Climate Change = Erratic Rainfall= 'big dam dilemma'

Hmm. Since all weather and hence climate on a longer scale is essentially chaotic, isn’t rainfall generally erratic as a consequence of that chaos?. Isn’t that why we have some areas that get droughts in one season and floods the next? Of course there are overriding patterns like El Niño, but it seems to me that this story is simply hyping the obvious known for years: better water storage helps in dry years.

Image above from NASA Earth Observeratory: Global Rainfall Patterns

From a press release, one more thing to worry about. The “big dam dilemma” is actually in the press release, I kid you not, see it unedited below. – Anthony


In a changing climate, erratic rainfall poses growing threat to rural poor, new report says.

Addressing big dam dilemma, experts call for diverse water storage options to reduce uncertainty and improve production of rainfed farming

STOCKHOLM (6 September 2010)—Against a backdrop of extreme weather wreaking havoc around the world, a new report warns that increasingly erratic rainfall related to climate change will pose a major threat to food security and economic growth, especially in Africa and Asia, requiring increased investment in diverse forms of water storage as an effective remedy.

“Millions of farmers in communities dependent on rainfed agriculture are at risk from decreasing and erratic availability of water,” said Colin Chartres, director general of the Sri Lanka-based International Water Management Institute (IWMI), which released the report to coincide with World Water Week in Stockholm. “Climate change will hit these people hard, so we have to invest heavily and quickly in adaptation.”

The report argues against over-reliance on single solutions like big dams, proposing instead an integrated approach that combines large- and small-scale storage options, including the use of water from natural wetlands, water stored in the soil, groundwater beneath the earth’s surface, and water collected in ponds, tanks and reservoirs.

“Just as modern consumers diversify their financial holdings to reduce risk, smallholder farmers need a wide array of ‘water accounts’ to provide a buffer against climate change impacts,” said Matthew McCartney, the report’s lead author and a hydrologist at IWMI, which is supported by the Consultative Group on International Agricultural Research (CGIAR). “That way, if one water source goes dry, they’ll have others to fall back on.”

“For millions of people dependent on rainfed agriculture, reliable access to water can make all the difference between chronic hunger and steady progress toward food security,” McCartney added. “Even small amounts of stored water, by enabling crops and livestock to survive dry periods, can produce large gains in agricultural productivity and in the well-being of rural people.”

IWMI and its research partners estimate that up to 499 million people in Africa and India can benefit from improved agricultural water management.

In Asia, where irrigation was greatly expanded in recent decades, rainfed agriculture is still extensive, accounting for 66 percent of the total cropped area, the IWMI study notes. In sub-Saharan Africa, the proportion is far greater at 94 percent. Yet, these are precisely the regions where water storage infrastructure is least developed.

“Unless we can reduce crippling uncertainty in rainfed agriculture through better water storage, many farmers in developing countries will face a losing battle with a more hostile and unpredictable climate.”

In response to increased demand for food and power supplies, the governments of developing countries with fast-growing economies have invested heavily in large dams during the current decade, ending a 10-year lull in their construction. Many of the 50,000 large dams built worldwide since the 1950s are intended to store water for irrigation.

The positive effects of such infrastructure development, in terms of flood control and improved agricultural productivity are well documented, the IWMI report explains. But so are the adverse social and environmental impacts, including displacement of up to 80 million people from their homes and disruption of the livelihoods of some 470 million people living downstream from dams as a result of altered river flows. As acrimonious debate about large dams continues, IWMI’s advice for governments is to do a better job of analyzing the potential benefits for economic development and poverty reduction and to pay more serious attention to the social and environmental consequences.

But the IWMI study also advocates giving more weight to a continuum of small-scale storage options, citing strong evidence that when such measures are well planned, they can contribute importantly to local food security and economic growth.

Field studies in various semi-arid environments, for example, have proven the effectiveness of using small planting basins to “harvest” water, together with targeted application of organic or inorganic fertilizer. In Zimbabwe, such basins have been shown to boost maize yields, whether rainfall is abundant or scarce, while in Niger, they have permitted three- or four-fold increases in millet yields.

In the northeast of India’s Rajasthan State, the construction of about 10,000 water harvesting structures—intended mainly to recharge groundwater—has made it possible to irrigate about 14,000 hectares, benefiting some 70,000 people. Whereas previously, farmers barely had enough water to produce grains, now they can also grow vegetables and other cash crops. Similarly, the construction of more than 90,000 underground water storage tanks in China is benefiting a million farmers.

