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Purpose This study tests the feasibility of using a submersible spectrophotometer as a novel method to trace and apportion suspended sediment sources in situ and at high temporal frequency. Methods Laboratory experiments were designed to identify how absorbance at different wavelengths can be used to un-mix artificial mixtures of soil samples (i.e. sediment sources). The experiment consists of a tank containing 40 L of water, to which the soil samples and soil mixtures of known proportions were added in suspension. Absorbance measurements made using the submersible spectrophotometer were used to elucidate: (i) the effects of concentrations on absorbance, (ii) the relationship between absorbance and particle size and (iii) the linear additivity of absorbance as a prerequisite for un-mixing. Results The observed relationships between soil sample concentrations and absorbance in the ultraviolet visible (UV–VIS) wavelength range (200–730 nm) indicated that differences in absorbance patterns are caused by soil-specific properties and particle size. Absorbance was found to be linearly additive and could be used to predict the known soil sample proportions in mixtures using the MixSIAR Bayesian tracer mixing model. Model results indicate that dominant contributions to mixtures containing two and three soil samples could be predicted well, whilst accuracy for four-soil sample mixtures was lower (with respective mean absolute errors of 15.4%, 12.9% and 17.0%). Conclusion The results demonstrate the potential for using in situ submersible spectrophotometer sensors to trace suspended sediment sources at high temporal frequency.

Purpose Modern agricultural practices have increased the losses of fine sediment and associated pollutants to watercourses with associated off-site degradation of water quality and aquatic biodiversity. Despite significant investment into agri-environment initiatives which aim to reduce these losses, limited empirical mechanistic evidence exists for the efficacy of many on-farm interventions. As the most likely mechanism of particle detachment in many landscapes, understanding the controls for rainsplash erosion is key to generating this mechanistic understanding. Methods Soil properties were compared to rainsplash erosion rates on a grassland and an arable field in Southwest England. Soil cores were retrieved and measured for dry density, aggregate stability, vegetation cover, and loss on ignition. A rainsplash erosion trap consisting of a plastic funnel containing filter paper was placed into the hole left by each core, and the mass of sediment trapped over a 1-month and 2-week period was recorded. Results Soil compaction on the grassland field was limited to a very small proportion of the total field area at its margins and close to water troughs and was not often associated with a reduction in aggregate stability. Neither soil dry density nor aggregate stability was associated with an increase in rainsplash erosion rate. On arable land, aggregate stability was significantly lower than on the grassland, and rainsplash erosion rates were higher. However, vegetation cover, rather than aggregate stability, was the major controlling factor. Conclusion The hypothesis that livestock are not causing an increase in erosion by raindrop impact on grassland fields but the same soils, when cultivated, experience a significant increase in erosion rate can be accepted based on the results generated. In lowland grassland landscapes with recommended best grazing management practices currently implemented, further reductions in sediment losses may therefore not be achievable. However, a reduction in elevated soil loss from arable fields can be achieved by ensuring vegetation cover is present during wet winter months.

Background Internationally increasing demand for emergency care is driving innovation within emergency services. The Alternative Pre-Hospital Pathway (APP) Team is one such Community Emergency Medicine (CEM) initiative developed in Cork, Ireland to target low acuity emergency calls. In this paper the inception of the APP Team is described, and an observational descriptive analysis of the APP Team’s service data presented for the first 12 months of operation. The aim of this study is to describe and analyse the APP team service. Methods The APP Team, consisting of a Specialist Registrar (SpR) in Emergency Medicine (EM) and an Emergency Medical Technician (EMT) based in Cork, covers a mixed urban and rural population of approximately 300,000 people located within a 40-min drive time of Cork University Hospital. The team are dispatched to low acuity 112/999 calls, aiming to provide definitive care or referring patients to the appropriate community or specialist service. A retrospective analysis was performed of the team’s first 12 months of operation using the prospectively maintained service database. Results Two thousand and one patients were attended to with a 67.8% non-conveyance rate. The median age was 62 years, with 33.0% of patients aged over 75 years. For patients over 75 years, the non-conveyance rate was 62.0%. The average number of patients treated per shift was 7. Medical complaints (319), falls (194), drug and alcohol related presentations (193), urological (131), and respiratory complaints (119) were the most common presentations. Conclusion Increased demand for emergency care and an aging population is necessitating a re-design of traditional models of emergency care delivery. We describe the Alternative Pre-Hospital Pathway service, delivered by an EMT and an Emergency Medicine SpR responding to low acuity calls. This service achieved a 68% non-conveyance rate; our data demonstrates that a community emergency medicine outreach team in collaboration with the National Ambulance Service offering Alternative Pre-Hospital Pathways is an effective model for reducing conveyances to hospital.

Organic tracers (bulk δ13C and δ15N) are being increasingly used in sediment/particulate organic carbon source apportionment studies at the catchment scale to support sustainable land management decisions. Here, the use of these isotopic tracers in sediment fingerprinting depends on the critical assumption that δ13C and δ15N values remain conservative during the sediment delivery continuum. Such assumption, however, requires critical evaluation, especially since standard tracer conservation tests applied in conjunction with catchment scale studies are prone to masking potential issues at smaller spatial scales including the field scale. Against this background, our study evaluated the subtle isotopic shift associated with sediment redistribution to edge-of-field and assessed the impact on sediment source apportionment. In a C3-grass dominated study field, δ13C and δ15N values exhibited differences (i.e., Δ13C = 1.4 ± 0.7‰ and Δ15N = 0.4 ± 0.4‰) between soil depths of 0–5 cm and 5–10 cm. Sampled sediments at the edge-of-field flume had higher δ13C values than the 0–5 cm soil layer; i.e., Δ13C values were 0.3 ± 0.4‰ and 0.8 ± 0.4‰ for suspended and deposited (materials deposited in a basket downstream of a flume) sediment, respectively. In contrast, δ15N values increased in suspended (Δ15N = 0. 8 ± 0.6‰) but decreased (Δ15N = − 0.4 ± 0.5‰) in deposited sediment, compared to the 0–5 cm soil layer, suggesting that the N isotopes can respond differently in edge-of-field sediment types. Although current fingerprinting work tends to not take explicit account of organic tracer alteration during transport or after deposition, our results demonstrate that correcting sediment δ13C and δ 15N values for tracer alteration in a Bayesian un-mixing model generated robust and reliable estimates of source contributions to both target sediment types. We therefore recommend taking account of the subtle but consistently altered δ13C and δ 15N values along the sediment cascade in un-mixing modelling to help better discriminate sources and to improve un-mixing model estimates at the catchment scale.

