Increasing riparian vegetation cover to improve water quality: The importance of considering land use

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J. Te Ngaio, Joesph M. McMahon, Deanna van den Berg, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8643825/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Environmental Management → Version 1 posted 9 You are reading this latest preprint version Abstract Riparian vegetation plays a crucial role in regulating sediment dynamics, reducing surface runoff, mobilising sediment, and stabilising streambanks. Despite extensive research on sediment loads and riparian vegetation individually, there remains a gap in understanding their interrelationship, particularly within the context of water quality and catchment management. This study investigates the statistical association between water quality and riparian vegetation cover within the Herbert catchment, Far North Queensland, Australia. Over one million total suspended sediment equivalent (TSSeq) data points were collected from 14 monitoring sites between December 2020 and December 2023, averaged into 361 monthly samples and paired with site-specific total cover (TC) values. Using Spearman’s rank correlation across land use disturbance classes (minimal, moderate, high) and seasonal subsets, results revealed a significant overall negative correlation between TSSeq and TC (ρ = -0.431, p < 0.0001). The strength of this relationship declined with increasing disturbance: minimal disturbance sites showed the strongest correlation (ρ = -0.530, p 0.05). Seasonal analysis showed stronger correlations during the wet season, except in high disturbance areas, where the dry season correlation was higher but still not statistically significant. Limitations in TC’s ability to distinguish vegetation types and capture dynamic cover changes in disturbed areas are discussed. These findings highlight the importance of riparian vegetation in improving water quality and underscore the need for refined remote sensing methods when integrating high-resolution temporal water quality datasets. Watershed sediment remote sensing catchment management sugarcane cropping riparian cover Figures Figure 1 Figure 2 Figure 3 1.0 Introduction The protection and rehabilitation of riparian zones is frequently advocated to balance land use pressures and in stream water quality (Alemu et al., 2017 ; Dosskey et al., 2010 ; Gomes et al., 2014 ; McKergow et al., 2004 ). Riparian restoration is globally recognised as an effective strategy for improving land and water quality by enhancing channel stability, reducing erosion and reducing nutrient runoff (González et al., 2015 ; Hamman et al., 2022 ; Singh et al., 2021 ). Riparian vegetation is critical to maintain essential ecosystem functions such as regulating sediment and nutrient transport (Fernandes et al., 2011 ; Fernández et al., 2014 ). The nature of riparian vegetation cover can result in varying ecosystem benefits specific to the landscape, such as channel stabilisation by large woody vegetation or surface runoff filtration by dense herbaceous vegetation (Alemu et al., 2017 ; Dosskey et al., 2010 ; McKergow et al., 2004 ). Studies have demonstrated that reduced riparian vegetation cover has led to increased rates of erosion (Bartley et al., 2008 ; McKergow et al., 2004 ). However, the extent of the impact on water quality depends on a range of hydrologic, climatic and biological factors like soil type and slope, vegetation structure and species composition (Dosskey et al., 2010 ). Understanding the environmental conditions unique to a given location enhance our ability to identify factors that influence the role of riparian vegetation in delivering water quality outcomes. This is critical for targeted restoration efforts, tailored to local ecological and hydrological conditions. In the catchments of the Great Barrier Reef (GBR), Australia, protection and rehabilitation of riparian vegetation forms a key component of strategies to reduce diffuse water pollution (Waterhouse et al., 2024 ). Numerous studies in the Great Barrier Reef catchment area (GBRCA) have been used to inform policy and guide improvement initiatives surrounding ongoing environmental pressures (Brodie et al., 2009 ; Waterhouse et al., 2024 ). Current estimates suggest that fine sediment export to the GBR is approximately 1.4 to 5 times greater than pre-development loads (Prosser & Wilkinson, 2024 ). Vegetation degradation through land/tree clearing, changes in structure and function of grass species and low ground cover are amongst key drivers of anthropogenic sediment from the GBRCA (Wilkinson et al., 2024 ). Despite ongoing efforts, current land management strategies are yet to fully combat the impact of historic landscape modification and subsequent environmental decline (Eberhard et al., 2017 ; Hamman et al., 2022 ; Lawson et al., 2007 ). Despite its widespread recommendation in policy documents, there has been limited research on water quality responses to varying riparian vegetation extents between sites at a catchment scale (Dosskey et al., 2010 ; Feld et al., 2018 ; Lorenz & Feld, 2013 ). Olley et al. ( 2015 ) is one of the few studies to explore this within Australia and highlighted a significant relationship between total suspended sediment (TSS) load and the proportion of remnant vegetation 1 in Southeast Queensland during high flow events. The paper estimated catchments containing no remnant vegetation produced 50 to 200 times more sediment per unit area than those with fully vegetated channels. These findings suggest there may be a correlation between sediment loads and riparian vegetation within GBR catchments. Water quality monitoring is commonly used to identify priority areas for intervention and indicate environmental improvement. High-frequency in situ sensors are increasingly employed for water quality monitoring due to their ability to capture fine-scale temporal variations, including event driven processes (Rode et al., 2016 ; Rozemeijer et al., 2025 ). The aim of this study was to investigate the relationship between water quality and riparian vegetation cover using total suspended sediment equivalents (TSSeq) data collected in the Herbert River catchment within the GBRCA. This dataset was paired with monthly spatial estimated measures of total cover in the riparian zone. The three primary research objectives were to (1) analyse the temporal interaction between water quality and riparian vegetation cover to understand the seasonal effect between the wet and dry season, (2) assess the spatial variation between water quality and riparian vegetation cover to understand the influence of dominant land use (cropping, conservation and grazing native vegetation) on water quality and riparian vegetation and (3) confirm whether the riparian zone influences water quality. 2.0 Methods and materials 2.1. Study area and sampling sites The Herbert catchment is located in far north Queensland and is the largest catchment in the Wet Tropics region (9, 842km 2 ) (State of Queensland, 2018b ). Rainfall and groundwater inputs to the Herbert basin are sustained intermittently throughout the year and can experience several high flow or flood periods during the wet season (Australia. Bureau of Meteorology et al., 2019). The Herbert River meanders through three distinct sections characterised by differing land uses and physiography (Fig. 1 )(Bartley et al., 2003 ; Johnson et al., 2000 ). The upper section is dominated by cattle grazing with minor areas of land for horticulture and dairy, the middle is protected Wet Tropics World Heritage Area and state forest, and the lower section has extensive sugarcane cropping along with areas of conservation and grazed native vegetation. The Herbert catchment has lost the largest proportion of riparian woody vegetation since pre-clearing among Wet Tropics catchments (State of Queensland, 2019 ). Streambank erosion is the primary contributor of sediment in the catchment, followed by hillslope erosion (McCloskey et al., 2021 ; Tims et al., 2010 ). Erosion rates are greater in areas with high land use intensity, particularly in regions dominated by grazing and sugarcane cultivation (Tims et al., 2010 ). The Herbert is listed as one of the top three priority catchments for sediment reduction in the GBRCA and has extensive water quality monitoring in the lower section (hereafter referred to as the lower Herbert) (State of Queensland, 2018a ). Each monitoring site is positioned at a singular point to capture upstream pollutant sources. The monitoring program uses topography to delineate the boundaries and shape of a monitoring site's catchment area by defining the direction of surface water flow and the area upstream of the monitoring site point that contributes to runoff. 2.2 Sampling design and processing Fourteen monitoring sites in the lower Herbert water quality monitoring program were selected to collate data on total suspended sediment equivalents (TSSeq) from December 2020 to December 2023. Samples are collected using TriOS™ Opus spectral sensors and transmitted hourly to the Department of the Environment, Tourism, Science and Innovation (DETSI) Water Quality Investigations online data portal, eagle.io. The data is processed through an automated data quality assurance and control pipeline adapted from a proposed framework by Leigh et al. ( 2019 ). Data is periodically reviewed to validate automated outputs, identify significant deviations, and flag values needing additional assessment and site-specific analysis. In total, over 1 million data samples were collated and averaged to monthly estimates. The sample periods and counts for each site are detailed in Table 1 . Table 1 Information summary of sites selected from Herbert catchment water quality monitoring sites including sub catchment, site ID, site name, latitude, longitude, site area (km 2 ), riparian area (km 2 ), number of samples collected and the sampling period. Sub Catchment Site ID Site name Latitude Longitude Site catchment area (km 2 ) Riparian area (km 2 ) Number of months sampled Herbert River 1160115 Broadwater Creek at Day Use -18.41633 145.94393 90 17 27 Francis Creek 1160118 Francis Creek at Weir -18.76673 146.13407 143 31 21 Herbert River 1160119 Herbert River at John Row Bridge -18.62831 146.16486 8608 1575 21 Herbert River 1160120 Herbert River at Nash's Crossing -18.41435 145.77097 6739 1191 25 Victoria Creek 1160121 Lagoon Creek at Five Mile Road -18.62341 146.2632 18 4 24 Lannercost Creek 1160122 Lannercost Creek at Lannercost Ext Road -18.6185 146.03286 180 26 21 Palm Creek 1160124 Palm Creek at Bosworths Road -18.69688 146.22796 32 2 29 Ripple Creek 1160126 Ripple Creek at Gangemis Road -18.5811 146.1323 31 6 32 Stone River 1160127 Stone River at Running Creek -18.76607 145.95028 164 39 26 Palm Creek 1160129 Trebonne Creek at Bruce Highway -18.7197 146.14842 95 9 36 Cattle Creek 1160130 Waterview Creek at Jourama Road -18.85141 146.12466 38 5 27 Cattle Creek 1160131 Waterview Creek at Mammarellas Road -18.82947 146.20546 53 9 23 Herbert River 1160133 Ripple Creek at Seymour Creek Gates -18.58571 146.18459 83 9 26 Palm Creek 1160134 Palm Creek at Cemetery Road -18.6643 146.2006 17 2 26 2.3 Riparian buffer data and processing A total riparian vegetation cover (TC) metric was derived using the monthly blended fractional cover dataset produced by the Joint Remote Sensing Research Program and Queensland Government’s DETSI Ground Cover Monitoring program (Joint Remote Sensing Research et