Wednesday, August 4, 2021

River linking and related concerns in India

India, with its vast length and breadth, manifests multifarious diversity in terms of people, land, animals, customs, traditions, history, water. It is this diversity that made various famous people made comment on it, to name a few Pt. Nehru said India has unity in diversity and very famous "Kos kos par badle paani char kos par bani" meaning that at every kos (ca. 200 meters) taste of water changes and at every four kos language also changes.  Geographically, this cannot be more true, India is notorious for its floods and droughts at the same time. Some regions have plenty of water (ground as well as surface) e.g. W.Bengal, Haryana, Punjab, Western UP etc. while others face chronic water shortage e.g. Bundelkhand, Rayalseema regions etc. Further, monsoon in India is also spatially as well as temporally varies i.e. approx. 75% of the total precipitation occurs in four monsoon months (June - September) and the remaining 25% occurs in rest of the 8 months. Further, the country faces high spatial variability of precipitation with a distinct pattern of distribution from east to the west (as in fig. 1).  As apparent from the fig.1 eastern and north eastern India experiences high rainfall while the western India has low rainfall. The water situation due to above explained reasons, water distribution in India is highly varied. This leads to flood situations in some parts of the country while other face drought creating a situation of water-surplus river basins and water-deficient river basins.

Distribution of Rainfall in India.

Challenges to India's Development:

1. India's population is increased and slated to be world's most populous country. It is expected to be stabilized at around 1640 million. Consequently, water availability per capita is bound to decline from 1820 m2/yr in 2001 to 1140 m2/yr in 2050. Further, additional water usage such as industry, agriculture, institutions etc will also increase. To some estimates, India's water availability is to be increased by 3 times. 

2. India's energy demand has been spiraling along with the economic development. India's energy demand is slated to increase by 2.5 times which require eco-friendly energy resources such as solar, hydro-electric power, nuclear power etc.

3. Monsoon dependent India needs water storage per person to deal with non-monsoon water shortages.

4. Water is a contentious issue in India, the infamous Cauveri river water distribution tussle between Karnataka, Tamil Nadu and Kerala needs no explanation. The issue is that every region (inter-state or intra-state) wants water but reluctant to share the water resources with other states. Since, India has over 60% of population in agriculture, irrigation water will remain a potent political issue.

It is in this background Inter-linking of rivers (ILR) has been proposed and re-proposed.

History of ILR:

Proposed By

Photo

Year

Features

Present Status

Sir Arthur Cotton

 


1839

He conceived a plan to link rivers in Southern India for inland navigation. While the project was partially implemented,

·         The river linking canals could not survive in the face of rapid development

Dr. K.L. Rao

 


1972

·         Linking the northern Ganga River with the southern Couvery River

·         Required lifting of water over 450 m

·         Envisaged to supply water to drought prone areas of south UP and South Bihar

·         Discarded because of high financial cost and very large energy requirement

Captain D.J. Dashtur

 


1977

·         Proposed the Himalayan and Garland canals to be inter-connected at two points (Delhi and Patna) by five pipelines of 3.7m in diameter

·         Surplus waters in the country to be utilized to irrigate 219 Mha of agriculture land

·         Found to be technically infeasible

National Water Development Authority (NDWA)

 


1980-2000

·         Two components were identified 

1) Himalayan River component (14 river linking) and 

2) Peninsular River component (16 river linking).        

·         Work is going on Ken-Betwa project.

The table is modified from Mirza, M., Ahmed A U (2006)

Issues related to ILR:

1. Other Inter Basin Transfer projects.

It has been claimed that adverse environmental impacts of IBT projects outweigh their beneficial impacts.

2. Food and Irrigation

Despite the expansion of irrigation over the years, availability of chemical fertilizers, introduction of high yield varieties, India's crop yield per hectare remains lower than other countries in the region e.g. wheat yield in China is 40 % more than what India produces. Further, China grows one kilo rice in 300-400 lts of water whereas India uses 4000 lts of water to grow same quantity of rice. So without adding more water to the agriculture it is still possible to increase crop yield. This argument opposes the need of more water for the crops. Further, flood irrigation has been widely criticized for tropical and subtropical areas and has adverse impacts on soil as well as ground water conditions.

