Land Cover Change, Irrigation, and their Impacts on Climate
Dr. Rezaul Mahmood
Author
09/30/2016
Added
175
Plays
Description
Part of the SNR Fall Research Seminar Series
Searchable Transcript
Toggle between list and paragraph view.
- [00:00:01.563]Good afternoon.
- [00:00:03.265]First of all, I would like to thank you for
- [00:00:06.441]inviting me and giving me the opportunity
- [00:00:09.454]to talk about some of my research.
- [00:00:13.321]Well, today I'm going to focus on the impacts
- [00:00:17.540]of these land cover change, particularly
- [00:00:20.142]irrigation on climate.
- [00:00:26.091]This is a map showing land cover change.
- [00:00:31.561]Referring to it here, particularly crop land
- [00:00:34.997]and pastures showing infraction.
- [00:00:40.343]It is showing us from 1500 through 2000,
- [00:00:47.639]but if you look at,
- [00:00:49.194]you know, we have little time,
- [00:00:51.703]but if you look at 1500 data versus 2000 data
- [00:00:55.460]or maps you can see the significant changes
- [00:00:58.767]over the last 500 years.
- [00:01:02.432]If you look at North America you can see
- [00:01:06.406]some land cover changes or agriculture going on
- [00:01:09.088]in 1500 in the central Americas,
- [00:01:11.586]but then if you look at 2000 you can see
- [00:01:13.832]the significant change of land cover.
- [00:01:17.945]Particularly in the great plains obviously
- [00:01:20.336]where we are located.
- [00:01:21.784]All this red color means higher,
- [00:01:24.367]almost very high intensity land cover change.
- [00:01:31.568]So what land cover does,
- [00:01:33.305]I think if you are a specialist
- [00:01:36.047]the next few slides are going to be
- [00:01:38.074]fairly rudimentary for you,
- [00:01:39.791]but my understanding is the audience could be diverse.
- [00:01:43.401]So I put together a few slides that
- [00:01:47.513]helps to everybody being to the same page.
- [00:01:50.408]So when we change land cover we change albedo.
- [00:01:54.786]We change surface roughness length.
- [00:01:58.335]We modify energy partitioning.
- [00:02:01.143]If we have lots of irrigation then we will have
- [00:02:03.349]a lot more latent energy plugs
- [00:02:05.511]versus say dry land agriculture
- [00:02:08.856]or natural grass land probably.
- [00:02:12.682]More sensible heat plugs, so even semi-radium
- [00:02:15.237]sensible heat plugs and so forth.
- [00:02:18.216]So these changes, in combination
- [00:02:21.632]changes location and timing of cloud development,
- [00:02:26.656]timing of convection, locations of precipitation,
- [00:02:31.306]temperature, atmospheric moisture and so forth.
- [00:02:35.976]So this is some of the photos that includes
- [00:02:40.002]land cover change.
- [00:02:41.277]If you look at this one right,
- [00:02:49.089]if you look at here we are seeing this cultivation.
- [00:02:52.749]Rice is growing, but the expense of modifying
- [00:02:56.087]this natural vegetation.
- [00:02:58.660]So we obviously are seeing the albedo has changed,
- [00:03:01.703]roughness length has changed
- [00:03:03.692]and of course, water consumption and water usage
- [00:03:09.127]has change, which would modify energy balance and so forth.
- [00:03:13.961]This is a photo showing we have modified
- [00:03:18.061]the land cover here.
- [00:03:19.619]It's significant changes in surface roughness and albedo.
- [00:03:27.150]Here we are looking into mining activities
- [00:03:30.345]that happens quite a bit in western Kentucky,
- [00:03:32.855]West Virginia, strip mining, surface mining,
- [00:03:35.284]mountaintop removal.
- [00:03:37.111]We can see that we modified
- [00:03:38.552]or completely wiped off all the forest
- [00:03:41.736]for this surface mining.
- [00:03:43.644]So there's significant change in albedo
- [00:03:46.207]from, you know, it was originally like this
- [00:03:49.313]where it was very low versus now very high albedo
- [00:03:52.488]in this area.
- [00:03:54.712]We can replace it with asphalt,
- [00:03:58.535]which also changes like here,
- [00:04:00.946]the sensible heat plugs is going to be very high.
- [00:04:04.216]So we can see these kind of changes
- [00:04:06.328]and a few years ago I was traveling
- [00:04:08.486]and I saw this landscape and
- [00:04:12.737]you can see, oops.
- [00:04:16.559]All the concretes, asphalts and concretes
- [00:04:20.221]is completely, there's nothing else is in there.
- [00:04:24.491]This is actually a weekend in Shanghai, empty streets,
- [00:04:28.301]but you see that land cover
- [00:04:29.839]and all these big buildings,
- [00:04:31.480]so humans significantly modified land cover
- [00:04:34.402]and land deals
- [00:04:37.699]this area or this significant development has altered.