Case studies suggest that combinations of different storage options can be particularly effective. In southern Sri Lanka, for example, the construction of a large water storage reservoir, which was then linked to five previously created small reservoirs brought about a 400 percent increase in crop production.

But in some places, the results of major water storage initiatives have been uneven. In Ethiopia, for example, one study showed that groundwater wells and small dams reduced poverty by 25 to 50 percent. But another analysis in the country’s Amhara region found that most of the approximately 4,000 water harvesting ponds constructed from 2003 to 2008 were no longer functioning, mainly because of poor site selection, technical failures and weak community involvement in maintenance.

“None of these options is a panacea,” said McCartney. “They all have pros and cons, which depend on their inherent characteristics, on the way they are planned and managed, and on the conditions at specific sites.”

A further hazard with any water storage option, the IWMI report notes, is that the practice itself will be subject to climate change impacts. In arid regions, for example, soil moisture may decline so rapidly as to reduce the effectiveness of practices like planting basins. Likewise, decreased rainfall could limit groundwater recharge, while rising sea levels will increase the risk of salt water intruding on coastal aquifers.

Another danger is that badly planned storage will not only waste money but actually worsen the negative affects of climate change, for example, by providing extra breeding habitats for malaria-infected mosquitoes.

To guard against such hazards, the report argues, governments need to assume greater responsibility for more integrated planning of water storage systems. In the past, storage schemes were often conceived in a piecemeal fashion at the local level, based more on political expediency than on evidence. An integrated approach would take into account the wide range of hydrological, economic, social and environmental factors that determine costs and benefits and would consider various storage options in combination. Well-planned water storage can help lift people out of poverty and provide them with an effective way to cope with climate change.

“The more we study climate change, the more we realize that water is the principal medium by which its impacts will be manifested in agriculture,” said Chartres. “We may not know exactly what those impacts will be, but we can be sure they will include greater rainfall variability. Water storage in all its forms offers a better way to manage risks during these times of increasingly uncertain weather.

###

The International Water Management Institute (IWMI) is a nonprofit, scientific research organization focusing on the sustainable use of water and land resources in agriculture, to benefit poor people in developing countries. IWMI’s mission is “Improving the management of water and land resources for food, livelihoods and the environment.” IWMI has its headquarters in Sri Lanka and regional offices in Africa and Asia. The Institute works in partnership with developing countries, international and national research institutes, universities and other organizations to develop tools and technologies that contribute to poverty reduction as well as food and livelihood security. www.iwmi.org

The Consultative Group on International Agricultural Research (CGIAR), established in 1971, is a strategic partnership of countries, international and regional organizations and private foundations supporting the work of a consortium of 15 international Centers. In collaboration with national agricultural research systems, civil society and the private sector, the CGIAR fosters sustainable agricultural growth through high-quality science aimed at benefiting the poor through stronger food security, better human nutrition and health, higher incomes and improved management of natural resources. www.cgiar.org

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Pamela Gray
September 7, 2010 6:40 pm

Good Lord All Mighty! Was this not covered in the Old Testament? Are these new age, post normal scientists not capable of reading texts about common sense (Genesis 42) regarding years of plenty and years of drought? Must they require money to research common sense? You don’t suppose these idiots would “discover” that dams are a good measure against years of drought if we threw a ton of cash at them. Maybe if we started a new journal dedicated to common sense research these young whipper snappers might get enlightened. Not.
And I’m not even a religious person, but I have at least studied the Bible for Gawdsake!

