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Optimal Groundwater Extraction under Uncertainty and a Spatial Stock Externality
Citation:
Merrill, N. AND T. Guilfoos. Optimal Groundwater Extraction under Uncertainty and a Spatial Stock Externality. American Journal of Agricultural Economics. Oxford University Press, Cary, NC, 100(1):220-238, (2018).
Impact/Purpose:
Groundwater is important to agriculture around the world as a source of irrigation water in dry years. It is often claimed that groundwater is used inefficiently by the many farmers that access it, but the size of that inefficiency has been shown to be small in previous work. This paper estimates the size of the economic gains associated with moving a groundwater resource, the Ogallala Aquifer in Kansas, from its current use to an optimal management regime. As groundwater is depleted, the spatial extent of the aquifer shrinks causing some farmers to lose access. We find that the spatial depletion of the aquifer is a significant factor influencing the size of the economic gains of moving towards optimal management. For a section of the Ogallala, improvements over current management ranged from 2.9-3.01%, larger than those found previously over the region. This implies there are possibly economic gains to be made by changing the way groundwater is managed when the spatial aspect of depletion is considered.
Description:
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.
