Quantifying the Impacts of Compound Extremes on Agriculture and Irrigation Water Demand

Abstract

Abstract. Agricultural production and food prices are affected by hydroclimatic extremes. There has been a large literature measuring the impacts of individual extreme events (heat stress or water stress) on agricultural and human systems. Yet, we lack a comprehensive understanding of the significance and the magnitude of the impacts of compound extremes. Here, we combine a high-resolution weather product with fine-scale outputs of a hydrological model to construct functional indicators of compound hydroclimatic extremes for agriculture. Then, we measure the impacts of individual and compound extremes on crop yields focusing on the United States during the 1981–2015 period. Supported by statistical evidence, we confirm that wet heat is more damaging than dry heat for crops. We show that the average damage from heat stress has been up to four times more severe when combined with water stress; and the value of water experiences a four-fold increase on hot days. In a robust framework with only a few parameters of compound extremes, this paper also improves our understanding of the conditional marginal value (or damage) of water in crop production. This value is critically important for irrigation water demand and farmer decision-making – particularly in the context of supplemental irrigation and sub-surface drainage.

Quantifying the impacts of compound extremes on agriculture

“…The historical weather data (PRISM) are available at http://www.prism.oregonstate.edu (PRISM Climate Group, 2019). The input data for estimations are available at https://doi.org/10.4231/0M14-EY38 (Haqiqi et al, 2020b).…”

Statistically bias-corrected and downscaled climate models underestimate the severity of U.S. maize yield shocks

“…Modeling the yield sensitivity to moisture poses greater challenges. Complex daily interactions between soil moisture extremes and heat stress can lead to drastically different yield outcomes 36 . As discussed, season-total precipitation is not always strongly correlated with seasonal mean soil moisture.…”

“…For season-total precipitation, there is less evidence of a clear bias that would affect maize yields-the most prominent feature is a tendency to overestimate the standard deviation across many central and southern counties. One potentially fruitful avenue for future work is to investigate the effects of bias-correction and downscaling on the representation of soil moisture, which is more important for plant growth 35 and may or may not be strongly correlated with cumulative precipitation depending on factors such as runoff, drainage, and irrigation 36 .…”

Uncertainty analysis in multi-sector systems: Considerations for risk analysis, projection, and planning for complex systems

“…The consequences of errors in marginal distribution estimation have been well-documented in the literature on single-sector systems, most predominantly with respect to floods (Wong et al, 2018). The negative consequences of incorrectly estimating the joint distribution of exogenous variables, particularly in the tails, or worse, assuming independence, have recently been raised in the literature with respect to coastal flooding (Moftakhari et al, 2017), agricultural production (Haqiqi et al, 2021), and wildfires (Brown et al, 2021), among others.…”