Monitoring land degradation & ecosystem resilience across Australian water-limited ecosystems

University of Technology Sydney. Faculty of Science.Degradation of dryland ecosystems has been of interest to ecologists for many decades, and has been reported on every populated continent. Rain-use efficiency (RUE), which describes the relationship between annual above-ground net primary productivity (ANPP) and annual precipitation (P), is a commonly used measure of ecosystem function across water-limited arid and semi-arid ecosystems. The goal of this thesis was to improve our understanding of spatial and temporal RUE relationships across Australian water-limited ecosystem in order to monitor land degradation and ecosystem resilience. A remote sensing approach was taken, as it is the only practical method that allows for spatially and temporally comprehensive assessment of RUE relationships at a continental scale. The first step was to assess spatial RUE variability in relation to spatial variability in precipitation (P) and potential evapotranspiration (PET), as water availability is primarily determined by hydro-meteorological conditions that encompass both water supply (P) and atmospheric evaporative demand, or PET. The results showed that water-limited ecosystems did not adhere to a well-defined spatial ANPP-rainfall relationship due to strong impacts of PET on RUE. Therefore, a new index that normalised RUE by PET was developed and tested - “effective RUE” (eRUE). The eRUE relationship (i.e. the regression between ANPP and the quotient of precipitation and PET) resulted in a spatially well-defined ANPP-water model compared to RUE (which does not consider the effect of PET). Also, during extreme dry years ecosystems showed stronger convergence to a common maximum ANPP-water relationship when the effects of both P and PET were included. This driest-years spatial eRUE relationship (i.e. cross-site eRUE_{dry}) defines theoretical water-limitation boundary conditions. Thus, while critically low rainfall can lead to vegetation water stress and contribute to ANPP losses, increasing PET caused by future climate change is likely to exacerbate drought-induced impacts on ecosystem structure and function, including the frequency of drought-induced mortality events. Vegetation type was also considered as a contributing factor to spatial RUE and eRUE variability. The results showed that vegetation types exhibited significant differences in eRUE (and RUE). Furthermore, these differences were also expressed during the driest years, suggesting that each vegetation type exhibits a unique spatial eRUE relationship during periods of severe water limitation. As such, if cross-site eRUE_{dry} is to be used as a theoretical drought resilience threshold, it should be defined by vegetation type-specific cross-site eRUE_{dry} relationships. Ecosystem function trends were assessed as indicators of land degradation. First, ANPP interannual variability was assessed in relation to interannual P variability, which revealed differences in sensitivity among vegetation types. Tussock grasslands, chenopod shrublands and agricultural lands were identified as the most sensitive to interannual P variability, suggesting that these vegetation types may be most sensitive to future climate change. The residuals trend (RESTREND) method was used to assess ecosystem function trends that were independent from climate trends. Sites with negative ecosystem function trends were observed across the study area, and represent potential sites of land degradation. Open woodlands, mulga shrublands, chenopod shrublands, hummock grasslands, and agricultural lands were identified as widely affected. This thesis has contributed to our understanding of spatial and temporal RUE relationships within the context of P, PET and vegetation type variability. At the continental scale ANPP spatial variability was strongly affected by P and PET. This led to the development of the eRUE metric, which was also applied during the driest years. The cross-site eRUE_{dry} represents theoretical water limitation boundary conditions that encompass water supply and atmospheric evaporative demand. Vegetation type was found to play a significant role in spatial eRUE relationships, suggesting that each vegetation type is likely to have a unique drought resilience threshold. The analysis did not reveal strong effects of PET trends on ANPP trends, perhaps indicating that negative effects of PET may be limited to drought periods. Finally, the possible presence of land degradation processes was identified across several vegetation types

Managing Grassland Systems in a Changing Climate: The Search for Practical Solutions

By the end of the XXIst century, a global temperature rise between 1.5 and 4°C compared to 1980-1999 and CO2 concentrations in the range 550-900 ppm are expected, together with an increased frequency of extreme climatic events (heat waves, droughts, and heavy rain) that is likely to negatively affect grassland production and livestock systems in a number of world regions. Grassland management has a large potential to mitigate livestock greenhouse gas emissions at a low (or even negative) cost, by combining a moderate intensification, the restoration of degraded pastures and the development of silvo-pastoral systems. Climate change vulnerability will be highest in regional hot spots with high exposure to climatic extremes and low adaptive capacity, such as extensive systems in dryland areas. Biome shifts, with expansion or contraction of the grassland biome, are projected by models. Resistance, resilience and transformation strategies can be used for grassland adaptation.With sown grasslands, adaptation options include changes in forage species (e.g. use of C4 grasses and of annual species) and genotypes and the use of grass-legume mixtures. Grazing management can be adapted to increase the resilience of plant communities to climatic variability. Our understanding of the synergies and trade-offs between adaptation and mitigation in the grassland sector is still limited and requires further research. Provided this understanding is gained, climate smart grassland systems that sustainably increase productivity and resilience (adaptation), reduce greenhouse gas emissions (mitigation), and enhance food security and development could be promoted. By reducing productivity gaps and increasing livestock production efficiency, they would also contribute to mitigate climate change from tropical deforestation and expansion of grasslands into savannahs