Purpose Intensive livestock grazing has been associated with an increased risk of soil erosion and concomitant negative impacts on the ecological status of watercourses. Whilst various mitigation options are promoted for reducing livestock impacts, there is a paucity of data on the relationship between stocking rates and quantified sediment losses. This evidence gap means there is uncertainty regarding the cost–benefit of policy preferred best management. Methods Sediment yields from 15 hydrologically isolated field scale catchments on a heavily instrumented ruminant livestock farm in the south west UK were investigated over ~ 26 months spread across 6 years. Sediment yields were compared to cattle and sheep stocking rates on long-term, winter (November–April), and monthly timescales. The impacts of livestock on soil vegetation cover and bulk density were also examined. Cattle were tracked using GPS collars to determine how grazing related to soil damage. Results No observable impact of livestock stocking rates of 0.15–1.00 UK livestock units (LU) ha−1 for sheep, and 0–0.77 LU ha−1 for cattle on sediment yields was observed at any of the three timescales. Cattle preferentially spent time close to specific fences where soils were visually damaged. However, there was no indication that livestock have a significant effect on soil bulk density on a field scale. Livestock were housed indoors during winters when most rainfall occurs, and best management practices were used which when combined with low erodibility clayey soils likely limited sediment losses. Conclusion A combination of clayey soils and soil trampling in only a small proportion of the field areas lead to little impact from grazing livestock. Within similar landscapes with best practice livestock grazing management, additional targeted measures to reduce erosion are unlikely to yield a significant cost-benefit.

Diffuse pollution of watercourses from agriculture represents a complex and persistent environmental problem in the UK. This paper provides insights into why UK policy interventions have had limited success to date, drawing on the disciplines of psychology, sociology and behavioural economics to more thoroughly understand farmer attitudes and behaviours towards pollution mitigation. Our analysis is based on eliciting the opinions of commercial farmers through a series of surveys and discussion groups in three catchments: the grassland dominated River Eden catchment; the arable dominated River Wensum catchment and the mixed farming area of the Hampshire River Avon catchment. Results strongly suggest that a fundamental shift in identities, normative behavioural beliefs and social norms is required within the farming community before mitigation behaviours become embedded. Simply offering financial incentives or imposing regulatory penalties is unlikely to achieve the desired results. Double loop learning has the potential to enable farmers to migrate from a productivist to a multifunctional outlook where pollution mitigation becomes internalised within a farm management system. Expert farm advisors will be required to facilitate this process.

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.

Purpose This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science. Methods Web of Science and Google Scholar were used to review published papers spanning the period 2013–2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018–2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017. Scope Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing. Conclusions The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach.

This research developed a more efficient integrated model (IM) based on combining the Nash-Sutcliffe efficiency coefficient (NSEC) and individual data mining (DM) algorithms for the spatial mapping of dust provenance in the Hamoun-e-Hirmand Basin, southeastern Iran. This region experiences severe wind erosion and includes the Sistan plain which is one of the most PM2.5-polluted regions in the world. Due to a prolonged drought over the last two decades, the frequency of dust storms in the study area is increasing remarkably. Herein, 14 factors controlling dust emissions (FCDEs) including soil characteristics, climatic variables, digital elevation map, normalized difference vegetation index, land use and geology were mapped. Correlation and collinearity among the FCDEs were examined by the Pearson test, tolerance coefficient (TC) and variance inflation factor (VIF), with the results suggesting a lack of collinearity between FCDEs. A tree-based genetic algorithm was applied to prioritize and quantify the importance weights of the FCDEs. Thirteen individual data mining models were applied for mapping dust provenance. The model performance was assessed using root mean square error, mean absolute error and NSEC. Based on clustering analysis, the 13 DM models were grouped into five clusters and then the cluster with the highest NSEC values used in an integrated modelling process. Based on the results, the IM (NSEC = 93%) outperformed the individual DM models (the NSEC values range between 51 and 92%). Using the IM, 11, 5, 7 and 77% of the total study area were classified into low, moderate, high and very high susceptibility classes for dust provenance, respectively. Overall, the results illustrate the benefits of an IM for mapping spatial variation in the susceptibility of catchment areas to act as dust sources.

In the context of the continued increased global uptake of fingerprinting procedures to explore fluvial sediment sources, far less attention has been paid to dust source tracing and especially using different particle size fractions and low-cost tracers such as colour and magnetic susceptibility. The objective of this study, therefore, was to apportion local dust storm source contributions for the < 63-μm and 63–125-μm fractions of dust samples in a case study in central Iran. Colour and magnetic susceptibility properties were measured on 62 source samples and six dust storm samples. Statistical methods were used to select four different composite fingerprints for discriminating the dust sediment sources. These statistical approaches comprised (1) the Kruskal–Wallis H test (KW-H), (2) a combination of KW-H and discriminant function analysis (DFA), (3) a combination of KW-H and principal components and classification analysis (PCCA), and (4) a combination of KW-H and a general classification and regression tree model (GCRTM). Local dust source contributions were ascribed using a Bayesian un-mixing model using the final composite fingerprints. For both the < 63- and 63–125-μm fractions, the different composite signatures consistently suggested that alluvial fan material was the dominant source of the dust samples. The root mean square differences between the apportionment results using the different fingerprints ranged from 0.5 to 1.6% for the < 63-μm fraction and from 1.8 to 5.8% for the 63–125-μm fraction. The Wald-Wolfowitz runs test was used to compare the posterior distributions of the predicted source proportions created using the alternative final composite fingerprints and the results indicated that most of the pairwise comparisons were significantly different (p ≤ 0.05). For the < 63-μm fraction, the RMSE and MAE estimates of divergence between the modelled and known virtual source mixtures using the different final composite signatures ranged between 1.5 and 23.4% (with a corresponding mean value of 9.4%). The equivalent estimates for the 63–125-μm fraction were 1.2–20.1% (8.3%). The findings clearly demonstrate that colour and magnetic susceptibility tracers offer low-cost options for apportioning dust sources.

Sediments are known to be a vector for nutrient and contaminant transfer because many substances partition preferentially to fine-grained sediment rather than remaining in solution. Despite the need to obtain reliable information on suspended sediment chemical composition, studies and monitoring programmes are often hampered by the difficulties associated with sampling, and by analytical costs. This, in turn, restricts high frequency sampling campaigns to a limited number of events and reduces the accuracy of the estimated fluxes and yields of sediment-associated chemical constituents.Over the past decade, progress in environmental monitoring and analytics has increasingly facilitated the collection of hydro-chemical data at high frequency using in-situ sensors (e.g., minutes). However, sensors to estimate sediment-associated chemical constituents are limited. Here, we propose the use of submerged spectrophotometers, which measure absorbance in the UV-VIS range, to predict sediment mineralogical composition, major and trace elements and colour. Submerged spectrophotometers have already been successfully used to predict mean particle size and sediment carbon content. In this study, we assess the performance of several regression models that relate light absorbance measurements with suspended sediment properties. To this end, spectrophotometers were installed at five different sites across Luxembourg. Preliminary results show that spectrophotometers allow simultaneous assessment of various sediment constituents and/or properties at high frequency, suggesting their deployment can assist in the estimation of reliable fluxes and yields of sediment-associated substances.

