Climatic changes have altered surface water regimes worldwide, and climate projections suggest that such alterations will continue. To inform management decisions, climate projections must be paired with hydrologic models to develop quantitative estimates of watershed scale water regime changes. Such modeling approaches often involve downscaling climate model outputs, which are generally presented at coarse spatial scales. In this study, Coupled Model Intercomparison Project Phase 5 climate model projections were analyzed to determine models representing severe and conservative climate scenarios for the study watershed. Based on temperature and precipitation projections, output from GFDL‐ESM2G (representative concentration pathway 2.6) and MIROC‐ESM (representative concentration pathway 8.5) were selected to represent conservative (ΔC) and severe (ΔS) change scenarios, respectively. Climate data were used as forcing for the soil and water assessment tool to analyze the potential effects of climate change on hydrologic processes in a mixed‐use watershed in central Missouri, USA. Results showed annual streamflow decreases ranging from −5.9% to −26.8% and evapotranspiration (ET) increases ranging from +7.2% to +19.4%. During the mid‐21st century, sizeable decreases to summer streamflow were observed under both scenarios, along with large increases of fall, spring, and summer ET under ΔS. During the late 21st century period, large decreases of summer streamflow under both scenarios, and large increases to spring (ΔS), fall (ΔS) and summer (ΔC) ET were observed. This study demonstrated the sensitivity of a Midwestern watershed to future climatic changes utilizing projections from Coupled Model Intercomparison Project Phase 5 models and presented an approach that used multiple climate model outputs to characterize potential watershed scale climate impacts.

Vegetation phenology plays a key role in terrestrial ecosystem nutrient and carbon cycles and is sensitive to global climate change. Compared with spring phenology, which has been well studied, autumn phenology is still poorly understood. In this study, we estimated the date of the end of the growing season (EOS) across the Greater Khingan Mountains, China, from 1982 to 2015 based on the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index third-generation (NDVI3g) dataset. The temporal correlations between EOS and climatic factors (e.g., preseason temperature, preseason precipitation), as well as the correlation between autumn and spring phenology, were investigated using partial correlation analysis. Results showed that more than 94% of the pixels in the Greater Khingan Mountains exhibited a delayed EOS trend, with an average rate of 0.23 days/y. Increased preseason temperature resulted in earlier EOS in most of our study area, except for the semi-arid grassland region in the south, where preseason warming generally delayed EOS. Similarly, EOS in most of the mountain deciduous coniferous forest, forest grassland, and mountain grassland forest regions was earlier associated with increased preseason precipitation, but for the semi-arid grassland region, increased precipitation during the preseason mainly led to delayed EOS. However, the effect of preseason precipitation on EOS in most of the Greater Khingan Mountains was stronger than that of preseason temperature. In addition to the climatic effects on EOS, we also found an influence of spring phenology on EOS. An earlier SOS led to a delayed EOS in most of the study area, while in the southern of mountain deciduous coniferous forest region and northern of semi-arid grassland region, an earlier SOS was often followed by an earlier EOS. These findings suggest that both climatic factors and spring phenology should be incorporated into autumn phenology models in order to improve prediction accuracy under present and future climate change scenarios.

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