• Energy Research

This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thunen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 models was less than 1 Mg ha−1. However under water deficit in one of the two years, the models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.

Abstract Our understanding of climate impacts and adaptations on crop growth and productivity can be accelerated by analyzing historical data over the past few decades. We used crop trial and climate data from 1981 to 2009 at 34 national agro-meteorological stations in the Huang-Huai-Hai Plain (HHHP) of China to investigate the impacts of climate factors during different growth stages on the growth and yields of winter wheat, accounting for the adaptations such as shifts in sowing dates, cultivars, and agronomic management. Maximum (Tmax) and minimum temperature (Tmin) during the growth period of winter wheat increased significantly, by 0.4 and 0.6 °C/decade, respectively, from 1981 to 2009, while solar radiation decreased significantly by 0.2 MJ/m2/day and precipitation did not change significantly. The trends in climate shifted wheat phenology significantly at 21 stations and affected wheat yields significantly at five stations. The impacts of Tmax and Tmin differed in different growth stages of winter wheat. Across the stations, during 1981–2009, wheat yields increased on average by 14.5% with increasing trends in Tmin over the whole growth period, which reduced frost damage, however, decreased by 3.0% with the decreasing trends in solar radiation. Trends in Tmax and precipitation had comparatively smaller impacts on wheat yields. From 1981 to 2009, climate trends were associated with a ≤ 30% (or ≤1.0% per year) wheat yield increase at 23 stations in eastern and southern parts of HHHP; however with a ≤ 30% (or ≤1.0% per year) reduction at 11 other stations, mainly in western part of HHHP. We also found that wheat reproductive growth duration increased due to shifts in cultivars and flowering date, and the duration was significantly and positively correlated with wheat yield. This study highlights the different impacts of Tmax and Tmin in different growth stages of winter wheat, as well as the importance of management (e.g. shift of sowing date) and cultivars shift in adapting to climate change in the major wheat production region.

Abstract. The Paris Agreement set a long-term temperature goal of holding the global average temperature increase to below 2.0 ∘C above pre-industrial levels, pursuing efforts to limit this to 1.5 ∘C; it is therefore important to understand the impacts of climate change under 1.5 and 2.0 ∘C warming scenarios for climate adaptation and mitigation. Here, climate scenarios from four global circulation models (GCMs) for the baseline (2006–2015), 1.5, and 2.0 ∘C warming scenarios (2106–2115) were used to drive the validated Variable Infiltration Capacity (VIC) hydrological model to investigate the impacts of global warming on runoff and terrestrial ecosystem water retention (TEWR) across China at a spatial resolution of 0.5∘. This study applied ensemble projections from multiple GCMs to provide more comprehensive and robust results. The trends in annual mean temperature, precipitation, runoff, and TEWR were analyzed at the grid and basin scale. Results showed that median change in runoff ranged from 3.61 to 13.86 %, 4.20 to 17.89 %, and median change in TEWR ranged from −0.45 to 6.71 and −3.48 to 4.40 % in the 10 main basins in China under 1.5 and 2.0 ∘C warming scenarios, respectively, across all four GCMs. The interannual variability of runoff increased notably in areas where it was projected to increase, and the interannual variability increased notably from the 1.5 to the 2.0 ∘C warming scenario. In contrast, TEWR would remain relatively stable, the median change in standard deviation (SD) of TEWR ranged from −10 to 10 % in about 90 % grids under 1.5 and 2.0 ∘C warming scenarios, across all four GCMs. Both low and high runoff would increase under the two warming scenarios in most areas across China, with high runoff increasing more. The risks of low and high runoff events would be higher under the 2.0 than under the 1.5 ∘C warming scenario in terms of both extent and intensity. Runoff was significantly positively correlated to precipitation, while increase in maximum temperature would generally cause runoff to decrease through increasing evapotranspiration. Likewise, precipitation also played a dominant role in affecting TEWR. Our results were supported by previous studies. However, there existed large uncertainties in climate scenarios from different GCMs, which led to large uncertainties in impact assessment. The differences among the four GCMs were larger than differences between the two warming scenarios. Our findings on the spatiotemporal patterns of climate impacts and their shifts from the 1.5 to the 2.0 ∘C warming scenario are useful for water resource management under different warming scenarios.

Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2°C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].

2022

AbstractTerrestrial ecosystem water retention (TEWR) service is subject to climate change and elevated atmospheric carbon dioxide concentration (eCO2), however, the relevant processes by which future climate change and eCO2 affect TEWR are poorly understood. Here, we use the factorial simulation experiments from the Inter‐Sectoral Impact Model Intercomparison Project to address this research question. The experiments are based on three dynamic global vegetation models forced with the same climate change scenarios. Results suggest that compared to the preindustrial level, during 2070–2099, (a) TEWR change is highly uncertain, especially in the Southern Hemisphere. (b) Climate change will dominate the pattern of future TEWR change compared with eCO2. (c) Precipitation and runoff change will dominate the future TEWR change in various regions, and the direct role of evapotranspiration (ET) on TEWR will be relatively small. (d) eCO2 will mainly affect vegetation dynamics in energy‐limited regions to affect the runoff, and consequently affecting TEWR change. (e) eCO2 will decrease ET and increase the runoff, resulting in a slight TWER change. These findings improve the understanding of the responses of TEWR to future climate change and eCO2.