Dataset Information

Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices.

ABSTRACT:

Study region

The Kamish River catchment (308?km²); a mountainous agricultural catchment under dry-land and rangeland farming located in Kermanshah province, in western Iran.

Study focus

The main objective of this study was to apportion sub-basin spatial source relative contributions to target channel bed sediment samples using a composite fingerprinting procedure including a Bayesian un-mixing model. In total, thirty-four geochemical tracers, eleven elemental ratios and different weathering indices were measured or estimated for 43 tributary sediment samples collected to characterise three sub-basin spatial sediment sources and eleven target bed sediment samples collected at the outlet of the main basin. Statistical analysis was used to select three different composite signatures.

New hydrological insights for the region

Using a composite signature based on KW-H and DFA, the respective relative contributions (with uncertainty ranges) from tributary sub-basins 1, 2 and 3 were estimated as 54.3% (47.8-62.0), 11.4% (4.2-18.7) and 34.3% (27.6-39.9), compared to 72.0% (61.6-82.7), 13.6% (9.0-18.5) and 14.2% (3.1-25.4) using a combination of KW-H and data mining, and 50.8% (42.8-59.9), 28.7% (20.2-37.3) and 20.3% (12.7-27.2) using a fingerprint selected by KW-H and PCCA. The root mean square difference between these source estimates highlighted sensitivity to the composite signatures. Evaluation of the un-mixing model predictions using virtual mixture tests confirmed agreement between modelled and known source proportions.

SUBMITTER: Raigani ZM

PROVIDER: S-EPMC6686636 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices.

Raigani Zeinab Mohammadi ZM Nosrati Kazem K Collins Adrian L AL

Journal of hydrology. Regional studies 20190801

<h4>Study region</h4>The Kamish River catchment (308 km<sup>2</sup>); a mountainous agricultural catchment under dry-land and rangeland farming located in Kermanshah province, in western Iran.<h4>Study focus</h4>The main objective of this study was to apportion sub-basin spatial source relative contributions to target channel bed sediment samples using a composite fingerprinting procedure including a Bayesian un-mixing model. In total, thirty-four geochemical tracers, eleven elemental ratios and ...[more]

PMID: 31417849

Similar Datasets

Project description:The mitigation of diffuse sediment pollution requires reliable provenance information so that measures can be targeted. Sediment source fingerprinting represents one approach for supporting these needs, but recent methodological developments have resulted in an increasing complexity of data processing methods rendering the approach less accessible to non-specialists. A comprehensive new software programme (SIFT; SedIment Fingerprinting Tool) has therefore been developed which guides the user through critical data analysis decisions and automates all calculations. Multiple source group configurations and composite fingerprints are identified and tested using multiple methods of uncertainty analysis. This aims to explore the sediment provenance information provided by the tracers more comprehensively than a single model, and allows for model configurations with high uncertainties to be rejected. This paper provides an overview of its application to an agricultural catchment in the UK to determine if the approach used can provide a reduction in uncertainty and increase in precision. Five source group classifications were used; three formed using a k-means cluster analysis containing 2, 3 and 4 clusters, and two a-priori groups based upon catchment geology. Three different composite fingerprints were used for each classification and bi-plots, range tests, tracer variability ratios and virtual mixtures tested the reliability of each model configuration. Some model configurations performed poorly when apportioning the composition of virtual mixtures, and different model configurations could produce different sediment provenance results despite using composite fingerprints able to discriminate robustly between the source groups. Despite this uncertainty, dominant sediment sources were identified, and those in close proximity to each sediment sampling location were found to be of greatest importance. This new software, by integrating recent methodological developments in tracer data processing, guides users through key steps. Critically, by applying multiple model configurations and uncertainty assessment, it delivers more robust solutions for informing catchment management of the sediment problem than many previously used approaches.

Project description:BackgroundPrecision Livestock Farming (PLF) is a promising approach to minimize the conflicts between socio-economic activities and landscape conservation. However, its application on extensive systems of livestock production can be challenging. The main difficulties arise because animals graze on large natural pastures where they are exposed to competition with wild herbivores for heterogeneous and scarce resources, predation risk, adverse weather, and complex topography. Considering that the 91% of the world's surface devoted to livestock production is composed of extensive systems (i.e., rangelands), our general aim was to develop a PLF methodology that quantifies: (i) detailed behavioural patterns, (ii) feeding rate, and (iii) costs associated with different behaviours and landscape traits.MethodsFor this, we used Merino sheep in Patagonian rangelands as a case study. We combined data from an animal-attached multi-sensor tag (tri-axial acceleration, tri-axial magnetometry, temperature sensor and Global Positioning System) with landscape layers from a Geographical Information System to acquire data. Then, we used high accuracy decision trees, dead reckoning methods and spatial data processing techniques to show how this combination of tools could be used to assess energy balance, predation risk and competition experienced by livestock through time and space.ResultsThe combination of methods proposed here are a useful tool to assess livestock behaviour and the different factors that influence extensive livestock production, such as topography, environmental temperature, predation risk and competition for heterogeneous resources. We were able to quantify feeding rate continuously through time and space with high accuracy and show how it could be used to estimate animal production and the intensity of grazing on the landscape. We also assessed the effects of resource heterogeneity (inferred through search times), and the potential costs associated with predation risk, competition, thermoregulation and movement on complex topography.DiscussionThe quantification of feeding rate and behavioural costs provided by our approach could be used to estimate energy balance and to predict individual growth, survival and reproduction. Finally, we discussed how the information provided by this combination of methods can be used to develop wildlife-friendly strategies that also maximize animal welfare, quality and environmental sustainability.

Project description:Despite fungi playing an important role in nutrient decomposition in aquatic ecosystems and being considered as vital actors in the ecological processes, they received limited attention regarding the community in aquaculture pond sediments which are extremely important and typically disturbed habitats. Using an ITS1 region of fungal rDNA, this study aimed to investigate sediment fungal communities in fish, crab, and crayfish ponds for decades of farming practices at representative aquaculture regions in the middle Yangtze River basin, China. We then aimed to explore the community patterns associated with species-based farming practices in the ponds at 18 farms. The results showed that the pond sediments harbored more than 9,000 operational taxonomic units. The sediments had significantly higher alpha diversity in crab ponds compared to that in fish and crayfish ponds. The fungal phyla largely belonged to Ascomycota and Chytridiomycota, and the dominance of Rozellomycota over Basidiomycota and Aphelidiomycota was observed. The majority of sediment fungal members were ascribed to unclassified fungi, with higher proportions in fish ponds than crab and crayfish ponds. Further, the fungal communities were markedly distinct among the three types of ponds, suggesting divergent patterns of fungal community assemblages caused by farming practices in aquaculture ponds. The community diversity and structure were closely correlated to sediment properties, especially sediment carbon content and pH. Thus, the distribution and pattern of fungal communities in the sediments appear to primarily depend on species-based farming practices responsible for the resulting sediment carbon content and pH in aquaculture ponds. This study provides a detailed snapshot and extension of understanding fungal community structure and variability in pond ecosystems, highlighting the impacts of farming practices on the assembly and succession of sediment fungal communities in aquaculture ponds.

Dataset Information

Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices.

Study region

Study focus

New hydrological insights for the region

Publications

Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets