Impacts of tracer type, tracer selection, and source dominance on source apportionment with sediment fingerprinting.

Sediment fingerprinting estimates the proportional contribution of fine sediment from distinct catchment sources delivered to downstream receiving environments. Increased attention has focused on assessing the accuracy of source contribution estimates, particularly in relation to tracer selection and statistical un-mixing procedures. However, no studies have systematically tested the impact of source combination or dominance on the accuracy of source estimates. Here, we assess sensitivity to tracer type, selection, and number of sources, and examine how variations in the dominant sediment source affect the accuracy of source apportionments using numerical mixtures. Sources were sampled according to erosion process and land cover from a New Zealand catchment. Topsoil and subsoil (landslide) samples were collected from pasture, harvested pine, kanuka scrub, and native forest, while banks were sampled along the main channel. Samples were analysed for bulk geochemistry, fallout radionuclides, and compound specific stable isotopes (CSSIs). Source apportionment accuracy tended to decrease as source number increased, which reflected decreasing source discrimination. Tracer selection showed variations in accuracy but exhibited no clear pattern overall. Source combination and particularly the dominant source had the largest impact on accuracy, reflecting the level of discrimination for each source. Notably, channel bank was frequently identified as the dominant source when using CSSI tracers. While this partly reflected lower levels of discrimination, the CSSI apportionment was particularly sensitive to the use of post-unmixing corrections routinely applied to derive soil proportional contributions from isotopic proportions. This sensitivity likely related to the low organic carbon content in bank material and the assumption that source apportionments based on isotopic proportions can be corrected using a linear relationship with organic carbon content. These results indicate that the use of CSSI tracers in catchments where erosion sources exhibit large differences in soil organic carbon content may introduce significant unquantified error in source apportionments.

Quantifying the influence of individual trees on slope stability at landscape scale.

Silvopastoralism in New Zealand's highly erodible hill country is an important form of erosion and sediment control. Yet, there has been little quantitative work to establish the effectiveness of space-planted trees in reducing shallow landslide erosion. We propose a method to provide high-resolution spatially explicit individual tree influence models at landscape scale for the dominant species in pastoral hill country. The combined hydrological and mechanical influence of trees on slopes is inferred through the spatial relationship between trees and landslide erosion. First, we delineate individual tree crowns and classify these into four dominant species classes found in New Zealand's pastoral hill country. This is the first species classification of individual trees at landscape scale in New Zealand using freely accessible data, achieving an overall accuracy of 92.6%. Second, we develop tree influence models for each species class by means of inductive inference. The inferred empirical tree influence models largely agree with the shape and distribution of existing physical root reinforcement models. Of exotic species that were planted for erosion and sediment control, poplars (Populus spp.) and willows (Salix spp.) make up 51% (109,000 trees) in pastoral hill country at a mean density of 3.2 trees/ha. In line with previous studies, poplars and willows have the greatest contribution to slope stability with an average maximum effective distance of 20 m. Yet, native kanuka (Kunzea spp.) is the most abundant woody vegetation species in pastoral hill country within the study area, with an average of 24.1 stems per ha (sph), providing an important soil conservation function. A large proportion (56% or 212.5 km2) of pastoral hill-country in the study area remains untreated. The tree influence models presented in this study can be integrated into landslide susceptibility modelling in silvopastoral landscapes to both quantify the reduction in landslide susceptibility achieved and support targeted erosion and sediment mitigation plans.

Dataset on the 6-year radiocesium transport in rivers near Fukushima Daiichi nuclear power plant.

Radiocesium released from the Fukushima Daiichi nuclear power plant (FDNPP) and deposited in the terrestrial environment has been transported to the sea through rivers. To study the long-term effect of riverine transport on the remediation process near the FDNPP, a monitoring project was initiated by the University of Tsukuba. It was commissioned by the Ministry of Education, Culture, Sports, Science, and Technology, and the Nuclear Regulatory Commission in June 2011, and was taken over by the Fukushima Prefectural Centre for Environmental Creation from April 2015. The activity concentration and monthly flux of radiocesium in a suspended form were measured in the project. This provides valuable measurement data to evaluate the impact of the accidentally released radiocesium on residents and the marine environment. It can also be used as verification data in the development and testing of numerical models to predict future impacts.

