A new river system modelling tool for sustainable operational management of water resources

Journal of Environmental Management 121 (2013) 13e28

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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman

A new river system modelling tool for sustainable operational management of water resources Dushmanta Dutta a, *, Kym Wilson b, Wendy D. Welsh a, David Nicholls c, Shaun Kim a, Lydia Cetin d a

CSIRO Land and Water, Canberra, Australian Capital Territory, Australia Goulburn-Murray Water, Victoria, Australia c DA Nicholls Pty Ltd, Nicholls, Australian Capital Territory, Australia d Lydia Cetin Sinclair Knight Merz, Victoria, Australia b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 14 June 2012 Received in revised form 31 October 2012 Accepted 5 February 2013 Available online 18 March 2013

The eWater Cooperative Research Centre of Australia has developed a river system modelling software called eWater Source that can be used to assist water managers and river operators in planning and operating river systems. It has been designed and developed within Australia to provide a consistent approach to underpin a wide range of water planning and management purposes. The software provides tools for the prediction and quantification of water from catchments to the end of a river system by integrating continuous rainfall-runoff and river system models. It includes three modes (catchment runoff, river management and river operations) for different applications. This paper introduces the operations mode of Source and compares its functionality with the existing tools used for daily river operations in Australia, with the Goulburn River as the case study. A 5-year period is used to compare modelled and observed results. Forecasts from Source and the existing tools are compared to observations over 7-day forecast periods that include an environmental water release. Source provided acceptable or improved results and required less user input than the existing method. Source provides a flexible software tool in which various forecast models can be incorporated. The application has demonstrated the potential of Source to provide an improvement on the existing river operations models in Australia at both the daily and seasonal time steps. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: eWater Source River system modelling Flow forecast River operation Goulburn River Water resources management

1. Introduction Operation of a regulated river system involves directing releases from storages and controlling diversions of water from the river for irrigation and environmental uses, and for consumers in urban areas. River operators of large regulated systems are increasingly facing complex challenges in operations due to a range of socio-economic and environmental issues (Welsh et al., 2012). The operational decisions in Australia take a range of technical considerations under different hydro-climatic conditions such as flow requirements, water level changes, estimated evaporation, forecast rainfall and the water-carrying capacity of the river at various locations. This is required for balancing competing objectives such as structural safety and maintenance, water orders,

* Corresponding author. E-mail addresses: [email protected] (D. Dutta), [email protected] (K. Wilson), [email protected] (W.D. Welsh), david@ danicholls.com.au (D. Nicholls), [email protected] (S. Kim), LCetin@ globalskm.com (L. Cetin). 0301-4797/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.02.028

water security, water trade, environmental outcomes, navigation and recreation (MDBA, 2011). River system modelling plays a key role in the operational decision-making process (Berris et al., 2001). In most of the countries around the world, river operations have historically utilized a diverse set of software in its decision-making and reporting processes (Carron et al., 2010). These tools require significant manual manipulation of data, are sometimes inconsistent, and do not always consider a priori all of the institutional constraints and policies in the decision-making process because such tools were not designed for planning purposes. For example, river operators in Australia have been using spreadsheet-based software for day-to-day river operations. These spreadsheets do not use sophisticated physical models and forecasting tools. Such tools are entirely dependent on input from an experienced user familiar with the nuances of the river system being modelled. Current operational tools in Australia model the river system in a manner that is distinctly different from the long-term planning tools used to develop water-sharing plans. Operational tools typically assess management options over a time scale in the order of

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weeks to months. Planning tools typically look at the impact of management decisions over a decadal to multi-decadal time scale. This creates a number of inconsistencies between the planning and operational management of river systems (Bridgart and Bethune, 2009). The large climatic variability in climate with prolonged droughts highlights the need of high efficiency in river operations in Australia. There is an increasing need for a new river system operational model that has a more robust decision-making foundation based on physical processes for reservoir and river operations and advanced streamflow forecasting tools. Such tools should allow comparisons of water orders with actual diversions, forecast inflows with actual river gains, and environmental target operations with actual deliveries. In recent years, with advancements in hardware and software technologies, several organisations in different countries have undertaken initiatives to develop new river systems modelling tools for planning (Wurbs, 2005) such as HEC-ResSim (USACE, 2010), MIKE BASIN (DHI, 2003), RIBASIM (Deltares, 2010), Decision Support Framework (DSF) (MRC, 2004), and RiverWare (Zagona et al., 1998, 2001), CALSIM (Draper et al., 2004; Van Lienden et al., 2006), CalLite (Islam et al., 2011), which incorporate physical models to represent underlying physical processes and advanced forecasting tools. Such modelling techniques in planning models have considerable potential to advance the accuracy and usefulness of river operations tools (Bridgart and Bethune, 2009). CALSIM and RiverWare can be effectively used for operational applications. Delft-FEWS is another operational system developed by Deltares (2012) for hydrological forecasting and warning. However, these models are not easily transferable to Australian River basins due to the complexity and diversity of the management and operational rules. A review of the user requirements for an operational tool was undertaken in 2006 (Nicholls, 2006) and reviewed in 2008 (eWater CRC, 2008). The key user requirements for a river operations tool are the ability to forecast future river flows and water use demands, to easily replace these with measured values, to re-set levels and flows to accurately reflect the controls river operators have, to rapidly assess several what-if scenarios, to see at-a-glance estimates of reservoir levels and river flows down the system, and to tailor the model to the simplicity or complexity of the actual system. A 2009 review of the existing operational tools by Barma (2009) reported that no existing operational tools in Australia met the above mentioned key user requirements. End users of these models have also expressed a strong desire for a tighter link between operational and planning tools, based on river management models (Nicholls, 2006). The complexity and diversity of challenges faced by water management organizations highlight a need for a new modelling tool to assist in decision making for operations of regulated river systems to not only meet today’s needs, but tomorrow’s as well. To meet these needs, a new integrated modelling software called “Source” has been recently developed by the eWater Cooperative Research Centre (CRC) of Australia in collaboration with several of its research and industry partners (Welsh et al., 2012). There are three scenario modes for modelling in Source namely catchment runoff, river management and river operations. River operations mode can be used to underpin daily river operations to support the efficient management of water storage, flow and delivery in regulated river systems. A study was undertaken to build an operational river system model in the Goulburn River basin using Source to improve the operational management of the river system by GoulburneMurray Rural Water Corporation (G-MW). The paper introduces the Source River Operations mode and analyses its performance through a case study application in the

