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1 Thapa, G. B.; Weber, K. E. 1994. Managing mountain watersheds in Nepal: Issues and policies. International Journal of Water Resources Development, 10(4):475-495.
(Location: IWMI-HQ Call no: PER Record No: H016123)
2 Mainali, J.; Chang, H. 2021. Environmental and spatial factors affecting surface water quality in a Himalayan Watershed, Central Nepal. Environmental and Sustainability Indicators, 9:100096. [doi: https://doi.org/10.1016/j.indic.2020.100096]
Surface water ; Water quality ; Environmental factors ; Spatial variation ; Watersheds ; Water pollution ; Land use ; Land cover ; Remote sensing ; Rivers ; Electrical conductivity ; Dissolved oxygen ; Models / Nepal / Himalayan Region / Setikhola Watershed / Pokhara Valley
(Location: IWMI HQ Call no: e-copy only Record No: H050227)
https://www.sciencedirect.com/science/article/pii/S2665972720300805/pdfft?md5=a19c47e58d2541d0695b643a17865928&pid=1-s2.0-S2665972720300805-main.pdf
https://vlibrary.iwmi.org/pdf/H050227.pdf
https://vlibrary.iwmi.org/pdf/H050227.pdf
(2.62 MB) (2.62 MB)
Various spatial interrelationships among sampling stations are not well explored in the spatial modeling of water quality literature. This research explores the relationship between water quality and various social, demographic, and topographic factors in an urbanizing watershed of Nepal with a comparison of different connectivity matrices to conceptualize spatial interrelationships. We collected electrical conductivity and dissolved oxygen data from surface water bodies using a handheld probe and used the data to establish relationships with land use, topography, and population density-based explanatory variables at both watershed and 100-m buffer scales. The linear regression model was compared with different eigenvector-based spatial filtering models. These spatial filtering models were constructed using five different spatial conceptualizations based on different graph types generated from the geographic coordinates of the sampling sites. Population density, elevation, and percentage of sand in the watershed and riparian regions are most important in explaining dissolved oxygen concentration and electric conductivity. A human signature as population density and increased sand and gravel cover can be detected in this watershed impacting water quality. Among different graph types compared, the relative graph type provided the highest model strength signifying a stronger upstream-downstream relationship of dissolved oxygen, while k-nearest graph types with four neighbors provided the strongest model performance, indicating the impact of local factors on electrical conductivity. The relationships between socio-environmental factors and water quality and their spatial interrelationships identified in this work shed light on the source, mobilization, and transport of dissolved oxygen and electrical conductivity and can assist the water quality management endeavor.
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