National Programme on

Isotope Fingerprinting of Waters of India (IWIN)

Scientific Progress Report (January to December, 2010)

A compilation of reports from various participating institutions

Index of Reports

Sr. No. Insitutions Page No.

1. Physical Research Laboratory (PRL), Ahmedabad 1-24

2. National Institute of Hydrology (NIH), Roorkee 25-39

3. Nuclear Research Laboratory (NRL), IARI, New Delhi

40-47

4. National Geophysical Research Institute (NGRI), Hyderabad

48-60

5. National Institute of Oceanography (NIO), Goa 61-67

6. Centre for Water Resource Development and Management (CWRDM), Kozhikode

68-76

7. Anna University 77 Notes: Details of samples collected by IWIN partners, namely, CGWB, CWC, CPCB, IMD and CRIDA is included in the progress report of PRL.

National Programme on

Isotope Fingerprinting of Waters of India (IWIN)

Scientific Progress Report (January to December, 2010)

Physical Research Laboratory (PRL) Navrangpura, Ahmedabad 380009

National Programme on Isotope Fingerprinting of Waters of India (IWIN) at

Physical Research Laboratory (PRL), Navrangpura, Ahmedabad 380 009

SCIENTIFIC PROGRESS REPORT (JANUARY-DECEMBER 2010)

1. Project Title: National Programme on Isotope Fingerprinting of Waters of India (IWIN)

DST No: IR/S4/ESF-05/2004

2. Principal Coordinator (Name & Address): Dr. S.K. Gupta, Physical Research Laboratory, Navrangpura, Ahmedabad 380 009 (up to 30.09.2010) Dr. R.D. Deshpande, Physical Research Laboratory, Navrangpura, Ahmedabad 380 009 (w.e.f. 30.09.2010)

Date of Birth 27th September, 1946 29th September, 1964

3. Co-Principal Coordinator (Name & Address): Prof. R. Ramesh, Physical Research Laboratory, Navrangpura, Ahmedabad 380 009

Date of Birth 2nd June, 1956

4. Broad area of Research: Earth & Atmospheric Science

4.1 Sub Area: Earth Science

5. Approved Objectives of the Proposal : • To generate isotopic data for addressing important hydrological questions related to origin

of water sources and the processes of redistribution by evapo-transpiration, stream flow generation, ground-water recharge/ discharge – from watershed to continental scale.

• To give quantitative estimates of residence time of the water/ vapour in each hydrological reservoir/setting and the fluxes across them in temporally and spatially distributed manner.

• Another important objective of the project is to nucleate detailed isotope hydrology activity in universities and academic institutions first by providing a framework of basic isotope hydrology data and then by providing measurement facilities and hands on experience to selected satellite projects.

Date of Start: September, 2007

Total cost of IWIN Project: 5,43,29,200/- Total DST Funds approved for IWIN-PRL: 2,46,50,000/- Total DST Funds approved for IWIN-Network: 30,00,000/- Total DST Component at PRL: 2,76,50,000/- Total PRL Contribution to IWIN: 68,35,200/-

Date of completion: Continuing

DST Funds Released at PRL: Rs. 2,19,50,000/= (September, 2007) Rs. 20,00,000/= (February, 2010) Expenditure as on 18 January, 2011: Rs. 2,27,97,196/-

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IWIN-PRL - Scientific Progress Report Jan-December 2010

6. Progress of Sampling and Analyses:

• Daily sampling of precipitation and atmospheric moisture from six stations (PRL-Ahmedabad, NIH-Roorkee, NIH-Sagar, NGRI-Hyderabad, NIO-Goa and NRL-IARI-New Delhi) for studying temporal variation in isotopic composition of atmospheric water vapour and its controlling factors is in progress. At PRL, NIH-Roorkee and NGRI-Hyderabad vapour samples are collected by both complete cryogenic trapping and liquid condensation methods to standardize the procedure of estimating isotopic composition of water vapour. (Samples collected: 559 at PRL).

• Fortnightly sampling of precipitation and atmospheric moisture at 19 stations of CRIDA and 20 stations of IMD is in progress (Samples received: 1917 = 1341 from CRIDA +576 from IMD).

• Monthly sampling of river water at 23 stations of CPCB and 39 stations of CWC is in progress (Samples received: 598 = 262 from CPCB + 336 from CWC).

• Periodic sampling of surface waters of Arabian Sea (AS) using NIO research vessels by NIO, and Bay of Bengal (BOB) using commercial ships by AU is in progress (Samples received: NIO-35; AU-to be delivered at PRL).

• Spatially distributed groundwater sampling from shallow unconfined aquifers across the country on pre- and post-monsoon basis is in progress by CGWB (Samples received: 995).

• A detailed break-up of various types of samples received is given in Table 1 • Standardized procedures for water sample collection and storage developed by PRL-IWIN are in

use at all the IWIN Sampling Network Stations. • The IWIN Isotope Ratio Mass Spectrometer (IRMS) Laboratory set up at PRL, Ahmedabad in

March, 2009, has been functioning efficiently. A total of 2821samples have been analysed for δ18O and 2103 for δD. For reconfirming the isotopic results of outliers 171samples were reanalysed for δ18O and 133 samples for δD. For quality assurance of the data generated from IWIN laboratory, in course of these analyses 1122 secondary laboratory standards were analysed for δ18O and 834 for δD. In addition, for regular health check up 240 test runs were undertaken for δ18O and 320 for δD. Thus, a total of 4354 analytical runs were performed for δ18O and 3390 for δD. This was possible due to remarkable dedication by the PDF (Dr. A.S. Maurya) and Project Associate (Ms. Miral Shah) at PRL, Ahmedabad.

• δ18O and δD analyses is also in progress at NIH-Roorkee and NGRI-Hyderabad (Separate reports of these Institutions attached).

Table 1. A detailed break-up of various types of samples received at IWIN Repository during 2010.

S.No. Name of Organisation RW GW DP FP DMC FMC DMPT SWTotal

Samples Received

1 PRL - - 49 8 251 - 251 - 5592 CWC 336 - - - - - - - 3363 CPCB 262 - - - - - - - 2624 CRIDA - - 336 117 750 138 - - 13415 IMD - - 86 224 16 250 - - 5766 CGWB - 995 - - - - - - 9957 NIH-R - 13 - 1 - - - - 148 NIH-S - 6 - - - - - - 69 NRL 1 62 63

10 NIO 35 35Total 598 1014 472 350 1079 388 251 35 4187

Samples Received at IWIN Repository at PRL : 01.01.2010 to 31.12.2010IWIN National Programme

RW = River Water; GW = Ground Water: DP = Daily Precipitation; FP = Fortnightly Precipitation; DMC = Daily Moisture by liquid condensation; FMC = Fortnighly Moisture by liquid condensation; DMPT = Daily Moisture by complete cryogenic trapping

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IWIN-PRL - Scientific Progress Report Jan-December 2010

7. Salient Research Achievements:

7.1 Inter-laboratory calibration

For inter-laboratory calibration (ILC) exercise under IWIN National Programme, four secondary laboratory standards were prepared. Two of the secondary laboratory standards, (IWIN-1 and IWIN-3) were synthesised at IWIN-IRMS-PRL laboratory, to have relatively depleted and enriched isotopic composition. This was achieved respectively by distillation and evaporation. The other two of the standards (IWIN-2 and IWIN-4) were natural water samples. A set of these four secondary laboratory standards were provided to all the IWIN Partners having their own IRMS laboratory, for the purpose of inter-laboratory calibration. These four laboratory standards were also sent to isotope hydrology laboratory of IAEA, Vienna.

The IWIN IRMS laboratory at PRL was calibrated with international standard reference materials (VSMOW2, GISP and SLAP2) obtained from IAEA. The secondary laboratory standards were analysed as samples in multiple aliquots in IWIN-IRMS-PRL laboratory using VSMOW2, GISP and SLAP2 as the laboratory reference. All other participating laboratories analysed the IWIN secondary laboratory standard samples with reference to their own secondary laboratory standards. The δ18O and δD analyses was performed at IAEA using both gas source IRMS and Laser Analysers (both LGR and Picaro), and average values were provided to IWIN. The values provided by IAEA are therefore used as calibrated benchmark values with respect to which results from all laboratories are compared. The δ18O and δD values obtained from IAEA and all other laboratories which participated in IWIN-ILC are shown in Table 2 and plotted in Figure 1 and Figure 2.

It can be noticed from Table 2 that various aspects of analytical method (e.g. Dual inlet/ continuous flow, sample volume, equilibration duration, equilibration temperature, sample introduction method, etc.), secondary laboratory standard used and IRMS equipments are not uniform for all laboratories. In spite of various differences in analytical set up between different laboratories, the δ18O and δD values are in good agreement with each other and with IAEA certified values, within a narrow range. Laboratory-wise average deviation from the IAEA calibrated δ18O is found to be: 0.06‰ for PRL, 0.42‰ for NIH, 0.25‰ for IIT-Kgp, 0.57‰ for CWRDM and 0.11‰ for NGRI. Laboratory-wise average deviation from the IAEA calibrated values for δD is found to be: 0.52‰ for PRL, 1.83‰ for NIH, 2.18‰ for IIT-Kgp and 0.50‰ for NGRI.

From the IWIN-ILC exercise it is observed that results provided by all IRMS laboratories participating in IWIN programme are in agreement with each other within 0.28‰ for δ18O 1.37 for δD. Therefore, combining the isotope data from various laboratories for a particular component of hydrological cycle is justifiable and can provide consistent results.

3

Table 2. δ18O and δD values of four secondary laboratory standards, measured at different laboratories as part of IWIN Inter-laboratory calibration exercise.

δ18O (‰) δD (‰) δ18O (‰) δD (‰)

δ18O (‰) δD (‰) δ18O (‰) δD (‰)

δ18O (‰) δD (‰) δ18O (‰) δD (‰)

IWIN-1 -6.57 -40.5 -6.54 -39.75 -6.33 -39.0 -6.79 -39.0 -5.86 -6.68 -40.45Standard Deviation (± ‰) 0.07 0.71 0.07 0.91 0.13 0.16 0.07 1.28 0.05 0.05 0.9

No. of aliquots 10 7 3 3 4 9 3 9 8IWIN-2 -11.09 -73.8 -11.01 -73.96 -10.38 -71.233 -10.90 -72.1 -10.65 -11.2 -73.79

Standard Deviation (± ‰) 0.07 0.75 0.09 1.05 0.14 0.23 0.06 2.76 0.18 0.12 1No. of aliquots 10 5 3 3 4 9 3 9 6

IWIN-3 15.1 36 15.07 36.80 15.062 37.4 14.63 38.4 15.27 14.96 37.29Standard Deviation (± ‰) 0.09 0.64 0.10 1.49 0.16 0.23 0.07 2.29 0.11 0.07 0.5

No. of aliquots 10 7 4 9 3 6 8IWIN-4 -2.29 -17.6 -2.21 -17.25 -1.6129 -15.771 −2.42 -14.5 -1.34 -2.36 -18.26

Standard Deviation (± ‰) 0.1 0.75 0.10 0.84 0.13 0.67 0.03 1.27 0.06 0.08 0.55No. of aliquots 20 17 4 9 2 9 10

Analytical Method Dual Inlet -- √

Continuous Flow √ --Sample Volume (μl) 300 300 400 400 500 500 200 150 150Equil. Temp. (°C) 32 32 40 40 32 32 30 40 40

Equil. Duration (hh:min) 16 1:30 7 2:30 16 2 18 6 1:30

Lab Std. for δ18O and δD δ18O (‰) δD (‰)

Lab Std. for δ18O and δD

δ18O (‰) δD (‰)

Lab Std. for δ18O and δD δ18O (‰) δD (‰)

Lab Std. for δ18O and δD

δ18O (‰) δD (‰)

Lab Std. for δ18O δ18O (‰) δD (‰)

Lab Std. for δD

VSMOW 0.00 ± 0.03 0.01 ± 0.8 Bangalore -1.84 -11.72 NARM- P -4.5 -35.8 NARM -4.68 -35.86 tapwater070410 -2.06 -17.9 mq waterSLAP –55.37 ± 0.09 –425.46 ± 064 Bisleri -7.89 -54.27 STAIL IT -4.1 -29.4 sccl-2 -3.6 -30.64 rw 020710GISP –24.74± 0.08 –188.91 ± 0.78 Gangotri -16.09 -113.94 sccl-3 5.43 -54.7 rw 210510

marleshwar2005 -10.09 5.92 sccl-3-19.67 dharwad-5

IWIN-1

IWIN-2

IWIN-3

IWIN-4

IWIN-1

IWIN-2

IWIN-3

IWIN-4

IWIN-1

IWIN-2

IWIN-3

IWIN-4

IWIN-1

IWIN-2

IWIN-3

IWIN-4

National Programme on Isotope Fingerprinting of Waters of India (IWIN)Inter-laboratory calibration (2010)

NIH, Roorkee NGRI, HyderabadIIT (Kgp)-Kharagpur CWRDM, KozhikodePRL, Ahmedabad

IWIN-3

IWIN-1

IWIN-4

IWIN-2

IWIN Secondary Laboratory Standard

IWIN-1

IWIN-2

IAEA, Viennna

√

Name and isotopic composition of Secondary Lab. Standard(s) used as

reference material for isotopic analyses

--√----

√

IWIN-3

IWIN-4

--√

4

Table 3. Summary of deviation from IAEA calibrated values

Calibrated δ18O values from

IAEA PRL NIH IIT-Kgp CWRDM NGRI

Sample wise avg. deviation from

calibrated values

Callibrated δD values from

IAEA PRL NIH IIT-Kgp CWRDM NGRI

Sample wise avg. deviation from

calibrated values

-6.57 -0.03 -0.24 0.22 -0.71 0.11 0.26 -40.5 -0.8 -1.5 -1.5 -- -0.05 0.95

-11.09 -0.08 -0.71 -0.19 -0.44 0.11 0.31 -73.8 0.16 -2.6 -1.7 -- -0.01 1.3515.10 0.03 0.04 0.47 -0.17 0.14 0.17 36.0 -0.8 -1.4 -2.4 -- -1.29 1.57-2.29 -0.08 -0.68 0.13 -0.95 0.07 0.38 -17.6 -0.4 -1.8 -3.1 -- 0.66 1.62

Lab wise Avg. deviation from

calibrated value

0.06 0.42 0.25 0.57 0.11 0.28Lab wise Avg. deviation from

calibrated value

0.52 1.83 2.18 -- 0.50 1.37

Deviation from IAEA Calibrated values of δ18O Deviation from IAEA Calibrated values of δD

* Sign (+ve or -ve) of deviation is ignored and only absolute value of deviation is considered for reporting average deviation

* –ve deviation means measured values are enriched compared to IAEA calibrated values* +ve deviation means measured values are depleted compared to IAEA callibrated valules

5

-11.

1

15.1

-2.3

-6.5

-11.

0

15.1

-2.2

-6.3

-10.

4

15.1

-1.6

-6.8

-10.

9

14.6

-2.4

-5.9

-10.

7

15.3

-1.3

-6.7

-11.

2

15.0

-2.4

-6.6

-15

-10

-5

0

5

10

15

20

IWIN-1 IWIN-2 IWIN-3 IWIN-4

δ18O

(‰)

IAEAPRLNIHIIT-KgpCWRDMNGRI

Figure 1 Comparison of δ18O values for four secondary laboratory standards IWIN-1 to IWIN-4 measured at IAEA-Vienna, PRL-Ahmedabad, NIH-Roorkee, IIT-Kharagpur, CWRDM- Kozhikode and NGRI-Hyderabad.

-73.

8

36.0

-17.

6

-39.

8

-74.

0

36.8

-17.

3

-39.

0

-71.

2

37.4

-15.

8

-39.

0

-72.

1

38.4

-14.

5

-40.

5

-73.

8

37.3

-18.

3

-40.

5

-100

-80

-60

-40

-20

0

20

40

60

80

IWIN-1 IWIN-2 IWIN-3 IWIN-4

δD (‰

)

IAEAPRLNIHIIT-KgpNGRI

Figure 2 Comparison of δD values for four secondary laboratory standards IWIN-1 to IWIN-4 measured at IAEA-Vienna, PRL-Ahmedabad, NIH-Roorkee, IIT-Kharagpur, CWRDM- Kozhikode and NGRI-Hyderabad.

