The utility of spatial data to delineate river riparian functions and management zones: A review

Stutter, Marc; Baggaley, Nikki; hUallacháin, Daire Ó; Wang, Chen

doi:10.1016/j.scitotenv.2020.143982

Cited by 49 publications

(20 citation statements)

References 106 publications

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“…Human activities resulting from urbanization and industrialization have deteriorated the riparian zones of many countries [ 1 , 2 , 3 , 4 ]. It is a fact that riparian zones naturally improve water quality since they ensure the safety of both the river and the environment [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Stressors on Riparian Health Indicators in the Upper and Lower Indus River Basins in Pakistan

Hira

Arif

Zarif³

et al. 2022

IJERPH

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Riparian zones along rivers and streams provide ecosystem services that may change over time as disturbances increase and deteriorate these buffer zones globally. The effect of stressors on ecosystem services along the rivers in underdeveloped countries is unclear, which impacts the environment directly in the form of riparian health indicators (RHIs). This study fills this gap and measures the impact of stressors on RHIs (parameters of habitat, plant cover, regeneration, exotics, and erosion) in the Indus River basin (IRB) in Pakistan. Data on 11 stressors and 27 RHIs were collected using a field-based approach in 269 transects in the upper and lower Indus basins (UIB and LIB) in 2020 and analyzed using multivariate statistical methods. The Kruskal–Wallis tests (p < 0.05) indicated that RHIs varied significantly under the influence of stressors in the UIB and LIB. However, their highest mean values were found in the UIB. Principal component analysis revealed the key RHIs and stressors, which explained 62.50% and 77.10% of the variance, respectively. The Pearson correlation showed that stressors had greater impacts on RHIs in LIB (with r ranging from −0.42 to 0.56). Our results also showed that stressors affected RHI indices with r ranging from −0.39 to 0.50 (on habitat), −0.36 to 0.46 (on plant cover), −0.34 to 0.35 (on regeneration), −0.34 to 0.56 (on erosion), and −0.42 to 0.23 (on exotics). Furthermore, it was confirmed by the agglomerative hierarchical cluster that indices and sub-indices of RHIs and stressors differ across the UIB and LIB. These findings may serve as guidance for managers of large rivers and ecosystem service providers to minimize the environmental impact of stressors in terms of RHIs.

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Section: Introductionmentioning

confidence: 99%

Impacts of Stressors on Riparian Health Indicators in the Upper and Lower Indus River Basins in Pakistan

Hira

Arif

Zarif³

et al. 2022

IJERPH

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“…Phosphorus presents challenges for developing catchment frameworks, because of spatio-temporal patterns in waterbody biotic responses, complex mixes of diffuse and point sources, differing P forms with varying pathways and the nature of interactions with physical aspects such as light, temperature, water residence, sedimentation, and redox (Jarvie et al, 2013). There is a growing availability and awareness of spatial data for catchment characterisation (Tomsett and Leyland, 2019;Stutter et al, 2021a). This, coupled with developing environmental P concepts (e.g., Records et al, 2016;Goyette et al, 2018), means that it should be possible to explore simultaneously multiple aspects of catchment pressure and condition when examining ecological and hydrochemical responses of freshwater ecosystems and targeting P mitigation.…”

Section: Introductionmentioning

confidence: 99%

“…Spatial data increasingly support catchment pollution investigations in connecting pollution sources to impacts. An example is progressing from whole catchment land cover proportions to land cover/management pressure characterisation in critical areas, such as near channel zones (Stutter et al, 2021a) or areas of high hydrological connectivity (Thomas et al, 2016). Close siting of P sources to rivers and alteration of pathways (e.g., ploughing, hard surfaces) maximises likelihood of P transport.…”

