44.165]That's the butterfly effect, oh,
[00:10:46.276]Ed Lorenz clearly said, no, it does not,
[00:10:49.451]and a thousand and a billion butterflies don't,
[00:10:51.725]why, because the natural system damps out
[00:10:53.213]those fine scale perturbations.
[00:10:57.503]But in our computer models, they can exist.
[00:11:01.222]Ah, when we're a partial differential equations,
[00:11:03.925]lots of geophysical processes, persistent excursions,
[00:11:07.526]as it's sometimes referred to, singularities.
[00:11:11.835]So in the computational word, within those processes
[00:11:14.266]we try to bust those calculation errors
[00:11:18.368]and those processes, to keep them from swamping a system,
[00:11:22.705]and a modeling process, but also we have
[00:11:24.599]limits to our knowledge of the system
[00:11:26.979]as well as errors in our measurements of the system.
[00:11:30.776]And those we try to deal with through ensemble forecasting
[00:11:33.668]systems and others, temping to limit that noise
[00:11:37.218]in the background and keep that model stable and functioning
[00:11:41.568]Okay, that's an inverted butterfly, that's cute.
[00:11:46.646]The basic thesis in this first part is
[00:11:48.436]that our knowledge of our environment
[00:11:50.721]and our ability to forecast these changes
[00:11:52.730]to that environment over the long and short haul
[00:11:55.349]are intrinsically tied to the quality, longevity,
[00:11:57.928]I used to use density, I changed it to representativeness
[00:12:00.642]of our observing systems, spatial and temporal
[00:12:05.247]representations of those observing systems.
[00:12:08.782]Okay, so my professional passion,
[00:12:13.021]what turns my crank in the morning,
[00:12:15.022]are capacity building, hydro-meteorological
[00:12:17.802]capacity building, locally and globally,
[00:12:20.559]and disaster risk reduction, I'm a warning coordination guy.
[00:12:25.482]So the second part, going into achieving food security
[00:12:28.318]amid disparate and volatile climate regimes.
[00:12:33.996]So again, WCM, warning coordination meteorologists,
[00:12:36.927]and looking, oh, moving out of our upper midwest
[00:12:40.361]maybe into some other parts of the globe here.
[00:12:43.709]Vulnerability, there's a lot of ways
[00:12:45.853]to assess vulnerability in the world.
[00:12:48.427]Food and food security being a major factor in that.
[00:12:53.171]Political systems can be another cause of that.
[00:12:58.501]Oh, weather could be a factor in that,
[00:13:00.613]and when Dave Hastings, one of my colleagues at NOAH
[00:13:02.503]was working on this particular one,
[00:13:04.173]I was criticizing him because he didn't crank up
[00:13:07.544]that weather impact, what is it like to have
[00:13:11.143]tornado threat or blizzard threat in your back door
[00:13:15.012]and what does that mean, but anyway,
[00:13:16.827]well, the US is kind of middling range
[00:13:19.201]as far as vulnerability because we have a lot
[00:13:22.207]of significant weather and other things going on,
[00:13:24.819]Canada's more resilient than us?
[00:13:29.695]Maybe, okay, but then you see the areas of vulnerability,
[00:13:34.159]and again, a lot of food security,
[00:13:35.601]a lot of instability also in political regimes,
[00:13:38.622]so we'll move over a little bit,
[00:13:42.561]oh, I said this is who I am, this is my world I live in,
[00:13:45.741]winter, spring, summer, fall, many of the same
[00:13:48.659]type of things you're dealing with here in Nebraska.
[00:13:52.912]And with that, a high degree of risk in there
[00:13:58.177]related to blizzard and tornadoes and floods,
[00:14:02.020]so the graph on the far right or my right over here,
[00:14:06.559]your left, with historic presidential declarations
[00:14:08.865]just showing again that upper Midwest area,
[00:14:11.935]my Red River valley of the north, having lots
[00:14:14.692]of presidential disaster declarations
[00:14:16.895]during any given period of years,
[00:14:21.222]but I deal a lot with emergency managers,
[00:14:22.520]with the broadcast media, with other
[00:14:24.251]local, state and federal agencies
[00:14:26.370]and the broadcast public to get information out
[00:14:28.964]and to them that is useful, and
[00:14:33.261]I chase tornadoes on occasion.
[00:14:37.192]But in that emergency management world,
[00:14:40.513]I've got the simplified version of these,
[00:14:42.555]but mitigation, preparedness, response, and recovery.
[00:14:45.939]You do not want to spend your life
[00:14:47.017]in response and recovery, tornado hits,
[00:14:50.153]respond, recover, flood hits, respond, recover,
[00:14:55.185]you want to be in mitigation and preparedness.
[00:14:57.441]So mitigation is removing some of that risk
[00:14:59.990]through certain means, preparedness is getting ready
[00:15:03.552]for what's coming at you.
[00:15:05.839]Winter's coming, how are the tires on your car?
[00:15:09.070]Are they gonna handle another snow and ice winter?
[00:15:12.133]Are you prepared, mitigation might be
[00:15:15.528]getting a different car, I don't know.
[00:15:18.791]But to break, to get into food security here,
[00:15:21.000]I theorize that to break the cycle
[00:15:23.271]of weather calamity and famine,
[00:15:25.322]our first step is adding risk management to the equation.
[00:15:29.426]We'll talk about some projects going on in Ethiopia.
[00:15:32.339]The first step that I encountered was this one,
[00:15:34.789]dealing with NIMS/ICS, National Incident Management System,
[00:15:40.527]Incident Command System, so those acronyms
[00:15:43.983]developed by the US Department of Agriculture,
[00:15:45.915]US Forest Service, our US Forest Service
[00:15:48.634]developed this methodology to deal with large-scale fires
[00:15:53.422]in the western US and how different agencies,
[00:15:56.644]different groups come together to battle that
[00:15:59.542]and how they deal among those agencies
[00:16:03.007]to prioritize what needs to be done.
[00:16:05.936]So we now as a country export that to other countries,
[00:16:09.080]and our Forest Service is the lead agent
[00:16:10.664]in trying to teach that to other countries,
[00:16:13.989]so NIMS is how we deal with a disaster,
[00:16:15.703]ICS, Incident Command System, is how those agencies
[00:16:18.707]interact in that emergency, so take you on that trip
[00:16:21.124]to Ethiopia, and again, hazards, shocks, vulnerabilities
[00:16:25.796]there, whether it's earthquake,
[00:16:27.220]whether it's fires, droughts, famine, insecurities
[00:16:30.151]of various types, and with those various types
[00:16:34.186]of hazards and shocks possible,
[00:16:35.606]the concept of looming climate change,
[00:16:38.554]so a lot of this is brought about
[00:16:39.996]by programs dealing with climate change
[00:16:45.278]preparations in these countries as well.
[00:16:47.770]And so with that, first of all we have to take that
[00:16:49.516]Incident Command System methodology
[00:16:51.222]and we have to map it to that context in Ethiopia,
[00:16:54.319]and so the government of Ethiopia, GOE,
[00:16:56.398]down to their Ministry of Agriculture and Rural Development,
[00:16:59.312]which would be like our US Department of Agriculture,
[00:17:01.462]within that, they have the Disaster Management
[00:17:05.382]Food Security sector, well this has a little red R in there,
[00:17:11.792]so as part of this development,
[00:17:13.554]they were adding risk management to the equation.