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IWIN-PRL - Scientific Progress Report January-December, 2010

7.2 Theoretical explanation and experimental evidence of unusual kinetic isotopic fractionation during liquid condensation of water vapour under supersaturated condition.

Theoretically consistent interpretation of an unusual kinetic fractionation for oxygen and hydrogen isotopes, during liquid condensation of water vapour under supersaturated condition, has been an important scientific achievement during the last year. Estimating the isotopic composition of atmospheric water vapour from liquid condensate collected at various IWIN Network Stations critically depends on theoretical premise behind the observed kinetic fractionation. Earlier, conceptual model for kinetic isotopic fractionation was offered and the model calculations were done to explain the observed values. This understanding was further consolidated using theory of diffusion across supersaturated boundary layer, propounded in 1984 by Jouzel and Merlivat.

According to the theory proposed by Jouzel and Merlivat (1984) the flux of various isotopologues of water (H2

16O, H218O and HD16O) reaching the condensing surface after crossing the

supersaturated boundary layer is proportional to diffusivity of various isotopologues and the difference between saturation vapour pressure at condensation temperature (~0ºC) and actual vapour pressure at ambient temperature and relative humidity. The differential flux of various isotopologues, which manifest itself as kinetic fractionation effect, therefore, depend on saturation index (Si = actual vapour pressure at ambient T and Rh / saturation vapour pressure at ~0ºC). Using this basic premise, following equation for kinetic fractionation factor (αk ) can be derived.

In above equation, D and D’ are diffusion coefficients respectively for lighter (H216O) and

heavier (H218O or HD16O) isotopologues of water and Si is the saturated index as defined above. Since

actual vapour pressure at condensing surface is not a measured parameter in experimental set up, maximum saturation index (Si-max = vapour pressure at ambient T and Rh / saturation vapour pressure at ~0ºC) has been used to calculate the kinetic fractionation factor (αk), with literature values of diffusivity ratios (D/D’) for oxygen (H2

16O/ H218O) and hydrogen (H2

16O/ HD16O).

The equilibrium and kinetic fractionation can be expressed in ‰ units as:

Equilibrium fractionation (in ‰) = (αe -1)×1000 = εe

Kinetic fractionation (in ‰) = (αk -1)×1000 = Δε

A plot of Si-max dependent variation in kinetic fractionation (Δε) during vapour to liquid phase change, for both oxygen and hydrogen isotopes, is shown in Figure 3. Also shown is the value of equilibrium fractionation (εe) for oxygen and hydrogen at 0ºC, which is independent of degree of saturation. The equilibrium fractionation tends to enrich the liquid condensate in heavier isotopes whereas kinetic fractionation tends to deplete the liquid condensate in heavier isotopes. The magnitude of kinetic fractionation exponentially increases with increasing saturation index whereas magnitude of equilibrium fractionation remains the same irrespective of degree of saturation. Therefore, at a certain value of saturation index magnitude of equilibrium (+ve) and kinetic (-ve) fractionation balance each other. This is defined as critical saturation index. When degree of saturation increases beyond this critical value, the magnitude of kinetic fractionation (-ve) exceeds magnitude of equilibrium fractionation (+ve) and consequently the liquid condensate is depleted in its isotopic composition compared to vapour from which it is condensed. For condensation at 0ºC the value of critical saturation index works out to be 1.4 for oxygen and 10.2 for hydrogen. In the ambient weather conditions at Ahmedabad, the maximum saturation index (Si-max) can easily exceed the critical saturation index for oxygen and consequently liquid condensate is depleted in 18O compared to ambient vapour. However, critical saturation index for hydrogen is not commonly attained in the ambient weather conditions at Ahmedabad. Therefore, liquid condensate is enriched in D compared to ambient vapour, albeit less enriched than expected under equilibrium condition.

( )i

k

e i'

SD S 1 1D

α =⎡ ⎤α × × − +⎢ ⎥⎣ ⎦

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IWIN-PRL - Scientific Progress Report January-December, 2010

Figure 3 A plot of variation in kinetic fractionation (Δε) with increasing saturation index, for oxygen and hydrogen isotopes, during vapour to liquid phase change at 0 °C. Also shown is the equilibrium fractionation (ε) at 0 °C.

Variation in δ18O of vapour and liquid condensate with increasing Si-max is shown in Figure 4. Similar variation for δD is shown in Figure 5. Both δ18O and δD of liquid condensate decrease progressively with increasing Si-max. Most of the liquid condensate samples collected under weather conditions at Ahmedabad are depleted in 18O compared to vapour. However, for smaller degree of saturation (Si-max < critical value for oxygen ~1.4) liquid condensate is found to be enriched in 18O compared to vapour. Beyond this cross over point liquid is depleted in 18O compared to vapour. In case of δD, liquid condensate is mostly enriched in D compared to ambient vapour. However, under weather conditions prevalent at Ahmedabad the Si-max does not exceed critical value for hydrogen (~10.2). Consequently, the δD of liquid condensate is not observed to be lower than that for vapour. Thus, theoretical premises discussed earlier are corroborated by observations. Strong dependence of isotopic composition of liquid on degree of saturation was also additionally verified by laboratory experiments of condensing ice and liquid at different temperatures.

In a controlled experiment, samples of solid condensates were collected at -5 ºC and -10ºC and liquid condensates at 0 ºC and 5ºC. The δ18O of solid and liquid condensates is plotted against Si-max in Figure 6. For a given ambient weather condition, relatively higher degree of supersaturation (and hence higher kinetic fractionation) is expected at progressively lower condensation temperatures. Consequently, solid or liquid condensate collected at lower temperature is expected to have isotopically depleted values. This theoretically expected trend was observed in controlled experiments as shown in Figure 6. As part of these controlled experiments, liquid condensates were collected at an interval of 10 minutes for 50 minutes. As the time passes after starting the experiment the temperature of the cone slightly increases due to melting of ice inside the cone. Therefore, the extent of supersaturation on the condensing surface decreases and consequently magnitude of kinetic fractionation also decreases. This is evident in Figure 6 (inset) where liquid condensate is progressively enriched as time passes after start of experiment.

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IWIN-PRL - Scientific Progress Report January-December, 2010

Figure 4. Variation in δ18O of liquid condensate with increasing value of Si-max.

Figure 5. Variation in δD of liquid condensate with increasing value of Si-max.

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IWIN-PRL - Scientific Progress Report January-December, 2010

Figure 6. The δ18O of solid condensate (collected at -5 and -10 °C) and liquid condensates (collected at 0 and 5 °C) plotted against Si-max. The inset plot shows variation in δ18O of liquid condensate collected at an interval of 10 minutes for 50 minutes.

That kinetic fractionation takes place during liquid condensation under supersaturated condition is clear from laboratory experiments. However, the same was also verified by analysing the samples of dew condensed under natural conditions at a Kothara village in Kutch district of Gujarat. Under natural condensation conditions, due to very small difference between temperatures of ambient air and condensing surface the degree of supersaturation is not very large. The Si-max for dew formation is much lower than Si-max at 0°C. Consequently, saturation index did not exceed the critical value. It was found that dew samples were enriched (δ18O = -2.2‰) compared to ambient vapour (δ18O = -5.8‰) but were less enriched than expected (δ18O = 4.4‰) by equilibrium fractionation at ambient temperature (16.3 °C)The dew samples are also found to have less enrichment compared to equilibrium fractionation.

This work was communicated for publication in high impact factor journal and is currently under revision.

7.3 Estimating isotopic composition of water vapour from liquid condensate

Monitoring the temporal and spatial variation in isotopic composition of ground level atmospheric water vapour is an important component of the IWIN programme. It was planned to estimate the isotopic composition of vapour from liquid condensates being collected at about 50 stations across the country on fortnightly basis. A sound understanding of fractionation during liquid condensation under supersaturated condition paved the way for determining empirical relationship between the isotopic composition of liquid condensate and the vapour collected by controlled experiments. As discussed in the previous section, the magnitude of fractionation depends on the degree of saturation attained during liquid condensation at 0°C. The degree of saturation in turn depends of ambient temperature and Rh conditions.

With a view to derive empirical equations defining relationship between isotopic composition of vapour and liquid condensate collected at 0°C, but under different degrees of supersaturation, systematic sampling efforts were going on at PRL, Ahmedabad. A dataset of 184 pairs of isotopic composition for liquid condensate and ambient vapour, collected during different seasons at

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IWIN-PRL - Scientific Progress Report January-December, 2010

Ahmedabad was analysed for deriving empirical relationship between isotopic composition of liquid condensate and vapour. The pairs of samples were sorted according to the degree of supersaturation (Si-max = vapour pressure at ambient T and Rh / saturation vapour pressure at ~0ºC). The data pairs were grouped in five classes with Si-max ranging from 1-2, 2-3, 3-4, 4-5 and 5-6. Samples in each class were treated separately and isotopic composition of vapour was regressed with respect to isotopic composition of liquid condensate and the value of Si-max. The coefficients of regression for different classes of samples are given in Table 4. Table 4. Coefficients of regression defining relationship between isotopic composition of vapour, liquid condensate and the saturation index

Si-max δ18O of Liq. Intercept R2 Si-max δD of Liq. Intercept R2

1 = Si-max < 2 1.33 0.22 -11.21 0.09 6.37 0.47 -68.23 0.352 = Si-max < 3 2.30 0.57 -8.61 0.69 5.49 0.83 -48.80 0.643 = Si-max < 4 1.77 0.77 -4.95 0.91 9.93 0.82 -63.90 0.964 = Si-max < 5 0.90 0.72 -2.63 0.78 9.76 0.93 -61.78 0.905 = Si-max < 6 0.08 0.97 5.77 0.92 3.37 0.92 -33.13 0.93

Si-max at 0°C = ( vapour pressure at ambient T and Rh) / (Saturation vapour pressure at 0°C)

Oxygen HydrogenCoefficients of Regression

Si-max

To verify the reliability of above regression coefficients, isotopic composition of vapour was

estimated from the isotopic composition of liquid condensate and the value of Si-max at Ahmedabad. The isotopic values of vapour as estimated above were compared with the measured values. A plot of estimated δ18O of vapour vs. measured δ18O is shown if Figure 7. For various classes of Si-max, 2σ uncertainty in estimated δ18O values of vapour range from ±1.7‰ to ±2.4‰ for Ahmedabad Station. The 2σ uncertainty in estimated δD values of vapour range from ±11‰ to ±15‰.

Figure 7. A plot of estimated vs. measured δ18O values of ground level atmospheric water vapour at Ahmedabad. The δ18O values are estimated using standard regression equations for five classes of Si-max, based on controlled experiments at Ahmedabad.

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IWIN-PRL - Scientific Progress Report January-December, 2010

These regression equations were used for estimating isotopic composition of ground level vapour at NIH-Roorkee and at NGRI-Hyderabad. Plots of estimated versus measured δ18O values of ground level atmospheric water vapour for Roorkee and Hyderabad are shown respectively in Figure 8 and Figure 9, along with the 2σ uncertainty values.

Figure 8. A plot of estimated vs. measured δ18O values of ground level atmospheric water vapour at Roorkee. The δ18O values are estimated using standard regression equations for five classes of Si-max, based on controlled experiments at Ahmedabad.

Figure 9. A plot of estimated vs. measured δ18O values of ground level atmospheric water vapour at Hyderabad. The δ18O values are estimated using standard regression equations for five classes of Si-max, based on controlled experiments at Ahmedabad.

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IWIN-PRL - Scientific Progress Report January-December, 2010

The uncertainty in the estimated isotopic composition of ground level vapour may seem to be large in comparison to analytical precision of 0.1‰ for δ18O and 1‰ for δD. However, it must be recognized that (i) satellite based estimates for δ18O and δD of ground level vapour are not available; (ii) The satellite based estimates are available only for δD for 550-850 hPa altitude range, with uncertainty of 10‰ in tropics and >20‰ for polar region. Considering these facts, estimates available from IWIN’s approach are extremely useful to understand the dynamics of ground level water vapour and its interaction with surface water sources. Further, the 2σ uncertainty values reported here also include those outliers which can be ignored considering their unusually different d-excess values which indicate possibility of errors related to sampling, storage or analyses. If such data points are ignored the uncertainty in the estimated isotopic composition can reduce considerably.

The applicability of standard regression equations based on controlled experiments at Ahmedabad, for estimating isotopic composition of vapour in other climatic regions, is proven by reasonably reliable estimates of isotopic composition of vapour at NIH-Roorkee and NGRI-Hyderabad, where similar experiments were undertaken to sample both vapour and liquid condensates. This assures that isotopic composition of vapour estimated from liquid condensates being collected at all other IWIN stations across India, will provide a reliable idea about trends and magnitudes of seasonal variations in the isotopic composition of ground level vapour. This activity will be done for all stations during the current year. However, to showcase what kind of information can be generated, isotopic composition of ground level vapour at NIH-Sagar is estimated where only liquid condensate samples are collected.

A time series of measured δ18O and δD of liquid condensate and δ18O and δD of vapour estimated from liquid condensate at NIH-Sagar is shown in Figure 10. An important observation from this data is that there is distinct seasonal variation in isotopic make up of ground level vapour as evident from δD. This is also reflected in time series of d-excess

Figure 10. A time series of measured δ18O and δD of liquid condensate and δ18O and δD of vapour estimated from liquid condensate at NIH-Sagar

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IWIN-PRL - Scientific Progress Report January-December, 2010

7.4 Insights about ground level vapour dynamics

One of the important capabilities developed due to IWIN National Programme is the monitoring of seasonal and spatial variation in the isotopic composition of ground level vapour. Until the developments under IWIN there was a total knowledge gap about the dynamics of ground level vapour front. Due to capability of isotopic tracing of ground level vapour under IWIN it is now possible to notice the seasonal and spatial variation in ground level vapour front and identify the factors responsible for it.

Time series of d-excess of ground level water vapour estimated from liquid condensates collected at Ahmedabad, Roorkee, Hyderabad and Sagar is plotted in Figure 11. It is seen that at each station ground level vapour front varies differently. It is already known from previous work that, in general, higher d-excess values of vapour indicate greater proportion of re-evaporated vapour under lower relative humidity and vice a versa. Therefore, at a given station, relatively lower d-excess values possibly indicate smaller proportion of re-evaporated component. It is seen from Figure 11 that lower d-excess values correspond to rainy season (Jun-Sept) when relative humidity is generally high and extent of evaporation is expected to be smaller. While Ahmedabad, Roorkee and Hyderabad have comparable trends of seasonal variation, Sagar behaves very differently. The pre-monsoon post-monsoon difference in d-excess is much larger at Sagar compared to other stations. Also the ground level vapour does not return to its pre-monsoon d-excess values even after the monsoon is over. Such apparent differences in the ground level water vapour at different stations can be studied in detail in light of the wind trajectories, rainfall, weather conditions, surface water availability etc at each station.

-60

-50

-40

-30

-20

-10

0

10

20

30

40

Jun/08 Sep/08 Dec/08 Apr/09 Jul/09 Oct/09 Feb/10Time

d-e

xces

s (‰

)

AhmedabadRoorkeeHyderabadSagar

Figure 11. Temporal variation in the d-excess of ground level water vapour at Ahmedabad, Roorkee, Hyderabad and Sagar stations, estimated from liquid condensates.

Liquid condensates from ambient water vapour are being sampled by IMD and CRIDA at several stations across the country. During the current year, isotopic composition of vapour will be estimated from the liquid condensate samples to obtain new insights about dynamics of ground level vapour.

14

IWIN-PRL - Scientific Progress Report January-December, 2010

7.5 Hydrograph separation and precipitation source identification using stable water isotopes and conductivity: River Ganga at Himalayan foothills

Understanding ground water and surface water interaction and quantifying the fluxes across various hydrological boundaries is one of the scientific objectives of IWIN. This aspect also has important socio-economic relevance. For example, observed retreat of several Himalayan glaciers and snow packs is a cause of concern for huge population in southern Asia dependent on the glacial-fed rivers emanating from Himalayas. There is considerable uncertainty about how cryospheric recession in the Himalayan region will respond to climate change, and how the water resource availability will be affected.

