Modelling changes in vegetation productivity and carbon balance under future climate scenarios in southeastern Australia.

Australia, characterized by extensive and heterogeneous terrestrial ecosystems, plays a critical role in the global carbon cycle and in efforts to mitigate climate change. Prior research has quantified vegetation productivity and carbon balance within the Australian context over preceding decades. Nonetheless, the responses of vegetation and carbon dynamics to the evolving phenomena of climate change and escalating concentrations of atmospheric carbon dioxide remain ambiguous within the Australian landscape. Here, we used LPJ-GUESS model to assess the impacts of climate change on Gross Primary Productivity (GPP) and Net Biome Productivity (NBP) of carbon for the state of New South Wales (NSW) in southeastern Australia. LPJ-GUESS simulations were driven by an ensemble of 27 global climate models under different emission scenarios. We investigated the change of GPP for different vegetation types and whether NSW ecosystems will be a net sink or source of carbon under climate change. We found that LPJ-GUESS successfully simulated GPP for the period 2003-2021, demonstrating a comparative performance with GPP derived from upscaled eddy covariance fluxes (R2 = 0.58, nRMSE = 14.2 %). The simulated NBP showed a larger interannual variation compared with flux data and other inversion products but could capture the timing of rainfall-driven carbon sink and source variations in 2015-2020. GPP would increase by 10.3-19.5 % under a medium emission scenario and 19.7-46.8 % under a high emission scenario. The mean probability of NSW acting as a carbon sink in the future showed a small decrease with a large uncertainty with >8 of the 27 climate models indicating an increased potential for carbon sink. These findings emphasize the significance of emission scenarios in shaping future carbon dynamics but also highlight considerable uncertainties stemming from different climate projections. Our study represents a baseline for understanding natural ecosystem dynamics and their key role in governing land carbon uptake and storage in Australia.

Extreme fire weather is the major driver of severe bushfires in southeast Australia.

In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019-2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25° grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires.

Deficit Irrigation at Pre-Anthesis Can Balance Wheat Yield and Water Use Efficiency under Future Climate Change in North China Plain.

BACKGROUND: Deficit irrigation (DI) is a feasible strategy to enhance crop WUE and also has significant compensation effects on yield. Previous studies have found that DI has great potential to maintain crop production as full irrigation (FI) does. Therefore, adopting DI to improve crop production and safeguard groundwater resources is of great importance in water scarce regions, e.g., the North China Plain (NCP). Under the background of global warming, it is worth investigating whether DI continues to play such a key role under future climate scenarios. METHODS: We studied the response of winter wheat yield and WUE to different DI levels at pre-anthesis under two Shared Socioeconomic Pathways (SSPs) scenarios (SSP245 and SSP585) using the Agricultural Production Systems Simulator (APSIM) model driven by 21 general circulation models (GCMs) from the Coupled Model Inter-Comparison Project phase 6 (CMIP6). Additionally, we explored the effects of different nitrogen (N) fertilizer application rates on DI. RESULTS: We found that simulated wheat yield would increase by 3.5-45.0%, with WUE increasing by 8.8-46.4% across all treatments under future climate change. Moderate deficit irrigation (DI3, ≤0.4 PAWC at the sowing to flowering stage) under the N3 (150 kg N ha-1) condition was identified as the optimum irrigation schedule for the study site under future climate change. However, compensation effects of DI3 on yield and WUE became weak in the future, which was mainly due to increased growing season rainfall projected by GCMs. In addition, we found that N fertilizer application could mitigate the effect of DI3. CONCLUSIONS: We highlight that in water scarce regions of NCP, DI remains an effective strategy to maintain higher yield and enhance water use under future climate scenarios. Results strongly suggest that moderate deficit irrigation under a 150 kg N ha-1 condition could mitigate the contradiction between production and water consumption and ensure food safety in the NCP.

Future climate impacts on forest growth and implications for carbon sequestration through reforestation in southeast Australia.

Reforestation is identified as one of the key nature-based solutions to deliver carbon dioxide removal, which will be required to achieve the net zero ambition of the Paris Agreement. However, the potential for sequestration through reforestation is uncertain because climate change is expected to affect the drivers of forest growth. This study used the process-based 3-PG model to investigate the effects of climate change on development of above-ground biomass (AGB), as an indicator of forest growth, in regenerating native forests in southeast Australia. We investigated how changing climate affects AGB, by combining historical data and future climate projections based on 25 global climate models (GCMs) for the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways. We found that the ensemble means of 25 GCMs indicated an increase in temperature with large variations in projected rainfall. When these changes were applied in 3-PG, we found an increase in the simulated AGB by as much as 25% under a moderate emission scenario. This estimate rose to 51% under a high emission scenario by the end of the 21st century across nine selected sites in southeast Australia. However, when CO2 response was excluded, we found a large decrease in AGB at the nine sites. Our modelling results showed that the modelled response to elevated atmospheric CO2 (the CO2 fertilization effect) was largely responsible for the simulated increase of AGB (%). We found that the estimates of future changes in the AGB were subject to uncertainties originating from climate projections, future emission scenarios, and the assumed response to CO2 fertilization. Such modelling simulation improves understanding of possible climate change impacts on forest growth and the inherent uncertainties in estimating mitigation potential through reforestation, with implications for climate policy in Australia.

