adaptability of irrigation to a changing monsoon in india...

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2016 IWREC MeetingSeptember 13, 2016

Adaptability of Irrigation to a Changing Monsoon in India: How far can we go?

This work was supported by the National Science Foundation, Water Sustainability and Climate program (Grant No. EAR-1038614), and the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, Integrated Assessment Program (Grant No. DE-SC0005171) and by a grant from the National Science Foundation’s Sustainable Research Network program (cooperative agreement GEO--‐1240507).

Esha ZaveriStanford University

In collaboration with: Karen Fisher-Vanden, Douglas H. Wrenn (Pennsylvania State University)

Danielle S. Grogan, Steve Frolking, Richard Lammers (University of New Hampshire)

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Motivation: Irrigation & India’s Water Crisis

Motivation Research Question Methods Results

"When the balloon bursts, untold anarchy will be the lot of rural India.“ – Tushar Shah (Taming the Anarchy, 2008 )

International Water Management Institute (IWMI)

“Of all sectoral water demands, the irrigation sector will be affected most strongly by climate change” (IPCC, 2007)

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Motivation: The Indian Monsoon



GFDL-ESM2G NorESM1-M

Motivation: Future Monsoon Precipitation is Uncertain

MIROC-ESM-CHEM CCSM4 GFDL-CM3

% change 1970-79 & 2040-49

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Motivation: “Water on Demand” & Groundwater Depletion


40,000

30,000

20,000

10,000

Irri

gate

d a

rea

in 1

00

0 h

a.

19

50

-51

19

60

-61

19

70

-71

19

80

-81

19

90

-91

20

00

-01

0

Source: Mukherji, Rawat and Shah (2013), Directorate of Economics and Statistics, Ministry of Agriculture, Government of India, several years.

Green Revolution

Groundwater

TankCanal

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Motivation: “Water on Demand” & Groundwater Depletion


• Groundwater depletion - a global problem (Konikow and Kendy 2005, Wada et al 2010)

• BUT India - world's largest consumer and the country probably most vulnerable to this threat (World Bank, 1998; Shah, 2010; Sekhri, 2011)

Source: Aeschbach-Hertig and Gleeson, Nature, 2012

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Motivation: Policy Implications


Policy implications for food security/poverty and ‘free’ electricity: ↑ Electricity subsidies -> ↑ rate of groundwater extraction

(Badiani and Jessoe, 2013)

At the same time, ↓ groundwater tables -> ↓ food production, ↑ poverty (Sekhri, 2012; Sekhri, 2013)

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Motivation: Policy Implications


Policy implications for food security/poverty and ‘free’ electricity: ↑ Electricity subsidies -> ↑ rate of groundwater extraction

(Badiani and Jessoe, 2013)

At the same time, ↓ groundwater tables -> ↓ food production, ↑ poverty (Sekhri, 2012; Sekhri, 2013)

Non-renewable groundwater/ fossil groundwater Extraction in excess of recharge

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Methods: Conceptual framework


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Methods: Conceptual framework


Q1: How will climate change affect the demand for non-renewable/ unsustainable groundwater (UGW)?

Q2: What would the agricultural impact be if groundwater abstractions were reduced to sustainable levels?

Q3: Can inter-basin water transfers (NRLP) alleviate groundwater stress?

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Research Question


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Methods: Multidisciplinary Approach


