Ireland’s Rural Environment: Research Highlights from Johnstown Castle

ReportThis booklet gives a flavour of the current research in Teagasc Johnstown Castle Research Centre and introduces you to the staff involved. It covers the areas of Nutrient Efficiency, Gaseous emissions, Agricultural Ecology, Soils and Water quality

Modelling shallow landslides: the importance of hydrological controls and lateral reinforcement

Shallow landslides are important as geomorphic agents of erosion, sources of catchment sediment and potential hazards to life and infrastructure. The importance of these mass movements is difficult to define using solely field- based approaches because these are often too limited in both duration and resolution to fully determine the magnitude and frequency of these processes. Modelling is a powerful alternative tool for providing insight into underlying processes governing shallow landslides and for testing new hypotheses regarding environmental and land-use change impacts. The explanatory power of models is a function of their process representation and predictive ability. Current models suitable for catchment-scale application provide valuable probabilistic information on failure, but not detailed deterministic predictions. Using the English Lake District as a study area, this thesis addresses three issues necessary to provide the process-basis of these probabilistic analyses. First, poorly constrained or spatially variable input parameters such as soil depth, root reinforcement or material properties are often used to explain the locations of failure within a larger area that has a high, sometimes equal, probability of failure. The thesis develops rigorous new methods to quantify and minimise error in these parameters, representing them as distributions to capture both their natural variability and the error in their measurement. Results suggest that lateral root reinforcement even for grasses and shrubs may provide important additional strength (as much as 6 kPa) in the top 0.5 m of the soil. Second, infinite slope stability analysis neglects important additional lateral friction and root reinforcement effects at the margins of an unstable block. More sophisticated three-dimensional stability analyses can represent this process but are limited in their applicability by computational and data resolution requirements. This thesis derives from first principles a set of analytical governing equations for three-dimensional analysis; tests these against benchmark geotechnical methods; and applies them to establish key landslide scaling relationships. Third, shallow landslides in the UK are almost exclusively hydrologically triggered, resulting from local high pore water pressures. In line with the current paradigm existing stability models assume that the topography plays a dominant role in defining the spatial pattern of soil moisture and therefore pore water pressures in the landscape. This hypothesis is tested: first at the hillslope scale (10(^1) km(^2)) with a network of ֊100 wells; then the catchment scale (10(^2) km(^2)) using high resolution orthorectified aerial photographs to identify vegetation indicative of wet habitats and applying these as a proxy for soil moisture. These studies indicate that, for the case-study, wet areas are controlled at the landscape scale by a set of broad topographic limits in terms of slope and contributing area. Within these there is considerable scatter, resulting from the interplay of local factors such as: bedrock topography, preferential flow and soil stratification. Lateral root cohesion represents an important source of additional strength which can be included within analytical stability equations to create a threshold dependence on landslide size. Patterns of instability will then depend on the spatial pattern of other influencing factors (e.g. soil strength and pore pressure). At present the limits to available data and our understanding of hillslope hydrology constrain our ability to predict slope instability in environments like the Lake District. Future research might usefully identify landscape scale controls on this predictability

Understanding Structure and Function in Semiarid Ecosystems: Implications for Terrestrial Carbon Dynamics in Drylands

This study advances understanding of how the changes in ecosystem structure and function associated with woody shrub encroachment in semi-arid grasslands alter ecosystem carbon (C) dynamics. In terms of both magnitude and dynamism, dryland ecosystems represent a major component of the global C cycle. Woody shrub encroachment is a widespread phenomenon globally, which is known to substantially alter ecosystem structure and function, with resultant impacts on C dynamics. A series of focal sites were studied at the Sevilleta National Wildlife Refuge in central New Mexico, USA. A space-for-time analogue was used to identify how landscape structure and function change at four stages over a grassland to shrubland transition. The research had three key threads: 1. Soil-associated carbon: Stocks of organic and inorganic C in the near-surface soil, and the redistribution of these C stocks by erosion during high-intensity rainfall events were quantified using hillslope-scale monitoring plots. Coarse (>2 mm) clasts were found to account for a substantial proportion of the organic and inorganic C in these calcareous soils, and the erosional effluxes of both inorganic and organic C increased substantially across the vegetation ecotone. Eroded sediment was found to be significantly enriched in organic C relative to the contributing soil with systematic changes in OC enrichment across the vegetation transition. The OC enrichment dynamics observed were inconsistent with existing understanding (derived largely from reductionist, laboratory-based experiments) that OC enrichment is largely insignificant in the erosional redistribution of C. 2. Plant biomass: Cutting-edge proximal remote sensing approaches, using a remotely piloted lightweight multirotor drone combined with structure-from-motion (SfM) photogrammetry were developed and used to quantify biomass carbon stocks at the focal field sites. In such spatially heterogeneous and temporally dynamic ecosystems existing measurement techniques (e.g. on-the-ground observations or satellite- or aircraft-based remote sensing) struggle to capture the complexity of fine-grained vegetation structure, which is crucial for accurately estimating biomass. The data products available from the novel SfM approach developed for this research quantified plants just 15 mm high, achieving a fidelity nearly two orders of magnitude finer than previous implementations of the method. The approach developed here will revolutionise the study of biomass dynamics in short-sward ecogeomorphic systems. 3. Ecohydrological modelling: Understanding the effects of water-mediated degradation processes on ecosystem carbon dynamics over greater than observable spatio-temporal scales is complicated by significant scale-dependencies and thus requires detailed mechanistic understanding. A process-based, spatially-explicit ecohydrological modelling approach (MAHLERAN - Model for Assessing Hillslope to Landscape Erosion, Runoff and Nutrients) was therefore comprehensively evaluated against a large assemblage of rainfall runoff events. This evaluation highlighted both areas of strength in the current model structure, and also areas of weakness for further development. The research has improved understanding of ecosystem degradation processes in semi-arid rangelands, and demonstrates that woody shrub encroachment may lead to a long-term reduction in ecosystem C storage, which is contrary to the widely promulgated view that woody shrub encroachment increases C storage in terrestrial ecosystems.NERC Doctoral Training Grant (NE/K500902/1)NSF Long Term Ecological Research Program at the Sevilleta National Wildlife Refuge (DEB-1232294

Smart Classifiers and Bayesian Inference for Evaluating River Sensitivity to Natural and Human Disturbances: A Data Science Approach

Excessive rates of channel adjustment and riverine sediment export represent societal challenges; impacts include: degraded water quality and ecological integrity, erosion hazards to infrastructure, and compromised public safety. The nonlinear nature of sediment erosion and deposition within a watershed and the variable patterns in riverine sediment export over a defined timeframe of interest are governed by many interrelated factors, including geology, climate and hydrology, vegetation, and land use. Human disturbances to the landscape and river networks have further altered these patterns of water and sediment routing. An enhanced understanding of river sediment sources and dynamics is important for stakeholders, and will become more critical under a nonstationary climate, as sediment yields are expected to increase in regions of the world that will experience increased frequency, persistence, and intensity of storm events. Practical tools are needed to predict sediment erosion, transport and deposition and to characterize sediment sources within a reasonable measure of uncertainty. Water resource scientists and engineers use multidimensional data sets of varying types and quality to answer management-related questions, and the temporal and spatial resolution of these data are growing exponentially with the advent of automated samplers and in situ sensors (i.e., “big data”). Data-driven statistics and classifiers have great utility for representing system complexity and can often be more readily implemented in an adaptive management context than process-based models. Parametric statistics are often of limited efficacy when applied to data of varying quality, mixed types (continuous, ordinal, nominal), censored or sparse data, or when model residuals do not conform to Gaussian distributions. Data-driven machine-learning algorithms and Bayesian statistics have advantages over Frequentist approaches for data reduction and visualization; they allow for non-normal distribution of residuals and greater robustness to outliers. This research applied machine-learning classifiers and Bayesian statistical techniques to multidimensional data sets to characterize sediment source and flux at basin, catchment, and reach scales. These data-driven tools enabled better understanding of: (1) basin-scale spatial variability in concentration-discharge patterns of instream suspended sediment and nutrients; (2) catchment-scale sourcing of suspended sediments; and (3) reach-scale sediment process domains. The developed tools have broad management application and provide insights into landscape drivers of channel dynamics and riverine solute and sediment export