Progress Report on the Mount Lofty Ranges Source Catchments Application Project

Transcription

1 Progress Report on the Mount Lofty Ranges Source Catchments Application Project Shaun Thomas, Ying He and Nigel Fleming ewater Technical Report

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3 Progress Report on the Mount Lofty Ranges Source Catchments Application Project Shaun Thomas 1, Ying He 1 and Nigel Fleming 2 1 South Australian Environment Protection Authority 2 South Australian Research and Development Institute October 20 ewater Cooperative Research Centre Technical Report

4 Contact for more information: Shaun Thomas A/Principal Scientific Officer (Water Quality) Water Quality Branch Environment Protection Authority 250 Victoria Sq Adelaide SA 5000 Please cite this report as: Thomas, S., He, Y. and Fleming, N. Progress Report on the Mount Lofty Ranges Source Catchments Application Project, ewater Technical Report 20. ewater Cooperative Research Centre 20 All rights reserved. No parts of this work may be reproduced in any form or by any means - graphic, electronic, or mechanical, including photocopying, recording, taping, or information storage and retrieval systems - without the written permission of the publisher. Products that are referred to in this document may be either trademarks and/or registered trademarks of the respective owners. The publisher and the author make no claim to these trademarks. While every precaution has been taken in the preparation of this document, the publisher and the author assume no responsibility for errors or omissions, or for damages resulting from the use of information contained in this document or from the use of programs and source code that may accompany it. In no event shall the publisher and the author be liable for any loss of profit or any other commercial damage caused or alleged to have been caused directly or indirectly by this document. Published online November 20 Innovation Centre, University of Canberra ACT 2601, Australia Phone (02) Fax (02) info@ewatercrc.com.au Web is a cooperative joint venture whose work supports the ecologically and economically sustainable use of Australia s water and river systems. was established in 2005 as a successor to the CRCs for Freshwater Ecology and Catchment Hydrology, under the Australian Government s Cooperative Research Centres Program.

5 Table of Contents Tables... iii Executive Summary... iv 1 Introduction Background to catchment modelling of the MLR Construction of MLR Source Catchments model Comparison of SILO and Point Source Rainfall Data to Observed Flow Data Houlgrave Weir - A Hahndorf - A Echunga - A Lenswood - A Summary Generation of new EMC / DWC for MLR and comparison with default data Trial Application of PEST to hydrological calibration of Myponga Catchments Myponga Catchment A Comparison of Local and SILO rainfall data Conclusions SILO vs local rainfall data EMC / DWC Trial Application of PEST Further Work References... 23

6 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Map showing the 8 distinct hydrologic regions for the MLR Source Catchments model. 3 Map showing the relative size and locations of the 14 local rainfall cells used to populate the Source Catchments model with localised rainfall data. 6 Monthly flow at site A (Houlgrave) as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. 7 Annual flow at site A (Houlgrave) from 1974 to 2006 as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. 8 Monthly flow at site A50301 (Hahndorf) as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. 9 Annual flow at site A50301 (Hahndorf) from 2003 to 2006 as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. 9 Monthly flow at site A (Echunga) as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. Annual flow at site A (Echunga) from 1974 to 2006 as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. 11 Monthly flow at site A (Lenswood) as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. 12 Annual flow at site A (Lenswood) from 1973 to 2006 as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. Note the gap in the data from 1989 to Observed and simulated loads of TSS, TN and TP at site A (Houlgrave) from 1998 to Observed and simulated loads of TP at site A (Echunga). This is a catchment with a large proportion of grazing land use and covers an area of 34 km2. 16 Section of the hydrograph for A (Myponga) showing simulated flow data from manual and PEST calibrated Source Catchments models using gridded SILO rainfall plotted along side the observed flows for comparison. 18 Monthly flow at site A (Myponga) as simulated from Source Catchments models that have been calibrated manually and with PEST using gridded SILO rainfall or grouped local rainfall plotted along side the observed flows at this gauging station for comparison. 18 Annual flow at site A (Myponga) from 1979 to 2008 as simulated from Source Catchments models that have been calibrated manually and with PEST using gridded SILO rainfall or grouped local rainfall plotted along side the observed flows at this gauging station for comparison. 19 Section of the hydrograph for A (Myponga) showing simulated flow data from 2 PEST calibrated Source Catchments models using SILO and local rainfall plotted along side the observed flows for comparison. 20 ii