As a first step towards quantifying the contribution of glacier melt-water, hydrograph separation of River Ganga at Rishikesh into its constituent components, namely (a) surface runoff; (b) glacial ice-melt; and (c) groundwater discharge was done. A three component mixing model was employed using the values of δ18O and electrical conductivity (EC) of the river water, and its constituents, to estimate the time varying relative fraction of each component.

Figure 12. Temporal variation in δ18O, d-excess and electrical conductivity (EC) of Ganga River water at Rishikesh.

15

IWIN-PRL - Scientific Progress Report January-December, 2010

Temporal variation of δ18O and d-excess in water samples from the Ganga River at Rishikesh is shown in Figure 12A. In general, there is inverse relationship between the two parameters (enriched 18O associated with low d-excess and vice versa), apparently suggesting contribution from sources undergoing evaporation during non-rainy period. A progressive decline in δ18O from the end of February till the onset of summer monsoon rain is a clear indication of progressively enhanced contribution of glacial melt component in the river discharge during this period. The observed pattern of isotope variation is typical of the monsoon dominated glacier fed rivers in Himalayas also known for Parbati and Beas in Himachal Pradesh and Teesta in West Bengal. The EC of Ganga River water at Rishikesh varied between 50–150 μS/cm during the period under investigation. The EC is high during summer and low during winter and an inverse relationship between EC and δ18O is clearly seen Figure 12B.

To estimate the temporal variation in proportions of various components, contributing to the total discharge of the Ganga River at Rishikesh, mass balance equations involving δ18O and EC of three end member (ice-melt, ground water and surface runoff) were formulated for hydrograph separation. The δ18O and EC values for various end members were decided based on combination of IWIN measurements and published data. The results of hydrograph separation are shown in Figure 13.

Estimated fractions of ‘surface runoff’, ‘groundwater discharge’ and ‘glacial ice-melt’ contributing to the total discharge of the Ganga River at Rishikesh corresponding to each observation of river water δ18O and EC, are shown in Figure 13a. Also shown is the temporal variation of river water d-excess. Time series of discharge values estimated at the time of water sample collection are shown in Figure 13b. Also shown are the corresponding discharge estimates of the three components made using the model discussed in the text. Rainfall distribution at few stations in the catchments of Bhagirathi and Alaknanda rivers during 2008 and 2009 is shown in Figure 13c. (Source: http://www.imd.gov.in).

To validate the above three component model, an attempt was also made to estimate the river water temperature using mixing of the water from the three components and with certain additional assumptions. These assumptions are based on the consideration that flowing water temperature responds to the prevailing atmospheric temperature on different time scales – groundwater on annual; surface water on seasonal and the temperature of glacial-melt progressively increases from 0ºC towards the regional air temperature. This simple model of the river water temperature is to be viewed as a time varying mixing of lower temperature glacial melt-water with relatively warm groundwater discharge and the surface water component responding to the regional air temperature. The modeled river water temperature at Rishikesh, using estimated discharge fractions of each component is shown in Figure 13d along with regional atmospheric temperature (estimated from AIRS of NASA) and measured river water temperature. Nearly parallel variation of the modeled temperatures with the measured temperatures of the river water is an indication that the water mixing model in terms of components is reasonable. The observed discrepancy between modeled and measured temperatures of river water is possibly due to uncertainties in the satellite based estimate of regional temperature and deviation of air/water temperature equilibration assumptions from actual.

Important findings from this study on River Ganga are: (i) relative fraction of the surface runoff peaks (70-90%) during winter, due to near zero contribution of glacial ice-melt, and essentially represents the melting of surface snow from the catchment. (ii) The contribution of glacial ice-melt to the stream discharge peaks during summer and monsoon, reaching a maximum value of ~ 40% with an average of 32%. (iii) The fraction of groundwater discharge varies within a narrow range (15±5%) throughout the year. (iv) Based on the variation in the d-excess values of river water, it is also suggested that the snow-melt and ice-melt component has significant fraction derived from winter precipitation with moisture source from mid-latitude westerlies (also known as western disturbances).

16

Figure 13. Results of isotope hydrograph separation for River Ganga at Rishikesh. (a) The estimated fractional discharge of various components contributing to Ganga River at Rishikesh, and corresponding variation in d-excess of river water. (b) Total discharge of River Ganga at Rishikesh and discharge of individual components. (c) Rainfall variation in 2008 and 2009 which corresponds to observed difference in total discharge for these two years. (d) Modeled river water temperature based on the fractions estimated in this study, together with measured river water temperature and regional atmospheric temperature.

17

7.6 Spatio-temporal variations in surface water dynamics of Bay of Bengal using oxygen isotope and salinity relationship

The Bay of Bengal (BOB) and Arabian Sea (AS) are two tropical semi-enclosed land-locked ocean basins, separated by Indian continent, which together comprise the north Indian Ocean. Both these basins are influenced by seasonally reversing monsoon winds and form primary marine sources of vapor for monsoon rains over India and adjoining countries. Although both these basins occupy the same latitudes and affected by the same seasonal wind reversals they exhibit stark contrast in terms of hydrological balance (precipitation−evaporation), surface stratification, vertical transport, wind strength, convective activity, frequency of cyclonic storm, biological productivity, heat budget etc.

Most of the studies undertaken so far to understand the Bay of Bengal surface water processes involved cruise based analyses of physical and chemical properties or satellite based observations and modeling. Application of δ18O and δ18O-S relationship as a tool to understand marine physical processes is well known because variation in δ18O and S, and the δ18O-S relationship in ocean water is a reflection of various physical processes operating in marine environment, such as evaporation, riverine influx, direct precipitation, upwelling, advection, diffusion, etc, and mixing of different water masses. Therefore, a few studies have also attempted to study oxygen isotopic composition (δ18O) in Bay of Bengal surface and deep water and its relationship with salinity (S). However, available data specifically from Bay of Bengal is too small to use it for delineating various controlling processes. To be precise there exist only 186 data pairs for surface waters of Bay of Bengal, which is too meagre compared to a very large area (~2.2×106 km2) of Bay of Bengal. Therefore, several cruises have been undertaken in the Bay of Bengal as part of IWIN. Results of first five cruises undertaken during December 2007 to December 2008, as shown in Figure 14, have been summarized in the following with preliminary interpretation.

Figure 14. Tracks of five cruises, undertaken by IWIN-Anna University, in the Bay of Bengal during 2007-2008 are shown along with sample locations. Also shown are major rivers draining into Bay of Bengal.

18

IWIN-PRL - Scientific Progress Report January-December, 2010

The seasonal variation (March to May, June to October, and December to January) in the geographical distribution of the salinity and δ18O of surface waters of Bay of Bengal is shown in contour maps (Figure 15). For plotting the contours, surface water salinity and δ18O values measured in this study (227 samples collected during 2007-08) have been combined with recently published data from earlier cruises (103 samples collected during 1988, 1991, 2002, 2003, 2004; Singh et al, 2010, Deep Sea Research), covering a wide spatio-temporal range. Therefore, it may be reasonable to believe that the observed spatio-temporal variation from this combined data-set nearly represents the usual scenario in Bay of Bengal.

The geographical distribution of surface salinity and δ18O during warm and non-rainy period (March-April) is shown in Figure 15 (top panel). Both, the salinity and δ18O values are seen to progressively increase from the central Bay of Bengal towards west-southwest ward. This indicates that dominant freshwater influx during March-April is from north-east Bay of Bengal. During non-rainy period, the river discharge from both peninsular rivers (Godavari, Krishna, Mahanadi, Cauvery) and Himalayan rivers (Ganga, Brahmaputra and Irrawaddy) reduce. In fact, nearly 75% of the total river discharge into Bay of Bengal takes place during southwest monsoon season (Jun-Sept). However, peninsular rivers are mainly fed by rain water whereas Himalayan rivers carry rainwater during rainy season and snow/glacial ice-melt during non-rainy period. Further, annual river runoff from Himalayan rivers is nearly seven times that from peninsular rivers. Therefore, even during lean period, when only 25% of total annual river discharge occurs in to Bay of Bengal, the Himalayan rivers can be expected to discharge much larger volumes of fresh water into Bay of Bengal than peninsular rivers. This possibly is the reason for progressive increase in the salinity and δ18O of surface waters from central Bay of Bengal to west-southwest Bay of Bengal. Although data of salinity and δ18O from east Bay of Bengal is not available for Mar-April, from the east-northeastward decreasing patterns of contours it is obvious that surface waters of east Bay of Bengal should have further lower values of salinity and δ18O compared to central Bay of Bengal.

The geographical distribution of salinity and δ18O during rainy season (June-October) is shown in Figure 15 (middle panel). A prominent change during rainy season is the much lower values of salinity and δ18O observed in the surface waters of west Bay of Bengal, along the east coast of India. This can possibly be ascribed to the increased proportion of fresh water contribution from Mahanadi and Godavari rivers. The available samples from east Bay of Bengal are limited to lower latitudes (~13 °N), however, from the contour map it seems that entire Bay of Bengal, north of 15 °N, has lower salinity and δ18O values during June to October. Also the surface waters of Bay of Bengal covering large area in the 10-15 °N has lower salinity and δ18O values during rainy period (June-October) compared to warm non-rainy period (March-April). Thus, fresh water front moves further southward during rainy season compared to summer. Another important feature is that in spite of considerable precipitation in Bay of Bengal during rainy season, ranging from ~1m off southeast India to ~3m in the Andaman Sea and coastal region north of it, the surface waters are observed to remain laterally inhomogeneous, maintaining the latitudinal gradient.

The geographical distribution of salinity and δ18O during winter months (November-December) is shown in Figure 15 (bottom panel). During winter months, surface water with low values of salinity (~31 PSU) and δ18O (< –0.15) is observed up to ~13 °N in Bay of Bengal. This is the period during which the surface waters north of ~13 °N tend to be nearly homogeneous with a very small range of variation in salinity and δ18O. The samples with high salinity (>34 PSU) are located only south of ~12 °N in Bay of Bengal. Therefore, it seems that ~12 °N demarcates a virtual boundary and that freshwater influx into Bay of Bengal can not move further southward of it.

Latitudinal variation in salinity and δ18O, and salinity vs. δ18O relationship is plotted in Figure 16. There is no systematic trend in latitudinal variation of salinity or δ18O of samples collected during an individual cruise undertaken as part of this study Figure 16 (top panel). Thus, while there is a definite latitudinal variation in salinity and δ18O, as observed in seasonal contour maps (Figure 15) based on collection of samples from several cruises during different seasons, the same is not detectable for an individual cruise. This signifies the fact that the surface waters of Bay of Bengal are considerably inhomogeneous in space, both along or across the latitudes. This possibly indicates that

19

Figure 15. Contour maps showing seasonal variation (March-May, June-October, and December-January) in the geographical distribution of the salinity and δ18O of surface waters of Bay of Bengal.

20

Figure 16. (Upper panel) Latitudinal variation in salinity and δ18O of surface waters of Bay of Bengal. (Lower Panel) A plot of δ18O vs. salinity for Bay of Bengal surface waters.

the fresh water influx from different rivers modify the salinity and δ18O of surface waters of Bay of Bengal as logically expected, but the fresh water plumes seem to retain their identity even far away (few hundred kms) from the mouth region. This is clearly evident in the distinctly lower δ18O values of samples between 14 to17 °N latitude, collected during Andaman-Kolkata cruise in Nov-Dec, 2008 (Figure 16, top panel). These samples are located in a region (see Figure 15, bottom panel) where

21

IWIN-PRL - Scientific Progress Report January-December, 2010

Irrawaddy River discharges huge volume of fresh water into Bay of Bengal. It is also important to recognize that region drained by Irrawaddy receives considerably depleted rain during Nov-Dec. This isotopically depleted rain water is discharged by Irawaddy into Bay of Bengal which is possibly manifested in the lower δ18O values of the Bay of Bengal surface water samples (enclosed in an oval; 14-17 °N; Figure 16 top panel and bottom panel). Very strong influence of isotopicallly depleted Irawaddy river water is also evident from the fact that there are a few samples which have conspicuously lower δ18O values but have salinity within normal range of Bay of Bengal. This signifies that Irrawaddy river water’s oxygen isotopic composition is strong enough to reduce the δ18O values of surface waters of Bay of Bengal but its volume is not large enough to reduce the salinity. An effect opposite to this is also signified by those samples (marked by ovals around 11-13 °N and 19-21 °N in Figure 16) which have relatively lower salinity but do not have correspondingly lower δ18O values. These samples indicate that there is sufficient enough river water discharge into Bay of Bengal to reduce its salinity but its isotopic composition is comparable to that of average value of Bay of Bengal and consequently there is no reduction in δ18O values. These samples are located in front of Krishna, Godavari and Mahanadi (Figure 15 and Figure 16).

7.7 Hydrograph separation to estimate water lost due to evaporation from southern tributaries of Ganga in peninsular part of Ganga Basin

Ganga is the major river of India with its source in the Himalayas. Several studies in recent year have highlighted vulnerability of Himalayan glacial melt and very large population of India living in the Ganga Basin while most tributaries of Ganga originate in Himalaya in the north, a significant component of river discharge and sediment load is also derived from the monsoon fed rivers of central India. These rivers are commonly known as southern tributaries of Ganga and drains through the semi-arid part of India. The hydrogeology of the region draining the southern tributaries is significantly different from those comprising the northern tributaries originating from Himalaya. The major streams that comprise southern tributaries are Chambal, Betwa, Ken and Son Figure 17. These rivers drain the catchment area spanning the states of Uttar Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajsthan and Chattisgarh. In recent years considerable attention has been focus on the impact of global climate change on the glacial melt component of the Himalayan rivers but it is not difficult to visualize that the impact of global climate change could be even more disastrous in the catchment area of southern tributaries of Ganga.

A set of four time series plots of river water monitoring at various stations located on rivers Chambal, Betwa, Ken and Son is presented in Figure 18. The temporal variation in the parameter δ18O at various stations is plotted in Figure 18a. Similar variations in parameters d-excess, EC and Temperature of river water at the monitoring stations are plotted in Figure 18b, Figure 18c and Figure 18d respectively. The δ18O and EC of river water have their lowest value during the rainy period (Summer Monsoon) and progressively increase till the onset of next year’s rainy season. The d-excess parameter varies opposite to the trend exhibited by δ18O and EC. The river water temperature follows the trend exhibited by the regional average atmospheric temperature of the study area.

A modeling exercise similar to hydrograph separation done earlier for the northern tributaries of Ganga up to Rishikesh is presently underway. Unlike in the previous case where the aim was to estimate the glacial melt component, the aim of the exercise underway is hydrograph separation to estimate water lost due to evaporation.

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IWIN-PRL - Scientific Progress Report January-December, 2010

Figure 17. Map showing southern tributaries of River Ganga

Figure 18. Time series plots for δ18O, d-excess, electrical conductivity (EC) and temperature of river Chambal at Kota and Udi, River Betwa at Rajghat and Shahijina, river Ken at Banda and River Sone at Kuldah Bridge and Chopan.

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IWIN-PRL - Scientific Progress Report January-December, 2010

8. Technical Personnel trained:

During 2010 four M.Sc. students worked in IWIN programme for their summer/ autumn internship projects at PRL. These projects were conceived to strengthen and supplement ongoing laboratory activities of IWIN. The names and affiliations of these students are: (1) Mr. Vikash Kumar, IISER, Kolkata; (2) Mr. A.K. Samal, BHU, Varanasi; (3) Praveen Kumar Mishra, BHU, Varanasi; (4) Sasiganesh Raja, Annamalai Uni.