Pyrolysis of invasive woody vegetation for energy and biochar has climate change mitigation potential.

Woody plant encroachment in agricultural areas reduces agricultural production and is a recognised land degradation problem of global significance. Invasive native scrub (INS) is woody vegetation that invades southern Australian rangelands and is commonly cleared to return land to agricultural production. Clearing of INS emits carbon to the atmosphere, and the retention of INS by landholders for the purpose of avoiding carbon emissions has been incentivized in Australia as an emission reduction strategy. Retaining INS, however, means land remains relatively unproductive because INS negatively impacts livestock production. This desktop study examined whether clearing INS to return an area to production, and pyrolysing residues to produce biochar, has the potential to provide climate change mitigation (the "pyrolysis scenario"). The syngas produced via pyrolysis was assumed to be used to generate electricity that was fed into the electricity grid and avoided the production of electricity from existing sources. In addition, the biochar was assumed to be applied to soils used for wheat production, giving mitigation benefits from reduced N2O emissions from fertiliser use and reduction in the use of lime to ameliorate soil acidity. Relative to clearing INS and burning residues in-situ, the pyrolysis scenario resulted in a reduction in radiative forcing of 1.28 × 10-4 W m2 ha-1 of INS managed, 25 years after clearing, and was greater than the reduction of 1.06 × 10-4 W m2 ha-1 that occurred when INS was retained. The greatest contribution to the climate change mitigation provided by the pyrolysis scenario came from avoided emissions from grid electricity production, while avoided N2O and lime emissions made a relatively minor contribution towards mitigation.

Using large-scale climate drivers to forecast meteorological drought condition in growing season across the Australian wheatbelt.

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.

Late planting has great potential to mitigate the effects of future climate change on Australian rain-fed cotton.

The rain-fed cotton industry in Australia is vulnerable to climate change due to its high dependence on seasonal climate and summer rainfall. The rain-fed cotton in eastern Australia is increasingly being incorporated into cereal crop rotations due to government regulation of water resources, restricting opportunities for irrigated cotton. The accurate quantification of future climate impacts on exposed cropping systems such as rain-fed cotton is required to identify effective agronomic practices and inform strategic industry planning for the expansion of Australian cotton industry. Our study utilized 32 General Circulation Model (GCMs) for four cotton-growing regions representing the geographic range of cotton production in eastern Australia. We assessed the climate impacts on rain-fed cotton yield for two future periods (2040s and 2080s) under the RCP4.5 (low) and RCP8.5 (high) emissions scenarios employing the processed-based APSIM-Cotton model. Our results showed that current cotton yields varied with planting date, and the magnitude of yield change was consistent with regional climate variations at four locations representing the current geographic distribution of rain-fed cotton production. Means from multi-GCM ensemble showed growth period temperature increased more under RCP8.5 in the longer-term (2080s). Growth period rainfall changes had significantly positive effects on yield at all planting dates over each site. The projected increases in rainfall were more evident at later planting dates for dry sites than early planting dates at wet sites. In addition, we found planting date had the greatest influence on cotton yield at wet sites, while GCMs accounted for a large portion of variation in cotton yield at dry sites. We conclude that later planting has a great potential to increase rain-fed cotton yields. This provides important insights for regional-specific adaptation strategies for the rain-fed cotton industry in eastern Australia.

Sources of uncertainty for wheat yield projections under future climate are site-specific.

Understanding sources of uncertainty in climate-crop modelling is critical for informing adaptation strategies for cropping systems. An understanding of the major sources of uncertainty in yield change is needed to develop strategies to reduce the total uncertainty. Here, we simulated rain-fed wheat cropping at four representative locations in China and Australia using eight crop models, 32 global climate models (GCMs) and two climate downscaling methods, to investigate sources of uncertainty in yield response to climate change. We partitioned the total uncertainty into sources caused by GCMs, crop models, climate scenarios and the interactions between these three. Generally, the contributions to uncertainty were broadly similar in the two downscaling methods. The dominant source of uncertainty is GCMs in Australia, whereas in China it is crop models. This difference is largely due to uncertainty in GCM-projected future rainfall change across locations. Our findings highlight the site-specific sources of uncertainty, which should be one step towards understanding uncertainties for more robust climate-crop modelling.

Future climate change likely to reduce the Australian plague locust (Chortoicetes terminifera) seasonal outbreaks.

