Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2035
Title: Quantifying water storage within the north of Lake Naivasha using sonar remote sensing and Landsat satellite data
Authors: Walker, D.
Shutler, J.
Morrison, E.
Harper, D,
Hoedjes, J.
Keywords: Landsat satellite data
Remote sensing
Quantifying water storage
Lake Naivasha
Issue Date: 2022
Publisher: Elsevier B.V
Citation: Ecohydrology & Hydrobiology Volume 22, Issue 1, Pages 12-20, January 2022
Series/Report no.: Ecohydrology & Hydrobiology;Volume 22, Issue 1, Pages 12-20
Abstract: Endorheic freshwater lakes can be vital water resources for sustaining large populations. However, their land-locked nature can lead to overexploitation and long-term sediment accumulation, reducing water storage and quality. Lake Naivasha supports a rapidly expanding population and agricultural industry. Therefore, maintaining good water storage and quality within this endorheic lake is crucial for the Kenyan economy and population. The lake has a long history of level fluctuations and the region is considered to be suffering from a chronic imbalance between water supply and demand. This study quantifies the sediment deposition rate and its impact on Lake Naivasha's water levels and volume, using inexpensive remote sensing techniques that could be easily replicated for future monitoring. Evidence of sedimentation in the northern area averaging 23 mm yr−1 was identified, which is likely annually displacing between 40.2 – 576 × 103 m³ of water. The volume displaced each year is equivalent to the water required to sustain between 40 – 1152 people. These results imply that current abstraction management, based purely upon lake level readings that govern a ‘traffic lights’ system, are detrimental to the long-term survival of the lake. The results also imply that lake health is decreasing. We recommend that future monitoring of this water resource and all endorheic lakes consider measurements of available water volume in combination with lake level data using the remote sensing methods we describe.
URI: http://hdl.handle.net/123456789/2035
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