Agriculture is challenged to produce healthy food and to contribute to cleaner energy whilst mitigating climate change and protecting ecosystems. To achieve this, policy-driven scenarios need to be evaluated with available data and models to explore trade-offs with robust accounting for the uncertainty in predictions. We developed a novel model ensemble using four complementary state-of-the-art agroecosystems models to explore the impacts of land management change. The ensemble was used to simulate key agricultural and environmental outputs under various scenarios for the upper River Taw observatory, UK. Scenarios assumed (i) reducing livestock production whilst simultaneously increasing the area of arable where it is feasible to cultivate (PG2A), (ii) reducing livestock production whilst simultaneously increasing bioenergy production in areas of the catchment that are amenable to growing bioenergy crops (PG2BE) and (iii) increasing both arable and bioenergy production (PG2A + BE). Our ensemble approach combined model uncertainty using the tower property of expectation and the law of total variance. Results show considerable uncertainty for predicted nutrient losses with different models partitioning the uncertainty into different pathways. Bioenergy crops were predicted to produce greatest yields from Miscanthus in lowland and from SRC-willow (cv. Endurance) in uplands. Each choice of management is associated with trade-offs; e.g. PG2A results in a significant increase of edible calories (6736 Mcal ha−1) but reduced soil C (−4.32 t C ha−1). Model ensembles in the agroecosystem context are difficult to implement due to challenges of model availability and input and output alignment. Despite these challenges, we show that ensemble modelling is a powerful approach for applications such as ours, offering benefits such as capturing structural as well as data uncertainty and allowing greater combinations of variables to be explored. Furthermore, the ensemble provides a robust means for combining uncertainty at different scales and enables us to identify weaknesses in system understanding.

Fine sediment has a critical role in river ecosystems and is essential for habitat heterogeneity, ecosystem structure and function. Expansion and intensification of specific land uses, including agriculture, have increased fine sediment inputs into river networks. The detrimental impacts of excessive fine sediment on river ecosystems have been well documented and numerous sediment targets have been proposed or adopted to assess the gap between target and current levels of fine sediment. Where sediment targets exist, these are often over-simplified and applied across a wide range of river environments irrespective of the processes of fine sediment deposition and the tolerance or sensitivity of river biota to fine sediment. Thus, targets often fail to provide a reliable basis for identifying the need for management interventions to restore ecosystem health. This review adopts a system-based approach to the impacts of fine sediment after reviewing the suitability of existing targets for guiding management in chalk stream catchments specifically. Chalk streams are groundwater-dominated systems characterised by stable hydrological, ecological and thermal regimes and thus respond differently to excessive fine sediment compared with other fluvial systems. Chalk streams are often subject to high levels of sedimentation and siltation despite their low suspended sediment loads. In this paper, we review the characteristic processes and dynamics of chalk streams and how these influence fine sediment accumulation. The impacts of excessive fine sediment on chalk stream habitats and biota and the role ecosystem engineers play in the processes of fine sediment dynamics are discussed. Finally, we discuss the application of fine sediment targets for chalk streams in relation to the implementation of both source and process-based techniques for meeting the requirement for improved ecosystem management.

Purpose: Nitrous oxide (N2O) and methane (CH4) are some of the most important greenhouse gases of the 21st century. Vegetated riparian buffers are primarily implemented for their water quality functions in agroecosystems and their location in the agricultural landscape allows them to intercept and process pollutants from immediately adjacent agricultural land. They recycle increase soil carbon (C), intercept nitrogen (N)-rich runoff from adjacent croplands, and are seasonally anoxic, promoting processes producing environmentally harmful gases including N2O and CH4. Against this context, the study quantified these atmospheric losses between a cropland and vegetated riparian buffers that serve it.Methods: We used the static chamber to measure N2O and CH4 emissions simultaneously with soil. Gas measurements were done simulataneously with soil and environmental variables for a 6-month period in a replicated plot-scale facility comprising of maize cropping served by three vegetated riparian buffers, namely: (i) a novel grass riparian buffer; (ii) a willow riparian buffer, and; (iii) a woodland riparian buffer. These buffered treatments were compared with a no-buffer control. Results: The no-buffer control generated the largest cumulative N2O emissions of 18 929 g ha-1 (95% confidence intervals: 524.1 - 63 643) whilst the maize crop upslope generated the largest cumulative CH4 emissions of 5 050 ± 875 g ha-1. Soil N2O and CH4-based global warming potential (GWP) were lower in the willow (1223.5 ± 362.0 and 134.7 ± 74.0 kg CO2-eq. ha-1 year-1, respectively) and woodland (1771.3 ± 800.5 and 3.4 ± 35.9 kg CO2-eq. ha-1 year-1, respectively) riparian buffers.. Conclusions: Our results suggest that maize production in general, and situations where such cropping is not undertaken in tandem with a riparian buffer strip, result in atmospheric CH4 and N2O concerns.

Transport and deposition of fine-grained sediment, a pervasive nonpoint source pollutant, cause deleterious off-site impacts for water quality and aquatic ecosystems. Sediment fingerprinting provides one means of identifying the spatial sources of mobilised sediment delivered to fluvial systems in order to help target sediment control strategies and uptake of such source tracing procedures has been steadily increasing. Nonetheless, there remains a need to continue testing and comparing different composite signatures for source discrimination including the incorporation of physically grounded information relevant to erosion patterns. Accordingly, the objective of this study was to compare the discrimination and apportionment of sub-basin spatial suspended sediment sources in a mountainous basin in northern Tehran, Iran, using composite signatures comprising conventional geochemical tracers combined with lithological weathering indices or only the former. The list of conventional geochemical properties comprised Al, Ca, Cu, Fe, K, Mg, Mn, Na, Ni, Sr, Ti, and Zn whilst three weathering indices were included: the chemical index of alteration (CIA), the weathering index of Parker (WIP), and the indicator of recycling (IR) which were all calculated based on elemental oxides. Using a composite signature combining conventional geochemical tracers and one weathering index (IR), the relative contributions from the sub-basin spatial sources were estimated at 1 (Imamzadeh Davood; 1.4%), 2 (Taloon; 13.4%), 3 (Soleghan; 35.9%), and 4 (Keshar; 48.4%) compared with corresponding respective estimates of 0.7%, 45.5%, 40.2%, and 13.3% using conventional geochemical tracers alone. Wald-Wolfowitz Runs test pairwise comparisons of the posterior distributions of predicted source proportions generated using the two different composite signatures confirmed statistically significant differences. These differing proportions demonstrated the sensitivity of predicted source apportionment to the inclusion or exclusion of a weathering index providing information reflecting the relative coverage of more erodible lithological formations in each of the sub-basins (32.7% sub-basin 1, 53.6% sub-basin 2, 58.5% sub-basin 3, and 63.2% sub-basin 4). The outputs of this study will be used to target sediment mitigation strategies.