[00:17:17.124]They were taking disaster management,
[00:17:19.482]learning risk management techniques,
[00:17:22.217]so that they would be less subject to that vulnerability,
[00:17:26.496]and then within that, the Early Warning and Response
[00:17:28.489]Directorate, in Ethiopia's case,
[00:17:30.382]pretty much their version of our FEMA in that context.
[00:17:35.314]So again, that's first step to add risk management,
[00:17:37.614]with that it comes as an assessment,
[00:17:40.005]so this is a typical type of hazard assessment,
[00:17:42.128]either a HIRA or a THIRA, hazardous risk
[00:17:48.974]or hazard risk assessment that goes to look
[00:17:52.313]in this case down to the woreda level and look at,
[00:17:55.164]or county level, and to looking at assessing
[00:17:57.300]the frequency of flooding, the frequency of drought,
[00:17:59.364]and that exposure that they may have over time.
[00:18:03.355]So again, climatology being important to develop
[00:18:06.938]that background, and the recording nature of that,
[00:18:09.365]to develop a history or a case for this occurring.
[00:18:12.747]There are certainly plenty of places
[00:18:14.707]in Ethiopia as well as worldwide
[00:18:17.203]where they do not have a very complete record.
[00:18:19.688]So it's very incomplete, if you will,
[00:18:21.345]but that's at least a first start,
[00:18:23.708]and then from that, going into Ethiopia
[00:18:25.737]to start training on that, and so I did
[00:18:27.997]get to go with a traveling team
[00:18:29.840]and train on the all hazards approach,
[00:18:33.648]again as a weather service, National Weather Service person
[00:18:36.107]bringing in that exposure to weather and climate data
[00:18:39.555]and those hazards, while the Forest Service
[00:18:42.643]is dealing with their specialty areas.
[00:18:44.342]Now with that, as we're training these people,
[00:18:47.153]we're also planning a study tour
[00:18:48.717]so they can come to the United States
[00:18:50.165]and see it in action, so I got some acronyms there.
[00:18:52.786]WFO, that's a forecast office, Weather Forecast Office
[00:18:55.729]and PSR, that's in Phoenix, Arizona,
[00:18:58.652]so I'm in Addis Ababa teaching Ethiopians
[00:19:02.319]about this methodology, they're gonna go
[00:19:04.552]to Phoenix, Arizona, San Francisco, or excuse me,
[00:19:07.506]Sacramento, California and Boise, Idaho,
[00:19:10.205]and they're gonna see that in action.
[00:19:14.219]Oh, by the way, weather forecast offices
[00:19:16.721]are co-located with those facilities,
[00:19:19.367]so that my counterpart there will be able to take
[00:19:21.706]those lessons I've been teaching
[00:19:23.557]and apply them to that context.
[00:19:26.854]And so there is pictures of me
[00:19:27.929]with some of the Ethiopian delegation
[00:19:30.525]as well as with Ethiopian met agency I was working with.
[00:19:35.328]2010 they were here, 2011, they were into
[00:19:38.567]the Pacific Northwest looking at forest fires,
[00:19:40.713]2011, there were no fires in the Pacific Northwest.
[00:19:45.899]So they call me up and say, what are we gonna do,
[00:19:47.613]and I'm battling flooding across the state
[00:19:51.217]of North Dakota at that time,
[00:19:52.565]and I said, well, can you get them here.
[00:19:55.129]We were able to bring them in,
[00:19:57.121]and they were able to drive in to flooding
[00:19:58.687]in the Missouri system up into the Souris River system
[00:20:01.182]and see what it means, and they were actually
[00:20:03.402]able to interact, so we have all these emergency management
[00:20:06.184]people from Ethiopia, and that's the equivalent
[00:20:09.258]of their Deputy Director there, their FEMA director,
[00:20:12.286]Deputy Director there, and we're in
[00:20:17.140]where in the city of Minot, North Dakota,
[00:20:19.563]they were battling, they just pretty much
[00:20:22.035]have lost part of the city to flood waters,
[00:20:25.195]and they're in there talking with the incident commander,
[00:20:28.099]who is dealing with this flood,
[00:20:31.092]which is a long-term preparation issue,
[00:20:34.752]and he's able to relate it to an anhydrous spill
[00:20:39.356]that happened several years earlier
[00:20:40.832]where rail cars of anhydrous ammonia
[00:20:43.407]derailed and a toxic plume of anhydrous
[00:20:47.253]was moving towards the city, short fuse,
[00:20:49.169]long fuse situation, and he's able to
[00:20:51.469]relate that to these people in that teaching environment.
[00:20:56.308]Oh, by the way, while he's fighting this flood.
[00:20:59.589]So of course, he's, again, walking through that,
[00:21:02.503]seeing that type of thing in action,
[00:21:04.451]and we went down the Missouri system
[00:21:05.859]all the way into Omaha, where they had the joint information
[00:21:08.180]Joint Operations Center, and they were able
[00:21:10.949]to go and see how, how does the core of engineers
[00:21:13.058]manage a flood along that whole length of the Missouri,
[00:21:16.197]where you're reaching floods in all of the big dams
[00:21:19.059]and reservoirs and floods of record
[00:21:21.209]in many areas along the Missouri system.
[00:21:26.212]So that's one aspect, adding risk management.
[00:21:30.018]Another would be adding agricultural adaptation,
[00:21:31.819]that's ag, agricultural adaptation/mitigation strategies.
[00:21:36.200]So with that, we continued with that guided implementation
[00:21:40.810]of NIMS/ICS in Ethiopia, but now food security,
[00:21:43.439]market, agricultural market stability.
[00:21:46.366]So again, dealing with the Ministry of Agriculture
[00:21:49.336]and Rural Development, further into a different group there,
[00:21:53.119]which is the Ethiopian Institute for Agricultural Research,
[00:21:56.279]so their version of agricultural schools,
[00:21:57.942]and that, the theory behind this being, of course,
[00:22:00.737]mitigation and adaptation will improve resiliency
[00:22:04.699]in the agricultural community,
[00:22:06.858]and there are projects through the US Agency
[00:22:09.422]for International Development, that's under
[00:22:11.582]our State Department, that are global climate change
[00:22:14.368]initiative and these land use type of projects
[00:22:17.484]that were developed, this one specifically, PRIME,
[00:22:20.544]has to do with pastoral areas, pastoral areas,
[00:22:23.879]herds, goat herders, camel herders,
[00:22:27.433]and their ability to be resilient to variability
[00:22:32.341]and change in climate and also to expand the market,
[00:22:35.915]because Ethiopia wanted to start producing
[00:22:39.057]not only to feed the population but maybe to export food.
[00:22:42.488]They wanted to also improve that aspect of stability.
[00:22:46.900]So in Ethiopia, large scale still to this day
[00:22:49.406]subsistence farming, not the type of production
[00:22:52.319]you are going to see here on the Great Plains.
[00:22:54.307]Where one farmer is feeding thousands,
[00:22:56.797]but you're gonna see large-scale subsistence farming
[00:23:00.481]at that point, 23% of the arable lands,
[00:23:05.018]very rain-dependent, not a lot of irrigation systems at all.
[00:23:09.352]A short growing season, as well as a longer,
[00:23:12.377]and this is in central and eastern parts of the country,
[00:23:15.668]in the west, it's more of a typical monsoonal pattern,
[00:23:18.441]and then of course land degradation issues
[00:23:20.841]and others that might crop up in any part of the world.
[00:23:25.284]There's a digital elevation map.
