Evaluation of impact of land use and landscape metrics on surface water quality in the Northeastern part along Lake Tanganyika, Africa

Abstract

Abstract As the second deepest lake in Africa, Lake Tanganyika plays an important role in supplying fish protein for the catchment’s residents, and is irreplaceable in global biodiversity. However, the lake's water environment is threatened by socio-economic development and rapid population growth along the lake. This study analyzed the spatial scales effects and seasonal dependence of land use types and landscape metrics on water quality in 16 sub-basins along northeastern Lake Tanganyika at different levels of urbanization. The results revealed that land use types had a higher influence on water quality in urban areas than that in rural areas; the explanatory variance in the urban area was 0.74-0.86, while it was 0.21-0.46 in the rural area. The water quality variation was better explained by sub-watershed scale and 500 m buffer scale in urban area in rainy season and dry season, respectively, and artificial surface was the most important factor and had a negative effect on water quality. While the 500 m buffer zone had the highest explained ability in rural area, and this phenomenon was more obvious in dry season than in rainy season. We identified that CONTAG was the key landscape metric in urban area and was positively correlated with nutrient variables, indicating that water quality degraded in less fragmented and highly dispersed landscapes. The sub-watershed scale had the highest explained ability. While in rural area, the 100 m buffer scale had the highest explained ability in the rainy season and IJI had the highest explanatory variance, the contribution rate reached 78.1%, which had a negative effect on water quality. During the dry season, the sub-watershed scale had highest explanatory ability, the IJI and CONTAG had higher explanatory variance, with 40.3% and 38.9%, respectively. And IJI had a positive effect on TN and TP, CONTAG had a negative effect on TN and TP. Thus, we found that the differences in the configuration of artificial surface and forest patches between different locations and areas with differing degrees of connectivity can explain the variability in stream water quality.