7 Tables Table 1 Table 2 SIMHYD parameters for the 8 MLR hydrological regions from the original E2 model calibration. 2 Landuse categories that have been compiled to construct the 14 Functional Units within the MLR Source Catchments model. 4 Table 3 Details of representative rainfall sites in the MLR 5 Table 4 Table 5 EMC and DWC values for TSS, TN and TP currently used in MLR Source Catchments model (normal font), and comparable values calculated from local data (bold). 14 Comparison of Hydrological calibration outputs for the PEST calibrated model using SILO or local rainfall data. 20 iii

8 Executive Summary The Mount Lofty Ranges catchments are a significant source of drinking water for Adelaide and home to a number of important aquatic environments. Unlike the water supply catchments of most other Australian capital cities they are also an important region for agriculture, and urban and rural living. Over time, this has led to fundamental landuse conflicts that have resulted in a number of water quality issues. Through the s Applications projects, the Source Catchments water quantity and quality model is being applied in the Mount Lofty Ranges to look at a range of water quality issues including the impacts of land use change, climate change, on-ground works and farm dam impacts. Source Catchments and its precursors have been developed and applied in the Mount Lofty Ranges Watershed for several years to look at a range of water quality issues. A review of the existing model over the last 12 months has highlighted deficiencies with previous modelling efforts. The latest improved Source Catchments platform is much better equipped to support natural resource management policy and planning initiatives. The objectives of this project are to update previous models to better represent the water quantity and quality in the Mount Lofty Ranges, apply the updated model to address a range of water quality issues and build the capacity in the use of the software within the South Australian ewater partners specifically the Environment Protection Authority (EPA), South Australian Research and Development Institute (SARDI), the Department for Water (DFW) and SA Water. This report outlines the progress made to date, highlights the major issues that have been encountered and outlines the future work required to complete the project. iv

9 1 Introduction The catchments of the Mount Lofty Ranges (MLR) are a crucial water resource that is important to the well-being of the people of Adelaide. There are seven reservoirs on rivers and streams of the MLR to harvest the relatively high rainfall and supply Adelaide with drinking water. This drinking water is supplemented with water diverted from the River Murray and soon it will also have desalinated water also included. However, water collected within the catchments is a significant component of the total supply needs of Adelaide and is the most cost effective water source. The MLR are used for different purposes including harvesting of drinking water, agriculture, intensive horticulture, recreation, rural living, tourism, environmental conservation and urban environments. These multiple uses place pressure on the water resource and can impact on water quality. The Source Catchments model being used as a decision support tool to help prioritise and assess the potential: benefits of on-ground works impacts of changes in land use and land management impacts of changes to government policy impacts of climate change This progress report provides an update for the MLR Source Catchments model including assessment of the hydrological and water quality calibration and defines the scenarios that will be pursued in the final application project report. 2 Background to catchment modelling of the MLR Catchment modelling tools have previously been developed for the MLR using precursors to the Source Catchments model (EMSS and E2). These products were mostly used as load estimation and catchment prioritisation tools although some link and node model functionality was examined. The base E2 model of the MLR was developed to examine major contributions of nutrients and sediments to Adelaide s water storages and key stressors to reservoir water quality and risk to water treatment capacity and performance. Secondary indicators of total organic carbon and E.Coli were also examined for Hazard assessment for risks to drinking water quality. Link models to simulate the major storages in the MLR were developed although these were limited due to relatively short time periods of storage water level and demand data to test the storage model simulation capability. Other limitations to this work included the use of constituent generation parameters (EMC/DWC values) from South East Queensland. The constituents used in the MLR E2 model were total suspended solids (TSS), total phosphorus (TP) and total nitrogen (TN), and were allocated to nine landuse categories. There was little variation between land uses for these EMC\DWC values and as they were based on the work undertaken in significantly different climate were considered a poor representation of local based data. 1