Econometric ModelIdentifying sensitivity of

irrigated area to weather changes

Climate Inputs

Water Balance Model

Hydrology model to represent spatial and temporal water cycle

Projections of Crop irrigated area by

season

Water Model Input

ResultsEcon ModelOutput

Δ Unsustainable Mined

Groundwater Demand

Step 1

Step 2

Step 3

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Identification comes from the exogenous variability in the weather:

log 𝐼𝑑𝑡= 𝛾0 + 𝛼 𝑙𝑜𝑔𝐼𝑑,𝑡−1+ 𝜷 𝑙𝑜𝑔 𝑹𝒅,𝒕 + 𝜸𝟏 𝑙𝑜𝑔𝐺𝐷𝐷

+ 𝛾2𝑙𝑜𝑔𝐴𝑑,𝑡−1,𝑡−6 + 𝜌𝑑 + 𝜆𝑡 + 𝐴𝑠𝑡 + 𝜖𝑑,𝑡

- district 𝑑, state 𝑠, year 𝑡 = [1970 − 2005] -

Methods: Step 1


*ICRISAT, Ministries of Agriculture & Water Resources, Central Groundwater Board of India*APHRODITE

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𝐼𝑑𝑡 ∶ Crop irrigated area for 6 crops: rice, wheat, sorghum, barley, maize, cotton

Main foodgrains

production: 30%, 35% value : 40%, 19% Coarse foodgrains

production: 2%, 5%, 6%

value : 2%, 3%, 5% Cash crop

production: 8%

value : 12%

Methods: Step 1


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𝐼𝑑𝑡 ∶ Crop irrigated area for 6 crops: rice, wheat, sorghum, barley, maize, cotton

Main foodgrains

production: 30%, 35% value : 40%, 19% Coarse foodgrains

production: 2%, 5%, 6%

value : 2%, 3%, 5% Cash crop

production: 8%

value : 12%

Methods: Step 1


- zero observations- nonlinear corner solution - Tobit

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𝐼𝑑𝑡 ∶ Crop irrigated area for 6 crops (rice, wheat, sorghum, maize, barley, cotton)

𝑹𝒅𝒕 : No. of rainy days: Precipitation > 0.1mm (Fishman, 2012)

Total precipitation : Monsoon rainfall from June to September

GDD : Growing degree days σ𝑑𝐷(𝑇𝑎𝑣𝑔,𝑑) (Schlenker et al., 2006)

Methods: Step 1


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• Controls: - Lag dependent - Previous 5 yr average crop area, 𝑙𝑜𝑔𝐴𝑑,𝑡−1,𝑡−6

- District fixed effects (𝜌𝑑) , year fixed effects (𝜆𝑡), state specific time trends (𝐴𝑠𝑡)

• Conley corrected standard errors (OLS), cluster standard errors at the district level (Tobit)

Methods: Step 1


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Methods: Step 1


Precipitation plays a larger role than GDD in driving changes in irrigated area.

Crop irrigated area increases, with increasing dry spells ( or decrease in no. of rainy days)

Crop irrigated area can either rise or fall in response to increases in total monsoon rainfall Varies by season +ve relationship in dry season

Estimating 𝛽𝑃𝑟𝑒𝑐𝑖𝑝 , 𝛽#𝑅𝑎𝑖𝑛𝐷𝑎𝑦𝑠 , 𝛾1𝐺𝐷𝐷 (Summary of Results):

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5 GCMs that come closest to describing the monsoon phenomenon well :NorESM1-M, GFDL-ESM2M, MIROC-ESM-CHEM, GFDL-CM3, CCSM4 (Menon et al., 2013)

Strongest warming scenario (RCP 8.5)

𝛽𝑃𝑟𝑒𝑐𝑖𝑝 , 𝛽#𝑅𝑎𝑖𝑛𝐷𝑎𝑦𝑠 , 𝛾1𝐺𝐷𝐷 + Future climate projections up to 2050

-> projections of crop irrigated area

Methods: Step 2


Dry seasonWet season

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5 GCMs that come closest to describing the monsoon phenomenon well :NorESM1-M, GFDL-ESM2M, MIROC-ESM-CHEM, GFDL-CM3, CCSM4 (Menon et al., 2013)

Strongest warming scenario (RCP 8.5)

Water balance model estimates :total irrigation water demand σ𝑐𝑊𝐷𝑔𝑐𝑡 and tracks supply of water (𝑊𝑆):

