Background

WQSAM is a water quality model that has been developed within the Institute for Water Research at Rhodes University, and is continuously being improved and extended. Development of WQSAM was initiated in 2012 during a climate change project when it was realised that there were no available water quality models that fulfilled the needs of water quality management in South Africa. To this end, WQSAM has been designed to be management focussed by:

1. Linking with routinely used yield models such as the Water Resources Yield Model (WRYM) and the Water Resources Modelling Platform (WReMP) so as to use the flow outputs of these models to drive water quality simulations.
2. Using relatively simple representations of water quality processes. This is relevant for:
   1. Ease of use by the model user.
   2. Allowing the model to be calibrated against the limited amount of observed DWS water quality data.
   The approaches taken to limit the complexity of the model include:
   1. Limiting water quality variables simulated to only those relevant to management.
   2. A requisite simplicity approach: representation of only the water quality processes that explain the majority of variation of relevant water quality variables.
3. Model simulation outputs that provide an indication of risk of exceeding certain management thresholds of water quality. This is achieved through plotting simulations as frequency distributions.

Water quality variables simulated

WQSAM currently simulates salinity as total dissolved solids (TDS), nutrients as nitrate plus nitrite, ammonium and phosphate, microbial water quality as Escherichia coli and water temperature.

Future development

A project is currently underway to incorporate a sediment delivery model within WQSAM. The erosion part of this model uses the Modified Universal Soil Loss Equation (MUSLE), and the transport part of the model is based on the conceptual understanding of sediment transport for South African catchments.

The monthly to daily flow disaggregation technique

Since WQSAM runs on a daily time step, it requires daily flows. Therefore, a method of disaggregating the monthly flows obtained from the yield models to daily was developed. This method uses the frequencies of daily observed rainfall to achieve the disaggregation. The method has proven to be very robust, and also has many potential applications besides in WQSAM, such as environmental flows for example.

Resources and data required to run WQSAM

WQSAM requires a systems model (yield model) setup to run, as it has adopted the same network structure (nodes and channels) as the yield models. Therefore, water quality modelling can be implemented on any catchment for which there is an existing yield model setup. WQSAM is run as part of the SPatial And Time Series Information Modelling framework (SPATSIM) on a normal windows desktop or laptop. Data requirements include:

1. Flow from a yield model setup (WRYM or WReMP)
2. Daily rainfall (either ground-based or satellite).
3. Some daily flow to facilitate calibration of the monthly to daily flow disaggregation.
4. Daily air temperature measures.
5. DWS historical monitoring water quality data.

Relevant contact information

SPATSIM is available on the Institute for Water Research website: https://www.ru.ac.za/iwr/research/software/spatsim/

The WQSAM addition to SPATSIM can be obtained from Dr Andrew Slaughter

Email: a.slaughter@ru.ac.za

Telephone: (046) 6224014

Relevant references

Hughes, D.A. and Slaughter, A.R. (2015) Daily disaggregation of simulated monthly flows using different rainfall datasets in southern Africa. Journal of Hydrology: Regional Studies. Volume 4. Pgs 153-171.

Hughes, D.A., Slaughter, A.R. (2016) Disaggregating the components of a monthly water resources system model to daily values for use with a water quality model. Environ. Modell. Softw. 80: 122–131.

Mantel, S.K., Hughes, D.A., and Slaughter, A.R. (2015) Water resources management in the context of future climate and development changes: a South African case study. Journal of Water and Climate Change 6 (4) 772-786.

Slaughter, A.R. & Hughes, D.A. (2013) A simple model to separately simulate point and diffuse nutrient signatures in stream flows. Hydrology Research, Volume 44(3), pgs 538–553.

Slaughter, A.R. & Mantel, S.K. (2013) A simple and rapid method to relate land cover and river flow rate to river nutrient concentration. Physics and Chemistry of the Earth, Volume 66, pgs 131–138.

Slaughter, A.R. and Hughes, D.A. (2014) Investigating possible climate change and development effects on water quality within an arid catchment in South Africa: a comparison of two models. Proceedings of the 7th International Environmental Modelling and Software Society (iEMSs) biennial meeting, San Diego,  USA 15-19 June 2014. http://www.iemss.org/sites/iemss2014/papers/iemss2014_submission_318.pdf

Slaughter, A.R. and Mantel S.K. (2016) The validation of algal growth processes in a water quality model using remote sensing data. Proceedings of the 8th International Environmental Modelling and Software Society (iEMSs) biennial meeting, Toulouse, France 10–14 July 2016. http://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1374&context=iemssconference

Slaughter, A.R., Hughes, D.A. and Mantel, S.K. (2012) The development of a Water Quality Systems Assessment Model (WQSAM) and its application to the Buffalo River catchment, Eastern Cape, South Africa. Proceedings of the 6th International Environmental Modelling and Software Society (iEMSs) biennial meeting, Leipzig,  Germany 1-5 July 2012. ISBN: 978-88-9035-742-8 http://www.iemss.org/sites/iemss2012//proceedings/I2_2_0497_Slaughter_et_al.pdf

Slaughter, A.R., Mantel, S.K., and Hughes, D.A. (2016) Water Quality Management in the Context of Future Climate and Development Changes: A South African Case Study. Journal of Water and Climate Change, jwc2016138.

Slaughter, A.R., Retief, D.C.H., and Hughes, D.A. (2015) A method to disaggregate monthly flows to daily using daily rainfall observations: model design and testing. Hydrological Sciences Journal. Volume 4(B), pgs 153–171. http://www.tandfonline.com/doi/pdf/10.1080/02626667.2014.993987

Last Modified: Mon, 07 Feb 2022 15:39:10 SAST