High-Frequency Water Isotopic Analysis Using an Automatic Water Sampling System in Rice-Based Cropping Systems
Abstract
:1. Introduction
- There are seasonal and crop effects on isotopic compositions in groundwater and surface water.
- The isotopic differences in water sources are driven by the evaporation fractionation process.
- Meteorological conditions control evaporation so that a day-night cycle is apparent for stable isotopes of ponding water, while this cannot be found for groundwater.
2. Materials and Methods
2.1. Field Site and Management
2.2. Automatic Analytical System
2.3. Isotopic Analysis
2.4. Raw Data Correction and Calibration
- The sample has already been in the diffusion cell for at least 300 s (and therefore safely beyond the lag time).
- The slope of a linear regression for the δ signals as a function of time is less than 0.002‰ s−1 for 2H and 0.005‰ s−1 for 18O.
- The coefficient of determination (R2) of the δ signals is less than 0.05 over plateau time.
- The vapor content is above 15,000 ppm.
2.5. Statistical Analysis
2.6. Estimation of the Relative Extent of Evaporation
2.7. Assessing Water Provenance of Surface and Groundwater
2.8. Daily Variation of Isotopic Values
3. Results
3.1. System Calibrated Data and Lab Data Comparison
3.2. Isotope Composition of Crops
3.3. Estimation of the Relative Extent of Evaporation
3.4. Assessment of Water Provenance in Surface and Groundwater
3.5. Daily Variation of Isotopic Values
4. Discussion
4.1. Isotope Composition of Water Sources
4.2. Relative Evaporation Extent
4.3. Origin of Water Sources
4.4. Sub-Daily Fluctuation of Surface and Groundwater
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Season | Water Type | Number of Observations | Calibrated Isotopic Values | |
---|---|---|---|---|
δ2H ± SD [‰] | δ18O ± SD [‰] | |||
DS | GW-Wet rice | 40 | −3.92 ± 7.6 | −0.14 ± 1.2 |
GW-Dry rice | 67 | −0.80 ± 6.3 | 0.07 ± 1.2 | |
GW-Maize | 88 | −13.5 ± 7.8 | −1.16 ± 2.0 | |
SW-Wet rice | 101 | 1.54 ± 12.1 | 0.48 ± 1.4 | |
WS | GW-Wet rice | 517 | −37.8 ± 11.6 | −5.2 ± 2.6 |
SW-Wet rice | 269 | −44.6 ± 10.6 | −6.9 ± 1.9 |
(a) | ||||||
GW | δ2Hday | δ2Hnight | δ18Oday | δ18Onight | δ2Hday− δ2Hnight | δ18Oday−δ18Onight |
RHday | −0.618 ** | −0.639 ** | −0.675 ** | −0.658 ** | (0.008) | (−0.083) |
RHnight | −0.530 ** | −0.547 ** | −0.547 ** | −0.551 ** | (−0.001) | (0.044) |
RHday − RHnight | −0.471 ** | −0.490 ** | −0.567 ** | −0.523 ** | (0.020) | (−0.256) |
Tday | 0.395 ** | 0.408 ** | 0.491 ** | 0.471 ** | (−0.002) | (0.106) |
Tnight | (0.086) | (0.088) | (0.133) | (0.146) | (0.004) | (−0.086) |
Tday − Tnight | 0.471 ** | 0.487 ** | 0.550 ** | 0.506 ** | (−0.008) | (0.263) |
(b) | ||||||
SW | δ2Hday | δ2Hnight | δ18Oday | δ18Onight | δ2Hday − δ2Hnight | δ18Oday − δ18Onight |
RHday | −0.679 ** | −0.682 ** | −0.797 ** | −0.785 ** | (0.081) | (−0.097) |
RHnight | −0.651 ** | −0.661 ** | −0.781 ** | −0.793 ** | (0.155) | (0.091) |
RHday − RHnight | (−0.331) | (−0.317) | (−0.351) | (−0.292) | (−0.135) | −0.466 * |
Tday | (0.345) | (0.354) | 0.465 * | 0.442 * | (−0.133) | (0.178) |
Tnight | (0.123) | (0.138) | (0.261) | (0.271) | (−0.175) | (−0.079) |
Tday − Tnight | (0.356) | (0.349) | (0.326) | (0.274) | (0.070) | 0.416 * |
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Mahindawansha, A.; Breuer, L.; Chamorro, A.; Kraft, P. High-Frequency Water Isotopic Analysis Using an Automatic Water Sampling System in Rice-Based Cropping Systems. Water 2018, 10, 1327. https://doi.org/10.3390/w10101327
Mahindawansha A, Breuer L, Chamorro A, Kraft P. High-Frequency Water Isotopic Analysis Using an Automatic Water Sampling System in Rice-Based Cropping Systems. Water. 2018; 10(10):1327. https://doi.org/10.3390/w10101327
Chicago/Turabian StyleMahindawansha, Amani, Lutz Breuer, Alejandro Chamorro, and Philipp Kraft. 2018. "High-Frequency Water Isotopic Analysis Using an Automatic Water Sampling System in Rice-Based Cropping Systems" Water 10, no. 10: 1327. https://doi.org/10.3390/w10101327