Applications of sediment source fingerprinting continue to increase globally as the need for information to support improved management of the sediment problem persists. In our novel research, a ...Bayesian fingerprinting approach using MixSIAR was used with geochemical signatures, both without and with informative priors based on particle size and slope. The source estimates were compared with a newly proposed Source Sensitivity Index (SSI) and outputs from the INVEST-SDR model. MixSIAR results with informative priors indicated that agricultural and barren lands are the principal sediment sources (contributing ∼5–85% and ∼5–80% respectively during two sampling periods i.e. 2018-2019 and 2021–2022) with forests being less important. The SSI spatial maps (using % clay and slope as informative priors) showed >78% agreement with the spatial map derived using the INVEST-SDR model in terms of sub-catchment prioritization for spatial sediment source contributions. This study demonstrates the benefits of combining geochemical sediment source fingerprinting with SSI indices in larger catchments where the spatial prioritization of soil and water conservation is both challenging but warranted.
•Sensitivity of Bayesian sediment fingerprinting to particle size and slope explored.•Proposed a novel method to translate fingerprinting outputs to spatial information.•Method combines INVEST-SDR catchment modelling and Bayesian fingerprinting.•Combined approach revealed agricultural and barren regions as crucial sediment sources.
•Three Gorges Dam significantly alters fluvial continuity and traps inflowing sediment.•Suspended sediment displays longitudinal and seasonal trends in grain-size composition.•Spatial and temporal ...particle size differentiation was also observed for sink sediment.•Flow regulation modulates sediment sorting in the reservoir water-level fluctuation zone.
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The Three Gorges Dam has significantly interrupted fluvial continuity and modified the mass transfer regime along river continuums. Flow regulation following regular dam operations drives dramatic hydrological regime shifts, which facilitates sediment dispersal in the water-level fluctuation zone over episodic inundation periods. How flow regulation modulates sediment redistribution, however, remains unclear. In this study, we depict absolute particle size composition of suspended sediment and sink sediment in the water-level fluctuation zone, and these are interpreted in the context of flow regulation controls on sediment sorting. Multiple sampling strategies were applied at different spatial and temporal scales, to overcome limitations of labour and cost input in a large-scale field study and to collect representative samples. The results revealed a longitudinal fining trend and seasonal variability in particle size composition for suspended sediment. Sink sediment collected from the water-level fluctuation zone during a single summer flood event displayed a similar longitudinal fining trend, reflecting preferential settling of coarser fractions in the backwater reaches where flow velocity declines sharply. Surface sediment demonstrated a laterally coarsening trend with increasing elevations along a slope profile. Flooding duration, frequency and timing represent key factors in determining the elevation-dependent variations in the magnitude of sedimentation and its source inputs. Relatively longer flooding duration and frequent intermediate summer floods with high suspended sediment flux are responsible for high sedimentation rates in the lower portions with distal upstream source inputs, while low sedimentation rates in the upper portions are principally associated with water impoundment and sediment produced from local bank erosion. Vertical particle size variability was observed along a sedimentary core profile, which most likely reflects seasonal differences in source supply with contrasting particle size characteristics. We conclude that absolute particle size differentiation explains flow regulation controls on sediment sorting in the water-level fluctuation zone of the Three Gorges Reservoir.
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.
Small, 1st and 2nd-order, headwater streams and ponds play essential roles in providing natural flood control, trapping sediments and contaminants, retaining nutrients, and maintaining biological ...diversity, which extend into downstream reaches, lakes and estuaries. However, the large geographic extent and high connectivity of these small water bodies with the surrounding terrestrial ecosystem makes them particularly vulnerable to growing land-use pressures and environmental change. The greatest pressure on the physical processes in these waters has been their extension and modification for agricultural and forestry drainage, resulting in highly modified discharge and temperature regimes that have implications for flood and drought control further downstream. The extensive length of the small stream network exposes rivers to a wide range of inputs, including nutrients, pesticides, heavy metals, sediment and emerging contaminants. Small water bodies have also been affected by invasions of non-native species, which along with the physical and chemical pressures, have affected most groups of organisms with consequent implications for the wider biodiversity within the catchment. Reducing the impacts and restoring the natural ecosystem function of these water bodies requires a three-tiered approach based on: restoration of channel hydromorphological dynamics; restoration and management of the riparian zone; and management of activities in the wider catchment that have both point-source and diffuse impacts. Such activities are expensive and so emphasis must be placed on integrated programmes that provide multiple benefits. Practical options need to be promoted through legislative regulation, financial incentives, markets for resource services and voluntary codes and actions.
