9 Lentic-Lotic Water System Response to Anthropogenic …
207
Molo River, which feeds into Lake Baringo, correspond to a lake level response of
between 2 and 4 m even when the discharge was at its highest in the late 1970s. But
between 2010 and 2013, the lake level response was +8 m with river inflow still
not as high as it was in the late 1970s (Fig. 3). In addition, it has been demonstrated
from palaeoclimate records and modeling that high amplitude climate variability and
corresponding hydrological responses by the lakes also occur on multi-decadal and
longer timescales which cannot be discerned from short instrumental records (e.g.,
Verschuren et al. 2000; Tierney et al. 2013; Shagerl and Renaut 2016) and should be
taken into account when considering lake responses to climate variability.
Britton et al. (2008) demonstrated in Lake Baringo that water level is significantly
and positively correlated with fish production. More generally, it has been noted that
lake level changes in shallow lakes and man-made reservoirs are highly correlated
with fish yield per unit area, and, therefore, the influence of water level changes on
aquatic productivity should be considered in management of lake resources (Kolding
and van Zwieten 2012). In a recent study of 13 African lakes, including Lakes Nakuru,
Naivasha, Turkana, and Victoria, it was observed that there was a positive correlation
between inter-annual water level fluctuations and primary and overall production, and
a negative correlation to fish diversity, transfer efficiency, and food chain length. Also,
seasonal water level fluctuation was positively correlated with biomass (Gownaris
et al. 2017). In Lake Naivasha, Stoof-Leichsenring et al. (2011) noted that the chemistry and biology of the lakes were affected mainly by natural climate variations in
the period 1820–1950, but that since 1950, anthropogenic activities have been the
dominant factor. Awange et al. (2013) note, for example, that there was a significant
correlation between the quantity of flowers produced and the level of Lake Naivasha
during the period 2002–2010, which suggests that anthropogenic activities had some
effect on the lake level drop that occurred during that period. Further effects on lake
level changes could arise from changes induced by siltation that alters the lake littoral
regimes (cf. UNEP 2004), bathymetry, and surface-volume ratios (cf. Shagerl and
Burian 2016), but these effects have not yet been quantified.
4 Management
While lakes are essential habitats for diverse flora and fauna and also serve to
support human livelihoods and economic development, they are much more vulnerable to stresses and more difficult to manage than river systems because they are
easily impacted by complex land and water relationships (ILEC 2007), which are
confounded by human activity (Fig. 5). It was conservatively estimated, in 2005,
that water resource degradation cost the country about 30 million dollars (0.5%
GDP) annually, while extreme events such as the El Niño-La Niña flood and drought
in 1997–2000 cost the country about 14% of its GDP, with the drought exacting
four times the cost of the flood (Mogaka et al. 2005). In addition, insufficient data
for planning, management, and decision-making can lead to failure of development
projects, frequent water supply disruption and rationing (Nyingi et al. 2013) and
207
Molo River, which feeds into Lake Baringo, correspond to a lake level response of
between 2 and 4 m even when the discharge was at its highest in the late 1970s. But
between 2010 and 2013, the lake level response was +8 m with river inflow still
not as high as it was in the late 1970s (Fig. 3). In addition, it has been demonstrated
from palaeoclimate records and modeling that high amplitude climate variability and
corresponding hydrological responses by the lakes also occur on multi-decadal and
longer timescales which cannot be discerned from short instrumental records (e.g.,
Verschuren et al. 2000; Tierney et al. 2013; Shagerl and Renaut 2016) and should be
taken into account when considering lake responses to climate variability.
Britton et al. (2008) demonstrated in Lake Baringo that water level is significantly
and positively correlated with fish production. More generally, it has been noted that
lake level changes in shallow lakes and man-made reservoirs are highly correlated
with fish yield per unit area, and, therefore, the influence of water level changes on
aquatic productivity should be considered in management of lake resources (Kolding
and van Zwieten 2012). In a recent study of 13 African lakes, including Lakes Nakuru,
Naivasha, Turkana, and Victoria, it was observed that there was a positive correlation
between inter-annual water level fluctuations and primary and overall production, and
a negative correlation to fish diversity, transfer efficiency, and food chain length. Also,
seasonal water level fluctuation was positively correlated with biomass (Gownaris
et al. 2017). In Lake Naivasha, Stoof-Leichsenring et al. (2011) noted that the chemistry and biology of the lakes were affected mainly by natural climate variations in
the period 1820–1950, but that since 1950, anthropogenic activities have been the
dominant factor. Awange et al. (2013) note, for example, that there was a significant
correlation between the quantity of flowers produced and the level of Lake Naivasha
during the period 2002–2010, which suggests that anthropogenic activities had some
effect on the lake level drop that occurred during that period. Further effects on lake
level changes could arise from changes induced by siltation that alters the lake littoral
regimes (cf. UNEP 2004), bathymetry, and surface-volume ratios (cf. Shagerl and
Burian 2016), but these effects have not yet been quantified.
4 Management
While lakes are essential habitats for diverse flora and fauna and also serve to
support human livelihoods and economic development, they are much more vulnerable to stresses and more difficult to manage than river systems because they are
easily impacted by complex land and water relationships (ILEC 2007), which are
confounded by human activity (Fig. 5). It was conservatively estimated, in 2005,
that water resource degradation cost the country about 30 million dollars (0.5%
GDP) annually, while extreme events such as the El Niño-La Niña flood and drought
in 1997–2000 cost the country about 14% of its GDP, with the drought exacting
four times the cost of the flood (Mogaka et al. 2005). In addition, insufficient data
for planning, management, and decision-making can lead to failure of development
projects, frequent water supply disruption and rationing (Nyingi et al. 2013) and