Goulburn River basin, Australia. The next section of the paper describes the Source Operations mode and its key features and functionalities. The case study area is then described. The model setup and outcomes of the case study application are presented in the results section. The advantages associated with Source when compared to the commonly used spreadsheet model for river operations in Australia are presented in the discussion section. The final section of the paper presents the key conclusions. 2. Source integrated modelling system Source provides tools for the prediction and quantification of water and associated constituents (such as salinity, suspended sediment and nutrient) from catchments to a river system and propagation along the river system. The components used to model regulated rivers within Source encompass (and enhance) the key functionalities of the three widely used river system modelling tools in Australia: IQQM (Podger, 2004; Simons et al., 1996; DLWC, 1999), REALM (Perera et al., 2005; VU and DSE, 2005) and MSMBigmod (Close, 1996a, b; Close and Sharma, 2003). It also incorporates several new components such as a suite of rainfallrunoff models, environmental and urban demand models and optimization tools for calibration of catchment rainfall-runoff and river routing parameters. The various components of Source and their functionalities are described in Welsh et al. (2012). The system as a whole is extensible through expression editor (Penton and Gilmore, 2009) and plug-in (Kim et al., 2011; eWater CRC, 2011) functionalities. The expression editor provides the capability to define a value using an expression based on the current or past state of another part of the system. A plug-in is a section of computer code compiled as a DLL (Dynamically Linked Library). Plug-ins can be created by the model user to add new modelling components to Source that are not otherwise available, including modifications to the simulation engine or the application user interface. Source includes three scenario modes: catchment runoff, river management and river operations. This paper describes the component of the river operations mode. River operations mode is primarily designed for river system operators to forecast and route flow for daily and seasonable river operations. It uses the same underlying node and link river network definitions and calculations as the river management mode to define and parameterise the river system being modelled. In the node-link system, a river network begins and ends with a node, and all nodes are interconnected by links. Runoff is fed into the network as an inflow at the relevant location in the network. Links represent a length of stream and are used to transfer flow and constituents between nodes, with or without routing and transformation. Two routing methods are available to model travel time and attenuation in links: pure lag or translation, and a generalised streamflow storage routing method, which can represent linear (Koussis, 1980), non-linear (Linsley et al., 1949), and variable parameter (Close, 1996a) Muskingum. Nodes are used to represent one or multiple physical locations along a river where flow and water quality constituents either enter the system or are stored, extracted, lost or measured. They are also used for the application of management rules. 2.1. River operations using Source There are many features and functions in Source that allow the presentation of system behaviour. Some of the major features and functions in river operations mode are briefly explained here, these include graphical user interface, daily and seasonable operations functionality.

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2.2. Graphical user interface A graphical user interface (GUI) allows river operators to interact with the underlying complex computer code and view and analyse model output. The GUI includes two views: schematic and tabular editors. 2.2.1. Schematic editor The schematic editor is used to construct a river system network by selecting graphical icons representing system components (nodes and links and their attributes) and run the software. Fig. 1 shows a schematic view of a river system operational model built using the schematic editor. 2.2.2. Tabular editor The tabular editor presents a tabular view of temporal observed data and predicted results at every node by reproducing the spreadsheet view that the operators are already familiar with. This allows an operator to simultaneously look at both temporal (y axis, columns) and spatial (x axis, rows) aspects of the river system. Time is represented with observed data starting from the top of the column and running to the forecast data at the bottom of the column. The various network elements (nodes and links) are shown in the column header row from upstream (left side) and heading downstream (right side). Users are also given the ability to rearrange the columns. The view provides the individual data points

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that are of interest to operators in an easily accessible way. The tabular editor (Fig. 2) also allows the user to access and configure the underlying models for each of the network elements. 2.3. Daily operation Source can be used for daily operations to model alternative storage release patterns and weir pool operations. It allows users to estimate an optimal operating scenario for a storage release while satisfying all the short-term system demands, and adhering to any operating rules in place for the system under the set of forecast conditions. Users can reconcile data collected from the field on a daily basis against the model predictions, and if necessary, reconfigure the inflow, demand or unaccounted difference forecasts to reflect the system behaviour that is currently being observed. Unaccounted Difference represents the difference between the modelled and observed data. 2.4. Seasonal operation The planning of seasonal operations is especially critical where a system has one or a combination of channel capacity constraints, storages in series and parallel, compressed demand periods or large volumes of water to transfer. Source can be used for seasonal operations to analyse system responses for a given level of resource allocation under a number of possible climate conditions and to

Fig. 1. A snapshot of a river system operation model showing the node-link setup and nodes with different functionalities.

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Fig. 2. A snapshot of tabular editor of a river system operation model similar to the spreadsheet model with both temporal (y axis, columns) and spatial (x axis, rows) aspects of the river system.

investigate system behaviour in terms of storage levels and flows, making sure the system has the ability to satisfy demands across the full delivery period. In general, users review extractions to date, storage levels and unaccounted differences on a monthly basis, and depending on these results, might increase allocation levels and modify operating plans for the remainder of the season. 2.5. Data import interface For any river operation, river operators need to use input data from a range of different sources. Data sources vary from industry databases such as Hydstra, a time-series data management system (Kisters, 2012), to text files and web services. Source includes a user interface capable of supporting interactions with some of the widely used databases, namely: Hydstra (a commercial software used across Australia for archiving of gauge data), MySQL and Oracle (Fig. 3). Through this interface, the software allows users to import data from these databases and convert it to a standard format for modelling (Delgado et al., 2012).

