Jane Ndungu

Evaluation of spatio-temporal variations in chlorophyll-a in Lake Naivasha, Kenya: remote-sensing approach

Restoration of the ecosystem services and functions of lakes requires an understanding of the turbidity dynamics in order to arrive at informed environmental management decisions. The understanding of the spatio-temporal dynamics of turbidity requires frequent monitoring of the turbidity components such as chlorophyll-a concentration. In this study, we explored the use of Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-Aqua) satellite data in studying the spatio-temporal changes in chlorophyll-a concentration in Lake Naivasha, a turbid tropical system. The temporal trend of chlorophyll-a concentration over the study period in the lake was also evaluated. The temporal trend assessment was achieved through the removal of periodic seasonal interference using Seasonal-Trend decomposition based on the LOESS (Local Regression) procedure. The resultant chlorophyll-a concentration maps derived from MODIS-Aqua satellite data give an indication of the monthly spatial variation in chlorophyll-a concentration from 2002 to 2012. The results of regression analyses between satellite-derived chlorophyll-a and in situ measurements reveal a high level of precision, but with a measureable bias with the satellite underestimating actual in situ measurements (R2 = 0.65, P < 0.001). Although the actual values of the chlorophyll-a concentrations are underestimated, the significant relationship between satellite-derived chlorophyll-a and in situ measurements provides reliable information for studying spatial variations and temporal trends. In 2009 and 2010, it was difficult to detect chlorophyll-a from the MODIS-Aqua imagery, and this coincided with a period of the lowest water levels in Lake Naivasha. An inverse relationship between de-seasoned water level and chlorophyll-a concentration was evident. This study shows that MODIS-Aqua satellite data provide useful information on the spatio-temporal variations in Lake Naivasha, which is useful in establishing general trends that are more difficult to determine through routine ground measurements.

Supporting IWRM through spatial integrated assessment in the Lake Naivasha basin, Kenya

This study describes the mismatch between required knowledge and efforts by scientists and stakeholders in the Lake Naivasha basin, Kenya. In the basin, integrated water resources management (IWRM) suffers from the absence of critically relevant knowledge. This study further presents a spatial integrated assessment framework for supporting IWRM in the basin. This framework resulted from an ongoing debate between stakeholders and scientists studying the basins issues. It builds on jointly identified indicators for sustainable governance, and their interdependency, and knowledge gaps. For IWRM in the basin this is a first important step towards a more structured debate on the implementation of IWRM.

A multivariate analysis of water quality in lake Naivasha, Kenya

Water quality information in aquatic ecosystems is crucial in setting up guidelines for resource management. This study explores the water quality status and pollution sources in Lake Naivasha, Kenya. Analysis of water quality parameters at seven sampling sites was carried out from water samples collected weekly from January to June and biweekly from July to November in 2011. Principal component analysis (PCA) and cluster analysis (CA) were used to analyse the dataset. Principal component analysis showed that four principal components (PCA-1 to PCA-4) explained 94.2% of the water quality variability. PCA-1 and PCA-2 bi-plot suggested that turbidity in the lake correlated directly to nutrients and iron with close association with the sampling site close to the mouth of Malewa River. Three distinct clusters were discerned from the CA analysis: Crescent Lake, a more or less isolated crater lake, the northern region of the lake, and the main lake. The pollution threat in Lake Naivasha includes agricultural and domestic sources. This study provides a valuable dataset on the current water quality status of Lake Naivasha, which is useful for formulating effective management strategies to safeguard ecosystem services and secure the livelihoods of the riparian communities around Lake Naivasha, Kenya

SST derivation from MSG for PUMA Pilot Projects in Fisheries

This study presents the technical and scientific results obtained through a collaboration between the Institute for Environment and Sustainability (Joint Research Centre of the European Commission) and two PUMA Pilot Projects, namely the Western Indian Ocean Satellite Application Project (WIOSAP), and the Monitoring of the Oceanographic and Meteorological Environment in support of the Management of Fisheries in Senegal (SEGEPS). Both projects are aimed at an optimized use of MSG (Meteosat Second Generation) satellite images to improve the management of regional fisheries and the current scientific knowledge on the natural variability of the fish stocks. A Sea Surface Temperature product from MSG is generated operationally at the native SEVIRI resolution (3 km at the sub‐satellite point) and can be used to locate upwelling regions and thermal fronts for the identification of Potential Fishing Zones (PFZs).

Assessing water quality in Lake Naivasha

Water quality in aquatic systems is important because it maintains the ecological processes that support biodiversity. However, declining water quality due to environmental perturbations threatens the stability of the biotic integrity and therefore hinders the ecosystem services and functions of aquatic ecosystems. Lake Naivasha is one of the aquatic ecosystems that have faced a myriad of environmental perturbations that have transformed the lake from a clear to muddy eutrophic turbid state, which has resulted in a decline in ecological quality, impacting heavily on fish population and tourism. Though there has been regular data collection on water levels and fish catches, little has been done in monitoring the water quality dynamics in Lake Naivasha. This research aimed at studying the water quality in Lake Naivasha, Kenya. The specific objectives were to assess the overall water quality status; establish the trophic status; assess retrospectively the water quality condition in the last decade; study effect of succession of fish community; and investigate the mechanisms that influence the water quality dynamics in Lake Naivasha. These objectives were achieved through coupling field measurements, geo-information and earth observation, and system modelling. The field measurements were collected weekly from January to June and bi-weekly from July to November 2011 at seven locations in the lake. Water temperature, pH, conductivity, Secchi depth, and turbidity were measured in-situ while others were analysed from water samples in the laboratory. Geo-information and earth observation was used in the retrieval of chlorophyll-a concentration from June 2002 to June 2012 from Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) satellite images. The modelling objective was achieved using Delft3D Flow module to simulate the hydrodynamics and simple stirred-tank reactor model to simulate the water quality in Lake Naivasha. Principal Component Analysis (PCA) and Cluster Analysis (CA) revealed spatial variability in physiochemical parameters, nutrients and main ions. Northern region, main lake, and Crescent Lake sectors of the lake were distinct. Water quality parameters association indicated that the quality of water is influenced by agricultural activities, and domestic effluent around Lake Naivasha. The Northern sector (close to rivers input) seemed to be influenced by agricultural activities. The North East sector of the Lake was dominated by domestic effluent and close association with the crescent lake which is influenced by natural mineral composition associated with its volcanic origin. Discriminant analysis (DA) of the trophic state indices (TSI) revealed that the trophic state was indeed heterogeneous with three distinct sectors which include: the northern part of the lake, the mid and southern sector, and the Crescent Lake. Water quality modelling revealed that response of the lake ecosystem to reduction of pollutants is gradual and it would take 40 years to reach the equilibrium state if the loading remained constant. Ground water seepage could be the main reason behind the freshness of Lake Naivasha despite non-existence of a visible outlet. Investigation of the driving forces behind the spatial variability in water quality revealed that currents which might have been responsible for the transport of sediment and other constituents from the input rivers, were mainly wind-driven in Lake Naivasha. There exists mixing which could be responsible of substance (suspended particles, sollutes, and pollutants) redistribution in the Lake. The nutrient mixing can enhance proliferation of algal biomass through nutrient enrichment in the water column leading to high turbidity levels.