Effect of Vegetation Cover Dynamics on Runoff and the Implication for Sediment Yield Estimation for the Great Barrier Reef Catchments

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Yu, Bofu
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Brooks, Andrew P
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2022-09-09
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Abstract

Large scale land clearing for livestock grazing has accelerated the soil loss and sediment delivery from Great Barrier Reef (GBR) catchments in Australia. Areas with low ground cover are known to generate large runoff volumes at a small scale (<100m2). However, the spatial effect of ground cover variation on runoff generation and soil loss is not well studied in the large catchment (~ ≥1 km2). Thus, it is essential to characterise the spatial distribution of ground cover to evaluate the effect of variation in cover on runoff in the GBR catchments. Despite recognising the potential effect of ground cover variation in runoff generation and soil loss, the current GBR eWater Source Catchment Modelling Framework uses lumped rainfall-runoff models that consider only rainfall and potential evapotranspiration as input variables. In addition, the Revised Universal Soil Loss Equation (RUSLE), which considers only effect of rainfall as rainfall-runoff erosivity has been used to estimate hillslope soil loss. However, the effect of runoff on rainfall-runoff erosivity and on hillslope soil loss is not studied well in Australia, particularly in Queensland. To evaluate the effects of ground cover variation on runoff and soil loss, this research was conducted in Central Queensland in 32 sub-catchments of Burnett-Mary Basin (35.84 - 231.17 km2), three experimental Brigalow catchments (11.7 – 16.8 ha) and Springvale catchment (9.6 ha) and Nogoa catchment (13,880 km2) in the Fitzroy basin and Weany Creek (Virginia Park) catchment (11.9 ha) in the Burdekin basin. Except the Burnett-Mary sub catchments, the measured runoff and sediment data were available for all the other catchments for the selected period. This study has three objectives: (1) to model the spatial distribution of vegetation cover in a parameter-efficient manner in grazing land, (2) to compare and evaluate the performance of Revised USLE and Modified USLE models for soil loss prediction, and (3) to integrate the ground cover variation with rainfall-runoff models for catchment scale runoff prediction. To address the first objective, the beta distribution was used to characterise cover variation in space at the sub-catchment scale. Three methods were used to test the appropriateness of the beta distribution: (i) visual goodness-of-fit assessment and Kolmogorov–Smirnov (K-S) test; (ii) the fractional area with cover ≤53%; and (iii) estimated runoff amount for a given rainfall amount for the fractional area with 3 | P a g e cover ≤53%. It was concluded that the two-parameter beta distribution is a parameter efficient method to characterise the spatial variation of cover and to evaluate the effect of cover variation on runoff in grazing catchments. In the second objective, the sediment yield changes due to the conversion of brigalow forest to cropping and grazing was assessed and the potential contributing factors to sediment yield were identified. It also evaluated the comparative performance of the Revised Universal Soil Loss Equation (RUSLE) and the Modified Universal Soil Loss Equation (MUSLE) in predicting the sediment yield from the three BCS catchments. The study supports applying the MUSLE model, which considers runoff (Q) and peak runoff rate (Qp) in BCS for improved sediment yield prediction. This work has been extended to three grazed catchments of Fitzroy and Burdekin basins to compare and evaluate the performance of RUSLE and MUSLE models for predicting soil loss/sediment yield for grazing catchments. The MUSLE models performed better as compared to the RUSLE model for all three catchments. Compared to the RUSLE model, the MUSLE1 model with factors Q and Qp, was able to predict sediment yield for Weany creek and Brigalow catchment and the MUSLE2 with factors EI30, Q and Qp performed well for Springvale and Brigalow catchment. The calibrated soil erodibility factor (K) was found to be 14%, 24%, and 60% higher for Springvale, Brigalow, and Weany Creek catchments, respectively, compared to the K-factor from the Australian Soil Resource Information System (ASRIS). This study recommends using the MUSLE model to improve sediment yield prediction from hillslope grazing lands in Australia. In the third objective, the influence of the spatial and temporal variation of cover on runoff using a conceptual framework to integrate the cover variation with lumped rainfall-runoff models for the Nogoa catchment (13,880 km2) was investigated. Preliminary findings of this study show that modified SimHyd cannot provide improved runoff estimation when the spatial and temporal variation of ground cover is taken into consideration. Use of an alternative approach, i.e., SCS Curve Number (CN) method for individual storm events shows that when the combined effect of rainfall and ground cover on CN is considered, the groundcover affect CN negatively, i.e., the lower the cover, the larger the value of CN, hence the higher the storm runoff amount for the same amount of rainfall. 4 | P a g e The exponent for ground cover is different from zero comparing to rainfall. Therefore, it can be concluded that though the modified SimHyd model wouldn’t provide the improved estimation of runoff, however, the alternative approach i.e., CN method shows that ground cover significantly affects runoff and cannot be ignored for the Nogoa catchment. Overall, this study highlights the need for an improved understanding of the effect of the spatial distribution of ground cover on runoff and sediment delivery from large grazing catchments, with the main findings as follows: (1) the spatial distribution of ground cover can be efficiently described using beta distribution, (2) runoff and peak runoff rate are the major variables responsible for variations in sediment yield from grazing catchments, (3) the MUSLE model should be used for grazing catchments irrespective of the catchment size for improved prediction of sediment yield with some modifications to soil erodibility values, (4) the effect of change in ground cover on runoff was identified using the SCS-CN method, hence, an improvement in the current lumped hydrological modelling framework would be achieved with inclusion of spatial variability of ground cover in addition to rainfall and potential evapotranspiration as input to hydrological models and should be further tested and developed for dry catchments with low ground cover for improved simulation of runoff from grazing catchments.

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Thesis (PhD Doctorate)
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Doctor of Philosophy (PhD)
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School of Eng & Built Env
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Great Barrier Reef
rainfall-runoff models
Vegetation Cover Dynamics
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