A sediment source fingerprinting method, including a Monte Carlo simulation framework, was used to quantify the contributions of terrestrial sources of fine- (< 63 μm) and coarse-grained (63–500 μm) sediments sampled from three categories of coastal sediment deposits in the Jagin catchment, south-east of Jask, Hormozgan province, southern Iran: coastal dunes (CD), terrestrial sand dunes or onshore sediments (TSD), and marine or offshore sediments (MD). Forty-nine geochemical properties were measured in the two size fractions and a three-stage statistical process consisting of a conservation test, the Kruskal–Wallis H test, and stepwise discriminant function analysis (DFA) was applied to select final composite fingerprints for terrestrial source discrimination. Based on the statistical tests, four final fingerprints comprising Be, Ni, K and Cu and seven final fingerprints consisting Cu, Th, Be, Al, La, Mg and Fe were selected for discriminating terrestrial sources of the coastal fine- and coarse grained sediments, respectively. Two geological spatial sources, including Quaternary (clay flat, high and low level fans and valley terraces) and Palaeocene age deposits, were identified as the main terrestrial sources of the fine-grained sediment sampled from the coastal deposits. A geological spatial source consisting of sandstone with siltstone, mudstone and minor conglomerate (Palaeocene age deposits) was identified as the main terrestrial source for coarse-grained sediment sampled from the coastal deposits.

Fine-grained cohesive sediment is the primary vector for nutrient and contaminant redistribution through aquatic systems and is a critical indicator of land disturbance. A critical limitation of most existing sediment transport models is that they assume that the transport characteristics of fine sediment can be described using the same approaches that are used for coarse-grained non-cohesive sediment, thereby ignoring the tendency of fine sediment to flocculate. Here, a modelling framework to simulate flow and fine sediment transport in the Crowsnest River, the Castle River, the Oldman River and the Oldman Reservoir after the 2003 Lost Creek wildfire in Alberta, Canada was developed and validated. It is the first to include explicit description of fine sediment deposition/erosion processes as a function of bed shear stress and the flocculation process. This framework integrates four existing numerical models: MOBED, RIVFLOC, RMA2 and RMA4 using river geometry, flow, fine suspended sediment characteristics and bathymetry data. Sediment concentration and particle size distributions computed by RIVFLOC were used as the upstream boundary condition for the reservoir dispersion model RMA4. The predicted particle size distributions and mass of fine river sediment deposited within various sections of the reservoir indicate that most of the fine sediment generated by the upstream disturbance deposits in the reservoir. Deposition patterns of sediment from wildfire-impacted landscapes were different than those from unburned landscapes because of differences in settling behaviour. These differences may lead to zones of relatively increased internal loading of phosphorus to reservoir water columns, thereby increasing the potential for algae proliferation. In light of the growing threats to water resources globally from wildfire, the generic framework described herein can be used to model propagation of fine river sediment and associated nutrients or contaminants to reservoirs under different flow conditions and land use scenarios. The framework is thereby a valuable tool to support decision making for water resources management and catchment planning.

To manage effectively excessive sediment inputs to rivers and streams, it is crucial to have detailed and reliable information on key sediment sources. Such evidence is important for implementing targeted measures for improving ecosystem functioning and meeting environmental objectives. Although sediment fingerprinting is increasingly adopted worldwide to provide such evidence, current procedures do not provide detailed information on how sediment sources can change over both short (e.g., events and in between events) and long (e.g., over seasons or years) time scales. These limitations are mainly due to the conventional methods used for target sediment sampling and the high workloads and costs associated with laboratory analyses for tracers, which limit both high-frequency and longer duration sampling campaigns. To address this issue, we propose the use of a submersible spectrophotometer, which measures absorbance in the UV-VIS range in situ and at high frequency (e.g., minutes) to trace suspended sediment sources. In our proof of concept investigation, the approach was first tested in a laboratory setting, using soil samples and artificial mixtures with known proportions of two, three and four soil source samples in an experimental water tank. A total of six soil samples were collected, which were sieved to different fractions to investigate the influence of particle size on the sensor absorbance readings. Both soil samples and artificial mixtures were suspended in the laboratory tank set-up at different concentrations to investigate the effects on: (i) absorbance, and; (ii) un-mixing accuracy. The results showed that absorbance was linearly additive and could be used to predict dominant samples in the artificial mixtures correctly using a Bayesian tracer un-mixing model, largely regardless of particle size and of the concentration inside the experimental tank. This approach is currently being tested in a field experiment in the Attert River Basin (Luxembourg) to investigate if the results found in the laboratory experiments hold under natural field conditions. Our preliminary insights into the use of absorbance for sediment source apportionment in the field will be presented.

Establishing quality reference values (QRVs) for rare earth elements (REEs) in soils is essential for the screening of these emergent contaminants. Currently, Brazil has the second-largest reserve of REEs, but data regarding background concentrations and distributions in soils remain scarce. The aim of this study was to establish the QRVs and assess the spatial distribution of REEs in soils, including REE fractionations and anomalies in (Piauí) state (251,529.186 km2), northeastern Brazil. This study reports the most detailed data on REE geochemistry in Brazilian soils. A total of 243 composite soil samples was collected at 0–20 cm depth. The mean background concentrations in soils followed the abundance of the earth’s upper crust: Ce > La > Nd > Pr > Sm > Dy > Gd > Er > Yb > Eu > Tb > Lu. The ∑REEs (mg kg−1) showed the following order based on the individual mesoregions of Piauí state: Southeast (262.75) > North and Central-North (89.68) > Southwest (40.33). The highest QRVs were observed in the Southeast mesoregion. The establishment of QRVs based on the mesoregion scale improves data representativeness and the monitoring of natural REE values by identifying hot spots. Geostatistical modeling indicated significant local variability, especially in the Southeast mesoregion. The levels of these elements in this spatial zone are naturally higher than the other values across Piauí state and the mesoregion itself and indicate a high potential to exceed the QRVs. Our approach provides much needed data to help strengthen policies for both human health and environmental protection.

Eight geochemical elements (Al, Ca, Fe, K, Na, Mg, Si, Ti) and 17 associated weathering indices were measured in 34 aeolian source samples and 10 sand dune target sediment samples in three absolute particulate size fractions. For each fraction, three final composite fingerprints (i.e., geochemical elements only, weathering indices only and a combination of the two) for discerning and ascribing the aeolian sediment sources were selected. The Modified MixSIR Bayesian un-mixing model was used to apportion aeolian source contributions using the final composite fingerprints. Regardless of the composite fingerprint used, all results across the different size fractions suggested that the south-eastern alluvial fan is the dominant (average contribution 50.6%, SD 19.0%) source of the sand dune samples, with the western alluvial fan being the second most important (average contribution 38.4%, SD 20.4%) source. Comparisons of the posterior distributions for the predicted source proportions generated using the nine composite fingerprints (three kinds of composite fingerprints*three particle size fractions) showed that the composite fingerprints combining the geochemical elements and weathering indices generated the most powerful source material discrimination. Our results demonstrate the use of weathering indices alongside more conventional elemental geochemistry tracers for investigations into sand dune sediment provenance.