9. Research Publications arising out of the present project:

9.1 Rain-Vapor Interaction and Vapor Source Identification using Stable Isotopes from Semi-Arid Western India. R.D. Deshpande, A.S. Maurya, B. Kumar, A. Sarkar, and S.K. Gupta Journal of Geophysical Research, 115, D23311, doi:10.1029/2010JD014458.(2010)

9.2 Hydrograph separation and precipitation source identification using stable water isotopes and conductivity: River Ganga at Himalayan foothills. A.S. Maurya, Miral Shah, R.D. Deshpande, R.M. Bhardwaj, A. Prasad and S.K. Gupta Hydrological Processes, DOI: 10.1002/hyp.7912, in press.(2010).

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PROGRESS REPORT 2010

NATIONAL PROGRAMME ON ISOTOPE FINGERPRINTING OF WATERS OF INDIA

(IWIN)

HYDROLOGICAL INVESTGATION DIVISION

NATIONAL INSTITUTE OF HYDROLOGY, ROORKEE

247 667 (Uttarakhand)

25 

ANNUAL PROGRESS REPORT (2010) National Programme on Isotope Fingerprinting of Waters of India(IWIN)

National Institute of Hydrology, Roorkee - 247 667 (Uttarakhand) 1. Project Title: National Programme on Isotope Fingerprinting of Waters of India (IWIN)

DST No: IR/54/ESF/05-2004 dated July17, 2007

2. Principal Investigator (National): Dr. Bhishm Kumar, Scientist F& Head, H.I. Division Principal Investigator (Internal): Dr.M.S.Rao, Scientist C, H.I. Division

Date of Birth 15th March,1954 1st June, 1965

3. Co-Investigators: •Dr. P.C. Nayak, Scientist C, Regional Centre, Kakinada •Dr. Renoj Thayyen, Scientist C, Regional Centre, Jammu •Sh. R.K. Jaiswal, Scientist B, Regional Centre, Sagar

Date of Birth 19th February, 1972 17th June, 1967

4. Project staff: Dr. Gopal Krishan, Research Scientist Dr. P. Purushothaman, Research Associate

25th June, 1973 26th December, 1979

5. Broad area of Research: Earth & Atmospheric Science Sub Area: Earth Science 6. Approved Objectives of the Proposal : Two new IWIN centres were established at Jammu and Kakinada. With addition of these two stations, NIH-IWIN will have total 4 stations well distributed along the air-moisture trajectory from Bay of Bengal coastline to NW Himalaya. The specific role of NIH, Roorkee and its Regional Centres, Sagar, Jammu and Kakinada in the IWIN-project is as given below:

NIH, Roorkee (Latitude: 29052’, Longitude: 77053’, Altitude: 268m, Date of Start: July, 2007)

a) Collection of Ground Level Vapour (GLV) (daily), precipitation (event based), Ganga Riverwater (weekly from Upper Ganga Canal) and local groundwater (twice in a month).

b) Monitoring of local temperature, Rain fall and Relative Humidity

c) Isotopic analysis of samples collected at NIH, Roorkee and its Regional Centres, Sagar, Jammu and Kakinada.

d) Isotopic analysis of samples received by NIH from the collaborating participating IWIN Organizations.

e) Basic experiments towards improving understanding of isotope hydrology.

Regional Centre, Sagar (Latitude: 23050’, Longitude: 78050’; Altitude: 517m; Date of start: April, 2008)

a) Sampling of GLV by condensation method on conical surface (daily), precipitation (event based), local groundwater (monthly) and lake samples (monthly).

b) Weekly collection of meteorological data from a nearby weather station.

26 

Regional Centre, Jammu (Latitude: 32042’, Longitude: 74051’; Altitude: 292m; Date of Start: April, 2010)

a) Sampling of GLV by condensation method on conical surface (daily), precipitation (event based) and local groundwater (monthly).

b) Weekly collection of meteorological data from a nearby weather station.

Regional Centre, Kakinada (Latitude: 16057’, Longitude: 82015’; Altitude: 02m; Date of start: May, 2010)

a) Sampling of GLV by condensation method on conical surface (daily) and precipitation (event based).

b) Weekly collection of meteorological data from a nearby weather station.

Date of Start : September, 2007 Total cost of Project : Rs. 5,47,18,000/- Funds allotted to NIH : Rs. 57,94,000/-

Date of completion: September, 2012 Expenditure as on 31st

December, 2010: Rs. 8,11,964/-(un-audited sum of known expenditure)

7. Methodology

NIH is carrying out following sample collection at Roorkee, Sagar, Jammu and Kakinada:

(a) Daily collection of GLV by condensation and push and trap method using ice and LN2, respectively,

(b) Ground water

(c) River water

(d) Rain water

Collected 1711 water samples at NIH (Roorkee, Sagar, Jammu and Kakinada) and analyzed for δ18O and δD. The details of samples are given below in table-1:

Table 1: Summary of the samples collected and analyzed

Station Sample type Frequency No. of samples collected & analyzed (January- December,10)

Roorkee GLV Using Ice Using LN2

(near ground) Rain samples Groundwater River Ganga

Daily Daily Event based Twice/month Weekly

328 278 74 88 39

Sagar GLV (Ice) Rain water Groundwater Surface water

Daily Event Based Monthly

349 91 12

27 

Twice/month 24

Jammu GLV (Ice) Rain water Groundwater

Daily Event Based Monthly

204 40 8

Kakinada GLV (Ice) Rain water

Daily Event Based

150 26

Total Samples Analyzed 1711 8. Salient Research Achievements

8.1 Summary of progress A) Isotopic variations (δD) of NIH stations (Roorkee, Sagar, Jammu and Kakinada)

As per the decision taken in the 4th Coordination Committee and 3rd Project Review Committee at NIO, Goa, NIH has established two new stations at its Regional Centres, (i) Jammu to initiate the observations from North-West part of India and (ii) at Kakinada to initiate the observations from east-coast. A total of 252 samples at Jammu and 176 samples at Kakinada have been collected and analysed for δD. The results obtained from all the four stations for the year 2010 are shown in fig. 1.

28 

Fig. 1. Isotopic variations (δD) of GLV at NIH Roorkee and at its Regional Centres

B) Temporal variation of rainfall and δD of GLV and rainfall Rainfall pattern for 2007-10 and variation of δD of GLVcond. (collected by condensation method) and rain for this period is shown in fig. 2. The variation clearly indicates; depletion in isotopic composition of GLVcond. whenever rain occurs, higher depleted levels of GLV during monsoon season compared to non-monsoon season and similarity in isotopic pattern of rain and GLV. As expected from the principle of isotopic fractionation, isotopic composition of rain is normally observed to be more enriched than the ambient vapour. The depleted/enriched isotopic pattern distinguishes vapours that are brought into the region by rains (vapors of depleted isotopic composition) from the local vapours (enriched). This also provides way to identify onset & withdrawal of monsoon. In fig. 2, the IMD declared dates of onset and withdrawal of monsoon at Roorkee is shown. The comparison of these dates with the dates that can be inferred from the isotopic pattern (onset of depleting trend as onset and start of enrichment trend as withdrawal) it can be said concluded that it is easier and prudent to identify onset and withdrawal of monsoon using isotopic trend analysis. This proves isotopic trend analysis to be a powerful technique to analyze the monsoon behavior (delay, onset, strength, breaks, withdrawals etc). For example, increasing monsoon strength can be observed from depleting isotopic composition of GLV during monsoon. This is demonstrated in the figure 3 by taking data for the period 2007-10. From 2007 to 2010, rainy days increased from 118 days to 142 days and the average rainfall increased from 6.5mm/day to 10.0 mm/day. This has resulted in depletion of average isotopic composition of GLV during monsoon from -71‰ in 2007 to -97 ‰ in 2010. Thus, isotope of GLV can be used as an important parameter in meteorological sciences.

29 

 

                                   Actual Date of monsoon onset ( ) and withdrawl ( ) (as per IMD) Fig. 2: Daily variation in δD in rainfall and atmospheric air-moisture, and the rainfall amount

 

δD = ‐0.037x Rainfall amount ‐ 42.44R² = 0.78

‐100

‐90

‐80

‐70

‐60

600 800 1000 1200 1400 1600

δD(‰

)

Rainfall (mm)

(2007, 118)

(2008, 124)

(2009, 118)

(2010, 142)

 Fig. 3. Variation of monsoon rainfall and isotopic composition of GLV for the period 2007‐10.  

Figures in the bracket indicates year and no. of monsoon days (onset to withdrawal).   

C) Inter‐relation of isotopic values of GLV obtained using P&T with the Condensation method   During  monsoon  period,  isotope  composition  of  cloud,  associated  vapour  and  condensing  rains  follow 

equilibrium fractionation. In the post monsoon, local vapors generating mainly from evaporation of surface water sources,  soil  moisture  etc  follow  kinetic  process.  It  was  conjectured  that  the  vapours  associated  with  non‐euqilibrium/equilibrium  fractionation  processes  can  be  distinguished  by  analyzing  by  condensing  it  at  two different temperatures. This is done by condensing ground level vapours at 00C (condensation method) and at ~ ‐750C (Push and Trap method) method. During non‐monsoon period, vapours condensed at 00C appear relatively enriched  compared  to  that  collected  by  condensed  at  ~  ‐750C  (fig.  4.) while,  during monsoon  period  no  such difference  is  observed.  This  phenomenon  can  be  developed  as  an  independent method  to  identify  onset  and 

30 

withdrawal  of monsoon  and  also  in  understanding  temperature  dependent  isotopic  fractionation  of  vapours during its condensation. 

 

0

20

40

60

80

100

120

140

160‐200

‐150

‐100

‐50

0

50

100

1.1.09 11.4.09 20.7.09 28.10.09 5.2.10 16.5.10 24.8.10 2.12.10

Rainfall (m

m)

δD (‰

)

Rainfall Condensation P&T Rain

Jun, 29

Jun, 29

Jul, 4

Oct, 2

Sep. 28

 Fig. 4. Seasonal effect of Push & Trap and condensation methods on air moisture sampling

 An  attempt  has  been made  to  develop  an  empirical  relation  to  predict  expected  isotopic  composition  of 

vapour condensed using P & T method (Pred(δDTrap) from the  isotopic data of GLV measured using condensation method at 00C (δDCond) and the observed absolute humidity (AH). Since with changing seasons vapour source and thermodynamic conditions are expected to change and thereby changing the equation coefficients; the observed data  set  was  split  into  4  seasons:  Monsoon  (July‐September),  Pre‐monsoon(March  to  June),  Post‐monsoon (October‐November) and winter (December to February). The relation between  Pred(δDTrap),  δDCond and AH was then obtained for these 4 season for the year 2010 at IWIN station Roorkee. The developed relations are shown in the table below.  

 The equations developed for various seasons are as follow: 

Pre‐Monsoon  Pred(δDtrap) = 0.38*( δDcond.) +0.67*(AH) ‐60.3  Monsoon  Pred(δDtrap) = 0.84*( δD cond.) +0.29*(AH) ‐30.2  Post‐monsoon  Pred(δDtrap) = 0.95*( δ Dcond.) +3.88*(AH) ‐75.3  Winter  Pred(δDtrap)  = 0.94*( δ Dcond.) +3.43*(AH) ‐73 

 The correlation between Pred(δDTrap) with that of the actual δDTrap for these seasons is shown in fig. 6.  A high 

correlation with slope ~1 provides high degree of confidence  in  the predicted  (δDTrap) using the observed δDcond and AH data.  

31 

y = 1.019xR² = 0.719

-140

-120

-100

-80

-60

-40

-20-140 -120 -100 -80 -60 -40 -20

Mea

sure

d δD

(Trap)

‰

Predicted δD(Trap)‰

Pre-Monsoon

y = xR² = 0.864

-140

-120

-100

-80

-60

-40

-20-140 -120 -100 -80 -60 -40 -20

Mea

sure

d δD

(Trap)

‰

Predicted δD(Trap)‰

Monsoon

y = 1.017xR² = 0.874

-140

-120

-100

-80

-60

-40

-20-140 -120 -100 -80 -60 -40 -20

Mea

sure

d δ

D(Trap)‰

Predicted δD(Trap)‰

Post-Monsoony = 1.003xR² = 0.710

-140

-120

-100

-80

-60

-40

-20-140 -120 -100 -80 -60 -40 -20

Mea

sure

d δD

(Trap)

‰

Predicted δD(Trap)‰

Winter

  

Fig. 6. Relationship of Predicted (Trap) and Measured (Trap)  

D) Isotopic Correlation with Absolute Humidity: Stable isotopic composition of vapour and humidity can be dependent parameters if the source is same. This 

aspect is examined by cross plotting these two parameters (fig. 2) which shows a high correlation between these two  parameters.  With  increasing  of  humidity  from  non  monsoon  season  to  monsoon  season,  the  isotopic composition of vapor depletes. During monsoon, absolute humidity reaches to ~25 g/m3 with δD ~ ‐115‰ while, in non‐monsoon season it Abs. humidity becomes as low as 10 g/m3 with δD ~ ‐25‰.   

 Fig. 7.: Correlation between stable isotopes in atmospheric air moisture and the absolute humidity 

 The  temporal  correlation  between  δDGLV,  δDrainfall  and  Absolute  Humidity  shown  in  fig.  8  also  confirms  a 

positive correlation between isotopic composition of ground level vapour and absolute humidity.  

32 

 

 Fig. 8: Temporal variation of absolute humidity and isotopic composition of air‐moisture & rain at Roorkee.  In the figure 8, depletion  in  isotopic composition and decrease  in humidity can be seen even at times of no 

rain events. A probable reason for this could be due to the fact that several times, rain spread occurs over a small local region although atmospheric moisture may be spread over a large region. Therefore, even if rain occurs at a distant place, its impact through its atmospheric moisture measurement can be observed. Therefore, atmospheric moisture can be used as signal for rain advancement.  Further analysis of correlation between absolute humidity (AH) and δDGLV at Roorkee has been done by cross-correlogram plots using the software MATLAB. The data series was taken for the period January, 2007 to August, 2010. Missing data was removed. Final data series contained 935 no. of data points. Cross-correlation between isotopic values of GLV of Roorkee and AH for per day lag has been examined. Correlation coefficients much higher than the confidence level 0.08 has been observed indicating a good match between the two series. The correlaogram plot is shown in fig. 9a. The plot clearly indicates maximum correlation at -0.63 with ‘0’ lag (no time lag between the two events). The autocorrelation parameters obtained for other series have also been analyzed in a similar manner to identify cross correlation indicates spatial/temporal relation between humidity and isotopic time series measured at various IWIN stations. The correlaogram parameters obtained is given in table 2 and plots are shown in fig. 9b. These correlograms provide possibility to develop a mathematical relation for predicting isotopic composition of air-moisture from humidity data and also a relation to predict isotopic composition of air-moisture of a station if data for other IWIN station. Table 2: Cross Correlation of Absolute Humidity vs. δD (Roorkee) and δD of different regional stations  

Cross correlation b/w stations/seasons

Parameters Significant values above +0.01

Cross correlation Coff. ,

Lag Roorkee AH vs. δD - -0.63

Roorkee/Monsoon AH vs. δD - -0.44 Roorkee/Non Monsoon AH vs. δD 0.31 +0.31

Roorkee-Sagar δDR vs. δDS

0.75 +0.75

Roorkee-Sagar/Monsoon δDR vs. δDS

0.62 +0.62

33 

Roorkee-Sagar/Non Monsoon δDR vs. δDS 0.65 +0.65 Roorkee-Jammu δDR vs. δDJ 0.51 +0.51

Roorkee-Jammu/Monsoon δDR vs. δDJ 0.41 +0.41 Roorkee-Jammu/ Non-Monsoon δDR vs. δDJ 0.39 +0.39,3 days

Lag

Cro

ss-C

orre

latio

n

Roorkee: Monsoon + Non- Monsoon (Absolute Humidity vs. δD)

Lag

Cro

ss-C

orre

latio

n

Roorkee: Monsoon (Absolute Humidity vsδD)

Fig 9a. Cross- Correlation Absolute Humidity Vs

34 

δD Lag

Cro

ss-C

orre

latio

n

Roorkee- Sagar: Monsoon (δDR vs δDS) Roorkee- Jammu: Monsoon (δDR vsδDJ)C

ross

-Cor

rela

tion

Lag

LagC

ross

-Cor

rela

tion

Roorkee- Sagar: Non- Monsoon(δDR vs. δDS) Roorkee- Jammu: Non-Monsoon (δDR vsδDJ)

Cro

ss-C

orre

latio

n

Lag

Lag

Cro

ss-C

orre

latio

n

Roorkee- Sagar: Monsoon + Non-Monsoon (δDR vs δDS)Roorkee- Jammu: Monsoon+ Non-Monsoon (δDR vsδDJ)

Cro

ss-C

orre

latio

n

Lag

 

Fig 9b. Cross- Correlation of δD between different regional stations with Roorkee

E) Wind Trajectory and isotopic data on GLV

Since, changing wind pattern changes the source of vapors and which also cause change  in  isotopic composition of  ground  level  vapors  a  correlation between wind  trajectory  and δ18O of GLV has been examined (fig 10). Some of the important inferences that could be drawn from the analysis are: δ18O of GLV of pre‐monsoon moisture originating  from Bay of Bengal are more depleted (c: δ 18 O= -6.07‰) compared to Western Disturbances (b: δ 18 O=  ‐1.85‰). This  is mainly due  to continental effect. Local vapors generated from evaporation of soil moisture are further depleted in isotopic composition (a: δ 18 O= ‐10.24‰). This is due to evaporation effect.  