10 3 Construction of MLR Source Catchments model The MLR Source Catchments model was constructed using the methodology outlined in Webber (2006). The model boundary and subcatchments was generated using automated pit filled 25m DEM data. The stream threshold was set up as 5 km 2. A total of 180 subcatchments were delineated to represent the MLR watershed extending over an area of approximately 1600 km 2. The original model was calibrated and validated against recorded stream flow data at 20 locations through the MLR region. Based on the calibration results, the MLR watershed were divided into 8 hydrological regions as shown in Figure 1. The SIMHYD parameters for these 8 regions are shown in Table 1. This calibration was based on monthly total flow total volumes. Table 1 SIMHYD parameters for the 8 MLR hydrological regions from the original E2 model calibration. Hydrological region Baseflow coefficient Impervious Threshold Infiltration coefficient Infiltration shape Interflow coefficient RISC Recharge coefficient SMSC It was assumed that land use changes which have occurred since the original model was built were unlikely to significantly alter the hydrological response of the catchment. Therefore the original SIMHYD parameters for the 8 regions were replicated in this model. This hydrological calibration is examined further in Section 4 In this application project significant work has been done to refine and update the MLR E2 model. The following is a list of the major areas of work that has been undertaken: 1. Rainfall/PET updated - The existing SILO rainfall and PET data sets have been upgraded to run from 1978 to A comparison between the updated SILO data and local pluviometer data has been performed to determine which is the preferred data set. This work is described further in Section Error! Reference source not found.. 2

11 Figure 1 Map showing the 8 distinct hydrologic regions for the MLR Source Catchments model. 2. Landuse updated - the land use data has been updated using the 2008 Department of Environment and Natural Resources (DENR) land use dataset. This data has been reclassified to combine similar land uses resulting in a final raster containing approximately 14 different functional units based on the land use classes as shown below in Table 2. 3

12 Table 2 Landuse categories that have been compiled to construct the 14 Functional Units within the MLR Source Catchments model. Functional Unit (FU) Broadscale agriculture Broadscale annual horticulture Broadscale perennial horticulture Conservation area Dense urban Grazing Intensive grazing Intensive production Managed forest Rural residential Suburban Utilities Water bodies Wetland Incorporating landuse classes Cropping, irrigated cropping Irrigated seasonal horticulture, seasonal horticulture Irrigated perennial horticulture, perennial horticulture Managed resource protection, nature conservation, other minimal use Manufacturing and industrial, urban residential, commercial service Grazing modified pastures, irrigated land in transition Intensive animal production, irrigated modified pastures Intensive horticulture, waste treatment and disposal Irrigated plantation forestry, plantation forestry Recreation and culture Rural residential, rural living, public service, research facilities Mining, transport and communication, utilities Reservoir/dam Marsh/wetland, river 3. EMC/DWC updated -The EMC/DWC values for each functional unit have been calculated based on the local composite sampler data. These EMC/DWC values have also been used to study the water quality and load calculations. This work is described further in Section 5 of this report and in Fleming et al Investigation of improved Source Catchments storage models - Significant effort has been given to further refine the E2 storage models. This was due to the additional features that have been added to the storage model module within Source Catchments which allow a wider range of storages types to be accurately represented. However, the storage models still could not simulate the complexity of storage operations in the MLR, due to problems obtaining the long term time series data of water transfers from the River Murray as well as issues previously mentioned regarding water release and monthly water extraction data. 5. Use of PEST for improved hydrological calibration PEST a parameter estimation tool has been used to systematically and objectively optimise the hydrological calibration within the MLR model (Stewart 20, Ellis et al 2009). This tool allows calibration of multiple gauges to be performed simultaneously and has initially applied to the Myponga catchments of the MLR model to test its performance. This work is described further in Section 6 of this report. 4

13 4 Comparison of SILO and Point Source Rainfall Data to Observed Flow Data The original MLR E2 model used SILO rainfall and PET datasets for all the climate data inputs. General experience on using these interpolated data sets is that they have a tendency to overestimate the number of rainfall days and as such total rainfall. A comparison between SILO rainfall data and a number of pluviometer datasets from within the catchment was performed. This work included reviewing how the local data was collated and analysed. Table 3 Details of representative pluviometer sites in the MLR Pluviometer cell number Representative Station Pluviometer location name Mean Annual Rainfall (mm) Years of Record Pooraka Adelaide (Tea Tree Gully Council) Williamstown Mount Pleasant North Adelaide Adelaide (Glen Osmond) Lobethal Harrogate Happy Valley Reservoir Cherry Gardens Echunga Golf Course Kanmantoo Yankalilla group 15 Port Elliott caravan park average of (Yundi) and (Prospect Hill) There are 138 current or historical rainfall stations within in the MLR land use panel boundaries, with daily rainfall records of up to 147 years duration. To determine representative sites, the locations of these were divided into geographical cells within the MLR boundary panel. Each cell was of about 0.15 degrees longitude and 0.15 degrees latitude. This gave 14 cells. There was one noticeable gap which was named cell 15. Daily rainfall of this cell was taken as the average of the two nearest adjacent rainfall stations. Within each cell the daily RF of each station was condensed to monthly rainfall for ease of analysis, and a new data set constructed as the averages of these monthly values for each cell. A correlation matrix was made between this averaging data set and the monthly values from each site within the group. A representative site was chosen from each cell as the site which had the best correlation to the average value, along with the most complete daily data record. Infrequent gaps in the daily record of 5

