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Abstract Ecosystems in arid and semi-arid regions are vulnerable to climatic and anthropogenic disturbances. However, our understanding of vegetation stability (including resistance and resilience, which are the abilities of ecosystems to resist perturbations and return to pre-disturbance structure or function, respectively) in response to environmental changes in dryland ecosystems remains insufficient, particularly in the absence of large-scale observations of water availability. Here we introduced GRACE monthly total water storage anomaly (TWSA) data into an autoregressive model with remote sensed EVI, air temperature and precipitation to investigate the short-term vegetation stability and its influencing factors in Central Asia (CA) during 2003–2015. The results showed that the grid-level vegetation resilience in CA increased logarithmically as mean annual precipitation (R2 = 0.33, P < 0.05) but decreased linearly with increasing mean annual temperature (R2 = 0.41, P < 0.05). Vegetation resilience was not correlated with TWSA, due to the decoupling of TWSA with precipitation both spatially and temporally in the majority of CA. At the biome level, vegetation resilience also increased as a logarithmical function of aridity index (R2 = 0.80, P < 0.05). Vegetation resistance to TWSA showed minor difference across biomes, while vegetation resistance to precipitation functioned as a parabolic curve along the aridity gradient (R2 = 0.59, P < 0.05). Our results suggest that accounting for the effects of total water column instead of precipitation only is critical in understanding vegetation-water relationships in drylands. The steep decrease in vegetation resilience in areas with high temperature and low water availability implies a high risk of collapse for these water-limited ecosystems if there are severe droughts. Furthermore, reduction in total water storage, induced by, e.g., large-scale extraction of surface runoff or shallow-layer groundwater for irrigation, can result in negative influences to natural biomes in dryland regions. Graphical abstract Unlabelled Image Highlights • Spatially vegetation resilience increased logarithmically with increasing mean annual precipitation or aridity index. • Vegetation resilience was not correlated with TWSA due to the decoupling of TWSA with precipitation. • Vegetation resistance to precipitation functioned nonlinearly along the aridity gradient. [ABSTRACT FROM AUTHOR]

Crop phenology is determined by both climatic factors and agronomic management practices such as sowing date and cultivar characteristics. Exploring the interactive effects of climate change and crop management practices on crop phenology can be used to devise adaptation strategies to mitigate climate change. The objectives of this study were to: 1) examined trends in soybean (Glycine max L.) phenological development in China from 1981 to 2010; 2) isolate and quantify impacts of climate change and crop management on changes in soybean phenology; 3) determine the relative contribution of climate change and crop management to observed changes in soybean phenology; and 4) determine the relative contribution of temperature, precipitation, and sunshine hours to changes in soybean phenology. Changes in soybean phenology were observed across the major soybean producing area of eastern China during 1981–2010. Observed dates of sowing, emergence, anthesis, and maturity were delayed by an average of 1.78, 0.83, 0.19, and 0.62 days decade−1, respectively. Additionally, the lengths of the vegetative growth period and the soybean growing season were shortened by an average of 0.62 and 1.16 days decade−1, respectively. Conversely, the reproductive period was lengthened by an average of 0.43 days decade−1. Crop management practices had greater influence on sowing, emergence, and maturity dates than climate change. The direction of the changes to phenology trends created by management and climate change were opposite to each other. The relative influence of climate change on dates of anthesis, lengths of the vegetative and reproductive growth periods and growing season was larger than the influence of crop management practices. Mean temperature was the dominant climatic factor influencing most soybean phenological stages and phases. Delayed sowing dates and use of longer-duration cultivars are management adaptations that farmers have used to adapt to climate change occurring in past decades and that can continue to be used. These results indicate that farmers have a wider sowing window in spring and can select cultivars with long growing season duration and frost-tolerance to mitigate detrimental effects of a future warmer climate. Unlabelled Image • Trends in soybean phenology in China from 1981 to 2010 were examined. • Impacts of climate change and crop management on changes in soybean phenology were isolated. • Crop management accounted for 80.9%, 69.3%, 79.6% of sowing, emergence, maturity trends. • Mean temperature accounted for >40% of soybean phenology variation. [ABSTRACT FROM AUTHOR]

