Abstract. The simulation of routing and distribution of water through a regulated river system with a river management model will quickly result in complex and nonlinear model behaviour. A robust sensitivity analysis increases the transparency of the model and provides both the modeller and the system manager with a better understanding and insight on how the model simulates reality and management operations. In this study, a robust, density-based sensitivity analysis, developed by Plischke et al. (2013), is applied to an eWater Source river management model. This sensitivity analysis methodology is extended to not only account for main effects but also for interaction effects. The combination of sensitivity indices and scatter plots enables the identification of major linear effects as well as subtle minor and nonlinear effects. The case study is an idealized river management model representing typical conditions of the southern Murray–Darling Basin in Australia for which the sensitivity of a variety of model outcomes to variations in the driving forces, inflow to the system, rainfall and potential evapotranspiration, is examined. The model outcomes are most sensitive to the inflow to the system, but the sensitivity analysis identified minor effects of potential evapotranspiration and nonlinear interaction effects between inflow and potential evapotranspiration.
Countries around the world are prioritising net zero emissions to meet their Paris Agreement goals. The demand for social impact assessment (SIA) is likely to grow as this transition will require investments in decarbonisation projects with speed and at scale. There will be winners and losers of these projects because not everyone benefits the same; and hence, trade-offs are inevitable. SIAs, therefore, should focus on understanding how the risks and benefits will be distributed among and within stakeholders and sectors and enable the identification of trade-offs that are just and fair. In this study, we used a hypothetical case of large-scale hydrogen production in regional Australia and engaged with multi-disciplinary experts to identify justice issues in transitioning to such an industry. Using Rawlsian theory of justice as fairness, we identified several tensions between different groups (national, regional, local, inter and intra-communities) and sectors (environmental and economic) concerning the establishment of a hydrogen industry. These stakeholders and sectors will be disproportionately affected by this establishment. We argue that Rawlsian principles of justice would enable the practice of SIA to identify justice trade-offs. Further, we conceptualise that a systems approach will be critical to facilitate a wider participation, and an agile process for achieving just trade-offs in SIA.
Water table elevations are usually sampled in space using piezometric measurements that are unfortunately expensive to obtain and are thus scarce over space. Most of the time, piezometric data are sparsely distributed over large areas, thus providing limited direct information about the level of the corresponding water table. As a consequence, there is a real need for approaches that are able at the same time to (1) provide spatial predictions at unsampled locations and (2) enable the user to account for all potentially available secondary information sources that are in some way related to water table elevations. In this paper, a recently developed Bayesian data fusion (BDF) framework is applied to the problem of water table spatial mapping. After a brief presentation of the underlying theory, specific assumptions are made and discussed to account for a digital elevation model and for the geometry of a corresponding river network. On the basis of a data set for the Dijle basin in the north part of Belgium, the suggested model is then implemented and results are compared to those of standard techniques such as ordinary kriging and cokriging. Respective accuracies and precisions of these estimators are finally evaluated using a “leave‐one‐out” cross‐validation procedure. Although the BDF methodology was illustrated here for the integration of only two secondary information sources (namely, a digital elevation model and the geometry of a river network), the method can be applied for incorporating an arbitrary number of secondary information sources, thus opening new avenues for the important topic of data integration in a spatial mapping context.
Conceptual uncertainty is considered one of the major sources of uncertainty in groundwater flow modelling. In this regard, hypothesis testing is essential to increase system understanding by refuting alternative conceptual models. Often a stepwise approach, with respect to complexity, is promoted but hypothesis testing of simple groundwater models is rarely applied. We present an approach to model-based Bayesian hypothesis testing in a simple groundwater balance model, which involves optimization of a model in function of both parameter values and conceptual model through trans-dimensional sampling. We apply the methodology to the Wildman River area, Northern Territory, Australia, where we set up 32 different conceptual models. A factorial approach to conceptual model development allows for direct attribution of differences in performance to individual uncertain components of the conceptual model. The method provides a screening tool for prioritizing research efforts while also giving more confidence to the predicted water balance compared to a deterministic water balance solution. We show that the testing of alternative conceptual models can be done efficiently with a simple additive and linear groundwater balance model and is best done relatively early in the groundwater modelling workflow.
Environmental impact assessment (EIA) relies on rigorous scientific assessment of all potential causal pathways by which large-scale developments may impact on valued assets in a region. Despite their importance to informed decision-making, many EIAs are flawed by incomplete analysis of causal pathways, limited spatial assessment and a lack of transparency about how risks have been evaluated across the region. To address these, we describe an EIA methodology based on network analysis of potential causal pathways in a given region. This network approach is coupled with a systematic evaluation of the likelihood, consequence and mitigation options for each causal pathway from one or more human activities to multiple valued assets. The method includes analysis of the confidence in these evaluations, recognizing where knowledge gaps constrain assessments of risks to particular assets. The causal network approach is complemented by a spatially explicit analysis of the region that allows residual risk (i.e. risk remaining after all feasible mitigations) to be mapped for all valued assets. This identifies which activities could lead to potential impacts of varying concern (rated from 'very low' to 'very high'), their likely pathways, which valued assets are at risk and where these residual risks are greatest. The output maps reveal 'risk hotspots' where more detailed local-scale assessments and monitoring should focus. The method is demonstrated by application to potential impacts on 8 valued assets (aquifers, ecosystems and protected species) due to unconventional gas resource development in the Cooper Basin, central Australia. Results show which activities and causal pathways are of potential concern to different valued assets and where residual risk is greatest for particular species and ecosystems. This spatial causal network provides a systematic, consistent and transparent assessment of potential impacts, improving the quality of decision-making about planned developments and their environmental risks.
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