14 these representative sites were filled from the average of the two geographically closest RF stations. All cells had at least 0 years of continuous daily RF data, with the exception of cell 15 which had 42 years of record. Details of the groups and representative sites are shown in Table 3 and a map displayed the relative size of each cell and their proximity to each other is shown in Figure 2. Figure 2 Map showing the relative size and locations of the 14 local rainfall cells used to populate the Source Catchments model with localised rainfall data. 6

15 Two Source Catchments scenarios were constructed, one with the gridded SILO rainfall and the other with the local rainfall data. Each scenario was run from 1 January 1970 to 1 January The monthly and annual modelled and observed runoff was then compared for upland and lowland sub catchments. This was undertaken to determine if the local rainfall data improved the modelled runoff predictions. 4.1 Houlgrave Weir - A Site A (Houlgrave weir) drains a catchment of 321 km 2, the single largest catchment in the MLR which flows directly into the Mt Bold Reservoir. The total simulated monthly runoff from both rainfall data sets are shown in Figure 3 with the observed flows for comparison. The annual flows are shown in Figure 4 to highlight the inter-annual variability in flow volumes and the relative accuracy of both rainfall datasets across the range of hydrological conditions. 800 Obs Flow Local RF 700 SILO RF Total Flow (GL) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 3 Monthly flow at site A (Houlgrave) as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. Runoff simulated from SILO and local rainfall at this site was very similar during the drier months of the year. In months of highest flow, however, runoff volumes were higher from SILO rainfall than from local rainfall. It is possible that this is due to the generalised nature of gridded rainfall data which may be increasing the number of rain days. Furthermore, days of high rainfall may be assigned to wider areas by SILO than actually occur. This would be due to localised climate and topographic effects which are not delineated in interpolated data, but show up in localised rainfall recordings. When compared with observed flows, both simulated runoff data sets tended to underestimate flows during the drier months (January to March) and overestimate the flows during the wetter months (April to October). Generally, the local rainfall data set demonstrated a closer match to the observed flows especially for the months July to October. 7

16 250 Obs Flow Local RF SILO RF 200 Annual Flow (GL) Year Figure 4 Annual flow at site A (Houlgrave) from 1974 to 2006 as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. When comparing both simulated runoff data sets with the observed flows recorded at the gauging station (A ) over the available time period the local rainfall provides a much better match to the observed flows over the SILO data as is clearly seen in Figure 4. Generally the SILO rainfall dataset significantly overestimate the total volumes whereas the local datasets does overestimate but not as often or to the same extent. There are some exceptions during low rainfall years such as 1976, 1982, 1994, 2002 and 2006 where the observed flows are greater than the simulated flows. The data described above for site A indicates that the calibrated SIMHYD parameters for this large region replicate observed flows reasonably well for the local rainfall data for average rainfall years but tend to over estimate flows for higher rainfall years and slightly underestimate the low rainfall year flows. The SILO data significantly overestimate the flow volumes for most years except for the low rainfall years where the simulated volumes correlate well with the observed flows. This indicates that the local data should be used to populate the model. 4.2 Hahndorf - A50301 The site A50301 (Hahndorf) is upstream of A and drains a catchment of around 227 km 2. The total simulated monthly runoff from both rainfall data sets are shown in Figure 5 with the observed flows for comparison. The annual flows are shown in Figure 6 to highlight the inter-annual variability in flow volumes and the relative accuracy of both rainfall datasets across a range of hydrological conditions which are limited for this site as only 4 years of daily flow data were available. 8

