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The downside risk of climate change in California’s Central Valley agricultural sector

Climatic Change volume 137, pages 15–27 (2016)Cite this article

Abstract

Downscaled climate change projections for California, when translated into changes in irrigation water delivery and then into profit from agriculture in the Central Valley, show an increase in conventional measures of variability such as the variance. However, these increases are modest and mask a more pronounced increase in downside risk, defined as the probability of unfavorable outcomes of water supply or profit. This paper describes the concept of downside risk and measures it as it applies to outcomes for Central Valley agriculture projected under four climate change scenarios. We compare the effect of downside risk aversion versus conventional risk aversion or risk neutrality when assessing the impact of climate change on the profitability of Central Valley agriculture. We find that, when downside risk is considered, the assessment of losses due to climate change increases substantially.

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Fig. 1
Fig. 2

Notes

  1. Downside risk was first modeled by Roy (1952), who noted that investors frequently put the conservation of their principal as their first goal and set some minimum acceptable return that will conserve the principal.

  2. See, for example, Mao (1970) and Waud (1976). Some other choice situations in which downside risk aversion has been observed include pricing decisions in large companies (Lanzillotti 1958) and peasant farming in Mexico (Masson 1974),

  3. Kiparsky (2010) interviewed water managers on the eastside of the San Joaquin Valley, part of our study area, to elicit their utility functions for water supply risk and found strong evidence of downside risk aversion on their part.

  4. Connell-Buck et al. (2011) employ the same approach.

  5. CALSIM output includes stream flow for the main rivers in the Central Valley, reservoir inflows, storage, and releases, and water deliveries to Central Valley Project (CVP) and State Water Project (SWP) contractors throughout the Central Valley. The CALSIM projections under climate change are in Chung et al. (2009). There are also projections for mid-century (2035–2064); our analysis of those projections is presented in an online Appendix. These deliveries were converted from monthly flow levels to annual deliveries based on the time in each month and matched to CVPM regions using CVPM files that match delivery nodes to CVPM regions. We modified historical non-project water deliveries by the same percentage as average changes in project water deliveries.

  6. In principle, some of the downside risk apparent in Fig. 1 could be mitigated with additional storage capacity. The Sacramento-San Joaquin River system has only about 1 year’s storage compared, say, to 3 years’ storage on the Colorado River. The need for additional storage has been discussed in California for over 25 years. So far, little progress has occurred because of disagreements about who should pay for new storage. Moreover, the over-riding concern currently is for major new investment in conveyance infrastructure to replace existing conveyance facilities highly vulnerable to seismic risks.

  7. Other applications of CVPM and similar models include BECS (2008a, b and Medellín-Azuara et al. (2011)). This is a positive mathematical programming model (Howitt 1995) calibrated to match farmers’ observed cropping choices.

  8. CVPM includes twenty crops, some of which are aggregates of several specific crops. The twenty crops are: grains (includes wheat, barley & oats), rice, cotton, sugarbeets, corn & corn silage, dry & lima beans, safflower, alfalfa (includes alfalfa hay, alfalfa seed, and clover), pasture, field crops (includes sudan grass, sunflower, other misc. field crops), process tomatoes, fresh tomatoes, onions & garlic, potatoes, almonds & pistachios, other deciduous (includes peaches, walnuts, nectarines, pears, cherries, apples, misc), subtropical (includes citrus, avocados, olives, figs, misc), vines (includes raisins, wine, and table grapes), truck crops (includes carrots, cauliflower, lettuce, peas, spinach, broccoli, asparagus, peppers, sweet potatoes, misc), and cucurb (includes cantelope, honeydew, watermelon, squash, and cucumbers).

  9. Output from other regions is implicitly held constant. To the extent that climate change reduces (increases) production worldwide, the price responses in the paper are under (over) estimates.

  10. The model does not consider the option of deficit irrigation resulting in reduced yields.

  11. However, adaptation can be costly, slow to occur or imperfect. Moreover, there can be some trends, such as population growth, that will increase the pressure on California’s water supply as the century progresses.

  12. By contrast, Medellín-Azuara et al. (2011), who simulate just the GFDL-A2 scenario, use the change in gross revenue from agriculture as their economic metric. Gross revenue exceeds net revenue by an order of magnitude and is not generally accepted as an appropriate metric of value in economics.

  13. To measure skewness, we report the Fisher-Pearson coefficient and also the probability that aggregate annual profit falls below the mean value, $549.3 million.

  14. The p-values are 0.00 for the GFDL scenarios, 0.010 for PCM-A2, and 0.202 for PCM-B1. In some sub-regions, the PCM-B1 results are also statistically different from the baseline (see online Appendix).

  15. The p-values are 0.0000 for the GFDL scenarios, 0.0182 for PCM-A2, and 0.1996 for PCM-B1.

  16. The certainty equivalent of a gamble is the outcome which, if it occurred for sure, would yield the same expected utility as the gamble itself.

  17. Markowitz (1952); Tobin (1958). The standard deviation is sometimes used instead of the variance.

  18. It is an exact measure of the certainty equivalent if the gamble involves normally distributed outcomes (which is not the case here) and the decision maker has a negative exponential utility function.

  19. Their first formulation in Kahneman and Tversky (1979), known as prospect theory, also assumes that the utility function for downside outcomes is risk seeking, while that for upside outcomes is risk averse.

  20. For a further generalization, see Zakamouline (2014).

  21. In an online Appendix, we also assess the impact of a lower level of downside risk aversion, α = 2.

  22. Many of the individual irrigation districts have their own surface water rights and/or pump groundwater in addition to receiving water from wholesale agencies.

  23. In an online Appendix, we perform a sensitivity analysis using values of λ = 0.5 and λ = 1.5.

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Acknowledgments

Dr. Hanemann’s research was supported by NSF Award 1204774 to Arizona State University. Our research relies on modeling results provided to us by California Department of Water Resources and CVPM computer code provided by Steven Hatchett. We thank Sydny Fujita and Nathaniel Bush for research assistance. This draft has benefitted from the helpful suggestions of several anonymous reviewers and the associate editor. Any remaining errors are our own.

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Authors and Affiliations

  1. Arizona State University, Tempe, AZ, USA

    Michael Hanemann

  2. Smith College, Northampton, MA, USA

    Susan Stratton Sayre

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Larry Dale

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  1. Michael Hanemann
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  2. Susan Stratton Sayre
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Correspondence to Larry Dale.

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Hanemann, M., Sayre, S.S. & Dale, L. The downside risk of climate change in California’s Central Valley agricultural sector. Climatic Change 137, 15–27 (2016). https://doi.org/10.1007/s10584-016-1651-z

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Keywords

  • Risk Aversion
  • Online Appendix
  • Water Delivery
  • Downside Risk
  • Certainty Equivalent