Craig Beverly
email: craig.beverly@depi.vic.gov.au

Anna Roberts

Geoff Park

Fred Bennett

Graeme Doole

Construction of a Bio-economic Model to Estimate the Feasibility and Cost of Achieving Water Quality Targets in the Burnett-Mary Region, Queensland

Keywords: decision support system, DSS, bio-economic optimisation framework, water quality

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Introduction

The Burnett-Mary region in Queensland, Australia includes the southern-most portion of the World Heritage listed Great Barrier Reef (GBR) Marine Park and the Ramsar listed Great Sandy Strait. The region hosts biodiversity values that are globally important. The health of the coastal and inshore marine areas are influenced by the quality and quantity of runoff from five river basins. Increased loads of nitrogen, phosphorus, sediments and pesticides from adjacent catchments have led to chronic changes in environmental conditions for GBR species and ecosystems. To help protect the values of coastal and marine receiving waters, the Burnett Mary Regional Group (BMRG) with funding from the Australian Government commissioned the development of a Water Quality Improvement Plan (WQIP). The focus of the WQIP is to improve water quality through the implementation of agricultural management practices based on the 'ABCD' water quality risk framework for the sugarcane and grazing industries ( [START_REF]Paddock to Reef Water Quality Risk Assessment Framework for Sugar Cane[END_REF], [START_REF]Reef Plan Water Quality Risk Framework for Grazing[END_REF]). 'A' practice represents cutting edge unproven technologies, 'B' represents current best-management practices, 'C' is common current industry practice and 'D' is below industry practice expectations. A bio-economic model was developed to assist the Burnett Mary region transparently evaluate the feasibility and costs of various management options to achieve two sets of pollutant load reduction targets, namely Reef Plan Targets (RPTs), the currently formally agreed targets, and more ambitious Ecologically Relevant Targets (ERTs) which might be needed to better protect the values of the GBR. This paper describes the construct and underpinning data used to develop the bio-economic model that explicitly considers the feasibility and net profits/costs of achieving water quality objectives.

The Burnett Mary Region

The Burnett Mary region in Queensland, Australia covers 56,000 square kilometres (5.6 million ha) of land and encompasses five major river basins (Baffle, Burnett, Burrum, Kolan and Mary). The Baffle, Kolan and the Burnett catchments flow to the GBR Reef Marine Park whilst the Burrum and Mary flow to the Great Sandy Strait Marine Park. The major primary industries are grazing, sugarcane, horticulture, forestry and mining.

Methodology

The development of the Burnett Mary WQIP involved integrating the outputs from a number of separate supporting projects and biophysical modelling activities across various scales. The approach has built on the outcomes from previous WQIPs, findings of the Scientific Consensus Statement and priorities from Reef Plan 2013 ([4]; [START_REF] Kroon | sediment, nutrient, pesticides and other pollutants in the Great Barrier Reef[END_REF]; [START_REF] Schaffelke | [END_REF]; [START_REF]Reef Water Quality Protection Plan[END_REF]) and new supporting studies. The key components of this approach have been:

• Use of assumptions and outputs from available science from paddock and catchment scale modelling conducted in Queensland;

• Coordination and engagement with technical experts and local stakeholders to integrate local knowledge and previous research; • Financial economic analysis of practices for grazing and sugar cane industries;

• Development and application of a bio-economic modelling framework to assess costs in attaining targets.

Data Sources

Land use data was based on the 2009 land use map derived from the Queensland Land Use Mapping project (QLUMP, [START_REF] Dsitia | Land use summary 1999-2009[END_REF]). The QLUMP data was used to represent all land uses with the exception of sugar cane. For sugarcane areas, Australian Bureau of Statistics (ABS) data was utilized because the ABS data was widely accepted by stakeholders as better representing the current area of land under sugarcane. Soil data layers were provided by the Queensland Department of Natural Resources and Mines. Stream and gully mapping was provided by the Burnett Mary Regional Group whereas gully and streambank effectiveness assumptions were provided by [START_REF] Thorburn | Conceptual frameworks for estimating the water quality benefits of improved agricultural management practices in large catchments 180[END_REF].

Landscape Modelling Software

The Burnett-Mary bio-economic model utilised predictions of catchment streamflow, baseflow and loads (nutrient, sediment and pesticide) derived using Source Catchments [9] and management impacts estimated using paddock scale models. Baseline data was based on a combination of point scale modelling and calibrated catchment scale model outputs. The key components of the linked modelling approach are described below.

Catchment Model

The landscape model Source Catchments was used to estimate constituent generation loads and attenuation coefficients for each of the 597 sub-catchment within the Burnett-Mary region. Each sub-catchment was defined based on topography, river basins, land use and the location of existing water quality and flow monitoring stations. Source Catchments modelling from the Paddock to Reef program provides a prediction of end of catchment loads for key pollutants of interest (nitrogen, phosphorus, sediment and pesticides). The variables modelled include: Total Suspended Solids (TSS), Dissolved Inorganic Nitrogen (DIN), PSII Herbicides (PSII), Particulate Nitrogen (PN), Particulate Phosphorus (PP), Dissolved Inorganic Phosphorus (DIP). The 2008-09 baseline results from the Source Catchments modelling were analysed to provide a summary of the relative contribution from the five river basins as a proportion of total load and anthropogenic load [START_REF] Waterhouse | Assessment of the relative risk of degraded water quality to ecosystems of the Burnett Mary Region, Great Barrier Reef DRAFT REPORT A Report for Burnett Mary Regional Group[END_REF].

Paddock-Scale Model

Farming system models were used to inform sediment and nutrient loads in Source Catchments and to estimate the constituent load reductions associated with various management practices. The APSIM [START_REF] Mccown | APSIM -An agricultural production system simulation model for operational research[END_REF] and Howleaky [START_REF] Mcclymont | Howleaky? Exploring water balance and water quality implications of alternative land uses. A computer program[END_REF] models were used to simulate sugar cane systems, whereas GRASP was used to model grazing systems.

Output from the farming system models were used to assign constitutive load reductions for each practice class by soil type by management combination representing the various abatement options.

Farm Level Financial Cane and Grazing Economics

Achievement of meaningful water quality targets require practice changes in agriculture. Although infrastructure and all agricultural industries contribute to constituent loads, this study was confined to the sugar cane and grazing industries because of their importance in contributing to water quality issues and the fact that estimates of the contributions of impacts from practice changes could be quantified from modelling assumptions. Financial economic analysis was undertaken for both these industries as part of the WQIP.

The farm level financial economic analysis for sugar cane was conducted for three representative farm sizes (small 75 ha, medium 125 ha, large 250 ha) and two soil types (well drained and less well drained). The analysis included consideration of non-profit related barriers and transaction costs [START_REF] Van Grieken | Economic Analysis of Farming Systems for Water Quality Improvement in the Burnett Mary Catchment. A report prepared for the Burnett Mary Regional Group in cooperation with Natural Decisions[END_REF].

Similarly the financial economic analysis for grazing in the Burnett Mary region was conducted for three representative farm sizes (small 288 ha, medium 880 ha, large 4134 ha) and across three land productivity class (high, medium and low; [START_REF] Whish | GRASP modelling of grazing systems in Great Barrier Reef catchments[END_REF]). Both profit and non-profit related financial barriers were also taken into account in the analysis, as outlined in [START_REF] Pannell | Economic analysis of grazing systems for water quality improvement in the Burnett Mary catchment[END_REF].

Optimisation Model

The bioeconomic model components were solved using nonlinear programming with the CONOPT solver in the General Algebraic Modelling System (GAMS) [START_REF] Brooke | GAMS-A user's Guide[END_REF]. The model seeks to maximise total net benefits for grazing and sugar cane enterprises as required to meet specified water quality targets at both the regional scale as well as by individual basin. The cost-effectiveness approach adopted in this study, where emissions goals are sought at least cost, is common ([3]; [START_REF] Doole | Cost-effective policies for improving water quality by reducing nitrate emissions from diverse dairy farms: An abatement-cost perspective[END_REF]; [START_REF] Doole | Empirical evaluation of nonpoint pollution policies under agent heterogeneity: regulating intensive dairy production in the Waikato region of New Zealand[END_REF]) because it avoids the difficulty and cost of assessing the benefits associated with improved water quality.