17 90 80 Obs Flow Local RF SILO RF Total Flow (GL) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 5 Monthly flow at site A50301 (Hahndorf) as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. Runoff simulated from SILO and local rainfall at this site was comparable to each other for most of the drier months. For the winter months the SILO dataset showed increased flow volumes compared to the local rainfall indicating an overestimate to rainfall volumes due to the generalised nature of gridded rainfall data. Both simulated datasets significantly overestimated the flow when compared to the observed monthly flows for this location indicating that the hydrological calibration was poor for this short time period Obs Flow Local RF SILO RF 0 Annual Flow (GL) Year Figure 6 Annual flow at site A50301 (Hahndorf) from 2003 to 2006 as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. 9

18 The annual flows were only from a short time period ( ) which were during a considerable drought and as such do not display much of the interannual variability. This is likely to be a key driver to the poor correlation between the simulated and observed monthly flow data. It is clear that the SIMHYD parameters for this catchment need to be re-calibrated to better represent the observed flow data. 4.3 Echunga - A The site A (Echunga) drains a relatively small catchment of 34 km 2 in the Onkaparinga catchment. The total simulated monthly runoff from both rainfall data sets are shown in Figure 7 with the observed flows for comparison. The annual flows are shown in Figure 8 to highlight the inter-annual variability in flow volumes and the relative accuracy of both rainfall datasets across a range of hydrological conditions. Figure 7 illustrates that the runoff simulated from SILO and local rainfall has an excellent correlation across most months although there was a tendency for the local rainfall data to produce slightly greater flow volumes for all but the wettest months (ie July & August). It would appear that SILO grid in this catchment coincided closely with the local rainfall group and there was no practical difference in runoff between the two rainfall sources Obs Flow Local RF SILO RF Total Flow (GL) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 7 Monthly flow at site A (Echunga) as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. When the simulated monthly data is compared to the observed flow data for this location it is again evident that there is a major over-estimation of flow volume data for all months from both simulated data sets. The annual data as shown in Figure 8 shows there is an even mix of years where the SILO data generates more flow than the local rainfall data. The annual chart indicates that the there is consistent and considerable over-estimation of flow volumes for all years recorded. These results indicate that the SIMHYD

19 parameters currently incorporated within the model require significant recalibration. 25 Obs Flow Local RF SILO RF 20 Annual Flow (GL) Year Figure 8 Annual flow at site A (Echunga) from 1974 to 2006 as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. 4.4 Lenswood - A Monthly runoff from another small catchment (17 km 2 ) is monitored at site A (Lenswood). The total simulated monthly runoff from both rainfall data sets are shown in Figure 9 with the observed flows for comparison. The annual flows are shown in Figure to highlight the inter-annual variability in flow volumes and the relative accuracy of both rainfall datasets across a range of hydrological conditions. Contrary to the previous site, there is a large discrepancy between runoff simulated from SILO rainfall and from local rainfall. The pattern is similar to that found with the larger catchments. It is likely that the relevant SILO grid cell and the local group had quite different rainfall characteristics, and that the local data gives a better estimate of runoff. The simulated flows from the local data slightly overestimate the flows in dry months and underestimate the high rainfall months. The simulated flows from the SILO dataset generate considerably more flow volume when compared to the local rainfall data (and even more so when compared against the observed data) across the whole year. 11

20 60 Obs Flow Local RF 50 SILO RF 40 Total Flow (GL) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 9 Monthly flow at site A (Lenswood) as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. Annual data shows that the local rainfall data is often a good predictor of the observed flows although there were some years where it generates too much flow (eg. 2001) and others where it considerably underestimates the flow (eg & 1975). Over the entire period the simulated flow generated by the local rainfall dataset provided a reasonable estimation of the observed flows Obs Flow Local RF SILO RF 12 Annual Flow (GL) Year Figure Annual flow at site A (Lenswood) from 1973 to 2006 as simulated by Source Catchments using either gridded SILO rainfall or grouped local rainfall plotted along side the observed flows for comparison. Note the gap in the data from 1989 to