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•Small Water Bodies (SWB) provide a suite of vital ecosystem services.•Hydromorphology of SWBs makes them highly vulnerable to anthropogenic pressures.•Land-use and environmental changes are disrupting the ecosystem functions of SWBs.•3-tier restoration is needed: channel, riparian and wider catchment management.•Success will require government prioritization, expert advice, and stakeholder buy-in.
Life cycle assessment is a multidisciplinary framework usually deployed to appraise the sustainability of various product or service supply-chains. Over recent decades, its use in the agri-food ...sector has risen sharply, and alongside this, a wide range of methodological advances have been generated. Spatial-life cycle assessment, defined in the current document as the interpretation of life cycle assessment results within a geographical nature, has not gone unexplored entirely, yet its rise as a sub-method of life cycle assessment has been rather slow relative to other avenues of research (e.g., including the nutritional sciences within life cycle assessment). With this relative methodological stagnation as a motivating factor, our paper combines a process-based model, the Catchment Systems Model, with various life cycle impact assessments (ReCiPe, Centre for Environmental Studies and Environmental Product Declaration) to propose a simple, yet effective, approach for visualising the technically feasible efficacy of various on-farm intervention strategies. As water quality was the primary focus of this study, interventions reducing acidification and eutrophication potentials of both arable and livestock farm types in the Southeast of England were considered. The study site is an area with a marked range of agricultural practices in terms of intensity. All impacts to acidification potential and eutrophication potential are reported using a functional unit of 1 ha. Percentage changes relative to baseline farm types, i.e., those without any interventions, arising from various mitigation strategies, are mapped using geographical information systems. This approach demonstrates visually how a spatially-orientated life cycle assessment could provide regional-specific information for farmers and policymakers to guide the restoration of certain waterbodies. A combination of multiple mitigation strategies was found to generate the greatest reductions in pollutant losses to water, but in terms of individual interventions, optimising farm-based machinery (acidification potential) and fertiliser application strategies (eutrophication potential) were found to have notable benefits.
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•A novel yet data intensive spatial-life cycle assessment framework is proposed.•Farm types with and without agri-environmental interventions are assessed.•Eutrophication and acidification potentials are calculated for each farm type.•Optimising machinery usage reduces water impacts through decreased sediment loss.•Spatial-life cycle assessment is an effective tool for visualising environmental impact.
•Information gained on sediment source contributions was evaluated using biotracers.•Compound-specific stable isotope tracers are powerful for differentiating land uses.•Bound-fatty acid carbon ...isotopes provided robust estimation of source contributions.•Prolonged rains increased catchment sediment delivery from arable and pasture land.•Catchment source especially pasture contributions were corroborated by field scale data.