For any given forecast variable, Source can include multiple forecast models. The first model can generate a short-term forecast of the near future (typically one week out) and the second generates a long-term forecast from the end of the first onwards. This enables the user to specify a suitable model for use in the immediate future, such as a simple regression model to predict streamflow, and to then revert to a model which might use historic, monthly or seasonal averages to produce its long-term forecast. A snapshot of short-term water demand forecasting using three different forecast options is shown in Fig. 4 with x-axis showing the forecast time scale and y-axis showing the daily forecast flow from a particular date. Several demand forecast models (such as environmental demand models, crop demand models e IQQM crop model (Podger, 2004) and PRIDE (SKM, 2007)) are built into Source to forecast irrigation and environmental demands. Source also allows users to incorporate user-specific forecasting techniques through expression editor and plug-in functionalities. Streamflow forecasts from unregulated catchments by rainfall-runoff (RR) models in the catchment runoff mode can also be linked to a river operations mode project scenario (Dutta et al., 2012).

2.6. Forecast models 3. Case study description Both observed and forecast data are required for river system operations. Forecast data, which refers to the prediction of future occurrences for any variable (e.g., inflow and demand) in the system, can be generated using various mathematical forecast models. Forecast models range in complexity from simple regression models to more complex models that use data such as rainfall predictions to approximate future tributary inflows. Source provides users with several options to incorporate different demand and streamflow forecast models.

The Goulburn River basin, located within the Murray-Darling Drainage Division of Australia, is the largest river basin of the state of Victoria, encompassing an area of approximately 16,200 km2 (Fig. 5a). The annual average rainfall in the south east of the basin is 1600 mm and in the far north of the basin is less than 450 mm per year. The Goulburn River is 570 km long, flowing from upstream of Woods Point to its confluence with the Murray River near Echuca. There are many tributaries flowing into the upper

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Fig. 3. GUI for importing time-series inputs from standard databases.

Goulburn River where the catchment is hillier and streams originate in the Great Dividing Range. These include the Goulburn, Delatite, Howqua, Jamieson and Big Rivers upstream of Lake Eildon and the Rubicon River, Acheron River, Yea River, King Parrot Creek, Creightons Creek, Seven Creeks and Hughes Creek downstream of

Lake Eildon. The Broken River flows into the Goulburn River at Shepparton (EPA, 2005). The Goulburn River is regulated with the river system supplying water for agriculture and urban uses. A schematic diagram of the river system is shown in Fig. 5b. Regulation of the river begins at

Fig. 4. A snapshot of a short-term water demand volume forecast using three different forecast options and the actual volume of water ordered.

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Fig. 5. a) Geographic view and (b) schematic view of the Goulburn River system showing the main river and the tributaries.

system’s primary storage, Lake Eildon (3334 GL capacity), in the headwaters of the main channel. Water storage in Lake Eildon along with tributary inflows to the Goulburn River is used to irrigate over 200,000 ha of land by a series of channels and weirs. On its release from Lake Eildon, water flows down the Goulburn River to Goulburn Weir where it is diverted north-east via the East Goulburn Main Channel and north-west via the Stuart Murray and Cattanach Canals. At full supply level, the Goulburn weir pool has a volume of 25 GL and covers an area of 130 ha. The Stuart, Murray and Cattanach Canals transfer water to Waranga Basin, an off-stream storage serving the western part of the Goulburn system. The capacity of Waranga Basin is 430 GL. From Waranga Basin water is transferred further westward via the Waranga Western Channel (WWC). This channel was capable of supplying water as far as Ouyen, some 600 km from Lake Eildon. In the present day G-MW only operates this channel to approximately 20 km west of Boort Township. The regulated river has a mean annual flow of about 3235 GL/year (between 1971 and 2011) at McCoys Bridge and the Goulburn Broken region generates 11% of Victoria’s runoff within the MDB region at the current level of development (Goulburn Broken CMA, 2003; CSIRO, 2008; DSE, 2011). The regulated Goulburn River system supports approximately 1,100 GL of high-reliability irrigation and urban water entitlements and a further 440 GL of low-reliability entitlements. In addition, the system supports approximately 100 GL of high-reliability and 160 GL of low-reliability environmental entitlements. High-reliability entitlements are those which are expected to be supplied with 97% reliability, based on historical records. This means the full volume of these entitlements would be available for use in 97 out of 100 years. Low-reliability entitlements receive allocation once all high-reliability entitlements can be supplied in the current year, and sufficient resource is held in reserve to allow fully supply of high-reliability entitlements in the following year.

The Goulburn River system is mostly operated to harvest and supply water for consumptive uses. Regular decisions must be made regarding the volume of water to release from Lake Eildon to meet demands. An assessment of the runoff from the catchment downstream of Lake Eildon must also be considered when determining appropriate releases. This is currently done based on a water balance calculation of inflow to Goulburn Weir. The volume of inflow calculated is compared to forecast demands, and used to determine the volume required from Lake Eildon. Rising observed streamflow in the tributaries below Lake Eildon after rainfall may warrant a reduction in the release from Lake Eildon in anticipation of additional water reaching Goulburn Weir. Waranga Basin is able to harvest unregulated flows from the catchment downstream of Lake Eildon and releases water to supply entitlement holders’ and minimum flow requirements via the WWC. During peak inflow months, regular assessments of catchment conditions must be made to determine the volume to be harvested at Goulburn Weir and diverted to Waranga Basin. The aim is to fill the storage to its interim 90% target level during winter and spring, followed by a final fill to capacity immediately prior to the onset of irrigation demand. Waranga Basin is then drawn down by irrigation demand until it reaches a pre-defined operating target. This triggers water to be ordered from Lake Eildon in order to ensure there is no shortfall in meeting irrigation and urban demands that are supplied through Waranga Basin. G-MW manages the Goulburn water supply system, including Lakes Eildon and Goulburn Weir Pool and the Waranga Basin. GMW is also the licencing authority responsible for managing private groundwater pumping and surface water diversions in the Goulburn Basin. Goulburn Valley Regional Water Corporation is responsible for urban water supply, whilst the Goulburn Broken Catchment Management Authority is responsible for waterway management.

