This study is conducted over the data-poor Limpopo basin centered over southern Africa using
reanalysis downscaled to useful resolution.
Reanalysis products are of limited value in hydrological applications due to the coarse spatial
scales they are available at. Dynamical downscaling of these products over a domain of interest
offers a means to convert them to finer spatial scales in a dynamically consistent manner.
Additionally, this downscaling also offers a way to resolve dominantatmospheric processes,
leading to improved accuracy in the atmospheric variables derived. This study thus evaluates
high-resolution downscaling of an objectively chosen reanalysis (ERA-I) over the Limpopo basin
using Weather Research and Forecasting (WRF) as a regional climate model.
The model generally under-estimates temperature and over-estimates precipitation over the
basin, although reasonably consistent with observations. The model does well in simulating
observed sustained hydrological extremes as assessed using the Standardized Precipitation Index
(SPI) although it consistently under-estimates the severity ofmoisture deficit for the wettest part
of the year during the dry years. The basin's aridity index (I) is above the severe drought
threshold during summer and is more severe in autumn. This practically restricts rain-fed agriculture to around 3 months in a year over the basin. This study presents possible beneficial use of
the downscaled simulations foroptimal hydrologic design and water resources planning in data
scarce parts of the world.
Fig. 14. Seasonal aridity index based on the model simulations over the study period showing that summer is generally free from moisture deficits.
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Last updated 23 January 2018