Effects of interaction of multiple large-scale atmospheric circulations on precipitation dynamics in China.

Different scenarios of precipitation, that lead to such phenomena as droughts and floods are influenced by concurrent multiple teleconnection factors. However, the multivariate relationship between precipitation indices and teleconnection factors, including large-scale atmospheric circulations and sea surface temperature signals in China, is rarely explored. Understanding this relationship is crucial for drought early warning systems and effective response strategies. In this study, we comprehensively investigated the combined effects of multiple large-scale atmospheric circulation patterns on precipitation changes in China. Specifically, Pearson correlation analysis and Partial Wavelet Coherence (PWC) were used to identify the primary teleconnection factors influencing precipitation dynamics. Furthermore, we used the cross-wavelet method to elucidate the temporal lag and periodic relationships between multiple teleconnection factors and their interactions. Finally, the multiple wavelet coherence analysis method was used to identify the dominant two-factor and three-factor combinations shaping precipitation dynamics. This analysis facilitated the quantification and determination of interaction types and influencing pathways of teleconnection factors on precipitation dynamics, respectively. The results showed that: (1) the Atlantic Multidecadal Oscillation (AMO), EI Niño-Southern Oscillation (ENSO), East Asia Summer Monsoon (EASM), and Indian Ocean Dipole (IOD) were dominant teleconnection factors influencing Standardized Precipitation Index (SPI) dynamics; (2) significant correlation and leading or lagging relationships at different timescales generally existed for various teleconnection factors, where AMO was mainly leading the other factors with positive correlation, while ENSO and Southern Oscillation (SO) were mainly lagging behind other factors with prolonged correlations; and (3) the interactions between teleconnection factors were quantified into three types: enhancing, independent and offsetting effects. Specifically, the enhancing effect of two-factor combinations was stronger than the offsetting effect, where AMO + NAO (North Atlantic Oscillation) and AMO + AO (Atlantic Oscillation) had a larger distribution area in southern China. Conversely, the offsetting effect of three-factor combinations was more significant than that of the two-factor combinations, which was mainly distributed in northeast and northwest regions of China. This study sheds new light on the mechanisms of modulation and pathways of influencing various large-scale factors on seasonal precipitation dynamics.

Meteorological drought migration characteristics based on an improved spatiotemporal structure approach in the Loess Plateau of China.

The development of drought has spatial and temporal synchronization. Previous studies usually explore the spatial and temporal evolution of drought separately. Moreover, existing approaches are based on a fixed overlapping area and do not consider the variable drought cluster area during development. This study proposes an improved and simple approach to derive dynamic overlapping area threshold for 3-dimensional droughts extraction. Based on the one monthly Nonparametric Standardized Precipitation Index (NSPI), this improved approach was applied for investigating the migration characteristics of meteorological drought events in the Loess Plateau of China. Then, Random Forest and Extreme Gradient Boosting model with Shapley additive explanation values were used to quantify the importance of driving factors on the dynamics of drought characteristics. The results showed that: (1) the improved approach has a better performance on identifying prolonged droughts than the method using a fixed overlap area threshold; (2) spatially, meteorological drought events with high severity (DS), long duration (DD), large effected area (DA) and fast migration velocity (DV) mainly occur in the central region; (3) temporally, droughts are expected to aggravate with significantly increased DS and DA which are mainly caused by increased temperature and vegetation; and (4) meteorological droughts have a preferred westward migration direction and three dominant migration paths, which are crucial for local drought prevention and control. The findings of this study provide new perspectives on drought migration characteristics, which are important for the exploration of drought-driven mechanisms, risk assessment and future prediction.

Precipitation and vegetation transpiration variations dominate the dynamics of agricultural drought characteristics in China.