There are a sub-catchments in each catchment labeled sc = [1,2..,a]. The total land area is allocated to b land use combinations labeled lu= [1,2,..,b]. These land use combinations contain permutations of sugarcane and grazing BMP options totaling 36 sugarcane management options (18 on good soil and 18 on poor soil) and 90 grazing management permutations (30 options on each of the high, medium and low productivity classes). Three decision variables describe the management options. First, the area allocated to each land use in each sub-catchment denoted by Asc,lu. Second, the intensity with which gully erosion is managed in each sub-catchment denoted by Gsc. Third, the intensity with which streambank erosion is managed in each sub-catchment denoted by Ssc. The intensity of gully, streambank and permanent waterway management is the percentage of the gully system or associated length that is fenced and revegetated. Subscripts eg refers to the length of gullies and es the length of streambanks. Total profit in each catchment is computed:

∑ ∑ ∑ ∑ ∑ ∑ = = = = = = - - = = - -
S C G C A C TC 1 1 1 1 , , (2)
The optimisation model seeks to maximize profit in each sub-catchment (grazing and sugar cane enterprises only) subject to target emission constraints. The model considered only transition states under the assumption that initial land use areas currently in A and B class practices would not be allowed to be reduced. As such, transitions can only move to an improved state (Figure 1). No land use change (land retirement) was also permitted -solutions could only be derived from practice change. Constraints including (1) farm distributions, (2) soil type, (3) productivity classes, (4) land practice class (A, B, C and D) and ( 5) land use transitions. Stream bank and gully remediation was also considered with associated opportunity costs.

Software environment

Source catchment prediction data sets were provided in an Access database. Key data was then exported from the database and manipulated using a Fortran program into a format consistent with the GAM model input data construct. The output results from the GAMS model was post-processed using software developed in Fortran. Outputs included summary text files and raster based gridded data for importing into GIS. Model sensitivity analysis was undertaken using a Fortran wrapper program that systematically modified GAMS input data, initiated the GAMS solver and collated simulation results.

The uniqueness of the developed models is the capacity to • Consider individual or combinations of constituent abatement targets;

• Consider meeting abatement targets at both the basin or regional scale;

• Evaluate the impact of different levels of funding;

• Estimate the area under A, B, C and D practice and the area transitioned into each of these conditions required to meet abatement targets;

• Estimate the length of streambank and gully remediation.

Application

Numerous targets were evaluated and reported to local stakeholders initially. Two sets of targets were selected as a focus for the WQIP, the Reef Plan Targets (RPTs) and Ecologically Relevant Targets (ERTs). Each target was evaluated at both the regional and individual basin scale and costs to achieve targets were assumed t be over a 20 year period The RPTs were a 20% overall reduction in anthropogenic suspended sediment load; a 20% (based on RPT 2013 target) and 50% (based on RPT 2009) reduction in anthropogenic loads of particulate nitrogen (PN) and particulate phosphorus (PP); 50% (based on Reef Plan 2013) reduction in anthropogenic loads of dissolved inorganic nitrogen (DIN); 50% (based on 'interpreted' Reef Plan 2009) reduction in anthropogenic loads of dissolved inorganic phosphorus (DIP) and 50% (Reef Plan 2009) and 60% (Reef Plan 2013) reductions of loads of PSII herbicides (i.e. the 2009 target and the 2013 target respectively).

The ERTs considered in this study are (based on reductions in anthropogenic load from the 2008-09 baseline): 20% overall reduction in suspended sediment load; a 50% reduction in particulate nitrogen (PN) and particulate phosphorus (PP); 80% reduction in dissolved inorganic nitrogen (DIN); 50% reduction in anthropogenic loads of dissolved inorganic phosphorus (DIP) and 60% reduction of loads of PSII herbicides. It should be noted that a 50% reduction in DIP proved to be infeasible and this constituent was limited to a 20% reduction in the tested scenarios.

Results

Costs and Land Use Implications of Achieving Targets

The annual cost of attaining scenario targets for each river basin are summarised in Table 1, with the associated spatial distribution shown in Figure 2. Figure 3 show the spatial patterning of reductions in DIP to meet scenario targets, whereas Figure 4 show the contrasting sediment reduction achievable under limited funding of $2M/year and $8M/year respectively.

Scenario

Land Use Management Changes

The bioeconomic model predicts that for both RPTs and ERTs in cane all land is required to move to at least B practice to meet whole of basin targets. For RPTs 81% of cane is required in B practice and 19% in A practice. For the ERTs the majority of land (over 46,000 ha or 81% cane) is predicted to be required to be in A practice. The transition in grazing land management is even more challenging, with an additional 131,349 ha required in A practice for RPTs and 243,815 ha to achieve ERTs. For RPTs 19% of grazing is required in A practice, 51% in B practice, 22% in C practice and 7% in D practice. In contrast for ERTs, 22% of grazing is required in A practice, 49% in B practice, 21% in C practice and 7% in D practice.

In the context of available funds (Figure 4), the proportional cane area in practices A, B and C are predicted to be 6%, 92% and 2% for $2M and 14%, 84% and 2% for $8M respectively. A similar transition is predicted for the proportion of grazing areas in practices A, B, C and D which are predicted to be 19%, 52%, 22% and 7% for $2M and 22%, 51%, 21% and 6% for $8M respectively.

Discussion and Conclusion

The results for Scenario 1 (meeting the RPTs in each basin, see Table 1) indicate that targets are predicted to be achieved at a modest net profit in the Kolan and Burrum basins. This occurs because of their lesser size compared with the Burnett and Mary and the increased area of sugarcane as a proportion of land use. Net costs are predicted in the Baffle, Burnett and Mary basins because the predictions require practice changes in grazing to achieve the targets, which always incurs a net loss. If RPTs only have to be met on a whole of region basis, then huge savings can be made (see Scenario 2 Table 1 compared to Scenario 1). The net cost is estimated to be approximately $3.0 million/year compared with a net loss of approximately $7.9 million/year from Scenario 1.

The difference between the RPTs and the ERTs are in the ambitiousness of the PN and PP targets -only 20% needs to be met under RPTs compared with 50% for ERTs. Meeting ERTs poses significant feasibility issues because particulate losses come mostly from grazing land uses, streambank and gully erosion, all of which incur large losses. As illustrated in Table 1, ERTs are infeasible to meet in the Mary catchment (Scenario 3). At a whole of region scale, ERTs pose at least an additional $13.4 million loss/year on agriculture than for RPTs.

Table 1 results also further illustrates that the whole of region targets (Scenarios 2 and 4) are much more cost effective than if targets have to be met on an individual basin level.

This study identified that (1) large and ongoing support will be needed for the grazing industry to achieve sediment and particulate P and N targets and (2) the most limiting constituent was DIP. Importantly ERTs cannot be met without substantial costs. This is the first time such a comprehensive and integrated bio-economic model has been constructed for the study region using environmental software that links available biophysical and economic modelling. The developed framework has a demonstrated capacity to test the effectiveness of alternative land management options, the optimal likely impact from available funding (Figure 4) and the environmental impacts of traditional incentive schemes (such as water way and gully remediation).

Importantly this study has informed the development of the Burnett Mary WQIP. Integration of available science and local knowledge through development of a bioeconomic model shows how net profits/costs of reaching relevant targets n can inform a more realistic implementation plan than previously possible . This is the first time such a comprehensive and integrated bio-economic model has been constructed for a region within GBR using environmental software that linked available biophysical and economic modelling.

  loads (TC) are calculated as follows where C refers to either DIN, DON, Particulate N, TN, FRP, DOP, Particulate P, TP, TSS and pesticides (hexazinone, ametryn, atrazine, diuron and tebuthiuron); PP and Gload, Sload refers to exports from gully and streambank respectively:

Fig. 1 .

 1 Fig. 1. Schematic of the optimisation model constituent pools.

Fig. 2 .

 2 Fig. 2. Net profit associated with the RPTs (left) and the ERTs (right).

Fig. 3 .

 3 Fig. 3. Reduction in DIP associated with the RPTs (left) and the ERTs (right).

Fig. 4 .

 4 Fig. 4. Reduction in TSS for associated with a $2M (left) and $8M (right) funding program.

Table 1 .

 1 Net profit or cost associated with meeting targets in the Burnett Mary region in individual basins or whole of regional scale. Red text indicates a net cost and black text indicates a net profit.

