Rainfall uncertainty, Climate change and Water resources in the
Limpopo basin, Botswana
*Kebuang Piet Kenabatho, Neil McIntyre, Howard Wheater
Department of Civil and Environmental Engineering, Environment and Water Resources Engineering,
Imperial College London, SW7 2AZ, UK.
1.INTRODUCTION
To keep up with the ever-escalating water demand in Botswana, there is
currently significant investment towards developing more dams in the
Limpopo basin. The major issues are that (1) the hydrology of this region
is poorly understood, (2) there is limited information on historic rainfall
observations yet with (3) extended periods of missing records. This could
lead to (4) high uncertainties on water resources planning models.
Furthermore, (5) knowledge on how uncertainty in future climate
projections will affect water resources systems is completely inadequate.
Focusing on rainfall and hydrological uncertainty, this project aims to
apply rainfall and hydrological models to further understand the hydrology
of this region under current and future climate states. Such work is
necessary to ensure more robust water resources plans for the future.
FIGURE1: The Limpopo basin
A multi-site continuous time stochastic rainfall model, the generalised
linear model (GLM) (Chandler and Wheater, 2002) was used to infill
historic rainfall data, to generate multiple realisations of rainfall (with
uncertainty) for the current rainfall series (Figure 2) in the study area
(Figure 1)
3. HYDROLOGICAL MODEL
A semi-distributed version of the IHACRES model (Ye et al., 1997) was
used for the hydrology. Stochastic infilling of rainfall data allows calibration
of a hydrological model under input uncertainty (Figure 3)
FIGURE 5: Monthly temp (a) and rainfall (b) projections from 2 GCMs
10
CSMK3
HADCM3
5
Temp Changes(deg.Celc)
2. STOCHASTIC RAINFALL MODEL
FIGURE 2: Rainfall results-GLMs
0
2
4
6
b
a
A2-2020s
8
10
12
8
10
12
10
CSMK3
HADCM3
5
0
2
4
A2-2050s
6
10
CSMK3
HADCM3
5
dam
A2-2080s
0
2
4
6
8
10
12
[months]
4. CLIMATE CHANGE PROJECTIONS
FIGURE 3: Calibration results for flow (left) and cumulated volume (right)
The stochastic rainfall model was used to downscale outputs of global
circulation models (GCMs) to generate rainfall at a basin scale under
scenarios of climate change using multiple GCM experiments (Figure 5)
5. RESERVOIR PERFORMANCE
The rainfall model, together with the uncertain hydrological model, is then
used to generate multiple realisations of reservoir inflow over a 100-year
period under the current and future rainfall scenarios. A proposed 382
106 m3 reservoir at the outlet of the catchment, which is part of
Botswana’s national water resource strategy, is re-evaluated in light of the
extended inflow data and the estimated uncertainty (Figure 4).
FIGURE 4: Reservoir performance for stationary climate and A2 scenario
6. CONCLUSIONS
Results show that the uncertainty has a considerable effect on the
reliability of the reservoir; for example, the proportion of time for which
demand for water was met ranged from [77 to 100% ]-stationary climate,
[0-76%]- projected future changes, over the different flow realisations used.
In view of these it is proposed that adaptation measures such as supply
restrictions should be imposed when the reservoir level reaches a certain
threshold to control shortfalls especially during dry periods
ACKNOWLEDGEMENTS
We thank the Department of Water Affairs and
Department of
Meteorological Services (Botswana) for providing data used in this study.
We also thank the Commonwealth Scholarship Commission (UK) for
sponsoring Piet’s research at Imperial College London.
*kp.kenabatho06@imperial.ac.uk
REFERENCES
1. Chandler, R. E. & Wheater, H. S. (2002) Analysis of rainfall variability
using generalized linear models: a case study from the west of Ireland.
Water Resources Research. 38, 1192, doi:10.1029/2001WR000906, 2002.
2. Ye, W., Bates, B. C., Viney, N. R., Sivapalan, M. & Jakeman, A. J. (1997)
Performance of conceptual rainfall–runoff models in low-yielding
catchments. Water Resour.ces Research. 33, 153–166.