The spatial arrangement of the river network is a fundamental
characteristic of the catchment, acting as a conduit between catchment-level
effects and reach morphology and ecology. Yet river network ...structure is
often simplified to reflect an upstream-to-downstream gradient of river
characteristics, commonly represented by stream order. The aim of this study
is to quantify network topological structure using two network density
metrics – one that represents network density over distance and the other
over elevation – that can easily be extracted from digital elevation models
and so may be applied to any catchment across the globe. These metrics should
better account for the multi-dimensional nature of the catchment than stream
order and be functionally applicable across geomorphological, hydrological
and ecological attributes of the catchment. The functional utility of the
metrics is assessed by appropriating monitoring data collected for regulatory
compliance to explore patterns of river characteristics in relation to
network topology. This method is applied to four comparatively low-energy,
anthropogenically modified catchments in the UK using river characteristics
derived from England's River Habitat Survey database. The patterns in river
characteristics explained by network density metrics are compared to stream
order as a standard measure of topology. The results indicate that the
network density metrics offer a richer and functionally more relevant
description of network topology than stream order, highlighting differences
in the density and spatial arrangement of each catchment's internal network
structure. Correlations between the network density metrics and river
characteristics show that habitat quality score consistently increases with
network density in all catchments as hypothesized.
For other measures of river character – modification score, flow-type speed and sediment size – there are varying
responses in different catchments to the two network density metrics. There
are few significant correlations between stream order and the river
characteristics, highlighting the limitations of stream order in accounting
for network topology. Overall, the results suggest that network density
metrics are more powerful measures which conceptually and functionally
provide an improved method of accounting for the impacts of network topology
on the fluvial system.
Global metrics of land cover and land use provide a fundamental basis to examine the spatial variability of human-induced impacts on freshwater ecosystems. However, microscale processes and site ...specific conditions related to bank vegetation, pollution sources, adjacent land use and water uses can have important influences on ecosystem conditions, in particular in smaller tributary rivers. Compared to larger order rivers, these low-order streams and rivers are more numerous, yet often under-monitored. The present study explored the relationship of nutrient concentrations in 150 streams in 57 hydrological basins in South, Central and North America (Buenos Aires, Curitiba, São Paulo, Rio de Janeiro, Mexico City and Vancouver) with macroscale information available from global datasets and microscale data acquired by trained citizen scientists. Average sub-basin phosphate (P-PO4) concentrations were found to be well correlated with sub-basin attributes on both macro and microscales, while the relationships between sub-basin attributes and nitrate (N-NO3) concentrations were limited. A phosphate threshold for eutrophic conditions (>0.1 mg L-1 P-PO4) was exceeded in basins where microscale point source discharge points (eg. residential, industrial, urban/road) were identified in more than 86% of stream reaches monitored by citizen scientists. The presence of bankside vegetation covaried (rho = -0.53) with lower phosphate concentrations in the ecosystems studied. Macroscale information on nutrient loading allowed for a strong separation between basins with and without eutrophic conditions. Most importantly, the combination of macroscale and microscale information acquired increased our ability to explain sub-basin variability of P-PO4 concentrations. The identification of microscale point sources and bank vegetation conditions by citizen scientists provided important information that local authorities could use to improve their management of lower order river ecosystems.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Multiple catchment controls contribute to the geomorphic functioning of river systems at the reach-level, yet only a limited number are usually considered by river scientists and managers. This study ...uses multiple morphometric, geological, climatic and anthropogenic catchment characteristics to produce a single national typology of catchment controls in England and Wales. Self-organising maps, a machine learning technique, are used to reduce the complexity of the GIS-derived characteristics to classify 4485 Water Framework Directive waterbodies into seven types. The waterbody typology is mapped across England and Wales, primarily reflecting an upland to lowland gradient in catchment controls and secondarily reflecting the heterogeneity of the catchment landscape. The seven waterbody types are evaluated using reach-level physical habitat indices (including measures of sediment size, flow, channel modification and diversity) extracted from River Habitat Survey data. Significant differences are found between each of the waterbody types for most habitat indices suggesting that the GIS-derived typology has functional application for reach-level habitats. This waterbody typology derived from catchment controls is a valuable tool for understanding catchment influences on physical habitats. It should prove useful for rapid assessment of catchment controls for river management, especially where regulatory compliance is based on reach-level monitoring.
The ecological degradation of urban rivers and streams has been termed the ‘urban stream syndrome’ and attributed to increased catchment urbanization. Limiting future degradation requires an ...understanding of the drivers of reduced water quality at both catchment and site scales. The goal of this study was to identify the probable drivers of turbidity in river ecosystems in highly urbanized areas, under the premise that turbidity does not respond consistently to urbanization. Catchment-scale data were compiled from remotely sensed datasets, whereas local-scale data were collected by citizen scientists as part of the global FreshWater Watch (FWW) program. The local-scale data included nearly 2600 coincident measurements of turbidity and observations of other local characteristics taken with a common method between March 2013 and June 2016 across 127 unique locations in 6 major population centers: Vancouver (Canada), São Paulo (Brazil), Curitiba (Brazil), Buenos Aires (Argentina), Hong Kong SAR (China), and Guangzhou-Foshan (China). Catchment- and site-scale information were modeled with Boosted Regression Trees (BRT) to identify likely drivers of increased turbidity both across the entire dataset and within individual cities. Urbanization was not consistently associated with turbidity. The global BRT model explained 60% of the variation in turbidity, and key predictors were catchment area, % of the catchment as grassland, rainfall, Gross Domestic Product, and % of the catchment as artificial surfaces. City-specific BRT models explained 35–67% of the variation in turbidity. Key predictors varied between cities and were often different than those observed at the global scale. Local-scale data collected by citizen scientists were less predictive of turbidity than catchment-scale factors and explained ~12% of the observed global variability in turbidity. Factors such as riverbank vegetation and the presence of point pollution sources explained some of the variation in turbidity, indicating their management could help mitigate elevated turbidity and sediment load in some urban rivers. Through this high-resolution, site-scale information, we highlight how community-sourced data may add value to freshwater monitoring programs.
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