al., 2022). This dataset consists of medoid-composited monthly fractional cover created from a combined Landsat 8 and 9 and Sentinel-2 time series, with at least three cloud free observations of fractional cover imagery required for a representative pixel to be included in the monthly composite. Riparian buffers were defined as 50 m around waterways above stream order 1 to align with the Paddock to Reef monitoring methodology (Australia and Queensland governments, 2024 ). The calculated riparian area within the contributing catchment area of each monitoring site is provided in Table 1 . Using ArcGIS Pro, we extracted the mean percentage of green vegetation (e.g. leaves, grass and growing crops), non-green vegetation (e.g. branches, dry grass, hay and dead leaf litter), and bare ground (soil or rock) within riparian buffers for catchments draining to each monitoring site. Green and non-green fractions represent a combination of woody and non-woody vegetation, and were combined to create the TC metric. The riparian buffers for catchments draining to each monitoring site were also used to extract the percent of land used for conservation, cropping and grazing native vegetation, woody vegetation extent, slope steepness (S) factor and soil erodibility (K) factor. Land use percentages were calculated using the GBR land use mapping 2021 dataset. Land use classes are based on a modified Australian Land Use Management Classification (ALUMC) Schema, Version 9 (October 2016) (State of Queensland, 2023 ). The presence of woody vegetation has been determined through the use of the Statewide Landcover and Trees Study (SLATS) woody vegetation extent dataset, where stands of woody vegetation greater than 0.5 ha with a canopy density greater than 10% crown cover are included (State of Queensland, 2024 ). The K factor and S factor were selected as the two most relevant factors in the revised universal soil loss equation to inform spatial differences in soil erosion potential across the study area. The K factor is derived from soil data in the Australian Soil Resource Information System (State of Queensland, 2016a ) and the S factor is derived from a Digital Elevation Model created by the Shuttle Radar Topography Mission (State of Queensland, 2016b ). Table S1 outlines the riparian buffer characteristics identified within the contributing catchment area for each monitoring site and incorporated into the data analysis. 2.4 Data analysis All available TSSeq monitoring data between December 2020 and December 2023 was included in the analysis, with the sampling date and total number of samples varying between sites. The total months of data for each site are provided in Table 1 . A Principal Component Analysis (PCA) was undertaken to explore the variables with the highest contribution to site variability. The analysis incorporated TSSeq, TC and riparian buffer conditions outlined in Table S1. Prior to analysis, all variables were standardised by subtracting the mean and dividing by the standard deviation, resulting in values with a mean of 0 and a standard deviation of 1. Sites were then plotted along the first two principal components to reveal clustering patterns and define groups. A biplot was utilised to understand how to variables responded to each other and corresponded to the sites and identified clusters in the PCA. The statistical relationship between TSSeq and TC was evaluated using paired samples. Initial data exploration indicated non-normal distributions for both variables. Consequently, Spearman’s ranks correlation was selected as a non-parametric method to assess the strength and direction of the association between TSSeq and TC. Statistical significance was determined at a threshold of p < 0.05. Correlation analysis was conducted on the entire dataset and separated by wet and dry season, to estimate the influence of temporal variation. Linearity was visually examined through a bivariate linear regression graph. All statistical analyses were conducted using the R 4.4.2 Hmisc package (R Core Team, 2016 ). 3.0 Results and discussion This study aimed to examine the statistical relationship between water quality (measured TSSeq) and riparian vegetation cover (measured total cover (TC)) within the Herbert catchment. The analysis of correlations between TC and TSSeq revealed seasonal and grouped patterns reflective of the spatial distribution of riparian characteristics that highlight the role of riparian vegetation in sediment retention across varying land-use conditions. Here we highlight the importance of maintaining high functioning riparian zones for water quality improvements and the effect of land use disturbance. Our findings provide valuable insight to support evidence-based riparian management practices and contribute to the development of more effective catchment scale environmental policies. 3.1 Cluster analysis The PCA cluster (Fig. 2 a) and corresponding biplot (Fig. 2 b) was employed to differentiate sites using TC, TSSeq, and variables outlined in Table S1. The ordination patterns indicate the presence of three clusters, with separation primarily driven by differences in dominant land use type (grazing native vegetation, conservation and cropping). The first and second principal components (PC1 and PC2) explained 54.6% and 23.5% of the total variability, respectively. The first principal component was primarily associated with a strong positive association for cropping (0.44) and k-factor (0.40), and a strong negative for s-factor (-0.43), woody vegetation (-0.41), TC (-0.40) and conservation (-0.37). The second principal component was primarily associated with a strong positive associated for grazing native vegetation (0.63). Comparatively, the third principal component accounted for 10.45% of the total variance in the data and was dominated by TSSeq (0.83), with only minor contributions from other variables (< 0.30). This indicates that the third axis primarily reflects a TSSeq gradient largely independent of the land use contrasts resolved by PC1 and PC2. Within Fig. 2 a, sites in cluster 1 (1160121, 1160124, 1160129 and 1160134, as identified by Fig. 2 b) are distinct due to their strong association with high cropping intensity and elevated k-factor. In contrast, the overlap between cluster 1 and 3 in Fig. 2 a and position of woody vegetation in Fig. 2 b indicates sites within these two groups share similar levels of woody vegetation cover. Cropping and woody vegetation show an inverse relationship, which is also observed, though less strong, between cropping and TC. However, TC is more closely related to conservation land use, suggesting that these sites may have a higher diversity of vegetation cover types beyond woody vegetation. 3.2 Site classification Based on the findings presented in Fig. 2 a and 2 b, percentage of woody vegetation cover and land used for grazing native vegetation, conservation and cropping was identified as the main separator between majority of sites. Comparatively, the s-factor and k-factor further differentiated the sites due to reflecting their variation in topography and land condition. Table 2 lists each site and their site classification which is defined as; 1) Minimal disturbance – site dominated by conservation land use or grazing present but 90% woody vegetation cover, 2) Moderate disturbance – sites dominated by grazing, forestry or conservation present but 90% woody vegetation cover, 3) High disturbance – site dominated by cropping land use. Table 2 Sites and defined site class Site Site class 1160115 Minimal disturbance 1160118 Moderate disturbance 1160119 Moderate disturbance 1160120 Moderate disturbance 1160121 High disturbance 1160122 Moderate disturbance 1160124 High disturbance 1160126 Minimal disturbance 1160127 Moderate disturbance 1160129 High disturbance 1160130 Minimal disturbance 1160131 Minimal disturbance 1160133 Minimal disturbance 1160134 High disturbance In addition to containing high proportions of cropping land use, the high disturbance sites (1160121, 1160124, 1160129, and 1160134) were distinguished by notably lower s-factor values and elevated k-factor values. Whereas, low and moderate disturbance sites exhibited comparable proportions of woody vegetation cover, cropping, and k-factor values. The moderate disturbance sites (1160118, 1160119, 1160120, 1160122, 1160127) demonstrated more heterogeneous land use patterns, with a greater mix between conservation and grazing of native vegetation. In contrast, the minimal disturbance sites (1160115, 1160126, 1160130, 1160131, 1160133) were characterised by similar proportions of conservation land use and woody vegetation cover. 3.2 Correlation analysis On a catchment scale, the results of our study aligned with Olley et al. 2015 indicating that in the Herbert catchment, TSSeq concentrations decrease (i.e. water quality improves) proportionally as total riparian vegetation cover increases, regardless of season of land use disturbance (ρ = -0.431, p < 0.0001). However, as land use disturbance intensifies from minimal disturbance (ρ = -0.530, p 0.05), the correlation weakens and direction of the correlation between water quality and riparian vegetation cover changes. Figure 3 Natural log of monthly mean total suspended sediment equivalents (TSSeq) against natural log of total riparian vegetation cover (%) between December 2020 to December 2023 with point shapes and regression line type representing season (wet and dry). The solid line and circle dots represent dry season samples, and the dash and triangle points represent wet season samples. Colours correspond to site classification of either high disturbance (red), minimal disturbance (green) or moderate disturbance (blue) TSSeq concentrations were consistently higher during the wet season across all site groups, with the moderate disturbance group exhibiting the highest values on average. This trend is likely driven by increased rainfall and surface runoff, which enhances erosion, facilitates the transport of suspended sediments into adjacent waterways and increases gully and streambank erosion potential. The minimal disturbance group maintained the highest levels of cover, while the moderate and high disturbance groups showed greater variability, particularly during the wet season. As shown in Table 3 , in the combined data set, the correlation between TSSeq and total riparian vegetation cover is strongest during the wet season. This suggests that riparian vegetation plays a more significant role in moderating sediment transport under high-flow conditions. During the dry season, reduced river discharge and lower erosion rates result in generally lower TSSeq concentrations, which may weaken the observed relationship regardless of differences in vegetation cover. Table 3 Summary of Spearman's rank correlation coefficient (ρ) between Total Cover (TC) and total suspended sediment (TSSeq) within each site class and between wet and dry season. Values in bold are significant at the 0.05 level. TSSeq Site class TC ~ Mean Mean - Wet Mean - Dry Minimal disturbance ρ -0.530 -0.668 -0.474 p-value < 0.0001 < 0.0001 < 0.0001 R 2 0.281 0.446 0.225 Moderate disturbance ρ -0.390 -0.443 -0.208 p-value < 0.0001 < 0.01 < 0.001 R 2 0.152 0.197 0.043 High disturbance ρ 0.075 0.039 0.305 p-value 0.429 0.795 0.011 R 2 -0.006 0.002 0.093 All sites combined ρ -0.431 -0.546 -0.326 p-value < 0.0001 < 0.0001 < 0.0001 R 2 0.186 0.298 0.106 Among the three site classifications, the minimal disturbance group exhibited the strongest negative correlation between