Future flow regimes will be different to today and imperfect knowledge of present and future climate variations, rainfall–runoff processes and anthropogenic impacts make them highly uncertain. Future water resources decisions will rely on practical and appropriate simulation tools that are sensitive to changes, can assimilate different types of change information and flexible enough to accommodate improvements in understanding of change. They need to include representations of uncertainty and generate information appropriate for uncertain decision-making. This paper presents some examples of the tools that have been developed to address these issues in the southern Africa region. The examples include uncertainty in present day simulations due to lack of understanding and data, using climate change projection data from multiple climate models and future catchment responses due to both climate and development effects. The conclusions are that the tools and models are largely available and what we need is more reliable forcing and model evlaution information as well as methods of making decisions with such inevitably uncertain information.
The previous (PUB) and present (Panta Rhei) science decades of the International Association of Hydrological Sciences (IAHS) recognised the need for improved understanding of catchment hydrology, incorporating different types of uncertainty into hydrological estimations and for translating science advances into practice (Hrachovitz et al., 2013; Montanari et al., 2013; Pomeroy et al., 2014). From the perspective of practising water resources engineers, the first two objectives may seem incompatible with the third (Pappenberger and Beven, 2006). However, the future flow regimes of rivers will be different to today and these differences are highly uncertain due to imperfect knowledge of present and future climate variations, rainfall–runoff processes and anthropogenic impacts. If future water resources development decisions are to be made, practical and appropriate simulation tools are needed, which represent natural hydrological functions and anthropogenic impacts. To be appropriate they need to be sensitive to changes, able to assimilate different types of change information and flexible enough to accommodate improvements in our understanding of existing and future processes. They need to include methods of representing uncertainty and generate outputs that provide information useful for uncertain decision-making methods (Matrosov et al., 2013).
This paper presents some examples of the tools that have been developed to address these issues within the southern Africa region. The examples are designed to demonstrate methods of dealing with uncertainty in present day situations due to lack of hydrological understanding and data associated with both natural and developed water resources. The examples also include the use of climate change projection data from multiple climate models and possible future catchment response changes due to both climate and development effects. All of the tools are developed to support the application of one of the most widely used hydrological models in southern Africa; the Pitman model (Hughes, 2013). The model is a semi-distributed, monthly time step, conceptual, rainfall–runoff model that also includes functions to represent anthropogenic impacts on water resources availability, including managed forestry plantations, small farm dams and large reservoirs (and associated abstractions and operating rules), as well as direct river abstractions.
Kapangaziwiri et al. (2012) presented an overview of an approach to
uncertainty analysis that was based on simple Monte Carlo sampling of the
parameter space that could be defined in different ways. The output
ensembles were then examined relative to regional constraints on assumed
hydrological behaviour following the approach suggested by Yadav et al. (2007).
One problem that emerged in large basins with multiple sub-basins
was that downstream ensembles that fall within the constraint bounds could
be made up of combinations of upstream ensembles that are both within and
outside the constraint bounds. Tumbo and Hughes (2015) present a revised
approach for the 86 000 km
Tumbo and Hughes (2015) discuss some of the practical issues with setting up
an uncertainty model of this type. These include ensuring that the
constraints are compatible with each other, an example being to ensure that
the groundwater recharge and low flow (
During the second step of the uncertainty model run, additional uncertainty in parameters that are not included during the first step (designed to focus on natural hydrology simulations) can be added. This is where uncertainties in the impacts of land use change, reservoir storage, abstractions and return flows, etc. can be added to simulate various scenarios of development. Hughes and Mantel (2010) investigated the uncertainties in simulating changes related to irrigation from small farm dams in three catchments of South Africa. The impacts were found to be variable, as was the confidence in the information that was available to quantify the volumes of storage and abstractions. Nevertheless, the Pitman model was able to generate realistic uncertainty bounds that bracketed the observed flows. Hughes (2014) used an unconstrained uncertainty version of the Pitman model to investigate how well the model can replicate temporal variability in observed data using two examples from Australia, one from Mali and one from South Africa. The approach adopted was to generate 10 000 ensembles and compare the optimum parameter sets based on objective functions using different periods of the observed data. The results indicated that the model was robust enough to simulate temporal variability in the Australian and Mali catchments that were largely caused by climatic variations, as well as variability in the South African catchment that was attributed to commercial afforestation under more-or-less stationary climate conditions. The South African catchment (NoordKaap River in Mpumalanga Province) is re-visited in this study.
Model parameters and results for the NoordKapp River for the two simulation periods.
The catchment is 126 km up to DATE1: MS between DATE1 and DATE2: MS after DATE2: MS
where S1
Flow duration curves for the NoordKaap River example based on data for 1948 to 2005, including observed, simulated using parameter set 1 (calibrated for 1947 to 1962; no afforestation) and parameter set 2 (calibrated for 1990 to 2005; 70 % afforestation) and a merged simulated time series (see text for details).
