Spatial and temporal dynamics of base flow in semi-arid montane watersheds and the effects of landscape patterns and topography.

Environ Monit Assess

State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, No 18 Shuangqing Road, Haidian District, Beijing, 100085, People's Republic of China.

Published: April 2023


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Article Abstract

Base flow (BF) is harder to predict than other hydrological signatures. The lack of hydrologically relevant information or adequately broad spectrum of typically selected catchment attributes (particularly landscape and topography) hinders the explanatory power. Our goals were to identify the most influential controls on base flow spatially and temporally and to elucidate the response relationships. Base flow in 19 semi-arid sub-watersheds was separated by digital filtering. One hundred and fourteen sub-watershed attributes were related to base flow using random forest regression. The main results were as follows: (1) Annual BF significantly declined since 1999 due to decreased precipitation, increased air temperature, afforestation, urban expansion, and increasing water consumption. Annual base flow index (BFI), varying between 0.319 and 0.695, showed less noticeable temporal trends. (2) Precipitation (P) and underlying carbonate rocks primarily controlled the spatial variation of annual BF and total flow (TF), with the impacts being positive. Landscape was less influential. After the abrupt runoff decline, landscape composition rather than configuration exerted greater impacts on spatial BF and TF, and the importance of forest increased, whereas landscape configuration was decisive for BFI during the whole observation period. The absence of significant links between landscape configuration and water quantity may result from a scale issue. Concave profile curvatures were found to be topographic variables more important than slopes. The impact of soil was the least. This study would benefit the selection of catchment attributes and spatial extents to quantify these attributes in building BF predicting models in future studies.

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http://dx.doi.org/10.1007/s10661-023-11193-xDOI Listing

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