			Annual Cost/Profit ($M/year)
		Baffle	Kolan	Burnett	Burrum	Mary
	1. Meet RPTs by indi-vidual basins	-1.4	0.6	-4.8	0.5	-2.8
	2. Whole catchment RPTs			-3.0
	3. Meet all ERTs by individual basins	-4.3	0.2	-7.8	0.2	infeasible
	4. Whole catchment ERTs			-16.4

Acknowledgements

The authors acknowledge the contributions from Dr Jon Brodie for defining the Ecological Reef Targets, Dr Mark Silburn for paddock scale effectiveness estimates and Dr Robert Ellis and Dr Banti Fentie for the provision of Source Catchments and farm scale modelling results without which the bio-economic model could not have been developed.


Mahesh Prakash
email: mahesh.prakash@csiro.au

James Hilton

Lalitha Ramachandran

Integrating Hydrodynamic and Hydraulic Modeling for Evaluating Future Flood Mitigation in Urban Environments

Keywords: modular mitigation analysis, drainage augmentation, local councils, evidence based cost-benefit analysis, land-use planning

We present an integrated flood modelling tool that is able to evaluate different mitigation solutions for areas that are prone to floods from storm surge and heavy rainfall. Our model integrates catchment and coastal flood modelling (spatio-temporally dynamic), including sea level rise, to provide a holistic inundation model for future flooding. Additionally, the model aims to enable simulation of a combination of flood mitigation and adaptation options. To date, the practice has been to model either drainage augmentation solutions alone, or (for coastal inundation) single coastal adaptation solutions. This tool aims to deliver the capacity to model a range of both coastal and drainage adaptation solutions to understand what combination of solutions might be effective. The model is demonstrated for example cases in the City of Port Phillip, Melbourne. Three mitigation strategies involving the use of a hypothetical off-shore reef and the combination of a single valve systems and retention/detention measures are evaluated for the region around Elwood canal for current and future scenarios.

Introduction

Australia has a very long coastline stretching almost 36,000 km in the mainland. A significant percentage of the population resides along this coastline especially along the eastern seaboard. Among these urban environments there are several locations that are low lying and are prone to flooding including storm surge, heavy rainfall and catchment. The Elwood Canal region in the City of Port Phillip (CoPP) in Melbourne, Victoria is particularly at risk of flooding which has been indicated through flood mapping studies carried out internally by engineers in the council as well as through AECOM [START_REF] Aecom | City of Port Phillip -Case Study for the Elwood Canal, Port Phillip Bay Coastal Adaptation Pathways Project[END_REF]. However the coastal flood inundation studies have been carried out using essentially static bath tub type models which do not provide details of flooding such as duration and areas that are prone to water logging (lack of sufficient drainage). This can be particularly important to decide future infrastructure needs including the effect of sea level rise (SLR). Additionally mitigation and adaptation options have not been investigated using a simulation and modelling approach in the past following on from a flood modelling exercise. An integrated flood modelling tool that is able to evaluate different mitigation solutions has therefore been developed by CSIRO for the CoPP so that the council:

1. Continues to have new (as becomes available) catchment and coastal data modelled into an integrated hydrodynamic model in order to understand what climate risks might present design and maintenance challenges to a range of municipal assets and land use planning processes. Specifically, we want to have a clear perspective on the future behaviour (shape, extent and duration) of flooding, and what changes how and when. 2. Is able to model a range of adaptation solutions and land use planning changes to test their effectiveness in mitigating flooding, in order to understand what future infrastructure and land-use needs might be. These then give councils a basis for understanding where and when critical infrastructure and land-use changes may need to be made, as well as the required investment plan for new adaptive infrastructure. The tool is currently being investigated by the Association of Bayside Municipalities (ABM), Victoria for Port Phillip bay-wide deployment.

Key Components of the Tool

The mitigation modeling tool being developed consists of the following key components:

1. Hydrodynamic module -A 2D shallow water based model will be used to simulate the flow hydrodynamics for the scenarios. The solver is able to concurrently simulate flood inundation due to sea level rise, storm surge, tides and rainfall allowing scenarios with multiple flood interactions possible. This is especially important for coastal councils where there is a significant likelihood of combined coastal and catchment flooding. 2. Topography clean-up module (terrain/bathymetry) -The solver is integrated with a LiDAR pre-processing capability that allows input of raw/processed Li-DAR data at varying resolutions to be used as input into the hydrodynamic model. The model allows specification of variable drag maps resulting from regions with varying vegetation composition and other resistance (infrastructure) overlays. 3. Hydraulic module -Drainage is handled either as an infiltration sink or by using an explicit 1D network model connection into the 2D solver for complex urban drainage infrastructure. 4. Mitigation module-Mitigation solutions are integrated into the hydrodynamic/hydraulic model either as modules (eg: drainage augmentation, retention/detention systems) or as structures that can be designed in AutoCAD or similar and imported into the model. 5. Analysis and Visualisation module -An integrated visualisation tool allows the results of the hydrodynamic solver to be produced in a visual 3D Google Earthlike environment. The flood inundation can be visualised as depth/velocity/force maps to provide a before and after quantitative assessment of the assessed mitigation scenario(s).

A detailed description of the computational models and the mitigation models is provided in the sections below.

Computational Models

The tidal and pluvial hydrodynamics were modelled using a shallow water model developed by CSIRO. The method has been specially adapted for modelling widespread areas of urban inundation. The model is well suited to both heavy rainfall events and tidal inundation. Recent applications include the impact of heavy rainfall events leading to the flash floods in Toowoomba in 2012 and the effects of possible SLR in the Kakadu region. The shallow water equations represent the depth averaged height and unit discharge of water above a fixed datum. The equations are discretised onto a fixed grid representing the land surface and computationally processed to predict the water dynamics. The underlying method is based on the finite-volume methods presented by Kurganov and Petrova [START_REF] Kurganov | A Second-Order Well-Balanced Positivity Preserving Central-Upwind Scheme for the Saint-Venant System[END_REF]. The method requires a combined topological and bathymetric height map above a given vertical datum as well as an initial distribution of water. In addition the method allows the input of a given boundary condition, which may represent a storm surge or tsunami, as well as a spatially distributed source map, which may represent a rainfall or other water source. The effect of base friction is imposed using a Manning drag model. The underground drains were incorporated into the hydrodynamic model using a 1D pipe network model (see Fig. 1). The solution to the 1D pipe network is derived as a steady state output at every hydrodynamic model time step. This integrated solution provides an accurate estimate of the dynamic flooding across a large urban region. The models have been extensively tested and validated against benchmark test cases presented by Neelz and Pender [START_REF] Neelz | Benchmarking the latest generation of 2D hydraulic modeling packages[END_REF].

Mitigation Models

A highlight of the software tool is the ability to incorporate different types of mitigation options as applications which then can be deployed to specific parts of the region of interest. The mitigation models can be classified into three broad categories:

• Drainage augmentation: These can be either a simple measure such as increasing the size of the drainage pipe, deploying back valves into existing drainage pipes or enhancing drainage capacity by incorporating pumps at specified locations in the urban drainage system. • Retention/detention systems: Retention/detention systems are typically useful for low to medium level flooding where a water storage system is also utilized for managed release of water to provide for additional capacity during a predicted flood event (such as a flash flood). Retention/detention systems can either be at a household level (tanks), street level (underground large storage tanks) and/or large parks/open spaces used as dug-out storage/release facilities. • Sea walls/off-shore reefs/protective barriers: These are large engineering or natural (such as mangroves) protective barriers that are specifically designed to protect a region in a council for its economic, heritage and/or environmental value typically against storm surge or similar events.

The software tool is able to incorporate all three forms of mitigation models. The drainage augmentation and retention/detention systems are built in the tool in such a way that they can be modularly incorporated into any mitigation analysis either by a point and click mechanism or by assigning the mitigation model(s) to a particular chosen location in the council. The protective barriers are incorporated typically via an AutoCAD or similarly designed input. This is because protective barriers are specifically designed for a region of interest and cannot be modularized.

Data Inputs

The data requirements for the tool are summarized below:

• Terrestrial and bathymetric LiDAR data

Modelling Process Flow Chart

The mitigation modeling process involves several components that have a specific purpose, these are described below, Fig. 2 provides a graphical flowchart summary describing the steps involved in the integrated mitigation modeling process: 1. Adoption of existing flood study data as input into an integrated flood mitigation model.