[00:23:27.840]So most of Ethiopia in the highlands in green,
[00:23:31.238]the areas we're studying on the red circles there,
[00:23:34.529]and one of them, as I'm trying to point to here,
[00:23:38.292]in the Awash River valley, which starts and flows
[00:23:42.436]to the north, northeastern into the Afar region,
[00:23:45.851]so this is a river valley and eventually
[00:23:48.243]that river disappears into the desert and into the lowlands,
[00:23:53.486]but that's one area, and then over in the far east
[00:23:57.025]in the Somali region just outside of Harar,
[00:24:00.426]and another in the southern Oromia area.
[00:24:05.931]So that's the Awash River, and I'm there during flood season
[00:24:08.416]I'm used to floods, and this guy's plowing with an ox
[00:24:12.731]next to the river, my role in this project.
[00:24:19.798]As a technical advisor to that is to help dig up
[00:24:21.821]those weather and climate records
[00:24:23.682]that might be important to this project,
[00:24:25.492]and there's a lot of 'em, they've had over 1200 different
[00:24:29.239]co-op agricultural type climate sites
[00:24:33.521]in different areas across Ethiopia,
[00:24:36.400]but at any one time, how many of them are operational?
[00:24:38.940]How many of them have a long enough period of record?
[00:24:42.985]To be used in this, but they also have
[00:24:45.241]maybe only 20 automated, at that time, 20 automated stations
[00:24:49.806]That's 24/7 hourly observations,
[00:24:52.541]generally at airports, but there might be some other
[00:24:55.994]automated stations sprinkled around
[00:24:58.319]in a couple of places around there.
[00:25:01.167]That's 20, there's probably double that
[00:25:03.592]in the state of Nebraska, this is a much larger area.
[00:25:07.424]And then we're also testing the expansion
[00:25:09.225]of perhaps what a increase in that mesonet help
[00:25:14.010]this project along, so the area in Harar
[00:25:16.818]over toward the eastern circle that we were talking about,
[00:25:19.782]we were also going to try and add in a little bit
[00:25:21.870]of a mesonet, mesonetwork of weather observation sites
[00:25:24.841]in there to see if that would help,
[00:25:26.605]and then of course communication,
[00:25:28.102]so across that area, you're dealing with a very high
[00:25:30.903]saturation rate of cell towers,
[00:25:33.879]so people still living in very rudimentary form,
[00:25:39.047]very rural existence, very primitive
[00:25:41.519]by any type of standard we'd see here in the US,
[00:25:44.101]but likely to have cell phones and good cell tower access.
[00:25:49.377]Somebody's gonna have an antenna
[00:25:51.149]and be picking up television signals,
[00:25:52.685]and a lot of that around there,
[00:25:54.240]but very good cellphone reception,
[00:25:57.220]so then we look at, ah, both for weather reporting,
[00:26:00.295]reporting of weather information,
[00:26:02.638]as well as getting alerts, that's gonna be something
[00:26:05.009]highly usable, well let's go back
[00:26:07.405]to this PRIME project now and USAID in Ethiopia.
[00:26:10.785]So here we are with a field team,
[00:26:12.236]and we're training them in the use of some GPS instruments
[00:26:17.033]and they're going to do a field reconnaissance of that area
[00:26:19.893]to determine the ground cover and canopy,
[00:26:23.139]so that image shows if a satellite is looking down
[00:26:25.275]onto that, what is that satellite gonna see?
[00:26:29.056]Is it gonna see what percentage of ground cover
[00:26:32.011]might it deduce, so we're sending people out
[00:26:34.891]to map areas and make a ground survey
[00:26:39.405]of what is going on at the ground level
[00:26:41.168]and then to try and associate that with satellite imagery.
[00:26:45.429]At the same time, we're trying to find the weather stations
[00:26:48.420]that are representative of that area
[00:26:50.572]so we can look at precipitation records,
[00:26:52.813]both current and historical, and then from historical
[00:26:56.513]archives of satellite data and weather instrumentation,
[00:26:59.640]to try and find out what are the patterns
[00:27:02.054]of crop growth, moisture in those areas?
[00:27:06.082]Ah, pastoralists, goat herders, sheep herders,
[00:27:09.381]camel herders, cattle herders, they're gonna be moving
[00:27:12.129]from season to season to different areas.
[00:27:14.764]How can you optimize that motion
[00:27:17.687]to take advantage of where the available forage is?
[00:27:22.454]Oh, and you better be able to avoid
[00:27:24.792]inter-tribal conflict in that process.
[00:27:29.631]So a lot of interesting things going on
[00:27:31.435]in developing that, original field form, on paper,
[00:27:36.430]with a handheld GPS going out and marking this.
[00:27:41.695]There's some of the field offices
[00:27:42.750]that the Mekelle Harar Agricultural School
[00:27:47.166]site near the river,
[00:27:49.726]and that guy in the background of the table there
[00:27:51.954]is the director of that Agricultural Research Center,
[00:27:56.350]and there's some of his scientists in there,
[00:27:59.636]and we're looking at the data that's available
[00:28:01.861]from that site and others and trying to find out
[00:28:03.910]how we can use it and get better access to that.
[00:28:08.204]Back, and this is the forecast office in Adama,
[00:28:10.281]we're looking at how they process data.
[00:28:13.321]Now, this office in this facility
[00:28:15.972]is pretty much, again, run by
[00:28:17.963]their Ministry of Agriculture and their data
[00:28:21.691]is flowing through that on paper forms
[00:28:25.293]and in that into their network of information.
[00:28:31.168]This office, which is the weather forecast office,
[00:28:33.405]that forecasts weather as well as short-term climate
[00:28:37.031]outlooks, six to 10 days, eight to 14 day,
[00:28:41.312]the 30-day, the 90-day, they are likely not
[00:28:45.147]going to see any of that data from these stations
[00:28:49.612]in their analysis and in their forecast process.
[00:28:52.505]Not gonna get there fast enough
[00:28:54.598]because you're gonna have people
[00:28:56.868]that are hand-entering data off of paper forms
[00:28:59.021]with maybe months before that data becomes available
[00:29:02.698]for that process, so right away we're looking at,
[00:29:05.484]you know, as a meteorologist, I wanna see
[00:29:07.265]what is occurring now, how it ties into that
[00:29:11.196]and how it affects that future, so I want near real time,
[00:29:15.682]the best that I can get that, whether it's for
[00:29:18.791]a tornado warning or for a rain forecast
[00:29:22.371]or for an outlook weeks or months down the road
[00:29:26.777]that could be drought or hydrologic in nature.
[00:29:31.235]What is going on now and how is it going to affect that.
[00:29:36.217]So then I bring this in, the Land-Potential Knowledge System
[00:29:41.912]So I threw these in, Jeff Herrick prepared and updated
[00:29:45.380]some of these slides for me for this presentation,
[00:29:47.949]but this is something that we knew of at that time
[00:29:51.167]and Jeff was developing this basically
[00:29:55.890]smartphone application to speed up that data collection
[00:29:59.525]process, but he's mainly concerned with soil issues.
[00:30:04.979]Previous to that, we're dealing with crop canopy,
[00:30:07.259]weather, climate data, he's using this to gather
[00:30:10.647]soils information, so this is work done in Namibia
[00:30:14.848]and in Kenya and a little bit going on in Ethiopia now.
[00:30:20.637]And here's a quote from one of the workshop participants
[00:30:22.953]in Namibia, we need to see beyond what we see,
[00:30:27.835]because that can help you decide what to do next.
[00:30:31.809]What does that mean?
