Understanding the potential drought impacts on agricultural production is critical for ensuring global food security. Instead of providing a deterministic estimate, this study investigates the likelihood of yield loss of wheat, maize, rice and soybeans in response to droughts of various intensities in the 10 largest producing countries. We use crop-country specific standardized precipitation index (SPI) and census yield data for 1961–2016 to build a probabilistic modeling framework for estimating yield loss risk under a moderate (−1.2 < SPI < −0.8), severe (−1.5 < SPI < −1.3), extreme (−1.9 < SPI < −1.6) and exceptional (SPI < −2.0) drought. Results show that there is >80% probability that wheat production will fall below its long-term average when experiencing an exceptional drought, especially in USA and Canada. As for maize, India shows the highest risk of yield reduction under droughts, while rice is the crop that is most vulnerable to droughts in Vietnam and Thailand. Risk of drought-driven soybean yield loss is the highest in USA, Russian and India. Yield loss risk tends to grow faster when experiencing a shift in drought severity from moderate to severe than that from extreme to the exceptional category, demonstrating the non-linear response of yield to the increase in drought severity. Sensitivity analysis shows that temperature plays an important role in determining drought impacts, through reducing or amplifying drought-driven yield loss risk. Compared to present conditions, an ensemble of 11 crop models simulated an increase in yield loss risk by 9%–12%, 5.6%–6.3%, 18.1%–19.4% and 15.1%–16.1 for wheat, maize, rice and soybeans by the end of 21st century, respectively, without considering the benefits of CO 2 fertilization and adaptations. This study highlights the non-linear response of yield loss risk to the increase in drought severity. This implies that adaptations should be more targeted, considering not only the crop type and region but also the specific drought severity of interest.

Bias corrected daily climate projections from five global circulation models (GCMs) under the RCP8.5 emission scenarios were fed into a calibrated Variable Infiltration Capacity (VIC) hydrologic model to project future hydrological changes in China. The standardized precipitation index (SPI), standardized runoff index (SRI) and standardized soil moisture index (SSWI) were used to assess the climate change impact on droughts from meteorological, agricultural, and hydrologic perspectives. Changes in drought severity, duration, and frequency suggest that meteorological, hydrological and agricultural droughts will become more severe, prolonged, and frequent for 2020-2049 relative to 1971-2000, except for parts of northern and northeastern China. The frequency of long-term agricultural droughts (with duration larger than 4 months) will increase more than that of short-term droughts (with duration less than 4 months), while the opposite is projected for meteorological and hydrological droughts. In extreme cases, the most prolonged agricultural droughts increased from 6 to 26 months whereas the most prolonged meteorological and hydrological droughts changed little. The most severe hydrological drought intensity was about 3 times the baseline in general whereas the most severe meteorological and agricultural drought intensities were about 2 times and 1.5 times the baseline respectively. For the prescribed local temperature increments up to 3°C, increase of agricultural drought occurrence is predicted whereas decreases or little changes of meteorological and hydrological drought occurrences are projected for most temperature increments. The largest increase of meteorological and hydrological drought durations and intensities occurred when temperature increased by 1°C whereas agricultural drought duration and intensity tend to increase consistently with temperature increments. Our results emphasize that specific measures should be taken by specific sectors in order to better mitigate future climate change associated with specific warming amounts. It is, however, important to keep in mind that our results may depend on the emission scenario, GCMs, impact model, time periods and drought indicators selected for analysis.

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[1] Previous studies on irrigation impacts on land surface fluxes/states were mainly conducted as sensitivity experiments, with limited analysis of uncertainties from the input data and model irrigation schemes used. In this study, we calibrated and evaluated the performance of irrigation water use simulated by the Community Land Model version 4 (CLM4) against observations from agriculture census. We investigated the impacts of irrigation on land surface fluxes and states over the conterminous United States (CONUS) and explored possible directions of improvement. Specifically, we found large uncertainty in the irrigation area data from two widely used sources and CLM4 tended to produce unrealistically large temporal variations of irrigation demand for applications at the water resources region scale over CONUS. At seasonal to interannual time scales, the effects of irrigation on surface energy partitioning appeared to be large and persistent, and more pronounced in dry than wet years. Even with model calibration to yield overall good agreement with the irrigation amounts from the National Agricultural Statistics Service, differences between the two irrigation area data sets still dominate the differences in the interannual variability of land surface responses to irrigation. Our results suggest that irrigation amount simulated by CLM4 can be improved by calibrating model parameter values and accurate representation of the spatial distribution and intensity of irrigated areas. Furthermore, through a set of numerical experiments, the deficiency in the current parameterization is evaluated and a critical path forward to a realistic assessment of irrigation impacts using an earth system modeling approach is recommended.Citation: Leng, G., M. Huang, Q. Tang, W. J. Sacks, H. Lei, and L. R. , Modeling the effects of irrigation on land surface fluxes and states over the conterminous United States: Sensitivity to input data and model parameters,