σ𝑐𝑊𝐷𝑔𝑐𝑡 = 𝑊𝐷𝑔𝑡 = 𝑊𝑆𝑠ℎ𝑎𝑙𝑙𝑜𝑤 𝐺𝑊 +𝑊𝑆𝑟𝑖𝑣𝑒𝑟 + 𝑊𝑆𝑈𝐺𝑊Water Supply Rank 1 2 3

Methods: Step 3


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* Groundwater levels (GWL) inferred from change in UGW needed to meet irrigation water needs

Results from 5 individual GCMS

Results: Demand for UGW


Q1: How will climate change affect the demand for unsustainable groundwater?

∗ ∆ between 1979-2000 & 2029-50

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Volume of Unmet Irrigation Water Demand

Future multi-model mean (solid line) and range (shaded) are

based on 5 GCM climate futures, with uncertainty due to

differences in GCM projections.

Q2: What would the agricultural impact be if UGW was absent?

Results: Policy (Unmet Demand and Food Security)


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Volume of Unmet Irrigation Water Demand

Future multi-model mean (solid line) and range (shaded) are

based on 5 GCM climate futures, with uncertainty due to

differences in GCM projections.



Crop Production Loss


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Crop Production Loss

Current (c. 2000) agricultural production: the caloric intake of 173 million people (14% India’s population) is dependent

upon UGW.

173 million is 50% of total U.S. population


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Source: National Water Development Authority

NRLP Plan• 37 river links (canals)• 15,000 km canals• 174 billion cubic meters of water transferred

annually

Goals• Supply water for 35 million ha cropland• 35 GW hydropower

$120 billion USD

Results: Policy (National River Linking Project)


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Source: National Water Development Authority



• On September 16, 2015 the first of the proposed links was launched

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2041-2050UGW without

Inter-Basin Transfers

2041-2050UGW with

Inter-Basin Transfers

UGW alleviated (%)UGW (cubic km / year)

Only NRLP canals NRLP canals + New reservoirs

1 - 4%

Q3: Can the NRLP alleviate groundwater stress?



10 - 20%

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Variation in the monsoon plays a fundamental role in irrigation decisions. Timing of the season matters.

Rainy season irrigation expands in response to rainfall deficiencies. This is not so for dry season irrigation• Dry season irrigation is vulnerable to accumulation of monsoon

rainfall .

Great deal of spatial variability in the change for UGW demand and supply with future climate change• 14% of the Indian population will be directly affected by a loss in

access • Role of NRLP canals alone in alleviating UGW demand is limited.

Reservoirs essential.

Conclusion

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Thank you for listening

Esha [email protected]

Zaveri E, Grogan D S, Fisher-Vanden K, Frolking S, Lammers R B, Wrenn D H, Prusevich A, and Nicholas R E (2016) Invisible water, visible impact: Groundwater use and Indian agriculture under climate change. Environmental Research Letters

mailto:[email protected]

Timing of the Seasons

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Historical period:

1 agriculture year:

Timing of seasons:

Timing of Monsoon:

1970 2005

‘70 ‘70 ‘70 ‘70 ‘70 ‘71 ‘71

Kharif (wet) Rabi (dry) planting planting

80% rainfall

May Jun Sep Oct Dec Feb Jun

Introduction Motivation Data Methodology Empirical results Link to WBM Conclusion

Results: Kharif (wet) crops

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Table 1: How does irrigation in the wet season respond to a variable monsoon?