observa
September 7, 2010 6:40 pm

With the ending of long General Drought over much of Australia and now vast flooding across much of the same including filling to overflowing the Murray Darling River Basin that had degenerated into a series of stagnant muddy pools recently, here is the poem by Dorothea Mackellar written in 1904 that generations of schoolchildren knew off by heart until the latestgeneration were inculcated by the new Malthusians and doomsdayers. Given Australia’s British background and due deference to the Mother Country at the time with its daffodil and green fields of home poets and longing, Dorothea makes the break we European migrants all came to love and understand-
The love of field and coppice, of green and shaded lanes,
Of ordered woods and gardens is running in your veins.
Strong love of grey-blue distance, brown streams and soft, dim skies-
I know but cannot share it, my love is otherwise.
I love a sunburnt country, a land of sweeping plains,
Of ragged mountain ranges, of droughts and flooding rains.
I love her far horizons, I love her jewel-sea,
Her beauty and her terror- the wide brown land for me!
The stark white ring-barked forests, all tragic to the moon,
The sapphire-misted mountains, the hot gold hush of noon,
Green tangle of the brushes where lithe lianas coil,
And orchids deck the tree-tops, and ferns the warm dark soil.
Core of my heart, my country! Her pitiless blue sky,
When, sick at heart, around us we see the cattle die –
But then the grey clouds gather, and we can bless again
The drumming of an army, the steady soaking rain.
Core of my heart, my country! Land of the rainbow gold,
For flood and fire and famine she pays us back threefold.
Over the thirsty paddocks, watch, after many days,
The filmy veil of greenness that thickens as we gaze.
An opal-hearted country, a wilful, lavish land –
All you who have not loved her, you will not understand –
Though earth holds many splendours, wherever I may die,
I know to what brown country my homing thoughts will fly.

PaddikJ
September 7, 2010 10:36 pm

wsbriggs says:
September 7, 2010 at 10:22 am
The long tailed distributions of water flow in the Nile river system have been well studied and Mandelbrot has written about them.
Why any of the Warmists would ignore centuries of data is still a mystery to me, but it continues. Willful ignorance is worse than outright stupidity.
Mandelbrot has also written about Harold Edwin Hurst; he named the “Hurst Coefficient” in his honor. Hurst developed his eponymous coefficient in the 1950s to estimate ideal storage capacities for resevoirs. Since annual stream flow variations (such as the Nile’s, in Hurst’s case) are now known to be chaotic, Hurst’s work anticapted this field, and fractal geometry, by a good 10 years.
Hydrologists routinely use Hurst’s methods, and are perplexed that climatologists get so huffy when hydrologists suggest they try it. Hydrologists are pragmatic engineers and don’t seem to understand that inerrency is the sine qua non of climatology.
There are a couple of good threads at Climate Audit about hydrologist Demetris Koutsoyiannis’ (futile, so far as I know) attempts to get climatologists to try the stochastic approach.

TomVonk
September 8, 2010 2:20 am

RW
While weather and climate are closely related, there are important differences … The chaotic nature of weather makes it unpredictable beyond a few days. Projecting changes in climate (i.e., long-term average weather) due to changes in atmospheric composition or other factors is a very different and much more manageable issue. As an analogy, while it is impossible to predict the age at which any particular man will die, we can say with high confidence that the average age of death for men in industrialised countries is about 75
Ignorance without bounds . The analogy is not even wrong .
The example postulates implicitly that there exists an invariant probability distribution of the life expectancy .
This is trivially falsified by the observation that the average life expectancy varies with time .
And as the dynamics of the major parameters influencing the life expectancy are unknown (f.ex when will be discovered the cure for AIDS ? What will be the financing of medical care ? etc) , there is no way to predict with high confidence anything concerning the probability of death unless one assumes that nothing ever changes .
For weather and climate it is even worse .
It is not known that such an invariant probability distribution may exist even in principle .
Experimental evidence in spatio-temporal chaos in geophysics is rather showing that such an invariant probability distribution generally does NOT exist .
It is not astounding that not only you but the IPCC studiously avoid the discussion of ergodicity , e.g the proof that weather and climate can admit a stochastical stationary interpretation .
It has been known and proven for a long time that time averages of a temporal chaotic system are as chaotic as the original system .
If I show you a plot of a Lorenzian chaotic system , you will not be able to make a difference between a plot showing the chaotic solutions and a plot showing time averages of the solutions .
Chaotic systems stay chaotic at all time scales and that’s why if the weather parameters have no invariant probability distribution , their time averages (so called climate) will not have an invariant probability distribution either .
The analogy you proposed is just silly .

RW
September 8, 2010 3:11 am

Tom Vonk – you missed the point completely. I did not propose the analogy. I merely posted an extract from the most recent IPCC report, in response to someone who seemed to think that it said something quite different.
As I’m sure you must know, chaotic systems are not immune to analysis. For example, the orbital parameters of solar system bodies vary in a chaotic manner. However, we can find out with some certainty the bounds within which they vary. These bounds (orbital climate, if you like) do not vary chaotically, and their response to an external perturbation is not a difficult problem to solve. The statement “Since all weather and hence climate on a longer scale is essentially chaotic” remains a non sequitur and a very basic error for someone supposedly interested in weather and climate to make.

