Hydrological modelling and uncertainty analysis
The Institute for Water Research has been involved in the development, testing and application of hydrological models for many years, but in recent years the focus shifted towards the use of uncertainty principles and the further development of the widely used, monthly time step and semi-distributed, Pitman rainfall-runoff model. The key motivation for this research focus was the recognition that we are always dealing with data scarcity or data accuracy problems in the use of hydrological models in Africa and therefore all of the associated uncertainties should be explicitly included, wherever possible. These include uncertainties in the climate forcing data, the model structure and the model parameters.
More recently the focus has been on developing a flexible uncertainty version of the Pitman model that can be used in many different situations. All of the developments have been included in the SPATSIM software framework that is available at no cost from the IWR. The key principle of this version of the model is that the simulated hydrological response of any incremental sub-basin should be constrained by a set of uncertain hydrological indices. These include the mean monthly stream flow volume, mean monthly groundwater recharge depth, three percentage points (10%, 50%, 90%) on the flow duration curve and the percentage time of zero flows (specifically added for arid and semi-arid basins). These indices are designed to represent the natural flow response of the sub-basins and could be quantified in different ways depending on how much information or knowledge and understanding is available about the spatial variability of the natural hydrological response.
The top part of Figure 1 illustrates that the model parameters are defined as ranges of possible values from which the model samples to generate ensembles of possible output time series. These time series are analysed and assessed against the hydrological response constraint indices. If a model output falls within all of the constraints then the result is accepted as ‘behavioural’ and the parameter set saved, otherwise it is rejected. Once a defined number (typically 5 000) of ‘behavioural’ parameter sets have been found for the specific sub-basin the first stage of the model run is complete. The second stage runs the model for the whole basin, including all the sub-basin downstream linkages to generate cumulative flows at the outlet of all sub-basins. The natural hydrological response is simulated using the parameter sets saved from stage 1, while additional uncertainty can be incorporated through independent random sampling of the ranges of routing and water use parameters (in situations where development impacts are important). The model is typically run 10 000 times to generate a large number of output ensembles. The advantages of the two stage approach is that all of the downstream outputs are made up of ‘behavioural’ upstream inputs, as measured against the realism of the hydrological response indices used in stage 1. The final outputs can then be compared with any available observed data (see Figure 2) to validate the success of the model and to assess the degree of uncertainty that has been simulated.
Figure 1: A 2-stage approach to incorporating uncertainty into the Pitman rainfall-runoff model
Figure 2: Example comparisons of simulated uncertain flow duration curves with observed data (Rundu gauging station on the Okavango River).
A further modelling option allows for different rainfall inputs (say 500) to be combined with parameter samples (also 500) to generate uncertainty outputs (250 000) that combine forcing data and parameter uncertainties. This approach has been used to investigate some of the uncertainties in climate change impacts on hydrological response. The different rainfall inputs represent the uncertainties in the predictions of future rainfall patterns across different downscaled Global Climate Models.
Further details of the contributions to hydrological modelling and uncertainty by the staff and students of the IWR can be found in the publications, research reports and postgraduate theses listed below:
Hughes, DA (2004) Incorporating ground water recharge and discharge functions into an existing monthly rainfall-runoff model. Hydrol. Sci. Journ. 49(2), 297-311.
Hughes, DA (2004) Three decades of hydrological modeling research in South Africa. Special Rhodes University Centenary Edition of the South African Journal of Science, 100, 638-642.
Hughes, DA (2006) Water resources estimation in less developed regions – issues of uncertainty associated with a lack of data. Prediction in Ungauged Basins: Promises and Progress (Proceedings of symposium S7 held during the Seventh IAHS Scientific Assembly at Foz do Iguaçu, Brazil, April 2005). IAHS Publ. 303, 72-79.
Hughes, DA, Andersson, L, Wilk, J and Savenije, HHG (2006) Regional calibration of the Pitman model for the Okavango River. Journ. Hydrology, 331, 30-42.
Andersson, L, Wilk, J, Todd, MC, Hughes, DA, Earle, A, Kniveton, D, Layberry, R and Savenije, HHG (2006) Impact of climate change and development scenarios on flow patterns in the Okavango River. Journ. Hydrology, 331, 43-57.
Hughes, DA (2006) Comparison of satellite rainfall data with observations from gauging station networks. Journ. Hydrology, 327(3-4), 399-410.
Hughes DA (2006) An evaluation of the potential use of satellite rainfall data for input to water resource estimation models in southern Africa. Climate Variability and Change – Hydrological Impacts (Proceedings of the Fifth FRIEND World Conference held at Havana, Cuba, November 2006), IAHS Publ. 308, 75-80.
Hughes, DA (2006) Modelling semi-arid and arid hydrology and water resources – the southern African experience. Ch. 3 in Hydrological Modeling in Arid and Semi-Arid Areas. Cambridge Univ. Press.