[00:30:34.301]Looking beyond your scale of sight,
[00:30:36.278]looking beyond your time reference,
[00:30:39.341]looking around to see what's going on
[00:30:41.843]and could be coming at you,
[00:30:45.240]cause weather and climate, you know, are like that.
[00:30:50.727]So there's Jeff and his crew,
[00:30:51.860]and they're mapping again in their world,
[00:30:55.278]and they're mapping and using this technique
[00:30:57.567]to develop that database and looking more,
[00:31:01.075]again, explicitly at soils, and then into
[00:31:03.919]crop coverance and all that, so he's been developing
[00:31:07.197]that Land PKS, so you can google that and find out more
[00:31:11.301]about that, but it's now a suite of mobile apps that are...
[00:31:17.759]Coordinated, cloud-based databases,
[00:31:22.816]it's not as tied into all the geophysical databases
[00:31:27.184]that it could be, but it's a start
[00:31:29.753]towards tying it into that, it's a system
[00:31:32.109]for sharing that data and information and knowledge,
[00:31:34.573]and then interpreting some of that data
[00:31:38.256]and applying it to land management decisions,
[00:31:40.372]so I'll just toss this out as a possibility.
[00:31:42.696]So this is off of his site, that's Land-PKS,
[00:31:46.411]so in the center there, it's taking that system,
[00:31:49.044]taking local knowledge, scientific knowledge
[00:31:51.178]that applies in geospatial information,
[00:31:53.997]and then providing that in a variety of different
[00:31:58.260]support functions, so again, you've got a weather station
[00:32:01.892]down here, and you have cell phones
[00:32:05.401]and charging stations for these rural inhabitants
[00:32:07.947]that might be moving across the countryside
[00:32:11.426]that are still gonna be accessing that information,
[00:32:14.224]and you've still got a lot of subsistence level farming
[00:32:17.024]and agriculture going on there,
[00:32:18.797]but you are starting to inform those farmers
[00:32:21.959]and the tribal leaders and all that
[00:32:25.431]on what type of practices might be good to employ.
[00:32:29.893]So again, just looking at that app,
[00:32:31.354]you're gonna have within that different modules,
[00:32:34.485]and there's some timeframes on there for their development,
[00:32:36.593]so the soil identification type of modules
[00:32:39.395]will be developed here shortly,
[00:32:41.664]there is some basic climate data in there,
[00:32:43.842]but it's climate data off of a very large-scale derivation.
[00:32:49.457]It doesn't have climate forecast information,
[00:32:53.867]so that would be short-term, 30-day to 90-day
[00:32:56.821]type of outlooks that could be important
[00:32:58.985]toward making that type of decision.
[00:33:03.442]Again, Land-PKS in the center, but now,
[00:33:07.449]and this is just a different look at that data
[00:33:10.101]with Land-PKS as an open platform now
[00:33:12.802]for application development, so you've got the soils
[00:33:15.784]and their potential from our National Resources
[00:33:18.341]Conservation Surface and other datasets,
[00:33:20.814]models, predictive models on soil behavior,
[00:33:23.910]weather, climate behavior, that can be integrated into that
[00:33:27.324]to start making management decisions in that process,
[00:33:32.197]and then back to using that in the background monitor.
[00:33:36.285]So that's just a "sure would be nice if" approach,
[00:33:39.661]but open-source, open data.
[00:33:43.554]That's just a look at some of the climate data in there.
[00:33:45.691]Oh, these types of maps, so there's slope
[00:33:48.034]and slope calculation and GPS,
[00:33:49.985]so the exact same things that we had on that paper form
[00:33:54.406]now on to the cellphone app, and with that,
[00:33:58.742]some of the soil data that's in there,
[00:34:01.458]and here's just some examples of that
[00:34:03.889]going in, providing a GPS, doing a transect on that
[00:34:07.514]soil surface, here's different ways
[00:34:10.770]that they were using to look at crop canopy and that,
[00:34:13.526]and again mapping that and showing that
[00:34:15.849]relative to the same paper form type of information
[00:34:18.704]you had before, so again, the current status of that,
[00:34:23.570]it's available for both Android and iPhone use,
[00:34:27.158]works globally, primarily for inventory and monitoring,
[00:34:31.042]with limited user feedback, downloadable
[00:34:35.321]from Google and Apple, sales pitch there.
[00:34:41.024]But into the data portal, moving back into Africa,
[00:34:43.454]so now there's looking at Namibia,
[00:34:45.727]and I just was with that idea of having
[00:34:49.569]now these are source points, where we have soil data
[00:34:53.087]that were taken as part of a survey
[00:34:54.795]and now you see some of the Land-PKS systems
[00:34:59.645]that are within that, not quite all of them
[00:35:02.130]linked into the Land-PKS system.
[00:35:05.695]And then applications under development to use
[00:35:08.382]effectively what is crowdsourced data
[00:35:11.744]into that soil inventory to improve that
[00:35:14.572]overall database, ah, when Jeff got back last Monday
[00:35:20.634]from his last trip to Kenya,
[00:35:22.234]he was saying ah, we've expanded,
[00:35:24.259]we're getting more information,
[00:35:25.885]so I look up into Ethiopia, and now,
[00:35:27.743]and I can look into that Awash valley
[00:35:29.650]and I can see some of the points
[00:35:31.109]where we're starting to use Land-PKS for plotting that,
[00:35:34.652]and I can go on there and I can find some of that data
[00:35:36.762]now that is in the system.
[00:35:41.551]And I wanna see the capacity continue to develop,
[00:35:43.709]so that we can look at extrapolation of that into forecasts,
[00:35:48.603]forecasts of weather and climate that could make
[00:35:52.863]or help people to make better decisions on their land use.
[00:35:58.373]So future examples, one of the things
[00:36:03.959]that Jeff talks about in this is right now
[00:36:06.626]you're looking at points, so I'll go back to this,
[00:36:08.784]and you're looking at a point-source information,
[00:36:12.685]well, how do you project that point to a larger area?
[00:36:17.432]So again, early on in the PRIME project,
[00:36:19.159]we were dealing with that field survey,
[00:36:21.554]trying to map out a five or 10-kilometer area
[00:36:24.752]with each of those applications,
[00:36:27.722]but how do you take this point data
[00:36:29.091]and expand it into that, that's a challenge
[00:36:33.820]that is being faced, so how to make,
[00:36:35.672]just like any observation system out there,
[00:36:38.750]how do you look at that and develop what it is
[00:36:42.859]representative both spatially and temporally too?
[00:36:46.183]Is it a one-kilometer resolution, a five-kilometer
[00:36:48.270]resolution, a 10-kilometer resolution, well,
[00:36:50.920]that could depend, in weather, it depends on
[00:36:55.622]the speed and intensity of weather systems
[00:36:57.268]moving through there, climate, it may depend
[00:36:59.497]on the variability of seasonal issues going on.
[00:37:04.916]And of course, soil, it may depend on
[00:37:07.093]for longer-scale type of degradation issues.
[00:37:14.434]So there's Land-PKS.
[00:37:17.847]It's a sale pitch as part of a larger process,
[00:37:20.579]which is toward a Global Earth Observing System of Systems.
[00:37:24.589]So when I talk about Global Earth Observing
[00:37:26.332]System of Systems, that's the attempt to take
[00:37:28.960]all those different types of platforms,
[00:37:32.116]international, satellite-based, earth-based,
[00:37:37.112]and to take that information from those different
[00:37:40.094]earth systems and to make that data available
[00:37:44.106]in consistent formats, in consistent ways
[00:37:47.722]over a geo portal, so there's a lot of the work
[00:37:51.024]that's being done here that is working toward
[00:37:54.209]that Global Earth Observing System of Systems.