Agricultural drought posing a significant threat to agricultural production is subject to the complex influence of ocean, terrestrial and meteorological multi-factors. Nevertheless, which factor dominating the dynamics of agricultural drought characteristics and their dynamic impact remain equivocal. To address this knowledge gap, we used ERA5 soil moisture to calculate the standardized soil moisture index (SSI) to characterize agricultural drought. The extreme gradient boosting model was then adopted to fully examine the influence of ocean, terrestrial and meteorological multi-factors on agricultural drought characteristics and their dynamics in China. Meanwhile, the Shapley additive explanation values were introduced to quantify the contribution of multiple drivers to drought characteristics. Our analysis reveals that the drought frequency, severity and duration in China ranged from 5-70, 2.15-35.02 and 1.76-31.20, respectively. Drought duration is increasing and drought intensity is intensifying in southeast, north and northwest China. In addition, potential evapotranspiration is the most significant driver of drought characteristics at the basin scale. Regarding the dynamic evolution of drought characteristics, the percentages of raster points for drought duration and severity with evapotranspiration as the dominant factor are 30.7 % and 32.7 %, and the percentages with precipitation are 35.3 % and 35.0 %, respectively. Precipitation in northern regions has a positive effect on decreasing drought characteristics, while in southern regions, evapotranspiration dominates the dynamics in drought characteristics due to increasing vegetation transpiration. Moreover, the drought severity is exacerbated by the Atlantic Multidecadal Oscillation in the Yangtze and Pearl River basins, while the contribution of the North Atlantic Oscillation to the drought duration evolution is increasing in the Yangtze River basin. Generally, this study sheds new insights into agricultural drought evolution and driving mechanism, which are beneficial for agricultural drought early warning and mitigation.

Synergistic effect of drought and rainfall events of different patterns on watershed systems.

The increase in extreme climate events such as flooding and droughts predicted by the general circulation models (GCMs) is expected to significantly affect hydrological processes, erosive dynamics, and their associated nonpoint source (NPS) pollution, resulting in a major challenge to water availability for human life and ecosystems. Using the Hydrological Simulation Program-Fortran model, we evaluated the synergistic effects of droughts and rainfall events on hydrology and water quality in an upstream catchment of the Miyun Reservoir based on the outputs of five GCMs. It showed substantial increases in air temperature, precipitation intensity, frequency of heavy rains and rainstorms, and drought duration, as well as sediment and nutrient loads in the RCP 8.5 scenario. Sustained droughts followed by intense precipitation could cause complex interactions and mobilize accumulated sediment, nutrients and other pollutants into surface water that pose substantial risks to the drinking water security, with the comprehensive effects of soil water content, antecedent drought duration, precipitation amount and intensity, and other climate characteristics, although the effects varied greatly under different rainfall patterns. The Methods and findings of this study evidence the synergistic impacts of droughts and heavy rainfall on watershed system and the significant effects of initial soil moisture conditions on water quantity and quality, and help to guide a robust adaptive management system for future drinking water supply.

Varying response of vegetation to sea ice dynamics over the Arctic.

Recent reduction of sea ice may have contributed to vegetation growth over the Arctic through albedo feedback effects to atmospheric warming. Understanding the varying response of vegetation to sea ice dynamics is critical for predicting future climate change over the Arctic and middle-high latitudes. Instead of looking at the direct response characteristics, we perform a systematic analysis of the time-lag and time-cumulation responses of vegetation to sea ice dynamics, using a long-term Arctic Normalized Difference Vegetation Index (NDVI) dataset and three sea ice indices (sea ice concentration (SIC), sea ice area (SIA) and sea ice extent (SIE)) from 1982 to 2015. The results show that annual NDVI in the Arctic has exhibited a significant (p < 0.05) increase during 1982 to 2015, while a significant (p < 0.05) decrease is detected for annual SIC, SIA and SIE. The results of a regression analysis on NDVI identify a lag time of 7-months, 8-months and 9-months for vegetation response to SIC, SIA and SIE in February, March and April, respectively, while no evident lag response is observed in summer except for August. For the cumulation response, NDVI in February, March and April shows the largest response to the previous 5, 7 and 9 months of sea ice variations, respectively, while a short cumulation response of 1 to 3 months is found in summer. The differences in the spatial patterns of lagged time are usually not statistically significant in autumn and winter. A shorter lag response (1-3 month) is found in the Yamalia region in June. Further analysis suggests that vegetation response to sea ice dynamics depends on bio - climatic characteristics and soil pH, with vegetation responding faster to sea ice changes in acidic soil. This study provides observational evidences on the varying response of vegetation to sea ice dynamics over the Arctic, which has great implications for predicting vegetation-climate feedback and climate change.