35 

8 Jan D30 Jan E

6 Feb D

‐30

‐25

‐20

‐15

‐10

‐5

0

3‐Jan 22‐Feb 12‐Apr 1‐Jun 21‐Jul 9‐Sep

δ18 O

 (‰)

‐10.24‰ ‐1.85‰ 

‐6.07‰

IMD IMD

IMD

a b

c

Local Vapour

Direction of movement of the vapour

From Top left 

Fig. 10: (a‐c) Wind trajectory and changes in isotopic composition of GLV at Roorkee in Jan‐Feb 2010 (D: Delhi). 

The Westerlies deplete GLV from δ 18 O= -8.39‰ (Fig. 10.a) to δ 18 O= -14.71‰ (Fig. 10.f) from June to September. Very high depletion occur -24‰ to -28 ‰ (Fig. 10.b & e) when winds bring moisture from the Arabian Sea.

 These analyses clearly demonstrate use of isotopes to fingerprint the source of moisture.  

4 Jun E16 Jun D

23 Jun E

‐30

‐25

‐20

‐15

‐10

‐5

0

3‐Jan 22‐Feb 12‐Apr 1‐Jun 21‐Jul 9‐Sep

δ18 O

 (‰)

‐8.39‰ ‐24.67‰

‐12.20‰

IMD

IMD

IMD

a b

c

13 Jun

Direction of movement of the vapour

From Top left 

Onset of monsoon

36 

‐30

‐25

‐20

‐15

‐10

‐5

0

3‐Jan 22‐Feb 12‐Apr 1‐Jun 21‐Jul 9‐Sep 29‐Oct

δ18 O

 (‰)

29 Sep

9 Sep E21 sep D

26 Sep E

‐15.03‰ ‐27.79‰

‐14.71‰

IMD IMD

IMD

d e

f

Withdrawal of MonsoonDirection of movement of the vapour

From Top left 

Fig. 11: Use of wind trajectory data for interpreting the isotopic data of GLV

    IWIN Sample Repository: 

Samples Communicated to PRL Date Groundwater  Rain water  Surface Water 

23.02.2010  Roorkee: 4 Sagar      : 2 

Roorkee: 1 Sagar     :  2 

Roorkee: 4 Sagar     : 2 

18.09.2010  Roorkee :7 Sagar      :6 Jammu   :5 

   

28.12.2010  Roorkee :4 Sagar      :6 Jammu   :2 

   

 New Experiments:        Work to be done in 2011-12:

The routine analysis of air moisture, rain, river and groundwater will continue in this year. Establishing new site in North-east preferably at Guwahati. Scientific/technical publication/reporting in consultation with IWIN Secretariat.

8.2 New Observations 1) Isotopic signatures of GLV can be used to confirm the arrival of monsoon vapors at any  location prior to 

the arrival of visible clouds. Therefore,  isotopes can be added as an additional tool for the prediction of 

37 

the monsoon arrival and its withdrawal. 2) The Isotopic composition of GLV shows a direct relation with absolute humidity. 3) Correlation of  isotopic  trend of GLV  for  collected using  condensation  and  P&T provides  a new way  to 

identify  onset  and  withdrawal  of monsoon  and  also  develops  new  understanding  to  look  at  isotopic fractionation moisture during condensation process at different cooling temperatures.   

4) An empirical  relation has been developed between  δDP&T  and  (δDCond & Absolute humidity)  for  various seasons  like pre‐monsoon  (March‐June), monsoon  (July‐September), post‐monsoon(October‐November) and winter (December‐February). 

5) The  cross‐correlation  analysis  of  spatial  and  temporal  relation  between  humidity  and  isotopic  data  of different IWIN (NIH) stations shows high degree of correlation.  

6) The wind trajectory data confirms isotope as a tool in meteorology to fingerprint source of moisture.  

8.3 Innovations: Nil 8.4 Application

The data provides base line information and if appropriately incorporated can support in short term weather forecast and in differentiating between pre-monsoon showers and monsoon rains.

8.5 Any other

9. Research work which remains to be done under project

10. Technical personnel trained (till date): 13 nos. Sh. Rahul Jaiswal, Scientist-B, Regional Centre, Sagar

Dr. A. K. Seth (Ex. Research Scientist), Dr. Shilpi Saxena (Ex Research Associate), Dr. Swati Srivastava (Ex

Research Associate), Sh. Himanshu Sharma and Sh. Gaurav Kothial (Ex. Sr. Technician.) have been trained in the project at various stages of the project. They have left the Institute and joined other organizations.

Dr. Gopal Krishan (Research Scientist), Dr. P. Purushotaman (RA), Sh. A. K. Gupta (RA), Sh. Y.S. Rawat (JRF), Ms Pooja Devi (PhD Student, IIT-Roorkee), Sh. P.R. Rao (RA, Kakinada) and Sh. Suraj Kotwal (Jammu) have been trained in various technical aspects of the project. Lectures Delivered:  

(i) M. S. Rao & B. Kumar (2010) Environmental Isotopes for Hydrological Studies; UGC Academic Staff College, BHU, Varanasi, 21st July, 2010

(ii) B. Kumar and M. S. Rao (2010) Isotope Analysis of Water Samples; UGC Academic Staff College, BHU, Varanasi; 21st July, 2010

(iii) M. S. Rao & B. Kumar (2010) Basics of Isotopes and Applications in Groundwater Hydrology; Training course for state PHED Engineers at Jaipur organized by CGWB, Jaipur 21-24 Sept, 2010.

M. S. Rao & B. Kumar (2010) Isotopes For Groundwater Studies: Project Planning, Sampling and Analysis; Training course for state PHED Engineers at Jaipur organized by CGWB, Jaipur, 21-24 Sept, 2010. 11. Ph.D. produced nos. nil

12. Research publications arising out of the current project:

List of publications from this project (including title, author(s), journals and year(s) Someshwar Rao M.; Bhishm Kumar; Gopal Krishan; Pankaj Garg,; Y. S. Rawat, and Jamil Ahmed (2010). Isotopic characterization of rainwater and air moisture at Roorkee. International conference of 7th Annual Meeting of Asia Oceania Geo-Sciences-2010 held at Hyderabad during July 5-9, 2010. Bhishm Kumar, S. P. Rai, U. Saravana Kumar, S. K. Verma, Pankaj Garg,S. V. Vijaya Kumar, Rahul Jaiswal, B. K. Purendra, S. R. Kumar, and N. G. Pande. 2010. Isotopic characteristics of Indian precipitation. Water Resources Research. 46: W12548.

38 

Patents filed/to be filed Major equipment (Model & Make)

39 

DST National Programme on Isotope Fingerprinting of Waters of India Network (I-WIN)

Progress Report

(Jan - December, 2010)

1. Principal Investigator: Prof. P. S. Datta Indian Agricultural Research Institute, New Delhi

2. Date of start: September 2007

Difficulties: • Since, no fund has been received from the DST for the FYs 2008-09, 2009-10 and 2010-2011, the

collection of daily ground level vapour at New Delhi by trapping method could not be implemented. 3. Accomplishments (Jan - December, 2010):

• Ensured implementation of the IWIN Programme activities as envisaged to the extent possible in view

of the difficulties outlined above. • One hundred and eighty seven daily atmospheric moisture samples (DMC) from about 6m above

ground level were collected by condensation cone method using ice cubes in the cone of the IWIN sampling device for daily atmospheric moisture collection. Sixty three DMC samples have been sent to the PRL, Ahmedabad, for repository and 18O & 2H isotopic analysis.

• Twenty one daily composite rainwater samples were collected using the IWIN sampling devices for

rainwater, installed at about 25m above ground level. The collected rainwater samples were mostly during the monsoon and the pre-monsoon period. 60ml of every water sample was preserved in sealed airtight polypropylene bottles, taking all precautions to avoid evaporation losses during storage and transport to the analytical laboratory at PRL.

• In the DMC and rainfall sampling program, the duration of each precipitation event, air temperature,

humidity, precipitation amount, and other relevant meteorological data were also recorded, and sent to the PRL, Ahmedabad.

• Data on 18O & 2H isotopic composition of rain and ground-level daily air-moisture at New Delhi for the period May, 2008 to September, 2009 have been processed. The δD and δ18O values are reported as deviations relative to international standard SMOW (Standard Mean Ocean Water) are expressed by:

δ (in ‰) = (Rsample/Rstandard -1) ×1000; Where, R is the ratio 18O/16O or D/H). • The Time Series of δD and δ18O of rainfall: The measured δD and δ18O of rainfall from May, 2008

to September, 2009 at New Delhi is shown in Figure-1. Rainfall δ18O values ranged from +6.16‰ to −15.67‰, while the δD values ranged from +38.9‰ to −114.05‰. Generally, enriched δD and δ18O values as compared to the weighted mean value (-6.09‰ for δ18O) were found in pre-monsoon months May to July, and depleted values were found in monsoon months August to September. The time-series of δ18O and δD in rainwater show similar trend through out the period under investigation, and a clear gradual depletion between Jun-end with onset of monsoon to mid-July, followed by enrichment up to July-end, and subsequently, a trend of depleted δ-values up to mid-September to September-end during the late monsoon with withdrawal of monsoon. The opposite behavior of δD and δ18O values in the 8th July, 2008 rainfall event seems to be due to experimental error. Within the rainy season, certain large rain events have depleted isotopic values compared to other equally large rain events with significantly enriched isotopic composition (Figure-2), clearly suggesting that such isotopic differences can not be explained by amount or rainout effects.

40

Time Series of Daily Rainfall δ18O and δD in New Delhi

-20.00

-15.00

-10.00

-5.00

0.00

5.00

10.00

19/0

6/20

0820

/06/

2008

24/0

6/20

0824

/06/

2008

07/0

7/20

0808

/07/

2008

08/0

7/20

0809

/07/

2008

14/0

7/20

0815

/07/

2008

18/0

7/20

0826

/07/

2008

28/0

7/20

0831

/07/

2008

04/0

8/20

0807

/08/

2008

08/0

8/20

0813

/08/

2008

14/0

8/20

0816

/08/

2008

18/0

8/20

0823

/08/

2008

23/0

8/20

0806

/09/

2008

08/0

9/20

0815

/09/

2008

18/0

9/20

0819

/09/

2008

20/0

9/20

0812

/02/

2009

30/0

3/20

0911

/05/

2009

26/0

5/20

0903

/06/

2009

30/0

6/20

0901

/07/

2009

02/0

7/20

0910

/07/

2009

24/0

7/20

0917

/08/

2009

22/0

8/20

0926

/08/

2009

27/0

8/20

0931

/08/

2009

01/0

9/20

0902

/09/

2009

04/0

9/20

0910

/09/

2009

11/0

9/20

09

Date of Collection

δ18O

SMO

W (%

0)

-140.00

-120.00

-100.00

-80.00

-60.00

-40.00

-20.00

0.00

20.00

40.00

60.00

δDSM

OW

(%0)

dD SMOW d18O SMOW Poly. (dD SMOW) Poly. (d18O SMOW) Fig. 1

• Amount Effect: In the present dataset, the δD and δ18O isotopic compositions of rainfall at New Delhi

show no evidence for the amount effect (Figure-2) and also no correlation with air temperature. To what extent the varying source regions (Arabian Sea/eastern continental plus Bay of Bengal), variable magnitude of evaporation of falling rain drops, and/or cloud height (or temperature) and liquid water fraction can explain the observed variability between the rains events need to be ascertained.

Time Series of δ18O, δD and Rainfall Amount, in New Delhi

-100.00

-80.00

-60.00

-40.00

-20.00

0.00

20.00

40.00

60.00

12/0

2/20

09

30/0

3/20

09

11/0

5/20

09

26/0

5/20

09

03/0

6/20

09

30/0

6/20

09

01/0

7/20

09

02/0

7/20

09

10/0

7/20

09

24/0

7/20

09

17/0

8/20

09

22/0

8/20

09

26/0

8/20

09

27/0

8/20

09

31/0

8/20

09

01/0

9/20

09

02/0

9/20

09

04/0

9/20

09

10/0

9/20

09

11/0

9/20

09

Date of Collection

δ18

O a

nd δ

D w

rt SM

OW

(%0)

0

10

20

30

40

50

60

70

80

Rai

nfal

l Am

ount

(mm

)

Rainfall d18O Rainfall dD Rainfall Amount

Fig.2

• δD and δ18O Relationship: There exists a good linear relationship (Figure-3) between δD and δ18O in New Delhi rainfall: δD = 7.041 δ18O + 3.335 (R2= 0.8531; r = 0:978). This LMWL has slope and intercept lower than the GMWL, suggesting that the rainfalls have been exposed to evaporative effects during the course of fall. The slope stands for the relationship between two kinds of fractionation rates from 18O and D, and the intercept gives the deviation degree of actual D from that

41

in the equilibrium state, and are controlled by phase change processes from evaporation at the vapor origin site to the falling site.

δ18O and δD Relationship in Rainfall of Delhi

y = 7.0417x + 3.3353R2 = 0.8531

-140.00

-120.00

-100.00

-80.00

-60.00

-40.00

-20.00

0.00

20.00

40.00

60.00

-20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00

δ18OSMOW (%0)

δDS

MO

W (%

0)

Fig. 3

• Factors influencing the rainfall δ18O and δD: The lower slope and intercept than that in the GMWL

indicates isobaric cooling processes. The slope of the water line depends on evaporation of droplets after condensation from the cloud, leading to slope <8. In the GWML, actual precipitation is generated neither in the wet adiabatic nor in the isobaric cooling processes. In reality, the process occurs in pseudo-adiabatic conditions, because, a small amount of energy is removed from the system by rainout. Since, adiabatic and isobaric processes have basically similar features, the influences of different supersaturation ratios and different liquid-water contents need to be considered. In nature, during the phase change in mixed-cloud, the different humidity conditions induce different rate of vapor movement of liquid and solid phases, and liquid-water content in cloud, and make the actual situation deviate from the ideal Rayleigh condensation model. Cloud with temperature below 00C usually have super-cooled water droplets, ice crystals, and vapor. The proportion of the ice crystal in the cloud increases with lowering of temperature. Under the same temperature, due to the lower saturated vapor pressure at the ice surface than at the water surface, the saturated or even the non-saturated environment for droplets remains probably supersaturated for ice crystals. Since, the rapid growth of ice crystals may lead to breakdown of the equilibrium state, the stable isotopic fractionation is composed of two parts: the Rayleigh fractionation in the equilibrium state, and the kinetic fractionation. The present dataset, lead us to search for factors other than the amount effect to explain the isotopic composition of rainfall in Delhi. The factors that could have possibly influenced the rainfall δ18O and δD include: (a) change in the moisture source and the distance of air mass travel over Bay of Bengal and Indian Ocean, and (b) isotopic disequilibrium between seawater and vapor.

