Rainfall partitioning by the vegetation canopy represents a significant component of the local hydrological cycle by reshaping the amount and spatial distribution of rainfall. Measuring the components of rainfall partitioning, however, has been a challenging task due to laborious- and time-consuming field experiments. In this study, to probe the influences of long-term afforestation on dynamic patterns of rainfall partitioning, the dominant sand-stabilizing shrub Haloxylon ammodendron at three different ages was selected for field measurements during the 2020-2021 growing season. The throughfall percentage for young H. ammodendron (YH, 75.9 %) was significantly higher than that for middle-aged H. ammodendron (MAH, 63.4 %) and mature H. ammodendron (MH, 62.4 %) (p < 0.05 for all cases). However, the interception loss percentage of YH (22.3 %) was significantly lower than that for MAH (35.0 %) and MH (36.5 %) (p < 0.05 for all cases). No significant difference was found for stemflow percentage among YH (1.8 %), MAH (1.5 %) and MH (1.1 %). Smaller rainfall events contributed to a higher interception loss percentage and a lower net rainfall percentage for all ages. Both throughfall and stemflow percentage first showed increasing trends and then tended to be stable with increasing rainfall amount and duration, whereas interception loss percentage showed the opposite patterns. Rainfall partitioning was significantly correlated with the plant area index, stem basal area and canopy height (p < 0.05 for all cases), which may account for significant differences in rainfall partitioning patterns, as all shrubs experienced the same weather conditions. The average funneling ratio was 56.6, 26.7 and 17.9 for YH, MAH and MH, respectively. These results suggested that H. ammodendron afforestation can have a significant impact on rainfall partitioning by reducing net rainfall reaching the soil and may have some implications for local water budget and ecosystem management in oasis-desert ecotones.
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http://dx.doi.org/10.1016/j.scitotenv.2022.159928 | DOI Listing |
The main objective of this study is to map and evaluate groundwater potential zones (GWPZs) using advanced ensemble machine learning (ML) models, notably Random Forest (RF) and Support Vector Machine (SVM). GWPZs are identified by considering essential factors such as geology, drainage density, slope, land use/land cover (LULC), rainfall, soil, and lineament density. This is combined with datasets used for training and validating the RF and SVM models, which consisted of 75 potential sites (boreholes and springs), 22 non-potential sites (bare lands and settlement areas), and 20 potential sites (water bodies).
View Article and Find Full Text PDFEnviron Technol
December 2024
Rothamsted Research, N Wyke, Devon, UK.
Soil erosion is a world-wide issue driven by land management and climate change. Research has focussed on soil loss rates from agricultural land. However, the loss of trace elements essential for soil and plant health, or potentially toxic elements that occur as impurities in fertilisers and manures, is poorly understood.
View Article and Find Full Text PDFJ Hazard Mater
November 2024
Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; Poyang Lake Wetland Research Station, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Jiujiang 332899, China.
A lack of hydro-biogeochemical models for catchment-scale antibiotic dynamics limits our mechanistic understanding of the transport and fate of antibiotics. This study addresses this gap by developing a distributed and process-based model that focuses on the complex water-sediment-antibiotic interactions. We applied the model to a typical agricultural catchment and selected tetracyclines (TCs) as the target antibiotics.
View Article and Find Full Text PDFEnviron Res
December 2024
College of Water Sciences, Beijing Normal University, Beijing, 100875, China. Electronic address:
Heavy metals, such as mercury, cadmium, and nickel, may contaminate human inhabited environments, with critical consequences for human health. This study examines the health impacts of heavy metal pollution from an iron slag pile in Hechi, China, by analyzing heavy metal contamination in water, sediment, soil, and crops. Here, the Nemerow pollution index (NI) indicated severe pollution at most sampling sites, the mean NI of groundwater, and surface water had reached 594.
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