Hughes, D & Kapangaziwiri, E (2007) The use of physical basin properties and runoff generation concepts as an aid to parameter quantification in conceptual type rainfall-runoff models. Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resource Management (Proceedings of Symposium HS2004 at IUGG2007, Perugia, July 2007). IAHS Publ. 313, 311-318.
Sawunyama, T & Hughes, D (2007) Assessment of rainfall and evaporation input data uncertainties on simulated runoff in southern Africa. Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resource Management (Proceedings of Symposium HS2004 at IUGG2007, Perugia, July 2007). IAHS Publ. 313, 98-106.
Kapangaziwiri, E and Hughes, D A (2008) Revised physically-based parameter estimation methods for the Pitman monthly rainfall-runoff model. Water SA, 32(2), 183-191.
Todd, MC, Andersson, L, Hughes, DA, Kniveton, D, Layberry, R, Murray-Hudson, M, Savenije, HHG, Wilk, J and Wolski, P (2008) Simulating climate change impacts on water resources: Experience from the Okavango River, southern Africa. In: Sorooshian et al. Hydrological Modelling and the Water Cycle: Coupling Atmospheric and Hydrological Models, Springer, 243-266.
Sawunyama, T and Hughes, DA (2009) Rainfall variability and uncertainty in water resource assessments in South Africa. New approaches to hydrological prediction in data sparse regions (Proc. symposium HS2 at the joint IAHS & IAH convention, Hyderabad, India, Sept. 2009). IAHS Publ. 333, 287-293.
Kapangaziwiri, E, Hughes DA, and Wagener, T (2009) Towards the development of a consistent uncertainty framework for hydrological predictions in South Africa. New approaches to hydrological prediction in data sparse regions (Proc. symposium HS2 at the joint IAHS & IAH convention, Hyderabad, India, Sept. 2009). IAHS Publ. 333, 84-93.
Hughes, DA and Mantel, SK (2010) Estimating the uncertainty in the impacts of small farm dams on stream flow regimes in South Africa. Hydrological Sciences Journal, 55(4), 578-592.
Hughes, DA, Kapangaziwiri, E and Sawunyama, T (2010) Hydrological model uncertainty assessment in southern Africa. Journ. Hydrology, 387, 221-232.
Hughes, DA and Mantel, S (2010) Estimating uncertainties in simulations of natural and modified streamflow regimes in South Africa. Global Change – Facing Risks and Threats to Water Resources (Proceedings of the Sixth FRIEND World Conference held in Fez, Morocco, November 2010), IAHS Publ. 340, 358-364.
Hughes, DA, Tshimanga, R and Tirivarombo, S (2010) Simulating the hydrology and water resources of large basins in southern Africa. Global Change – Facing Risks and Threats to Water Resources (Proceedings of the Sixth FRIEND World Conference held in Fez, Morocco, November 2010), IAHS Publ. 340, 591-597.
Hughes, DA (2010) Hydrological models: mathematics or science? Hydrological Processes, 24, 2199-2201.
Hughes, DA, Kingston, DG and Todd, M.C. (2011) Uncertainty in water resources availability in the Okavango River Basin as a result of climate change. Hydrol. Earth Syst. Sci. , 15, 931-941.
Hughes, DA (2011) Regionalisation of models for operational purposes in developing countries: An introduction. Hydrology Research 42(5), 331-337.
Tshimanga, R, Hughes, DA and Kapangzawiri, E (2011) Understanding hydrological processes and estimating model parameter values in large basins: The case of the Congo River basin. Conceptual and modelling studies of integrated groundwater, surface water and ecological systems (Proceedings of Symposium H01, IUGG Conference held in Melbourne, Australia, July 2011), IAHS Publ. 345, 17-22.
Kapangzawiri, E, Hughes, DA, Tanner, J and Slaughter, S (2011) Resolving uncertainties in the source of low flows in South African rivers using conceptual and modelling studies. Conceptual and modelling studies of integrated groundwater, surface water and ecological systems (Proceedings of Symposium H01, IUGG Conference held in Melbourne, Australia, July 2011), IAHS Publ. 345, 127-132.
Hughes, DA, Mallory, SJL, Haasbroek, B and Pegram, GGS (2011) Hydrological and Stochastic Uncertainty: Linking Hydrological and Water Resources Yield Models in an Uncertainty Framework. Risk in water resources management. (Proceedings of Symposium H03, IUGG Conference held in Melbourne, Australia, July 2011), IAHS Publ. 347, 127-132.
Sawunyama, T, Hughes, DA and Mallory, SJL (2011) Evaluation of combined contribution of uncertainty sources to total output uncertainty in water resource estimation in South Africa. Risk in water resources management. (Proceedings of Symposium H03, IUGG Conference held in Melbourne, Australia, July 2011), IAHS Publ. 347, 133-138.
