Abstract. Water jurisdictions in Australia are required to prepare and implement water resource plans. In developing these plans the common goal is realising the best possible use of the water resources – maximising outcomes while minimising negative impacts. This requires managing the risks associated with assessing and balancing cultural, industrial, agricultural, social and environmental demands for water within a competitive and resource-limited environment. Recognising this, conformance to international risk management principles (ISO 31000:2009) have been embedded within the Murray-Darling Basin Plan. Yet, to date, there has been little strategic investment by water jurisdictions in bridging the gap between principle and practice. The ISO 31000 principles and the risk management framework that embodies them align well with an adaptive management paradigm within which to conduct water resource planning. They also provide an integrative framework for the development of workflows that link risk analysis with risk evaluation and mitigation (adaptation) scenarios, providing a transparent, repeatable and robust platform. This study, through a demonstration use case and a series of workflows, demonstrates to policy makers how these principles can be used to support the development of the next generation of water sharing plans in 2019. The workflows consider the uncertainty associated with climate and flow inputs, and model parameters on irrigation and hydropower production, meeting environmental flow objectives and recreational use of the water resource. The results provide insights to the risks associated with meeting a range of different objectives.
Water quality models are replete with implicit assumptions. Some of the assumptions may be legacy in nature, having originated from early development of a model and subsequently taken for granted within the scientific community. For instance, Easton et al. (2008) reported that the SWAT model implicitly assumes an infiltration-excess response to rainfall when predicting storm runoff, which is not applicable to humid, well vegetated regions and where saturation-excess is the dominant process. The re-conceptualisation of the SWAT model then led to the development of a new modelling approach SWAT-VSA. Development of an appropriate suite of conceptual models should be one of the first steps in water quality modelling-appropriate in the sense that they capture key operating processes (runoff generation, transport and delivery) and landscape connectivities within catchments. The history of water quality modelling is one of evolution with most of the popular models built on, or adapted from, existing models or experimental studies. As a consequence, the conceptualisations underpinning these models are rarely scrutinised sufficiently. In this paper, we use the eWater Source modelling framework as a case study for critically analysing conceptualisations of constituent generation and transport fluxes. We have selected this product as its water quality modelling component is currently being enhanced and our analysis can usefully inform its future. Our observations, which are pertinent to many water quality models, include: • The current filtration and transport processes are basic representations. Depending on the water quality parameters being modelled, these may not be sufficient to capture the range of processes needing to be quantified in catchment modelling projects. • The base spatial unit for generation is an unlinked functional unit (FU), which is a property-based semidistributed approach to spatial discretisation. This limits the ability to capture important factors such as the distance of FU to streams, drainage/channelisation on FUs and relationships between FUs in the same subcatchment. • The temporal scale for constituent generation is generally daily. Moving to sub-daily scales may be needed to better capture the influence of rainfall intensity, especially in urbanised catchments. • Some aspects of management impacts are not adequately considered, such as lag-time between catchment management actions and system response. It could be argued that many of these conceptualisations are simplifications designed to balance complexity with usability and computational efficiency. Nevertheless, they need to be challenged and alternatives explored if we are to advance the science and practical effectiveness of water quality modelling. In this paper we report our initial investigations and propose some potential improvements in conceptualisation to allow for better representation of the system for water quality management.
Tracking the progress of water management actions against Basin Plan objectives in the Murray-Darling Basin requires an ability to forecast the condition of the Basin's environmental assets into plausible hydrological futures. Understanding and modelling how asset condition changes through time is referend to as trajectory modelling. Asset trajectories originating from a particular starting condition are bound by a range of possible future conditions. This range increases through time in association with different sequences of environmental conditions (created through the flow regime), and is bound by the rate of response of the environmental asset. This rate of response is associated with factors largely intrinsic to the different environmental assets, for example, the rate at which generation of biomass is associated with vegetation recovery. Tracking ecological outcomes through time requires understanding and quantifying environmental water needs, responses to event sequencing and antecedent condition within a broader systems framework. Many factors are likely to influence the extent to which environmental watering can achieve Basin Plan objectives. These include natural variability in the flow regime, strategic (long-term) water management decisions, short-term prioritisation of environmental water and other threats and influences outside of water management (such as multi-species interactions). Throughout the record of historical flows, natural variability has been a major cause of change in environmental condition. Short-term incremental decision-making (or prioritization based upon annual objectives and opportunities) and the uncertainty of future conditions influence the ability to achieve longer-term objectives. The Murray-Darling Basin Authority (MDBA) currently uses a range of ecological modelling tools and methods to inform water management priorities and decision-making within the Basin. In this paper, we outline the development of a method that builds upon existing frameworks and methods used by MDBA and integrates them into a trajectories modelling architecture. The trajectories architecture uses an automated workflow to incorporate the variability of historic flow regimes combined with scenario analyses that are linked to eco-hydrological models. The goal is to develop methods to inform possible outcomes of water management over periods amenable to both long-and short-term decision making processes and align with timelines for Basin Plan objectives and beyond. We demonstrate the architecture using a case-study of woody floodplain vegetation.
Recent water reforms in Australia and the release of the Murray-Darling Basin Plan have been supported by climate models and detailed hydrological modelling including river system models. Sensitivity analysis of these river system models provides valuable insights into the often complex and non-linear relationships between uncertainty in input variables and parameters and model outputs. An understanding of these relationships is an important component of assessing the risks in the planning process.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.