Land Cover Change, Irrigation, and their Impacts on Climate

Dr. Rezaul Mahmood Author

09/30/2016 Added

175 Plays

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Part of the SNR Fall Research Seminar Series

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[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)

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Land Cover Change, Irrigation, and their Impacts on Climate

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