Optimal Groundwater Extraction under Uncertainty and a Spatial Stock Externality

Nathaniel H Merrill and Todd Guilfoos

American Journal of Agricultural Economics, 2018, vol. 100, issue 1, 220-238

Abstract: We introduce a model that incorporates two important elements to estimating welfare gains from groundwater management: stochasticity and a spatial stock externality. We estimate welfare gains resulting from optimal management under uncertainty as well as a gradual stock externality that produces the dynamics of a large aquifer being slowly exhausted. This groundwater model imposes an important aspect of a depletable natural resource without the extreme assumption of complete exhaustion that is necessary in a traditional single cell (bathtub) model of groundwater extraction. Using dynamic programming, we incorporate and compare stochasticity for both an independent and identically distributed as well as a Markov chain process for annual rainfall. We find that the spatial depletion of the aquifer is significant to welfare gains for a parameterization of a section of the Ogallala Aquifer in Kansas, ranging from 2.9% to 3.01%, which is larger than those found previously over the region. Surprisingly, the inclusion of stochasticity in rainfall increases welfare gains only slightly.

Keywords: Groundwater management; Ogallala Aquifer; stochastic dynamic programming; welfare analysis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:oup:ajagec:v:100:y:2018:i:1:p:220-238.

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