• The δ18O and δD in DMC ranged from about -38‰ to -6.0‰ and about -10‰ to -198‰ respectively (Fig – 4) showing isotopic enrichment trend from October to February, while depletion trend from August to September. 18O and D in DMC on rainy days were ~2 to 3 times depleted as compared to that in rainfall in August, but, depletion was ~3 to 8 times in September. Moisture origins make an effect upon the fluctuations of δ18O of atmospheric water vapor. In addition, the strengthening of the supply effect of monsoon rainfall on the evaporation of surface water may also affect the δ18O value of water vapor. In the absence of isotopes data on ground level vapour, for interpretation, ground-level daily air-moisture has been presumed to be representing the ground level vapour. Due to frequent and abundant rainfall in August from the southwest monsoon, the influence of evaporation on the falling raindrop, and the atmospheric water vapor approaching the saturation level, the δ18O values are depleted in rainfall and atmospheric water vapor. In early September, due to retreat of the southwest monsoon and the influence by the continental air mass, the δ18O values of atmospheric water vapor are enriched than those in August. In theory, the moisture origins of rainfall are

42

transported from high layer. The temperature of water vapor of the higher layer being very low, the δ18O of water vapor is expected to be lower than that of the lower layer, and need to be monitored.

Time Series of δ18O and δD in Daily Moisture Condensed in Delhi

-40.00

-35.00

-30.00

-25.00

-20.00

-15.00

-10.00

-5.00

0.0021/07/2008 09/09/2008 29/10/2008 18/12/2008 06/02/2009 28/03/2009

δ18

OSM

OW

(%0)

-250.00

-200.00

-150.00

-100.00

-50.00

0.00Date of Collection

δD

SMO

W (%

0)

d18O wrt SMOW Corrected Final

dD

Fig. 4

The higher intercept in Fig-5 suggests adiabatic process, but, lower slope suggests evaporation.

δ18O-δD Relationship in Daily Moisture Condensed in Delhi

y = 5.4407x + 32.146R2 = 0.9242

-200.00

-180.00

-160.00

-140.00

-120.00

-100.00

-80.00

-60.00

-40.00

-20.00

0.00-40.00 -35.00 -30.00 -25.00 -20.00 -15.00 -10.00 -5.00 0.00

δ18OSMOW (%0)

δDS

MO

W (%

0)

Fig. 5

• Comparison of δ18O and δD values in ground level rainfall and cloud base: During a rain event,

a rapid interaction of ground level water vapour (GLV) may occur with the falling raindrops to establish equilibrium with isotopic composition of the rain, which can induce very different isotopic composition of a rain event. This vapour is possibly transported from the surrounding area. Therefore, the composite samples of rainfall and DMC may not properly reflect the isotopic variability that may be observed in near-ground water vapour. In order to explain the possible influences within the present dataset, the relationships of the isotopic compositions of ground level vapour and rainfall, first condensate and cloud base are discussed in this section. Presuming that the δ18O and δD of ground level vapour is same as that of the rainfall, the δ18O and δD in first condensate and cloud base has been computed using the software provided by the PRL based on the following equation:

43

Where, δz and δg denote δ values at some desired altitude (z, in km) and at ground level (g), respectively; zs denotes scale height of atmospheric water vapor distribution, i.e., altitude at which specific humidity (qz) is 1/e times the specific humidity at ground level; and αe is the equilibrium fractionation factor at temperature prevalent at altitude z.

A comparison of the δ18O and δD and the slopes and intercepts of the respective δ18O–δD relationship (Figs. 3, 6 and 7) suggests that the rain had initially enriched isotope content because it was probably produced by low altitude clouds and was subjected to evaporation during its fall. Subsequently, with increase in the altitude of the rain formation, either because of the ascent of warm and humid air to surmount a cold front, or because of the convective rise of air accompanied by inflow of new air from the bottom, the heavy isotope content of rainwater gradually depleted, reflecting progressive adiabatic condensation of atmospheric vapour by Rayleigh-type process. Three processes could have possibly occurred: (i) formation of the residual precipitation at lower elevation, (ii) exchange between falling raindrops and the near ground air vapour, and (iii) inputs of new air masses with lower effective rainout, which move behind the front.

Cloud Base δ 18O and δD Relationship in Delhi

y = 6.3925x - 24.204R2 = 0.8531

-250.0

-200.0

-150.0

-100.0

-50.0

0.0-30.00 -25.00 -20.00 -15.00 -10.00 -5.00 0.00

δ 18OSMOW (%0)

δD

SMO

W (%

0)

Relationship of Ground Level Rain δ18O and Cloud Base δ18O in Delhi

y = 7.0417x + 3.3353R2 = 0.8531

-140.00

-120.00

-100.00

-80.00

-60.00

-40.00

-20.00

0.00

20.00

40.00

60.00

-20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00

Ground Level Rain δ 18OSMOW (%0)

Clo

ud B

ase δ

18O

SMO

W (%

0)

Fig.6 Fig.7

• d-excess: Because the d-excess value is expected to be relatively constant during transport and

during formation of condensate, the d-excess value can be used as the indicator reflecting the origin of moisture. The temporal variation of the d-excess value is large. It is seen from the time series (Figs. 8 and 9) that most Ground level rain samples had d-excess >10 ‰ much higher than the global average value (d=10) that is common over oceans. However, an abrupt increase in the d-excess is noticed almost in the beginning of the first monsoon rain in July, 2008. Thereafter, the d-excess shows a steady declining trend. Another abrupt increase in the d-excess is noticed almost in the first week of August, 2008, and thereafter remaining almost steady with fluctuations up to November, 2009. Partial evaporation of the rain sample during the storage in the rain gauge under warm and dry atmospheric conditions may also be accountable for a part of the observed decrease of the d-excess. Large d-excess values (d > 20) suggest contributions of recycled moisture to precipitation. This seasonal variation in d-excess is a reflection of the kinetic fractionation during evaporation of water under different relative humidity. Despite advantages, the use of d-excess has some drawbacks. Compared with the application of the 18O or D, d-excess variations can be complicated, and theoretical understanding of d-excess and related rainfall processes has not yet been fully explored. Because, changes in the d-excess depend on changes in both 18O and D, the analytical uncertainty of this parameter can be relatively high in comparison with its natural variability.

44

Time Series of δ18O and dex in GL Rainfall, Cloudbase and First Condensate in Delhi

-60.00

-40.00

-20.00

0.00

20.00

40.00

60.00

80.00

100.00

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Date of Collection

δ18O

SMO

W (%

0)

Ground Level d18O Ground Level Rainfall dex

Cloud base d18O First Condensate d18O

Fig. 8

Time Series of δD and dex in GL Rainfall, Cloudbase and First Condensate in Delhi

-250.00

-200.00

-150.00

-100.00

-50.00

0.00

50.00

100.00

150.00

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Date of Collection

δD

SMO

W (%

0 )

Ground Level Rainfall dD Ground Level Rainfall dex

Cloudbase dD Cloudbase dex

First Condensate dD

Fig. 9

• Ground level Rh and temperature: It is noticed (Fig. 10) that ground level Rh and temperature at

New Delhi are inversely correlated (Fig. 11), and a significant change in their pre-monsoon values occurred. In the present dataset, the δD and δ18O isotopic compositions of rainfall show no correlation with air temperature. However, during the monsoon period August 2008 and September 2008, both in rainfall and DMC, depleted δD and δ18O compositions are associated with higher average Rh (80-86%) and enriched isotopic compositions are associated with relatively lower average Rh (54-60%). The following sequence can then be possibly expected:

Low Rh → increased kinetic fractionation → higher d-excess in vapour & lower in liquid. High Rh → decreased kinetic fractionation → lower d-excess in vapour & higher in liquid.

45

Time Series of Av. Relative Humidity and Av. Temperature in Delhi

0102030405060708090

100

13/0

8/20

0814

/08/

2008

16/0

8/20

0818

/08/

2008

19/0

8/20

0820

/08/

2008

21/0

8/20

0822

/08/

2008

23/0

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/08/

2008

26/0

8/20

0827

/08/

2008

28/0

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/08/

2008

01/0

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0802

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0804

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2008

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2008

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/09/

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2008

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/09/

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/09/

2008

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23/0

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0824

/09/

2008

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9/20

0826

/09/

2008

27/0

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/09/

2008

30/0

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0801

/10/

2008

03/1

0/20

0804

/10/

2008

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0/20

0807

/10/

2008

08/1

0/20

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/10/

2008

13/1

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0814

/10/

2008

15/1

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/10/

2008

17/1

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0820

/10/

2008

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0822

/10/

2008

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0/20

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/10/

2008

25/1

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0827

/10/

2008

29/1

0/20

0831

/10/

2008

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0803

/11/

2008

04/1

1/20

0805

/11/

2008

06/1

1/20

0807

/11/

2008

10/1

1/20

0811

/11/

2008

12/1

1/20

0814

/11/

2008

15/1

1/20

0817

/11/

2008

18/1

1/20

0819

/11/

2008

20/1

1/20

0821

/11/

2008

22/1

1/20

0825

/11/

2008

26/1

1/20

0827

/11/

2008

28/1

1/20

0801

/12/

2008

03/1

2/20

0804

/12/

2008

05/1

2/20

0806

/12/

2008

08/1

2/20

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/12/

2008

11/1

2/20

0812

/12/

2008

15/1

2/20

0816

/12/

2008

17/1

2/20

0818

/12/

2008

19/1

2/20

0820

/12/

2008

22/1

2/20

0823

/12/

2008

24/1

2/20

0826

/12/

2008

27/1

2/20

0829

/12/

2008

30/1

2/20

0831

/12/

2008

01/0

1/20

0902

/01/

2009

03/0

1/20

0905

/01/

2009

06/0

1/20

0909

/01/

2009

12/0

1/20

0913

/01/

2009

14/0

1/20

0915

/01/

2009

16/0

1/20

0917

/01/

2009

19/0

1/20

0920

/01/

2009

21/0

1/20

0922

/01/

2009

23/0

1/20

0924

/01/

2009

27/0

1/20

0928

/01/

2009

29/0

1/20

0931

/01/

2009

02/0

2/20

0903

/02/

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06/0

2/20

0907

/02/

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2/20

0910

/02/

2009

11/0

2/20

0912

/02/

2009

13/0

2/20

0916

/02/

2009

Date of Collection

Rel

ativ

e H

umid

ity (%

)

0

5

10

15

20

25

30

35

Tem

pera

tue

(0 C)

Rain / MoistureAvg. RH % Rain / Moisture Avg. Temp°C

Fig.10

Relationship of Relative Humidity with Temperature and Rainfall δ18O in New Delhi

-20

-10

0

10

20

30

40

0 20 40 60 80 10

Av. Relative Humidity (%)

δ18

O (%

0)

T

emp

0 C

0

Rain / Moisture Avg. Temp°C DP d18O

Fig. 11

Concluding Remarks: The local meteoric water line and seasonal variations in the isotopic composition of precipitation for

Delhi in the present dataset provide insight to some extent into the meteorological regime. The effect of evaporation on the stable isotopes of rainfall is often found. However, a few dry-season rainwater samples do not show secondary evaporation, as they all fall close to the GMWL. Apparently, it seems that these samples fell through high-humidity air below the cloud. In addition, there is no correlation between δ18O and the amount of rainfall in the present data. The temporal changes in the isotopes of rainfall and DMC are pronounced and seem to depend on the seasonal dynamics of moisture sources and transport pathways. Temporal variation in isotopes reflects the contribution of recycled moisture. However, it is difficult to determine or discuss the contributions of recycled moisture using only the isotopic content of rainfall, because, newly formed ground level water vapour does not form rainfall directly. The vapor first mixes with background moisture in the atmospheric boundary layer, and convective activity transports the well-mixed moisture across over the atmospheric boundary layer; this moisture then forms precipitation. A shift from continental moisture source to a marine source (during ISM) seem to lead to distinct isotopic signatures in rainfall, and disequilibrium conditions between surface water and water vapor during monsoonal weather can further enhance the isotopic depletion. Future reconstructions based on variations in isotopic signatures as related to frequency, intensity and distributions of rain and trajectory of wind, in relation to the isotopic composition of ground level vapour (which can be extrapolated from the isotopes measurements of NIH, Roorkee) on the respective rainy day, may be helpful for a detailed understanding of the rainfall sources and the influencing processes.

46

47

48

PROGRESS REPORT ( November 2009 to November 2010)

1. Project Title: National Program on “ Isotope Finger

Printing of waters of India”(IWIN)

DST No: IR/S4/ESF-05/2004

2. PI (Name & Address): Principal Co-ordinator Dr.S.K.Gupta ,Visiting Scientist, Physical Research Laboratory(PRL), Navrangpura, P.O.BOX 4218, Ahmedabad, Gujarat-380 009,India. skgupta@prl.res.in

Date of Birth : 27th sept 1946

3. Co-PI (Name & Address): P.Nagabhushanam Scientist , NGRI, Uppal Road, Habshiguda, Hyd-500 606 nagpasupu@rediffmail.com

Date of Birth : 6th Aug 1952

4. Broad area of Research : Earth and Atmospheric Science

4.1 Sub Area - Earth Science 5. Approved Objectives of the Proposal : (i). Daily collection of atmospheric moisture using two methods. (ii). Collection of rainfall samples. (iii). Measurement of Deuterium and Oxygen-18 isotope ratios of atmospheric moisture and rainfall. Date of Start: 17/7/2007

Total cost of Project: : Rs.19.36 Lakhs (NGRI share)

Date of completion: continuing

Expenditure : Rs. 10.577 lakhs as on 31/12/2010.

49

6. Methodology : NGRI component of work & methodology

1. Daily collection of atmospheric moisture by condensation (DMC), and Pump and trap method (PTMC).

2. Collection of rainfall samples.

3. Measurements of Oxygen -18 and Deuterium of atmospheric moisture collected

daily and rainfall.

7. Salient Research Achievements:

7.1 Summary of Progress

1. Collected 296 samples of daily atmospheric moisture by condensation method (DMC) ( from 01/01/10 to 31/12/10).

2. Collected 286 samples of Atmospheric moisture using pump and trap method

(PTMC) (from 01/01/10 to 31/12/10). 3. Oxygen-18 measurements on 320 DMC samples. 4. Oxygen-18 measurements on 303 PTMC samples.

5. Deuterium measurements on 283 DMC samples.

6. Deuterium measurements on 277 PTMC samples.

7. Deuterium measurements on 67 Rainfall samples.

7.2 New Observations : Please see Annexure-I

50

7.3 Innovations: The Isotope data of Atmospheric (daily collection) provides an opportunity to understanding the moisture kinematics vis-à-vis ambient temperature

7.4 Application Potential:

7.4.1 Long Term : As mentioned in the project document, the isotope data of various components of hydrological cycle would be used in modeling studies to elicit hydrological processes affecting isotope fractionation as well as contribution of various components to the hydrological cycle.

7.4.2 Immediate

7.5 Any other 8. Research work which remains to be done under the project (for on-going projects)

i) Continuation of atmospheric moisture and rainfall collection. ii) Measurements of Oxygen-18 and Deuterium of collected waters. iii) Data communication, interpretation and publication.

Ph.Ds Produced no: -

Technical Personnel trained: ONE

Research Publications arising out of the present project: -

51

List of Publications from this Project (including title, author(s), journals & year(s) (A) Papers published only in cited Journals (SCI) : nil

(B) Papers published in Conference Proceedings, Popular Journals etc.: nil

Patents filed/ to be filed: nil

Major Equipment (Model and Make) : nil S

No Sanctioned List Procured

(Yes/ No) Model & make

Cost (Rs in lakhs)

Working (Yes/ No)

Utilisation Rate (%)

52

ANNEXURE-I

7.2 New Observations:

The time series of δ18O and δD of atmospheric moisture samples (both DMC –condensation method and PTMC-pump and trap method) collected and measured during October /November 2009 to October/November 2010 are shown in Figs. 1 a, b, c, d. The δ18O of measured DMC samples varied from -30 to + 2‰ (Fig. 1a) with a baseline value of ~ -16‰. In case of PTMC samples, the δ18O varied from -22 to -1.6‰ with a baseline value of ~ -11‰ (Fig. 1c). The δD of the DMC samples vary from -138 to +23‰ (Fig. 1b). A baseline value of ~ -47‰ can be found from the δD time series (Fig. 1b). The PTMC samples’ δD shows a range of -125 to +89‰ with a baseline value of ~ -66‰ (Fig. 1d). The time series of δ values indicate negative excursions (heavy isotope depletion) during rainfall events/period and positive shifts (enrichment of heavy isotope) during dry times.