TSSeq and TC (ρ = -0.530, p < 0.0001). These sites were characterised by higher proportions of conservation land use and woody vegetation. The strength of this relationship is likely due to the presence of well-established and relatively undisturbed canopy structures, which enhance sediment retention and filtration capacity within riparian zones. Although the moderate disturbance group showed a weaker correlation, the results were consistent with those of the minimal disturbance group. Significant negative correlations were observed in both the combined seasonal dataset (ρ = -0.390, p < 0.0001) and the wet season subset (ρ = -0.443, p < 0.01). In contrast, the high disturbance group showed a non-significant correlation (ρ = 0.075, p = 0.429). Notably, this group was the only one to exhibit a stronger and statistically significant correlation during the dry season (ρ = 0.305, p = 0.011), unlike the wet season where the correlation was weak and not significant (ρ = 0.039, p = 0.795). The underlying cause of the stronger dry season correlation is unclear. This result may be attributed to the role of vegetation in facilitating the accumulation of already entrained sediment, as reduced water flow and increased sediment deposition during this period create conditions favourable to sediment accumulation (Huai et al., 2021 ). The results indicate that in regions experiencing minimal to moderate land use disturbance, riparian vegetation continues to play a vital role in enhancing water quality. These findings align with those of Olley et al. ( 2015 ) who identified a statistically significant relationship between TSS load and the proportion of remnant vegetation in Southeast Queensland. Similar land use observations were observed in de Mello et al. ( 2018 ) who found a negative correlation between forest cover and TSS and organic suspended solids concentrations, alongside a positive correlation between agricultural land use and elevated levels of the sediment indicators in Southeastern Brazil. Comparatively, our results suggest that as disturbance levels increase to highly disturbed, it becomes more difficult to detect the relationship between TC and reduced TSSeq as other sediment generating process become prevalent. In addition, inherent complexities and constraints associated with satellite-based remote sensing could be a factor. The monthly fractional cover dataset, created from a combined Landsat 8 and 9 and Sentinel-2 time series, predicts vegetation cover at medium spatial resolution (30 m). In areas with minimal disturbance, vegetation cover is expected to remain relatively consistent within the monthly time period, and the observations used to create the composite image are likely to be relatively similar. The vegetation cover in highly disturbed agricultural landscapes is more variable, due to the dynamic nature of cropping systems. The observations used for a monthly composite may include abrupt changes due to harvest or planting for example, with the change in cover more rapid than the monthly time scale. Changes in cropping areas may also be missed due to cloud cover and therefore not represented in the monthly dataset. The spatial arrangement of cover in highly disturbed areas is also more variable than areas with minimal disturbance. Cropping areas with high cover may still present with persistently bare inter-rows for example, contributing to substantial surface runoff during rainfall events. Similar limitations of remote sensing methods are highlighted in Allred et al. ( 2025 ) and Saldarriaga ( 2022 ) who note challenges specific to unique vegetation patterns and cover type in riparian areas. Saldarriaga ( 2022 ) discusses Sentinel-2 as optimal for large areas and suggests complex ecosystems, like fine-scale riparian buffers, should employ UAV platforms to provide more detailed analysis. This is especially when analysing small areas with mixed land use and vegetation types. Allred et al. ( 2025 ) provides a similar perspective with consideration to understorey vegetation, which may not be well represented, particularly in areas with dense overstory canopies. Based on our results and observations provided in Allred et al. ( 2025 ) and Saldarriaga ( 2022 ), future work could evaluate the integration of additional satellite-derived vegetation products to separate ground cover from woody vegetation to further understand the influence of vegetation cover types. In addition, our findings corroborate the results shown in Olley et al. ( 2015 ), indicating that areas with high woody vegetation cover, subject to minimal land use disturbance, result in decreased amounts sediment present in waterways. These results support policy direction for increasing woody riparian vegetation cover through revegetation works streambank restoration which can generating water quality improvements. However, as land use disturbance begins to transition from minimal to high, implementing strategies to address policy goals focussed on improving riparian vegetation cover for water quality benefits need to acknowledge that in a fragmented riparian zone may not result in a measurable water quality improvement until a substantial woody vegetation corridor is established. Integrating long-term monitoring would provide greater insight into the full benefit of riparian restoration efforts and to quantify how much riparian restoration is needed to see a measurable water quality improvement. Many riparian management studies are confined to short-term observations (Feld et al., 2018 ; Muller et al., 2016 ). This can limit identification of lag effects between the time of management intervention and ecological outcome. Whilst the magnitude of lag time can vary between sites and pollutant type (Meals et al., 2010 ; Swanson et al., 2017 ), increasing data resolution to evaluate water quality before and after riparian management intervention could improve lag identification and provide insight into long-term water quality improvement benefits (Feld et al., 2018 ; Muller et al., 2016 ). 4.0 Conclusion Riparian vegetation plays a critical role in regulating in-stream sediment loads by reducing runoff, trapping sediments, and stabilising banks. This study found a significant relationship between total suspended sediment equivalents (TSSeq) and total riparian cover (TC) across 14 sites in the Herbert catchment, particularly during the wet season. However, this relationship weakened with increasing disturbance, due to the influence of land use variability and seasonal cropping dynamics. These findings highlight the importance of riparian vegetation restoration for sediment control, water quality improvements and the need for careful interpretation of remotely sensed cover in highly disturbed catchments. Our results highlight the importance of maintaining high riparian (woody) vegetation cover. In addition, river restoration approaches (improving riparian vegetation cover for water quality improvements) need to understand the fragmentation of the riparian zone in relations to measurable water quality improvements. Future research should investigate dominant sediment delivery processes linked to specific land uses within riparian buffers, both at localised scales and across the broader catchment. While cropping remains the dominant high disturbance land use in the lower Herbert riparian zone, policy interventions should prioritise the restoration of riparian zones in a systematic and connected approach with a focus on restoring woody vegetation. Declarations Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Payton Te Ngaio. The first draft of the manuscript was written by Payton Te Ngaio and all affiliated authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements This work was conducted as part of an honours research project, which was supported by the Reef Catchments Science Partnership (The University of Queensland and Queensland Department of the Environment, Tourism, Science and Innovation) and funded by the Michael St John Warne Memorial Scholarship. I extend my thanks to Angela Marsh who co-supervised the honours research project, Richard Gardiner for sediment data processing from eagle.io, Jen Strauss who acted as an advising statistician, Peter Hairsine for guiding the writing process and Steph Atkins for being a trusted external advisor. Lastly, thank you to the team at Water Quality and Investigations, the Earth Observation and Social Sciences team within the Queensland Department of the Environment, Tourism, Science and Innovation (DETSI) and all those involved in the Great Barrier Reef Catchment Loads Monitoring Program, for collecting, managing and processing the sediment and fractional cover monitoring data. References Alemu, T., Bahrndorff, S., Hundera, K., Alemayehu, E., & Ambelu, A. (2017). Effect of riparian land use on environmental conditions and riparian vegetation in the east African highland streams. Limnologica , 66 , 1–11. Allred, B. W., McCord, S. E., Assal, T. J., Bestelmeyer, B. T., Boyd, C. S., Brooks, A. C., Cady, S. M., Fuhlendorf, S. D., Green, S. A., & Harrison, G. R. (2025). Estimating rangeland fractional cover and canopy gap size class with Sentinel-2 imagery. bioRxiv , 2025.2003. 2013.643073. Australia and Queensland governments. (2024). 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Target setting for pollutant discharge management of rivers in the Great Barrier Reef catchment area. Marine and Freshwater Research , 60 (11), 1141–1149. de Mello, K., Valente, R. A., Randhir, T. O., dos Santos, A. C. A., & Vettorazzi, C. A. (2018). Effects of land use and land cover on water quality of low-order streams in Southeastern Brazil: Watershed versus riparian zone. Catena , 167 , 130–138. Dosskey, M. G., Vidon, P., Gurwick, N. P., Allan, C. J., Duval, T. P., & Lowrance, R. (2010). The role of riparian vegetation in protecting and improving chemical water quality in streams 1. JAWRA Journal of the American Water Resources Association , 46 (2), 261–277. Eberhard, R., Brodie, J., & Waterhouse, J. (2017). Managing water quality for the Great Barrier Reef. Decision Making in Water Resources Policy and Management , 265–289. Feld, C. K., Fernandes, M. R., Ferreira, M. T., Hering, D., Ormerod, S. J., Venohr, M., & Gutiérrez-Cánovas, C. (2018). 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Restoration of riparian vegetation: A global review of implementation and evaluation approaches in the international, peer-reviewed literature. Journal of Environmental Management , 158 , 85–94. Hamman, E., Brodie, J., Eberhard, R., Deane, F., & Bode, M. (2022). Regulating land use in the catchment of the Great Barrier Reef. Land Use Policy , 115 , 106001. Huai, W.-x., Li, S., Katul, G. G., Liu, M.-y., & Yang, Z.-h. (2021). Flow dynamics and sediment transport in vegetated rivers: A review. Journal of Hydrodynamics , 33 (3), 400–420. Johnson, A., Ebert, S., & Murray, A. (2000). Land cover change and its environmental significance in the Herbert River catchment, north-east Queensland. Australian Geographer , 31 (1), 75–86. Joint Remote Sensing Research, P., Department of the Environment, T. S., & Innovation, Q. G. (2022). Monthly Blended Fractional Cover - Landsat and Sentinel-2, JRSRP Algorithm Version 3.0, Queensland Coverage (Version 3.0) Terrestrial Ecosystem Research Network. https://portal.tern.org.au/metadata/TERN/8d3c 8b36-b4f1-420f-a3f4-824ab70fb367 Lawson, T., Gillieson, D., & Goosem, M. (2007). Assessment of riparian rainforest vegetation change in tropical North Queensland for management and restoration purposes. Geographical Research , 45 (4), 387–397. Leigh, C., Alsibai, O., Hyndman, R. J., Kandanaarachchi, S., King, O. C., McGree, J. M., Neelamraju, C., Strauss, J., Talagala, P. D., & Turner, R. D. (2019). A framework for automated anomaly detection in high frequency water-quality data from in situ sensors. Science of the Total Environment , 664 , 885–898. Lorenz, A. W., & Feld, C. K. (2013). Upstream river morphology and riparian land use overrule local restoration effects on ecological status assessment. Hydrobiologia , 704 (1), 489–501. McCloskey, G., Baheerathan, R., Dougall, C., Ellis, R., Bennett, F., Waters, D., Darr, S., Fentie, B., Hateley, L., & Askildsen, M. (2021). Modelled estimates of fine sediment and particulate nutrients delivered from the Great Barrier Reef catchments. Marine pollution bulletin , 165 , 112163. McKergow, L. A., Prosser, I. P., Grayson, R. B., & Heiner, D. (2004). Performance of grass and rainforest riparian buffers in the wet tropics, Far North Queensland. 2. Water quality. Soil Research , 42 (4), 485–498. Meals, D. W., Dressing, S. A., & Davenport, T. E. (2010). Lag time in water quality response to best management practices: A review. Journal of environmental quality , 39 (1), 85–96. Muller, I., Delisle, M., Ollitrault, M., & Bernez, I. (2016). Responses of riparian plant communities and water quality after 8 years of passive ecological restoration using a BACI design. Hydrobiologia , 781 (1), 67–79. Neldner, V. J., Wilson, B. A., Dillewaard, H. A., Ryan, T. S., Butler, D. W., McDonald, W. J. F., Richter, D., Addicott, E. P., & Appelman, C. N. (2023). Methodology for survey and mapping of regional ecosystems and vegetation communities in Queensland . (Version 7.0). Queensland Herbarium, Queensland Department of Environment, Tourism, Science and Innovation, Brisbane Olley, J., Burton, J., Hermoso, V., Smolders, K., McMahon, J., Thomson, B., & Watkinson, A. (2015). Remnant riparian vegetation, sediment and nutrient loads, and river rehabilitation in subtropical Australia. Hydrological Processes , 29 (10), 2290–2300. Prosser, I., & Wilkinson, S. (2024). Question 3.3 How much anthropogenic sediment and particulate nutrients are exported from Great Barrier Reef catchments (including the spatial and temporal variation in delivery), what are the most important characteristics of anthropogenic sediments and particulate nutrients, and what are the primary sources. Waterhouse J, Pineda MC, Sambrook K (Eds) . R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www . R-project . org/ . Rode, M., Wade, A. J., Cohen, M. J., Hensley, R. T., Bowes, M. J., Kirchner, J. W., Arhonditsis, G. B., Jordan, P., Kronvang, B., & Halliday, S. J. (2016). Sensors in the stream: the high-frequency wave of the present. In: ACS Publications. Rozemeijer, J., Jordan, P., Hooijboer, A., Kronvang, B., Glendell, M., Hensley, R., Rinke, K., Stutter, M., Bieroza, M., & Turner, R. (2025). Best practice in high-frequency water quality monitoring for improved management and assessment; a novel decision workflow. Environmental Monitoring and Assessment , 197 (4), 353. Saldarriaga, P. B. (2022). Multi-scale fluvial remote sensing-a study on spatial scaling discrepancies between Sentinel-2 and UAV multispectral data on riparian zones in Northwest Portugal Universidade do Minho (Portugal)]. Singh, R., Tiwari, A., & Singh, G. (2021). Managing riparian zones for river health improvement: an integrated approach. Landscape and ecological engineering , 17 (2), 195–223. State of Queensland. (2016a). Soils - universal soil loss equation - K factor https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={8A72D6B 8-2DBD-4538-A2DC-84ACAE66DFCE} State of Queensland. (2016b). Soils - universal soil loss equation - S Factor https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={594F3CCD-95AC- 447B-86FB-A028E049FE70} State of Queensland. (2018a). Reef 2050 Water Quality Improvement Plan 2017–2022 . Queensland Government Retrieved from https://www.reefplan.qld.gov.au/__data/assets/pdf_file/0017/46115/reef-2050-water-quality-improvement-plan-2017-22.pdf State of Queensland. (2018b). Wet Tropics Region Herbert catchment water quality targets. https://www.reefplan.qld.gov.au/__data/assets/pdf_file/0017/46061/catchment-targets-wet-tropics-herbert.pdf State of Queensland. (2019). Riparian vegetation extent methods: Reef Water Quality Report Card 2017 and 2018. Reef 2050 Water Quality Improvement Plan . https://www.reefplan.qld.gov.au/__data/assets/pdf_file/0027/82926/report-card-2017-2018-methods-riparian-extent.pdf State of Queensland. (2023). Land use mapping – 2021 - Great Barrier Reef NRM regions https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={35C9BBE8- 5EB7-451B-B191-0650098DAC37} State of Queensland. (2024). Statewide Landcover And Trees Study (SLATS) Sentinel-2–2022 woody vegetation extent - Queensland - Whole of state https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={91B442A 3-FB5F-4E24-B3AF-4C90A983631B} Swanson, S., Kozlowski, D., Hall, R., Heggem, D., & Lin, J. (2017). Riparian proper functioning condition assessment to improve watershed management for water quality. Journal of Soil and Water Conservation , 72 (2), 168–182. Tims, S., Everett, S., Fifield, L. K., Hancock, G., & Bartley, R. (2010). Plutonium as a tracer of soil and sediment movement in the Herbert River, Australia. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms , 268 (7–8), 1150–1154. Waterhouse, J., Pineda, M., Sambrook, K., Newlands, M., McKenzie, L., Davis, A., Pearson, R., Fabricius, K., Lewis, S., & Uthicke, S. (2024). Scientific Consensus Statement: Conclusions. Waterhouse J, Pineda MC Sambrook K (Eds) . Wilkinson, S., Murray, B., & Prosser, I. (2024). Question 3.4 What are the primary biophysical drivers of anthropogenic sediment and particulate nutrient export to the Great Barrier Reef and how have these drivers changed over time. Waterhouse J, Pineda MC, Sambrook K (Eds) . Footnotes Woody vegetation classified as remnant under the Vegetation Management Act 1999 when the dominant canopy retains more than 70% of its original height and over 50% of its original cover, and primarily consists of species typical of an undisturbed canopy layer. Neldner, V. J., Wilson, B. A., Dillewaard, H. A., Ryan, T. S., Butler, D. W., McDonald, W. J. F., Richter, D., Addicott, E. P., & Appelman, C. N. (2023). Methodology for survey and mapping of regional ecosystems and vegetation communities in Queensland . (Version 7.0). Queensland Herbarium, Queensland Department of Environment, Tourism, Science and Innovation, Brisbane Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Environmental Management → Version 1 posted Editorial decision: Revision requested 02 Mar, 2026 Reviews received at journal 25 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers agreed at journal 01 Feb, 2026 Reviewers invited by journal 29 Jan, 2026 Editor assigned by journal 21 Jan, 2026 Submission checks completed at journal 20 Jan, 2026 First submitted to journal 19 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8643825","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578430135,"identity":"c27ce4b8-44e7-4e88-a583-c20be0d98d69","order_by":0,"name":"Payton A. J. Te Ngaio","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBADHgb2BhDNTIxiZqgWngMkamFgkEggUos5A//BxwW/7GT4Jd8Y3mCosE5sYD9jgFeLZQMzs/HMvmQeydk5xhYMZ9ITG3hy8GsxOMDMJs3bw8xjcDvHTIKx7XBiAwNhLey/eXvqeQxungFq+QfUwv+GsC3MPD8O8xjc4AFqaQBqkSBgi2Uzs7E0b8NxHsmetGKLhGPpxm0SzwrwajFnb3z4medPtT0/++GNNz7UWMv28ydvwO8wUEQwtkE44Khhw6sepAVM/oFqIaR6FIyCUTAKRiYAALO7O//Z4AdeAAAAAElFTkSuQmCC","orcid":"","institution":"The University of Queensland","correspondingAuthor":true,"prefix":"","firstName":"Payton","middleName":"A. J. Te","lastName":"Ngaio","suffix":""},{"id":578430136,"identity":"9c762d6d-c695-49bd-9f0a-bbe12d39da4b","order_by":1,"name":"Joesph M. McMahon","email":"","orcid":"","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Joesph","middleName":"M.","lastName":"McMahon","suffix":""},{"id":578430137,"identity":"056fbc12-8858-44d9-9e29-b9f305ced9bf","order_by":2,"name":"Deanna van den Berg","email":"","orcid":"","institution":"Queensland Department of Environment and Science","correspondingAuthor":false,"prefix":"","firstName":"Deanna","middleName":"van den","lastName":"Berg","suffix":""},{"id":578430139,"identity":"10e0fc36-13ac-4c41-87f5-11b3659535a4","order_by":3,"name":"Al Healy","email":"","orcid":"","institution":"Queensland Department of Environment and Science","correspondingAuthor":false,"prefix":"","firstName":"Al","middleName":"","lastName":"Healy","suffix":""},{"id":578430142,"identity":"ba319793-1549-4c6d-8264-0f335969c94a","order_by":4,"name":"Ryan D.R. Turner","email":"","orcid":"","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Ryan","middleName":"D.R.","lastName":"Turner","suffix":""},{"id":578430143,"identity":"fe4623b0-2b9e-4bb9-a5f1-ff0065b0dd1a","order_by":5,"name":"Angela Marsh","email":"","orcid":"","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Angela","middleName":"","lastName":"Marsh","suffix":""}],"badges":[],"createdAt":"2026-01-20 01:54:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8643825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8643825/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00267-026-02478-1","type":"published","date":"2026-04-27T15:56:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101302498,"identity":"2719acdf-1910-4c56-9e13-6123b0b04b63","added_by":"auto","created_at":"2026-01-28 09:54:08","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":386205,"visible":true,"origin":"","legend":"\u003cp\u003eMap of lower Herbert catchment, located within the Ingham region, Queensland, showing the Herbert water quality monitoring sites, catchment areas and dominant land uses. Conservation (green), cropping (yellow) and grazing native vegetation (light green) areas are highlighted, along with highways (grey lines) and water bodies (blue). Red dots indicate monitoring sites, with site codes labelled and site catchment areas outlined with black dashes. The insert map shows the location of the study area within Queensland and the Great Barrier Reef catchment Natural Resource Management regions\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8643825/v1/50ab0ac40a9ebeeffce32084.jpeg"},{"id":101301376,"identity":"0b971a29-e4cb-4ac0-8068-f2cb5d3de0d3","added_by":"auto","created_at":"2026-01-28 09:51:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1309556,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) of site-level riparian buffer conditions. a) PCA ordination plot showing the distribution of sites across the first two principal components (PC1 and PC2), coloured by cluster assignment. Ellipses represent the 95% confidence region for each cluster. b) Corresponding loadngs plot displaying the contribution and direction of environmental variables used to define clusters, including, percent woody vegetation (WV), total riparian vegetation cover (TC), mean monthly total suspended sediment equivalents (TSSeq), slope stability (s-factor), soil erodibility (k-factor) and percent land used for grazing native vegetation (GNV), conservation (Consrv) and cropping (Crop). Vector lengths indicated the strength of each variable’s influence, with points coloured by site and shape by season (Dry or Wet)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8643825/v1/9023d47b8b0bcc8794340114.png"},{"id":101302346,"identity":"dfa2a6fe-3f85-4d2c-a229-2e448500523a","added_by":"auto","created_at":"2026-01-28 