Where is the Planetary Boundary for freshwater being exceeded because of livestock farming?

Livestock production has significant impacts on the environment, including due to the use of water. In this study, we provide a spatially explicit estimation of livestock blue water use, by analyzing feed crop water use and livestock drinking water. For the past four decades, livestock water use has increased from 145 km3/year in 1971 to 270 km3/year in 2012 with an increasing trend of 1.36%/year. The proportion of livestock drinking water use has remained relatively stable at around 10% of total water use attributable to livestock production. Several hotspots of water use, including eastern China, northern India, US high plains, are identified in terms of the long-term averages, while South America and Central Africa show the most rapidly increasing trends. In USA, climate change is found to contribute most to the changes in water use attributable to livestock, while feed cropping intensity and land use change are the dominant driver in China and India, respectively. Though, in total, livestock water use makes a relatively modest contribution to the Planetary Boundary (PB) that has been proposed for anthropogenic water use (4000 km3/year), we argue that this aggregate number is not particularly meaningful, so we identify places where livestock is a major contributor to the unsustainable use of water, in northern India, part of the Middle East, Northern China and Central US. 7% of rivers where excessive water withdrawals mean that there is insufficient residual flow to sustain the aquatic environment (which we take to be the local manifestation of a PB) have been tipped over that boundary because of livestock farming, whilst in a further 34% of rivers, livestock farming on its own exceeds the water PB. Our results provide new and more geographically specific evidence about the impact that the meat industry makes on the PB for water.

Time-scale dependent mechanism of atmospheric CO2 concentration drivers of watershed water-energy balance.

The elevated atmospheric carbon dioxide concentration (CO2), as a key variable linking human activities and climate change, seriously affects the watershed hydrological processes. However, whether and how atmospheric CO2 influences the watershed water-energy balance dynamics at multiple time scales have not been revealed. Based on long-term hydrometeorological data, the variation of non-stationary parameter n series in the Choudhury's equation in the mainstream of the Wei River Basin (WRB), the Jing River Basin (JRB) and Beiluo River Basin (BLRB), three typical Loess Plateau regions in China, was examined. Subsequently, the Empirical Mode Decomposition method was applied to explore the impact of CO2 on watershed water-energy balance dynamics at multiple time scales. Results indicate that (1) in the context of warming and drying condition, annual n series in the WRB displays a significantly increasing trend, while that in the JRB and BLRB presents non-significantly decreasing trends; (2) the non-stationary n series was divided into 3-, 7-, 18-, exceeding 18-year time scale oscillations and a trend residual. In the WRB and BLRB, the overall variation of n was dominated by the residual, whereas in the JRB it was dominated by the 7-year time scale oscillation; (3) the relationship between CO2 concentration and n series was significant in the WRB except for 3-year time scale. In the JRB, CO2 concentration and n series were significantly correlated on the 7- and exceeding 7-year time scales, while in the BLRB, such a significant relationship existed only on the 18- and exceeding 18-year time scales. (4) CO2-driven temperature rise and vegetation greening elevated the aridity index and evaporation ratio, thus impacting watershed water-energy balance dynamics. This study provided a deeper explanation for the possible impact of CO2 concentration on the watershed hydrological processes.

Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models.

Pervious assessments of crop yield response to climate change are mainly aided with either process-based models or statistical models, with a focus on predicting the changes in average yields, whilst there is growing interest in yield variability and extremes. In this study, we simulate US maize yield using process-based models, traditional regression model and a machine-learning algorithm, and importantly, identify the weakness and strength of each method in simulating the average, variability and extremes of maize yield across the country. We show that both regression and machine learning models can well reproduce the observed pattern of yield averages, while large bias is found for process-based crop models even fed with harmonized parameters. As for the probability distribution of yields, machine learning shows the best skill, followed by regression model and process-based models. For the country as a whole, machine learning can explain 93% of observed yield variability, followed by regression model (51%) and process-based models (42%). Based on the improved capability of the machine learning algorithm, we estimate that US maize yield is projected to decrease by 13.5% under the 2°C global warming scenario (by ~2050s). Yields less than or equal to the 10th percentile in the yield distribution for the baseline period are predicted to occur in 19% and 25% of years in 1.5°C (by ~2040s) and 2°C global warming scenarios, with potentially significant implications for food supply, prices and trade. The machine learning and regression methods are computationally much more efficient than process-based models, making it feasible to do probabilistic risk analysis of climate impacts on crop production for a wide range of future scenarios.