Fig.1a. Time series of δ18O of measured DMC samples

-35

-30

-25

-20

-15

-10

-5

0

5

Oct/09

Nov/09

Dec/09

Jan/1

0

Feb/10

Mar/10

Apr/10

May/10

Jun/1

0Ju

l/10

Aug/10

Sep/10

Oct/10

Nov/10

Dec/10

δ18O

53

Fig.1b. Time series of δD of measured DMC samples

-160

-140

-120

-100

-80

-60

-40

-20

0

20

40

Oct/09

Nov/09

Dec/09

Jan/1

0

Feb/10

Mar/10

Apr/10

May/10

Jun/1

0Ju

l/10

Aug/10

Sep/10

Oct/10

Nov/10

Dec/10

δD

Fig.1c.Time series of δ18O of measured PTMC samples

-25.00

-20.00

-15.00

-10.00

-5.00

0.00

Sep/09Oct/

09Nov/0

9Dec/0

9Jan/10

Feb/10Mar/1

0Apr/1

0

May/10

Jun/10Ju

l/10

Aug/10Sep/10

Oct/10

Nov/10

Dec/10

δ18O

54

Fig.1d. Time series of δD of measured PTMC samples

-150.00

-100.00

-50.00

0.00

50.00

100.00

150.00

Sep/09 Oct/09 Nov/09 Dec/09 Jan/10 Feb/10 Mar/10 Apr/10 May/10 Jun/10 Jul/10 Aug/10 Sep/10 Oct/10 Nov/10

δD

In order to visualise clearly the heavy isotope variation month-wise, the delta (δ ) values of both (DMC and PTMC) atmospheric moisture samples are averaged for each month. DMC samples’ δ18O varied from -22 to -8.8‰ (Fig. 2a), while its δD showed a range of -72 to -20‰ (Fig. 2b). Both the delta values show heavy isotopic enrichment during pre-monsoon time. The depleted delta values are observed for the October month (2009) and August to November 2010 months having low average ambient temperature (20 to 25 °C) and fairly high relative humidity (70%). It is observed from the δ data (Fig. 2a, b) that the atmospheric moisture attains peak positive trend in pre-monsoon, and the transition from enriched values to depleted values takes place around March/April. This trend indicates change in the isotopic composition of air mass induced by monsoonal winds. A similar observation can be made from the δ values of PTMC samples (Fig. 2c and d). The monthly average δ18O of PTMC samples ranged from -15 to -8.5‰ and δD from -88 to -49‰ (Fig. 2c & d respectively). It is to be noted that there is an off-set between δ values of both types of samples, i.e., the δ18O of DMC samples are characterised by more depletion than PTMC samples, and vice versa in the case of δD values.

55

Fig.2a.Monthly average δ18O of DMC samples

-25.00

-20.00

-15.00

-10.00

-5.00

0.00

Oct-09

Nov-09

Dec-09

Jan-1

0

Feb-10Mar-1

0Apr-1

0

May-10

Jun-1

0Ju

l-10

Aug-10

Sep-10

Oct-10

Nov-10

δ18

O

Fig.2b. Monthly average δD of DMC samples

-80.00

-70.00

-60.00

-50.00

-40.00

-30.00

-20.00

-10.00

0.00

Oct-09

Nov-09

Dec-09

Jan-1

0

Feb-10Mar-1

0Apr-1

0

May-10

Jun-1

0Ju

l-10

Aug-10

Sep-10

Oct-10

Nov-10

δD

56

Fig.2c. Monthly average δ18O of PTMC samples

-18.00

-16.00

-14.00

-12.00

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

Oct-09

Nov-09

Dec-09

Jan-1

0

Feb-10Mar-1

0Apr-1

0

May-10

Jun-1

0Ju

l-10

Aug-10

Sep-10

Oct-10

Nov-10

δ18

O

Fig.2d. Monthly average δD of PTMC samples

-100.00

-90.00

-80.00

-70.00

-60.00

-50.00

-40.00

-30.00

-20.00

-10.00

0.00

Oct-09 Nov-09 Dec-09 Jan-10 Feb-10 Mar-10 Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10

δD

57

Relation between monthly average δ18O and δD of both types of samples are shown in Fig. 3a & b. DMC samples’ delta values show fairly good linear relation (Fig. 3a) than PTMC samples (Fig.3b). In the case of DMC samples, the isotopic value range is more pronounced than PTMC values. This shows the effect of the kinetic evaporation of heavy isotopes during condensation using ice and cone.

Fig.3a. Relation between monthly averages δ 18 O & δ D of DMC samples

y = 4.6541x + 25.529 R2 = 0.9298

-80.000 -70.000 -60.000 -50.000 -40.000 -30.000 -20.000 -10.000

0.000

-25.00 -20.00 -15.00 -10.00 -5.00 0.00 δ18O

δD

Fig.3b. Relation between monthly average δ 18 O & δD of PTMC samples

y = 6.8598x + 8.8773R 2 = 0.6911

-100.00 -90.00 -80.00 -70.00 -60.00 -50.00 -40.00 -30.00 -20.00 -10.00

0.00

-16.00 -14.00 -12.00 -10.00 -8.00 -6.00 -4.00 -2.00 0.00δ18O

δD

58

ISOTOPIC ANALYSES OF RAINFALL OF 2008 & 2009 The rainfall samples collected at NGRI are measured for δ18O and δD. The total rainfall is observed to be 974.5 mm in 2008, and 506 mm during 2009. If only the monsoon period (June to September) is considered, then the rainfall in 2008 is 724.5 mm and 443 mm in 2009. That shows non-monsoonal rainfall contribution is ~25% of total rainfall in 2008, and ~12% in 2009. It is observed that maximum rainfall (432 & 302 mm respectively) occurred in the month of August in both the years. The δ18O variation in the rainfall of 2008 is from -12.6 to +1.5‰, and the δD varied from -95 to +14‰. The weighted average δ18O and δD for the 2008 rainfall are -5.4‰ and -33.7‰ respectively. The 2008 rainfall shows a d-excess value of +9.5, which is very close to the d-excess value (+10) of the global meteoric water line (δD = 8* δ18O + 10). For the 2008 rainfall, the local meteoric water line is shown in Fig. 4a. Its regression equation (Y = 8.3333X + 8.9278; R2 = 0.9865) shows close relation to the global meteoric water line equation (δD = 8* δ18O + 10). The 2009 rainfall shows δ18O from -14.27 to +4.78‰ with a weighted average value of -4.0‰. Its δD varied from -102 to +35‰, with a weighted average value of -20‰. Its regression equation (Y = 7.4065X + 8.4631; R2 = 0.9826) also closely related to the global meteoric water line equation (δD = 8* δ18O + 10). The differences in the regression equations of both rainfall and the global meteoric water line can be related to the kinetic evaporation effect of heavy isotopes during precipitation process.

Fig.4a. Relation between δ18 O and δD of rainfall in 2008

y = 8.3333x + 8.9278R2 = 0.9865

-120

-100

-80

-60

-40

-20

0

20

40

-14.00 -12.00 -10.00 -8.00 -6.00 -4.00 -2.00 0.00 2.00 4.00 δ18O

δD

59

δD

Fig.4b. Relation between δ 18 O and δD of rainfall in 2009

y = 7.4065x + 8.4631 R2 = 0.9826

-120.00 -100.00

-80.00 -60.00 -40.00 -20.00

0.00 20.00 40.00 60.00

-20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00 δ18 O

In order to visualise the relation between monthly average δ18O of DMC and Rainfall samples, the data is plotted in Figs. 5a & b for the years 2008 and 2009 respectively. The DMC δ18O is considered only for those months when rainfall occurred. From the figures it is obvious, in general, that the δ18O of atmospheric moisture follows the δ18O of rainfall. That is the monsoon and also non-monsoon winds are carried into the landmass effecting a change in the isotope content of the local atmospheric moisture.

Fig. 5a. Relation between monthly average δ18O of DMC and Rainfall in 2008

-12.00

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

feb mar apr jun jul aug sep octMonth

rf δ18

O

-25.00

-20.00

-15.00

-10.00

-5.00

0.00D

MC

δ18

O

Rain fall

DMC

60

Fig. 5b.Relation between monthly average δ18O of DMC and Rainfall in 2009

-12.00

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

may jun jul aug sep octMonth

Rf δ

18O

-20.00

-18.00

-16.00

-14.00

-12.00

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

δ18

O D

MCRain

fall

DMC

National Institute of Oceanography PROGRESS REPORT

1. Project Title: ISOTOPE FINGERPRINTING OF WATERS OF INDIA (IWIN)

DST No: IR/S4/ESF-05/2004

2. PI (Name & Address): Dr. P.M.Muraleedharan Scientist, P.O.D, National Institute of Oceanography Dona Paula, Goa 403004

Date of Birth 8 October 1955

3. Co-PI (Name & Address): Not Applicable

Date of Birth Not Applicable

4. Broad area of Research Hydrology

4.1 Sub Area Atmospheric Science 5. Approved Objectives of the Proposal : 1. Setting up upper atmosphere and surface meteorology observatory at NIO,

Goa to collect vertical profiles of temperature, humidity, wind speed, wind direction etc using Vaisala Radio Sonde system.

2. Computation of moisture transports from the surrounding ocean to the sub

continent and estimate the amount of moisture trapped within the designated land segments.

3. Collection of surface water samples from Arabian Sea for salinity and isotope analysis to cover all four seasons using the ships of opportunities. 4. Collection of precipitation and atmospheric water vapour from the station

set up at NIO.

Total cost of Project: 1. Rs. 95.53 lakhs (revised vide

DST order No. IR/S4/ESF-05/2004(3) dated 30 Sept. 2009)

Date of completion: September 2012

Expenditure as on 31-12-2010 Rs. 78.893 lakhs

6. Methodology : Item 5 (1) Equipments are assembled as per the instructions provided. Operating procedures are followed as per the training given. The collected data are exported to ascii format for further use.

61

Item 5 (2) Moisture transport is computed from the gridded reanalysis data products by integrating from specified height to the surface level. Item 5 (3) Arabia sea surface water samples are collected from ships of opportunities and analysed in the mass spectrometer at PRL for stable oxygen and hydrogen isotope activity. Item 5 (4) Precipitation and atmospheric moisture samples are collected in a daily basis at NIO, Goa using IWIN protocol and then analysed at PRL for isotope activity. 7. Salient Research Achievements:

7.1 Summary of Progress Surface Observations as per the IWIN protocol are continuing without any interruption. As on today about 140 radio sonde ascends have been made out of which 75 were taken during the period 1st January to 31st December 2010. The frequency of observation during monsoon and non monsoon months were once in three days and weekly respectively. Surface meteorological parameters (temperature, humidity, wind speed, wind direction, solar radiation, rainfall) were also measured every 10 minutes during this period. Two types of work have been done using this data. The upper air data collected are best suited to validate the reanalysis products such as NCEP/NCAR, ECMWF, NCMRWF etc. We have also made an attempt to compare the IMD radio sonde profiles taken from IMD station at Goa with the vaisala sonde observation. This will be useful for IMD to take stock of their observational network and subsequent policy decisions. The manuscript based on the validation/comparision results is communicated to the journal Current Science. The upper air data along with Automatic Weather Station data will be useful for comparing the monsoon rainfall and its association with troposphere-stratosphere interaction during the contrasting years 2009 and 2010 as a case study. We have also made an attempt to understand the troposphere-stratosphere interaction during the extreme solar radiation cutoff during mid day due to the extremely rare incident of annular solar eclipse occurred on 15th of January 2010. The effect of the eclipse was partial over Goa but the effect was phenomenal. The results are compiled in the form of a manuscript and communicated to the journal of Atmospheric and Solar Terrestrial Physics. In brief the NCMRWF data product is closely following the vaisala trends in the lower and middle troposphere followed by ECMWF for all three parameters. But in the upper troposphere and lower stratosphere all three reanalysis products (and IMD sonde) exhibited substantial error especially for relative humidity.

Sl.no Obs. No. of ascends Since Jan ’10 Total

1 Vaisala sonde 75 140 Arabian sea water sample collection program is going in full swing by making use of the ships of opportunities. Arrangements were made to collect the surface water whenever these ships ventures into the Arabian Sea (30 to 80 E and 0 to 25 N). Instructions were given to collect three samples (50 ml, 100ml and 250 ml) from each station for isotope analysis, salinity measurement and for PRL repository respectively. A Memorandum of Understanding was signed between NIO and FSI (Fisheries Survey of India) on 4th October 2010 at NIO to make use of the 7 FSI vessels

62

surveying the EEZ of west coast of India. It has been observed that due to the piracy problems less number of cruises have been planned to survey the western Arabian Sea since January 2010. The vessels currently participating in the IWIN observational program are (a) Sagar Kanya (b) Boris Petrov (c) Sagar Sampada (d) Sagar Sukti (e) Sagar manjusha (f) Sagar Purvi (g) MV Kavarathi etc. About 743 sets of samples were collected under this program during the period Jan 2009 to Dec. 2010. The details of the Arabian Sea samples collected are tabulated below. Sl.no Vessel No of

cruises No. of samples Sent to PRL

Balance

1 Sagar Kanya  14 2 M V Kavrati  10 3 Sagar Paschimi  5 4 Sagar Purvi  6 5 Sagar Sampada  5 6 A Boris Petrov  2 

743

350

393

Total 44 743 350 393 Daily atmospheric moisture samples were collected by conical condensation method following IWIN protocol. Fortnightly moisture samples were also collected by push and trap method to make correction for evaporation. Altogether we have collected about 767 DMC samples and 33 DMPT samples covering three. The respective samples collected during the period starting from January 2010 to December 2010 are 302 and 12 respectively. Stable isotope analyses of these samples are pending due to non availability of mass spectrometer. 241 Daily precipitation (DP) samples were collected since January 2009 out of which 80 has been deposited at PRL repository for isotope analysis. Details of samples collected are displayed below in tabular form.

Sl.no Sample No. of samples collected Entire period Since Jan 10

Deposited in PRL

Starting Date

1 DMC 767 302 250 12-08-2008 2 DMPT 33 12 80 01-10-2008 3 DP 241 120 23 12-08-2008

7.2 New Observations:

An experiment was planned to study the effect of annular solar eclipse on troposphere and the boundary layer by slightly modifying the upper air measurement periodicity. The annular solar eclipse was partially visible at Goa for about 4 hours on 15th January 2010 peaking at about 13 hours. Seventy percent of solar radiation was cut off (Fig.1) during the eclipse to force the tropopause descend (700 m) thereby warming the upper troposphere (Fig.2). The inverse relationship between tropopause pressure and surface

63

pressure during non monsoon months obtained in our earlier experiments predicts convection at the surface layer in the event of a high pressure formation at the tropopause. An abrupt decrease of air temperature (Fig.3) and humidity (Fig. 4) in the surface layer in response to the tropopause warming is an indication of enhanced convection. The observed low wind speed also support a low level convergence associated with the convection (Fig. 5). The time lag observed between peak of eclipse and the response of surface temperature and humidity field is attributed to the distance between the path of totality of the eclipse and the observation point and the response time between tropopause and surface boundary layer.

(Figure. 1) (Figure 2)

(Figure 3) (Figure 4)

(Figure 5) 7.3 Innovations:

64

NIL 7.4 Application Potential:

7.4.1 Long Term Upper atmosphere observations and Arabian Sea surface water samples, once analyzed, will have long term applications. Vaisala radio sonde data accurately measures both troposphere and stratosphere profiles of various parameters that has the potential to draw conclusions on troposphere-stratosphere interaction especially the vertical propagation of moisture which has profound influence on global climate in general and Asian monsoon in particular. Thorough knowledge of activities of Oxygen and Hydrogen stable isotopes measured from the seas around India has the potential to source the precipitation received at various parts of the sub continent. Such information is vital in understanding the predictive nature of monsoon rainfall over the subcontinent.