- [00:04:43.162]So where we see the land cover land has changed
- [00:04:45.426]so it's changing albedo, roughness length,
- [00:04:49.763]changes the fluxes which modifies,
- [00:04:53.690]in these cases, local scale atmosphere,
- [00:04:56.226]local scale weather and climate.
- [00:04:58.909]So in summary we want to move forward,
- [00:05:01.938]we can say that land cover plays an important role
- [00:05:04.887]in land atmosphere interactions.
- [00:05:07.267]We alter these interactions.
- [00:05:11.758]The impacts can be of varying degrees
- [00:05:14.109]depending on what kind of
- [00:05:17.743]the magnitude of those changes.
- [00:05:20.324]If we change grassland to a large city
- [00:05:23.430]versus irrigated crops or short grass land cover
- [00:05:28.926]then each of them would have a different
- [00:05:31.152]kind of impacts.
- [00:05:34.138]So here, going forward, I'm going to present
- [00:05:36.801]results from selected studies
- [00:05:38.447]and quite a bit of it involves irrigation impacts
- [00:05:41.684]on this part of the country.
- [00:05:45.159]This map shows the Ogallala Aquifer
- [00:05:50.146]and the irrigation and how much water
- [00:05:53.601]we basically pump into the surface.
- [00:05:58.100]When you look at, we can see where we are located,
- [00:06:02.132]this is almost like the blue side
- [00:06:04.705]or ground zero of high irrigation.
- [00:06:07.267]We can see some of the areas in Kansas and Texas,
- [00:06:10.881]but all of these areas uses significant of water
- [00:06:16.793]for irrigation to support agricultural activities
- [00:06:20.636]and we can see that in some areas we pump in
- [00:06:24.350]annually almost far over,
- [00:06:27.199]much greater than 100 million gallons
- [00:06:30.098]per square mile per year.
- [00:06:32.213]So this is a lot of water
- [00:06:33.571]and we irrigate in a very short period of time
- [00:06:37.118]in growing seasons, okay?
- [00:06:40.060]And we know that water consumption
- [00:06:42.111]varies over the growing season.
- [00:06:43.705]We irrigate in the beginning of the season
- [00:06:47.123]then irrigation peaks during July, August,
- [00:06:50.417]when plant is actively growing.
- [00:06:54.907]So there's an interest in variation of that.
- [00:07:01.097]I'm sure you're familiar with this
- [00:07:03.823]landscape here around this part of the country or state
- [00:07:10.479]and this is centipede irrigation
- [00:07:13.657]from the photos from the west.
- [00:07:17.327]This one is from Flint County, Kansas,
- [00:07:22.049]southwestern Kansas.
- [00:07:23.729]In fact, I have taken this photo and asked
- [00:07:26.980]a number of people at different times
- [00:07:28.602]to tell me what they are looking at
- [00:07:30.869]and pretty much the answer goes from,
- [00:07:34.275]"I don't know."
- [00:07:35.891]to, "A painting."
- [00:07:41.429]I cannot remember anyone that told me that,
- [00:07:44.218]"Oh, this is the centipede irrigation.
- [00:07:45.943]"I know exactly what you are talking about."
- [00:07:47.952]Obviously, the folks I asked,
- [00:07:49.075]they are not specialists in this kind of research,
- [00:07:51.765]but I expected to have some idea, but they did not.
- [00:07:56.795]At the same time, this is very spectacular
- [00:07:59.549]from one perspective, but at the same time
- [00:08:01.466]not every part of the world irrigation is like this.
- [00:08:05.415]So all this kind of extensive and intensity
- [00:08:08.051]at the same time.
- [00:08:10.977]So going back to this map,
- [00:08:12.317]if we look at it, I started to do some research
- [00:08:15.800]a number of years ago looking into the impacts
- [00:08:19.092]of irrigation and particular of the climate scale,
- [00:08:21.865]what it does and the expectation was that
- [00:08:24.712]it impacts first and foremost would be temperatures
- [00:08:28.614]because how irrigation changes energy balance.
- [00:08:35.146]So this is an example of land cover change.
- [00:08:38.601]This is from your county
- [00:08:41.281]and you can see from '50s and the mid-'40s, early '50s
- [00:08:44.941]when irrigation became very prevalent
- [00:08:49.434]and you can see how this irrigated land cover
- [00:08:54.032](mumbles) irrigated agriculture is significantly increased.
- [00:08:59.253]So this unbroken line is the area of irrigation
- [00:09:04.414]and you can see that at the same time
- [00:09:06.688]non-irrigated agriculture largely decline
- [00:09:09.687]and there's a point there's just almost went down
- [00:09:12.074]to nothing then came back a little bit
- [00:09:14.744]after the (mumbles).
- [00:09:20.031]So what I did at the time that okay,
- [00:09:22.003]I was interested
- [00:09:24.381]to see the impacts of land cover change on climate.