[00:37:59.276]So to break the cycle of weather calamity and famine,
[00:38:02.712]oh, we've added risk management,
[00:38:04.636]trying to work on agricultural issues,
[00:38:06.950]infrastructure improvements for agriculture.
[00:38:10.701]I say stirring vigorously, so capacity building in Ethiopia
[00:38:15.239]is also involving infrastructure, roads.
[00:38:19.129]So when I was first there, there was only two-lane road
[00:38:22.399]from the capital city down to the coast of Djibouti.
[00:38:27.036]There was no active rail service at all.
[00:38:31.064]How do you deal with transport issues
[00:38:32.754]on a two-lane road for a city of, what,
[00:38:36.843]three or four million people in a country
[00:38:39.581]of 80 to 90 million people with one two-lane road
[00:38:44.730]to the coast, not very easily.
[00:38:48.993]So that, the Grand Renaissance Dam,
[00:38:51.673]which is building a huge hydroelectric dam,
[00:38:54.083]now they had some smaller ones, but now huge,
[00:38:56.127]starts to stabilize the power grid within that area,
[00:38:59.783]very important to development.
[00:39:02.575]PRIME as an agricultural process
[00:39:05.043]was to aid pastoralists, it was not meant
[00:39:08.008]to significantly change their way of life
[00:39:10.784]but to improve their lot in life
[00:39:14.148]and their decision-making capability,
[00:39:15.838]to improve their overall sustainability,
[00:39:19.686]but there is some unrest here,
[00:39:25.736]so with that, in any international circle,
[00:39:27.745]you face that possibility of civil unrest,
[00:39:30.689]and there is some dysfunction that's been going on
[00:39:32.357]the last few months in Ethiopia.
[00:39:36.104]Just an example from the silver medalist
[00:39:38.468]as he's crossing the finish line in the Olympics,
[00:39:41.099]crossing his hands above the head,
[00:39:43.523]and here, in Ethiopia, the crossed hands above the head
[00:39:45.822]as there are various types of demonstrations,
[00:39:51.006]peacefully oriented, but still nonetheless
[00:39:54.910]resulting in injuries and death as a result of those.
[00:39:58.106]Where people are looking at marginalization,
[00:40:00.867]are looking at issues within their civil society
[00:40:05.309]and trying to address it.
[00:40:08.515]And so in different parts of the country,
[00:40:09.934]those still need to be addressed,
[00:40:12.780]and those can affect the outcome of some of these projects
[00:40:16.911]that we're dealing with, so there's a map
[00:40:20.604]of some of those areas, Amhara, Oromia,
[00:40:22.858]and the city of Addis sitting there,
[00:40:26.243]which is growing and developing,
[00:40:27.715]it would like to annex more parts of Oromia
[00:40:31.154]to expand and develop, maybe the people in Oromia
[00:40:34.827]don't want that annexation, maybe they want
[00:40:36.733]to see that development as part of their structure.
[00:40:41.131]So all of those kind of processes in there.
[00:40:44.208]Meanwhile, so NIMS/ICS, Incident Management,
[00:40:47.333]public safety is tied into that,
[00:40:50.284]originally we had two projects there back to back,
[00:40:52.333]three years each, and the projects began in 2010,
[00:40:56.094]officially ended in February of this year.
[00:40:58.550]What we looked at, well, that was at the federal level,
[00:41:00.799]but really incident management, emergency management
[00:41:03.604]occurs at the local state level and the local level,
[00:41:06.766]so can we do a follow-on project
[00:41:08.202]to try and develop more in that scale,
[00:41:12.164]and so we got approval for that in spring of this year.
[00:41:15.356]The summer, they started to get a job opening
[00:41:17.312]for somebody in Ethiopia to head up that project,
[00:41:19.766]but as of now, I think that it's still kind of in limbo,
[00:41:22.753]waiting for some of these other things to stabilize
[00:41:25.343]before that takes off.
[00:41:27.392]PRIME project is going great guns from what I understand.
[00:41:31.158]Our additional project continues through 2017,
[00:41:34.212]there's a follow-on project that will be,
[00:41:37.884]is in the works for development,
[00:41:39.986]and I've been encouraging Tsegaye here as a possibility
[00:41:43.667]that maybe there could be some things with his projects
[00:41:46.918]that will also tie into that, it would be great
[00:41:49.819]to see that type of thing happen with the cradle horn
[00:41:52.032]projects as well.
[00:41:56.516]More topics, more things going on.
[00:41:58.134]So it's not just in Ethiopia.
[00:41:59.510]So Kazakhstan, I'll throw a little bit in here very quickly,
[00:42:01.765]so this is a wheat project in Kazakhstan, CRW,
[00:42:07.097]so that's Central Asian Resiliency for Wheat.
[00:42:11.683]So it's targeting wheat farmers in Kazakhstan,
[00:42:14.458]which once upon a time was the breadbasket of central Asia.
[00:42:18.409]Well, then, what happened, the Soviet Union dissolved,
[00:42:21.591]and all of the stans became Uzbekistan, Kurdistan,
[00:42:26.313]all of those became separate countries,
[00:42:28.137]and with that, the overarching Soviet system
[00:42:32.778]went in decline, and they basically have had
[00:42:35.732]to redevelop some of those institutions.
[00:42:41.232]Like agricultural schools.
[00:42:44.550]Like their meteorological service.
[00:42:47.353]They have a lot of infrastructure,
[00:42:49.478]but it was vacated when the Soviets left,
[00:42:52.498]and they had to take over and redevelop,
[00:42:55.107]and so they're looking for assistance in that process.
[00:42:58.062]So for this project, I actually went there last summer
[00:43:00.976]not to set the project up, but to look at it
[00:43:03.946]and evaluate the midterm status of this.
[00:43:07.130]At the same time somebody else was evaluating
[00:43:09.446]the midterm status of my Ethiopian project, PRIME.
[00:43:14.654]So I'm there looking at that, and I'm looking at,
[00:43:16.884]is it meeting its objectives, is it doing the things
[00:43:18.921]it set out to do, again, both of these projects
[00:43:22.116]are funded under those global climate change initiatives,
[00:43:25.813]part of the US Agency for International Development
[00:43:28.943]under our State Department.
[00:43:32.976]So there with that money over time,
[00:43:35.661]to catalyze that process of adaptation
[00:43:37.990]in Kazakhstan's food sector.
[00:43:40.423]The important institutions in there
[00:43:43.952]were KazAgroInnovation, that's under
[00:43:47.504]their Ministry of Agriculture.
[00:43:49.358]KazHydromet, which is their meteorological service,
[00:43:54.002]and then the National, now I'm forgetting what S
[00:43:58.029]stands for, but the satellite, National Satellite Agency,
[00:44:04.209]Satellite Imagery, agricultural sites,
[00:44:09.280]meteorological sites, satellite imagery.
[00:44:13.718]So there's agricultural sites,
[00:44:15.335]that's an agricultural school location,
[00:44:18.902]there's some test plots, that's the director
[00:44:20.504]of one of the schools out there,
[00:44:23.575]looking at wheat fields, look at that nice little
[00:44:25.539]automated sensor sitting out in that field.
[00:44:28.816]Again, he's got that data at his site
[00:44:32.279]and several other datasets like that.
[00:44:35.026]They're collecting that data once a week for their purposes.
[00:44:39.993]Why not once a day, well, they don't need it once a day,
[00:44:42.840]but down at the weather office,
[00:44:44.701]the KazHydromet office, they sure would like to see
[00:44:47.633]that data on a daily basis.
