Climate change is expected to increase the scarcity and variability of fresh water supplies in some regions with important implications for irrigated agriculture. By allowing for increased flexibility in response to scarcity and by incentivizing the allocation of water to higher value use, markets can play an important role in limiting the economic losses associated with droughts. Using data on water demand, the seniority of water rights, county agricultural reports, high-resolution data on cropping patterns, and agronomic estimates of crop water requirements, we estimate the benefits of market-based allocations of surface water for California’s Central Valley. Specifically, we estimate the value of irrigation water and compare the agricultural costs of water shortages under the existing legal framework and under an alternate system that allows for trading of water. We find that a more efficient allocation of curtailments could reduce the costs of water shortages by as much as $362 million dollars per year or 4.4% of the net agricultural revenue in California in expectation, implying that institutional and market reform may offer important opportunities for adaptation.
Irrigated agriculture in snow-dependent regions contributes significantly to global food production. This study quantifies the impacts of climate change on irrigated agriculture in the snow-dependent Yakima River Basin (YRB) in the Pacific Northwest United States. Here we show that increasingly severe droughts and temperature driven reductions in growing season significantly reduces expected annual agricultural productivity. The overall reduction in mean annual productivity also dampens interannual yield variability, limiting yield-driven revenue fluctuations. Our findings show that farmers who adapt to climate change by planting improved crop varieties may potentially increase their expected mean annaul productivity in an altered climate, but remain strongly vulnerable to irrigation water shortages that substantially increase interannual yield variability (i.e., increasing revenue volatility). Our results underscore the importance for crop adaptation strategies to simultaneously capture the biophysical effects of warming as well as the institutional controls on water availability.
more » « less- Award ID(s):
- 1639458
- NSF-PAR ID:
- 10170214
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Aquifer depletion due to extensive and intensive irrigation in the High Plains has threatened the environmental sustainability of the region. The change of crop evapotranspiration (
ET ), the major form of agriculture water consumption, presents a critical signature of hydrologic cycle change. This study evaluates the relative contributions of climate and groundwater‐fed irrigation toET temporal and spatial pattern change over the High Plains Aquifer, one of the most severely depleted aquifers in the US. We developed a framework to extend the Budyko hypothesis to assess the impact of catchment storage change on long‐termET . It is found that irrigation from groundwater pumping contributes more than half of the increase inET (74.1 mm) from the period of 1940–1975 to 1976–2010, despite an increase of precipitation (35.0 mm) in the region.ET seasonal variance is decreased by the decline in precipitation variability (at −20.7 mm) and increase in irrigation (at +14.2 mm). As expected, irrigation decreasesET coefficient of variability (i.e., the ratio of standard deviation to mean). Spatially, we find that the human‐inducedET heterogeneity post‐1975 superimposes over the natural east‐to‐west gradient (in precipitation andET ) of this region. A correlation between the statistics (mean vs. coefficient of variation) onET and crop yield provides promising signatures for understanding the coupled natural and human system of High Plains agriculture. Guides are discussed regarding how to handle the tradeoffs between agricultural development and natural resource sustainability under climate variability in the High Plains and other regions with similar conditions. -
Data-driven technologies are employed in agriculture to optimize the use of limited resources. Crop evapotranspiration (ET) estimates the actual amount of water that crops require at different growth stages, thereby proving to be the essential information needed for precision irrigation. Crop ET is essential in areas like the US High Plains, where farmers rely on groundwater for irrigation. The sustainability of irrigated agriculture in the region is threatened by diminishing groundwater levels, and the increasing frequency of extreme events caused by climate change further exacerbates the situation. These conditions can significantly affect crop ET rates, leading to water stress, which adversely affects crop yields. In this study, we analyze historical climate data using a machine learning model to determine which of the climate extreme indices most influences crop ET. Crop ET is estimated using reference ET derived from the FAO Penman–Monteith equation, which is multiplied with the crop coefficient data estimated from the remotely sensed normalized difference vegetation index (NDVI). We found that the climate extreme indices of consecutive dry days and the mean weekly maximum temperatures most influenced crop ET. It was found that temperature-derived indices influenced crop ET more than precipitation-derived indices. Under the future climate scenarios, we predict that crop ET will increase by 0.4% and 1.7% in the near term, by 3.1% and 5.9% in the middle term, and by 3.8% and 9.6% at the end of the century under low greenhouse gas emission and high greenhouse gas emission scenarios, respectively. These predicted changes in seasonal crop ET can help agricultural producers to make well-informed decisions to optimize groundwater resources.more » « less
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Abstract Groundwater extraction in the United States (US) is unsustainable, making it essential to understand the impacts of limited water use on irrigated agriculture. To improve this understanding, we integrated a gridded crop model with satellite observations, recharge estimates, and water survey data to assess the effects of sustainable groundwater withdrawals on US irrigated agricultural production. The gridded crop model agrees with satellite‐based estimates of evapotranspiration (
R 2 = 0.68), as well as survey data from the United States Department of Agriculture (R 2 = 0.82–0.94 for county‐level production and 0.37–0.54 for county‐level yield). Using the optimistic assumption that groundwater extraction equals effective aquifer recharge rate, we find that sustainable groundwater use decreases US irrigated production of maize, soybean, and winter wheat by 20%, 6%, and 25%, respectively. Using a more conservative assumption of groundwater availability, US irrigated production of maize, soybean, and winter wheat decreases by 45%, 37%, and 36%, respectively. The wide range of simulated losses is driven by considerable uncertainty in surface water and groundwater interactions, as well as accounting for the many aspects of sustainability. Our results demonstrate the vulnerability of US irrigated agriculture to unsustainable groundwater pumping, highlighting the difficulty of expanding or even maintaining irrigated food production in the face of climate change, population growth, and shifting dietary demands. These findings are based on reducing pumping by fallowing irrigated farmland; however, alternate pumping reduction strategies or technological advances in crop genetics and irrigation could produce different results. -
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Primary productivity response to climatic drivers varies temporally, indicating state-dependent interactions between climate and productivity. Previous studies primarily employed equation-based approaches to clarify this relationship, ignoring the state-dependent nature of ecological dynamics. Here, using 40 y of climate and productivity data from 48 grassland sites across Mongolia, we applied an equation-free, nonlinear time-series analysis to reveal sensitivity patterns of productivity to climate change and variability and clarify underlying mechanisms. We showed that productivity responded positively to annual precipitation in mesic regions but negatively in arid regions, with the opposite pattern observed for annual mean temperature. Furthermore, productivity responded negatively to decreasing annual aridity that integrated precipitation and temperature across Mongolia. Productivity responded negatively to interannual variability in precipitation and aridity in mesic regions but positively in arid regions. Overall, interannual temperature variability enhanced productivity. These response patterns are largely unrecognized; however, two mechanisms are inferable. First, time-delayed climate effects modify annual productivity responses to annual climate conditions. Notably, our results suggest that the sensitivity of annual productivity to increasing annual precipitation and decreasing annual aridity can even be negative when the negative time-delayed effects of annual precipitation and aridity on productivity prevail across time. Second, the proportion of plant species resistant to water and temperature stresses at a site determines the sensitivity of productivity to climate variability. Thus, we highlight the importance of nonlinear, state-dependent sensitivity of productivity to climate change and variability, accurately forecasting potential biosphere feedback to the climate system.