Excessive sediment loss degrades freshwater quality and is prone to further elevation and variable source contributions due to the combined effect of extreme rainfall and differing land uses. To quantify erosion and sediment source responses across scales, this study integrated work at both field and catchment scale for two hydrologically contrasting winters (2018–19 and 2019–20). Sediment load was estimated at the field scale (grassland-arable conversion system). Sediment source apportionment work was undertaken at the catchment scale (4.5 km2) and used alkanes, and both free and bound fatty acid carbon isotope signatures as diagnostic fingerprints to distinguish sediment sources: arable, pasture, woodland and stream banks. Sediment source apportionment based on bound fatty acids revealed a substantial shift in contributions, from stream banks dominating (70 ± 5%) in winter 2018–19, to arable land dominating (52 ± 7%) in the extreme wet winter 2019–20. Increases in sediment contributions from arable (∼3.9 times) and pasture (∼2.4 times) land at the catchment outlet during the winter 2019–20 were consistent with elevated sediment losses monitored at the field scale which indicated that low-magnitude high frequency rainfall alone increased sediment loss even from pasture by 350%. In contrast, carbon isotope signatures of alkanes and free fatty acids consistently estimated stream banks as a dominant source (i.e., ∼36% and ∼70% respectively) for both winters regardless of prolonged rainfall in winter 2019–20. Beyond quantifying the shifts in field scale sediment load and catchment scale sediment sources due to the changes in rainfall patterns, our results demonstrate valuable insight into how the fate of biotracers in soil and sediment manifests in the δ13C values of homologues and, in turn, their role in information gain for estimating sediment source contributions. Discrepancies in the estimated sediment source contributions using different biotracers indicate that without a careful appreciation of their biogeochemical limitations, erroneous interpretation of sediment source contributions can undermine management strategies for delivering more sustainable and resilient agriculture.
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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.
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•Land subsidence probability was derived through ANN, Bagging, ANN-Bagging.•Land subsidence inventory map and twelve conditioning factors were considered.•ANN-Bagging provides the prediction accuracy of 94,1, followed by ANN and Bagging.•Nearly 13% of the study area is under very high land subsidence probable zone.
Impoundment of the Three Gorges Reservoir on the upper Yangtze River has remarkably altered hydrological regime within the dammed reaches, triggering structural and functional changes of the riparian ...ecosystem. Up to date, how vegetation recovers in response to compound habitat stresses in the water level fluctuation zone remains inexplicitly understood. In this study, plant above-ground biomass (AGB) in a selected water level fluctuation zone was quantified to depict its spatial and temporal pattern using unmanned aerial vehicle (UAV)-derived multispectral images and screened empirical models. The contributions of multiple habitat stressors in governing vegetation recovery dynamics along the environmental gradient were further explored. Screened random forest models indicated relatively higher accuracy in AGB estimation, with R2 being 0.68, 0.79 and 0.62 during the sprouting, growth, and mature periods, respectively. AGB displayed a significant linear increasing trend along the elevational gradient during the sprouting and early growth period, while it showed an inverted U-shaped pattern during late growth and mature period. Flooding duration, magnitude and timing were found to exert greater negative effects on plant sprouting and biomass accumulation and acted as decisive factors in governing the elevation-dependent pattern of AGB. Localized spatial variations in AGB were modulated by other stressors such as sediment burial, soil erosion, soil moisture and nutrient content. Occurrence of episodic summer floods and vegetation distribution were responsible for an inverted U-shaped pattern of AGB during the late growth and mature period. Generally, AGB reached its peak in August, thereafter an obvious decline by an unprecedent dry-hot climatic event. The water level fluctuations with cumulative flooding effects exerted substantial control on AGB temporal dynamics, while climatic condition played a secondary role. Herein, further restorative efforts need to be directed to screening suitable species, maintaining favorable soil condition, and improving vegetation pattern to balance the many trade-offs.
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•Vegetation recovery in a water level fluctuation zone was studied by modelling AGB.•UAV imagery and screened random forest models provide reliable AGB estimates.•AGB increased at growth stage but declined after an extreme heat-drought event.•AGB exhibited a linear trend at elevations but an inversed U-shaped trend thereafter.•Flooding governs overall AGB variability, other stressors generate local variances.