Purpose: Effective use of existing data and to reduce rework. Existing data will also be used for validation purposes.

Statistical extreme event analysis

Purpose: To understand and estimate the occurrence likelihood of extreme events such as storm surges and rainfalls for current day and for the future. This is important since there could be large variations in climatic conditions in a study region of interest. Rigorous statistical analysis provides a better basis for estimating flood frequency and amplitude. Using this approach will provide an envelope that can then be used as input into a hydrodynamic/hydraulic model for scenario simulations.

Statistical joint probability analysis

Purpose: To understand and estimate the occurrence likelihood of joint flood events such as coastal and catchment flooding for current day and for the future. Very few of the previous flood studies investigated the joint likelihood of flood-ing. Joint probability analysis is an emerging and robust approach for a holistic analysis of encompassing all possible avenues for flooding.

Integration of hydrodynamic modelling with a comprehensive drainage network (hydraulic) model

Purpose: To accurately predict flooding in an urban environment. A key outcome from the case study (described below) conducted for the City of Port Phillip study (2014) was that drainage networks contribute to flood inundation especially when there is a storm surge event and/or sea level rise due to the drainage pipes working as a reverse carrier of water from the sea further inland. This will be especially important for low-lying inland locations. 5. Incorporation of outputs from the statistical modelling into the hydrodynamic/hydraulic model. Purpose: For simulation scenario analysis that uses an occurrence likelihood approach including joint (coastal and catchment) flood events. This provides a robust statistically based envelope for flood inundation predictions that is relevant for coastal councils. 6. Analysis of future flooding to investigate the effect of sea level rise.

Purpose: This is important since any future infrastructure (overland or underground) will need to be justified from a cost/benefit perspective. 7. Integration of suggested mitigation and adaptation strategies.

Purpose: This relates to quantitatively assessing the effect of suggested mitigation and adaptation strategies for specific "hot spot" locations. This approach provides a quantitative method of assessing the effectiveness of flood mitigation and adaptation strategies especially for future flooding. 8. Provide further guidance on flood mitigation following on from the integrated analysis in step 7 above. Purpose: This is a feedback loop that allows the planners/engineers to test various suggested strategies and potentially enhance their effectiveness.

The modelling components are linked and packaged (see Fig. 3) utilizing the CSIRO Workspace: A scientific workflow platform [START_REF]Workspace: A scientific workflow platform[END_REF]. Workspace has the following advantages that are advantageous for the development of the mitigation modelling tool including:

1. Ability to modularly include different mitigation options 2. Ability to seamlessly connect computational models such as the 2D hydrodynamic model with the 1D network model for drainage 3. In-built capability to manage distributed computing such that a set of simulation scenarios can be assigned to different GPUs/CPUs and results drawn back into a unified analysis/visualization environment 4. In-built visualization facility 7 Case Studies

Simulation Scenarios

Table 1 summarises the simulation scenarios investigated for the integrated flood impact study. These scenarios were derived following on from a preliminary joint probability analysis as described in section 6 above. A maximum storm surge height of 1.3 m was chosen on the basis of the gauge measurements at St Kilda marina by Melbourne Water [START_REF] Hoang | Frequency analysis of tide levels for St Kilda marina (Site No: 229670[END_REF]. It is acknowledged that the maximum storm surge height recorded in St Kilda marina is higher than the one recorded at Williamstown (1.15 m) in the work of McInnes et al. [START_REF] Mcinnes | The effect of climate change on extreme sea levels in Port Phillip Bay[END_REF]. The higher value is used for the purposes of evaluating a severe but very likely future scenario.

The rainfall input condition was finalised following on from discussions with Melbourne Water for a typical storm event in CoPP. The same scenarios were repeated for evaluating the effect of mitigation described in section 4.

Designed Mitigation -Off-Shore Reef

The first case study is used to demonstrate the application of the tool to evaluate the effectiveness of a hypothetical off-shore reef designed to attenuate the effect of a storm surge wave impacting the Elwood region in the City of Port Phillip. The off-shore reef was introduced into the model by importing a design concept proposed by Prof. Rob Roggema (Urban Design, Swinburne University, Melbourne). The off-shore reef is 3 m tall, 150-200 m in width and approximately 2 km offshore. Fig. 4 shows the case without the seawall (left) and with the seawall (right), where the initial water level is shown in blue to highlight the difference between the maps. Fig. 5 shows the change in maximum flood extent without and with the off-shore reef for a high storm surge for current day conditions.

Modular Mitigation -Back Calves and Retention/Detention

In the second example two mitigation models (as shown in Fig. 6) have been investigated namely:

1. Non return valves included along the stretch of Elwood canal and main drains at St Kilda and end of Langridge Street and 2. Retention detention consisting of water tanks installed on all lots in the Elwood region, along roads identified as being prone to flooding and a cricket oval in north-west of Elsternwick Park. d. Such a model also enables the capacity to assess and determine areas that will not be able to provide effective adaptation solutions -this then enables an evidence-base for the consideration of different land uses or drastic changes to current land use. e. The tool currently does not have the capacity to process point cloud LiDAR data and requires a "clean and processed" LiDAR input. CSIRO is aware of this limitation and work is underway to address this issue.

d. Visualisation of the results currently requires the LiDAR obtained terrain/bathymetry to match up with textural information. This is always not straightforward due to the inherent inconsistency associated with frames of reference for these layers. This issue is being investigated so that a Workspace based frame reference fixing algorithm can be provided to clients.

Conclusions

The flood modelling tool is an integrated system for stakeholders to investigate potential mitigation options in response to flood scenarios. The tool provides end-to-end modelling, analysis and visualization capability, as demonstrated by the City of Port Phillip case study. The model has also been developed to test the effectiveness of customised adaptation solutions. The next phase of development will be to customize the tool for use by land-use and infrastructure managers, especially in Local Government through a Port Phillip bay-wide approach.

Fig. 1 .

 1 Fig. 1. Flow in drainage network for the City of Port Phillip 3 hours after a 1 in a 10 year ARI rainfall.

Fig. 2 .Fig. 3 .Fig. 4 .

 234 Fig. 2. Flowchart describing the steps involved in the integrated assessment of flood mitigation and adaptation strategies

Fig. 5 .

 5 Fig. 5. Maximum flood extent with water coloured by depth without (left) and with (right) the offshore reef for current day storm surge conditions.The flood extent averaged over time for a 0.4 m sea level rise (SLR) scenario with and without mitigation is compared in Figure7. This demonstrates that the mitigation options selected is able to reduce the flooding to essentially nuisance levels (retention times of only around 2 hrs with flood height of 0.5 m or lower) in most regions around Elwood canal at a 0.4 m SLR.

Fig. 6 .Fig. 7 .

 67 Fig. 6. Modular mitigation types and location around Elwood Canal

  • Land-use classification data to derive a variable drag map for the region of interest

• Historical storm surge data • Tidal data • Sea level rise scenario(s) • Rainfall data • Drainage network information (ArcGIS compatible) • Mitigation solutions ─ Modular (retention/detention, back valves, pumps) ─ Designed (AutoCAD or similar for custom sea walls and protective barriers and off shore reefs) • Historic flood data for validation • Previously performed flood study information for calibration

Table 1 .

 1 Simulation scenarios

	No	Max.	Rain	Rain	SLR	Max ht
		Stm ht. (m)	(ARI)	(hrs)	(m)	(AHD)
	1	1.30	100	3	0.0	1.30
	2	1.30	100	3	0.4	1.70
	3	1.30	100	3	0.8	2.10
	4	1.30	100	3	1.1	2.40

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

Acknowledgements

The authors would like to acknowledge financial support provided by the City of Port Phillip and the CSIRO Digital Productivity and Services Flagship for conducting research towards developing the integrated flood mitigation analysis tool. The authors would also like to acknowledge the in-kind expertise and review provided by a range of key stakeholders including Melbourne Water, the Association of Bayside Municipalities, AECOM, and SGS Consulting to consider data and methodological inputs and likely adaptation solutions to be tested. Finally the authors would like to thank Fletcher Woolard from CSIRO for assistance in producing the visualisations related to this project.


