A toolbox of different recharge values and a distributed recharge model have been applied to
estimate the recharge values over Malawi. The toolbox is prepared within Microsoft Excel and
coded using Visual Basics. The distributed recharge calculation is undertaken using the BGS
ZOODRM model. The model uses gridded daily rainfall and potential evaporation data as well
as gridded landuse, topography, soil, and river data to calculate recharge.
The distributed recharge model is calibrated by matching the simulated overland flows to the
observed ones at selected gauging stations. However, difficulties were encountered during the
calibration of the recharge model due to: (i) the resolution of the model grid being relatively
coarse so that the topographical characteristics could not be fully captured, (ii) the number of
runoff zones specified in the model not being enough to represent the characteristics of the study
area, and (iii) there being a need to improve the representation of land cover in the model since
the land cover affects the estimated recharge values.
The estimated recharge values presented in this study are highly affected by the quality of data
used in the distributed recharge model. Comparing the recharge values estimated from the
recharge model and averaged over the district areas to the recharge values calculated using the
recharge toolbox, it was clear that the former agree with the values of at least one analytical
method included in the toolbox. However, there was no consistency of agreement, i.e. the
recharge values produced by the distributed model did not agree with one particular method. The
sensitivity analysis results indicate that the recharge values are highly affected by the soil type
parameter values specified in the model and by the definition of spatial distribution of land
cover. To improve the accuracy of recharge calculations using the distributed recharge model, it
is recommended that maps with a better representation of these features are included in the
model. In addition, further model calibration runs are needed to improve the quality of the
estimated recharge values. This can be only achieved by obtaining better field data