Preprints
https://doi.org/10.5194/hessd-8-8737-2011
https://doi.org/10.5194/hessd-8-8737-2011
26 Sep 2011
 | 26 Sep 2011
Status: this preprint was under review for the journal HESS but the revision was not accepted.

Water balance modelling in a semi-arid environment with limited in-situ data: remote sensing coupled with satellite gravimetry, Lake Manyara, East African Rift, Tanzania

D. Deus, R. Gloaguen, and P. Krause

Abstract. Accurate and up to date information on the status and trends of water balance is needed to develop strategies for conservation and the sustainable management of water resources. The purpose of this research is to estimate water balance in a semi-arid environment with limited in-situ data by using a remote sensing approach. We focus on the Lake Manyara catchment, located within the East African Rift of northern Tanzania. We use remote sensing and a semi-distributed hydrological model to study the spatial and temporal variability of water balance parameters within Manyara catchment. Satellite gravimetry GRACE data is used to verify the trend of the water balance result. The results show high spatial and temporal variations and characteristics of a semi-arid climate with high evaporation and low rainfall. We observe that the Lake Manyara water balance and GRACE equivalent water depth show comparable trends a decrease after 2002 followed by a sharp increase in 2006–2007. Despite the small size of Lake Manyara, GRACE data are useful and show great potential for hydrological research on smaller un-gauged lakes and catchments in semi-arid environments. Our modelling confirms the importance of the 2006–2007 Indian Ocean Dipole fluctuation in replenishing the groundwater reservoirs of East Africa. The water balance information can be used for further analysis of lake variations in relation to soil erosion, climate and land cover/land use change as well as different lake management and conservation scenarios. We demonstrate that water balance modelling can be performed accurately using remote sensing data even in complex climatic settings.

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D. Deus, R. Gloaguen, and P. Krause
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
D. Deus, R. Gloaguen, and P. Krause
D. Deus, R. Gloaguen, and P. Krause

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