A B A B A B A B

Rice Maize Sorghum Cotton

No. of rain days 0.03 -0.059 -0.327*** -0.234** -0.054+ 0.037 -0.199*** -0.264***

(0.038) (0.067) (0.087) (0.083) (0.030) (0.039) (0.000) (0.000)

Rainfall JJAS 0.070*** 0.083*** -0.014 -0.025 -0.039*** -0.057*** 0.045*** 0.076***

(0.020) (0.025) (0.019) (0.022) (0.011) (0.015) (0.000) (0.000)

Kharif degree days -0.048 0.095 -0.099* 0.036 0.027 0.117* -0.310*** -0.150***

(0.040) (0.091) (0.039) (0.053) (0.027) (0.055) (0.000) (0.000)

Model Tobit

Lag irrigation+ previous

5 year avg crop area

Yes No Yes No Yes No Yes No

Fixed Effects District, Year, State-Year Time Trends

N 8248 8613 7244 7544 5178 5903 3244 3359

N left censored at zero 632 665 1628 1723 3520 3649 277 289

Notes: Table reports all average partial effects for Tobit models. The dependent variable is log irrigated area.

Standard errors reported in parentheses are clustered at the district level. Statistical significance is given by + p<0.10

* p<0.05 ** p <0.01 ***p < 0.001.



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A B A B A B A B


No. of rain days 0.03 -0.059 -0.327*** -0.234** -0.054+ 0.037 -0.199*** -0.264***

(0.038) (0.067) (0.087) (0.083) (0.030) (0.039) (0.000) (0.000)

Rainfall JJAS 0.070*** 0.083*** -0.014 -0.025 -0.039*** -0.057*** 0.045*** 0.076***

(0.020) (0.025) (0.019) (0.022) (0.011) (0.015) (0.000) (0.000)


(0.040) (0.091) (0.039) (0.053) (0.027) (0.055) (0.000) (0.000)

Model Tobit





N 8248 8613 7244 7544 5178 5903 3244 3359




* p<0.05 ** p <0.01 ***p < 0.001.



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A B A B A B A B


No. of rain days 0.03 -0.059 -0.327*** -0.234** -0.054+ 0.037 -0.199*** -0.264***

(0.038) (0.067) (0.087) (0.083) (0.030) (0.039) (0.000) (0.000)

Rainfall JJAS 0.070*** 0.083*** -0.014 -0.025 -0.039*** -0.057*** 0.045*** 0.076***

(0.020) (0.025) (0.019) (0.022) (0.011) (0.015) (0.000) (0.000)


(0.040) (0.091) (0.039) (0.053) (0.027) (0.055) (0.000) (0.000)

Model Tobit





N 8248 8613 7244 7544 5178 5903 3244 3359




* p<0.05 ** p <0.01 ***p < 0.001.



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A B A B A B A B


No. of rain days 0.03 -0.059 -0.327*** -0.234** -0.054+ 0.037 -0.199*** -0.264***

(0.038) (0.067) (0.087) (0.083) (0.030) (0.039) (0.000) (0.000)

Rainfall JJAS 0.070*** 0.083*** -0.014 -0.025 -0.039*** -0.057*** 0.045*** 0.076***

(0.020) (0.025) (0.019) (0.022) (0.011) (0.015) (0.000) (0.000)


(0.040) (0.091) (0.039) (0.053) (0.027) (0.055) (0.000) (0.000)

Model Tobit





N 8248 8613 7244 7544 5178 5903 3244 3359




* p<0.05 ** p <0.01 ***p < 0.001.


Results: Rabi (dry) crops

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Table 2: How does irrigation in the dry season respond to a variable monsoon?

A B A B A B

Rice Wheat Barley

No. of rain days -0.003 -0.073 -0.005 -0.122 -0.180* -0.099

(0.032) (0.054) (0.071) (0.094) (0.091) (0.198)

Rainfall JJAS 0.166*** 0.146*** 0.250*** 0.284*** -0.026 -0.090

(0.023) (0.026) (0.034) (0.046) (0.043) (0.077)

Kharif degree days 0.301** 0.489** 0.089 -0.017 0.066 0.143

(0.115) (0.179) (0.069) (0.123) (0.110) (0.176)

Rabi degree days -0.316 0.212 -0.159 -0.178 0.068 -0.035

(0.243) (0.479) (0.099) (0.128) (0.184) (0.221)

Model Tobit Tobit OLS OLS OLS OLS



Yes No Yes No Yes No


N 3770 3989 7460 7739 3882 4054

N left censored at zero 586 617 19 19 128 128


Standard errors reported in parentheses are clustered at the district level (tobit), or corrected for spatial and serial

correlation (OLS) Statistical significance is given by + p<0.10 * p<0.05 ** p <0.01 ***p < 0.001.