[00:44:52.234]Okay, so how do we get them to share that data
[00:44:55.353]among those groups, so then that first question
[00:44:58.855]we're evaluating is are these entities
[00:45:02.087]successfully sharing their data?
[00:45:04.841]And the question was, well, kinda,
[00:45:07.203]it brought them together, but there's careful political
[00:45:10.202]words, collaboration is a challenge.
[00:45:12.908]There's an inadequate surface observing network to start,
[00:45:16.421]there's a lot of undigitized historical records
[00:45:18.952]from which to try and derive climates statistics
[00:45:22.138]is very difficult, they are getting new tools,
[00:45:24.995]they are starting to digitize that and develop those,
[00:45:27.049]but they still need a lot of work.
[00:45:29.714]Another question had to do with, okay,
[00:45:31.717]are they working together, and is that information now,
[00:45:35.578]the new climate information, actually getting out
[00:45:37.940]to agricultural producers, ehhh,
[00:45:42.114]and the answer was, not really.
[00:45:45.569]They're used to charging farmers for that information,
[00:45:48.089]they're not used to giving it away for free.
[00:45:50.847]Most of the farmers can't get access to it
[00:45:52.684]easily over the internet, that site was not yet up to speed,
[00:45:58.062]they're not getting the paper products
[00:46:00.650]through their distribution system,
[00:46:03.127]and they were still getting costly,
[00:46:06.454]they were having to pay for a lot of their information,
[00:46:09.250]which meant small farmers cannot do that.
[00:46:13.604]So if any of this is working, can it be sustained?
[00:46:17.520]Well that, the enthusiasm level was high.
[00:46:20.338]Both in the KazHydromet and the satellite world,
[00:46:25.474]they both have the technical capability and expertise
[00:46:27.939]to move this forward, but they still have to deal with
[00:46:31.651]the institutional roadblocks to sharing data.
[00:46:35.054]And developing that internet site
[00:46:36.776]and the capability to get that information out.
[00:46:41.644]So again, there are my bookends.
[00:46:45.081]You have agricultural mitigation and adaptation
[00:46:47.674]strategies you wanna employ, you have
[00:46:49.685]agricultural infrastructure and improvements you wanna make,
[00:46:51.748]but the bookends are risk management on the one side
[00:46:54.716]and building that hydrometeorological capacity
[00:46:57.494]on the other, so that you can improve
[00:46:59.158]that overall mix, we were able to do some of that,
[00:47:03.485]or at least start to set that up in Ethiopia,
[00:47:05.369]creating the Early Warning Response Director
[00:47:07.648]together with the Ethiopian Institute for Ag Research,
[00:47:10.131]the Ethiopian Met Agency, there's other partnerships in play
[00:47:14.173]One of those is sitting right here,
[00:47:16.079]and that's the Drought Mitigation Center here
[00:47:18.816]and the work under Tsegaye and others in this office
[00:47:22.741]that are working in projects in Africa
[00:47:24.545]and other places like that so that they can develop
[00:47:27.556]that capacity and that research
[00:47:30.137]to inform these projects.
[00:47:32.720]We also have, this is our National Center
[00:47:34.461]for Environmental Prediction, the National Weather Service,
[00:47:36.853]and Wassila in the red shirt is training African
[00:47:39.978]meteorologists as he has for over 20 years now
[00:47:43.898]that are going back into Africa to both work
[00:47:46.163]in forecast offices, to work at their climate centers,
[00:47:48.407]and to work in other fields in that arena.
[00:47:53.434]So again, a very good export, bringing them here,
[00:47:56.515]training them into processes that they can take back and use
[00:48:02.319]Oh, and you expect, I stole this off your website,
[00:48:06.206]so there's this guy talking here last August
[00:48:08.416]in Namibia, but the climate prediction
[00:48:11.051]application science workshop, there's that CPASW,
[00:48:14.285]where we met up in March and from where he invited me here,
[00:48:16.916]AfriGEOSS, the African Global Earth Observing
[00:48:20.497]System of Systems, that started as the African water
[00:48:24.148]cycle conference, and then morphed into that.
[00:48:27.844]The African Drought Conference hopefully tying in
[00:48:30.609]and working on those problems, depth of water,
[00:48:34.007]water shortage, or water super-abundance.
[00:48:37.452]So clock ticks, what did we learn over time
[00:48:44.509]from these type of development programs?
[00:48:47.117]So this is extracted just quickly
[00:48:48.989]from one of my colleagues, Curt Barrett,
[00:48:51.685]who worked with me in Weather Service International
[00:48:53.774]Activities Office for several years,
[00:48:55.409]and he's looking at his experience over time
[00:48:58.148]and Sezin is with the World Bank and with USAID
[00:49:01.983]and OFKA, which helps to develop these programs.
[00:49:06.706]Some of those hydro lessons learned.
[00:49:10.215]We need to refine that implementation and improve
[00:49:13.664]the sustainability of these early warning systems.
[00:49:16.562]We need to refine the implementation
[00:49:19.399]and improve that sustainability.
[00:49:21.658]The donors that are coming together to provide
[00:49:23.956]those resources, we need to coordinate that better
[00:49:26.985]so we're not duplicating on the one hand
[00:49:29.158]or inadequate in another, we have to build up
[00:49:33.185]the capacities of those national hydromet agencies.
[00:49:37.329]Because if we come in there and try to interject
[00:49:39.050]something that they are not able to work with,
[00:49:42.533]it will not be sustainable, so it has to be something
[00:49:45.556]that invests in the locally sustainable system.
[00:49:51.970]There are lots of places all over the world
[00:49:53.202]where well-meaning university systems,
[00:49:55.714]government systems have come in and set something up
[00:49:58.226]over there, and then they left,
[00:49:59.867]and that something over there stopped.
[00:50:04.255]And those thousands or millions of dollars stopped.
[00:50:08.521]And that is a waste, now, blending that
[00:50:11.181]with this other work, we look at most countries,
[00:50:13.568]many and most countries, here in the US here,
[00:50:15.701]you have an amazing access to data that is freely shared
[00:50:20.038]among agencies, that is freely accessible
[00:50:22.308]to university researchers, step outside this country
[00:50:25.526]and you will not see that, so most countries
[00:50:29.995]do not have free distribution of weather and climate data
[00:50:32.660]among their component agencies or among their citizens.
[00:50:39.754]And then in most of these worlds,
[00:50:41.190]the agriculture, the hydromet, and the satellite agencies
[00:50:44.013]are not really sharing that among themselves,
[00:50:46.615]yet they need it to make their respective products
[00:50:49.917]whether it's a drought analysis, river forecast,
[00:50:52.672]climate outlook, or a weather forecast.
[00:50:58.750]So we need real-time reports, we need to pool
[00:51:01.395]our awareness, responsiveness,
[00:51:03.120]to provide decision support services
[00:51:07.303]to our public and to our officials.
[00:51:11.197]And every development program needs follow-through
[00:51:13.263]to complete that plan and follow-up
[00:51:14.952]to make sure it's working.
[00:51:19.434]I've only got a couple minutes,
[00:51:20.589]gotta let questions be asked,
[00:51:22.367]but bringing us back home, there's my Grand Forks,
[00:51:25.077]there's floods in 97, there's water flowing through
[00:51:28.214]that dragged me back to that area to work on these issues,
[00:51:32.148]and so I got involved with programs
[00:51:33.797]for long-term flood solutions that continue
[00:51:36.213]and our Forecast Working Group toward that,
[00:51:39.110]and we were able to come up
[00:51:40.335]with this river forecast, what do we need
[00:51:42.707]to make better river forecasts, what data's out there,
[00:51:46.551]how do we integrate it, and how do we stabilize it?
