Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Solum depth and its spatial distribution play an important role in different types of environmental studies. Several approaches have been used for fitting quantitative relationships between soil properties and their environment in order to predict them spatially. This work aimed to present the steps required for solum depth spatial prediction from knowledge-based digital soil mapping, comparing the prediction to the conventional soil mapping approach through field validation, in a watershed located at Mantiqueira Range region, in the state of Minas Gerais, Brazil. Conventional soil mapping had aerial photo-interpretation as a basis. The knowledge-based digital soil mapping applied fuzzy logic and similarity vectors in an expert system. The knowledge-based digital soil mapping approach showed the advantages over the conventional soil mapping approach by applying the field expert-knowledge in order to enhance the quality of final results, predicting solum depth with suited accuracy in a continuous way, making the soil-landscape relationship explicit

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes

Abstract: 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

Rainfall erosivity in subtropical catchments and implications for erosion and particle-bound contaminant transfer: a case-study of the Fukushima region

International audienceThe Fukushima Dai-ichi nuclear power plant (FDNPP) accident in March 2011 resulted in a significant fallout of radiocesium over the Fukushima region. After reaching the soil surface, radiocesium is almost irreversibly bound to fine soil particles. Thereafter, rainfall and snow melt run-off events transfer particle-bound radiocesium downstream. Erosion models, such as the Universal Soil Loss Equation (USLE), depict a proportional relationship between rainfall and soil erosion. As radiocesium is tightly bound to fine soil and sediment particles, characterizing the rainfall regime of the fallout-impacted region is fundamental to modelling and predicting radiocesium migration. Accordingly, monthly and annual rainfall data from ~ 60 meteorological stations within a 100 km radius of the FDNPP were analysed. Monthly rainfall erosivity maps were developed for the Fukushima coastal catchments illustrating the spatial heterogeneity of rainfall erosivity in the region. The mean average rainfall in the Fukushima region was 1387 mm yr-1 (σ 230) with the mean rainfall erosivity being 2785 MJ mm ha-1 yr-1 (σ 1359). The results indicate that the majority of rainfall (60 %) and rainfall erosivity (86 %) occurs between June and October. During the year, rainfall erosivity evolves positively from northwest to southeast in the eastern part of the prefecture, whereas a positive gradient from north to south occurs in July and August, the most erosive months of the year. During the typhoon season, the coastal plain and eastern mountainous areas of the Fukushima prefecture, including a large part of the contamination plume, are most impacted by erosive events. Understanding these rainfall patterns, particularly their spatial and temporal variation, is fundamental to managing soil and particle-bound radiocesium transfers in the Fukushima region. Moreover, understanding the impact of typhoons is important for managing sediment transfers in subtropical regions impacted by cyclonic activity

Rainfall erosivity in catchments contaminated with fallout from the Fukushima Daiichi nuclear power plant accident

The Fukushima Daiichi nuclear power plant (FDNPP) accident in March 2011 resulted in the fallout of significant quantities of radiocesium over the Fukushima region. After reaching the soil surface, radiocesium is quickly bound to fine soil particles. Thereafter, rainfall and snowmelt run-off events transfer particle-bound radiocesium downstream. Characterizing the precipitation regime of the fallout-impacted region is thus important for understanding post-deposition radiocesium dynamics. Accordingly, 10 min (1995–2015) and daily precipitation data (1977–2015) from 42 meteorological stations within a 100 km radius of the FDNPP were analyzed. Monthly rainfall erosivity maps were developed to depict the spatial heterogeneity of rainfall erosivity for catchments entirely contained within this radius. The mean average precipitation in the region surrounding the FDNPP is 1420 mm yr^−1 (SD 235) with a mean rainfall erosivity of 3696 MJ mm ha^−1 h^−1 yr^−1 (SD 1327). Tropical cyclones contribute 22 % of the precipitation (422 mm yr^−1) and 40 % of the rainfall erosivity (1462 MJ mm ha⁻¹ h⁻¹ yr⁻¹ (SD 637)). The majority of precipitation (60 %) and rainfall erosivity (82 %) occurs between June and October. At a regional scale, rainfall erosivity increases from the north to the south during July and August, the most erosive months. For the remainder of the year, this gradient occurs mostly from northwest to southeast. Relief features strongly influence the spatial distribution of rainfall erosivity at a smaller scale, with the coastal plains and coastal mountain range having greater rainfall erosivity than the inland Abukuma River valley. Understanding these patterns, particularly their spatial and temporal (both inter- and intraannual) variation, is important for contextualizing soil and particle-bound radiocesium transfers in the Fukushima region. Moreover, understanding the impact of tropical cyclones will be important for managing sediment and sediment-bound contaminant transfers in regions impacted by these events

Spatial and seasonal patterns of rainfall erosivity in the Lake Kivu region: insights from a meteorological observatory network

In the Lake Kivu region, water erosion is the main driver for soil degradation, but observational data to quantify the extent and to assess the spatial-temporal dynamics of the controlling factors are hardly available. In particular, high spatial and temporal resolution rainfall data are essential as precipitation is the driving force of soil erosion. In this study, we evaluated to what extent high temporal resolution data from the TAHMO network (with poor spatial and long-term coverage) can be combined with low temporal resolution data (with a high spatial density covering long periods of time) to improve rainfall erosivity assessments. To this end, 5 minute rainfall data from TAHMO stations in the Lake Kivu region, representing ca. 37 observation-years, were analyzed. The analysis of the TAHMO data showed that rainfall erosivity was mainly controlled by rainfall amount and elevation and that this relation was different for the dry and wet season. By combining high and low temporal resolution databases and a set of spatial covariates, an environmental regression approach (GAM) was used to assess the spatiotemporal patterns of rainfall erosivity for the whole region. A validation procedure showed relatively good predictions for most months (R2 between 0.50 and 0.80), while the model was less performant for the wettest (April) and two driest months (July and August) (R2 between 0.24 and 0.38). The predicted annual erosivity was highly variable with a range between 2000 and 9000 MJ mm ha−1 h−1 yr−1 and showed a pronounced east–west gradient which is strongly influenced by local topography. This study showed that the combination of high and low temporal resolution rainfall data and spatial prediction models can be used to improve the assessments of monthly and annual rainfall erosivity patterns that are grounded in locally calibrated and validated data

Terraced Landscapes: Land Abandonment, Soil Degradation, and Suitable Management

International audienc