21 4.5 Summary In general the simulated flow using local rainfall resulted in a closer match of modelled to observed runoff when compared to the simulated flows from the SILO rainfall. The generalised nature of gridded SILO rainfall overlooking local effects of climate and topography and averaging out flow events over a wider area is most likely the cause of the increased runoff volumes. At larger scales there is major overestimation of runoff volumes from SILO rainfall data during the wetter months of the year in the MLR, although generally good agreement is found between SILO and local rainfall in the drier months. It is likely that the impact of the generalised nature of gridded SILO rainfall overlooking local effects of climate and topography is reduced during the drier months. This pattern is also found in some smaller catchments, although other small catchments have close agreement between runoff generated from SILO rainfall data and that from local rainfall data. The effectiveness of the existing SIMHYD rainfall runoff parameters to effectively predict runoff volumes was mixed for the flow gauges examined in this report. For the sites at Houlgraves weir and Lenswood the local rainfall and SIMHYD rainfall runoff parameters were relatively effective at predicting the observed flows although this was reduced during high flow events. However for the sites at Echunga and Hahndorf neither the local nor SILO derived simulated flows effectively replicated the observed flows indicating that the SIMHYD parameters for these catchments were poorly calibrated. The overestimation of flows in these catchments was well beyond the accepted bounds of model error and indicated that the entire MLR Source Catchments model needs to be re-calibrated. The task of recalibrating the MLR Source Catchments model is discussed further in Section 6. 13

22 5 Generation of new EMC / DWC for MLR and comparison with default data New EMC values were generated from local water quality data in South Australia, as opposed to generic EMC values used in the existing Source Catchments model (see Fleming et al. 20 for detail). The following table shows new and old EMC values for land uses in the MLR. Table 4 EMC and DWC values for TSS, TN and TP currently used in MLR Source Catchments model (normal font), and comparable values calculated from local data (bold). landuse Conservation area Managed forest Plantations Grazing Broadscale agriculture Broadscale annual horticulture Broadscale perennial horticulture Rural Residential Suburban Dense Urban TSS TN TP EMC DWC EMC DWC EMC DWC mg/l Utilities Water The effect of changing EMC values on TSS, TN and TP loads was tested at a number of monitoring sites. Given the poor correlation between simulated and observed flows (as discussed in Section 4) a simple comparison of predicted to observed loads was not used. Instead, the analysis was done by running simulations of these catchments using SILO rainfall data with separate scenarios set up for each set of EMC values old and new. The SILO data was used 14

23 because the comparison with local data had not yet been completed. The daily concentrations of TSS, TN and TP were exported from the simulation runs. To calculate daily loads for comparison at a site, the daily concentrations from each simulation and observed concentrations were then multiplied by observed daily flows. This produced three sets of load data from a single observed flow series. These data sets were then compared. Figure 11 shows observed loads vs loads simulated with old and new EMC values for site A (Houlgrave). This is a mixed landuse catchment of around 321 km 2 the largest in the MLR. 900 ) 750 (T P T 600 a n d N T 0 ), x (T 300 S T 150 observed new EMC's old EMC's 0 TSS TN TP Figure 11 Observed and simulated loads of TSS, TN and TP at site A (Houlgrave) from 1998 to There are some differences between observed and simulated loads of TSS, TN and TP. Loads calculated using new EMC values are generally closer to observed loads than loads calculated using old EMC values. The improvement in predictive capacity of total nitrogen loads is particularly evident in Figure 11. This is typical of the monitoring sites studied and indicates an improvement in prediction of constituent loads using EMC values developed from local data. The above comparison is over a lengthy monitoring period ( ). Within this time period, observed and simulated loads varied somewhat. Figure 12 shows annual TP loads for the Echunga site. While the observed loads varied about the simulated ones from year to year, they generally showed the same trends. Observed TP loads showed greater variation from year to year than simulated ones. TP loads simulated using the new EMC values followed observed loads more closely than TP loads simulated using the old EMC values. This confirms the value of EMC values generated from local data. 15

24 14 12 e a r) /y 8 (T a d o L 6 g e e ra 4 v A 2 observed loads new EMC's old EMC's Figure 12 Observed and simulated loads of TP at site A (Echunga). This is a catchment with a large proportion of grazing land use and covers an area of 34 km2. The final technical report will expand considerably on the water quality calibration outputs to further highlight the improvements in predictive capacity for nutrient and sediment loads as a result of the analysis of local water quality data sets as described in Fleming et al