[00:51:49.956]With uncertain futures and budgets in all these processes.
[00:51:54.608]Again, a lot of disparate groups coming together,
[00:51:59.339]agriculture industry interests,
[00:52:01.351]government interests, a lot of different sensor sets
[00:52:03.891]out there, for aviation at an airport,
[00:52:06.015]along a highway as part of a transportation,
[00:52:08.721]at a river, perhaps, in a field as part of agriculture,
[00:52:13.501]how do we bring those different data
[00:52:15.189]into consistent formats, into consistent frameworks
[00:52:19.857]and then overcome issues of timing, this and that.
[00:52:26.033]Lot of different data points out there,
[00:52:28.682]none of 'em built explicitly to deal with flooding issues.
[00:52:35.252]So that's my area, all those green dots reflect
[00:52:39.892]possible daily precipitation points,
[00:52:43.877]but on any given day, how many of them
[00:52:45.816]are actually reporting, and in the winter,
[00:52:49.663]only 64 of them were reporting when we started this in 2013.
[00:52:54.594]Only 20 of those were reporting hourly.
[00:52:58.638]So if you look at that in that area,
[00:53:00.297]it's roughly a 40-kilometer grid scale resolution,
[00:53:02.633]but if you look at this lower picture,
[00:53:05.028]you'll see they're concentrated in certain areas
[00:53:06.938]and conspicuously absent in others.
[00:53:10.754]But we could easily increase the resolution
[00:53:12.431]to 20-kilometer or less grid scale resolution,
[00:53:15.377]which for modeling would be critical
[00:53:16.708]simply by, oh, telemetry, so the North Dakota
[00:53:22.099]Agricultural Weather Network, we went from having
[00:53:24.121]that data dumped once a day at night
[00:53:27.468]to now it's five-minute accessible.
[00:53:31.078]Which is a huge improvement, so now I can see that data
[00:53:36.616]at that type of near-real time.
[00:53:41.726]Data logs in that process, even tipping buckets,
[00:53:44.681]now if you've got snow measurements,
[00:53:46.958]and you're dropping into heating tipping buckets,
[00:53:49.935]there's a problem with that, but it's better than nothing
[00:53:52.821]for information, it's not the best,
[00:53:55.422]but it's better than nothing, volunteer networks.
[00:54:00.031]The Land-PKS looks at crowdsourcing,
[00:54:03.120]and we're looking at crowdsourcing applications
[00:54:05.081]to get that, to bring in citizen science, crowdsourcing,
[00:54:08.125]and to take what is anecdotal information,
[00:54:11.817]somebody sees something off in the distance,
[00:54:15.137]coupling that with GIS information,
[00:54:17.334]and with other types of data,
[00:54:20.258]to become qualified, tying that to regular reporting
[00:54:23.364]type of stations, so that it becomes more quantified
[00:54:27.256]in nature and it becomes useful to the forecast.
[00:54:33.997]Skip through, MADIS is something that is,
[00:54:37.810]again, a NOAH derivative for shining meteorological
[00:54:40.352]observation data all over the globe.
[00:54:43.757]We've just gone through an update, and how that works,
[00:54:45.773]it was experimental, it's now become operational,
[00:54:49.203]it's a way of bringing in some of those different datasets,
[00:54:53.346]bringing those different platforms in
[00:54:55.309]so that I can see them as a weather forecaster,
[00:54:58.496]so it's available to people like you for research.
[00:55:05.112]And then lastly, climate change, how does this apply to it?
[00:55:09.540]You need to know what's been going on,
[00:55:13.232]you need to know what's going on now,
[00:55:14.589]and you need to be able to project into the future.
[00:55:18.758]And with those projections, you need to be able
[00:55:21.649]to relate it to something, physical representative
[00:55:25.688]that people can understand, so this was a workshop
[00:55:27.952]we did last November around this time,
[00:55:30.198]where we took some climate change scenarios.
[00:55:33.317]So this graphic, quickly, is looking at
[00:55:35.151]our current point in time at the black
[00:55:37.419]where we might have our current state
[00:55:40.477]of temperature and precipitation in Central North Dakota.
[00:55:43.393]Most climate change scenarios, and these are off of
[00:55:45.257]the CMEC3 database, CMEC5 is out there, for new projections
[00:55:50.622]or climate outlook possibilities,
[00:55:53.916]and we looked at the scatter plot of scenarios,
[00:55:56.848]and those scenarios say that you're definitely
[00:55:58.502]gonna get warmer, and you could either get
[00:56:00.700]wetter or dryer, well warmer, we've selected
[00:56:05.918]some of those wetter, most of the three of the four
[00:56:09.588]scenarios were wetter, one of them was warmer and dryer.
[00:56:14.098]What does it mean in Nebraska,
[00:56:15.984]if you were here in Lincoln, and you wanted to go
[00:56:19.239]to a wetter area somewhere in the Great Plains,
[00:56:23.875]what direction would you go?
[00:56:28.118]Oh, come on, that's easy, east, you wanna go warmer,
[00:56:32.034]and you're in Lincoln, what direction do you want to go?
[00:56:35.876]South, so Adenon and I are talking,
[00:56:38.665]and there might have been a beer involved in this.
[00:56:41.026]Are we still on camera?
[00:56:42.806]But we're talking after this, we said,
[00:56:44.531]well, you know, moving toward that wetter and warmer
[00:56:47.069]or wetter and dryer, or excuse me,
[00:56:50.349]warmer and wetter or warmer and dryer type of environment,
[00:56:54.125]for a lot of people in Central North Dakota,
[00:56:56.065]is like either heading toward the southeast,
[00:56:58.440]maybe toward Minneapolis, maybe on a trajectory
[00:57:01.273]more toward Omaha, depending on how wet you're getting,
[00:57:04.400]or due south, toward North Platte,
[00:57:07.525]where you're not getting wetter,
[00:57:08.538]but you're getting hotter, what are you gonna expect to see
[00:57:12.102]for land use, what are you gonna expect to see for plants?
[00:57:15.234]So wet here in Central North Dakota
[00:57:17.722]would be called a sweet clover year, the sweet clover
[00:57:20.225]starts to grow very naturally
[00:57:22.040]and as an invasive species, where in most years it doesn't.
[00:57:27.619]Okay, it's gonna affect flora, fauna,
[00:57:30.871]it's gonna affect land use, it's gonna affect
[00:57:33.050]a lot of things, but that gives a way of
[00:57:38.467]looking at scenarios and how people and different agencies
[00:57:41.296]can respond, we need to see beyond what we see,
[00:57:47.021]so that can help you decide what to do next.
[00:58:06.087]Hi, with the Land-PKS app that you have,
[00:58:10.030]is there a way that you verify any of the data
[00:58:12.283]that is collected by volunteers?
[00:58:14.970]Well, again, the Land-PKS allows crowdsourcing,
[00:58:18.160]so you can load up the app, you can go there
[00:58:19.456]and download this, in the one slide I showed,
[00:58:24.049]it showed where we had some known plots
[00:58:26.151]and then what's coming in through crowdsourced data,
[00:58:31.186]so in that example I said at the end,
[00:58:33.798]about taking that and tying it to something physically,
[00:58:37.707]that's one way to take, okay, we have already a report
[00:58:40.909]near there that is like this, is it reasonable,
[00:58:43.714]so like any observation, you would look at
[00:58:46.201]nearest neighbor comparison if you had something like that
[00:58:48.972]and look at its representativeness.