3. Hydrological situation:

The whole ILR project is based upon the concept of transferring water from areas of surplus to areas of deficit. However, this concept was contested by the fact that every drop of water in a river perform some function and thus there is no surplus water even in chronic flooding rivers. Flood water is seen as the source of free minerals for the land, free recharge for the groundwater resources, free medium for the growth and transportation of fish and conversation of biological diversity, free bumper harvest for humans etc. Further, at the micro level, the flood flows flush the silt from the riverbeds in the plains to the delta areas free of cost. Flushing of water to sea support the rich fisheries in the estuaries and keep away the saline incursion from the sea. Transferring water from a "surplus" area takes away the multi-ecological services, the flood water does. The idea of surplus water is considered as reductionist approach. Therefore, to keep the ecological services water should not be transferred.

4. Risk and ILR as a system:

This argument is one of the most powerful. The ILR is a system, whereby one component receives water from another component e.g. Peninsular component is planned to receive water from Himalayan component. If a link is become non-functional or not constructed; the other parts might not work as efficiently as has been thought or planned. Thus, this can risk the whole system of ILR. 

5. Flood and Drought mitigation

Ganga River has been identified as a "surplus basin". Now, lets see how Ganga varies in its flow. Ganga's peak discharge is 55,415 cubic meters per second (cumecs) at Farakka during the four monsoon months, a 100 m wide 10 m deep canal can divert at most 2,000 cumecs to provide 4 % relief. For the rest of 8 months (non-monsoon), Ganga flows at 5,280 cumecs and diversion of 2,000 cumecs will deny Bihar 38 %  of Ganga water when it is needed the most. Alternatively, using a canal only for 4 months would be economically a non-sense. Thus notion of flood mitigation seems flawed. 

6. Health Concerns

Dams and reservoirs are evidenced to cause favourable conditions for the growth of various disease causing vectors. When long canals transport water from one location to multiple locations they also carry disease causing vectors. Since the water is a medium of growth for these vectors, an likely disease outbreak, an epidemic can occur. Diseases such as malaria, guinea worm, river blindness and multiple diarrhoel diseases are common diseases related to water projects. Further, fertilizers in the canal water, water effluents, pesticides, hazardous chemicals can jeopardize environment as well as public health.

7. Social and Ecological impacts

Big water are know to displace people and wildlife attributed to submerged area behind dams. Since 1950, big dam projects have displaced 40 million people. This causes heightened social tensions in both rural and urban India. Narmada bachao is one of the most popular movement against the dams on Narmada river. More such movements might rise up if the people perceives the issue threatening their security of livelihood and shelter.

8. Economic costs

The ILR project is the largest inter-basin water transfer ever taken in the world. The estimated cost of the entire project is in the range of $ 200 billion; by any estimate the cost is huge! One argument is that if one component of the ILR fails, the probability of failure of other parts or the whole is huge, threatening the survival of whole ILR project. In such a scenario, debt of $200 billion is too huge to forget and can have serious political, social and economical consequences.

The above issue might show the ILR project in bad light but these issues must be considered and discussed at lengths to make ILR a success.  

Conclusion:

The ideas and arguments discussed above were taken from the book "Interlinking of Rivers in India: Issues and concerns". However, the issue is not a simple one and India has to grow and to achieve sustainable development goals #1 End poverty, #2 Zero hunger #6 clean water and sanitation #7 affordable and clean energy, India needs to distribute its water resources spatially and remove the temporal barrier to its access. There are a number of anti-ILR views, majority of them recommend for a more ecological friendly approach i.e. water-shed development, switching to less water intensive crops etc. The benefit of these alternative approaches is that they are well distributed, can be built using bottom-up approach. More on this will be addressed in another blog.


Bibliography:

“Interlinking of Rivers in India: Issues and Concerns.” Routledge & CRC Press, https://www.routledge.com/Interlinking-of-Rivers-in-India-Issues-and-Concerns/Mirza-Ahmed-Ahmad/p/book/9780415404693. Accessed 4 Aug. 2021.