Rajiv Pratap

Ramesh Rayudu
email: ramesh.rayudu@vuw.ac.nz

Manfred Plagmann

Modelling of Air Flow Analysis for Residential Homes using Particle Image Velocimetry

Keywords: Particle Image Velocimetry, PIV, air flow measurement, building ventilation analysis, Angle filtering

The purpose of this paper is to simulate the designed physical Particle Image Velocimetry (PIV) system, as a simulation platform to physically build and implement a system for residential building and housing research. The focus is on the angle filter; the filter used to process the images of a laser progressively scanning through a space by adjusting its angle.

An indicator of heat escape and ventilation in buildings is the airflow itself. Conventional airflow measurement techniques are typically intrusive, interfering with the data or the environment. For small flows such as that in residential housing, the error introduced can sometimes be large relative to the measured data. In contrast, PIV is a relatively a non-intrusive measurement tool that measures flows. However, there are a few problems with standard PIV techniques, for implementation in an attic space.

The proposed solution is to use dust particles, already present in the air, as tracers for the PIV system. In conclusion, our PIV system with a non-diverging laser beam produces a velocity field of similar quality to a velocity field of a standard PIV system.

Introduction

NZ homes are well known to be cold, damp and poorly ventilated. There have been several papers reporting on the quality of these conditions [START_REF] Lloyd | The efficacy of an energy efficient upgrade program in New Zealand[END_REF][START_REF] Isaacs | Energy Use in New Zealand Households: Final Report on the Household Energy End-use Project (HEEP)[END_REF], as well as how to mitigate these problems. In some cases it can be relatively easy to identify causes and find solutions to them, such as by adding insulation [START_REF] Howden-Chapman | Retrofitting houses with insulation to reduce health inequalities: Aims and methods of a clustered, randomised community-based trial[END_REF]. Other times it can be more difficult, such as finding gaps where heat is escaping, or identifying poor ventilation. These problems require less conventional methods to solve. One indicator of both heat escape and ventilation is the airflow itself. Conventional airflow measurement techniques are typically intrusive, interfering with the data or the environment. For small flows such as that in housing, the error introduced can sometimes be large relative to the data measured. In contrast, Particle Image Velocimetry (PIV) is a relatively non-intrusive measurement tool that measures such flow. PIV systems have been researched and implemented for decades, especially useful in wind tunnel experiments and other complex flow scenarios [START_REF] Raffel | Particle Image Velocimetry, A Practical Guide Springer[END_REF]. The typical setup for a PIV system uses a camera, tracer particles, a laser to illuminate the tracers and a data processing unit, typically a computer, to compute a vector field of the space [START_REF] Adrian | Particle-imaging techniques for experimental fluid mechanics[END_REF]. The use of tracer particles in combination with a laser, through a diverging lens, gives a clear representation of the flow through a space, providing high contrast to the background which makes it easier to visualise and process the space.

There are a few problems with this standard setup, for implementation in an attic space. Firstly, the use of tracer particles involves adding potential pollutants to the housing space. These could get stuck in cracks and other gaps where air escapes, disrupting or changing the airflow, which would defeat the purpose of such a system. Another problem is that the reflected light intensity of particles decreases dependant on distance from the lens. This is because the mirror modifies the laser's beam to be diverging, and so the beam's power density decreases as a result of distance. When the incoming light intensity decreases, so does the reflected intensity of the particles. This limits the dimensions which the system can be used, and to compensate for it the laser's power may need to be adjusted for various scenarios.

The proposed solution uses dust particles, already present in the air, as tracers for the PIV system. Research has previously been done in use of small particle tracers for PIV [START_REF] Cao | Particle image velocimetry measurement of indoor airflow field: A review of the technologies and applications[END_REF], outlining specifications for suitable tracer particles. The scattering intensity of dust particles prevents its use as a tracer particle in standard PIV systems. However, through experimental observation it was found that the space containing dust particles could be illuminated using a non-diverging, high power laser beam. The level of illumination produced was comparable to the images used in existing PIV systems, suggesting this modified system could perform equivalently.

The purpose of this paper is to simulate the modified PIV system, as a confirmation to build and apply the system for building and housing research. The simulation provides insight to algorithm design for a new system, allowing for rapid adjustment and testing. Once simulated, the final algorithm in the simulation was implemented in a physical system, and tested in a building. This paper focuses on the simulation side of the project, in particular the angle filter used to process the images of a laser progressively scanning through a space.

Modelling of PIV System

The simulated PIV system was implemented in Mathematica. The PIV simulation takes in images and generates a vector field overlaid on top of the two images used for correlation, blended together.

The PIV simulation utilises a multi-frame single pulse PIV technique [START_REF] Adrian | Particle-imaging techniques for experimental fluid mechanics[END_REF], with some additional features. The method uses two images taken a small time frame apart, divide each image into cells, and cross correlate each cell with the corresponding cell in the second image. By observing various shifts in both vertical and horizontal direction, the shift that results in the highest correlation corresponds to the estimated displacement in that time for the particles in that cell.

The convolution is calculated using the Fast-Fourier Transform (FFT), by utilizing the Convolution Theorem of Fourier Transforms. By using this property the convolution can be found significantly faster than by directly calculating the convolution. The Convolution Theorem property can be observed in the equation below.

F[ a * b ] = F[a] . F[b] (1) a * b = F -1 [ F[a] . F[b] ] (2)
To prevent aliasing, the data was zero padded. This allows the correlation at the edges of the cells to be calculated without distortion from the opposite side of the cell. The details of the developed algorithm are discussed in the following sections.

Correlation Adjustment

2.1.1

Background Correlation Background correlation as described in [START_REF] Westerweel | Fundamentals of digital particle image velocimetry[END_REF] was removed by subtracting the mean from each image. Background correlation is caused by images' direct correlation, resulting in a high correlation at '0' displacement. This is prevalent in a PIV system, as it involves repeated photographing of and at a relatively stationary point. The background would remain constant, and using small tracer particles of dust would result in a majority of the image consisting of the background. The majority of the image would have a net velocity of 0, and so the algorithm would indicate a '0' vector field. By removing the constant background correlation, the exclusive velocity vector field of the particles can be observed.

The difference in clarity of the correlation is significant, and can be seen in Fig. 1. Here, the cross correlation values can be graphically observed, both with and without the mean. Notice the sharper peak after the mean is removed, allowing for an easier decision of net velocity vector.

2.1.2

Correlation Peak averaging Cross correlation calculates the correlation at each integer displacement by considering the peak displacement and thereby choosing the most likely integer displacement.

One drawback with this method is that velocities are locked into integer values, and so the error introduced can be ±0.5 pixels. A second drawback is the decision method cannot consider several velocities as it only takes the single peak. Both the drawbacks can be solved by peak averaging, as described in [START_REF] Bastiaans | Cross-correlation PIV; theory, implementation and accuracy[END_REF]. Fig. 1 depicts the case where the true velocity lies between two integer pixel displacements. Cross-Correlation PIV [START_REF] Bastiaans | Cross-correlation PIV; theory, implementation and accuracy[END_REF] method looks at the estimated region of peak, and determines the 'centre of gravity' by using a weighted average at that location to improve the accuracy. The modified algorithm used in the simulation takes a weighted average of all local maxima within 80-100% of the largest correlation. For this simulation, taking above the 80 th percentile of peaks empirically provides a good balance between accurate vector estimation and speed.

Angle Filtering Systems

The final feature that is unique to this simulation is the filter for the laser's scanning angle. The laser may not be able to sweep the entire field in a single time frame, so utilisation of these filters allows the system to obtain partial information and can be combined to provide an accurate full field.

Physical Setup

There are two physical configurations (methods) for the considered angular filter: 1. Successive images per step (pre-processing) and 2. Scan the field and correlate (post processing).

Method 1 iterates through a range of angles, and for each iteration, takes two successive images. These images are directly compared against each other, and the resultant vector fields combined. Method 2 iterates through a range of angles twice; each time taking a single image. Iterating twice produces two images per angle, with a longer time step between each image.

The limiting factor for these systems is the time gap between correlations, which determines the maximum measurable velocity. To compare each system, the time gap was set constant and the space to scan was divided up into 20 segments. It was found that the time to scan the field using Method 1 required 20 times that of Method 2, and the time between each angle was 40 times that of Method 2. This suggests that for measuring a velocity field, Method 1 takes longer to scan however puts less strain on equipment. It should also be noted that Method 2 has a significant memory cost, as the system must hold the image in memory until another image appears for comparison. For 20 segments, this would require 20 images to be stored in memory at any time. The extension of this project will have access to a high speed scanner, and hence Method 2 was chosen.

