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  • CLIGEN as a weather generator for predicting rainfall erosion using USLE based modelling systems

    Author(s)
    Kinnell, PIA
    Yu, Bofu
    Griffith University Author(s)
    Yu, Bofu
    Year published
    2020
    Metadata
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    Abstract
    CLIGEN is a stochastic weather generator that has been used as input to WEPP. Normally, USLE based models predict erosion using parameter values that are long-term averages but RUSLE2 has a facility to predict erosion for single storms through user entered data. This enables CLIGEN to be used as a weather generator for RUSLE2 when EI30 values for CLIGEN generated rainfall are determined separately. This can be achieved using daily erosivity density (EI30 per unit quantity of rain) data generated by RUSLE2 for each location or by other methods that have the capacity to determine daily EI30 values independently of RUSLE2. One ...
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    CLIGEN is a stochastic weather generator that has been used as input to WEPP. Normally, USLE based models predict erosion using parameter values that are long-term averages but RUSLE2 has a facility to predict erosion for single storms through user entered data. This enables CLIGEN to be used as a weather generator for RUSLE2 when EI30 values for CLIGEN generated rainfall are determined separately. This can be achieved using daily erosivity density (EI30 per unit quantity of rain) data generated by RUSLE2 for each location or by other methods that have the capacity to determine daily EI30 values independently of RUSLE2. One such method developed by Yu was compared with the RUSLE2 based method in terms of its ability to predict temporal variations in soil loss during the calendar year from bare fallow and cropped areas. The process of determining EI30 values by the Yu method involves generating EI30 values so as to match R-factor values used by RUSLE2. This enables CLIGEN to predict soil loss values that are as useful those generated using RUSLE2 erosivity densities in terms of predicting long-term variations in soil loss during the year. However, CLIGEN does not necessarily produce stochastic rainfall data evenly over decades. Consequently, the process of matching R-factors associated with RUSLE2 with those generated by using CLIGEN should be undertaken using the same time frame as used for obtaining the long-term mean soil loss amounts.
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    Journal Title
    CATENA
    Volume
    194
    DOI
    https://doi.org/10.1016/j.catena.2020.104745
    Subject
    Geology
    Physical Geography and Environmental Geoscience
    Soil Sciences
    Science & Technology
    Physical Sciences
    Life Sciences & Biomedicine
    Geosciences, Multidisciplinary
    Publication URI
    http://hdl.handle.net/10072/402184
    Collection
    • Journal articles

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