09:53:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1226634,"visible":true,"origin":"","legend":"\u003cp\u003eNatural log of monthly mean total suspended sediment equivalents (TSSeq) against natural log of total riparian vegetation cover (%) between December 2020 to December 2023 with point shapes and regression line type representing season (wet and dry). The solid line and circle dots represent dry season samples, and the dash and triangle points represent wet season samples. Colours correspond to site classification of either high disturbance (red), minimal disturbance (green) or moderate disturbance (blue)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8643825/v1/27913aaec87a9d3eae25063b.png"},{"id":108437551,"identity":"6d7eaa00-5774-49ef-a573-b02c77dcfa4b","added_by":"auto","created_at":"2026-05-04 15:59:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2909491,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8643825/v1/c941d155-664c-4ab3-9e18-ba0e9ba325d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Increasing riparian vegetation cover to improve water quality: The importance of considering land use","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eThe protection and rehabilitation of riparian zones is frequently advocated to balance land use pressures and in stream water quality (Alemu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dosskey et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gomes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; McKergow et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Riparian restoration is globally recognised as an effective strategy for improving land and water quality by enhancing channel stability, reducing erosion and reducing nutrient runoff (Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hamman et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Riparian vegetation is critical to maintain essential ecosystem functions such as regulating sediment and nutrient transport (Fernandes et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fern\u0026aacute;ndez et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The nature of riparian vegetation cover can result in varying ecosystem benefits specific to the landscape, such as channel stabilisation by large woody vegetation or surface runoff filtration by dense herbaceous vegetation (Alemu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dosskey et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; McKergow et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Studies have demonstrated that reduced riparian vegetation cover has led to increased rates of erosion (Bartley et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; McKergow et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, the extent of the impact on water quality depends on a range of hydrologic, climatic and biological factors like soil type and slope, vegetation structure and species composition (Dosskey et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Understanding the environmental conditions unique to a given location enhance our ability to identify factors that influence the role of riparian vegetation in delivering water quality outcomes. This is critical for targeted restoration efforts, tailored to local ecological and hydrological conditions.\u003c/p\u003e \u003cp\u003eIn the catchments of the Great Barrier Reef (GBR), Australia, protection and rehabilitation of riparian vegetation forms a key component of strategies to reduce diffuse water pollution (Waterhouse et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Numerous studies in the Great Barrier Reef catchment area (GBRCA) have been used to inform policy and guide improvement initiatives surrounding ongoing environmental pressures (Brodie et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Waterhouse et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Current estimates suggest that fine sediment export to the GBR is approximately 1.4 to 5 times greater than pre-development loads (Prosser \u0026amp; Wilkinson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Vegetation degradation through land/tree clearing, changes in structure and function of grass species and low ground cover are amongst key drivers of anthropogenic sediment from the GBRCA (Wilkinson et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite ongoing efforts, current land management strategies are yet to fully combat the impact of historic landscape modification and subsequent environmental decline (Eberhard et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hamman et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lawson et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its widespread recommendation in policy documents, there has been limited research on water quality responses to varying riparian vegetation extents between sites at a catchment scale (Dosskey et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Feld et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lorenz \u0026amp; Feld, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Olley et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) is one of the few studies to explore this within Australia and highlighted a significant relationship between total suspended sediment (TSS) load and the proportion of remnant vegetation\u003csup\u003e1\u003c/sup\u003e in Southeast Queensland during high flow events. The paper estimated catchments containing no remnant vegetation produced 50 to 200 times more sediment per unit area than those with fully vegetated channels. These findings suggest there may be a correlation between sediment loads and riparian vegetation within GBR catchments.\u003c/p\u003e \u003cp\u003eWater quality monitoring is commonly used to identify priority areas for intervention and indicate environmental improvement. High-frequency in situ sensors are increasingly employed for water quality monitoring due to their ability to capture fine-scale temporal variations, including event driven processes (Rode et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rozemeijer et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aim of this study was to investigate the relationship between water quality and riparian vegetation cover using total suspended sediment equivalents (TSSeq) data collected in the Herbert River catchment within the GBRCA. This dataset was paired with monthly spatial estimated measures of total cover in the riparian zone. The three primary research objectives were to (1) analyse the temporal interaction between water quality and riparian vegetation cover to understand the seasonal effect between the wet and dry season, (2) assess the spatial variation between water quality and riparian vegetation cover to understand the influence of dominant land use (cropping, conservation and grazing native vegetation) on water quality and riparian vegetation and (3) confirm whether the riparian zone influences water quality.\u003c/p\u003e"},{"header":"2.0 Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area and sampling sites\u003c/h2\u003e \u003cp\u003eThe Herbert catchment is located in far north Queensland and is the largest catchment in the Wet Tropics region (9, 842km\u003csup\u003e2\u003c/sup\u003e) (State of Queensland, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). Rainfall and groundwater inputs to the Herbert basin are sustained intermittently throughout the year and can experience several high flow or flood periods during the wet season (Australia. Bureau of Meteorology et al., 2019). The Herbert River meanders through three distinct sections characterised by differing land uses and physiography (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)(Bartley et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Johnson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The upper section is dominated by cattle grazing with minor areas of land for horticulture and dairy, the middle is protected Wet Tropics World Heritage Area and state forest, and the lower section has extensive sugarcane cropping along with areas of conservation and grazed native vegetation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Herbert catchment has lost the largest proportion of riparian woody vegetation since pre-clearing among Wet Tropics catchments (State of Queensland, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Streambank erosion is the primary contributor of sediment in the catchment, followed by hillslope erosion (McCloskey et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tims et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Erosion rates are greater in areas with high land use intensity, particularly in regions dominated by grazing and sugarcane cultivation (Tims et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Herbert is listed as one of the top three priority catchments for sediment reduction in the GBRCA and has extensive water quality monitoring in the lower section (hereafter referred to as the lower Herbert) (State of Queensland, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). Each monitoring site is positioned at a singular point to capture upstream pollutant sources. The monitoring program uses topography to delineate the boundaries and shape of a monitoring site's catchment area by defining the direction of surface water flow and the area upstream of the monitoring site point that contributes to runoff.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling design and processing\u003c/h2\u003e \u003cp\u003eFourteen monitoring sites in the lower Herbert water quality monitoring program were selected to collate data on total suspended sediment equivalents (TSSeq) from December 2020 to December 2023. Samples are collected using TriOS\u0026trade; Opus spectral sensors and transmitted hourly to the Department of the Environment, Tourism, Science and Innovation (DETSI) Water Quality Investigations online data portal, eagle.io. The data is processed through an automated data quality assurance and control pipeline adapted from a proposed framework by Leigh et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Data is periodically reviewed to validate automated outputs, identify significant deviations, and flag values needing additional assessment and site-specific analysis. In total, over 1\u0026nbsp;million data samples were collated and averaged to monthly estimates. The sample periods and counts for each site are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation summary of sites selected from Herbert catchment water quality monitoring sites including sub catchment, site ID, site name, latitude, longitude, site area (km\u003csup\u003e2\u003c/sup\u003e), riparian area (km\u003csup\u003e2\u003c/sup\u003e), number of samples collected and the sampling period.