7.4.2 Immediate Salinity data collected over the Arabian Sea has the immediate use of validating algorithms that retrieve salinity values from the satellite data. Vaisala radio sonde profiles are also has the immediate use of validating satellite data. The nation’s prestigious meteorological satellite ‘Megatropiques’ is planned to launch in early 2011 and the high resolution (height) Vaisala sonde data is the best data to validate the sensor MADRAS onboard MEGATROPIQUES.

7.5 Any other

With the installation of vaisala radio sonde facility, monsoon research at our Institute will get the long awaited boost. The Vaisala radiosonde data availability and the infra structure developed prompted us to under take useful collaboration with the experts available at Cochin University of Science and Technology (CUSAT), Kerala. It is also planned to submit proposal in collaboration with CUSAT for funding so that optimum utilization of the Vaisala facility is envisaged. The facility created at NIO generated capability and confidence to take up international programs like “Cooperative Indian Ocean Experiment on intra-seasonal variability in the year 2011 (CINDY2011) to understand the initiation process of MJO convection. Apart from Japan, USA, Canada, NIO is an active participant and core member from Indian side.

8. Research work which remains to be done under the project (for on-going projects) Stable isotope activities of Hydrogen and Oxygen in the samples collected are yet to be completely undertaken. First batch of samples were sent to PRL for analysis Ph.Ds Produced no: NIL

Technical Personnel trained:

TWO

Research Publications arising out of the present project: 2 manuscripts communicated.

65

List of Publications from this Project (including title, author(s), journals & year(s) (A) Papers published only in cited Journals (SCI)

(a) Validation of realanysis data products using Vaisala Radio Sonde RS92SGP. Akhil, V.P,, Keerthi, M.G., P.M.Muraleedharan. (Communicated to Current Science, 2010)

(b) Effect of January 15, 2010 Annular Solar Eclipse on Meteorological Parameters over Goa, India. Muraleedharan, P.M., Nisha, P.G., K. Mohankumar (Communicated to Journal of Atmospheric and Solar Terrestrial Physics, 2010)

(B) Papers published in Conference Proceedings, Popular Journals etc.

NIL Patents filed/ to be filed:

NIL

Major Equipment (Model and Make) S

No Sanctioned List Procured

(Yes/ No) Model & make

Cost (Rs in lakhs)

Working (Yes/ No)

Utilisation Rate (%)

1

2

DigiCORA MW 31 Vaisala Radio

sonde system

Radio Sonde

Yes DigiCORA MW 31

Vaisala

Yes RS92-SGP

Vaisala

54

7

Yes

Yes

100

100

Activities planned during next year.

1. Continue surface observations, upper air observations and Arabian Sea sample collection the same fashion until the end of the project.

2. Next year Fishery Survey of Indian is also going to collect samples for IWIN using their six ships in the west coast.

3. Will concentrate on analyzing upper air data to understand the contrasting years 2009 and 2010.

4. Once the isotope data over Arabian Sea is made available, an attempt can be made to link the isotopic fractionation with possible oceanographic and meteorological processes.

66

FUNDING STATUS

Points to be raised in the PRC Meeting: Although the project started in July 2007, installation of the upper air system and recruitment of project assistants were made on the second half of 2008. By July 2011, the project completes fourth year. Project assistant’s tenure complete its three year term in July 2011.

1. If the surface observations are to be continued beyond July 2011, the tenure for project assistants (PA II) approved for three years are to be given extension till the end of the five year term of the project. We seek approval of the extension of PA’s for one more year along with the sanction of wages (Rs. 24,000 X 12 = Rs. 288,000).

2. An amount of Rs. 10 lakhs are required for Vaisala consumables to continue upper air observation till the end of the project five year term. Request approval and fund.

3. Under TA/DA, funds are available as per the DST order (No. IR/S4/ESF-05/2004) but approval is required to carry forward and use it until the end of the project. Carry forwarded fund needs to be transferred to NIO.

4. Approval is required to carry forward the funds available under consumables so that the money can be utilized till the end of the project.

5. Rs. 5 lakhs are sanctioned under ‘other costs’ and approval is required to carry forward the balance to the 5th year of the project.

6. A negative balance of Rs. 10742/- has been occurred under the head ‘equipment’ as the actual expenditure was more than the sanctioned amount. A revised approval is sought for this.

Expenditure Incurred Total Exp-enditure Sr

No

(I)

Sanctioned Heads (in lakhs)

(II)

Funds Allocated (indicate

sanctioned or revised

(III)

1st Year

IV + V + VI

+ VII

2nd Year 3rd Year 4th Year(01-04-08 (01-04-09 (17-07-07 (01-04-10

to to to to 31-03-09 31-03-10) 31-03-08) 31-12-10)

(VIII) (V) (VI) (IV)

(VIII)

Balance availabe as on (31-12-

2010)

(IX) = III – VIII

Balance available at NIO as on

31st Dec 2010

1. Manpower costs 864000 35354 176744 288000 199304 699402 164598 133600 2. Consumables 2000000 0 546169 605323 98270 1249762 750238 250200 3. Travel 400000 31256 97459 68365 55513 252593 147407 47400 4. Contingencies 500000 15334 77453 79615 36510 208912 291088 136100 5. Others, if any ---- --- --- --- --- ---- --- --- 6. Equipment 5489058 0 5499764 0 --- 5499764 -10706 -10742 7. Overhead 300000 100000 100000 62000 --- 262000 38000 10000 8. Total 9553058 181944 6497589 1103303 389597 8172433 1401658 575800

67

CWRDM, Kozhikode. Project Profile Title of the Project : National programme on Isotope fingerprinting of Waters of India (IWIN) Satellite project title: Isotope and Hydro-chemical Mapping of two River Basins in Kerala and an Island in Lakshadweep

DST Sanction Reference: No.SR/S4/ES-460/2009 Dt.03.06.2010

Date of Birth : 06.05.1956

Name of the Investigator(s) and Address: Principal Investigator:

Dr. A Shahul Hameed Scientist and Head, Isotope Hydrology Division, CWRDM, Kozhikode.

Co-Principal Investigator: Dr Resmi T R Scientist-B, Isotope Hydrology Division, CWRDM, Kozhikode.

25.04.1976

Project Staff: Mr. Sui A.S Junior Research Fellow 21.04.1982

Objectives: • To study the stable isotope variations in atmospheric moisture, rain, river and

groundwater in the two river basins in Kerala and one island in Lakshadweep on a seasonal basis

• To investigate how these variations contribute to our understanding of the precipitation origin and formation conditions

• To assess the interaction of surface water and groundwater in the river basins and if possible, to carry out the hydrograph separation

• To provide necessary isotope data of Kerala region to the National Database of IWIN for addressing various water related issues of the Nation as a partner of the IWIN National Programme

Date of Commencement of the Project : August, 2010 Total Sanctioned Budget : Rs.16.59 lakhs First installment received : Rs.5,40,000/- (for 3 years) Expenditure as on November,2010:

Rs. 60,000/- (Approx)

Date of Completion of the Project : Continuing (Expected date : July, 2013)

68

Progress achieved: (Satellite project was initiated in August, 2010)

Preamble: The southwest monsoon air masses enters the Indian subcontinent striking Kerala first, which is located uniquely in the tip of the subcontinent. Since, the prime climatic determinant of the State is the southwest monsoon, the calendar year can be categorized as pre-monsoon period (Feb-May), monsoon period (Jun-Sep) and post-monsoon period or northeast monsoon period (Oct-Jan). The special topographic features of the state forces the up-lift of incoming monsoon vapours causing heavy downpour in the windward side. During, north east monsoon period, the region does not experience heavy rains and hence modification in isotopic composition of the rain and corresponding vapours are expected. Manifestation of these isotopic variations is reflected in the ground and surface water resources also. In this context, two river basins located in the southern and northern part of the State were selected for detailed investigation. Neyyar River basin in the south and Chaliyar River basin in the north are being monitored in the project. Neyyar River originates from the Agasthya Hills at an elevation of 1860m. The river has a basin area of 497km2 and the average annual rainfall is 2300mm. The average stream flow is 207Mm3 and the length of the mainstream is 56 km. The Chaliyar River (an inter-state river) originating from the Ilambalari Hills at an Elevation of 2066m covers a basin area of 2535km2 in Kerala. Length of the main stream is 169 km. The average annual rainfall in the basin is 3800mm and the average annual stream flow is 5902Mm3. In addition to this, periodical collection of rainwater and atmospheric moisture samples are carried out in selected locations as part of IWIN programme. Major activities performed during the period under report are listed below:

Continued the collection of fortnightly composite rain water samples at CWRDM HQ (northern Kerala) and at its sub-centres viz, Manimalakkunnu (central Kerala) and Neyyattinkara, Thiruvananthapuram (southern Kerala.

Continued the daily collection of atmospheric moisture samples at ground level by

condensation method at Kozhikode

Continued data collection and compilation of climatological parameters such as rainfall, temperature and relative humidity pertaining to the sites of investigation

Drainage map, land use map and other relevant maps for Neyyar and Chaliyar

river basins were prepared

Field work has been initiated in Neyyar river basin. Rain water collectors were installed in selected stations and identified representative locations for stream water and groundwater sampling in the basin.

One set of water sample was collected from different locations along the river

course and at different locations from open dug wells in the basin (10nos. of Surface water samples and 15 nos. of groundwater samples)

Isotope analysis of the collected samples of precipitation, atmospheric moisture,

surface water and ground water are in progress

69

Present Status of sample collection:

Rain Water Atmospheric Moisture

Month CWRDM,HQ Manimalakkunnu Neyyatinkara Status

CWRDM, HQ

Status

Feb, 09 × ×

×

× × 1 ×

× 0

Mar, 09 × ×

4 ×

× 0

Apr, 09

6

2

May, 09

× 5

2

Jun, 09

6

2

Jul, 09

6

2

Aug, 09

× 5

2

Sep, 09

6

2

Oct, 09

×

5

2

Nov, 09

6

2

Dec, 09

×

5

2

Jan, 10

× ×

× ×

× 1

2

Feb, 10

× ×

× ×

× × 0

2

Mar, 10 × ×

×

× × 1

2

Apr, 10

× 5

Daily

May, 10

6

Daily

Jun, 10

6

Daily

Jul, 10

6

Daily

Aug, 10

6

Daily

Sep, 10 ×

5

Daily

Oct, 10

6

Daily

Nov, 10

6

Daily

Dec, 10

Daily 6

70

Results and Salient Observations

Isotopic composition of rain samples of three geographically varied locations:

The three locations identified for rain water sampling are CWRDM, HQ at Kozhikode (northern Kerala:11°17’ 07”N and 75°52’15”E) and at Manimalakkunnu (central part of Kerala:9°53’ 21” N and 76°33’54”’E) and Trivandrum (southern Kerala:8°23’N and 76°05’E). Although the distance from the sea coast is similar for the southern and northern stations, their elevation is quite different (8.3m amsl & 79m amsl, respectively). The central Kerala region comes under the mid land category with a distance of 36km from the coast. This site is intervened from the sea by the largest estuary of the State, the Vembanad Lake.

Time series analysis of oxygen isotope composition of the rain water collected from these sites exhibited almost similar trend (Fig.1). Most depleted δ values were observed during the north-east monsoon period or during the withdrawal phase of the southwest monsoon. The pre-monsoon rains and monsoon rains did not vary much in isotope composition though slightly enriched values were obtained for the pre-monsoon rains.

Fig 1: Variation of δ 18O in rain water of three locations

The δ2H & δ18O plot of the few rain water samples are shown in Fig.2. It can be seen that the local meteoric water lines followed the global trend. However, the regression line for the Manimalakkuunnu (central Kerala) region deviated from the rest with a higher intercept.

71

Fig 2: δ 2H & δ 18O plot for rain water of three locations

Time series data of Atmospheric Moisture: Fortnightly variation of δ 18O of ground-level vapor collected by the condensation method of the Kozhikode (northern Kerala) station is given in Fig.3.

Fig.3. Temporal variation of ground-level vapor

δD = 8.31 δ18O + 17.8R2 = 0.85 (MMK)

δ D = 8.9 δ 18O + 13.2 R 2 = 0.89 (KKD)

-40

-30

-20

-10

0

10

20

-6 -5.5 -5 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0

δ18O

δ D

δD = 7.6 δ 18 O + 8.9 R2 = 0.97 (TVM)

RW-KKD KKD RW-MMK RW-TVM MMK TVM

72

Similar to the variation of rainwater isotopes, the ground level vapor exhibited depleted δ values during the withdrawal phase of the monsoon. A gradual enrichment can be seen for the pre-monsoon rains and the monsoon rains were showing similar values.

Relation with temperature, Relative humidity and rainfall: Correlation of δ 18O of atmospheric moisture (AM) with relative humidity (RH) indicated depletion of heavier isotopes from pre-monsoon to monsoon and to the post-monsoon seasons (Fig.4). Despite the relatively high humid condition and torrential showers of the monsoon season, the vapours were rather enriched which may be a reflection of the oceanic vapour input. In the case of rainwater, correlation with relative humidity exhibited similar characteristics of the atmospheric moisture though the monsoon and pre-monsoon rains had more or less same isotope composition.

Air temperature and intensity of rainfall correlated poorly with both rain water and atmospheric moisture heavier isotopes (Fig.5&6). The correlation patterns of the isotopes were very similar to that of relative humidity.

Fig. 4. Correlation of relative humidity with atmospheric moisture and rainwater at Kozhikode station

73

Fig.5. Correlation of air temperature with atmospheric moisture and rainwater at Kozhikode station

Fig.6 5. Correlation of intensity of rainfall with atmospheric moisture and rainwater at Kozhikode station

74

Variation of weighted average of δ18O values of rainwater at Kozhikode station are presented in Table 1. The plot of the weighted mean data (Fig.7) reveals that post- monsoon samples are most depleted and the pre-monsoon showers are more enriched.

Table 1. Weighted mean average of oxygen -18 data (Kozhikode)

δ18O - Weighted mean Period

Apr-09 -1.88 May-09 -1.80 Jun-09 -4.07 Jul-09 -2.51 Aug-09 -2.07 Sep-09 -2.20 Oct-09 -0.50 Nov-09 -10.06 Dec-09 -4.68 Apr-10 -2.10 May-10 -2.16 Jun-10 -2.63 Jul-10 -3.04 Aug-10 -2.83

Sep -10 -4.67

Fig.7. Temporal variation of weighted mean δ values of oxygen-18 at Kozhikode

From the results of the isotope data of the rainwater and atmospheric moisture obtained

so far, seasonal changes in the monsoon vapour masses could be deduced.

75

Data transfer to IWIN National data base:

• Oxygen-18 and a few D/H results of the samples from 01.05.2009 to 30.06.2010

along with the available meteorological data have been forwarded.

• The Oxygen-18 results of moisture samples collected during 01.04.2009 to 31.03.2010 in fortnightly intervals and during 05.04.2010 to 31.08.2010 on daily intervals have been forwarded. (The atmospheric moisture collection by condensation method was carried out at Kozhikode station alone).

Bottlenecks:

• Due to technical problem in Deuterium measurement in the IRMS at CWRDM (which is still under service), over 400 samples are yet to be analysed for D/H.

Requirements:

• The following materials are required to be spared form IWIN-PRL.

Funnel, Rubber Cork and SS tubing-10 sets. for rain water collector.

Low temperature thermometer to initiate moisture collection by Push and

Trap method.

• Permission to carry forward to the unspent balance to the next financial year, as the satellite project was initiated in August, 2010.

76

Report 2010-2011.

National programme DST I WIN: Isotope Fingerprinting of waters of India

Department of Geology Anna University Chennai 600 025.

1. Carried out sea surface water between Chennai-

Vishakhapatnam, Andaman –Kolkata (2)using

commercial ship. Collected rainwater and atmospheric

moisture samples and submitted to PRL for isotope

analyses.

2. The samples have been submitted for Isotope analyses.

3. Isotope Results awaited.

4. Paper prepared for publication on earlier results.

5. Final UC fort this year will be submitted.

77