Transport and Redistribution of Radiocesium in Fukushima Fallout through Rivers.

The Fukushima Daiichi Nuclear Power Plant (FDNPP) accident released the most significant quantity of radiocesium into the environment since Chernobyl, and detailed measurements over the initial 5 years provide new insights into fluvial redistribution of radiocesium. We found that the high initial activity concentration of 137Cs-bearing suspended sediment in rivers was followed by a steep exponential decline (λ1) which extended to approximately 1 year after the accident, while the rate of initial decline in radiocesium activity concentration in water was an order of magnitude higher than rates measured after Chernobyl. Fluvial transport of 137Cs to the ocean from the Abukuma river totaled 12 TBq between June 2011 and August 2015 and almost all this radiocesium (96.5%) was transported in the particulate form. The primary sources of 137Cs were paddy fields, farmland, and urban areas [plaque-forming unit (PFU)], discharging 85% of the exported 137Cs from 38% of the watershed area. After 1 year, activity concentrations were lower and exhibited a more gradual secondary decline (λ2) which was associated with reduced radiocesium losses from PFU areas, while forest areas continue to represent more stable contaminant stores.

A deconvolutional Bayesian mixing model approach for river basin sediment source apportionment.

Increasing complexity in human-environment interactions at multiple watershed scales presents major challenges to sediment source apportionment data acquisition and analysis. Herein, we present a step-change in the application of Bayesian mixing models: Deconvolutional-MixSIAR (D-MIXSIAR) to underpin sustainable management of soil and sediment. This new mixing model approach allows users to directly account for the 'structural hierarchy' of a river basin in terms of sub-watershed distribution. It works by deconvoluting apportionment data derived for multiple nodes along the stream-river network where sources are stratified by sub-watershed. Source and mixture samples were collected from two watersheds that represented (i) a longitudinal mixed agricultural watershed in the south west of England which had a distinct upper and lower zone related to topography and (ii) a distributed mixed agricultural and forested watershed in the mid-hills of Nepal with two distinct sub-watersheds. In the former, geochemical fingerprints were based upon weathering profiles and anthropogenic soil amendments. In the latter compound-specific stable isotope markers based on soil vegetation cover were applied. Mixing model posterior distributions of proportional sediment source contributions differed when sources were pooled across the watersheds (pooled-MixSIAR) compared to those where source terms were stratified by sub-watershed and the outputs deconvoluted (D-MixSIAR). In the first example, the stratified source data and the deconvolutional approach provided greater distinction between pasture and cultivated topsoil source signatures resulting in a different posterior distribution to non-deconvolutional model (conventional approaches over-estimated the contribution of cultivated land to downstream sediment by 2 to 5 times). In the second example, the deconvolutional model elucidated a large input of sediment delivered from a small tributary resulting in differences in the reported contribution of a discrete mixed forest source. Overall D-MixSIAR model posterior distributions had lower (by ca 25-50%) uncertainty and quicker model run times. In both cases, the structured, deconvoluted output cohered more closely with field observations and local knowledge underpinning the need for closer attention to hierarchy in source and mixture terms in river basin source apportionment. Soil erosion and siltation challenge the energy-food-water-environment nexus. This new tool for source apportionment offers wider application across complex environmental systems affected by natural and human-induced change and the lessons learned are relevant to source apportionment applications in other disciplines.

Community managed forests dominate the catchment sediment cascade in the mid-hills of Nepal: A compound-specific stable isotope analysis.