Watershed water-energy balance dynamics and their association with diverse influencing factors at multiple time scales.

The Budyko parameter, which controls the shape of Budyko curve, represents the superimposed impact of various periodic factors (including climatic factors, catchment characteristics, large-scale climate patterns, solar activity and anthropogenic activity) on the watershed water-energy balance dynamics. However, this superimposition is not conducive to identifying the drivers of Budyko parameter dynamics at different time scales, and thus affects parameter estimation. Here we obtain the Budyko parameter ω in the Fu's equation (one form of the Budyko framework) for the Wei River Basin (WRB), and then adopt the Empirical Mode Decomposition method to reveal the relationships between factors and ω series at multiple time scales by considering the interplay among different influencing factors. Results indicate that (1) ω series are decomposed into 4-, 12-, 20-, exceeding 20-year time scale oscillations and a residual component with an significantly increasing trend in the mainstream of the WRB, a non-significantly decreasing trend in the Jing River Basin and Beiluo River Basin; (2) by analyzing the residual trend component, evaporation ratio, soil moisture and effective irrigated area are found to induce the significant increase of ω in the upstream of the WRB, whereas that in the middle and lower reaches is dominated by baseflow and Niño 3.4; (3) ω dynamics at the 4-year time scale is dominated by evaporation ratio, aridity index, baseflow and soil moisture; baseflow, Pacific Decadal Oscillation (PDO) and sunspots attribute to the dynamics at 12-year time scale; all the factors except baseflow and soil moisture contribute to the dynamics at 20- or exceeding 20-year time scales. The results of this study will help identify the connection between watershed water-energy balance dynamics and changing environment at multiple time scales, and also be beneficial for guiding water resources management and ecological development planning on the Loess Plateau.

Propagation thresholds of meteorological drought for triggering hydrological drought at various levels.

What the extent of meteorological drought could trigger the corresponding hydrological drought with different levels? This question is an important topic in the field of drought propagation, which however has not been resolved. Therefore, a novel model based on a Bayesian network was proposed to address this issue in this study. In this model, the drought pooling and excluding methods were applied to eliminate minor drought events. A drought matching approach based on drought propagation time was proposed to achieve the one by one matching between different types of drought. Moreover, based on the matched drought events and the copula-based conditional probability model, the drought propagation thresholds of meteorological drought for triggering hydrological drought at various levels were determined. In addition, the interval conditional probability was calculated to further explore the sensitivity of hydrological drought response to different meteorological drought conditions. Furthermore, the propagation ratio was proposed to characterize the differences of drought propagation threshold among various regions. The Wei River Basin was selected as a case study. Results indicated that the results of drought propagation threshold were reliable and accurate. The increase of interval conditional probability showed a typical S-curve, which can intuitively obtain the probability of hydrological drought occurrence at different levels under specific meteorological drought condition, so as to effectively guide drought preparedness and mitigation. The propagation ratio can describe the overall resistance of the basin to meteorological drought, and it mainly depended on the meteorological and underlying surface conditions as well as groundwater supply.

Long-term spatiotemporal variation of drought patterns over the Greater Horn of Africa.