Understanding the spatial distribution of soil salinity is required to conserve land against degradation and desertification. Against this background, this study is the first attempt to predict soil salinity in the Jaghin basin, in southern Iran, by applying and comparing the performance of four deep learning (DL) models (deep convolutional neural networks—DCNNs, dense connected deep neural networks—DenseDNNs, recurrent neural networks-long short-term memory—RNN-LSTM and recurrent neural networks-gated recurrent unit—RNN-GRU) and six shallow machine learning (ML) models (bagged classification and regression tree—BCART, cforest, cubist, quantile regression with LASSO penalty—QR-LASSO, ridge regression—RR and support vectore machine—SVM). To do this, 49 environmental landsat8-derived variables including digital elevation model (DEM)-extracted covariates, soil-salinity indices, and other variables (e.g., soil order, lithology, land use) were mapped spatially. For assessing the relationships between soil salinity (EC) and factors controlling EC, we collected 319 surficial (0–5 cm depth) soil samples for measuring soil salinity on the basis of electrical conductivity (EC). We then selected the most important features (covariates) controlling soil salinity by applying a MARS model. The performance of the DL and shallow ML models for generating soil salinity spatial maps (SSSMs) was assessed using a Taylor diagram and the Nash Sutcliff coefficient (NSE). Among all 10 predictive models, DL models with NSE ≥ 0.9 (DCNNs was the most accurate model with NSE = 0.96) were selected as the four best models, and performed better than the six shallow ML models with NSE ≤ 0.83 (QR-LASSO was the weakest predictive model with NSE = 0.50). Based on DCNNs-, the values of the EC ranged between 0.67 and 14.73 dS/m, whereas for QR-LASSO the corresponding EC values were 0.37 to 19.6 dS/m. Overall, DL models performed better than shallow ML models for production of the SSSMs and therefore we recommend applying DL models for prediction purposes in environmental sciences.

Excess diffuse fine sediment losses from agriculture are associated with a reduction in the ecological status of lakes and rivers. Agri-environment initiatives aim to help reduce these excessive losses through targeted on-farm advice and capital grants. However, to deliver optimum cost-benefit such initiatives must target the most important sediment sources. Sediment source fingerprinting provides an approach by which sediment provenance data can be generated; however, conventional tracers are expensive and state-of-the-art data processing for source discrimination and apportionment requires significant expertise. In consequence, sediment fingerprinting is not routinely used for catchment management. Against this context, sediment colour measured using an ordinary document scanner is trialled as an inexpensive and accessible tracer in eight catchments classed as high priority for an agri-environment initiative in England. Colour is interpreted using simple scatter bi-plots of source and sediment samples and compared to the results produced by a conventional composite tracer sediment source fingerprinting methodology. It was found that in two study catchments, sediment colour was as effective as conventional sediment fingerprinting. In four catchments, sediment colour provided less source discrimination or minor differences in results but still identified the most important targets for sediment mitigation. In two catchments, colour was un-successful; however, significant challenges were also encountered with more conventional tracers due to specific local conditions. The findings suggest that use of low-cost colour tracers by non-experts has the potential to significantly increase the uptake of sediment source tracing to improve the cost-benefit of agri-environment initiatives combating the degradation of water quality and aquatic ecology by accelerated sediment losses.

This study undertook a comprehensive application of 15 data mining (DM) models, most of which have, thus far, not been commonly used in environmental sciences, to predict land susceptibility to water erosion hazard in the Kahorestan catchment, southern Iran. The DM models were BGLM, BGAM, Cforest, CITree, GAMS, LRSS, NCPQR, PLS, PLSGLM, QR, RLM, SGB, SVM, BCART and BTR. We identified 18 factors usually considered as key controls for water erosion, comprising 10 factors extracted from a digital elevation model (DEM), three indices extracted from Landsat 8 images, a sediment connectivity index (SCI) and three other intrinsic factors. Three indicators consisting of MAE, MBE, RMSE, and a Taylor diagram were applied to assess model performance and accuracy. Game theory was applied to assess the interpretability of the DM models for predicting water erosion hazard. Among the 15 predictive models, BGAM and PLS respectively returned the best and worst performance in predicting water erosion hazard in the study area. The most accurate model, BGAM predicted that 22%, 8.2%, 9.4% and 60.4% of the total area should be classified as low, moderate, high and very high susceptibility to soil erosion by water, respectively. Based on BGAM and game theory, the factors extracted from the DEM (e.g., DEM, TWI, Slope, TST, TRI, and SPI) were considered the most important ones controlling the predicted severity of soil erosion by water. We conclude that overall, game theory is a valuable technique for assessing the interpretability of predictive models because this theory through SHAP (Shapley additive explanations) and PFIM (permutation feature importance measure) addresses the important concerns regarding the interpretability of more complex DM models.

Soil erosion and sediment losses are likely to increase with projected climate change and increasing storm intensity, causing detrimental impacts to agriculture and the degradation of aquatic ecosystems. Agri-environment initiatives aim to mitigate these losses through farmer engagement and incentivisation for best practice. There is, however, limited evidence regarding the impacts of their delivery and their capacity to deliver resilience to extreme wet weather. As such, the aim of this study was to determine how fine-grained sediment provenance changes with flow condition in eight high priority English catchments targeted through a strategic agri-environment initiative and to compare the estimated sediment source proportions to the targeted advice being delivered to farmers. The provenance of fine-grained sediment was determined using sediment source fingerprinting over low flow conditions and the winter of 2019–2020 which was characterised by severe flooding. Details on the advice delivered to farmers through the Catchment Sensitive Farming initiative were obtained from a delivery agency database and interviews with catchment officers. Dominant sediment sources varied considerably between catchments and were not easily predicted based upon their characteristics meaning that targeting on-farm advice effectively is challenging. Critically, however, changes in sediment sources rarely occurred with the extreme wet winter of 2019–2020. It is recommended that the greater availability of empirical catchment-specific evidence could significantly improve the delivery of such agri-environment initiatives. In the current absence of this evidence an assumption that riparian woodland is effective at preventing sediment losses and the targeting of advice to sediment sources proportionally based upon the area of the catchment they cover may improve the benefits of such initiatives.