Tshimanga, R, Hughes, DA and Kapangzawiri, E (2011) Initial calibration of a semi-distributed rainfall runoff model for the Congo River basin. Understanding hydrological processes and estimating model parameter values in large basins: Physics and Chemistry of the Earth 36(14-15), 761-774
Kapangaziwiri, E, Hughes, D.A. and Wagener, T. (2012) Incorporating uncertainty in hydrological predictions for ungauged basins in southern Africa. Hydrol. Sci. Journ. 57(5), 1000-1019.
Hughes, D.A. and Mohobane, T. (2012) Reducing uncertainty in hydrological models using local observed data: examples from South Africa. Proc. 11th National Symposium, British Hydrological Society. doi:
Tshimanga, R.M. and Hughes, D.A. (2012) Climate change and impacts on the hydrology of the Congo Basin: The case of the northern sub-basins of the Oubangui and Sangha Rivers. Physics and Chemistry of the Earth 50-52, 72-83.
Hughes, D.A., Kapangaziwiri, E. and Tanner, J. (2013) Spatial scale effects on model parameter estimation and predictive uncertainty in ungauged basins. Hydrology Research 44(3), 441-453.
Linhoss, A.C, Munoz-Carpena, R., Kiker, G. and Hughes D. (2013) Hydrologic modeling, uncertainty, and sensitivity in the Okavango Basin: Insights for scenario assessment. J. Hydrologic Eng. doi: 10.1061/(ASCE)HE.1943-5584.0000755.
Hrachowitz, M., Savenije, H.H.G., Blöschl, G., McDonnell, J.J., Sivapalan, M., Pomeroy, J.W., Arheimer, B., Blume, T., Clark, M.P., Ehret, U., Fenicia, F., Freer, J.E., Gelfan, A., Gupta, H.V., Hughes, D.A., Hut, R.W., Montanari, A., Pande, S., Tetzlaff, D., Uhlenbrook, S., Wagener, T., Winsemius, H.C. and Woods, R.A. (2013) A decade of Predictions in Ungauged Basins (PUB) - a review. Hydrological Sciences Journal, 58(7), 1198-1255.
Montanari, A., G. Young, H. Savenije, D. A. Hughes, T. Wagener, L Ren, D. Koutsoyiannis, C. Cudennec, S. Grimaldi, G. Bloeschl, M. Sivapalan, K. Beven, H. Gupta, B. Arheimer, Y. Huang, A. Schumann, D. Post, V. Srinivasan, E. Boegh, P. Hubert, C. Harman, S. Thompson, M. Rogger, M. Hipsey, E. Toth, A. Viglione, G. Di Baldassarre, B. Schaefli, H. McMillan, S.J. Schymanski, G. Characklis, B. Yu, Z. Pang and V. Belyaev (2013) “Panta Rhei – Everything Flows”: Change in hydrology and society – The IAHS Scientific Decade 2013-2022. Hydrological Sciences Journal, 58(7), 1256-1275.
Hughes, DA. (2013) A review of 40 years of hydrological science and practice in southern Africa using the Pitman rainfall-runoff model. Journal of Hydrology 501, 111-124.
Castellarin, A., Botter, G., Hughes, D.A., Liu, S., Ouarda, T.B.M.J., Parajka, J., Post. D.A.., Sivapalan, M., Spence, C., Viglione, A. and Vogel, R.M. (2013) Prediction of flow durations curves in ungauged basins. Chapter 7 in: G. Blöschl, M. Sivapalan, T. Wagener, A. Viglione and H. Savenije (Editors), Runoff Prediction in Ungauged Basins. Synthesis across Processes, Places and Scales. Cambridge Univ. Press, UK.
Hughes, D.A. (2013) Seasonal flow prediction with uncertainty in South Africa and Lesotho. Chapter 11.6 (Part of Chapter 11; PUB in practice: case studies) in: G. Blöschl, M. Sivapalan, T. Wagener, A. Viglione and H. Savenije (Editors), Runoff Prediction in Ungauged Basins. Synthesis across Processes, Places and Scales. Cambridge Univ. Press, UK.
Tanner, J.L. and Hughes, D.A. (2013) Assessing uncertainties in surface-water and groundwater interaction modelling - a case study from South Africa using the Pitman model. Chapter 9 In: J. Cobbing, S. Adams, I. Dennis and K. Riemann (Editors), Assessing and Managing Groundwater in Different Environments, International Association of Hydrogeologists Selected Papers. CRC Press, Taylor and Francis Group, London UK, 121-134.
Hughes, D.A., Gush, M., Tanner, J. and Dye, P. (2014) Using targeted short-term field investigations to calibrate and evaluate the structure of a hydrological model. Hydrological Processes , 28(5), 2794-2809.