Understanding historical patterns of changes in drought is essential for drought adaptation and mitigation. While the negative impacts of drought in the Greater Horn of Africa (GHA) have attracted increasing attention, a comprehensive and long-term spatiotemporal assessment of drought is still lacking. Here, we provided a comprehensive spatiotemporal drought pattern analysis during the period of 1964-2015 over the GHA. The Standardised Precipitation-Evapotranspiration Index (SPEI) at various timescales (1 month (SPEI-01), 3 month (SPEI-03), 6 month (SPEI-06), and 12 month (SPEI-12)) was used to investigate drought patterns on a monthly, seasonal, and interannual basis. The results showed that despite regional differences, an overall increasing tendency of drought was observed across the GHA over the past 52 yr, with trends of change of -0.0017 yr-1, -0.0036 yr-1, -0.0031 yr-1, and -0.0023 yr-1 for SPEI-01, SPEI-03, SPEI-06, and SPEI-12, respectively. Droughts were more frequent, persistent, and intense in Sudan and Tanzania, while more severe droughts were found in Somalia, Ethiopia, and Kenya. Droughts occurred frequently before the 1990 s, and then became intermittent with large-scale impacts occurred during 1973-1974, 1984-1985, and 2010-2011. A turning point was also detected in 1989, with the SPEI showing a statistically significant downward trend during 1964-1989 and a non-statistically significant downward trend from 1990 to 2015. Seasonally, droughts exhibited an increasing trend in winter, spring, and summer, but a decreasing trend in autumn. The research findings have significant implications for drought adaptation and mitigation strategies through identifying the hotspot regions across the GHA at various timescales. Area-specific efforts are required to alleviate environmental and societal vulnerabilities to drought events.

Can We Use Satellite-Based FAPAR to Detect Drought?

Drought in Australia has widespread impacts on agriculture and ecosystems. Satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has great potential to monitor and assess drought impacts on vegetation greenness and health. Various FAPAR products based on satellite observations have been generated and made available to the public. However, differences remain among these datasets due to different retrieval methodologies and assumptions. The Quality Assurance for Essential Climate Variables (QA4ECV) project recently developed a quality assurance framework to provide understandable and traceable quality information for Essential Climate Variables (ECVs). The QA4ECV FAPAR is one of these ECVs. The aim of this study is to investigate the capability of QA4ECV FAPAR for drought monitoring in Australia. Through spatial and temporal comparison and correlation analysis with widely used Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite Pour l'Observation de la Terre (SPOT)/PROBA-V FAPAR generated by Copernicus Global Land Service (CGLS), and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index, as well as the European Space Agency's Climate Change Initiative (ESA CCI) soil moisture, the study shows that the QA4ECV FAPAR can support agricultural drought monitoring and assessment in Australia. The traceable and reliable uncertainties associated with the QA4ECV FAPAR provide valuable information for applications that use the QA4ECV FAPAR dataset in the future.

Recent changes in county-level maize production in the United States: Spatial-temporal patterns, climatic drivers and the implications for crop modelling.

Despite the fact that it is the total crop production that shapes future food supply rather than one of its single component, previous studies have mainly focused on the changes in crop yield. It is possible that recent gains in crop production are mainly due to improvement of yield rather than growth of harvest area. However, it remains unclear about the geographical patterns of their relative contributions at fine scales and the possible mechanisms. Analysis of US maize production shows that maize production has increased significantly at a rate of 2.1%/year during 1980-2010. Although yield is the dominant factor contributing to production growth for the country as a whole, the importance of harvest area has become more evident with time. In 56% of US's maize growing counties, harvest area has also contributed more than yield to production changes. High spatial correlation between the change rates of harvest area and production is observed (R = 0.96), while a weak relation (R = 0.21) is found between the spatial patterns of yield and production. This suggests that harvest area has exerted the dominant role in modulating the spatial distribution pattern of maize production changes. Further analysis suggests that yield and harvest area respond differently to climate variability, which has great implications for adaptation strategies. Comparing 11 state-of-the-art crop model simulations against census data reveals large bias in the simulated spatial patterns of maize production. Nevertheless, such bias can be reduced substantially by incorporating the observed dynamics of harvest area, pointing to a potential pathway for future model improvement. This study highlights the importance of accounting for harvest area dynamics in assessing agricultural production empirically or with crop models.

Assessing the non-stationarity of low flows and their scale-dependent relationships with climate and human forcing.