Applying maximum dust speed calculations to the model, with an estimate of the laser being 1 pixel in diameter and a 1200x1000 pixel image, the time taken to scan the entire field using Method 2 is about 0.5 seconds ignoring the camera delay. A large delay is introduced from the high resolution required due to the small diameter of the laser. However, by utilising the exposure of the camera and the indirect illumi-nation from the beam, the effective resolution and hence the time can be vastly improved. This will introduce an error due to blurring, and will require calibration and filtering to reduce the error to an acceptable level.

2.2.2

Vector Merging Algorithm. It is possible that the laser may not cover the field fast enough to include it within one frame, and even if it does, the exposure may increase noise beyond an acceptable threshold. This scenario would instead require multiple frame comparisons, and a method to merge the resultant fields. The method of merging can be done by adding using a rectangular window; however by using more complex functions a higher accuracy can be achieved per vector coordinate, via a weighted average.

The proposed method uses a triangle function. For a particular measured angle, the following algorithm is used:

For each Vector Position,

If[θ_laser -angleStepSize < θ_V < θ_laser + angleStep- Size ] Vector V += 1-Abs[ (θ_laser -θ_V) ] / angleStepSize * VectorV_0
where θ_laser is the angular position of the laser. angleStepSize is the space between laser positions, determined by 'resolution'.

θ_V is the polar coordinate angle of the vector's position in the vector field.

Vector_V is the current state of the output vector at a position, and initialised as {0,0}.

Vector_V0 is the original state of the vector, produced using the correlation algorithm with the unfiltered images.

This results in the following function per vector point:

a = (θ1 -θ_a)/(θ1 -θ2).a2 + (θ_a -θ2)/(θ1 -θ2).a1 (3)
where θ1 and θ2 are the two closest possible angular positions of the laser θ_a is the polar coordinate angle of the vector's position a1 is the vector at the position generated from images at laser angle θ1 a2 is the vector at the position generated from images at laser angle θ2 The above is a basic weighing function which weighs the vectors according to the closeness to the laser in each laser's iteration. The sum across the full range of angles results in a full field with unit scaling on all vectors.

For regular repeating angles, the algorithm can be pre-processed, which allows the merging function to be implemented by simply multiplying a 'kernel' to each field, effectively applying a scalar weighting to each vector. By estimating the angle to match that of the kernel, the processing speed can be vastly improved.

The use of this algorithm allows a continuous sum that emphasises vectors that contain accurate information, by being closer to the beam. It should be noted that although a simple triangle function was used, any other window function that sums to unity would also work.

3

Simulation and Testing

Methodology

Initially the original, non-angle filtered PIV system was tested using a simulation of randomly moving pixels, with a net velocity applied to the pixels. The purpose of this simulation was to confirm that the system was processing and displaying the right velocities. Once confirmed, the original system was used as a benchmark for comparison with the new system. A particle-based fluid dynamics simulation was developed to test the PIV system for complex flow. Here, a typical attic space was modelled in Mathematica. This attic space allowed for particles to flow in from the sides of the attic, and force applied to eject them from the top. This simulation was based on algorithms described in a fluid modelling thesis [START_REF] Roy | Physically-Based Fluid Modeling using Smoothed Particle Hydrodynamics[END_REF]. A screen-shot of the simulation can be seen in Figure 2, and the applied angle filtering algorithm in Figure 3.

The fluid simulation has several features, including particle collision, diffusion, advection, velocity damping, boundary conditions, and force. It also simulates the laser by displaying the particles at a particular depth, as well as within a range of a polar coordinate angle with a Gaussian decaying boundary. A sample of this can be seen in Figure 3. This display method assumes the light scattering of the laser on the particles is Mie scattering. Physical dust particles have a highly irregular shape and hence irregular scattering. However, for this system the dust particles move and rotate relatively slow compared to the camera and scanner, even after considering their volatility. Hence it can be assumed that scattering will not fluctuate significantly within the time frame.

The fluid simulation was run with 100 particles, simulating an attic space of dimensions 4mx5mx2m. The PIV system was then applied to these images, in various sample steps. The original and angle filtered systems were measured over the same time interval. The original system was tested over 50 steps at 0.01 seconds while the angle filtered system was measured over 1000 steps, angular resolution 1/20 of a frame, at 0.01 seconds. This provided a 0.5 sec total interval.

The test used to measure the quality of the system was correlation distance. To measure consistency, the correlation distance was measured between the original and the angle filtered system.

Results

The results of the pixel simulation can be seen in Figure 4. The net velocity applied to the pixels were {30, 20, 1} as {x,y,z} coordinates, with uniform randomness of up to ±5. On empirical analysis, the velocity field displays an accurate velocity where pixels are present, and displays a small error for velocities where the cell contains few pixels.

A second set of results can be seen in Figure 4. Here, a system describing an attic space, as displayed in the Methodology, is analysed for particle flow where air feeds from the sides and force applied to remove them from the top.

A noticeable problem with the system is that velocities appear to change slowly across sharp borders. An example of this can be observed in Figure 5 between the boundary of visible and non-visible particles. Only the beam of particles (visible as white dots) show a visible displacement per frame, and so it is expected that non-zero velocities only exist at the location of the particles. However, in the Figure the nonzero velocities extend to no particle space in a cell. This can be explained as an effect of linear spatial smoothing, in an attempt to generate smoother flow. The presence of this smoothing is questionable, so the effect may need to be removed or improved using a different function, depending on the desired effect.

Conclusion

In conclusion, a PIV system utilising a non-diverging laser beam produces a velocity field of similar quality to a velocity field of a standard PIV systems. By using appro-priate filters the accuracy of this new system is comparable to that of standard ones, while maintaining constant beam power across a distance. The downside to this filtering produces errors at boundaries, and so is a consideration for implementation of such systems. The simulation suggests the use of dust particle tracers works for PIV systems, and provides evidence for proceeding to implement a PIV system with this methodology.

Fig. 1 .

 1 Fig. 1. (a) Mean Adjustment: before.

Fig. 1 .

 1 Fig. 1. (b) Mean Adjustment: after.

Fig. 2 .

 2 Fig. 2. Screen-shot of a 3d view of the fluid simulation. Arrows indicate a force field, replicating the force due to negative pressure.

Fig. 3 .

 3 Fig. 3. Screen-shot of 2d view (rotated), during scan and generation of velocity field. Overlaid is the interrogation cells and their effective weighing (brighter is stronger weighting).

Fig. 4 .

 4 Fig. 4. Vector field describing fluid flow in an attic space.

Fig. 5 .

 5 Fig. 5. Vector field of velocities due to pixel drift.

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011


Brian Miles
email: brian_miles@unc.edu

Lawrence E Band

Ecohydrology Models Without Borders? Using Geospatial Web Services in EcohydroLib Workflows in the United States and Australia

Keywords: hydroinformatics, workflows, ecohydrology modeling, RHESSys

Ecohydrology models require diverse geospatial input datasets (e.g. terrain, soils, vegetation species and leaf area index), the acquisition and preparation of which are labor intensive, yielding workflows that are difficult to reproduce. EcohydroLib is a software framework for managing spatial data acquisition and preparation workflows for ecohydrology modeling, while automatically capturing metadata and provenance information. The goal of EcohydroLib is to enable water scientists to spend less time acquiring and manipulating geospatial data and more time using ecohydrology models to test hypotheses, while making it easier for models to be shared and scientific results to be reproduced. This increased reproducibility, ease of sharing, and researcher productivity can enable both model inter comparison of interest within a country, and site inter comparison of interest across national borders. Currently, EcohydroLib allows modelers to work with geospatial data stored locally as well as high spatial resolution U.S. national spatial data available via web services, for example 30meter digital elevation model and land cover data, and 1:12,000 scale soils data. While researchers working in watersheds outside the U.S. can use EcohydroLib, they must manually download data for their study areas before these data can be imported into EcohydroLib workflows. Though national agencies in the U.S. and Australia offer some datasets via web services, with a few exceptions these are either lower resolution datasets or data made available via Open Geospatial Consortium (OGC) Web Map Service (WMS) interfaces of use primarily for cartography, rather than via OGC Web Coverage Service (WCS) or Web Feature Service (WFS) interfaces needed for integration with numerical models. In this paper we explore: (1) availability of high-resolution national geospatial data web services in the United States and Australia; and (2) integration of Australian web services with EcohydroLib.