Thursday, July 1, 2021

The Joy of Plotting

Joy of Plotting

Plots

In statistics, Exploratory Data Analysis or EDA is an approach where an understanding about the data and its behavior is made by making plots between or or more variables. This is in contrast to confirmatory analysis wherein a definite relation between variables is made. This leads to model building, a model is a mathematical relationship between two or more variables. For example, below is a model representing a line, also popularly known as linear regression model.

\[ x = \alpha + \beta y + \epsilon \] However, there are numerous models and all of them have certain assumptions. These assumptions can be made through careful investigation of EDA plots. People use number of plots such as boxplots, histograms, scatterplots, linecharts, lollipop plots to name as few. Here I take some freely avaialable data and plot some nice (only try :-p) plots. In R, one of the (arguably) most popular programming languages, there are three plotting systems:

1. Base plotting system

penguins <- modeldata::penguins
penguins <- penguins |> 
  filter(!is.na(bill_length_mm) & !is.na(bill_depth_mm))
billL <- penguins$bill_length_mm
billD <- penguins$bill_depth_mm
plot(billL, billD, type = 'p', xlab = "Bill Length", ylab = "Bill Depth")

2. Lattice plotting system

library(lattice)
xyplot(billD ~ billL, type = 'p', xlab = "Bill Length", ylab = "Bill Depth")


Now, this is a great improvement over the base plotting system with neat and nice tick marks.

3. Grammar of graphic or ggplot plotting system

library(ggplot2)
penguins <- modeldata::penguins
penguins |> 
  filter(!is.na(bill_length_mm) & !is.na(bill_depth_mm)) |> 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point() + 
  labs(x = "Bill Length",
       y = "Bill Depth") + 
  theme_bw()


As you can see the difference, the graph is made by layers of commands joined by ‘+’. This is a special philosophy of making a graph using the grammar of graphics, developed by Leland Wilkinson and implemented in ggplot2 package of R.


In any of the plotting system, it is possible to produce very wide variety of graphs, and some of the most popular ones are boxplot, histograms, scatterplots, linegraphs etc.

Boxplots

library(ggplot2)
penguins <- modeldata::penguins
penguins |> 
  filter(!is.na(bill_length_mm) & !is.na(bill_depth_mm)) |> 
  ggplot(aes(x = bill_length_mm, colour = species)) + 
  geom_boxplot() + 
  scale_color_brewer(palette = "Set2") + 
  coord_flip() + 
  labs(x = "Bill Length",
       title = "Bill Lengths of Penguin Species") + 
  theme_dark()


It is not very hard to see that Adelie penguins have shorter bill than other two species. In just one graph, we are able to see the differences in the data.

Scatterplots

penguins <- modeldata::penguins
penguins |> 
  filter(!is.na(bill_length_mm) & !is.na(bill_depth_mm)) |> 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + 
  geom_point() + 
  scale_color_brewer(palette = "Set2") + 
  labs(x = "Bill Length",
       y = "Bill Depth",
       title = "Bill Lengths of Penguin Species") + 
  theme_dark()


OK! things are getting clearer, isn’t it? we are able to see the difference and see the characteristics of three species are quite different!

Histograms

library(ggplot2)
library(patchwork)
penguins <- modeldata::penguins
bilL <- penguins |> 
  filter(!is.na(bill_length_mm) & !is.na(bill_depth_mm)) |> 
  ggplot(aes(x = bill_length_mm, fill = species)) + 
  geom_histogram(bins = 50) + 
  facet_wrap(~species) +
  coord_flip() + 
  scale_color_brewer(palette = "Set2") + 
  labs(x = "Bill Length") + 
  theme_dark()

bilD <- penguins |> 
  filter(!is.na(bill_length_mm) & !is.na(bill_depth_mm)) |> 
  ggplot(aes(x = bill_depth_mm, fill = species)) + 
  geom_histogram(bins = 50) + 
  facet_wrap(~species) + 
  coord_flip() + 
  scale_color_brewer(palette = "Set2") + 
  labs(x = "Bill Depth") + 
  theme_dark()

bilL / bilD


Voila! A lot is clearer now with just two variables. I once attended a conference, and there a statistician said: “If you dont understand your scatterplot, print it in the largest possible paper roll it out and see how they are distributed” - well this is a bit extreme but EDA is taken seriously everywhere!

Spatial Plots or Maps

Now I want to show the power of spatial plots aka Maps! There has been a number of software packages released in recent year to give tough competition to tradition GUI based proprietory software. In R, it is quite possible to use grammar of graphics and make impressive maps, thanks to tmap package.

library(tmap)
library(spData)
usState<- spData::us_states
names(usState)[2] <- "States"
# Using sf packages plotting function
tm_shape(usState) +
  tm_grid() + 
  tm_polygons(col = "States", legend.show = FALSE) +
  tm_compass(position = c("right", "top")) +
  tm_scale_bar()


The maps and plots above are not even scratching the surface of the capabilities of R and other progamming languages such as python but these are pointers - in the direction of tremendous possibilities.