TomVonk
September 8, 2010 4:32 am

RW
No I didn’t miss the point , I merely explained why the point and the analogy were silly .
The example of gravitationaly bound systems is even sillier .
It is not a scoop that we know since more than a century that the N body system orbits are chaotic .
But we also know that this particular kind of chaos concerning Hamiltonian conservative systems is NOT ergodic ! That means that there can be no stochastical description of planetary orbits . There is no invariant “probability” that the orbit will be this or that .
Precisely because the system is conservative , there are results concerning the stability of some orbits (look up the KAM theorem) but you must not confuse these stability results with predictability .
You are very mistaken if you think that the N body system is “not a difficult problem to solve” .
Poincare took a really long time and even he did a mistake .
On the contrary it is extremely difficult to solve and the results exist only for very simplified particular cases .
The difficult , strongly coupled cases show such an extreme sensibility on initial conditions that they can’t be “solved” for any practical purposes .
Of course all this has nothing at all to do with the spatio temporal chaos in non conservative systems like weather and climate .
The statement that a chaotic system is chaotic at all time scales is trivial to prove as I have already shown with the Lorenzian example that you apparently either didn’t read or didn’t understand .
If you still believe the contrary , would you care to sketch a proof how chaotic solutions to a system of ODE or PDE become non chaotic by time averaging ?
It certainly should not be difficult if you consider that hamiltonian chaos is something very easy .
Something is telling me that you will not want to go beyond hand waving 🙂

Pascvaks
September 8, 2010 5:02 am

Ref – observa says:
September 7, 2010 at 6:40 pm
Beautiful! Shame kids don’t memorize things anymore, especially poetry.

Frank K.
September 8, 2010 5:33 am

“If you still believe the contrary , would you care to sketch a proof how chaotic solutions to a system of ODE or PDE become non chaotic by time averaging ?
It certainly should not be difficult if you consider that hamiltonian chaos is something very easy .
Something is telling me that you will not want to go beyond hand waving :)”
Yes – I have noticed this pattern of posters like RW making sweeping statements and claiming “basic errors” on our part, but then running for the tall grass when we start to discuss the behavior of partial differential equations, boundary conditions, and (my specialty) the numerical solution of these PDEs. It always disappoints me as they never want to get into the interesting details. But then they point to the models as evidence of impending climate disaster in 50-100 years
Anyways, I too look forward to the rigorous proof that climate is not chaotic and that its mathematical description is in fact a “boundary value problem” [cue crickets chirping…]

TomVonk
September 8, 2010 5:39 am

Btw as this is a science blog , it is time to put this question of chaos at all scales to rest once and for all .
Only undergrad maths are necessary to stop the misinterpretations .
Let’s take f(t,X0) the solution describing the system with X0 being the initial condition : f is weather .
Let’s define g(t,X0) = 1/T Int from t-T to t [f(y,X0)dy] the time average of f over some arbitrary averaging period T (f.ex 30 years) : g is climate .
We will prove that if f is chaotic , g is chaotic too .
Since f is chaotic there exists an L >0 such as :
f(t,X0+dX0) – f(t,X0) = h(dX0).Exp(L.t) for dX0 being a small difference in initial conditions and h some function of dX0 .
This translates the exponential divergence of orbits what is the definition of chaos .
Let’s compute now the difference of the orbits of g for a small difference in the initial conditions dX0 .
g(t,X0+dX0) – g(t,X0) = (by definition of g)
1/T Int from t-T to t [f(y,X0+dX0)dy] – 1/T Int from t-T to t [f(y,X0)dy] =
1/T Int from t-T to t [f(y,X0+dX0) -f(y,X0)]dy =
1/T Int from t-T to t [h(dX0).Exp(L.y)]dy (because f is chaotic) =
h(dX0) . (1/L.T) . {Exp[L.(t-T)] – Exp[ L.t]} =
h(dX0) . (1/L.T) . {Exp[- L.T)] – 1} . Exp (L.t)
QED
There is an exponential divergence of trajectories of g which has been defined as the time average of f .
It is therefore proven that if f (weather) is chaotic then g (climate) is chaotic too and as this is true for every averaging period T , it is true at all time scales .

