Acting to Prepare for Severe Droughts in the Yakima River Basin

By Mengqi Zhao, recent PhD graduate, Washington State University

For more than fifty years, individuals and organizations in the Yakima River Basin (YRB) have been working toward improving water availability, especially for agriculture. The mismatch between rainfall (and snowmelt) timing and the irrigation season has focused these efforts on strategies for increasing water storage. However, farmers frequently encounter insufficient irrigation water supply and large demands from agricultural activities, resulting in prorationing across irrigation districts during every severe drought of record since 1970s. In the Pacific Northwest, projected water scarcity situations under future climate change scenarios could increase to 68% of years in the 2080s if no actions are taken, compared to only 14% of years on average historically (Vano et al., 2010).

Facing such frequent low water availability conditions, what methods can improve the reliability of irrigation systems? How might people’s decisions on adopting those methods affect system vulnerability to droughts? The fundamental solutions to these questions rely on strategies that either improve water supply when it is needed or reduce water demand. In our study, we explored three climate adaptation methods:

1. Increase irrigation supply: managed aquifer recharge (MAR);
2. Reduce irrigation demand: upgrading irrigation technology (IT) for higher irrigation efficiency, and
3. Reduce irrigation demand: switching low value cropland to greenhouse (GH), which use efficient irrigation systems requiring less water than open field crops.

We analyzed these three climate adaptation methods using a system dynamics model (described in an earlier article) that quantifies physical processes, including how water moves throughout the water cycle, and how socio-economic factors affect decisions. To show the possible future climate conditions, we projected both mild and extreme climate change scenarios based on representative carbon pathways RCP 4.5 (mild) and RCP 8.5 (extreme). With “no adaptation methods” serving as a baseline, we compared the improvement in irrigation reliability when implementing these methods individually or in combination. Irrigation reliability is defined as the ratio of irrigation supply over irrigation demand.

Our results indicated the optimum solution under both climate change scenarios relies on combining climate adaptation methods. Under future climates, projected increases in temperature lead to higher water demand for irrigation due to increased evapotranspiration. Earlier snowmelt caused by warming reduces available water supply during the irrigation season. With no adaptation actions, we expect to see a decline in annual irrigation reliability (“Baseline” for each climate change scenario in Figure 2). Assuming irrigation reliability lower than 0.7 represents drought years when prorationing occurs and potential severe agricultural loss may happen (USBR, 2006), proratable water right holders (e.g. junior farmers) could expect water curtailment from their entitlements every year from 2050 onward under the extreme climate change scenario (RCP 8.5). Looking at individual methods, implementing MAR appeared to be more effective at improving irrigation reliability than greenhouses or irrigation efficiency technology under the mild climate change scenario (RCP 4.5; S-MAR vs S-GH and S-IE in Figure 2, left panel), whereas both greenhouses and irrigation efficiency performed better under the extreme scenario (RCP 8.5; S-GH and S-IE vs S-MAR in Figure 2, right panel).  However, the best solution for both mild and extreme climate change was to implement all the adaptation methods (S-ALL in Figure 2, both panels) to alleviate most of the drought impacts on agriculture.

The climate adaptation methods’ ability to mitigate drought impacts on irrigation reliability appears promising, at least under the scenarios we explored. However, these results are significantly affected by what assumptions we made about people’s adoption of these methods. We assumed that the adoption of all three methods would grow over time and would be above 75% of potential adopters by the end of 2090. Adoption behaviors will be influenced by word of mouth, effectiveness of the adaptation methods, adoption incentive programs and outreach, costs, and drought occurrence. For example, implementing MAR with existing irrigation canals costs much less than constructing and maintaining greenhouses. Consequently, we assumed the adoption ratio of MAR will increase faster than the rest of the methods, especially after drought frequency increases, around 2050s. If we have overestimated how and when each technology is adopted, we can expect more impacts of climate change on irrigation reliability than shown in our results.

Mitigating the impacts of the potential extreme droughts expected in the second half of the century requires action sooner rather than later. Considering the time needed for projects to be designed, prepared, evaluated, and implemented, our water system management model can provide insights on innovations and their ability to mitigate drought impacts. Given the importance of the adoption ratio on our findings, in this study, our results also suggest that preventive actions should be supported by outreach and incentives for stakeholders to adopt climate adaptation methods in light of future climate change drought impacts.

References:

U.S. Bureau of Reclamation (USBR) 2006. Yakima River Basin Storage Alternatives Appraisal Assessment. A component of Yakima River Basin Water Storage Feasibility Study, Washington. Technical Series No. TS-YSS-8. Feasibility study available online.

Vano, J.A., M.J. Scott, N. Voisin, C.O. Stöckle, A.F. Hamlet, K.E.B. Mickelson, M.M.G. Elsner, and D.P. Lettenmaier. 2010. Climate Change Impacts on Water Management and Irrigated Agriculture in the Yakima River Basin, Washington, USA. Climatic Change 102:287–317.

The work described in this article was supported jointly by the National Science Foundation under EAR grant #1639458 and the U.S. Department of Agriculture’s National Institute of Food and Agriculture under grant #2017- 67004-26131, as well as the Washington State University Graduate School.