Detection of trends in extreme streamflow due to climate variability in the Lake Naivasha basin, Kenya

Abstract

Variability of streamflow has far-reaching impacts especially in developing countries. This is aggravated by climate change which has adversely affected the water resources and food security. This paper presents the characterization trends in extreme streamflow regimes with a view to providing information for planning local coping mechanisms to climate variability and change using streamflow data recorded from 1959 to 2008 in the Lake Naivasha basin in Kenya. The maxima and percentiles of streamflow distributions were investigated to identify changes in extreme intensity and frequency, respectively, using the Mann -Kendall test. The results indicate significant increases in annual maxima at all gauging stations. The flows in the month of November increased significantly at gauging stations 2GB4 and 2GC4. Flow percentile exceedance revealed that the annual 95th percentile exceedance increased significantly at gauging stations 2GB1 and 2GB5 with a decrease in annual 90th and 97th percentile exceedance at gauging stations 2GB4 and 2GC4. The results presented in this paper are useful for climate change adaptation planning and management especially in water supply, hydropower generation and agriculture.

Quantifying water storage within the north of Lake Naivasha using sonar remote sensing and Landsat satellite data

“…Lake Naivasha sits at the highest elevation and has a contemporary average area of 154 km². The basin receives an average annual rainfall of 610 mm yr -1 (Kyambia and Mutua, 2015) and the lake supports an expanding population and industry (figure 1).…”

“…The town of Naivasha has, as a consequence, has more than doubled from 160,000 people in 1999 to >355,000 by 2019 (Onywere et al, 2012;KNBS, 2019), with workers bringing families from the rural communities to work at the large farms, or the tertiary and quaternary industries that are a byproduct of a growing industrial town; all of which has increased the pressure on local water resources. The 2013 water volume within the Lake Naivasha basin were considered to be able to provide 647 m³ per capita per year (Kyambia and Mutua, 2015), which falls within the definition of 'chronic water scarcity', defined as between 500 to 1000 m³ (500,000 to 1,000,000 litres) per capita per year, (Falkenmark and Widstrand, 1992). The Naivasha basin is therefore both vital for, and increasingly at risk of over exploitation from, those that live and work around the lake and within the upper catchment (Onywere et al, 2012).…”

An Assessment of the Role of Water Hyacinth in the Water Level Changes of Lake Naivasha Using GIS and Remote Sensing

“…For ETM+ images, the first step was conversion of thermal bands' (band 61 and band 62) digital numbers into spectral radiance using the algorithm presented below. = × !QCal − QCalMin* + LMinλ (3) where: Lλ = Spectral Radiance at the sensor's aperture in watts/(m 2 * ster * µm) QCal = the quantized calibrated pixel value in DN LMinλ = the spectral radiance that is scaled to QCALMIN in watts/(m 2 * ster * µm) LMaxλ = the spectral radiance that is scaled to QCALMAX in watts/(m 2 * ster * µm) QCalMin = the minimum quantized calibrated pixel value (corresponding to LMinλ) in DN = 1 for LPGS products and NLAPS products processed after 4/4/2004; and DN = 0 for NLAPS products processed before 4/5/2004 QCalMax = the maximum quantized calibrated pixel value (corresponding to LMAXλ) in DN = 255…”

“…This means that changes in vegetative cover around or on it can affect the lake's water level, other factors held constant. However, studies which have investigated the lake's water level fluctuations have mainly focused on the role of climate change on the water level [3][4][6][7][8]. For example, using parameters such as the hydrological cycle, temperature and precipitation, Arlan predicted that climate change would cause the lake water level to decline by 4 m by 2039 [6].…”

Forty years' channel change on the Yongdinghe River, China: patterns and causes

“…And the latter is used to quantitatively characterize the fluvial geomorphology (Winterbottom and Gilvear 1997, Whited et al 2002, Sinha et al 2005. For analysing the driving factors, the statistical correlation method, moving T-test, Crammer's method, Yamamoto method, Mann-Kendall method, and Pettitt-test method have often been used, based on the site observation data (Zhang et al 2006, Mu et al 2007, Yang and Tian 2009, Jiang et al 2014, Kyambia and Mutua 2015, Lu et al 2015.…”