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub Catchment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSite name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSite catchment area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRiparian area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNumber of months sampled\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerbert River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBroadwater Creek at Day Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.41633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e145.94393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrancis Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrancis Creek at Weir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.76673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146.13407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerbert River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHerbert River at John Row Bridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.62831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146.16486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerbert River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHerbert River at Nash's Crossing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.41435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e145.77097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVictoria Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLagoon Creek at Five Mile Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.62341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146.2632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLannercost Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLannercost Creek at Lannercost Ext Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.6185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146.03286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalm Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePalm Creek at Bosworths Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.69688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146.22796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRipple Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRipple Creek at Gangemis Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.5811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146.1323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStone River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStone River at Running Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.76607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e145.95028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalm Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrebonne Creek at Bruce Highway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.7197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146.14842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCattle Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaterview Creek at Jourama Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.85141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146.12466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCattle Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaterview Creek at Mammarellas Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.82947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146.20546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerbert River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRipple Creek at Seymour Creek Gates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.58571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146.18459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalm Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePalm Creek at Cemetery Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.6643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146.2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Riparian buffer data and processing\u003c/h2\u003e \u003cp\u003eA total riparian vegetation cover (TC) metric was derived using the monthly blended fractional cover dataset produced by the Joint Remote Sensing Research Program and Queensland Government\u0026rsquo;s DETSI Ground Cover Monitoring program (Joint Remote Sensing Research et al., 2022). This dataset consists of medoid-composited monthly fractional cover created from a combined Landsat 8 and 9 and Sentinel-2 time series, with at least three cloud free observations of fractional cover imagery required for a representative pixel to be included in the monthly composite. Riparian buffers were defined as 50 m around waterways above stream order 1 to align with the Paddock to Reef monitoring methodology (Australia and Queensland governments, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The calculated riparian area within the contributing catchment area of each monitoring site is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Using ArcGIS Pro, we extracted the mean percentage of green vegetation (e.g. leaves, grass and growing crops), non-green vegetation (e.g. branches, dry grass, hay and dead leaf litter), and bare ground (soil or rock) within riparian buffers for catchments draining to each monitoring site. Green and non-green fractions represent a combination of woody and non-woody vegetation, and were combined to create the TC metric.\u003c/p\u003e \u003cp\u003eThe riparian buffers for catchments draining to each monitoring site were also used to extract the percent of land used for conservation, cropping and grazing native vegetation, woody vegetation extent, slope steepness (S) factor and soil erodibility (K) factor. Land use percentages were calculated using the GBR land use mapping 2021 dataset. Land use classes are based on a modified Australian Land Use Management Classification (ALUMC) Schema, Version 9 (October 2016) (State of Queensland, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The presence of woody vegetation has been determined through the use of the Statewide Landcover and Trees Study (SLATS) woody vegetation extent dataset, where stands of woody vegetation greater than 0.5 ha with a canopy density greater than 10% crown cover are included (State of Queensland, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The K factor and S factor were selected as the two most relevant factors in the revised universal soil loss equation to inform spatial differences in soil erosion potential across the study area. The K factor is derived from soil data in the Australian Soil Resource Information System (State of Queensland, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e) and the S factor is derived from a Digital Elevation Model created by the Shuttle Radar Topography Mission (State of Queensland, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e). Table S1 outlines the riparian buffer characteristics identified within the contributing catchment area for each monitoring site and incorporated into the data analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cp\u003eAll available TSSeq monitoring data between December 2020 and December 2023 was included in the analysis, with the sampling date and total number of samples varying between sites. The total months of data for each site are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eA Principal Component Analysis (PCA) was undertaken to explore the variables with the highest contribution to site variability. The analysis incorporated TSSeq, TC and riparian buffer conditions outlined in Table S1. Prior to analysis, all variables were standardised by subtracting the mean and dividing by the standard deviation, resulting in values with a mean of 0 and a standard deviation of 1. Sites were then plotted along the first two principal components to reveal clustering patterns and define groups. A biplot was utilised to understand how to variables responded to each other and corresponded to the sites and identified clusters in the PCA.\u003c/p\u003e \u003cp\u003eThe statistical relationship between TSSeq and TC was evaluated using paired samples. Initial data exploration indicated non-normal distributions for both variables. Consequently, Spearman\u0026rsquo;s ranks correlation was selected as a non-parametric method to assess the strength and direction of the association between TSSeq and TC. Statistical significance was determined at a threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Correlation analysis was conducted on the entire dataset and separated by wet and dry season, to estimate the influence of temporal variation. Linearity was visually examined through a bivariate linear regression graph.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using the R 4.4.2 Hmisc package (R Core Team, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results and discussion","content":"\u003cp\u003eThis study aimed to examine the statistical relationship between water quality (measured TSSeq) and riparian vegetation cover (measured total cover (TC)) within the Herbert catchment. The analysis of correlations between TC and TSSeq revealed seasonal and grouped patterns reflective of the spatial distribution of riparian characteristics that highlight the role of riparian vegetation in sediment retention across varying land-use conditions. Here we highlight the importance of maintaining high functioning riparian zones for water quality improvements and the effect of land use disturbance. Our findings provide valuable insight to support evidence-based riparian management practices and contribute to the development of more effective catchment scale environmental policies.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Cluster analysis\u003c/h2\u003e \u003cp\u003eThe PCA cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) and corresponding biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) was employed to differentiate sites using TC, TSSeq, and variables outlined in Table S1. The ordination patterns indicate the presence of three clusters, with separation primarily driven by differences in dominant land use type (grazing native vegetation, conservation and cropping).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe first and second principal components (PC1 and PC2) explained 54.6% and 23.5% of the total variability, respectively. The first principal component was primarily associated with a strong positive association for cropping (0.44) and k-factor (0.40), and a strong negative for s-factor (-0.43), woody vegetation (-0.41), TC (-0.40) and conservation (-0.37). The second principal component was primarily associated with a strong positive associated for grazing native vegetation (0.63). Comparatively, the third principal component accounted for 10.45% of the total variance in the data and was dominated by TSSeq (0.83), with only minor contributions from other variables (\u0026lt;\u0026thinsp;0.30). This indicates that the third axis primarily reflects a TSSeq gradient largely independent of the land use contrasts resolved by PC1 and PC2.\u003c/p\u003e \u003cp\u003eWithin Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, sites in cluster 1 (1160121, 1160124, 1160129 and 1160134, as identified by Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) are distinct due to their strong association with high cropping intensity and elevated k-factor. In contrast, the overlap between cluster 1 and 3 in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and position of woody vegetation in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb indicates sites within these two groups share similar levels of woody vegetation cover. Cropping and woody vegetation show an inverse relationship, which is also observed, though less strong, between cropping and TC. However, TC is more closely related to conservation land use, suggesting that these sites may have a higher diversity of vegetation cover types beyond woody vegetation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Site classification\u003c/h2\u003e \u003cp\u003eBased on the findings presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, percentage of woody vegetation cover and land used for grazing native vegetation, conservation and cropping was identified as the main separator between majority of sites. Comparatively, the s-factor and k-factor further differentiated the sites due to reflecting their variation in topography and land condition. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists each site and their site classification which is defined as; 1) Minimal disturbance \u0026ndash; site dominated by conservation land use or grazing present but \u0026lt;\u0026thinsp;10% (approx.) of site and with \u0026gt;\u0026thinsp;90% woody vegetation cover, 2) Moderate disturbance \u0026ndash; sites dominated by grazing, forestry or conservation present but \u0026lt;\u0026thinsp;35% (approx.) with \u0026gt;\u0026thinsp;90% woody vegetation cover, 3) High disturbance \u0026ndash; site dominated by cropping land use.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSites and defined site class\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite class\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimal disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimal disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimal disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimal disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimal disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1160134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh disturbance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition to containing high proportions of cropping land use, the high disturbance sites (1160121, 1160124, 1160129, and 1160134) were distinguished by notably lower s-factor values and elevated k-factor values. Whereas, low and moderate disturbance sites exhibited comparable proportions of woody vegetation cover, cropping, and k-factor values. The moderate disturbance sites (1160118, 1160119, 1160120, 1160122, 1160127) demonstrated more heterogeneous land use patterns, with a greater mix between conservation and grazing of native vegetation. In contrast, the minimal disturbance sites (1160115, 1160126, 1160130, 1160131, 1160133) were characterised by similar proportions of conservation land use and woody vegetation cover.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation analysis\u003c/h2\u003e \u003cp\u003eOn a catchment scale, the results of our study aligned with Olley et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e indicating that in the Herbert catchment, TSSeq concentrations decrease (i.e. water quality improves) proportionally as total riparian vegetation cover increases, regardless of season of land use disturbance (ρ = -0.431, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). However, as land use disturbance intensifies from minimal disturbance (ρ = -0.530, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) to high disturbance (ρ\u0026thinsp;=\u0026thinsp;0.075, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), the correlation weakens and direction of the correlation between water quality and riparian vegetation cover changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003eNatural log of monthly mean total suspended sediment equivalents (TSSeq) against natural log of total riparian vegetation cover (%) between December 2020 to December 2023 with point shapes and regression line type representing season (wet and dry). The solid line and circle dots represent dry season samples, and the dash and triangle points represent wet season samples. Colours correspond to site classification of either high disturbance (red), minimal disturbance (green) or moderate disturbance (blue)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTSSeq concentrations were consistently higher during the wet season across all site groups, with the moderate disturbance group exhibiting the highest values on average. This trend is likely driven by increased rainfall and surface runoff, which enhances erosion, facilitates the transport of suspended sediments into adjacent waterways and increases gully and streambank erosion potential.\u003c/p\u003e \u003cp\u003eThe minimal disturbance group maintained the highest levels of cover, while the moderate and high disturbance groups showed greater variability, particularly during the wet season. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, in the combined data set, the correlation between TSSeq and total riparian vegetation cover is strongest during the wet season. This suggests that riparian vegetation plays a more significant role in moderating sediment transport under high-flow conditions. During the dry season, reduced river discharge and lower erosion rates result in generally lower TSSeq concentrations, which may weaken the observed relationship regardless of differences in vegetation cover.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Spearman's rank correlation coefficient (ρ) between Total Cover (TC) and total suspended sediment (TSSeq) within each site class and between wet and dry season. Values in bold are significant at the 0.05 level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eTSSeq\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTC ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean - Wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean - Dry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMinimal disturbance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eρ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.530\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.668\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.474\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModerate disturbance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eρ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.390\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.443\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.208\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHigh disturbance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eρ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAll sites combined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eρ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.431\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.546\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.326\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the three site classifications, the minimal disturbance group exhibited the strongest negative correlation between TSSeq and TC (ρ = -0.530, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These sites were characterised by higher proportions of conservation land use and woody vegetation. The strength of this relationship is likely due to the presence of well-established and relatively undisturbed canopy structures, which enhance sediment retention and filtration capacity within riparian zones. Although the moderate disturbance group showed a weaker correlation, the results were consistent with those of the minimal disturbance group. Significant negative correlations were observed in both the combined seasonal dataset (ρ = -0.390, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and the wet season subset (ρ = -0.443, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, the high disturbance group showed a non-significant correlation (ρ\u0026thinsp;=\u0026thinsp;0.075, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.429). Notably, this group was the only one to exhibit a stronger and statistically significant correlation during the dry season (ρ\u0026thinsp;=\u0026thinsp;0.305, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), unlike the wet season where the correlation was weak and not significant (ρ\u0026thinsp;=\u0026thinsp;0.039, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.795). The underlying cause of the stronger dry season correlation is unclear. This result may be attributed to the role of vegetation in facilitating the accumulation of already entrained sediment, as reduced water flow and increased sediment deposition during this period create conditions favourable to sediment accumulation (Huai et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results indicate that in regions experiencing minimal to moderate land use disturbance, riparian vegetation continues to play a vital role in enhancing water quality. These findings align with those of Olley et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) who identified a statistically significant relationship between TSS load and the proportion of remnant vegetation in Southeast Queensland. Similar land use observations were observed in de Mello et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) who found a negative correlation between forest cover and TSS and organic suspended solids concentrations, alongside a positive correlation between agricultural land use and elevated levels of the sediment indicators in Southeastern Brazil. Comparatively, our results suggest that as disturbance levels increase to highly disturbed, it becomes more difficult to detect the relationship between TC and reduced TSSeq as other sediment generating process become prevalent. In addition, inherent complexities and constraints associated with satellite-based remote sensing could be a factor.\u003c/p\u003e \u003cp\u003eThe monthly fractional cover dataset, created from a combined Landsat 8 and 9 and Sentinel-2 time series, predicts vegetation cover at medium spatial resolution (30 m). In areas with minimal disturbance, vegetation cover is expected to remain relatively consistent within the monthly time period, and the observations used to create the composite image are likely to be relatively similar. The vegetation cover in highly disturbed agricultural landscapes is more variable, due to the dynamic nature of cropping systems. The observations used for a monthly composite may include abrupt changes due to harvest or planting for example, with the change in cover more rapid than the monthly time scale. Changes in cropping areas may also be missed due to cloud cover and therefore not represented in the monthly dataset. The spatial arrangement of cover in highly disturbed areas is also more variable than areas with minimal disturbance. Cropping areas with high cover may still present with persistently bare inter-rows for example, contributing to substantial surface runoff during rainfall events.\u003c/p\u003e \u003cp\u003eSimilar limitations of remote sensing methods are highlighted in Allred et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Saldarriaga (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) who note challenges specific to unique vegetation patterns and cover type in riparian areas. Saldarriaga (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) discusses Sentinel-2 as optimal for large areas and suggests complex ecosystems, like fine-scale riparian buffers, should employ UAV platforms to provide more detailed analysis. This is especially when analysing small areas with mixed land use and vegetation types. Allred et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) provides a similar perspective with consideration to understorey vegetation, which may not be well represented, particularly in areas with dense overstory canopies. Based on our results and observations provided in Allred et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Saldarriaga (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), future work could evaluate the integration of additional satellite-derived vegetation products to separate ground cover from woody vegetation to further understand the influence of vegetation cover types.