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As the manager of the Goulburn River system, G-MW has an obligation to ensure minimum passing flows are maintained along the Goulburn River downstream of Lake Eildon and Goulburn Weir. This requires the river operators to determine the appropriate releases from Goulburn Weir to meet river losses, downstream demands from urban and irrigation customers and minimum flow requirements at McCoys Bridge. The travel time between the Goulburn Weir and McCoys Bridge is 7 days under normal regulated conditions. The purpose of this case study application is to assess the capability and suitability of Source to model the operation and management of a complex and highly regulated river system in comparison with the existing spreadsheet-based method used for the river operations. The existing spreadsheet-based model is designed to forecast based on a receding trend from the most recent observed data. To forecast any flow regime other than a receding trend requires significant manual input. Other flow regimes need to be modelled during events such as in-stream delivery of environmental entitlements, or the supply of trade commitments to the Murray River. The specific objectives of the operational model developed in Source for the Goulburn River system included routing of flow with appropriate lag and attenuation, representing the demands of water users, river losses and diversion of unregulated flows to an off-stream storage. In addition, it was necessary for the model to have the capability to forecast river flows and storage releases based on demand inputs, minimum flow requirements and a recession of tributary flows. 4. Application results 4.1. Source setup Fig. 6 shows a schematic diagram of the node-link setup of the Goulburn River system operational model built in Source. Links represent river reaches and canals; nodes represent the physical processes (inflow, storages, gauging) and regulatory and management activities (demands, off allocation, minimum flow requirements) in the river system. 4.1.1. Inflow Inflows to Lake Eildon are represented with a single inflow node. These inflows are based on a daily mass balance calculation, which provides a good estimate of inflow prior to forecast period because it incorporates the contributions of ungauged tributaries. The catchment downstream of Lake Eildon has large networks of tributaries that drain to the Goulburn River. To simplify the tributary networks, inflow nodes represent flows into Goulburn River from all gauged tributaries, which directly join the Goulburn River. To account for the ungauged tributaries between Lake Eildon and Goulburn Weir, two inflow nodes are included in the model. These nodes estimate the contribution of these tributaries based on a factor of the observed or forecast inflow for a selected gauged tributary. The factor was derived from an assessment of outflows from all gauged tributaries, including the release from Lake Eildon. This was compared with the observed flows at Trawool gauge to determine the approximate contribution of ungauged tributaries, observed as a gain. The pattern of this gain was matched with a tributary showing similar flow patterns and a factor applied to scale the flow appropriately. There are two unregulated gauged tributaries downstream of Goulburn Weir, but daily data are not available from these locations. To compensate for this, a relationship was determined between these tributaries and a nearby tributary for which daily data are available (Sevens Creek). These tributaries

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are incorporated into a single inflow node and modelled based on a regression with the flow at the Sevens Creek inflow node. Broken River, which is represented by an inflow node downstream of Sevens Creek, is a regulated system and is the largest tributary to the Goulburn River below Goulburn Weir. Goulburn River receives the majority of its tributary flow from Broken River during winter and spring. The only aspect of the Broken which influences Goulburn operations is the outflow from the Broken River as measured at the “Orrvale” river gauge. As such, it is treated as an inflow to the Goulburn River. 4.1.2. Storages All storages and weirs in the model are represented using the storage node. They are configured to represent the dimensions of the storage, as well as maximum and minimum operating targets and outlet release capacities. The actual level-discharge relationship is included for some storage outlets where appropriate (diversion channels from Goulburn Weir and Waranga Basin). However, a true-to-life representation of all outlet paths from Lake Eildon is not necessary and these are represented via a single valve and single gated spillway. Waranga Basin is configured with a monthly pattern of operating targets representative of the minimum operating level in each month. When required, Waranga Basin orders water from Lake Eildon to maintain its minimum operating level. 4.1.3. Stuart Murray and Cattanach Canals The Stuart Murray and Cattanach Canals are represented by a single link (Fig. 6). The Cattanach Canal is a diversion channel to Waranga Basin that does not have irrigation extracted directly from it. Consequently, it does not require an irrigation demand to be associated with it. Its diversion capacity can be combined with that of the Stuart Murray Canal by configuring the outlet from Goulburn Weir to be equal to the combined capacity of both channels. 4.1.4. Demands For the purpose of simplicity and for consistency with existing approaches, irrigation area demands have been represented as the total diversion for the area. For example, the demand for the entire Shepparton Irrigation Area is represented as a single demand point supplied from Goulburn Weir. In Source this is represented using a water user node, which defines the demand and supply storage, and a supply point to define the physical constraints of supplying water from a give location. The supply point is configured with the maximum diversion rate, and with the storage at which orders should be placed. All the Shepparton demands are ordered from Goulburn Weir, which subsequently will order water from Lake Eildon if required. Demand from the Stuart Murray Canal is included as a Water User between Goulburn Weir and Waranga Basin. This allows any demand to be extracted from the volume diverted at Goulburn Weir, with the remainder left to flow through to Waranga Basin. Conversely, where no water is being diverted to Waranga Basin, the model will only order what is required to supply the Rodney Offtakes via the Stuart Murray e Cattanach Canal link, and the remainder will pass into the Lower Goulburn River. By using total diversion for irrigation area demand, management of the system in the model is aligned with what is done operationally. Demand and system losses within irrigation districts are combined and treated as a single demand or order at the area offtake. Private river diversions are not included as a demand node, due to their distribution along the entire length of the Goulburn River and also because of the small size (only 4%) compared to the total diversion. An allowance for these demands forms part of the

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Fig. 6. Node-link setup of the Goulburn River Source river operations model showing different links (rivers and canals) and nodes (inflow, storages, demands, losses, gauges, water users, minimum flow). The nodes with “þ/” represents gauge node with “Unaccounted Difference” functionality activated.

monthly river loss assumptions, as is addressed in the planning of river operations. 4.1.5. Losses In the Goulburn River system, it is current practice to assess losses in three reaches; Lake Eildon to Goulburn Weir, Goulburn Weir to Shepparton and Shepparton to McCoys Bridge. The loss between Lake Eildon and Goulburn Weir is assumed to be the greater of 300 ML/day, or 8.7% of the release from Lake Eildon during fully regulated conditions, i.e., where all water in the river is

committed; either to extraction for consumptive use, or in-stream environmental requirements. Losses downstream of Goulburn Weir vary seasonally, and a monthly loss assumption is made for the two reaches below the weir. The monthly loss assumptions are adjusted based on observed loss rates. In Source, losses are represented as an extraction immediately upstream of the relevant gauge site. This represents the loss in the entire reach as would be observed at that gauge location. Currently, losses downstream of Goulburn Weir are incorporated into the model using a Supply Point and Water User nodes because this