[00:58:51.061]Obviously there could be a database
[00:58:53.623]to some degree of soil types that may not
[00:58:56.689]have that degree of specificity,
[00:58:58.560]but there maybe a larger mapping.
[00:59:01.117]The FAO has larger scale land use type of mapping
[00:59:06.125]that can give you some idea of if you're at least
[00:59:08.908]in the right ballpark, but with anything,
[00:59:10.955]you're relying on, well, is somebody trying
[00:59:13.658]to give you good information, like I say,
[00:59:15.415]if somebody's reporting a tornado
[00:59:17.376]that they're observing outside of town here,
[00:59:19.375]I'd like to know about that, okay,
[00:59:21.664]they don't need to know how strong it is,
[00:59:23.299]they don't even need to know exactly how far away
[00:59:26.179]and what direction it's moving,
[00:59:27.599]I just want to know that they're seeing it,
[00:59:29.458]and then we'll go into detail from there,
[00:59:31.529]but it's a beginning to that information.
[00:59:35.352]But I think eventually they,
[00:59:36.843]true satellite, as you're integrating that
[00:59:39.306]into the system, nearest neighbor comparison
[00:59:41.666]and some other things would help to identify that.
[00:59:44.796]Jeff's the expert on that.
[00:59:49.017]Any other questions?
[00:59:52.309]I have one question.
[00:59:54.830]You mentioned on the stationarity
[00:59:56.738]or non-stationarity, as a model I am really worried
[00:59:59.506]about that because we use them for modeling
[01:00:03.433]the non-spherical pattern, then,
[01:00:07.960]that is non-stationarity thing,
[01:00:09.962]especially in the context of climate change.
[01:00:12.841]What do you advise us?
[01:00:15.196]Well, really the advice that is coming out of that section
[01:00:18.901]is that do we have a long enough record and knowledge
[01:00:22.361]to actually provide that type of determination,
[01:00:25.798]so we can see what looks to us
[01:00:28.823]as being a significant change, is that really,
[01:00:31.162]and again, I point to this part, this area
[01:00:34.125]of this country where you can see very dramatic changes
[01:00:36.920]from year to year and season, look at this week
[01:00:40.103]versus last week in temperature.
[01:00:42.428]Now, if you're from this area, if you're used to that,
[01:00:45.279]if it makes sense, it's not a problem.
[01:00:48.132]People that are coming from an area
[01:00:50.025]that does not have that high degree of variability
[01:00:51.626]would say this is dramatically different,
[01:00:55.800]so in the context for here, eh, it's not abnormal.
[01:00:59.194]Not abnormal is the thing, so again,
[01:01:05.057]we may have too short of a time view of that data,
[01:01:08.547]we may not have enough, and so that's,
[01:01:10.719]so we may not be able to decide
[01:01:13.762]whether it's a stationary or non-stationary,
[01:01:16.804]but we may see yet a trend, and that's why I said
[01:01:19.435]transition and trend, we can try to allude to it,
[01:01:22.745]but when I see people say non-stationarity
[01:01:26.453]and stationarity, I start to get suspicious
[01:01:29.632]of the math involved, and so that's an important thing
[01:01:33.427]as scientists to really look at
[01:01:35.497]whether or not we've changed, whether or not stochastic
[01:01:38.940]processes is significantly altered.
[01:01:42.944]So I don't have an answer, I just, it's important,
[01:01:46.242]because there is a lot, there's been an argument
[01:01:48.979]in my field, while dealing with whether or not
[01:01:53.476]different type of ocean signals and change signals
[01:01:57.036]in ocean, in the seas, would help us improve
[01:02:00.190]our knowledge of the number of tornadoes
[01:02:02.452]in, say, eastern Nebraska during the summer season.
[01:02:07.060]And there's people that are coming out,
[01:02:09.024]making firm pronunciations that yes, they've done that,
[01:02:12.064]and now there's a lot of people that are saying that
[01:02:14.641]no, your data isn't showing that,
[01:02:16.113]it's showing a slight hint that maybe,
[01:02:18.995]well that's not even suggestive.
[01:02:24.925]Any other questions?
[01:02:32.135]Thank you, I was wondering
[01:02:33.626]if we could back to this idea of social vulnerability
[01:02:36.221]and linking it to geography, so as a political scientist
[01:02:41.105]one thing I think about is political boundaries
[01:02:45.702]and the data are paired to geographic units
[01:02:48.955]that are outlined by government systems that are
[01:02:52.090]somewhat arbitrary and aren't necessarily tied
[01:02:55.337]to resources or river basins, and I was wondering
[01:02:59.527]if you could comment a little bit
[01:03:01.043]about promising avenues for coupling weather and climate
[01:03:06.008]information with some...
[01:03:07.627]Well, in my colleague Dave Hastings that did that work
[01:03:11.868]originally, let's see, and I clicked to something here,
[01:03:15.138]this was another vulnerability score
[01:03:18.485]that came out of the UK Met Office
[01:03:20.687]and some work by Krishnamurthy
[01:03:22.651]and our friend Richard that was at CPASW
[01:03:26.530]was involved in this, and that's looking more distinctly
[01:03:28.902]at climate and hunger vulnerabilities,
[01:03:32.754]so they were separating out those issues
[01:03:36.948]directly involving food production and availability.
[01:03:40.716]Now that's not to say that you can separate politics
[01:03:43.562]out from production and availability,
[01:03:47.334]because political can affect production,
[01:03:51.951]I mean, if you can't harvest a crop
[01:03:53.547]because you've got people rampaging through the fields
[01:03:56.373]or the distribution of food is affected by
[01:04:01.875]shutting down of infrastructure,
[01:04:04.717]but this was an attempt to try and isolate
[01:04:07.390]that from those other metrics, but,
[01:04:10.842]again, it's on those same geophysical boundaries
[01:04:13.575]that you were looking at before.
[01:04:17.632]The same analysis, at least in the US
[01:04:19.193]and in largely North America has been drove down
[01:04:21.676]to the county scale, just like when I showed you
[01:04:25.205]for Ethiopia, we had it down to the woreda scale
[01:04:27.638]for flooding and for drought, but that's
[01:04:31.780]where you have to go into that country,
[01:04:34.752]into their emergency management professionals,
[01:04:36.976]if they have them, to then try and,
[01:04:40.841]to extract that information, because those are
[01:04:43.060]the experts that would be able to identify
[01:04:46.750]and separate out some of that risk,
[01:04:49.316]but in all countries, that will not be easy,
[01:04:53.071]you know, so again, to try and look at that
[01:04:55.692]from a large scale and drill in,
[01:04:57.959]you're not getting the actual background data
[01:05:00.388]from those areas, you are not going to have
[01:05:03.123]a well-constructed model, I don't think.
[01:05:08.199]You'll have a guess, and you'll have some of these things
[01:05:10.857]to go on, but that's, you know, look at the Kurdish
[01:05:14.490]population in the borders of Syria and Iraq
[01:05:17.126]and where do they actually live
[01:05:18.472]and which country are they part,
[01:05:20.645]and how do you look at that, and there's no geophysical
[01:05:24.455]representation of that, that's way outside
[01:05:28.291]of my weather and climate deal.
[01:05:31.985]Thank you, Greg, and thank you all for coming.
[01:05:35.285]And I apologize for talking late, thank you.
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