It is necessary to assess the non-stationarity of a hydrological series under changing environments. This study aimed to determine the validity of the stationarity of low flow series in terms of trends and possible change points, as well as the time-scale that is responsible for the production of trends and change points in low flow series. Further, we investigated how climatic variables affect low flow variations by studying their scale-dependent relationships. The modified Mann-Kendall trend test, heuristic segmentation method, discrete wavelet transform, and Pearson correlation coefficient were co-utilized to achieve these objectives. The Wei River Basin (WRB), a typical Loess Plateau region in China, was selected as the case study. Results showed significantly decreasing trends and change points in the low flow series, indicating that its stationarity assumption is invalid. The 2-year and 4-year events were the most important time-scales contributing to the trend of the original low flow series, and the 8-year periodic scale was the most influential frequency component for change point generation. Additionally, the strongest scale-dependent relationships among high frequency components (2-year and 4-year scales) of the low flow series and climatic variables (precipitation, potential evaporation, and soil moisture) demonstrated the importance of climatic factors for driving the trends of a low flow series. In contrast, human activities, including water withdrawals and water and soil conservation projects showed strong influences on the non-stationarity of low flows via affecting the low frequency component (8-year frequency and approximate components). These findings contribute to a better understanding temporal variations of low flow and their responses to changing environments, and the results also would be helpful for local water resources management as well as agricultural and ecological sustainable development.

Climate change will pose challenges to water quality management in the st. Croix River basin.

Responses of streamflow and nutrient export to changing climate conditions should be investigated for effective water quality management and pollution control. Using downscaled climate projections and the Soil and Water Assessment Tool (SWAT), we projected future streamflow, sediment export, and riverine nutrient export in the St. Croix River Basin (SCRB) during 2020-2099. Results show substantial increases in riverine water, sediment, and nutrient load under future climate conditions, particularly under the high greenhouse gas emission scenario. Intensified water cycling and enhanced nutrient export will pose challenges to water quality management and affect multiple Best Management Practices (BMPs) efforts, which are aimed at reducing nutrient loads in SCRB. In addition to the physical impacts of climate change on terrestrial hydrology, our analyses demonstrate significant reductions in ET under elevated atmospheric CO2 concentrations. Changes in plant physiology induced by climate change may markedly affect water cycling and associated sediment and nutrient export. Results of this study highlight the importance of examining climate change impacts on water and nutrient delivery for effective watershed management.

Comparison of urbanization and climate change impacts on urban flood volumes: Importance of urban planning and drainage adaptation.

Understanding the drivers behind urban floods is critical for reducing its devastating impacts to human and society. This study investigates the impacts of recent urban development on hydrological runoff and urban flood volumes in a major city located in northern China, and compares the urbanization impacts with the effects induced by climate change under two representative concentration pathways (RCPs 2.6 and 8.5). We then quantify the role of urban drainage system in mitigating flood volumes to inform future adaptation strategies. A geo-spatial database on landuse types, surface imperviousness and drainage systems is developed and used as inputs into the SWMM urban drainage model to estimate the flood volumes and related risks under various urbanization and climate change scenarios. It is found that urbanization has led to an increase in annual surface runoff by 208 to 413%, but the changes in urban flood volumes can vary greatly depending on performance of drainage system along the development. Specifically, changes caused by urbanization in expected annual flood volumes are within a range of 194 to 942%, which are much higher than the effects induced by climate change under the RCP 2.6 scenario (64 to 200%). Through comparing the impacts of urbanization and climate change on urban runoff and flood volumes, this study highlights the importance for re-assessment of current and future urban drainage in coping with the changing urban floods induced by local and large-scale changes.

Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future.

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 CO2 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.

Keeping global warming within 1.5 °C reduces future risk of yield loss in the United States: A probabilistic modeling approach.