Section: Introductionmentioning

confidence: 99%

“…This may also help to explain the observed disconnects between catchment source mitigation and expected ecological improvements, including lags and timescales for recovery (Meals et al, 2010). Structural degradation of riparian zones is associated with negative aspects of bank erosion, thermal stress, loss of carbon, and woody debris sources and loss of benthic habitat diversity (Stutter et al, 2021a). Hence, improving understanding of how waterbody condition interacts with pollution via experimental, empirical, and modelling approaches is a developing topic, with potential to inform risk and complimentary pollution and ecological improvement actions.…”

Section: Introductionmentioning

confidence: 99%

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Can Prediction and Understanding of Water Quality Variation Be Improved by Combining Phosphorus Source and Waterbody Condition Parameters?

et al. 2022

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Phosphorus (P) pollution impairs river systems globally. There is long-standing interest in understanding catchment source P loads to inform mitigation to improve water quality. However, P sources to the hydrosphere differ individually in discharge behaviour, P intensity, bioavailability, and cumulative impacts. River condition also varies (e.g., riparian disturbance, climate change impacts) such that source and river resilience are likely synergistic but poorly studied controls on water quality variation. To challenge the use of overly-simplistic factors (e.g., basic soils and landcover) in empirical catchment pollution source-impact assessments, we pooled spatial data according to conceptual aspects of P source mechanisms and waterbody riparian condition. These were related empirically to P concentrations and loads, and trophic diatom indices, for 19 Scottish catchments (~10–250 km2) representing some mechanistic aspects of pollution loading and river impacts. Sources of P from septic tanks and farmyards influenced loads and ecological impacts. Some secondary calculations pooling spatial data such as septic tank source-delivery methods were novel, involving complex, but available, soil water flowpath data. In contrast, inclusion of channel condition and farmyard P loads used simple aerial imagery. Multiple Factor Analysis combined with Redundancy Analysis showed that source P loads expressed as bioavailable forms of P were better explanatory factors of diatom classification groups than stream soluble reactive P concentrations, although used together they improved explanation further. Riparian quality metrics were less powerful predictors than expected, likely with more scale-dependant effects on ecological functions than can be quantified by visual condition assessment on isolated short reaches. There was strong justification for examining separate P fractions (total, dissolved, particulate and bioavailable forms) by distinct catchment source types to understand better nutrient dynamics across land to waters, ecosystem degradation and waterbody impacts in the contemporary hydrosphere.

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“…Currently, there is no monitoring system available to quantify and map the spatial and temporal distribution of macro litter abundance in the Brantas River riparian zone. Existing methods of monitoring and mapping the riparian zone have been published 8 – 14 , however, research regarding monitoring and mapping macro litter abundance is limited.…”

Section: Introductionmentioning

confidence: 99%

Mapping riparian zone macro litter abundance using combination of optical and thermal sensor

Ramdani

Sianturi

Furqon

et al. 2022

Sci Rep

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A significant increase in the world's population will lead to an increase in consumption and, therefore, an increase in global waste. Various attempts have been made to monitor and map waste, but the proposed approaches are difficult and complicated, and they incur high costs. In this study, to overcome limitations in monitoring and mapping plastic waste, using combined optical and thermal sensors installed on drones is proposed. The study area is the riparian zone, or the zone around the river, where the accumulation of plastic waste at the mouth of the river eventually reaches the sea. The image data obtained were processed using machine learning methods to produce high accuracy and precision. To determine the effectiveness of the proposed method, an accuracy assessment was conducted. The results of this study indicate that the combination of optical and thermal sensors provides the best accuracy compared to using only single optical or thermal image data.

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The utility of spatial data to delineate river riparian functions and management zones: A review

Cited by 49 publications

References 106 publications

Impacts of Stressors on Riparian Health Indicators in the Upper and Lower Indus River Basins in Pakistan

Impacts of Stressors on Riparian Health Indicators in the Upper and Lower Indus River Basins in Pakistan

Can Prediction and Understanding of Water Quality Variation Be Improved by Combining Phosphorus Source and Waterbody Condition Parameters?

Mapping riparian zone macro litter abundance using combination of optical and thermal sensor

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