This study aims to predict land susceptibility (a term that indicates the degree of sensitivity of land to detachment of soil particles by wind) to dust emissions in Yazd province, central Iran, by combining a new integrated data mining (DM) model and the RegCM4 climatic model. The study further determines the relative importance of key factors controlling dust emissions by applying 12 individual DM models. The integrated model is based on the individual models returning Nash Sutcliffe coefficient (NSC) values > 90% for the spatial modelling of land susceptibility to dust emissions and using the area under the curve (AUC) for validation. 13 key factors controlling dust emissions are mapped including soil characteristics, climatic variables, vegetation cover, a Digital Elevation Model (DEM), geology and land use. Based on Spearman clustering analysis and multi-collinearity tests (tolerance coefficient -TC and variance inflation factor -VIF), the effective factors for dust emissions are classified into nine clusters and no multi-collinearity is found among the effective factors. DEM, NDVI (normalized difference vegetation index), geology and calcium carbonate are identified as the most important factors controlling dust emissions. Seven individual models return NSC in the range of 90–98% and are used to generate the integrated model for the final mapping of land susceptibility to dust emissions. Among 851 pixels located in the dust sources, 30% (255 pixels) and 70% (596 pixels) are randomly selected as validation and training datasets, respectively for the new integrated model. Using this model, 9%, 17%, 7% and 67% of the study area correspond to low, moderate, high and very high susceptibility classes, while the validation results in AUC = 99.3%. Simulations with the RegCM4 model reveal high consistency regarding the spatial distribution of the most susceptible areas and dust emissions. Overall, combining DM approaches and physical models is useful in aeolian geomorphology studies.

Identifying the key sources of fine-grained sediment is essential for protecting and improving soil and water quality. Accordingly, this contribution tested a combination of low-cost analytical procedures for assembling information on key sediment sources in an agricultural catchment in Brazil and in so doing, tested 24 components derived from a conventional printer scanner using various colourimetric models, 18 organic compounds derived from near-infrared (NIR) spectra and 13 soil constituents derived from mid-infrared (MIR) spectra. In combining the application of these low-cost tracers, the study also aimed to investigate potential scale-dependency in sediment sources. Four main sediment sources were sampled: (i) sugarcane; (ii) unpaved roads; (iii) cropland, and; (iv) channel banks and samples were collected from these at two different scales; sub-catchment (~1453 km2) and catchment-wide (~2857 km2). At both scales, channel banks were the most important sediment source followed by sugarcane. At catchment-wide scale, channel banks accounted for 75.9 ± 6.7%, 56.4 ± 16.3%, 39.1 ± 20.7% and 68.3 ± 4.9% of sampled suspended sediment using composite signatures comprising NIR, MIR or colour tracers only, or a combination of all three types of low-cost tracers, respectively. For bed sediment samples, the corresponding respective source contributions were estimated to be 43.4 ± 4.7%, 32.8 ± 7.8%, 49.2 ± 18.6% and 32.0 ± 4.6%. Our results, regardless of optical property, target sediment type, or scale, suggest that channel banks represent the primary source of the sediment problem in the study area. Targeted remedial actions therefore especially need to deliver protection for eroding channel banks.

The authors would like to thank the Faculty of Agriculture and Natural Resources, University of Hormozgan, Iran, for supporting this joint research Project. The contribution to this work by ALC was funded by UKRI-BBSRC (UK Research and Innovation-Biotechnology and Biological Sciences Research Council) Grant BBS/E/C/000I0330 - Soil to Nutrition Project 3., Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts. Here, we identify and analyze desertification trends in Iran for the period 2001–2015 via a combination of three indices for vegetation (NPP—net primary production, NDVI—normalized difference vegetation index, LAI—leaf area index) and two climate indices (LST—land surface temperature, P— precipitation). We combine these indices to identify and map areas of Iran that are susceptible to land degradation. We then apply a simple linear regression method, the Mann–Kendall non-parametric test, and the Theil–Sen estimator to identify long-term temporal and spatial trends within the data. Based on desertification map, we find that 68% of Iran shows a high to very high susceptibility to desertification, representing an area of 1.1 million km2 (excluding 0.42 million km2 classified as unvegetated). Our results highlight the importance of scale in assessments of desertification, and the value of high-resolution data, in particular. Annually, no significant change is evident within any of the five indices, but significant changes (some positive, some negative) become apparent on a seasonal basis. Some observations follow expectations; for instance, NDVI is strongly associated with cooler, wet spring and summer seasons, and milder winters. Others require more explanation; for instance, vegetation appears decoupled from climatic forcing during autumn. Spatially, too, there is much local and regional variation, which is lost when the data are considered only at the largest nationwide scale. We identify a northwest–southeast belt spanning central Iran, which has experienced significant vegetation decline (2001–2015). We tentatively link this belt of land degradation with intensified agriculture in the hinterlands of Iran’s major cities. The spatial and temporal trends identified with the three vegetation and two climate indices afford a cost-effective framework for the prediction and management of future environmental trends in developing regions at risk of desertification., Faculty of Agriculture and Natural Resources, University of Hormozgan, Iran, UK Research & Innovation (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) BBS/E/C/000I0330

Sediment fingerprinting in data sparse regions, such as the mountainous areas of Iran, is more suited to a confluence-based sample design wherein tributary sub-basins are characterised by sediment samples using different size fractions. Our objective was therefore to fingerprint spatial source contributions to the < 37 and 37–63 μm fractions of fine-grained channel bed sediment samples collected in a large erodible mountainous river basin in Iran based on a number of statistical and machine learning approaches. Geochemical elements were measured in channel bed surface drape sediments from seven sub-basins and in delivered sediments from the basin outlet. A Bayesian mass balance model (modified MixSIR) was applied to apportion sub-basin spatial sources with four composite signatures selected using the different statistical approaches. For the < 37 and 37–63 μm fractions, the signatures all indicated that sub-basins 3 (Andajroud; 46.7%) and 7 (Ninehroud; 36.8%) were the dominant spatial sources of the fine-grained bed drape sediment samples, respectively, identifying the most active erosional zones spatially. The statistical error between known and predicted spatial source contributions using virtual mixture tests for both size fractions demonstrated the importance of using multiple different composite fingerprints to decrease the model prediction uncertainties. Despite the difficult terrain in such data sparse areas, the source fingerprinting approach provides a basis for assembling new evidence which, in turn, is of interest to scientists and managers alike.

Clean water is a precious resource, and policies/programmes are implemented worldwide to protect and/or improve water quality. Faecal pollution can be a key contributor to water quality decline causing eutrophication through nutrient enrichment and pathogenic contamination. The robust sourcing of faecal pollutants is important to be able to target the appropriate sector and to engage managers. Biomarker technology has the potential for source confirmation, by using, for example the biomarker suite of steroids. Steroids have been used in the differentiation of human and animal faeces; however, there is no unequivocal extraction technique. Some of the methods used include (i) Soxhlet extraction, (ii) Bligh and Dyer (BD) extraction, and (iii) accelerated solvent extraction (ASE). The less costly and time intensive technique of ASE is particularly attractive, but a current research gap concerns further comparisons regarding ASE lipid extraction from soils/slurries compared with the more traditional Soxhlet and BD extractions. Accordingly, a randomised complete block experiment was implemented to assess differences between the three extraction methods, differences between the different sample types, and the interactions between these two factors. Following GC-MS, it was found that there was no significant difference between the results of the steroid extraction methods, regardless of the type of sample used, for the quantity of each steroid extracted. It was concluded that ASE could be used confidently instead of the more established steroid extraction methods, thereby delivering time and cost savings.

