Abstract
The success of the Green Revolution in Punjab, India, is threatened by a significant decline in water resources. Punjab, a major agricultural supplier for the rest of India, supports irrigation with a canal system and groundwater, which is vastly overexploited. The detailed data required to estimate future impacts on water supplies or develop sustainable water management practices is not readily available for this region. Therefore, we use Bayesian methods to estimate hydrologic properties and irrigation requirements for an under-constrained mass balance model. Using the known values of precipitation, total canal water delivery, crop yield, and water table elevation, we present a method using a Markov chain Monte Carlo (MCMC) algorithm to solve for a distribution of values for each unknown parameter in a conceptual mass balance model. Model results are used to test three water management strategies, which show that replacement of rice with pulses may be sufficient to stop water table decline. This computational method can be applied in data-scarce regions across the world, where integrated water resource management is required to resolve competition between food security and available resources.
| Original language | English (US) |
|---|---|
| Title of host publication | Sustainability of Integrated Water Resources Management |
| Subtitle of host publication | Water Governance, Climate and Ecohydrology |
| Publisher | Springer International Publishing |
| Pages | 147-162 |
| Number of pages | 16 |
| ISBN (Electronic) | 9783319121949 |
| ISBN (Print) | 9783319121932 |
| DOIs | |
| State | Published - Sep 4 2015 |
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All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)
- Environmental Science(all)
Cite this
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Assessment of agricultural water management in Punjab, India, using bayesian methods. / Russo, Tess Alethea; Devineni, Naresh; Lall, Upmanu.
Sustainability of Integrated Water Resources Management: Water Governance, Climate and Ecohydrology. Springer International Publishing, 2015. p. 147-162.Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Assessment of agricultural water management in Punjab, India, using bayesian methods
AU - Russo, Tess Alethea
AU - Devineni, Naresh
AU - Lall, Upmanu
PY - 2015/9/4
Y1 - 2015/9/4
N2 - The success of the Green Revolution in Punjab, India, is threatened by a significant decline in water resources. Punjab, a major agricultural supplier for the rest of India, supports irrigation with a canal system and groundwater, which is vastly overexploited. The detailed data required to estimate future impacts on water supplies or develop sustainable water management practices is not readily available for this region. Therefore, we use Bayesian methods to estimate hydrologic properties and irrigation requirements for an under-constrained mass balance model. Using the known values of precipitation, total canal water delivery, crop yield, and water table elevation, we present a method using a Markov chain Monte Carlo (MCMC) algorithm to solve for a distribution of values for each unknown parameter in a conceptual mass balance model. Model results are used to test three water management strategies, which show that replacement of rice with pulses may be sufficient to stop water table decline. This computational method can be applied in data-scarce regions across the world, where integrated water resource management is required to resolve competition between food security and available resources.
AB - The success of the Green Revolution in Punjab, India, is threatened by a significant decline in water resources. Punjab, a major agricultural supplier for the rest of India, supports irrigation with a canal system and groundwater, which is vastly overexploited. The detailed data required to estimate future impacts on water supplies or develop sustainable water management practices is not readily available for this region. Therefore, we use Bayesian methods to estimate hydrologic properties and irrigation requirements for an under-constrained mass balance model. Using the known values of precipitation, total canal water delivery, crop yield, and water table elevation, we present a method using a Markov chain Monte Carlo (MCMC) algorithm to solve for a distribution of values for each unknown parameter in a conceptual mass balance model. Model results are used to test three water management strategies, which show that replacement of rice with pulses may be sufficient to stop water table decline. This computational method can be applied in data-scarce regions across the world, where integrated water resource management is required to resolve competition between food security and available resources.
UR - http://www.scopus.com/inward/record.url?scp=84955407400&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84955407400&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-12194-9_9
DO - 10.1007/978-3-319-12194-9_9
M3 - Chapter
AN - SCOPUS:84955407400
SN - 9783319121932
SP - 147
EP - 162
BT - Sustainability of Integrated Water Resources Management
PB - Springer International Publishing
ER -