\u003c/p\u003e \u003cp\u003eIn addition, our findings corroborate the results shown in Olley et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), indicating that areas with high woody vegetation cover, subject to minimal land use disturbance, result in decreased amounts sediment present in waterways. These results support policy direction for increasing woody riparian vegetation cover through revegetation works streambank restoration which can generating water quality improvements. However, as land use disturbance begins to transition from minimal to high, implementing strategies to address policy goals focussed on improving riparian vegetation cover for water quality benefits need to acknowledge that in a fragmented riparian zone may not result in a measurable water quality improvement until a substantial woody vegetation corridor is established. Integrating long-term monitoring would provide greater insight into the full benefit of riparian restoration efforts and to quantify how much riparian restoration is needed to see a measurable water quality improvement. Many riparian management studies are confined to short-term observations (Feld et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muller et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This can limit identification of lag effects between the time of management intervention and ecological outcome. Whilst the magnitude of lag time can vary between sites and pollutant type (Meals et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Swanson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), increasing data resolution to evaluate water quality before and after riparian management intervention could improve lag identification and provide insight into long-term water quality improvement benefits (Feld et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muller et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Conclusion","content":"\u003cp\u003eRiparian vegetation plays a critical role in regulating in-stream sediment loads by reducing runoff, trapping sediments, and stabilising banks. This study found a significant relationship between total suspended sediment equivalents (TSSeq) and total riparian cover (TC) across 14 sites in the Herbert catchment, particularly during the wet season. However, this relationship weakened with increasing disturbance, due to the influence of land use variability and seasonal cropping dynamics. These findings highlight the importance of riparian vegetation restoration for sediment control, water quality improvements and the need for careful interpretation of remotely sensed cover in highly disturbed catchments.\u003c/p\u003e \u003cp\u003eOur results highlight the importance of maintaining high riparian (woody) vegetation cover. In addition, river restoration approaches (improving riparian vegetation cover for water quality improvements) need to understand the fragmentation of the riparian zone in relations to measurable water quality improvements. Future research should investigate dominant sediment delivery processes linked to specific land uses within riparian buffers, both at localised scales and across the broader catchment. While cropping remains the dominant high disturbance land use in the lower Herbert riparian zone, policy interventions should prioritise the restoration of riparian zones in a systematic and connected approach with a focus on restoring woody vegetation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Payton Te Ngaio. The first draft of the manuscript was written by Payton Te Ngaio and all affiliated authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was conducted as part of an honours research project, which was supported by the Reef Catchments Science Partnership (The University of Queensland and Queensland Department of the Environment, Tourism, Science and Innovation) and funded by the Michael St John Warne Memorial Scholarship. I extend my thanks to Angela Marsh who co-supervised the honours research project, Richard Gardiner for sediment data processing from eagle.io, Jen Strauss who acted as an advising statistician, Peter Hairsine for guiding the writing process and Steph Atkins for being a trusted external advisor.\u003c/p\u003e \u003cp\u003eLastly, thank you to the team at Water Quality and Investigations, the Earth Observation and Social Sciences team within the Queensland Department of the Environment, Tourism, Science and Innovation (DETSI) and all those involved in the Great Barrier Reef Catchment Loads Monitoring Program, for collecting, managing and processing the sediment and fractional cover monitoring data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlemu, T., Bahrndorff, S., Hundera, K., Alemayehu, E., \u0026amp; Ambelu, A. (2017). Effect of riparian land use on environmental conditions and riparian vegetation in the east African highland streams. \u003cem\u003eLimnologica\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e, 1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllred, B. W., McCord, S. E., Assal, T. J., Bestelmeyer, B. T., Boyd, C. S., Brooks, A. C., Cady, S. M., Fuhlendorf, S. D., Green, S. A., \u0026amp; Harrison, G. R. (2025). Estimating rangeland fractional cover and canopy gap size class with Sentinel-2 imagery. \u003cem\u003ebioRxiv\u003c/em\u003e, 2025.2003. 2013.643073.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustralia and Queensland governments. (2024). \u003cem\u003eRiparian Vegetation Extent Monitoring Methods, Reef Water Quality Report Card 2021 and 2022\u003c/em\u003e. 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Patterns of erosion and sediment and nutrient transport in the Herbert River catchment, Queensland. \u003cem\u003eConsultancy Report, CSIRO Land and Water\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartley, R., Keen, R. J., Hawdon, A. A., Hairsine, P. B., Disher, M. G., \u0026amp; Kinsey-Henderson, A. (2008). Bank erosion and channel width change in a tropical catchment. \u003cem\u003eEarth Surface Processes and Landforms\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(14), 2174\u0026ndash;2200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrodie, J., Lewis, S., Bainbridge, Z., Mitchell, A., Waterhouse, J., \u0026amp; Kroon, F. (2009). 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Scientific Consensus Statement: Conclusions. \u003cem\u003eWaterhouse J, Pineda MC Sambrook K (Eds)\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkinson, S., Murray, B., \u0026amp; Prosser, I. (2024). Question 3.4 What are the primary biophysical drivers of anthropogenic sediment and particulate nutrient export to the Great Barrier Reef and how have these drivers changed over time. \u003cem\u003eWaterhouse J, Pineda MC, Sambrook K (Eds)\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Woody vegetation classified as remnant under the \u003cem\u003eVegetation Management Act\u003c/em\u003e 1999 when the dominant canopy retains more than 70% of its original height and over 50% of its original cover, and primarily consists of species typical of an undisturbed canopy layer. Neldner, V. J., Wilson, B. A., Dillewaard, H. A., Ryan, T. S., Butler, D. W., McDonald, W. J. F., Richter, D., Addicott, E. P., \u0026amp; Appelman, C. N. (2023). \u003cem\u003eMethodology for survey and mapping of regional ecosystems and vegetation communities in Queensland\u003c/em\u003e. (Version 7.0). Queensland Herbarium, Queensland Department of Environment, Tourism, Science and Innovation, Brisbane\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Watershed, sediment, remote sensing, catchment management, sugarcane cropping, riparian cover","lastPublishedDoi":"10.21203/rs.3.rs-8643825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8643825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRiparian vegetation plays a crucial role in regulating sediment dynamics, reducing surface runoff, mobilising sediment, and stabilising streambanks. Despite extensive research on sediment loads and riparian vegetation individually, there remains a gap in understanding their interrelationship, particularly within the context of water quality and catchment management. This study investigates the statistical association between water quality and riparian vegetation cover within the Herbert catchment, Far North Queensland, Australia. Over one million total suspended sediment equivalent (TSSeq) data points were collected from 14 monitoring sites between December 2020 and December 2023, averaged into 361 monthly samples and paired with site-specific total cover (TC) values. Using Spearman\u0026rsquo;s rank correlation across land use disturbance classes (minimal, moderate, high) and seasonal subsets, results revealed a significant overall negative correlation between TSSeq and TC (ρ = -0.431, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The strength of this relationship declined with increasing disturbance: minimal disturbance sites showed the strongest correlation (ρ = -0.530, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while at high disturbance sites the correlation was not significant (ρ\u0026thinsp;=\u0026thinsp;0.075, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Seasonal analysis showed stronger correlations during the wet season, except in high disturbance areas, where the dry season correlation was higher but still not statistically significant. Limitations in TC\u0026rsquo;s ability to distinguish vegetation types and capture dynamic cover changes in disturbed areas are discussed. These findings highlight the importance of riparian vegetation in improving water quality and underscore the need for refined remote sensing methods when integrating high-resolution temporal water quality datasets.\u003c/p\u003e","manuscriptTitle":"Increasing riparian vegetation cover to improve water quality: The importance of considering land use","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 09:35:12","doi":"10.21203/rs.3.rs-8643825/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-03T02:05:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T04:02:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T05:12:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72581031177239016416588500648250334087","date":"2026-02-04T23:18:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263232494376016949126667170896418664124","date":"2026-02-01T22:19:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T05:45:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T01:53:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-20T05:57:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Management","date":"2026-01-20T01:46:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"14e691ab-c06a-4d02-959c-e736fc3c86c5","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T15:58:45+00:00","versionOfRecord":{"articleIdentity":"rs-8643825","link":"https://doi.org/10.1007/s00267-026-02478-1","journal":{"identity":"environmental-management","isVorOnly":false,"title":"Environmental Management"},"publishedOn":"2026-04-27 15:56:50","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2026-01-28 09:35:12","video":"","vorDoi":"10.1007/s00267-026-02478-1","vorDoiUrl":"https://doi.org/10.1007/s00267-026-02478-1","workflowStages":[]},"version":"v1","identity":"rs-8643825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8643825","identity":"rs-8643825","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}