Introduction

Researchers working in the interdisciplinary field of ecohydrology are concerned with the cycling of energy, carbon, water, and nutrients through coupled climate-soilvegetation systems (Rodríguez-Iturbe 2000), and with the interaction between water cycling and the ecological community. Ecohydrology models require diverse geospatial datasets (e.g. terrain, soils, vegetation species and leaf area index), the acquisition and preparation of which are labor intensive, yielding workflows that are difficult to reproduce. When applied to sites with complex terrain, for example mountainous forested ecosystems or urbanized ecosystems, high spatial resolution data (<= 30-m) are needed to accurately simulate hydrologic processes [START_REF] Band | Effect of land surface representation on forest water and carbon budgets[END_REF][START_REF] Band | Heterogeneity in urban Ecosystems: Patterns and Processes[END_REF].

Environmental modeling can incorporate information from diverse sources and relies heavily on cyberinfrastructure (CI; e.g. hardware, software, sensors, networks, and the human and social capital necessary to make use of these) to assist in the collection, analysis, and visualization of observed and model output data. The increasing use of cyberinfrastructure to carry out complex modeling, data analysis, and visualization tasks has led to the adoption of workflow systems of increasing complexity [START_REF] Deelman | Workflows and e-Science: An overview of workflow system features and capabilities[END_REF]). Workflow construction tools (e.g. Cyberintegrator, Kepler, VisTrails) enable scientists to create workflows by combining a series of services needed to complete a set of tasks (e.g. data preparation, analyses and modeling, visualization; [START_REF] Goble | myExperiment: a repository and social network for the sharing of bioinformatics workflows[END_REF]. Over the past decade, workflow systems have been gaining use across the sciences (e.g. high-energy physics, biological science as well as climate science). More recently, domain and information scientists have turned their attention to improving cyberinfrastructure for geoscience workflows in general (cf. U.S. National Science Foundation EarthCube; http://www.earthcube.org/) and water science in particular. In an analysis of Australia's Water Information Research and Development Alliance (WIRADA), [START_REF] Plale | The challenges and opportunities of workflow systems in environmental research[END_REF] identifies the benefits of adopting workflow systems to carry out tasks common to water sciences, and considers how amenable these tasks are to representation in scientific workflow systems. The tasks include: (1) data discovery; (2) data cleaning and formatting; (3) data ingest; (4) data assimilation and forecast model execution; and (5) data analysis. The challenge for workflow systems in geosciences is to enable easy: (1) workflow creation, including debugging; (2) validation of workflows; (3) running of workflows across workflow systems; (4) results visualization; (5) results publishing; [START_REF] Goble | myExperiment: a repository and social network for the sharing of bioinformatics workflows[END_REF] sharing and reuse of workflows among scientists in a community and across disciplines to allow results to be reproduced [START_REF] Duffy | Designing a Road Map for Geoscience Workflows[END_REF][START_REF] Guo | Scientific workflow challenges[END_REF].

EcohydroLib (https://github.com/selimnairb/EcohydroLib) is a software framework for managing spatial data acquisition and preparation workflows for ecohydrology modeling, and provides library code and workflow commands for acquiring, manipulating, and managing geospatial data needed to run a variety of ecohydrology models (e.g. RHESSys, SWAT, VIC). Workflow steps common across models can be performed using EcohydroLib commands, for example identifying a study area, or acquiring terrain or soils data via web services (Fig. 1). Fig. 1. Illustration of layered architecture of EcohydroLib. Read from the bottom, raw data sources are accessed using web services (yellow), or as static data stored on local files systems (yellow), using library code (green) in EcohydroLib. Generic data acquisition tools (black) are built on top of EcohydroLib library code. These tools yield data in generic formats not specific to any model (grey). Model-specific tools (magenta) use these generic data to produce modelspecific input data parameterizations (grey, above magenta). The model specific data make possible ecohydrology modeling science workflows (cyan) whose goal is to answer particular science and management questions.

Modelers can then use model-specific workflow commands built on top of Ecohy-droLib to transform input data into formats appropriate for direct use by a particular model. In this way, EcohydroLib increases the amount of code devoted to data acquisition that can be shared across ecohydrologys, reducing duplication of programming effort, and facilitating model and site inter comparison. EcohydroLib workflows are composed of loosely coupled commands for performing geospatial data acquisition and preparation; data acquired by these commands are stored in a directory on local disk specific to each project (i.e. the project directory). These workflows are orchestrated via a metadata store in the project directory. Workflow commands are built using task-oriented APIs defined in the package ecohydrolib. These commands provide tools for downloading and manipulating geospatial data needed to run ecohydrology models, information such as: digital elevation model (DEM), soils, land cover. The metadata store, essentially a key-value store (e.g. a dictionary), is used to orchestrate a series of workflow commands used to prepare data for an ecohydrology model. The metadata contain information related to the study area (e.g. bounding box coordinates, spatial reference, spatial resolution), provenance information for each spatial data layer imported, and a processing history that records the order in which commands were run as well as the parameters used to invoke each command. Currently, EcohydroLib allows modelers to work with geospatial data stored locally as well as high spatial resolution U.S. national spatial data available via web services, for example 30-meter digital elevation model and land cover data, and 1:12,000 scale soils data. While researchers working in watersheds outside the U.S. can use Ecohy-droLib, they must manually download data for their study areas before these data can be imported into EcohydroLib workflows.

Compared to systems for manually downloading geospatial data, web services can make it easier to acquire and integrate such data into geoscience workflows. Manualdownload workflows typically require a modeler to use a web browser-based GIS tool to define the region of interest and choose a data product to download before being able to download data, perhaps after a delay of minutes to hours while the data are fetched from offline storage or otherwise processed. Once downloaded, these data may consist of several image tiles that need to be joined together before being usable in an ecohydrology model. Instead, web services can offer more-or-less instant access to seamless geospatial data covering the entire study area. Further, when accessed via workflow tools that store study area information (e.g. geographic bounding box, spatial reference) such as EcohydroLib, users are not required to specify spatial information each time within a given workflow that they acquire datasets via web services. Lastly, compared to manual-download systems, web services make it possible to acquire geospatial data in spatial reference systems and resolutions specific to each workflow, with conversions being done on the server side, reducing the work required of the modeler. It must be noted that both web services and manual-download data acquisition workflows require the modeler to take care when combining geospatial datasets to ensure that the data being combined are of commensurate spatial scales or resolutions as well as temporal and semantically compatible.

To be of use in numerical ecohydrology models, web services must provide data as Open Geospatial Consortium (OGC) Web Coverage Service (WCS) end points (http://www.opengeospatial.org/standards/wcs) or OGC Web Feature Service (WFS; http://www.opengeospatial.org/standards/wms), rather than as OGC Web Map Service (WMS; http://www.opengeospatial.org/standards/wms), which are of use primarily for cartography applications. While national agencies in the U.S. and Australia are offering some datasets via web services, these are either lower resolution datasets or are offered as WMS end points. In this paper we explore: (1) availability of highresolution national geospatial data web services in the United States and Australia; and (2) integration of Australian web services with EcohydroLib. 1); both high-resolution U.S. national data (National Elevation Dataset, NED; SRTM) as well as global DEM data (e.g. GLSDEM, GTOPO) coverages are available via WCS4DEM. High spatial resolution (30-m) land cover data for the continental U.S. are available over a number of years (1992, 2001, 2006, and 2011) as part of the National Land Cover Database (NLCD), which is available for download via WCS interfaces from the NASA Distributed Active Archive (DAAC) for Biogeochemical Dynamics center at Oak Ridge National Laboratory (ORNL) for years 1992, 2001, and 2006, as well as through the U.S. Geological Survey for 2011 (Table 1). Finally, U.S. soils data are available via WFS and Simple Object Access Protocol (SOAP) web services interfaces provided by the U.S. Department of Agriculture (USDA).

Unfortunately, at present there are no national-scale high spatial resolution vegetation leaf area index (LAI) data products available for either the U.S. or Australia, and thus none are available via WCS interfaces. However, it is possible to derive 30-m peak LAI data from Landsat satellite data as well as LAI phenology (e.g. green-up, senescence) from MODIS satellite data, which are available globally. ORNL DAAC offers seasonal and yearly Landsat mosaics for 2008 and 2009 via WCS (http://webmap.ornl.gov/wcsdown/dataset.jsp?ds_id=111112) from the USGS Webenabled Landsat Data (WELD) project (http://weld.cr.usgs.gov/). These data could serve as a data source for a prototype WCS-enabled peak LAI service.