The Loess Plateau, the largest arid and semi-arid zone in China, has been confronted with more severe water resource pressure and a growing demand for food production under global changes. For developing sustainable agriculture in this region, it is critical to learn spatiotemporal variations in water use efficiency (WUE) of main crops (e.g. winter wheat in this region) under various water management practices. In this study, we classified irrigated and rainfed wheat areas based on MODIS data, and calculated the winter wheat yield by using an improved light use efficiency model. The actual evapotranspiration (ETa) of winter wheat and the evapotranspiration drought index (EDI) were also investigated. Then we mainly examined the synergistic relationship between crop yield, ETa, and WUE, and analyzed the variations in WUE of irrigated and rainfed wheat under water stress during the 2010–2011 growing season. The results suggested that winter wheat in the Loess Plateau was primarily dominated by rainfed wheat. The average yield of irrigated wheat was 3928.4 kg/ha, 22.2% more than that of rainfed wheat. High spatial heterogeneities of harvest index (HI) and maximum light use efficiency (ε max ) were found in the Loess Plateau. The ETa of irrigated wheat was 10.2% more than that of rainfed wheat. The ratio of irrigated and rainfed wheat under no water stress was 31.55% and 17.16%, respectively. With increasing water stress, the WUE of rainfed wheat decreased more quickly than that of irrigated wheat. The WUE variations in winter wheat under water stress depended strongly on the synergistic effects of two WUE components (crop yield and ETa) and their response to environmental conditions as well as water management practices (irrigated or rainfed). Our findings enhance our current understanding of the variations in WUE as affected by water stress under various water use conditions in arid and semi-arid areas. [ABSTRACT FROM AUTHOR]

The Earth's Critical Zone, where physical, chemical and biological systems interact, extends from the top of the canopy to the underlying bedrock. In this study, we investigated soil moisture controls on phenology and productivity of an Acacia woodland in semi-arid central Australia. Situated on an extensive sand plain with negligible runoff and drainage, the carry-over of soil moisture content (θ) in the rhizosphere enabled the delay of phenology and productivity across seasons, until conditions were favourable for transpiration of that water to prevent overheating in the canopy. Storage of soil moisture near the surface (in the top few metres) was promoted by a siliceous hardpan. Pulsed recharge of θ above the hardpan was rapid and depended upon precipitation amount: 150 mm storm − 1 resulted in saturation of θ above the hardpan (i.e., formation of a temporary, discontinuous perched aquifer above the hardpan in unconsolidated soil) and immediate carbon uptake by the vegetation. During dry and inter-storm periods, we inferred the presence of hydraulic lift from soil storage above the hardpan to the surface due to (i) regular daily drawdown of θ in the reservoir that accumulates above the hardpan in the absence of drainage and evapotranspiration; (ii) the dimorphic root distribution wherein most roots were found in dry soil near the surface, but with significant root just above the hardpan; and (iii) synchronisation of phenology amongst trees and grasses in the dry season. We propose that hydraulic redistribution provides a small amount of moisture that maintains functioning of the shallow roots during long periods when the surface soil layer was dry, thereby enabling Mulga to maintain physiological activity without diminishing phenological and physiological responses to precipitation when conditions were favourable to promote canopy cooling. [ABSTRACT FROM AUTHOR]

Recurring drought has caused large crop yield losses in Australia during past decades. Long-term drought forecasting is of great importance for the development of risk management strategies. Recently, large-scale climate drivers (e.g. El Niño-Southern Oscillation) have been demonstrated as useful in the application of drought forecasting. Machine learning-based models that use climate drivers as input are commonly adopted to provide drought forecasts as these models are easy to develop and require less information compared to physical-based models. However, few machine learning-based models have been developed to forecast drought conditions during growing season across all Australian cropping areas. In this study, we developed a growing season (Apr.-Nov.) meteorological drought forecasting model for each climate gauging location across the Australian wheatbelt based on multiple lagged (past) large-scale climate indices and the Random Forest (RF) algorithm. The Standardized Precipitation Index (SPI) was used as the response variable to measure the degree of meteorological drought. Results showed that the RF model could provide satisfactory drought forecasts in the eastern areas of the wheatbelt with Pearson's correlation coefficient r > 0.5 and normalized Root Mean Square Error (nRMSE) < 23%. Forecasted drought maps matched well with observed drought maps for three representative periods. We identified NINO3.4 sea surface temperature and Multivariate ENSO Index as the most influential indices dominating growing season drought conditions across the wheatbelt. In addition, lagged impacts of large-scale climate drivers on growing season drought conditions were long-lasting and the indices in previous year could also potentially affect drought conditions during current year. As large-scale climate indices are readily available and can be rapidly used to feed data driven models, we believe the proposed meteorological drought forecasting models can be easily extended to other regions to provide drought outlooks which can help mitigate adverse drought impacts. Unlabelled Image • A machine learning-based model was developed to predict drought. • Lagged large-scale climate indices were adopted as input predictors. • Growing season drought across all Australian cropping areas was predicted. • Forecasted drought maps matched well with observed drought maps. • NINO3.4 SST and MEI were identified as the most influential indices. [ABSTRACT FROM AUTHOR]