- [00:09:29.947]What intrigued me that when I started to look at
- [00:09:32.477]the literage of linear I saw there are some studies
- [00:09:35.238]that a meteorological time scale
- [00:09:38.092]and when I use this hydro-logic energy balance model
- [00:09:43.455]and I ran this model for 16 years,
- [00:09:45.739]a daily time scale and I found this
- [00:09:50.099]incredible increase irreparable transformation
- [00:09:53.348]from irrigated land cover.
- [00:09:57.263]Almost on the average it was 36% higher
- [00:10:00.348]compared to if land cover was natural grass land.
- [00:10:05.090]So when I baffled with that numbers
- [00:10:07.744]and obviously we did, we start asking question
- [00:10:09.819]is the model doing a good job?
- [00:10:12.863]It is in a well, well first I looked at
- [00:10:14.744]whether I did my experiment or set up was correct or not,
- [00:10:18.985]well everything looked all right.
- [00:10:21.670]Then I went to the model validation literature
- [00:10:24.413]which I found it very poorly validated
- [00:10:28.107]over multiple state under eight or nine
- [00:10:30.523]different land covers and the different kind
- [00:10:33.715]of soil conditions, so I had a feeling that,
- [00:10:37.044]well, the model is doing a fine job.
- [00:10:39.856]So I said, "Well, if this is true
- [00:10:42.459]"or in other words, if the model is doing a fine job,
- [00:10:45.244]"then I should see some
- [00:10:49.255]"response or signal in the climate scale."
- [00:10:53.179]Because I already saw the meteorological time scale,
- [00:10:55.440]what can happen if (mumbles) concept to a model
- [00:10:58.439]and things like that.
- [00:10:59.890]So then I went back, I said, "Okay, let's start small.
- [00:11:02.698]"I don't want to spend too much time
- [00:11:04.192]"in something that can end up taking us somewhere else."
- [00:11:08.117]So I started to look at data from Nebraska
- [00:11:10.465]then I expanded for the entire (mumbles) area.
- [00:11:14.432]So again, going back to this slide which is showing
- [00:11:16.892]almost 36% increase of irreparable transformation
- [00:11:21.405]under irrigated foundation versus natural grass.
- [00:11:26.321]So we have taken data from irrigated
- [00:11:28.379]and non-irrigated areas about 50 long term
- [00:11:33.653]stations in that great plains,
- [00:11:36.007]but then this is some examples.
- [00:11:38.485]Here I am showing the difference of
- [00:11:42.822]temperatures between irrigated and selected irrigated
- [00:11:46.518]and non-irrigated sites.
- [00:11:48.860]What I found is, first of all,
- [00:11:50.875]that the response was the most robust in Nebraska
- [00:11:55.601]or magnitude is greater compared to other areas
- [00:11:59.052]or other say Texas or New Mexico.
- [00:12:03.311]But here we are seeing,
- [00:12:05.272]this is most of the data that is close to 90 plus years,
- [00:12:11.130]the length of the data I said is about 90 plus years.
- [00:12:14.738]So all of these lines, Oakland, New York,
- [00:12:19.006]those are irrigated sites,
- [00:12:20.685]Auburn, Holsey and Pony, those are non-irrigated sites
- [00:12:25.387]and we have seen in the long term climatological scale
- [00:12:29.295]the second half of the 20th century
- [00:12:31.815]almost 1.4 degrees Celsius cooling.
- [00:12:36.344]And that trend holds and we did a very detailed,
- [00:12:39.912]robust analysis of data including applying
- [00:12:42.836]bootstrapping, including applying robust statistics
- [00:12:46.031]so I had to take all the out layers out.
- [00:12:48.573]Almost 40% of the two tales are out
- [00:12:50.580]then we did statistical testing and the results hold.
- [00:12:54.439]So it was fairly interesting, exciting at the same time.
- [00:12:59.912]We found that obviously signal is as expected.
- [00:13:03.556]Most consistent with T-max, maximum temperatures,
- [00:13:09.439]the result is mixed when it comes to minimal temperatures,
- [00:13:13.189]which we expected and it also,
- [00:13:17.350]similar kind of study afterwards done in California,
- [00:13:20.160]they found similar kind of results.
- [00:13:24.718]This is actual difference between
- [00:13:28.895]irrigated and non-irrigated sites
- [00:13:33.599]in New Mexico and three in post 1945.
- [00:13:39.056]So the dark bar is temperature,
- [00:13:42.743]pre 1945 and the emptier white bar
- [00:13:48.992]is temperature post 1945 and you can see that, oops.
- [00:13:56.573]That during post 1945 in most of this four comparies
- [00:14:01.208]and in post 1945 is temperature cooled down.
- [00:14:09.585]So then we also looked at just traditional
- [00:14:12.923]trained analysis after smoothing the data
- [00:14:15.239]and everything in New York what we found
- [00:14:18.475]the T-max is declining, long term trends.