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configuration provides greater functionality and usability than other options. 4.1.6. Flow routing Travel time in the Goulburn River system is 2e3 days between Lake Eildon and Goulburn Weir, and a further 6e8 days to McCoys Bridge (dependant on flow rate). A combination of both storage and lagged routing has been used. Storage routing has been applied to links upstream of key gauge locations to reflect natural attenuation, which is observed as flows pass down the river system. The use of storage routing also allows some variation in travel time depending on flow rate. Lagged routing has been used for short sections of river (e.g. 1 day travel time) where minimal attenuation is observed, but travel time is important. No routing has been applied for the reaches between Lake Eildon and Trawool, Murchison and Shepparton and Shepparton and McCoys Bridge. The reach between Seymour and Goulburn Weir has a single day lag. 4.1.7. Minimum flow requirements There are three minimum flow requirements in the Goulburn River system; one downstream of Lake Eildon; one at Murchison; and one at McCoys Bridge. These have been represented as Minimum Flow nodes, with a monthly pattern applied to them. The pattern reflects the minimum daily flow required at these locations at any time throughout the year. 4.1.8. Gauges Six gauge nodes are included within the Goulburn Operations model. These represent all flow gauging stations along the Goulburn River between Lake Eildon and the Murray River. These nodes are useful for comparison of model outputs with observed flows for historic model runs, but can also be used to override modelled flows with observed flows. This assists operational forecasting by utilising all observed data available, and so river flow forecasts are always based on the most recent observed data at each gauge station. This is identical to the procedure currently used in river operations. In addition, the “Unaccounted Difference” can be activated for a gauge node, which records the difference between the modelled and observed data values during the warm up period. The warm up period in Source Operator is used to seed the model with available historical data before forecasting to ensure modelled fluxes equal observed values. This can be very useful for forecasting purposes, as the unaccounted difference may indicate, for example, a continual loss or gain in a river reach. This information can be used to adjust the loss assumption for that reach during the forecast period based on the observed river losses during the preceding weeks. This would result in a more accurate forecast of river flows and expected loss or gain in the river reach. 4.1.9. Off allocation An Off allocation node is used to instruct the model to divert water from Goulburn Weir to Waranga Basin within the defined operating target levels where unregulated flows exist. In the Goulburn River operations model, access to off allocation water is permitted when the volume of inflow to Goulburn Weir in any time step is greater than the sum of the offtake channel demands, Waranga Basin orders (to maintain minimum operating level) and downstream river requirements. This has proved to be an effective way of representing the actual management of unregulated diversion to Waranga Basin. “Excess” inflows to Goulburn Weir are defined as those which are above the necessary volume to meet offtake demands and downstream requirements (regulated requirements). In many cases these flows are generated from the catchment below Lake Eildon

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(unregulated flows), or where Lake Eildon spills. Where excess flows exist, they are diverted to Waranga Basin. In Source this excess flow, and the process to determine the volume of excess flow, is referred to as “Off allocation”. The off allocation process will only be active where the inflow to Goulburn Weir is in excess of the regulated requirement. It can also occur where too much is released from Lake Eildon. Where water is released from Lake Eildon and diverted to Waranga Basin to maintain a target level, this is a result of the storage generating an order in response to the storage level reaching an operating target trigger. This is not recognised by the model as off allocation flow, as there is an order for that water. 4.2. Data collection and preparation The hydrometeorological and storage data required for setting up the Goulburn River operations model were extracted from the following sources: (i) Daily data for river flow, diversion from the river system to irrigation districts, evaporation, rainfall and storage level were extracted from G-MW’s operational database; and (ii) Where observed daily flow data were available, these were extracted from the Victorian Data Warehouse and used in preference to G-MW’s operational data. Where there were missing data values, these were rectified using linear interpolation between available data points. Storage dimension data and level-volumedischarge relationships were provided by G-MW. 4.3. Forecast models 4.3.1. Inflow forecast Recession models are used to forecast recession of tributary inflows. The recession models are based on an analysis of observed streamflow recessions over the period of flow record for each tributary. Fig. 7 shows an example of how the different models forecast the streamflow recession. They are configured to represent streamflow recessions under following three scenarios: Scenario 1: Forecast commencing at the initial part of the falling limb, i.e. effectively from the peak of the hydrograph. In this forecast, the flow initially has a high rate of fall, then gradually recedes to baseflow. This is intended to be reflective of streamflow behaviour following the flow peak immediately after rainfall. Scenario 2: Forecast commencing during the mid-section of the falling limb. This forecast initially has a lesser rate of fall than Scenario 1. It is intended to represent conditions where there has not been any rainfall or significant runoff in the immediate past, but streamflows are still receding from previous rainfall. Scenario 3: Forecast commencing during the tail of the recession. This forecast is intended to be reflective of dry periods where streamflows are near baseflow conditions and the rate of recession is very slow. While the recession model cannot reflect the precise recession of streamflows under all catchment conditions and flow rates, they are intended to be an average representation of what could be expected to occur. Forecasting the recession of tributary flow in this manner is not dissimilar from what is done operationally. The main difference is the use of three defined-recession models in Source, compared with the existing spreadsheet method that uses operator-defined recession patterns, which might be adjusted daily. The use of fixed recession models, while not having a high level of precision under all conditions, allows for adequate and replicable forecasting of tributary flows. While it is not feasible to adjust the recession patterns daily in Source, the forecast recession which is produced by the model can be manually adjusted by the operator in the Tabular Editor. The forecasts produced in Source are generally

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D. Dutta et al. / Journal of Environmental Management 121 (2013) 13e28

Fig. 7. An example of streamflow recession forecast by different recession models.