From a practical perspective, the NoordKaap example represents a relatively
simple way to explore different impacts of water use (in this case through
land use change effects) over different historical periods. The simulations
reported in Hughes (2014) were based on uncertainty analysis and the
exploration of multiple possible parameter sets, while this study has
applied a simple linear merging approach. The parameters in Table 1 suggest
that before afforestation there were greater amounts of surface and
interflow runoff coupled with lower rates of evapotranspiration loss. These
results are consistent with existing concepts about the impacts of
commercial forest plantations in South Africa (Görgens and Van Wilgen,
2004). It is possible that the S1
Hughes et al. (2014) refer to a previous approach to incorporating climate change uncertainties into hydrological modelling. This was based on the use of rainfall and temperature data for 9 downscaled GCMs obtained from the Climate Systems Analysis Group (CSAG) of the University of Cape Town (Hewitson and Crane, 2006). These consist of daily rainfall, maximum and minimum temperature for baseline (1961 to 2000), near-future (2046 to 2065) and far-future (2081 to 2100) periods. As the statistical characteristics of the baseline rainfall simulations are very different, across the 9 GCMs, to the available historical data, Hughes et al. (2014) proposed a bias correction approach to generate corrected near-future rainfall time series that could be used with a hydrological model established using historical rainfall data. There was less difference in the predicted temperature scenarios across the 9 models. There are three potential practical problems with this approach. The first is that the sequences of near-future simulated flows for the 9 GCMs cannot be compared because they all have different starting conditions. The second is that the near-future simulations are only 20 years compared with the 85 year (1920 to 2005) historical data simulations that are currently used in South Africa for water resources availability assessments. The third is that there is quite a lot of data preparation and the hydrological model has to be run 9 times.
An alternative approach has been adopted in this study that is based on generating 500 rainfall ensembles for the same period as the historical data and that are designed to reflect uncertainties in future rainfall given the changes between the baseline and near-future patterns predicted by the 9 downscaled GCMs. The rainfall ensembles are generated by a single pre-processing program, after which an uncertainty version of the Pitman model is run that combines the 500 rainfall ensembles with 500 random parameter samples to generate a total of 250 000 uncertainty ensembles. Given the similarities in the predictions of future temperatures, the historical evaporation demand seasonal distributions are scaled by the average of the scaling factors (baseline to near-future) suggested by the 9 GCMs (see Hughes et al., 2014 for more details).
The pre-processing program calculates the calendar month means and standard
deviations for all the rainfall data using a square root transformation,
which was found by Hughes et al. (2014) to give the lowest skewness values
(and therefore the best approximation to Normal distributions) across all
calendar months and GCMs. The delta change in means ( Determine the historical standard variate based on the appropriate
calendar month mean and standard deviation: HV Generate the random delta change values for the calendar month mean
and standard deviations: Apply the delta change values to the historical calendar month ( Re-scale the historical standard variate by the future ensemble means
and standard deviations: Finally, back-transform the FP
The fixed random value (FR) ensures that each ensemble is not a complete
mixture of delta changes from all GCMs, while the use of the same random
number for the mean and standard deviation changes assumes that a GCM with
higher means will have higher standard deviations, an assumption that is
only partly supported by an analysis of the GCM data. The additional random
values (MR
Upper Caledon uncertainty example.
The approach has been applied to a headwater sub-catchment (474 km
It was not the main purpose of this paper to demonstrate the validity of the Pitman model for simulating water resources assessments in southern Africa as this has been done a number of times in the past (Hughes, 2013). The purpose was to illustrate the practical application of the model where the scientific issues of uncertainty analysis and potential change (Montanari et al., 2013) are not neglected. The 2-step approach for simulating uncertain natural hydrological regimes (Tumbo and Hughes, 2015) offers a practical and flexible approach to uncertainty analysis of natural hydrology as well as anthropogenic impacts. The NoordKaap afforestation example illustrates that the model is sensitive to at least some land use impacts and that a simple approach can be used to merge natural and changed conditions over an extended time period. If necessary it would be straightforward to replace the simple linear merging used here, with a more complex non-linear merging process involving more than two simulations.
The approach used to incorporate climate change effects based on multiple climate model outputs is also simple and can be applied to as many climate models or emission scenarios as required. With the previous approach (Hughes et al., 2014), increasing the number of climate models would increase the number of model runs, while the approach presented is based on a single pre-processing program and one run of the model. It assumes that all of the GCM predictions are equally likely and that the effects of temperature on evaporation demand are similar for all GCMs. It is possible that future GCM outputs will change as new methods of simulating climate and of downscaling are generated by the climate science community (Kalognomou et al., 2013). The new approach makes it far easier to incorporate these updates into existing model setups. The approach could possibly be further enhanced by using a stochastic rainfall generator (Srikanthan and Pegram, 2009), where more statistical properties of the climate change projections are accounted for than just the calendar month means and standard deviations. The example provided uses the same uncertainty parameter sets that were established for historical conditions. However, if there is evidence to suggest that atmosphere-land feedback mechanisms exist (such as changes to root zone storage; Gao et al., 2014), then these can be incorporated as changes in the parameter ranges.
Ultimately, the practical value of the simulations using the methods described here are highly dependent upon the data used to set up the model (climate forcing data, local and regional output constraint information and water use or land use change information). The value is also dependent upon appropriate decision-making processes that can incorporate uncertainty (Matrosov et al., 2013).
The author is grateful to T. Mohobane for the use of the results from the earlier climate change assessments on the Caledon River catchment and to the Climate Systems Analysis Group of the University of Cape Town for the downscaled GCM data. Some of the research for this paper was supported by the Water Research Commission of South Africa, while T. Mohobane was supported by a PhD bursary by the Carnegie Corporation of New York under the Regional Initiative for Science Education (RISE) programme.