25 6 Trial Application of PEST to hydrological calibration of Myponga Catchments The issues raised from simplified approach to hydrological calibration using rainfall runoff library as described in Section 4 lead the application project team to review different approaches to optimise hydrology calibration. Utilising PEST (a model-independent parameter estimation program) with e2commandline.exe to optimise rainfall runoff parameters calibration appeared the most advanced approach available. In addition, significant progress has recently been made by a number of ewater partners in simplifying the use of PEST which make its application with Source Catchments more practical and user friendly. PEST allows for a number of gauged catchments within a single model to be simultaneously calibrated which is relevant to the MLR model that uses 20 flow gauges. This process allows Source Catchment models to be more systematically calibrated by matching the simulated flow with observed flow dataset and optimising the daily and monthly Nash-Sutcliffe coefficients and exceedence curves. To accomplish this task the MLR team completed a training course Guidance on Applying PEST to Source Catchments facilitated by Dr Joel Stewart of WBM which enabled this tool to be examined in the MLR application project. 6.1 Myponga Catchment A To test the application of PEST within the MLR a smaller model of the Myponga catchment was constructed. The Myponga catchment is at the southernmost extent of the MLR model. It only has 1 flow gauge and was seen as a suitable candidate to examine the application of PEST and refine some of the process issues in combining PEST & e2commandline which need to be resolved before PEST can be successfully applied. An added reason for using the Myponga catchment as a test case is that a number of South Australian Government agencies have found that the hydrology of this catchment difficult to model in various software packages notably WaterCAST and WaterCRESS. The initial process in building a model for PEST analysis involves consolidating the number of functional units (FUs) within the model back to the smallest number to represent the dominant hydrologic response units (HRUs). For the Myponga model 14 FUs were consolidated down to 3 HRUs which were considered to dominate the hydrologic response within the catchment. The PEST hydrologic calibration of the model was initially run with SILO rainfall data so that a straight comparison with outputs from the original MLR Source Catchments model - which was manually calibrated with Rainfall Runoff Library (RRL) could be performed. Once the first few bugs in the PEST process were resolved a calibration of the SIMHYD parameters within the model was achieved using the SIMHYD parameters from the manual calibration as the initial values. An excerpt of the daily hydrograph output from this calibration is displayed in Figure 13 alongside the manually calibrated and observed hydrographs. The hydrograph produced from the PEST calibrated model is a vastly improved match to the observed data with significant reductions to the peaks heights during flow events as well as a more realistic return to baseflow rates. These reduced peaks and improved return to base flow levels significantly reduce the overestimation of flow volumes which is evident in Figure 14 and Figure 15 which show the total monthly and annual volumes respectively. 17

26 1800 SILO RF - PEST SILO RF - Manual 1600 Observed ML/day /04/ /05/2004 /07/ /08/ //2004 Date Figure 13 Section of the hydrograph for A (Myponga) showing simulated flow data from manual and PEST calibrated Source Catchments models using gridded SILO rainfall plotted along side the observed flows for comparison. The monthly volumes in Figure 14 show that although the predicted volumes in the drier months are still higher than those observed they are significantly better than the manual predictions. During the main flow period of the year (June to October) the PEST calibrated model shows excellent correlation with the observed flows Observed SILO RF PEST SILO RF Manual Total Monthly Runoff (GL) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 14 Monthly flow at site A (Myponga) as simulated from Source Catchments models that have been calibrated manually and with PEST using gridded SILO rainfall or grouped local rainfall plotted along side the observed flows at this gauging station for comparison. 18

27 The annual data shown in Figure 15 also indicates that the PEST calibrated model is vastly improved at representing the different volumes generated due to inter-annual variability seen over long time periods. It is clear from Figure 15 that the PEST calibrated SIMHYD rainfall runoff model is very good as predicting the runoff volumes certainly when compared with the manually calibrated model Observed SILO RF - PEST SILO RF - Manual Annual Flow (GL) Year Figure 15 Annual flow at site A (Myponga) from 1979 to 2008 as simulated from Source Catchments models that have been calibrated manually and with PEST using gridded SILO rainfall or grouped local rainfall plotted along side the observed flows at this gauging station for comparison. 6.2 Comparison of Local and SILO rainfall data Following the significant improvements that were seen in Section 4 when local rainfall data was compared to SILO rainfall data a similar scenario was performed with the trial Myponga model. The local rainfall data for Myponga was incorporated into the trial Myponga model and calibrated using PEST. This calibration took a higher number of iterations for Phi (the objective function) to plateau when compared to the SILO rainfall model but once optimised produced a SIMHYD rainfall runoff model that very accurately predicted observed flow volumes. This improved correlation is evident in Figure 16 where the hydrographs of observed flows and simulated flows from PEST calibrated SILO and local rainfall models are compared. The local rainfall PEST calibrated model appears to further reduce the major flow peaks which again improves the predictive capacity of the rainfall runoff model. 19