Prediction of dissolved organic carbon (DOC) based on catchment characteristics is a useful tool for efficient and effective water management, but in the case of arid and semi-arid regions, such predictive capacity is scarce. Accordingly, the main objective of this study was to evaluate the significance of principal components for predicting DOC concentrations and fluxes in nine headwater catchments of the Hiv catchment located in the Southern Alborz Mountains in the west of Tehran, Iran. To achieve this aim, data were assembled on 24 headwater catchment characteristics comprising soil properties, physiography, seasonal rainfall, and flow attributes, as well as estimates of DOC concentrations and fluxes across four seasons. The results revealed a major positive correlation between DOC and soil organic matter parameters related to soil biological processes. Using general linear modelling, an organic matter component related to soil biology, a seasonal component related to the dummy effect of sampling seasons, and a soil physical component related to soil texture were found to be the best predictors for DOC responses in the study area.

The contribution of quarrying in the context of multiple catchment sources of finegrained sediment has rarely been investigated. This study assessed the relative importance of quarrying as a sediment source alongside rangeland surface soils and channel banks in a mountainous catchment in northern Tehran, Iran, using fingerprinting. Eight geochemical tracers were measured on 24 potential sediment source samples and four fine‐grained sediment samples. Statistical analysis to select three different composite fingerprints for discriminating the potential sediment sources comprised: (a) the Kruskal–Wallis H test (KW‐H), (b) a combination of KW‐H and discriminant function analysis (DFA), and (c) a combination of KW‐H and principal components and classification analysis (PCCA). A Bayesian unmixing model was used to apportion sediment source contributions using the three composite fingerprints. Using the KW‐H composite signature, the respective relative contributions (with uncertainty ranges) from channel banks, rangeland surface soils, and quarrying were estimated as 28.4% (10.9–46.8), 15.1% (6.6–22.7), and 56.6% (38.3–74.2), compared with 35.4% (11.9–60.1), 13.4% (4.1–22.2), and 51.3% (26.5–74.3) using a composite signature selected using a combination of KW‐H and DFA, or 20.7% (3.9–41.7), 17.2% (4.4–29.9), and 61.4% (44–78.8) using a fingerprint selected using KW‐H and PCCA. The different composite signatures therefore all consistently suggested that quarrying is the dominant source of the fine‐grained sediment samples. Potential mitigation measures targeting this land use include closure to permit revegetation to reduce exposure of bare surfaces to sediment mobilization. Limitations and uncertainties associated with this preliminary investigation are briefly discussed.

espanolAlrededor del 40 % de los lagos europeos no cumplen con los objetivos de calidad quimica del agua que se han establecido en la Directiva Marco del Agua de la UE (DMA). Una de las principales causas es la entrada excesiva de fosforo (P) a los lagos proveniente de sus cuencas, ya sea por fuentes agricolas o efluentes de plantas de tratamiento de aguas residuales. Este estudio utilizo datos de monitoreo de la DMA de Inglaterra y Gales para desarrollar un metodo para identificar lagos que no estan cumpliendo con los objetivos de calidad del agua de la DMA en terminos de concentraciones de fosforo total (TP) y cuyos problemas podrian resolverse reduciendo las entradas de P de fuentes agricolas. Se presenta un marco de referencia para la toma de decisiones que (1) identifica los lagos que no logran alcanzar un buen estado debido a los altos aportes de TP de la agricultura; (2) predice como las concentraciones de TP en el lago parecen responder a aportes reducidos de fuentes agricolas, y (3) identifica los sitios donde es probable que la recuperacion se retrase debido a factores que inducen resistencia a las medidas de mitigacion de TP. A traves del ejemplo estudiado, demostramos como puede ser usado un marco de referencia para la toma de decisiones, que nos ayude a filtrar los datos de monitoreo de lagos de la DMA y poder identificar donde son mas efectivas las costosas medidas de restauracion. Tambien demostramos que en general, reducir la carga externa de TP de origen agricola solo es efectivo si se introduce como parte de un programa de restauracion mas completo el cual tenga como objetivo identificar otras fuentes de TP, como por ejemplo las estaciones depuradoras, y teniendo en cuenta factores especificos de los lagos que afectan su respuesta. Los resultados tambien permiten a las personas encargadas de la gestion del agua identificar sitios donde la recuperacion puede retrasarse por las caracteristicas biologicas, quimicas o fisicas especificas del lago, lo que permite gestionar mejor las expectativas del proceso de recuperacion al proporcionar escalas de tiempo realistas. EnglishAbout 40 % of European lakes are failing water quality targets for chemistry that have been set under the EU Water Framework Directive (WFD). One of the main causes of this problem is excessive inputs of phosphorus (P) to lakes from their catchments, including those from agricultural sources and wastewater treatment works. This study used WFD monitoring data from England and Wales to develop a method of identifying lakes that are failing WFD water quality targets for good status in terms of total phosphorus (TP) concentrations and whose problems could be solved by reducing P inputs from agricultural sources. A decision support framework is presented that (1) identifies lakes that are failing to achieve good status due to high TP inputs from agriculture; (2) predicts how in-lake TP concentrations are likely to respond to reduced inputs from agricultural sources, and (3) identifies sites where recovery is likely to be delayed by factors that induce resistance to TP mitigation measures. Through a worked example, we demonstrate how a decision support framework can be used to screen lake WFD monitoring data to identify where expensive restoration measures are likely to be more cost effective. We demonstrate that, in general, reducing the external TP load from agricultural sources would be effective only if introduced as part of a more comprehensive programme of restoration measures targeting other sources of TP, such as waste water treatment works, and taking into account lake-specific factors that affect lake response. The outputs from this decision support framework are designed to enable water managers to identify sites where recovery may be delayed by lake specific biological, chemical or physical characteristics, allowing them to manage expectations of the recovery process better by providing realistic timescales.