A farm-to-landscape scale modelling framework combining regulating services and life cycle assessment mid-point impacts for air and water was used to explore the co-benefits and trade-offs of ...alternative management futures for grazing livestock farms. Two intervention scenarios were compared: one using on-farm interventions typically recommended following visual farm audits (visually-based) and the other using mechanistical understanding of nutrient and sediment losses to water (mechanistically-based). At farm scale, reductions in business-as-usual emissions to water of total phosphorus (TP) and sediment, using both the visually-based and mechanistically-based scenarios, were <5%. These limited impacts highlighted the important role of land drains and the lack of relevant on-farm measures in current recommended advisory lists for the soil types in question. The predicted impacts of both scenarios on free draining soils were significantly higher; TP reductions of ∼9% (visually-based) and ∼20% (mechanistically-based) compared with corresponding respective estimates of >20% and >35% for sediment. Key co-benefits at farm scale included reductions in nitrous oxide emissions and improvements in physical soil quality, whereas an increase in ammonia emissions was the principal trade-off. At landscape scale, simulated reductions in business-as-usual losses were <3% for both pollutants for both scenarios. The visually-based and mechanistically-based scenarios narrowed the gaps between current and modern background sediment loads by 6% and 11%, respectively. The latter scenario also improved the reduction of GWP100 relative to business-as-usual by 4%, in comparison to 1% for the former. However, with the predicted increase of ammonia emissions, both eutrophication potential and acidification potential increased (e.g., by 7% and 14% for the mechanistically-based scenario). The discrepancy of on-farm intervention efficacy across spatial scales generated by non-agricultural water pollutant sources is a key challenge for addressing water quality problems at landscape scale.
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•Monitoring data frames visually- and mechanistically-based intervention scenarios.•Farm to landscape modelling explores mitigation scenario co-benefits/trade-offs.•Farm scenarios reduced total phosphorus (TP) and sediment by <5% on drained soils.•Farm scenarios reduced TP and sediment by up to ∼20% and >35% on free draining soils.•Reductions in nitrous oxide emissions are the main co-benefit at farm and landscape scales.
Previous studies comparing sediment fingerprinting un‐mixing models report large differences in their accuracy. The representation of tracer concentrations in source groups is perhaps the largest ...difference between published studies. However, the importance of decisions concerning the representation of tracer distributions has not been explored explicitly. Accordingly, potential sediment sources in four contrasting catchments were intensively sampled. Virtual sample mixtures were formed using between 10 and 100% of the retrieved samples to simulate sediment mobilization and delivery from subsections of each catchment. Source apportionment used models with a transformed multivariate normal distribution, normal distribution, 25th–75th percentile distribution and a distribution replicating the retrieved source samples. The accuracy and precision of model results were quantified and the reasons for differences were investigated. The 25th–75th percentile distribution produced the lowest mean inaccuracy (8.8%) and imprecision (8.5%), with the Sample Based distribution being next best (11.5%; 9.3%). The transformed multivariate (16.9%; 17.3%) and untransformed normal distributions (16.3%; 20.8%) performed poorly. When only a small proportion of the source samples formed the virtual mixtures, accuracy decreased with the 25th–75th percentile and Sample Based distributions so that when <20% of source samples were used, the actual mixture composition infrequently fell outside of the range of uncertainty shown in un‐mixing model outputs. Poor performance was due to combined random Monte Carlo numbers generated for all tracers not being viable for the retrieved source samples. Trialling the use of a 25th–75th percentile distribution alongside alternatives may result in significant improvements in both accuracy and precision of fingerprinting estimates, evaluated using virtual mixtures. Caution should be exercised when using a normal type distribution, without exploration of alternatives, as un‐mixing model performance may be unacceptably poor.
The representation of source group tracer concentrations is perhaps the largest difference between sediment fingerprinting un‐mixing models. Despite this, the effects of different distributions on model accuracy have not been explored explicitly. ‘This study compared a transformed multivariate normal, a normal and a 25th–75th percentile distribution as well as a distribution replicating the retrieved source samples. The 25th–75th percentile distribution produced the lowest mean inaccuracy (8.8%), with the Sample Based being next best (11.5%). The transformed multivariate (16.9%) and untransformed normal distributions (16.3%) performed poorly.
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