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A B A B A B

Rice Wheat Barley

No. of rain days -0.003 -0.073 -0.005 -0.122 -0.180* -0.099

(0.032) (0.054) (0.071) (0.094) (0.091) (0.198)

Rainfall JJAS 0.166*** 0.146*** 0.250*** 0.284*** -0.026 -0.090

(0.023) (0.026) (0.034) (0.046) (0.043) (0.077)


(0.115) (0.179) (0.069) (0.123) (0.110) (0.176)

Rabi degree days -0.316 0.212 -0.159 -0.178 0.068 -0.035

(0.243) (0.479) (0.099) (0.128) (0.184) (0.221)






N 3770 3989 7460 7739 3882 4054







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A B A B A B

Rice Wheat Barley

No. of rain days -0.003 -0.073 -0.005 -0.122 -0.180* -0.099

(0.032) (0.054) (0.071) (0.094) (0.091) (0.198)

Rainfall JJAS 0.166*** 0.146*** 0.250*** 0.284*** -0.026 -0.090

(0.023) (0.026) (0.034) (0.046) (0.043) (0.077)


(0.115) (0.179) (0.069) (0.123) (0.110) (0.176)

Rabi degree days -0.316 0.212 -0.159 -0.178 0.068 -0.035

(0.243) (0.479) (0.099) (0.128) (0.184) (0.221)






N 3770 3989 7460 7739 3882 4054






Results: Robustness Checks

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Results are robust to:

Adding more controls• Smooth spatial function at five year increments (Banzhaf and

Lavery, 2010)• Different trends in electrification, technological evolution

Using correlated random effects Tobit models

Using standardized measures of the weather variables


Trends over time

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Research Agenda This paper Model Conclusions

99

.51

01

0.5

11

11.5

-20

2

Mon

soo

n

1970 1980 1990 2000 2010year

Monsoon Log irrigated area

Log crop area Log production

Wheat

05

01

00

150

200

Log

whe

at yie

ld

-20

2

Mo

nso

on

1970 1980 1990 2000 2010year

Monsoon Log wheat yield

Note: Each panel reports values aggregated in each period over the district sample used in analysis.

Blue bars are the standardized deviation of monsoon rainfall (left axis)

Aggregate changes in Wheat

Trends over time

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Research Agenda This paper Model Conclusions

Note: Each panel reports values aggregated in each period over the district sample used in analysis.

Blue bars are the standardized deviation of monsoon rainfall (left axis)

Aggregate changes in Rice

99

.51

01

0.5

11

11.5

-20

2

Mon

soo

n

1970 1980 1990 2000 2010year

Monsoon Log irrigated area

Log crop area Log production

Rice

-100

-50

05

01

00

150

Log

ric

e y

ield

-20

2

Mo

nso

on

1970 1980 1990 2000 2010year

Monsoon Log rice yield

Why India?

% change Rainy Days between 1970-79 and 2040-49



A

B

Changes in Temperature over time

Notes: Seasonal growing degree days in the (A) wet (Kharif) and (B) dry (Rabi) seasons.

The solid lines represent the multi-model mean of five different GCM climate futures, and the shade bands

the five-model range.