An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions

Drylands cover about 40% of the world’s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people’s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010–2019) using an integrative approach of remotely sensed and physical process-based numerical modeling (e.g., Global Land Data Assimilation System (GLDAS) and Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) models) data. Interaction between hydrological and ecological indicators including precipitation, evapotranspiration, surface soil moisture and vegetation indices are presented in the global four types of arid and semi-arid areas. The trends followed by precipitation, evapotranspiration and surface soil moisture over the decade are also mapped using harmonic analysis. This study also shows that some hotspots in these global drylands, which exhibit different processes of land cover change, demonstrate strong coherency with noted groundwater variations. Various types of statistical measures are computed using the satellite and model derived values over global arid and semi-arid regions. Comparisons between satellite- (NASA-USDA Surface Soil Moisture and MODIS Evapotranspiration data) and model (FLDAS and GLDAS)-derived values over arid regions (BSh, BSk, BWh and BWk) have shown the over and underestimation with low accuracy. Moreover, general consistency is apparent in most of the regions between GLDAS and FLDAS model, while a strong discrepancy is also observed in some regions, especially appearing in the Nile Basin downstream hyper-arid region. Data-driven modelling approaches are thus used to enhance the models’ performance in this region, which shows improved results in multiple statistical measures ((RMSE), bias (ψ), the mean absolute percentage difference (|ψ|)) and the linear regression coefficients (i.e., slope, intercept, and coefficient of determination (R2))

Rangeland Systems: Processes, Management and Challenges

environmental management; environmental law; ecojustice; ecolog

Australasia

Observed changes and impacts Ongoing climate trends have exacerbated many extreme events (very high confidence). The Australian trends include further warming and sea level rise sea level rise (SLR), with more hot days and heatwaves, less snow, more rainfall in the north, less April–October rainfall in the southwest and southeast and more extreme fire weather days in the south and east. The New Zealand trends include further warming and sea level rise (SLR), more hot days and heatwaves, less snow, more rainfall in the south, less rainfall in the north and more extreme fire weather in the east. There have been fewer tropical cyclones and cold days in the region. Extreme events include Australia’s hottest and driest year in 2019 with a record-breaking number of days over 39°C, New Zealand’s hottest year in 2016, three widespread marine heatwaves during 2016–2020, Category 4 Cyclone Debbie in 2017, seven major hailstorms over eastern Australia and two over New Zealand from 2014–2020, three major floods in eastern Australia and three over New Zealand during 2019–2021 and major fires in southern and eastern Australia during 2019–2020

Dynamic Feedbacks Between Vegetation and Hydrology in the Long Term

The interaction between vegetation and hydrology is complex and varies dynamically at both spatial and temporal scales. A close-to-reality representation of this interaction requires models that are able to depict spatiotemporal dynamics between the vegetation (green) and the hydrology (blue) world in an integrated way. However, most current hydrological models simulate plants by either pre-defining their properties or by considering few plant species only. Vice-versa, most plant models simplify the hydrological conditions and ignore the temporal dynamics of spatially distributed hydraulic conditions. Simplifying or pre-defining hydrological or ecological components may limit the capability of models to fully investigate this dynamic interplay. In this thesis, an innovative modelling approach is presented, which is coupling an advanced 3-D hydrogeological model to an individual-based plant model. The modelling system consists of a fully integrated surface and subsurface flow model HydroGeoSphere (HGS) that is dynamically coupled with a highly flexible plant model (PLANTHeR). This coupled modelling framework is then used to explore the dynamic feedbacks between hydrology and plant communities on a long-term time scale. Three main research questions are being investigated: 1) What benefit and which additional insights are provided by using the PLANTHeR-HGS model instead of uncoupled models? 2) Under which climate condition is it most meaningful to use the PLANTHeR-HGS model? 3) Are high plant diversity communities able to buffer ecosystems against extreme climate events? The results show that the PLANTHeR-HGS model is superior to uncoupled HGS and PLANTHeR models in quantifying hydrological processes and plant community dynamics. For quantifying the transpiration, soil water dynamics, plant community richness and aboveground biomass in a drier climate, or analyzing the evaporation process and plant community diversity in a wetter climate, it is recommended to use the PLANTHeR-HGS model instead of uncoupled models. In addition, under dry climates, high diversity communities have been shown able to buffer ecosystems against extreme flood events and extreme drought and heavy rainfall events, through increasing their ecosystem stabilities. In summary, this dissertation demonstrates that using pre-defined plant properties in hydrological models flattered the climate change effects over plant growth. Vice-versa, simply the hydrological conditions in ecological models resulted in unrealistic plant distribution pattern, such as competing for water resources generated regular pattern. The PLANTHeR-HGS model developed in this study, is not only able to advance our understanding of both spatial and temporal water resource heterogeneity effects on plant community diversity and richness, but also can advance our understanding of multiple climate drivers impact on plant community performance and hydrological dynamics simultaneously

Supporting evidence-based adaptation decision-making in the Australian Capital Territory: a synthesis of climate change adaptation research

This research synthesis provides policy-makers and practitioners with an understanding of the building blocks for effective adaptation decision-making, as evidenced through the NCCARF research program. It synthesised a portfolio of adaptation research for each Australian state and territory and addressing the complex relationships between research and policy development.   Each state and territory synthesis report directs users to research relevant identified priorities. Authored by Jennifer Cane, Laura Cacho, Nicolas Dircks and Peter Steele