This study assess the possible outcomes of yield changes in the United States which is responsible for 40% of global maize supply under 1.5 °C and 2 °C global warming scenarios. Instead of providing deterministic estimates, this study introduces a probability-based approach that allow for examination of the associated probability of each outcome, which has great implications for decision-makings. Results show distinct spatial patterns in future yield loss risk associated with temperature rise at the county scale, with highest probability in central and southeastern US, and lowest risk in western US and high production regions such as Iowa. Comparing the estimates under 1.5 °C global warming against that in 2.0 °C warming indicates that keeping global warming within 1.5 °C has great benefits for reducing future yield loss risk. Based on the ensemble mean of 97 climate model simulations, the risk of yield dropping below historical long-term mean is projected to decrease from 81% to 75% for the country as a whole. Such benefit is more evident when considering the risk of yield reduction by 10% and 20%, which is expected to decrease by 25% and 28%, respectively. This suggests that constraining global temperature rise to 1.5 °C has more benefits for reducing extreme yield reductions. Spatially, keeping global warming within 1.5 °C would benefit more in Missouri, South Dakota, Eastern Kansas, Southern Texas and southeastern part of the country than other regions, highlighting the spatially variable benefits of climate mitigation efforts. The analysis framework introduced in this study can also be easily extended to other regions and crops. The results of this study highlight the areas where maize yield is most vulnerable to temperature rise, and the spatially variable benefits for reducing yield loss risk by keeping global warming within 1.5 °C.

Recent changes in county-level corn yield variability in the United States from observations and crop models.

The United States is responsible for 35% and 60% of global corn supply and exports. Enhanced supply stability through a reduction in the year-to-year variability of US corn yield would greatly benefit global food security. Important in this regard is to understand how corn yield variability has evolved geographically in the history and how it relates to climatic and non-climatic factors. Results showed that year-to-year variation of US corn yield has decreased significantly during 1980-2010, mainly in Midwest Corn Belt, Nebraska and western arid regions. Despite the country-scale decreasing variability, corn yield variability exhibited an increasing trend in South Dakota, Texas and Southeast growing regions, indicating the importance of considering spatial scales in estimating yield variability. The observed pattern is partly reproduced by process-based crop models, simulating larger areas experiencing increasing variability and underestimating the magnitude of decreasing variability. And 3 out of 11 models even produced a differing sign of change from observations. Hence, statistical model which produces closer agreement with observations is used to explore the contribution of climatic and non-climatic factors to the changes in yield variability. It is found that climate variability dominate the change trends of corn yield variability in the Midwest Corn Belt, while the ability of climate variability in controlling yield variability is low in southeastern and western arid regions. Irrigation has largely reduced the corn yield variability in regions (e.g. Nebraska) where separate estimates of irrigated and rain-fed corn yield exist, demonstrating the importance of non-climatic factors in governing the changes in corn yield variability. The results highlight the distinct spatial patterns of corn yield variability change as well as its influencing factors at the county scale. I also caution the use of process-based crop models, which have substantially underestimated the change trend of corn yield variability, in projecting its future changes.

Evidence for a weakening strength of temperature-corn yield relation in the United States during 1980-2010.

Temperature is known to be correlated with crop yields, causing reduction of crop yield with climate warming without adaptations or CO2 fertilization effects. The historical temperature-crop yield relation has often been used for informing future changes. This relationship, however, may change over time following alternations in other environmental factors. Results show that the strength of the relationship between the interannual variability of growing season temperature and corn yield (RGST_CY) has declined in the United States between 1980 and 2010 with a loss in the statistical significance. The regression slope which represents the anomalies in corn yield that occur in association with 1 degree temperature anomaly has decreased significantly from -6.9%/K of the first half period to -2.4%/K--3.5%/K of the second half period. This implies that projected corn yield reduction will be overestimated by a fact of 2 in a given warming scenario, if the corn-temperature relation is derived from the earlier historical period. Changes in RGST_CY are mainly observed in Midwest Corn Belt and central High Plains, but are partly reproduced by 11 process-based crop models. In Midwest rain-fed systems, the decrease of negative temperature effects coincides with an increase in water availability by precipitation. In irrigated areas where water stress is minimized, the decline of beneficial temperature effects is significantly related to the increase in extreme hot days. The results indicate that an extrapolation of historical yield response to temperature may bias the assessment of agriculture vulnerability to climate change. Efforts to reduce climate impacts on agriculture should pay attention not only to climate change, but also to changes in climate-crop yield relations. There are some caveats that should be acknowledged as the analysis is restricted to the changes in the linear relation between growing season mean temperature and corn yield for the specific study period.