Tristam, D., Hughes, D.A. and Bradshaw, K. (2014) Accelerating a hydrological uncertainty ensemble model using Graphics Processing Units (GPUs). Computers and Geosciences, 62, 178-186.
Hughes, DA, Tshimanga, R, Tirivarombo, S. and Tanner, J. (2014) Simulating wetland impacts on stream flow in southern Africa using a monthly hydrological model. Hydrological Processes, 28, 1775-1786.
Hughes, D.A. (2014) PUB in practice at the national scale: the case of South Africa. Chapter 11 in: P. Whitfield (Editors), Putting Prediction in Ungauged Basins into Practice, Canadian Water Resources Association, Canada.
Hughes, D.A., Spence, C. and Woods, R. (2014) Synthesis of major findings at PUB 2011 and recommendations for future directions. Chapter 21 in: J.W. Pomeroy, C. Spence, and P.H. Whitfield (Editors), Putting Prediction in Ungauged Basins into Practice, Canadian Water Resources Association, Canada.
Hughes, D.A., Mantel, S and Mohobane, T. (2014) An assessment of the skill of downscaled GCM outputs in simulating historical patterns of rainfall variability. Hydrology Research 45(1), 134-147.
Tshimanga, R.M. and Hughes, D.A. (2014) Basin-scale performance of a semi-distributed rainfall-runoff model for hydrological predictions and water resources assessment of large rivers: the Congo River. Water Resources Research. 50(2), 1174-1188.
Vaze, J., Chiew, F., Hughes, D. and Andreassian. (2015) Preface: HSO2 – Hydrologic Non-Stationarity and Extrapolating Models to Predict the Future. Proc. IAHS, 371, 1-2.
Hughes, D.A. (2015) Scientific and practical tools for dealing with water resource estimations for the future. Proc. IAHS, 371, 23-28. doi:10.5194/piahs-371-23-2015
Hughes, D.A. (2015) Simulating temporal variability in catchment response using a monthly rainfall-runoff model. Hydrological Sciences Journal, 60 (7-8), 1286-1298.
Tanner, J.L. and Hughes, D.A. (2015) The role of surface water - groundwater interactions in catchment scale water resources assessments - understanding and hypothesis testing with a hydrological model. Hydrological Sciences Journal, 60(11), 1880-1895. Doi:10.1080/02626667.2015.1052453
Tumbo, M. and Hughes, D.A. (2015) Uncertain hydrological modelling: Application of the Pitman model in the Great Ruaha River Basin, Tanzania. Hydrological Sciences Journal, 60(11), 2047-2061. doi:10.1080/02626667.2015.1016948.
Hughes, D.A. (2016) Hydrological modelling, process understanding and uncertainty in a southern African context: lessons from the northern hemisphere. Hydrological Processes. 30(14), 2419-2431. DOI: 10.1002/hyp.10721
Pienaar, G.W. and Hughes, D.A. (2016) Linking hydrological uncertainty with equitable allocation for water resources decision-making. Paper accepted for publication in Water Resources Management (Oct 2016).
Hughes, DA, Kapangaziwiri, E, Mallory, SJL, Wagener, T and Smithers, J (2011) Incorporating uncertainty in water resources simulation assessment tools in South Africa. Water Research Commission Report No. 1838/1/11.
Hughes, D.A., Mohobane, T. and Mallory, S.J.L. (2014) Implementing uncertainty in water resources assessment and planning. Water Research Commission Report No. 2056/1/14 (In press).
Tanner, J.L. and Hughes, D.A. (2014) Understanding and modelling surface water – groundwater interactions. Water Research Commission Report No. 2056/2/14.
Kapangaziwiri, E (2008) Revised parameter estimation methods for the Pitman monthly rainfall-runoff model. MSc Thesis awarded with distinction.
Sawunyama, T (2009) Evaluating uncertainty in water resources estimation in southern Africa: a case study of South Africa. PhD Thesis.
Kapangaziwiri, E (2011) Regional application of the Pitman monthly rainfall-runoff model in southern Africa incorporating uncertainty. PhD Thesis.
Tshimanga, R (2012) Hydrological uncertainty analysis and scenario based streamflow modelling for the Congo River basin. PhD Thesis.
Tirivarombo, S (2013) Climate variability and climate change in water resources management of the Zambezi River basin. PhD Thesis.
Tanner, J (2014) Understanding and modelling of surface and groundwater interactions. PhD Thesis.
Tumbo, M (2015) Uncertainties in modelling hydrological responses in gauged and ungauged sub-basins. PhD Thesis.
Mohobane, T (2015) Water resources availability in the Caledon River basin: past, present and future. PhD Thesis.
Last Modified: Tue, 22 Nov 2016 12:23:44 SAST