Resistance to relocation in flood-vulnerable coastal areas: a proposed composite index

The increasing number of coastal floods in recent years in France has resulted in the design of new adaptation principles for the most endangered coastal areas. The aim of the government is to reduce the vulnerability of these areas by relocating property and infrastructure. These measures have, however, come up against considerable opposition from the population concerned. Using a survey of 421 inhabitants of Hyères, a coastal town in the South of France, this article proposes the study of resistance to relocation through the creation of an index for resistance that incorporates attachment to place, residential mobility and risk perception. The results show a correlation for the index and distance from the sea that highlights the existence of conflicting interests with adaptation measures depending upon population categories.

Key policy insights

• In France, although coastal flooding risk is a key issue in numerous populated coastal areas, coastal dwellers show little willingness to relocate.

• Resistance to relocation can be assessed through a composite index integrating place attachment, residential mobility and risk perception.

• Application of such an index shows a correlation between willingness to relocate and distance from the sea.

• Conflicts of interest with adaptation measures also depend on the age of the dwellers, their standard of living and on home ownership.

     

Evaluation of Simulated CO_2 Concentrations from the CarbonTracker-Asia Model Using In-situ Observations over East Asia for 2009–2013

The CarbonTracker(CT) model has been used in previous studies for understanding and predicting the sources, sinks, and dynamics that govern the distribution of atmospheric CO_2 at varying ranges of spatial and temporal scales. However, there are still challenges for reproducing accurate model-simulated CO_2 concentrations close to the surface, typically associated with high spatial heterogeneity and land cover. In the present study, we evaluated the performance of nested-grid CT model simulations of CO_2 based on the CT2016 version through comparison with in-situ observations over East Asia covering the period 2009–13. We selected sites located in coastal, remote, inland, and mountain areas. The results are presented at diurnal and seasonal time periods. At target stations, model agreement with in-situ observations was varied in capturing the diurnal cycle. Overall, biases were less than 6.3 ppm on an all-hourly mean basis, and this was further reduced to a maximum of 4.6 ppm when considering only the daytime. For instance, at Anmyeondo, a small bias was obtained in winter, on the order of 0.2 ppm. The model revealed a diurnal amplitude of CO_2 that was nearly flat in winter at Gosan and Anmyeondo stations, while slightly overestimated in the summertime. The model's performance in reproducing the diurnal cycle remains a challenge and requires improvement. The model showed better agreement with the observations in capturing the seasonal variations of CO_2 during daytime at most sites, with a correlation coefficient ranging from 0.70 to 0.99. Also, model biases were within-0.3 and 1.3 ppm, except for inland stations(7.7 ppm).     

Employing ground and satellite-based QuickBird data and random forest to discriminate five tree species in a Southern African Woodland

With the emergence of very high spatial and spectral resolution data set, the resolution gap that existed between remote-sensing data set and aerial photographs has decreased. The decrease in resolution gap has allowed accurate discrimination of different tree species. In this study, discrimination of indigenous tree species (n?=?5) was carried out using ground based hyperspectral data resampled to QuickBird bands and the actual QuickBird imagery for the area around Palapye, Botswana. The purpose of the study was to compare the accuracies of resampled hyperspectral data (resampled to QuickBird sensors) with the actual image (QuickBird image) in discriminating between the indigenous tree species. We performed Random Forest (RF) using canopy reflectance taking from ground-based hyperspectral sensor and the reflectance delineated regions of the tree species. The overall accuracies for classifying the five tree species was 79.86 and 88.78% for both the resampled and actual image, respectively. We observed that resampled data set can be upscale to actual image with the same or even greater level of accuracy. We therefore conclude that high spectral and spatial resolution data set has substantial potential for tree species discrimination in savannah environments.     

Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods

In this study, the strength and reliability of internal accuracy estimate built in random forest (RF) ensemble classifier was evaluated. Specifically, we compared the reliability of the internal validation methods of RF with independent data-sets of different splitting options for defoliation classification. Furthermore, we set out to statistically validate the best independent split option for image classification using RF and multispectral Rapideye imagery. Results show that the internal accuracy measure yields comparable results with those derived from an independent test data-set. More important, it was observed that the errors produced by the internal validation methods of RF were relatively stable as statistically shown by the lower confidence interval obtained as compared to the independent test data. Results also showed that the 70–30% split option had the lowest mean standard errors (0.2351) and hence highest accuracy when compared to the other split options. The study confirms the reliability and stability of the internal bootstrapping estimate of accuracy built within the random forest algorithm.     

