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Ai L.,CAS Institute of Soil and Water Conservation | Shi Z.H.,CAS Institute of Soil and Water Conservation | Shi Z.H.,Huazhong Agricultural University | Yin W.,The Yangtze river Water Resources Protection Science Institute | Huang X.,Huazhong Agricultural University
Journal of Hydrology | Year: 2015

Landscape characteristics are widely accepted as strongly influencing stream water quality in heterogeneous watersheds. Understanding the relationships between landscape and specific water contaminant can greatly improve the predictability of potential contamination and the assessment of contaminant export. In this work, we examined the combined effects of watershed complexity, in terms of land use and physiography, on specific water contaminant across watersheds close to the Danjiangkou Reservoir. The land use composition, land use pattern, morphometric variables and soil properties were calculated at the watershed scale and considered potential factors of influence. Due to high co-dependence of these watershed characteristics, partial least squares regression was used to elucidate the linkages between some specific water contaminants and the 16 selected watershed characteristic variables. Water contaminant maps revealed spatial and seasonal heterogeneity. The dissolved oxygen values in the dry season were higher than those in the wet season, whereas the other contaminant concentrations displayed the opposite trend. The studied watersheds which are influenced strongly by urbanization, showed higher levels of ammonia nitrogen, total phosphorus, potassium permanganate index and petroleum, and lower levels of dissolved oxygen. The urban land use, largest patch index and the hypsometric integral were the dominant factors affecting specific water contaminant. © 2015 Elsevier B.V. Source

Huang X.,Huazhong Agricultural University | Shi Z.H.,Huazhong Agricultural University | Shi Z.H.,CAS Institute of Soil and Water Conservation | Zhu H.D.,Huazhong Agricultural University | And 3 more authors.
Catena | Year: 2016

Knowledge of soil moisture dynamics within soil profiles can improve hydrologic process modeling. The heterogeneity of environmental factors (e.g., topographic attributes, soil properties, land use types and antecedent precipitation) influences soil moisture dynamics. In this study, we measured the soil moisture content at 10-cm depth intervals between the soil surface and the soil-bedrock boundary at 39 sites within a small catchment in 2008 and 2009; we primarily analyzed the environmental controls on soil moisture dynamics over the entire soil profile (0-10, 10-20, 20-30, 30-40, 40-50, and > 50 cm). Soil properties, terrain attributes, land use types and precipitation were evaluated as the potential controls. Given the high co-dependence of environmental factors, partial least squares regression (PLSR) was used to understand the links between soil moisture dynamics and the 14 selected environmental indices. The results show that PLSR considerably reduced the dimensionality of the soil moisture data. The first two components explained 73.5%, 69.9%, 65.7%, 60.1%, 51.5% and 47.6% of the soil moisture variability at the measurement depths of 0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm, 40-50 cm and > 50 cm, respectively. Antecedent precipitation dominates the first and second positive components of the PLSR models at all of the measurement depths. The first-order controls were determined by calculating the Variable Influence on Projection (VIP). The VIP values revealed that the soil moisture in the top layer (0-10 cm) is controlled by antecedent precipitation. In the intermediate layer (10-30 cm), the first-order controls of soil moisture are antecedent precipitation, soil thickness, topographic wetness index, and soil texture; while in the deeper layer (> 30 cm), antecedent precipitation, soil thickness, topographic wetness index, relative elevation and slope gradient control soil moisture dynamics. The results indicate that soil moisture is closely associated with environmental factors in measurement catchments at various soil layers and provide a deeper understanding of hydrological processes. © 2015 Elsevier B.V.. Source

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