Frank K.
September 8, 2010 8:22 am

TomVonk says:
September 8, 2010 at 5:39 am
Thanks Tom. That’s a very profound analysis. I have a question – does this result have implications for ensemble averaging of chaotic solutions? That is, the trend these days is to run multiple weather/climate simulations and averaging them in hopes that this process will yield a unique (and hopefully more accurate) “ensemble” result. My sense it that this can be effective over short time periods, but as solutions are taken farther out in time, the accuracy diminishes rapidly. What are your thoughts on this?

Jaye Bass
September 8, 2010 9:25 am

RW…chirp, chirp, chirp…

Jaye Bass
September 8, 2010 9:28 am

You might want to say “finite difference” instead of “small difference”…since small doesn’t really make any sense in Rn or C or any other metric space for that matter.

RW
September 8, 2010 11:29 am

Tom Vonk – yes, you did miss the point. The point was that the IPCC report said what I said it did, and not what “PaulM” thought it did. Whether you like or agree with what it said is irrelevant.
Climate is not chaotic over all timescales. You can see this simply by looking at the geological temperature record. Similar input (variations of insolation at 65N) gives similar output (ice ages). Similarly, plot a Lorenz oscillator as many times as you like for the same σ, ρ and β – after a given time you’ll find that the plots all look very similar even though the initial conditions were different. The long term average does not vary chaotically.
REPLY: Well that’s progress, you started with this in a previous comment:

“This statement is illogical and wrong. Climate is not chaotic. This is really, really basic knowledge and I am astonished that you would say something like this.”

to now saying

“Climate is not chaotic over all timescales. ”

Yep, definitely progress. 😉
– Anthony

Enneagram
September 8, 2010 11:58 am

Pamela Gray says:
September 7, 2010 at 6:40 pm
And I’m not even a religious person…
Don’t feel ashamed of it, as it transpires from what you wrote. Be ashamed, instead, of not questioning enough yourself about the validity or truthfulness of everything you received from our “liberal” and post French revolution culture.

Enneagram
September 8, 2010 12:02 pm

TomVonk says:
September 8, 2010 at 5:39 am
LOL!, that’s chaotic Calculus instead. Do you know anything real, apart from Ghosts of course, which can tend to the infinite or to zero?. Anything that is real is a measurable quantity. Period.Check this:
http://www.milesmathis.com/calcsimp.html

RW
September 8, 2010 2:26 pm

Anthony – your statement remains illogical and wrong, and you compounded it by implying that you think ice ages are somehow indicative of chaotic variation, when in fact they show the opposite. My statements do not contradict each other. One is merely a specific restatement of the other.
REPLY: Yeah sure whatever, anything to avoid having to admit you made conflicting statements. -A

Frank K.
September 8, 2010 6:15 pm

RW says:
September 8, 2010 at 11:29 am
No differential equations? No proofs? Come now – let’s see ’em…[chirp, chirp, chirp]

Pamela Gray
September 8, 2010 6:51 pm

Now this makes good sense. Our public climate zones in Central and NE Oregon were pretty grossly defined, which meant that forecasts were pretty useless at the zonal boundaries. Weather variability was and is a very big deal when your forecast says all is well and then you get hit with rain right before you start harvesting wheat.
Well, based on climatological re-evaluation, our zones are being redrawn. And from the look of it, all for the better.
http://www.wrh.noaa.gov/pdt/reference/Zone_Alterations_PDT_2010.pdf
Maybe someone at the NWS has decided that weather, interacting with topography, IS the most important thing to consider and they had better do a better job of feeding the populace more accurate information.