- [00:14:22.355]DTR was declining and our
- [00:14:25.594]hypothesis is nighttime evaporation
- [00:14:29.183]keeping the T-mean high as a result we are seeing,
- [00:14:34.087]and T-max also declines so we are seeing
- [00:14:37.806]lowering of DTR over the entire century
- [00:14:41.624]or particularly the impact of the second half
- [00:14:43.840]of the 20th century.
- [00:14:45.643]I was interested that, okay,
- [00:14:47.831]let's look at we see interest seasonal conditions.
- [00:14:52.259]In other words, irrigation,
- [00:14:55.539]application of irrigation and how intensively
- [00:14:57.888]how much we do, it varies through the season
- [00:15:00.184]because of crop water decline and changes.
- [00:15:03.262]So if that holds, the idea would be that
- [00:15:06.853]July and August, when you are going to see
- [00:15:10.262]the maximum changes and I was focusing only on
- [00:15:15.060]the extreme maximum temperatures data.
- [00:15:17.541]So extreme data from extreme high temperatures
- [00:15:20.502]from each month of the stations we have taken
- [00:15:23.420]and then calculated the trend
- [00:15:25.594]and then what we found is, yes,
- [00:15:28.960]that in most cases in July and August
- [00:15:32.633]when you see the maximum changes impact,
- [00:15:37.274]there's a manuscript came out
- [00:15:40.784]very recently, I was looking for it I think,
- [00:15:42.969]this year or late last year which
- [00:15:46.202]expanded this study to the entire US
- [00:15:49.394]and they found the similar kind of results
- [00:15:51.951]for extreme temperatures and precipitations
- [00:15:54.176]and things like that.
- [00:15:57.692]So how it is happening as I have mentioned
- [00:16:00.124]that this is from model data and you can see that
- [00:16:04.630]latent energy plugs, how it increases during
- [00:16:08.262]the growing season because we are applying the irrigated
- [00:16:11.874]water and how sensible heat plugs
- [00:16:14.752]declines at the time of, when you look at
- [00:16:17.201]in other words, energy partitioning,
- [00:16:19.620]latent energy plug, dominant energy partitioning
- [00:16:22.102]and as a result which helped to decrease
- [00:16:26.026]maximum temperatures.
- [00:16:29.552]Then as a follow up, you guys have this nice
- [00:16:32.863]data in Nebraska, so these are.
- [00:16:38.024]I don't know, which includes dew point temperatures
- [00:16:41.179]and so I said, "Okay, let's look at the dew point
- [00:16:44.149]"temperatures, it doesn't go as far back,
- [00:16:46.254]"but as a follow up, we are building it up."
- [00:16:48.152]We can explain, so I have taken data from
- [00:16:53.101]irrigated and non-irrigated sites and here the
- [00:16:57.897]non-irrigated sites are
- [00:17:00.499]the black boxes and the triangles are irrigated sites
- [00:17:04.068]and as you can see we have taken, for example,
- [00:17:10.502]this is a non-irrigated area, whereas the irrigated sites.
- [00:17:16.751]So when you analyze the data of what we found that
- [00:17:21.553]for most cases if we look into dew point temperature data
- [00:17:27.364]that it has increased over irrigated areas
- [00:17:31.605]and so we looked at month-by-month and then seasonal.
- [00:17:35.969]So this is for month of May and
- [00:17:39.550]for most of the cases the dew point temperature,
- [00:17:44.243]as you can see, up to one degree increase
- [00:17:47.597]in dew point temperatures, which is quite a bit,
- [00:17:50.492]but then in June it remained the same,
- [00:17:52.551]almost similar level, but then we see this rapid
- [00:17:56.643]increase in July when we apply lots and lots of irrigation.
- [00:18:02.280]Then in August as crop water demand goes down
- [00:18:05.331]and we see slight decline,
- [00:18:08.165]but then it nicely declines in September,
- [00:18:10.955]you know, when we don't do much.
- [00:18:14.356]So these are data, monthly data from
- [00:18:18.972]the top one is for Europe and the bottom one is for Shelton
- [00:18:22.868]and we can see this nice sort of bell curve type of
- [00:18:29.366]dew point increase and reaches to it's maximum
- [00:18:33.668]during middle of the season and then it declined.
- [00:18:43.968]We have also done some simulation work using ramps model
- [00:18:52.283]in Nebraska so after the application that these
- [00:18:55.767]are some of the snapshots
- [00:18:57.212]that what they found, if we do irrigation
- [00:19:00.173]minus natural vegetation temperatures
- [00:19:03.421]as you could see that lots of this negative balance,
- [00:19:07.225]which means under natural vegetation temperature
- [00:19:10.612]was higher where it was lower under irrigated condition.