Climate is a major limiting factor for insect distributions and it is expected that a changing climate will likely alter spatial patterns of pest outbreaks. The Australian plague locust (APL) Chortoicetes terminifera, is the most economically important locust species in Australia. Invasions cause large scale economic damage to agricultural crops and pastures. Understanding the regional-scale and long-term dynamics is a prerequisite to develop effective control and preventive management strategies. In this study, we used a 32-year locust survey database to uncover the relationship between historical bioclimatic variables and spatial seasonal outbreaks by developing two machine learning species distribution models (SDMs), random forest and boosted regression trees. The explanatory variables were ranked by contribution to the generated models. The bio-climate models were then projected into a future climate change scenario (RCP8.5) using downscaled 34 global climate models (GCMs) to assess how climate change may alter APL seasonal distribution patterns in eastern Australia. Our results show that the model for the distribution of spring outbreaks performed better than those for summer and autumn, based on statistical evaluation criteria. The spatial models of seasonal outbreaks indicate that the areas subject to APL outbreaks were likely to decrease in all seasons. Multi-GCM ensemble means show the largest decrease in area was for spring outbreaks, reduced by 93-94% by 2071-2090, while the area of summer outbreaks decreased by 78-90%, and 67-74% for autumn outbreaks. The bioclimatic variables could explain 78-98% outbreak areas change. This study represents an important step toward the assessment of the effects of the changing climate on locust outbreaks and can help inform future priorities for regional mitigation efforts in the context of global climate change in eastern Australia.

Individualized Fall Prevention Program in an Acute Care Setting: An Evidence-Based Practice Improvement.

BACKGROUND: A 245-bed community hospital established patient fall prevention as its patient safety priority. PROBLEM: The hospital's fall prevention program was not consistently effective. The baseline fall rate was 3.21, higher than the National Database of Nursing Quality Indicators' median of 2.91. APPROACH: An interprofessional fall prevention team evaluated the hospital's fall program using the evidence-based practice improvement model. A clinical practice guideline with 7 key practices guided the development of an individualized fall prevention program with interventions to address 4 fall risk categories and an algorithm to identify interventions. Interventions included nurse-driven mobility assessment, purposeful hourly rounding, and video monitoring for confused and impulsive fall-risk patients. OUTCOMES: The fall rate decreased to 1.14, with a 72% expense reduction based on decreased sitter usage. CONCLUSIONS: An interprofessional team successfully reduced falls with an evidence-based fall prevention program.

High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia.

Efficient and effective modelling methods to assess soil organic carbon (SOC) stock are central in understanding the global carbon cycle and informing related land management decisions. However, mapping SOC stocks in semi-arid rangelands is challenging due to the lack of data and poor spatial coverage. The use of remote sensing data to provide an indirect measurement of SOC to inform digital soil mapping has the potential to provide more reliable and cost-effective estimates of SOC compared with field-based, direct measurement. Despite this potential, the role of remote sensing data in improving the knowledge of soil information in semi-arid rangelands has not been fully explored. This study firstly investigated the use of high spatial resolution satellite data (seasonal fractional cover data; SFC) together with elevation, lithology, climatic data and observed soil data to map the spatial distribution of SOC at two soil depths (0-5cm and 0-30cm) in semi-arid rangelands of eastern Australia. Overall, model performance statistics showed that random forest (RF) and boosted regression trees (BRT) models performed better than support vector machine (SVM). The models obtained moderate results with R2 of 0.32 for SOC stock at 0-5cm and 0.44 at 0-30cm, RMSE of 3.51MgCha-1 at 0-5cm and 9.16MgCha-1 at 0-30cm without considering SFC covariates. In contrast, by including SFC, the model accuracy for predicting SOC stock improved by 7.4-12.7% at 0-5cm, and by 2.8-5.9% at 0-30cm, highlighting the importance of including SFC to enhance the performance of the three modelling techniques. Furthermore, our models produced a more accurate and higher resolution digital SOC stock map compared with other available mapping products for the region. The data and high-resolution maps from this study can be used for future soil carbon assessment and monitoring.

Hypnotic enhancement of creative drawing.

A hypnotically based intervention to enhance creativity in drawing was evaluated in a controlled study. Participants were randomly assigned to either a hypnotic treatment or a nonhypnotic (task-motivational) control treatment. Subjects drew a standard still-life tableau twice. The first drawing involved no special instructions and provided a baseline measure of creativity in drawing. The second drawing was completed after the creativity-enhancement procedure. The drawings were rated blindly on several dimensions of artistic creativity. Hypnotizability, absorption, and debriefing measures were also administered. Results indicated that the hypnotic procedure had significantly greater effects on creativity in drawing. However, there were no significant main effects or interactions involving hypnotizability or absorption. Hypnotic and task-motivational groups did not differ on debriefing measures regarding their experience.