within acceptable limits and therefore this method has been deemed suitable for the Goulburn River operations model. 4.3.2. Demand forecast Forecast demand is a manual input to the model. G-MW’s operations support team compiles five-day demand forecasts for each irrigation area. The forecasts are based on orders lodged by customers. Beyond the five-day forecast, a conservative continuation of current demand is assumed, with consideration made for the impact of forecast hot weather or rainfall. Due to the limitations of climate forecasts (available for upto 7 days), and the travel time required for ordered water, it is recognised that demand forecasts are only truly meaningful for a maximum of 5e7 days from the time they are estimated. For operational planning, forecasting of future demand cannot use historic demand patterns. This is due to the large variability in the irrigation demand patterns observed from year to year. The introduction of “carry over” water allocations also makes the use of historic usage patterns less reliable. Carry over allows entitlement holders to “carry over” unused water allocation for use in the following season. In addition, hot weather or rainfall can result in large increases or decreases in irrigation demand within a matter of days, and this must be captured within the model to efficiently operate the river system. Infrastructure upgrades in some areas has reduced the required notice for irrigation delivery to as little as 24 h prior to the desired delivery time. This adds further complexity to forecasting, and highlights the need for an operations model which can easily run a number of demand scenarios. 4.4. Model calibration The capability of Source for modelling the Goulburn River system has been assessed using two different approaches. For the first approach the model was run using 5 years of historic data from July 2006 to August 2011 on a daily time step. The model outputs were then compared to observed data for streamflow, storage level and storage releases. The model was validated through analysis of its outputs compared with the observed data. The second approach was used to assess the capability of the model for forecasting. This approach used a 3-month warm up period (daily time step) and required the model to forecast 15 time

steps based on expected demands and streamflow recession forecasts. Again these outputs were compared to the actual data for the relevant period. This approach was also assessed on its ability to replicate existing operational planning and modelling techniques through comparison with spreadsheet-based models, where these were previously used. 4.4.1. Streamflow calibration Calibration of streamflow was undertaken first using a trial-anderror approach. To calibrate flows, the “Unaccounted Difference” feature was activated in the gauge node at the start of the target reach. The purpose of this was to ensure that the node’s downstream flow was identical to the observed flow at that gauge station, which allowed for calibration of travel time to the next gauge downstream. The flow routing was adjusted based on a comparison of the modelled flows and gauged flows in each reach, over the range of flows observed since 2006 using only a lagged routing method. This method allowed successful calibration of travel time for specific flow ranges. However, travel time was not accurate for all flow ranges and flow attenuation was not adequately represented by the model using only the lagged routing method. To rectify this, storage routing was configured to define the storage relationship and lag time for reaches with a travel time of greater than one day. 4.4.2. Storage calibration Storage calibration involved ensuring that modelled storage behaviour was within acceptable limits of what was observed when running the model using historic data. This involved a comparison of observed storage levels with the modelled storage levels over a 5-year model run. This proved to be a simple but useful method to identify where specific node configurations were resulting in unexpected model results, causing the storage level to deviate from the expected results. 4.4.3. Full model calibration and testing Following the streamflow and storage calibration, the model as a whole was assessed and adjusted to ensure it was producing tangible results for all required outputs. This involved checking that (i) the model was supplying orders correctly, (ii) orders from upstream storages were only generated when needed and there was no over-ordering, (iii) water orders maintained Waranga Basin

D. Dutta et al. / Journal of Environmental Management 121 (2013) 13e28

target levels, (iv) unregulated flows were diverted to Waranga Basin when airspace was available, (v) minimum flow requirements were met, (vi) inflow forecasts produced the expected results, (vii) flow was routed appropriately along the entire river, and (viii) river losses were applied correctly. It is possible to streamline the calibration of the model using an auto-calibration approach such as presented by Hughes et al. (2012). Following this, the forecasting capability of the model was tested. The model was setup to forecast for a 15-day period. The model was then advanced by 7 days, and re-run. The modelled outputs were compared with the observed data for river flow and storage level as well as forecasts produced for the corresponding period using the existing spreadsheet-based model. 5. Results The study found that the use of non-linear storage routing with a power function (“generic” method) and flow-travel time index (“piecewise” method) to define the storage relationship generally provide a similar balance of travel time and attenuation except for flow ranges above 10,000 ML/d Fig. 8 shows a comparison between the observed and simulated daily flow from July to November 2010 at McCoys Bridge using both “generic” and “piecewise” storage routing schemes. The study found that the “piecewise” storage routing provides better results for flows above 10,000 ML/d for river reaches downstream of Murchison, whereas the “generic” method provided better results for reaches between Eildon and Seymour at flowes above 10,000 ML/d. The Nash-Sutcliffe Coefficients of efficiencies (NSE) are 0.88 and 0.98 for the generic and piecewise routing, respectively for the reach between Shepparton and McCoys Bridge over the period July 2006 to September 2011. Fig. 9 compares the simulated and observed flow at several gauging stations and Table 1 summarises the NSE values for modelled flow in key reaches of the Goulburn River for the period July 2006 to August 2011 using the “generic” storage routing method. Table 2 summarises the NSE values for modelled flow in key reaches of the Goulburn River for the period July 2006 to August 2011 using the “piecewise” storage routing method. NSE values have been calculated for three flow ranges, low (1e999 ML/ d), medium (1000e9999 ML/d) and high (>10,000 ML/d). The efficiency noted in the summary table is affected by the flow rate and the storage routing method used.

23

Fig. 10a and b shows a comparison of the observed and modelled storage levels from July 2006 to December 2010 at Warranga Basin and Lake Eildon, respectively. The modelled storage level at Warranga Basin is maintained at or above the minimum target operating level of approximately 116 mAHD. The observed level drops below this due to drought response measures taken during the 2006/07 to 2008/09 summer seasons, when the dead storage of Waranga Basin was pumped prior to the close of the irrigation season. Pumping of Waranga Basin only occurs in years with very poor water availability, and as such is not undertaken annually. As a result, it has not been deemed necessary to replicate this operation in the model. In the event that pumping was required in a future year, the Waranga Basin target operating levels could be adjusted to represent the additional drawdown of the storage due to pumping. It can be seen from Fig. 10b that the modelled level closely follows the observed trend at Lake Eildon. Variations between the modelled and observed level are driven by the volume released from Lake Eildon. This is due to the model releasing water from Lake Eildon to maintain the Waranga Basin operating target as noted in Fig. 10a. This results in the model showing a slightly greater drawdown of Lake Eildon than was observed. To test the forecasting capability of the Goulburn River operations model, it was setup to produce a series of 10-day forecasts. Fig. 11a and b provides example of the forecast’s deviation from the observed storage volume for 12 individual 10-day forecasts for Waranga Basin and Lake Eildon, respectively. Fig. 13a shows that the difference between the observed and simulated storage levels for all forecasts was less than 10 GL over a 7-day period and less than 15 GL over the full ten day forecast. While forecasting storage level and volume did not form part of routine operational planning in the past, the ability to produce short-term forecasts for these parameters in Source adds value to operational planning. A 7-day forecast of storage level or volume is generally sufficient for daily planning needs. From the perspective of operational planning, a forecast for Waranga Basin which is within 10 GL over a 7-day period is within the tolerances currently employed in system planning. Therefore, the ability to provide a reasonable storage forecast for a 7-day period is generally sufficient for daily planning needs. It may be possible to further reduce the deviation between the modelled and observed value after the initial 7 days of the forecast through additional refinement of the model such as incorporation of improved recession models for tributaries and through applying a forecast model for evaporation. It is important