- [00:19:13.878]So what we found, the long term climate scale
- [00:19:18.319]it is also holding up in short term application
- [00:19:20.572]further in the model study.
- [00:19:23.478]This is showing latent energy plugs.
- [00:19:26.583]We found that the results of comparable completely
- [00:19:30.720]in this short term application and what we found
- [00:19:33.133]in the long term application as you could see
- [00:19:35.387]that the positive latent energy plugs
- [00:19:38.732]under irrigated conditions.
- [00:19:44.381]This is some work we are currently doing
- [00:19:47.123]at the very early stage,
- [00:19:48.993]but what I can say this is from application of
- [00:19:52.589]wharf model for 2013 and this is for one event.
- [00:19:59.088]Late May and as you can see this is,
- [00:20:01.550]if you look at this figure, while we are looking at
- [00:20:03.923]accumulated precipitation under controlled environment,
- [00:20:07.247]but when we apply irrigation we can see
- [00:20:10.262]these changes in precipitation.
- [00:20:12.069]In other words, and also the enhancement in certain areas
- [00:20:15.933]and we see the down wind effect of irrigation
- [00:20:21.758]which is also been discussed by other authors.
- [00:20:26.947]We plan to do further extensive work on this,
- [00:20:29.464]but that is right now this is where we are.
- [00:20:34.341]Then another really interesting area
- [00:20:36.852]is northwest India where there's lots
- [00:20:38.930]of irrigation during dry season.
- [00:20:41.845]Their dry season is more of a starting late November
- [00:20:45.535]and November through after the retreat of the monsoon
- [00:20:49.279]basically November through June
- [00:20:52.436]the arrival of monsoon in northwestern India
- [00:20:55.519]is mid June or around that time period.
- [00:21:00.212]So what we did, we looked into,
- [00:21:03.306]in the '50s, early '50s
- [00:21:08.165]the famous Green Revolution began in India
- [00:21:11.967]which is growing crops,
- [00:21:14.526]high yielding varieties with lots of
- [00:21:18.795]pesticides and fertilizer and this is a dry areas.
- [00:21:22.302]There's lots of irrigation,
- [00:21:24.255]so we looked into pre Green Revolution
- [00:21:27.593]and then post Green Revolution data.
- [00:21:29.467]We divide the times within a manner
- [00:21:31.366]that captured those two periods.
- [00:21:33.537]I can speak more detail afterwards,
- [00:21:36.209]but if we look at the data that is
- [00:21:39.289]green bars are representing post Green Revolution
- [00:21:45.715]temperatures and you can see that December,
- [00:21:50.660]one is January to December and particularly
- [00:21:55.213]from January through June we can see that clearly
- [00:21:59.462]there is a decline in long term temperatures.
- [00:22:05.022]So in other words, during post Green Revolution
- [00:22:08.064]we have experienced similar kind of things
- [00:22:10.428]that we experienced in Ocallala Aquila Region
- [00:22:13.768]where we do irrigation including Nebraska.
- [00:22:18.906]To further understand the results we used,
- [00:22:21.741]we applied both point scale hydrologic model
- [00:22:25.448]and also RAMS, Regional Atmospheric Modeling Systems
- [00:22:29.379]from Colorado State University.
- [00:22:32.196]So what we found here is showing latent energy plugs
- [00:22:35.627]and you see that there's a significant almost
- [00:22:38.172]twice as much latent energy plugs
- [00:22:41.778]from irrigated land cover
- [00:22:46.124]during peak period of a given day.
- [00:22:49.921]Okay, broken line is as if it was under natural foundation.
- [00:22:54.170]We looked into specific humidity.
- [00:22:56.121]We see the same under irrigated conditions,
- [00:22:58.713]significant increase in specific humidity,
- [00:23:01.683]atmospheric moisture.
- [00:23:07.019]And when you look at...
- [00:23:13.811]Checking time, sorry about that.
- [00:23:18.429]When you look at
- [00:23:21.810]the air temperature over
- [00:23:25.710]local time, the previous one and let me,
- [00:23:28.444]over several days I believe.
- [00:23:31.764]No, this is also local time.
- [00:23:33.066]That was latent energy plugs.
- [00:23:34.372]Now, look at temperature, I'm sorry.
- [00:23:36.624]Different variables, so when we look at the temperature
- [00:23:38.899]we also see that under irrigated condition
- [00:23:43.180]temperature is almost five degrees,
- [00:23:46.916]close to four to five degrees cooler
- [00:23:50.157]under irrigated condition,
- [00:23:51.631]which you expect and it was our
- [00:23:53.861]conceptual understanding holds.
- [00:23:58.092]Then for the six days of applications
- [00:24:01.494]we clearly see the temperature as you could see
- [00:24:05.272]that this line circle showing temperature from
- [00:24:09.582]irrigated land cover which is significantly cooler than
- [00:24:14.518]natural land cover in every single day.