Integrating EcohydroLib with U.S. and Australian National Spatial Data Web Services

When EcohydroLib 1.0 was released in July 2013, it provided the ability to acquire the following geospatial datasets from remote web services: U.S. and global DEM data from WCS4DEM, NLCD 2006 data from ORNL DAAC, and U.S. surface soil texture data from USDA SSURGO. Here we describe an additional module recently developed for EcohydroLib 1.20 to provide access to national-scale 1-second (~30-m) spatial resolution SRTM-based DEM data provided by Geoscience Australia. Geoscience Australia (GA) provides 1-second spatial resolution DEM data for all of mainland Australia and Tasmania based on the 2000 Shuttle Radar Topography Mission data. These data are offered in three forms: unsmoothed data (DEM); smoothed data (DEM-S; with noise removed); and a hydrologically enforced version of the smoothed data (DEM-H; which includes hydrologic flowpaths derived from both SRTM data and mapped streams). The hydrologically enforced data are deemed to be suitable for watershed delineation (Geoscience Australia 2011). EcohydroLib provides access to all three datasets using the GetGADEMForBoundingBox command (for a full description EcohydroLib, including installation instructions and tutorials, see: https://github.com/selimnairb/RHESSysWorkflows). The EcohydroLib workflow steps required to acquire GA DEM data are summarized in Fig. 2.

Fig. 2. Initial EcohydroLib workflow steps including acquisition of DEM data from Geoscience

Australia WCS web service interface

The first workflow step, RegisterStudyAreaShapefile, defines the spatial extent of the study area using a single polygon encoded in an ESRI shapefile file format. This shapefile, accessed locally via a disk mounted on the computer where EcohydroLib is running, is copied into the project directory specified to RegisterStudyAreaShapefile; as it is copied, the shapefile is reprojected into the WGS84 geographic coordinate system (EPSG:4326). This reprojected shapefile can be visualized using a GIS such as QGIS.

Once the study area is defined, the bounding box of the study area must be derived from the study area polygon using the GetBoundingboxFromStudyareaShapefile command, which stores the bounding box in the metadata.txt file stored in the project directory. The bounding box (a.k.a. minimum bounding rectangle) is defined by two coordinate pairs that represent the upper right (e.g. northeast) and lower left (e.g. southwest) corners of a rectangle that circumscribes the study area polygon. With the bounding box defined, it is possible to obtain GIS data for the study area via web services; by default GetBoundingBoxFromStudyareaShapefile will slightly buffer the bounding box rectangle to help ensure that any data downloaded provide sufficient coverage of the study area watershed.

The GetGADEMForBoundingBox command is used to download the 1-second DEM data from web services provided by Geoscience Australia. The type of DEM to be downloaded (e.g. DEM, DEM-S, or DEM-H) can be specified using the -d (a.k.a.

-demType) option. A sample DEM for the above study area is shown in Fig. 3. By default, GetGADEMForBoundingBox will reproject the DEM into the UTM (WGS84) zone appropriate for the centroid of the study area; the native resolution of the DEM will be maintained. Both the spatial reference and resolution of the DEM can be specified as optional arguments to GetGADEMForBoundingBox. With the DEM data in hand, subsequent EcohydroLib commands can be run to acquire and manipulate data needed to run ecohydrology models (Fig. 2). All subsequent data imported into the project (either acquired via web services, or imported from locally stored data) will be resampled to the spatial reference system and resolution of the DEM; the user can choose any re-sampling method supported by GDAL, or can disable raster re-sampling on import. When working in Australia, land cover, soils, and other necessary geospatial data would need to be downloaded by hand before being imported into an EcohydroLib project (e.g. via the RegisterRaster command), given the lack of suitable web services for these data (see above).

Discussion and Conclusion

In this paper we have described common high spatial resolution (<=30-m) geospatial data needed for parameterizing ecohydrology models, and listed known OGC web services-based interfaces for these data for the United States and Australia. We have also described a new module for EcohydroLib that provides access to high spatial resolution (~30-m) national-scale Australian DEM data accessible via OGC WCS interfaces provided by Geoscience Australia. While this new module does not address all data gaps for EcohydroLib users working in Australia, it is a useful first step toward providing easy access to geospatial data needed for parameterizing ecohydrology models for Australian catchments. Once other necessary geospatial datasets are available via suitable OGC web services interfaces (e.g. high resolution land cover raster data via WCS, and soils raster or vector data via WCS or WFS), these can be similarly integrated into EcohydroLib, providing similar ease of access currently afforded to users working in U.S. watersheds. In the mean time, EcohydroLib can still be of benefit when applied to Australia due to its ability to improve metadata and provenance information capture, even for datasets downloaded manually. Further, it is our hope that this work will show the benefits to the water science community when providers of national geospatial data make these data available via OGC WCS and WFS web services interfaces required for integration with numerical models, rather than cartography-oriented WMS services. Such web services, when integrated with

Fig. 3 .

 3 Fig. 3. QGIS visualization of Geoscience Australia DEM acquired for bounding box of study area defined by watershed shapefile.

2 Availability of National Spatial Data Web Services in Australia and the United States Geospatial

  datasets needed to parameterize process-based ecohydrology models include: digital elevation model (DEM); land cover; soil surface texture; and vegetation leaf area index. Some of these data are available via web services interfaces as national coverages for both Australia and the United States (Table1). In Australia, all data listed are provided by Geoscience Australia (GA). Currently, Australia has national-scale 1-second (~30-m) spatial resolution DEM data available via a WCS interface, though these data date from the 2000 Shuttle Radar Topography Mission. There is a relatively coarse 250-m land cover data dataset for Australia, however at present the GA only offers a WMS web service for these data, not WCS. Soils data (e.g.

	AU	LiDAR	20-cm	WCS	GA	Murray Dar-
						ling basin only
	AU	DEM (LiDAR)	1-m	WCS	GA	Murray Dar-
						ling basin only
	US	NLCD (1992-	N/A	WMS	USGS
		2011)
	US	NLCD (2011)	30-m	WCS	USGS
	US	NLCD (1992-	30-m	WCS	ORNL DAAC
		2011)			/ NASA
	US	DEM (SRTM,	30-m	WCS	GeoBrain
		NED)			GMU / NASA
	US	Soil Survey	~1:12,000 WFS,
		Geographic		SOAP
		Database
		(SSURGO)
	Table 1. U.S. and Australia national geospatial data of use in ecohydrology modeling available
	via web services interfaces. AU = Australia, US = United States, GA = Geoscience Australia,
	USGS = U.S. Geological Survey, NLCD = National Land Cover Database, ORNL DAAC =
	Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynam-
	ics, NASA = National Aeronautics and Space Administration, GMU = George Mason Universi-
		ty, USDA = U.S. Department of Agriculture. More info on GA web services:
		http://www.ga.gov.au/data-pubs/web-services/ga-web-services
		USGS web services: http://viewer.nationalmap.gov/example/services/serviceList.html
		ORNL web services: http://webmap.ornl.gov/wcsdown/index.jsp
		GeoBrain WCS4DEM web service: http://geobrain.laits.gmu.edu/wcs4dem.htm
		SSURGO web services: http://sdmdataaccess.nrcs.usda.gov
	Country Dataset	Spatial	Web	Hosting or-	Notes
			resolution	service	ganization
				type
	AU	Land cover	N/A	WMS	GA
	AU	DEM	9-second	WCS,	GA
			(~250-m)	WMS
	AU	DEM (SRTM)	1-second	WCS,	GA
			(~30-m)	WMS

Australian Soil Resource Information System; ASRIS) do not appear to be available via web services at this time, though a web browser-based download tool is provided by CSIRO (the Commonwealth Scientific and Industrial Research Organisation) and the Department of Agriculture, Fisheries and Forestry (http://www.asris.csiro.au/). USDA In the U.S., DEM data are available via WCS4DEM, a WCS service provided by the GeoBrain project at George Mason University and funded by NASA (Table

tools like EcohydroLib, hold the potential to enable transformative water science by improving scientific reproducibility and researcher productivity while making model and site inter comparisons easier to achieve.