Soil erosion by water is critical for soil, lake and reservoir degradation in the mid-hills of Nepal. Identification of the nature and relative contribution of sediment sources in rivers is important to mitigate water erosion within catchments and siltation problems in lakes and reservoirs. We estimated the relative contribution of land uses (i.e. sources) to suspended and streambed sediments in the Chitlang catchment using stable carbon isotope signature (δ13C) of long-chain fatty acids as a tracer input for MixSIAR, a Bayesian mixing model used to apportion sediment sources. Our findings reveal that the relative contribution of land uses varied between suspended and streambed sediment, but did not change over the monsoon period. Significant over- or under-prediction of source contributions could occur due to overlapping source tracer values, if source groups are classified on a catchment-wide basis. Therefore, we applied a novel deconvolutional framework of MixSIAR (D-MixSIAR) to improve source apportionment of suspended sediment collected at tributary confluences (i.e. sub-catchment level) and at the outlet of the entire catchment. The results indicated that the mixed forest was the dominant (41 ±â€¯13%) contributor of sediment followed by broadleaf forest (15 ±â€¯8%) at the catchment outlet during the pre-wet season, suggesting that forest disturbance as well as high rainfall and steep slopes interact for high sediment generation within the study catchment. Unpaved rural road tracks located on flat and steep slopes (11 ±â€¯8 and 9 ±â€¯7% respectively) almost equally contributed to the sediment. Importantly, agricultural terraces (upland and lowland) had minimal contribution (each <7%) confirming that proper terrace management and traditional irrigation systems played an important role in mitigating sediment generation and delivery. Source contributions had a small temporal, but large spatial, variation in the sediment cascade of Chitlang stream. D-MixSIAR provided significant improvement regarding spatially explicit sediment source apportionment within the entire catchment system. This information is essential to prioritize implementation measures to control erosion in community managed forests to reduce sediment loadings to Kulekhani hydropower reservoir. In conclusion, using compound-specific stable isotope (CSSI) tracers for sediment fingerprinting in combination with a deconvolutional Bayesian mixing model offers a versatile approach to deal with the large tracer variability within catchment land uses and thus to successfully apportion multiple sediment sources.

Comparing catchment sediment fingerprinting procedures using an auto-evaluation approach with virtual sample mixtures.

Information on sediment sources in river catchments is required for effective sediment control strategies, to understand sediment, nutrient and pollutant transport, and for developing soil erosion management plans. Sediment fingerprinting procedures are employed to quantify sediment source contributions and have become a widely used tool. As fingerprinting procedures are naturally variable and locally dependant, there are different applications of the procedure. Here, the auto-evaluation of different fingerprinting procedures using virtual sample mixtures is proposed to support the selection of the fingerprinting procedure with the best capacity for source discrimination and apportionment. Surface samples from four land uses from a Central Spanish Pyrenean catchment were used i) as sources to generate the virtual sample mixtures and ii) to characterise the sources for the fingerprinting procedures. The auto-evaluation approach involved comparing fingerprinting procedures based on four optimum composite fingerprints selected by three statistical tests, three source characterisations (mean, median and corrected mean) and two types of objective functions for the mixing model. A total of 24 fingerprinting procedures were assessed by this new approach which were solved by Monte Carlo simulations and compared using the root mean squared error (RMSE) between known and assessed source ascriptions for the virtual sample mixtures. It was found that the source ascriptions with the highest accuracy were achieved using the corrected mean source characterisations for the composite fingerprints selected by the Kruskal Wallis H-test and principal components analysis. Based on the RMSE results, high goodness of fit (GOF) values were not always indicative of accurate source apportionment results, and care should be taken when using GOF to assess mixing model performance. The proposed approach to test different fingerprinting procedures using virtual sample mixtures provides an enhanced basis for selecting procedures that can deliver optimum source discrimination and apportionment.

Initial flux of sediment-associated radiocesium to the ocean from the largest river impacted by Fukushima Daiichi Nuclear Power Plant.

This study aimed to quantify the flux of radiocesium in the Abukuma Basin (5,172 km(2)), the largest river system affected by fallout from the Fukushima Daiichi Nuclear Power Plant (FDNPP) event. In the period from 10 August 2011 to 11 May 2012 an estimated 84 to 92% of the total radiocesium transported in the basin's fluvial system was carried in particulate form. During this monitoring period Typhoon Roke (September 2011) was observed to induce a significant and temporally punctuated redistribution of radiocesium. The storm-mobilised radiocesium was an estimated 6.18 Terabecquerels corresponding to 61.4% of the total load delivered to the coastal zone during the observation period. The total flux of radiocesium into the Pacific Ocean estimated at the outlet station (basin area 5,172 km(2)) was 5.34 TBq for (137)Cs, and 4.74 TBq for (134)Cs, corresponding to 1.13% of the total estimated radiocesium fallout over the basin catchment (890 TBq). This was equivalent to the estimated amount of direct leakage from FDNPP to the ocean during June 2011 to September 2012 of 17 TBq and the Level 3 Scale Leakage on 21 August 2013 (24 TBq).