Fundamental issues

James Ball, Mark Babister, Monique Retallick, Fiona Ling, Mark Thye

The importance of spatiotemporal variability in irrigation inputs for hydrological modelling of irrigated catchments

Irrigation contributes substantially to the water balance and environmental condition of many agriculturally productive catchments. This study focuses on the representation of spatio‐temporal variability of irrigation depths in irrigation schedule models. Irrigation variability arises due to differences in farmers' irrigation practices, yet its effects on distributed hydrological predictions used to inform management decisions are currently poorly understood. Using a case study of the Barr Creek catchment in the Murray Darling Basin, Australia, we systematically compare four irrigation schedule models, including uniform vs variable in space, and continuous‐time vs event‐based representations. We evaluate simulated irrigation at hydrological response unit and catchment scales, and demonstrate the impact of irrigation schedules on the simulations of streamflow, evapotranspiration and potential recharge obtained using the Soil and Water Assessment Tool (SWAT). A new spatially‐variable event‐based irrigation schedule model is developed. When used to provide irrigation inputs to SWAT, this new model: (i) reduces the over‐estimation of actual evapotranspiration that occurs with spatially‐uniform continuous‐time irrigation assumptions (biases reduced from ∼40% to ∼2%) and (ii) better reproduces the fast streamflow response to rainfall events compared to spatially‐uniform event‐based irrigation assumptions (seasonally‐adjusted Nash‐Sutcliffe Efficiency improves from 0.15 to 0.56). The stochastic nature of the new model allows representing irrigation schedule uncertainty, which improves the characterization of uncertainty in simulated catchment streamflow and can be used for uncertainty decomposition. More generally, this study highlights the importance of spatio‐temporal variability of inputs to distributed hydrological models and the importance of using multi‐variate response data to test and refine environmental models.David McInerney, Mark Thyer, Dmitri Kavetski, Faith Githui, Thabo Thayalakumaran, Min Liu, George Kuczer

Uncertainty, sensitivity and scenario analysis: how do they fit together?

Session J5. Advances and applications in decision making in the face of multiple plausible futuresDealing with uncertainty is becoming increasingly important in model-based decision support. Various methods have been developed in order to do this, including uncertainty, sensitivity and scenario analysis. Although these different methods serve their purpose, the availability of a large number of methods can make it difficult for practitioners to understand the similarities and differences between them and when the use of one is more suitable than another, resulting in confusion. In addition, researchers often identify with belonging to a group dealing with a particular approach, which can lead to a lack of crossfertilisation and understanding. In order to assist with bridging the gap between researchers working on different approaches to dealing with uncertainty and eliminate confusion for practitioners, the objective of this paper is to examine the relationship between uncertainty, sensitivity and scenario analysis in the context of model-based decision support, and to take the first steps towards establishing common ground between these methods and assess the contexts under which they are most suitable. This is achieved by conceptualising the various methods as different approaches to “sampling” the hyperspace of model inputs, although this is done from different perspectives and for different ends (Figure 1). It is therefore also necessary to think about the assumptions each method is making about the space being explored, and there are benefits to be gained in thinking about how best to sample the space for each purpose. The approaches identified in this conference paper provide a first level of coarse characterisations. Further refinements in categorisation is possible (with the differentiation between narrative and stress testing scenarios as a first example), and likely to be useful. There are connections to be made to other disciplines, such as philosophy and decision theory, regarding the assumptions each method makes.H.R. Maier, J.H.A. Guillaume, C. McPhail, S. Westra, J.H. Kwakkel, S. Razavi, H. van Delden, M.A. Thyer, S.A. Culley and A.J. Jakema

Experimental evaluation of the dynamic seasonal streamflow forecasting approach

The primary focus of this experimental evaluation project is to answer the key question: is it possible to provide accurate and reliable seasonal streamflow forecasts using the dynamic hydrologic modelling approach? To address this issue, this experimental project evaluated the performance of the dynamic modelling approach to key catchments in the Murray-Darling Basin, where statistical seasonal streamflow forecasts are currently available.Tuteja NK, Shin D, Laugesen R, Khan U, Shao Q, Wang E, Li M, Zheng H, Kuczera G, Kavetski D, Evin G, Thyer M, MacDonald A, Chia T & Le

Optimisation of pump operations in water distribution systems taking into account the seasonality of the demand

Although the optimisation of pump operations in water distribution systems has previously been researched considering different types of pumps and controls, the variation of the water demand throughout the year and its impact on pumping have never been considered explicitly. This paper analyses two different demand patterns, characterized by a different degree of variability throughout the year, and their effects on pumping operations. A case study is presented that considers different pump types (fixed speed pumps and variable speed pumps) and the different monthly demand patterns. The comparison of results shows that the optimisation of variable speed pumps can lead to more effective pump controls, especially when the demand variability is larger. In addition, pump operations are optimised taking into account a seasonal temporal resolution: results show up to 12.5% in possible savings, highlighting the importance of considering seasonality in the optimisation of pump operations.Angela Marchi, Angus R. Simpson, Mark A. Thyer, Samuel Sutanto, Nhu Cuong D

Understanding and predicting household water use for Adelaide

Nicole Arbon, Mark Thyer, Darla Hatton MacDonald, Kym Beverley, Martin Lamber

Controlling rainwater storage as a system: An opportunity to reduce urban flood peaks for rare, long duration storms

Available online 01 October 2018Globally, urban infill is stressing existing stormwater systems, necessitating costly infrastructure upgrades. Although household rainwater tanks provide significant distributed storage, they have virtually no impact on reducing peak flows for rare, long duration events. This study introduces an innovative “smart systems” approach to operating tanks to overcome this limitation. Smart tanks are operated as systems and tank opening/closing is optimised to reduce peak flows. To evaluate the proposed approach, we develop a simulation-optimization model by coupling SWMM with a multi-objective genetic algorithm. The results for a two allotment case study show a consistent reduction in peak flows for a 24 h, 1 in 100-year storm for a range of rainfall patterns and tank sizes. For example, a system of 10 kL smart tanks reduced peak flows by 39%–48% compared with the same sized retention tanks. This smart systems approach provides an opportunity to reduce the cost of stormwater infrastructure.M. Di Matteo, R. Liang, H.R. Maier, M.A. Thyer, A.R. Simpson, G.C. Dandy, B. Erns

A simplified approach to produce probabilistic hydrological model predictions

Abstract not availableDavid McInerney, Mark Thyer, Dmitri Kavetski, Bree Bennett, Julien Lerat, Matthew Gibbs, George Kuczer

The SA Climate Ready Project

Climate change is anticipated to bring about significant changes to the capacity of, and the demand on, South Australia's water resources. As future changes to these water resources are uncertain, a scenario approach using global climate models (GCMs), combined with downscaling and hydrological models, is critical in the planning required to adapt the state's water resource management strategies to future climate conditions. A recently completed Goyder Institute project, SA Climate Ready, has developed an agreed set of downscaled climate change projections for South Australia to support proactive responses to climate change in water resource planning and management at a state and regional scale.Mark Siebentritt, Graham Green, Steve Charles, Guobin Fu, Seth Westra, Mark Thyer and Leon van der Linde