TomVonk
September 9, 2010 2:34 am

Frank K
Yes there is a relation with “ensemble averaging” .
The proof that a chaotic system is chaotic at all time scales that I sketched in the post above is a proof about exponential divergence of orbits .
This proof doesn’t tell and can’t tell much in this form about what happens for very long times when t goes against infinity .
Indeed the exponential divergence can’t go forever because the chaotic orbits are bounded .
So when the system has described many times an orbit (each being different) , the orbits may cluster and often do cluster in privileged regions of the phase space .
These regions are called chaotic attractors .
Then these regions (e.g physical sates) are more probable to be hit by a chaotic orbit than other regions .
It is the ergodic theory which studies the questions when t goes against infinity .
If there exists an invariant probability density function (e.g independent of initial conditions) then the system is ergodic and it is possible to predict probabilities that the system will take this or that state even if it is still impossible to predict individual trajectories .
For example the whole body of statistical thermodynamics uses the fact that a large number of colliding material particles is chaotic and ergodic .
“Ensemble averaging” is a tool related to the ergodic theory and aims to provide a stochastical interpretation of a chaotic system .
Of course if no invariant PDF exists and the system is not ergodic (like f.ex the hamiltonian dynamics governing the N body gravitational problem) then no stochastical interpretation is possible .
RW
Similarly, plot a Lorenz oscillator as many times as you like for the same σ, ρ and β – after a given time you’ll find that the plots all look very similar even though the initial conditions were different. The long term average does not vary chaotically.
There is some progress but it’s about time you stopped insisting on silly statements .
The short of it is that Anthony is right and you are wrong , admit it and live with it .
The average of a Lorenz solution is EXACTLY as chaotic as the solution itself . You substitute in the proof I sketched the Lorenzian variables for f and it will stay perfectly valid . The average of a chaotic Lorenzian variable defined as g in the proof is simply chaotic too .
The variability will obviously decrease with increasing averaging period (T in the proof) but that is due to the 1/T factor that eliminates all variations in the infinite limit – the “smoothing effect” . It is sufficient to rescale the time axis (e.g to zoom) to see the chaotic behaviour of averages just by eye balling .
Of course the Lorenz system admits a chaotic attractor which doesn’t depend on initial conditions but that has nothing to do with chaoticity (or exponential divergence) of averages .
You seem to be very confused about what a chaotic system really is and especially about the proven fact that once a system admits chaotic solutions , then the averages of these solutions stay chaotic at all time scales too .

TomVonk
September 9, 2010 3:25 am

Enneagram
Why would you put this link in a discussion on a science blog ?
This totally unknown guy tells us :
No one understands or ever understood calculus, not Einstein, not Cauchy, not Cantor, not Russell, not Bohr, not Feynman, no one. Not even Leibniz or Newton understood it. That is a big statement, I know, but I have already proved it and I will prove it again below.
Already with this he has exploded the crackpot index (http://math.ucr.edu/home/baez/crackpot.html) .
He scores almost on every point . Congratulations !
Then he delivers us following pearls of wisdom :
To measure a length you don’t need a watch. To measure velocity, you do. Velocity has a “t” in the denominator, which makes it a rate of change. A rate is just a ratio, and a ratio is just one number over another number, with a slash in between .
There is more of the same . Of course as could be predicted from his truly impressive crackpot rating , he has also demonstrated that the relativity is wrong . Yes , quantum mechanics too .
Please try to avoid making people waste their time with things like : http://www.milesmathis.com/calcsimp.html
Thanks in advance .

Frank K.
September 9, 2010 6:20 am

Thanks Tom. There’s a new post up describing a NAS report on the predictability of weather and climate:
http://wattsupwiththat.com/2010/09/09/nas-report-plateau-in-our-ability-to-make-accurate-seasonal-forecasts/#more-24594
I’d be interested in your opinions of the report (which appears to be a useful summary of the ability of modern forecast systems to predict climate on seasonal time scales), as it ties into what we’ve been discussing here.

RW
September 12, 2010 7:04 am

Oh dear, Tom Vonk. You contradict yourself so much, it’s clear you don’t know what you are talking about. This is the key bit that you say yourself but then seek to deny:
“Indeed the exponential divergence can’t go forever because the chaotic orbits are bounded.”

September 13, 2010 4:11 am

Poor people will always suffer because of the unequal distribution of resources, particularly food and water.
The ideas for water storage are a good idea. However, work also has to be done to improve the political and economic infrastructures of underdeveloped countries so that resources can get to those who need them.
I suppose using the term climate change just makes more people pay attention to the problem.

September 22, 2010 5:32 am

In Pakistan, there is no need of big dam like Kalabagh dam project in Punjab as three out of four Provincial Assemblies have passed about 10 resolutions against construction of Kalabagh dam in Punjab.
A pro-dam lobby belonging to Punjab are bent upon to prove people of three out of four provinces are enemies of Pakistan and also enemies of their own provinces..
The underground of water in Punjab is sweet and sufficient to meet all requirements of province but the underground water of Sindh is brackish. Punjab wants to control the water of Sindh and turn it to desert. Presently Indus delta – sixth largest in the world is already on verge of death as it was in Sindh. Had Indus delta been in Punjab, Punjab and Pakistan Govt both would have not allowed it to die. What a shame!

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