28 Figure 16 Section of the hydrograph for A (Myponga) showing simulated flow data from 2 PEST calibrated Source Catchments models using SILO and local rainfall plotted along side the observed flows for comparison. To better quantify these improvements the Nash-Sutcliffe coefficients have been determined for the daily, monthly and total volumes as shown in Table 5. These coefficients show the impact of changing the rainfall data from SILO to local with good improvements in the daily Nash-Sutcliffe coefficient and in the reduction in relative error associated with prediction of total runoff volume from 5.8 to 1.3%. Table 5 Comparison of Hydrological calibration outputs for the PEST calibrated model using SILO or local rainfall data. Rainfall data Gauge Daily Nash- Sutcliffe Monthly Nash- Sutcliffe Modelled Volume (GL) Measured Volume (GL) % Error SILO % Local % 20

29 7 Conclusions 7.1 SILO vs local rainfall data The work completed as part of this application project has highlighted a number of issues that the MLR team was unaware of. The significant issues with the hydrological calibration have been highlighted in this report and were previously unidentified. The project begun on the basis that the model was effectively calibrated but the comparative analysis using both SILO and local rainfall data has shown that the calibration needs to be reviewed. The variations between the simulated flows from SILO and local rainfall data have highlighted the issues regarding the quality of climate data. Generally the local rainfall data appeared to give closer match to the observed flows but this was not always the case. It is envisaged that upcoming work to re-calibrate the whole MLR Source Catchments model will use both SILO and local rainfall data and the final choice for each catchment will depend upon which input data best matches the observed flow records. 7.2 EMC / DWC There was a significant body of work involved in generating the locally derived EMC / DWC values for TSS, TN and TP. The comprehensive analysis of up to 30 years of composite sampler flow and load data using a range of tools (including the ewater Water Quality Analyser) has produced high quality event and baseflow data for 14 functional units specifically in the MLR but also for greater South Australia. Previously default values generated in South-East Queensland were used to generate pollutant loads and these lacked both local relevance and landuse definition. Furthermore the local basis for the new EMC / DWC values has increased the confidence external stakeholders place in outputs produced by Source Catchments models produced in South Australia. 7.3 Trial Application of PEST The application of PEST to the hydrological calibration of the Myponga catchment was very successful. The use of PEST to calibrate the SIMHYD rainfall runoff parameters has vastly improved the accuracy of the hydrological prediction. This work has demonstrated to the MLR team what is feasible when using PEST as an optimisation tool with Source Catchments and is clearly a very powerful tool that will be incorporated in the re-calibration of the full MLR Source Catchments model. 21

30 7.4 Further Work The key tasks for the MLR application project team to complete prior to July 2011 are Re-calibration of MLR model using PEST with SILO and local data to best match observed flows Run scenarios with new hydrology including: o Impacts of climate change using LARS WG to generate climate inputs o Re-assess comparison of loads from old EMC's vs new EMC's vs observed loads Assess Farm Dam capacity by: o Running scenarios of using link storage models for Currency Creek, Myponga and Lenswood to mimic WaterCRESS approach to farm dam analysis. o Test the Farm Dam plugin module at Currency Creek, Myponga, Lenswood Assess relative benefits of potential On-ground works programs specifically: o Scenarios of application of Sediment and Nutrient Filter modules for grazing on-ground works 22

31 References Ellis, R., Doherty, J., Searle, R.D. and Moodie, K.. Applying PEST (Parameter ESTimation) to improve parameter estimation and uncertainty analysis in WaterCAST models, MODSIM Congress, Australia, Fleming, N., Cox, J., He, Y., Thomas, S. and Frizenschaf. J. Analysis of Constituent Concentrations in the MLR, ewater Technical Report 20. Stewart, J. Calibrating Source Catchments Models using PEST: Assessing model performance, WBM Report 20. Weber, T. Mt Lofty Ranges Storage Catchments EMSS to E2 Conversion Report, WBM Project Report