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Water Balance Model: Hydrology model to represent spatial and temporal water cycle

• India is divided into grid boxes (0.5 degree resolution)

• The hydrologic cycle is represented on a daily time step

• Water flows (arrows) between stocks (boxes) each day:

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WDgct = f(IAgct, Tempgt, Precipgt, Soilparg, Cropparc)

g = grid (spatial)c = crop typeT = time (day)WD = Water Demand (volume)IA = Irrigated AreaTemp = temperature (daily ave)Precip = precipitation (daily total, mm)Soilpar = soil parameters

(wilting point, field capacity, drainage class)Croppar = crop parameters

(root depth, crop evapotranspiration coefficient, planting day, season length, length of growth stages, available water depletion factor)

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WDgt = ΣWDgctc

WDgt = WSsrg+ WSrr + WSmg

Water supply rank: 1 2 3

WSsrg = Water supplied by shallow rechargeable groundwaterWSrr = Water supplied by rivers and reservoirs WSmgw = Water supplied by mined groundwater

Note: WSsrg and WSrr are limited by the volume of water available

WSmg is limitless

% change Rainy Days between 1970-79 and 2040-49



A

B

Changes in Temperature over time

Notes: Seasonal growing degree days in the (A) wet (Kharif) and (B) dry (Rabi) seasons.

The solid lines represent the multi-model mean of five different GCM climate futures, and the shade bands

the five-model range.

A

B

Projections of Crop Irrigated Area to 2050

Notes: Econometric model generated irrigated area projections for (A) dry season and (B) wet season

crops in million hectares. The historical period (1970-2005) reflects data from ICRISAT.

For the future period (2006-2050), the solid line reflects the multi-model mean of econometric

projections based on five different GCM climate futures, with the range of uncertainty due to differences

in GCM projections in the shaded region. Note that the y-axis scales are different.

Decreased rate of GWL declines

Same rate of GWL declines

Increased rate of GWL declines

GWL decline begins in the future

GWL recovers/stays static

< 10% of national UGW demand

No UGW demand

Not modeled



Trends in Groundwater Levels between 1979-2000 and 2029-2050

* Results using Multi-Model Mean

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Change in groundwater level validation

Table S 2.11: Model validation: comparison of historical district-level groundwater level data and hydrologic model simulation of changes in groundwater levels.

• Existing irrigation reservoir capacity: 157 km3• Number existing irrigation reservoirs in India, GranD

database: 56• Number used by WBM (remove incomplete data, and

aggregate reservoirs within same grid cell): 37

• Added irrigation reservoir capacity: 158 km3• Number of added reservoirs: 16

3.3 km3

2.1 km3

2 km3

0.9 km3

4 + 1.9 km3

Total new reservoir capacity in NW = 14.2 km3

NW states annual UGW demand = ~20 km3

NRLP

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NRLP

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Un

sust

ain

able

Gro

un

dw

ate

r D

em

and

[km

3/y

ear

]

historical NRLP transfers only NRLP reservoirs only NRLP transfers & reservoirs

Unsustainable groundwater (UGW) is defined as:

Any groundwater extracted in excess of recharge

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Background: Unsustainable Groundwater Definition

This definition:• Allows for continental- to global-scale simulations of changes in groundwater

storage

• Appropriate for aquifers in which extraction far exceeds recharge. • Example: NW India, extraction is 2 orders of magnitude larger than recharge.

• Does not account for complex surface-water – groundwater interactions

• Is not appropriate for sub-basin scale analysis, or aquifers in which extraction and recharge rates are similar.

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Hydrology• Models require irrigated crop areas as input.• Most studies apply a single global expansion factor (Bruinsma

2009; Hanasaki et al. 2012) or only account for surface water variation (Elliott et al. 2014)



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Hydrology• Models require irrigated crop areas as input.• Most studies apply a single global expansion factor (Bruinsma

2009; Hanasaki et al. 2012) or only account for surface water variation (Elliott et al. 2014)

Economics• Previous studies of projected agricultural yields do not assess

changing water supplies or water sources (India: Fishman, 2012; Guiteras, 2009)

• Assume that water will be available to meet the demand for irrigated yields

• Do not model underlying irrigation behavior



adaptability of irrigation to a changing monsoon in india...

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