Fig. 8. Comparison of generic and piecewise storage routing.

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D. Dutta et al. / Journal of Environmental Management 121 (2013) 13e28

b)

a)

d)

c)

Fig. 9. Comparison of the simulated and observed flow at (a) McCoys Bridge, (b) Seymour, (c)Trawal and (d) Shepparton of the Goulburn River, respectively.

to note that at times variations to river operations can occur on a daily basis. As such, an operational decision made 2 days into the future may have a significant impact on a forecast which was produced in the days prior to that decision being made. As can be seen from Fig. 11b, the difference between the observed and simulated storage levels for all but one forecast was less than 10 GL both over a 7-day period and the full 10-day forecast. Table 3 shows the difference between the forecast value and the subsequently observed value as a percentage for both Source forecast and the spreadsheet model forecast for the flow flow at Goulburn River at McCoys Bridge. The relatively small differences (upto 6%) between the forecast by Source and the observed data for up to 7 days suggest that the Source forecast is an appropriate indicator of storage behaviour for short-term operational planning.

Fig. 12 compares a Source forecast for McCoys Bridge with a spreadsheet-based forecast during the initial days of an environmental water release from Goulburn Weir. An actual environmental release was used an example as it shows variation in flow, rather than a flat, constant flow hydrograph. Both the forecasts commence on 8 November 2011 and are compared with the observed flow at McCoys Bridge. Source model run on 8 November deviates from both the spreadsheet forecast, and the observed data (Fig. 10). This is largely a result of the additional losses incurred from bank wetting as the flow increases. In Source, the operator can manually override the pre-defined seasonal loss pattern for the required time steps. This is done by assessing the unaccounted loss or gain between two gauges over the prior one to two weeks. This is made easier by Source’s “unaccounted difference” function, which calculates the unaccounted loss or gain between two flow gauges. The

Table 1 Summary of calibration results at different gauging stations with “generic” storage routing.

Table 2 Summary of calibration results at different gauging stations with “piecewise” storage routing.

River reach

NSE for defined flow ranges e “generic” storage routing

River reach

NSE for defined flow ranges e “piecewise” storage routing

Eildon to Trawool Eildon to Seymour Murchison to Shepparton Shepparton to McCoy Bridge Murchison to McCoy Bridge

0.81 0.64 0.86

0.9 0.89 0.86

0.45 0.46 0.82

0.9 0.86 0.94

0.91

0.96

0.95

0.98

0.85

0.89

0.87

0.94

1e999 ML/d 1000e9999 ML/d >10,000 ML/d Full range

1e999 ML/d 1000e9999 ML/d >10,000 ML/d Full range Eildon to Trawool Eildon to Seymour Murchison to Shepparton Shepparton to McCoy Bridge Murchison to McCoy Bridge

0.79 0.66 0.86

0.90 0.87 0.85

0.47 0.63 0.71

0.88 0.86 0.88

0.91

0.89

0.61

0.88

0.91

0.89

0.61

0.88

D. Dutta et al. / Journal of Environmental Management 121 (2013) 13e28

25

Fig. 10. Comparison of the modelled and observed water levels at: (a) Waranga Basin, (b) at Lake Eildon.

a)

b)

Fig. 11. Difference between the observed volume and the source forecasts at: (a) Waranga Basin, (b) Lake Eildon.

result is a forecast, which is much closer to both the observed flow and spreadsheet forecast, with significantly less operator input than the spreadsheet model. Furthermore, Fig. 13 compares a Source forecast for Shepparton, upstream of McCoys Bridge, with a spreadsheet-based forecast from 8 November 2011. These are compared with the observed flow at McCoys Bridge. As Fig. 13 shows, Source model run on 8 November is significantly closer to the observed flow than the spreadsheet forecast. In this reach, river losses have a lesser impact on the flow observed at Shepparton when compared with the reach

from Shepparton to McCoys Bridge. Due to this, the river loss assigned by the operator only provides a slight improvement in the forecast when compared with the unadjusted Source forecast. 6. Discussion Simulations of the Goulburn River system operations over a 5year historic period have demonstrated that Source is able to produce results for travel time and flow attenuation that are very close to the observations over the same period. In addition, the modelled

Fig. 12. Comparison of the source forecast and the spreadsheet-based forecast for river flows at McCoys Bridge.

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D. Dutta et al. / Journal of Environmental Management 121 (2013) 13e28

Fig. 13. Comparison of source forecast and spreadsheet-based forecast for river flows at Shepparton.

behaviour of Lake Eildon was close to the observed storage level. Where differences exist, these are able to be explained, and are generally a result of operations (mostly due to the pumping of Waranga basin), which are not necessary to replicate in Source for a historical model run. These forecast runs showed that river flow forecasting was similar to or better than forecasts produced with existing models. Storage level forecasts were within acceptable limits of observed levels for the first 7 days of the forecast, after which the deviation from observed level became larger than desirable. Dutta et al. (2012) have shown that it is possible to further improve forecasting capability of the model by integrating well-calibrated rainfall-runoff models to forecast inflows from the tributaries using forecast precipitation by numerical weather prediction models (Anderson et al., 2002; Clark and Hay, 2004; BoM, 2010). This can be easily achieved by linking Source Catchment mode to Operation mode (Delgado et al., 2012; eWater CRC, 2012). Source offers many extra capabilities and functionality compared with the current spreadsheet-based models. The largest single improvement is the ability to model full system operations using one model. Existing methods utilise a number of spreadsheets to model different aspects of system operations.