Use of natural tracers (in particular, δ13C and δ15N, total carbon and nitrogen) for discriminating and apportioning soil sources of eroded sediment has increased. Some critical issues, however, including the variability of such tracers over time and space, merit further investigation. This study used a 25 m grid in a 1.78 ha hydrologically-isolated permanent pasture field in SW England to assess temporal and spatial variability in the discrimination (using Kruskal-Wallis H-tests) of top (0–2 cm) and subsoil (2–10 cm) samples afforded by these tracers. Grid sampling was repeated four times (twice in each of June and July 2015) to assess the impact of temporal variability and Monte Carlo random sub-sampling of the 25 m grid was used to assess the significance of spatial variability for discriminatory efficiency. Using grouped (combining all time-periods) data, all tracers were statistically significant (p ≤ 0.05) for discriminating soil samples by depth. For the repeat sample collection campaigns, δ13C discriminated samples by soil depth in three out of four, δ15N in one and both TC and TN in all periods. Grouped data (all time-periods, both soil depths) yielded statistically significant results for all tracers. All tracers apart from δ15N yielded statistically significant tests for the comparison of topsoil tracers across the four time periods, whilst all tracers except δ13C were significantly different in the subsoil samples across the sampling campaigns. All tracers therefore exhibited some statistically significant temporal variation. δ13C and δ15N exhibited less temporal dependence than TC and TN. Random sub-sampling (n = 10) with 100 iterations of the tracer data grid sampling points did not alter the discriminatory efficiency of the properties. The spatial nature of the tracer data, therefore, did not compromise or confound discrimination between the soil source samples by depth or across time. Un-mixing modelling with virtual sediment mixtures based on equal proportions of the field soil source tracers from each time period and using the field soil source data from each individual period, revealed sensitivity to the temporal dependence of the tracers. Overall, the findings suggest that the temporal variation in the tracers tested requires more consideration than the spatial dimension at the scale of an individual field, which in a conventional erosion source tracing study, would represent one location of many within the target landscape or catchment.

Highlights • Fingerprinting was used to assess the contribution of recreational roads to sediment yield. • Three statistical approaches were used to select composite fingerprints. • The Modified MixSIR Bayesian model was used for source apportionment. • The source apportionment estimates were sensitive to the tracers. • Recreational roads were the dominant source of suspended sediment., Road construction associated with land development generally increases erosion and sediment yields. Construction of unpaved roads has the potential to alter hydro-sedimentological behavior and catchment sediment source dynamics and, to date, this has largely been investigated in forested environments. The objective of this study, therefore, was to assess the relative importance of unpaved recreational roads as a sediment source alongside hillslope surface soils and stream channel banks in a non-forested mountainous catchment in northern Tehran, Iran, using a fingerprinting procedure. Eleven geochemical tracers were measured on 27 samples collected to characterise the sediment sources and five suspended sediment samples collected at the study catchment outlet. The statistical analysis employed to select three different composite fingerprints for discriminating the sediment sources comprised: (1) the Kruskal–Wallis H test (KW-H), (2) a combination of KW-H and discriminant function analysis (DFA), and (3) a combination of KW-H and principal components & classification analysis (PCCA). A Bayesian un-mixing model was used to ascribe sediment source contributions using the three composite fingerprints. Using the KW-H composite signature, the respective relative contributions (with uncertainty ranges) from recreational roads, hillslope surface soils and channel banks were estimated as 64.5% (57.7–73.1), 1.1% (0.1–4.9), and 33.9% (24.9–41.0), compared to 55.3% (45.5–68.5), 1.9% (0.1–7.9) and 42.1% (27.8–52.4) using a composite signature selected using a combination of KW-H and DFA, or 82.0% (69.7–93.8), 8.2% (0.7–22.7) and 7.3% (0.7–21.0) using a fingerprint selected using KW-H and PCCA. The root mean square difference between the apportionment results using the fingerprints identified on the basis of the three different statistical approaches ranged from 5.5% to 25.7%, highlighting the sensitivity of source estimates to the tracers used. Regardless, the different composite signatures all suggested that unpaved recreational roads were the dominant source of the suspended sediment samples, underscoring the need for mitigation measures targeting these anthropogenic features of the catchment system, including closure to permit re-vegetation, surface ripping and/or mulching to improve infiltration or gravel re-surfacing to reduce exposure of bare surfaces to sediment mobilisation.

Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total of 96 locations of LS and 12 LS conditioning factors (LSCFs) were collected. Each feature in the LS inventory map (LSIM) was randomly assigned to one of four groups or folds, each comprising 25% of cases. The novel ensemble model was trained using 75% (3 folds) and validated with the remaining 25% (1 fold) in a four-fold cross-validation (CV) system, which is used to control for the effects of the random selection of the training and validation datasets. LSCFs for LS prediction were selected using the information-gain ratio and multi-collinearity test methods. Factor significance was evaluated using a random forest (RF) model. Groundwater drawdown, land use and land cover, elevation, and lithology were the most important LSCFs. Using the k-fold CV approaches, twelve LS susceptibility maps (LSSMs) were prepared as each fold employed all three models (ANN-bagging, ANN, and bagging). The LS susceptibility mapping showed that between 5.7% and 12.6% of the plain had very high LS susceptibility. All three models produced LS susceptibility maps with acceptable prediction accuracies and goodness-of-fits, but the best maps were produced by the ANN-bagging ensemble method. Overall, LS risk was highest in agricultural areas with high groundwater drawdown in the flat lowlands on quaternary sediments (Qcf). Groundwater extraction rates should be monitored and potentially limited in regions of severe or high LS susceptibility. This investigation details a novel methodology that can help environmental planners and policy makers to mitigate LS to help achieve sustainability.

Reliable and detailed information on the primary sources of suspended sediment (SS) and sediment-associated nutrient and contaminant transfers is needed to target mitigation measures for delivering healthy ecosystems and meeting environmental policy objectives. To this end, the SS source fingerprinting approach is proven an effective tool for assembling reliable information on the sources of SS and SS-associated nutrients and contaminants within a catchment. However, SS source estimates at a high temporal resolution are often lacking due to the high workload and costs involved in collecting and analysing SS and soil samples using conventional means. Given this background, here, we propose the use of submersible spectrophotometers that measure absorbance spectra at 2.5 nm intervals in the 200-750 nm range (UV-VIS) in-situ and at high temporal frequency (i.e. minutes) to fingerprint SS sources. We hypothesise that increasing the measurement frequency will eventually help to better characterise changes in sources over time, whilst also giving further insights on how to improve the classical sediment fingerprinting approach, which is currently based on the use of temporally-lumped data. In this research, we first test our approach under fully controlled conditions in a laboratory experiment. To this end, we use a large cylindrical tank (40-L) equipped with a spectrophotometer as well as a LISST sensor (measuring the effective particle size distribution (PSD)). A mechanical stirring device ensures homogeneous conditions in the system and prevents the settling of soil particles (added in solution). The used soil samples originate from different areas within Luxembourg, whereby a selection was made based on differences in tracer properties and colour. The soils were sieved to three different fractions to take account of PSD control on tracer properties. Using the laboratory experiment, we investigated how suspended particle properties affect the absorbance spectra readings. In particular, we looked at the effects of: (i) increasing concentrations of suspended particles, and; (ii) differences in PSD. We then created artificial mixtures composed of two, three and four soil types mixed in different proportions to investigate if the absorbance readings at different wavelengths (i.e., considered as tracers or fingerprints) can be used to un-mix the known proportions of the SS sources. For this, we used the predictions of MixSIAR, a well-established Bayesian tracer un-mixing model. Our preliminary results indicate the promising use of high resolution absorbance data to un-mix artificial sediment mixtures. Ongoing work is testing the approach at larger scales.