- [00:24:22.388]If we look at data, earlier especially,
- [00:24:26.147]we can see that cooler temperature in the entire
- [00:24:29.786]experimental domain, this is we are talking about
- [00:24:33.046]application of RAMS and you can see up to six degree
- [00:24:36.280]cooler temperature under irrigated areas.
- [00:24:42.418]So subsequently this is still,
- [00:24:45.116]we decided while still it's quite interesting
- [00:24:48.002]and I wish I could do follow up work to
- [00:24:52.640]understand the results better,
- [00:24:54.578]look into precipitation data that okay,
- [00:24:56.594]if there's an increase in precipitation
- [00:24:58.347]due to increase in irrigation.
- [00:25:01.226]So this is the area, northwestern India again,
- [00:25:05.229]looking to precipitation data.
- [00:25:10.531]So these red bars are showing a post Green revolution
- [00:25:15.102]precipitation from different areas
- [00:25:19.466]of northwestern India.
- [00:25:21.699]So you can see in most cases we see this,
- [00:25:24.857]this is a dry season, so precipitation is fairly small
- [00:25:28.476]so we see, nonetheless, increase in precipitation
- [00:25:32.180]and asterisks are showing when results
- [00:25:34.817]are statistically significant, okay?
- [00:25:38.184]So we have found where results are statistically
- [00:25:41.034]significant and there are other areas
- [00:25:44.024]where our results are not statistically significant,
- [00:25:46.866]but still it shows increase in precipitation.
- [00:25:54.140]Let's go back there and I would like to also state
- [00:25:56.910]that there's an ongoing work
- [00:26:00.567]in the University of Colorado.
- [00:26:03.638]They're looking into these NDVIs during
- [00:26:09.631]for the dry season or irrigated period
- [00:26:13.457]and they're finding the initial results,
- [00:26:15.211]the time series is not that long,
- [00:26:17.130]so it's almost 20 years of time series,
- [00:26:19.018]but the argument is that if irrigation makes
- [00:26:23.376]northwestern India cooler than
- [00:26:27.157]monsoon may not evolve
- [00:26:28.975]as much as normally to evolve so how it is
- [00:26:32.367]effecting the monsoon trends and the early results
- [00:26:35.597]indicates that it impacts monsoon.
- [00:26:39.397]In other words, monsoon is a little bit weaker.
- [00:26:43.219]I'm sure a lot more work needs to be done,
- [00:26:45.747]but I think that's the early results indication.
- [00:26:54.805]So there is a downstream effect time wise.
- [00:26:57.482]Subsequently, we did again the model applications
- [00:26:59.974]and what we have found,
- [00:27:01.936]I think to understand the results better
- [00:27:03.842]that let's focus on the bottom two panel.
- [00:27:07.660]These results, this is showing precipitation
- [00:27:12.095]for somehow we managed to do it from (mumbles) experiment.
- [00:27:16.992]So this blue colors indicating where after
- [00:27:21.687]adding irrigation this is control or dry conditions
- [00:27:25.357]and this is after wetting the soil
- [00:27:28.112]represent irrigation so we see significant increase
- [00:27:31.658]in precipitation in these areas.
- [00:27:34.274]Okay, so again, this is the kind of thing
- [00:27:37.376]we would expect that if we add additional water
- [00:27:40.740]and if all the other ingredients for convection,
- [00:27:44.044]cloud development is there and precipitation
- [00:27:46.806]may develop in the whole.
- [00:27:53.617]This is showing higher yield equivalent potential
- [00:27:57.450]temperatures under dry condition, left two panels
- [00:28:02.215]and under wet conditions
- [00:28:04.932]and you can see in both cases under wet condition
- [00:28:07.987]higher yield equivalent potential temperature
- [00:28:11.909]also known as more static energy,
- [00:28:15.062]it increases significantly which explains
- [00:28:17.931]part of the changes in precipitation and so forth.
- [00:28:20.945]We also see the changes in the wind field.
- [00:28:27.018]This is where we are looking into soil latent
- [00:28:31.516]energy flux when we have increase soil moisture.
- [00:28:35.441]Five, 10, I'm sorry, we increase soil moisture
- [00:28:38.570]five, 10 and 15%.
- [00:28:41.380]These bright colors means
- [00:28:44.857]latent energy flux was lower
- [00:28:48.932]where it is under very wet or irrigated condition,
- [00:28:51.871]the latent energy flux was much higher,
- [00:28:54.913]which you would expect.
- [00:28:58.074]Now I'm going back from irrigation just to make some point
- [00:29:00.896]that if you know, the other intense changes of what we see
- [00:29:04.170]in the urban areas, urban heated is the classic example
- [00:29:07.922]of land cover change in the syntax and this is
- [00:29:11.453]recent data from London.
- [00:29:14.433]You can see then the city center
- [00:29:16.708]temperature is about 11 degrees Celsius,
- [00:29:21.277]it's meet may, whereas it's outskirts is going
- [00:29:24.803]five degrees Celsius so there's a six degrees
- [00:29:27.044]temperature difference within maybe 20 mile distance,
- [00:29:32.562]which is significant.
- [00:29:35.804]This is examples from Atlanta area,
- [00:29:40.500]the impact of urbanization and precipitation,
- [00:29:43.926]what we have found that
- [00:29:48.162]in the east of Atlanta, concentrated it's moist,
- [00:29:52.038]tropical air mass
- [00:29:54.404]in the afternoons we see significant increase
- [00:29:57.537]in precipitation and this blue lines represents
- [00:30:01.027]that when it's more than 100,
- [00:30:07.071]equal then 4.5, or this is average precipitation, I'm sorry.
- [00:30:12.482]When precipitation was more than 100 hours
- [00:30:16.674]of time period was that much as that color represents.
- [00:30:22.767]This is about four year radar data.
- [00:30:27.781]Under this one shows that when precipitation
- [00:30:30.207]is at least 4.5 millimeters in eastern Atlanta,
- [00:30:34.762]consistently higher for at least for 100 hours.
- [00:30:39.170]This is showing
- [00:30:42.520]increase in precipitation far east of Atlanta,
- [00:30:45.432]even in the late evening hours, 9:00 to 10:00 pm.
- [00:30:50.229]The previous results are early afternoon.
- [00:30:56.311]One of our colleagues imparted a detailed study of
- [00:31:01.288]convection's development around Indianapolis
- [00:31:04.842]and this is what he found that this is
- [00:31:07.046]some examples from radar data that how
- [00:31:10.088]convection cells actually get
- [00:31:13.408]split and spread out and in some cases
- [00:31:18.152]that it comes from the north in Atlanta
- [00:31:23.782]and then the split and sometimes the march
- [00:31:27.212]in the south, southeast of Atlanta.
- [00:31:29.965]He posed a really interesting question that
- [00:31:32.765]what about when it comes to the water managers
- [00:31:35.939]that locations are getting more rain that they
- [00:31:38.954]were not ready for, what to do with that extra water?
- [00:31:42.304]Versus the areas that used to get really bit more rain,
- [00:31:45.044]but they don't get that rain because it is happening
- [00:31:47.332]somewhere else and as you could imagine that
- [00:31:50.521]large metropolitan area how much roughness we change,
- [00:31:55.367]this is major built up in the middle of the plains.
- [00:32:02.780]I would say another flat, mid-western landscape.
- [00:32:07.722]So this is basically showing or discussing the
- [00:32:10.971]complex relationship all these different variables
- [00:32:15.928]that eventually led to the impacts that we see
- [00:32:20.443]are due to land cover change.
- [00:32:24.639]In conclusion we can say that
- [00:32:27.356]impacts of meso and regional scale land cover change
- [00:32:30.731]in climate is undeniable and significant.
- [00:32:35.261]Impacts of land cover change on sub-continental scale,
- [00:32:38.973]climate is also quite evident.
- [00:32:41.646]Right here in the US or India or China,
- [00:32:45.597]land cover change and climate collections remained
- [00:32:49.016]and as an unresolved issue,
- [00:32:51.617]but we have found early indication that shows
- [00:32:55.054]that there can be distant impact from
- [00:32:58.968]land cover change from climate.
- [00:33:04.988]Recent studies clearly shows that land cover change
- [00:33:08.167]impacts could be equal or greater than impacts
- [00:33:11.170]of carbon dioxide in some parts of the world
- [00:33:16.407]and due to this volumes of significant water
- [00:33:20.419]by our colleagues now NCA, or National Climate Assessment,
- [00:33:25.828]included land cover change in their report.
- [00:33:31.318]The CMiP5, it means for the first time in the last
- [00:33:34.558]IPCC report has an entire chapter devoted
- [00:33:38.631]to land cover change and it's impacts on climate
- [00:33:42.207]and the CM6 experiment for the next
- [00:33:47.285]IPCC report is also designing a detailed study
- [00:33:52.117]on land cover change impacts on climate.
- [00:33:57.721]I think I'm going to stop here.
- [00:33:59.799]Thank you so very much
- [00:34:02.383]for being here and I'm happy to respond
- [00:34:07.523]to some questions.
- [00:34:09.304](applause)
- [00:34:15.054]Thank you very much.
- [00:34:16.404]So this is being recorded so I'll just pass the mic around
- [00:34:19.598]so that your questions are recorded.
- [00:34:21.271]So any questions raise your hand.
- [00:34:26.130]So as I understand things right,
- [00:34:29.586]with the irrigation, afternoon temperatures are reduced,
- [00:34:33.349]but how about nighttime temperatures?
- [00:34:35.255]It seems like they would be potentially increased
- [00:34:38.918]in an irrigated area.