A significant improvement on the existing methods is centred around the complex operation on Waranga Basin. This storage must harvest unregulated flows from Goulburn Weir when it has spare capacity, but also order water from upstream storages to maintain its target level when drawn down by irrigation demand. This was very difficult to model using a spreadsheet model, however the functionality within Source has allowed the model to be configured in a way that it can effectively model the operation of Waranga Basin as would occur in reality. In addition, Source can determine where unregulated flows downstream of Lake Eildon will be sufficient to meet demand from Goulburn Weir. Where these flows are not sufficient, it will automatically order from Lake Eildon to avoid any shortfall in supply. The model also takes into account the travel time to Goulburn Weir, and so places orders ahead of time. Forecasting of releases from Lake Eildon with this level of detail is something that has not been achieved effectively in the past using spreadsheet models. These forecasts relied greatly on the operator’s experience and knowledge of the river system. The routing capability of Source is a significant improvement from the routing capability of existing spreadsheet models, which is lag routing. Storage routing within Source allows travel time to be

Table 3 Difference between forecasts by source and spreadsheet (SS) models and the observed data at Goulburn River at McCoys Bridge as a percentage. Days from forecast start

Date forecast produced (forecast start date) 11-Apr

0 1 2 3 4 5 6 7 8 9 10 10-day Average of variation from observed flow

16-Apr

14-May

21-May

Source (%)

SS (%)

Source (%)

SS (%)

Source (%)

SS (%)

Source (%)

SS (%)

0 0 3 5 3 1 2 4 2 3 1 2

0 3 7 8 8 10 11 11 13 18 16 10

0 3 0 1 2 10 7 6 11 19 14 7

0 0 1 4 9 4 4 4 3 19 16 6

0 3 1 0 1 2 3 6 8 8 6 4

0 0 1 1 2 5 7 12 16 19 18 8

0 8 9 13 12 8 3 3 14 42 64 16

0 2 7 9 16 23 29 29 32 50 67 24

D. Dutta et al. / Journal of Environmental Management 121 (2013) 13e28

varied with flow rate and attenuation of hydrograph in a river reach, as is observed in the real world. An example of river flow forecasting is undertaken for McCoys Bridge, which effectively measures the outflow to the Murray River. G-MW provides weekly flow forecasts for McCoys Bridge to Murray River Operations to assist in their planning of releases from Hume dam and Yarrawonga Weir. To obtain a useful forecast under these circumstances, significant knowledge of the system is required in order to manipulate the model to produce the behaviour expected by the operator. The operator must also make assumptions of travel times at higher flow rates and flow attenuation along the river. Source has improved significantly on this with its in-stream delivery of environmental water implemented as water orders. This means that the required flow will be supplied at the required location in the model, and orders will be placed upstream to ensure this requirement is met. The flow is then routed through the model with appropriate lag and attenuation, without need for manipulation of the model. 7. Conclusions The paper has described the functionality and applicability of the River Operations mode of eWater source, a newly developed tool for integrated water resources management, for improving daily operations of regulated river systems in the 21st Century, and demonstrated the performance of the model through a case study application in the Goulburn River System, Australia. The purpose of case study application was to assess the capability and suitability of Source (River Operations mode) to model the operation and management of the Goulburn River system in comparison to the existing spreadsheet model. The case study application has shown that Source is able to produce more accurate forecasts than existing models for key requirements, such as river flow, whilst requiring less user input. The storage routing functionality in Source has resulted in greatly improved capability to produce river flow forecasts that can replicate both travel time and flow attenuation within the river system (compared with spreadsheet models). It also has the added advantage of enabling the whole system to be modelled together as one single model. Overall, Source is a significant improvement from the existing spreadsheet model used for operational planning and management of the Goulburn River system. With the tailored interface of the River Operations mode, the ability to dynamically link to RR models and plug-in functionality to incorporate third party forecast techniques, Source is expected to contribute to improved decision-making in river operations, leading to increased economic benefits through more efficient river operations and sustainable management of water resources. Acknowledgement The authors acknowledge the contributions from eWater CRC and its partners; members of the Source team and CSIRO manuscript reviewers: Alice Brown and Andrew Davidson. References Anderson, M.L., Chen, Z.-Q., Kavva, M.L., Feldman, A., 2002. Coupling HEC-HMS with atmospheric models for prediction of watershed Runoff. Journal of Hydrologic Engineering 7 (3), 312e318. Barma, D. 2009. Review of Operational Tools and Techniques for Efficient River Operation. Consultancy Report Prepared for Water for Rivers, Albury, Australia. Berris, S.N., Hess, G.W., Bohman, L.R., 2001. River and Reservoir Operations Model, Truckee River Basin, California and Nevada, 1998. Water-resources Investigations Report 01-4017. USGS, USA.

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BoM, 2010. Operational Implementation of the ACCESS Numerical Weather Prediction Systems. NMOC Operations Bulletin No. 83. Bureau of Meteorology, Australian Government, 34 p. Bridgart, R.J., Bethune, M., 2009. Development of RiverOperator: a tool to support operational management of river systems, Proceedings of the 18th World IMACS/MODSIM Congress, Cairns, Australia, pp. 3782e3788. Carron, J., Walker, D., Wheeler, K., Saunders, G., Brown, R., 2010. The Lower Colorado river authority daily river operations model. Proceedings of the 2nd Joint Federal Interagency Conference, Las Vegas, USA. Clark, M., Hay, L.E., 2004. Use of medium-range numerical weather prediction model output to produce forecasts of streamflow. Journal of Hydrometeorology 5, 15e32. Close, A.F., 1996a. A New Daily Model of Flow and Solute Transport in The River Murray. 23rd Hydrology and Water Resources Symposium, Hobart, Australia. Close, A.F., 1996b. The BIGMOD Model of Flow in the River Murray. 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