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The influence of land use patterns on water quality in a river system is scale‐dependent. In this study, a four‐order hierarchical arrangement method was used to select water sampling sites and to delineate sub‐basins in the Daliao River Basin, China. The 20 sub‐basins were classified into four spatial scales that represented four different stream orders. Pearson correlation analysis was used to quantify relationships between land use composition and the river's physical‐chemical variables for all samples collected. This analysis showed that the presence of forest cover was associated with higher water quality at the scale of the whole basin. The scale effects of land use patterns on water quality were then examined using stepwise multiple regression analysis that compared different land use types with water quality variables. The results from this analysis showed that urban areas, as opposed to forest areas, became the most significant contributors of water pollutants when scale effects were considered. The influence of urban land cover on water pollution was significantly higher at larger scales. The lack of a significant regression correlation for the forest land use type at smaller scales revealed that forest located upstream of the Daliao River Basin did not provide a buffer for improved water quality. Further analysis showed that this result could be because of disproportionate spatial distributions for forest and urban land use types. The topographic characteristics of sub‐basins, such as average slope (S) and size (A), were determined to be secondary explanatory variables that affected land use impacts on stream water quality. Areas with steep slopes were associated with increased water oxygenation, whereas areas with flatter slopes were associated with higher concentrations of pollutants. These results are significant because they can provide a better understanding of the appropriate spatial scale required for effective river basin management in the future. Copyright © 2013 John Wiley & Sons, Ltd.

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Soil moisture distribution plays a significant role in soil erosion, evapotranspiration, and overland flow. Infiltration is a main component of the hydrological cycle, and simulations of soil moisture can improve infiltration process modeling. Different environmental factors affect soil moisture distribution in different soil layers. Soil moisture distribution is influenced mainly by soil properties (e.g., porosity) in the upper layer (10 cm), but by gravity-related factors (e.g., slope) in the deeper layer (50 cm). Richards’ equation is a widely used infiltration equation in hydrological models, but its homogeneous assumptions simplify the pattern of soil moisture distribution, leading to overestimates. Here, we present a modified Richards’ equation to predict soil moisture distribution in different layers along vertical infiltration. Two formulae considering different controlling factors were used to estimate soil moisture distribution at a given time and depth. Data for factors including slope, soil depth, porosity, and hydraulic conductivity were obtained from the literature and in situ measurements and used as prior information. Simulations were compared between the modified and the original Richards’ equations and with measurements taken at different times and depths. Comparisons with soil moisture data measured in situ indicated that the modified Richards’ equation still had limitations in terms of reproducing soil moisture in different slope positions and rainfall periods. However, compared with the original Richards’ equation, the modified equation estimated soil moisture with spatial diversity in the infiltration process more accurately. The equation may benefit from further solutions that consider various controlling factors in layers. Our results show that the proposed modified Richards’ equation provides a more effective approach to predict soil moisture in the vertical infiltration process.