- [00:34:40.960]So I'm wondering if the magnitude of change
- [00:34:44.477]could be actually better detected by
- [00:34:47.078]the daily temperature range variable
- [00:34:51.461]and have you looked at that any?
- [00:34:53.672]Yes, I have a slide.
- [00:35:14.667]Now, what we have found here in other places.
- [00:35:17.623]This is showing daily temperature range over a period.
- [00:35:21.431]Okay, so in other words we saw that some sites,
- [00:35:24.683]we found basically the result we found was mixed.
- [00:35:28.370]Almost like half and half of the station shows
- [00:35:31.097]increase in T-mean, nighttime, the minimum temperatures
- [00:35:37.023]and other half almost doesn't show anything.
- [00:35:39.752]In other places found the same kind of results.
- [00:35:44.746]One part is that it's more complex than we understand.
- [00:35:49.424]The other part is about the question of
- [00:35:53.047]how we, in some cases data was an issue,
- [00:35:56.838]but mostly is that we do not understand
- [00:35:59.830]all the details of the mechanism, how it effects.
- [00:36:02.445]But the result is mixed that we found.
- [00:36:06.049]So the best signal we found is from T-max.
- [00:36:09.357]That's what we found,
- [00:36:10.671]but here it's like try to find good signals like York,
- [00:36:14.144]you can see that daily temperature range
- [00:36:17.338]declined over a period of time.
- [00:36:19.770]It's a combination of both.
- [00:36:21.215]It's the T-max sub rest, T-mean was increased, okay?
- [00:36:36.999]So I'm curious as to whether or not
- [00:36:38.741]you looked at how different species of irrigated crops
- [00:36:41.501]might have affected your results
- [00:36:43.445]or are there differences in the response
- [00:36:46.380]due to the species of crop that's being irrigated?
- [00:36:50.725]In terms of impacts on temperature,
- [00:36:53.474]I did not, but when we did the model simulations
- [00:36:57.658]I have applied the model for irrigated corn,
- [00:37:02.394]rain fed corn and grass.
- [00:37:05.260]So what we found under irrigated corn, yes ET increases,
- [00:37:08.693]but the difference is
- [00:37:11.345]much smaller, it's almost only 2% increase
- [00:37:14.979]compared to grass, but irrigated corn gives us
- [00:37:20.237]huge differences in ET.
- [00:37:23.374]So that's all it, now how temperature is going to
- [00:37:28.216]vary from irrigated corn versus rain fed corn,
- [00:37:32.707]no I did not look at the different types of vegetation.
- [00:37:42.749]Is there a reason
- [00:37:43.701]that you have such old temperature data
- [00:37:46.359]and that you didn't include
- [00:37:47.759]more current temperature readings?
- [00:37:50.388]So that I can see the reason that I looked at,
- [00:37:53.168]I looked at basically taking the longest time set
- [00:37:56.227]as possible, so most of the irrigation,
- [00:37:59.565]particularly in the US or anywhere that happens post '50s,
- [00:38:03.777]like in India post '50, we are looking at
- [00:38:06.546]post 1950, mid '40s, 1945 that it really picked up.
- [00:38:10.764]We actually did a sensitivity study of
- [00:38:13.352]when we get the best signals,
- [00:38:14.840]so we divided the data in 1945 pre and post,
- [00:38:17.997]1950 pre and post, 1955 pre and post.
- [00:38:22.528]Our working hypothesis is we should get best signal
- [00:38:25.665]from pre and post 1945 if we divide the data in '45.
- [00:38:31.029]That's what we have found.
- [00:38:34.086]So we have looked at the old data when land fields
- [00:38:37.385]was not changed and then we looked at the data
- [00:38:41.591]after the land deals has changed
- [00:38:43.342]so then we can compare and see and say,
- [00:38:45.532]"Okay, this is the impact of land that is changed
- [00:38:48.933]on temperature or whatever variable."
- [00:38:51.681]That's why we looked at all that.
- [00:38:59.922]Any other questions?
- [00:39:07.839]All right, well thank you again Dr. Mahmood for coming.
- [00:39:10.914]He'll be here for a little while longer
- [00:39:12.789]if you want to come up and talk with him.
- [00:39:15.180]So thanks for coming.
- [00:39:17.115]Thank you, thank you. (applause)
The screen size you are trying to search captions on is too small!
You can always jump over to MediaHub and check it out there.
Log in to post comments
Embed
Copy the following code into your page
HTML
<div style="padding-top: 56.25%; overflow: hidden; position:relative; -webkit-box-flex: 1; flex-grow: 1;"> <iframe style="bottom: 0; left: 0; position: absolute; right: 0; top: 0; border: 0; height: 100%; width: 100%;" src="https://mediahub.unl.edu/media/6280?format=iframe&autoplay=0" title="Video Player: Land Cover Change, Irrigation, and their Impacts on Climate" allowfullscreen ></iframe> </div>
Comments
0 Comments