The effect of pH and natural microbial phosphatase activity on the speciation of uranium in subsurface soils

The biomineralization of U(VI) phosphate as a result of microbial phosphatase activity is a promising new bioremediation approach to immobilize uranium in both aerobic and anaerobic conditions. In contrast to reduced uranium minerals such as uraninite, uranium phosphate precipitates are not susceptible to changes in oxidation conditions and may represent a long-term sink for uranium in contaminated environments. So far, the biomineralization of U(VI) phosphate has been demonstrated with pure cultures only. In this study, two uranium contaminated soils from the Department of Energy Oak Ridge Field Research Center (ORFRC) were amended with glycerol phosphate as model organophosphate source in small flow-through columns under aerobic conditions to determine whether natural phosphatase activity of indigenous soil bacteria was able to promote the precipitation of uranium(VI) at pH 5.5 and 7.0. High concentrations of phosphate (1-3 mM) were detected in the effluent of these columns at both pH compared to control columns amended with U(VI) only, suggesting that phosphatase-liberating microorganisms were readily stimulated by the organophosphate substrate. Net phosphate production rates were higher in the low pH soil (0.73 ± 0.17 mM d⁻¹) compared to the circumneutral pH soil (0.43 ± 0.31 mM d⁻¹), suggesting that non-specific acid phosphatase activity was expressed constitutively in these soils. A sequential solid-phase extraction scheme and X-ray absorption spectroscopy measurements were combined to demonstrate that U(VI) was primarily precipitated as uranyl phosphate minerals at low pH, whereas it was mainly adsorbed to iron oxides and partially precipitated as uranyl phosphate at circumneutral pH. These findings suggest that, in the presence of organophosphates, microbial phosphatase activity can contribute to uranium immobilization in both low and circumneutral pH soils through the formation of stable uranyl phosphate minerals.     

Provenance, tectonics and source weathering of modern fluvial sediments of the Brahmaputra–Jamuna River, Bangladesh: Inference from geochemistry

This present study describes the elemental geochemistry of fluvial sediments in the Kurigram (upstream) to Sirajganj–Tangail (downstream) section of the Brahmaputra–Jamuna River, Bangladesh, with the aim of evaluating their provenance, weathering and tectonic setting. Petrographically, the sediments are rich in quartz (68%), followed by feldspars (8.5%) and lithic grains (7%). The bulk sediment chemistry is influenced by grain size. Concentrations of TiO₂, Fe₂O₃, MgO, K₂O, P₂O₅, Rb, Nb, Cr, V, Y, and, Ce, Th and Ga slightly decrease with increasing SiO₂/Al₂O₃ and grain size, suggesting clay matrix control. In contrast, concentrations of CaO, Na₂O, Sr and Pb increase with increasing SiO₂/Al₂O₃ and grain size, suggesting residence of these substances in feldspar. Decrease in Zr as grain size increases is likely controlled both by clay matrix and heavy minerals. In addition, heavy minerals' sorting also influences Ce, Th, Y and Cr abundances in some samples. The sediments are predominantly quartzose in composition with abundant low-grade metamorphic and sedimentary lithics, low feldspars and trace volcanic detritus, indicating a quartzose recycled orogen province as a source of the sediments. Discriminant diagrams together with immobile element ratio plots show that, the Brahmaputra–Jamuna River sediments are mostly derived from rocks formed in an active continental margin. Moreover, the rare earth element ratios as well as chondrite-normalized REE patterns with flat HREE, LREE enrichment, and negative Eu anomalies indicate derivation of the sediments of Brahmaputra–Jamuna River from felsic rock sources of upper continental crust (UCC). The chemical indices of alteration suggest that Brahmaputra–Jamuna River sediments are chemically immature and experienced low chemical weathering effects. In the A–CN–K ternary diagram, most of the samples close to the plagioclase–K-feldspar join line and to the UCC plot, and in the field of various lithologies of Higher Himalayan Crystalline Series, suggesting that rocks in these series are likely source rocks. Therefore, the elemental geochemistry of the Brahmaputra–Jamuna River sediments is controlled mostly by mechanical breakdown of lithic fragments and subsequent preferential attrition of muscovite > albite > quartz.     

Fine-scale population structure in a deep-sea teleost (orange roughy, Hoplostethus atlanticus)

Microsatellite and otolith chemistry variability were analysed to assess fine scale genetic structure in the deep-sea teleost orange roughy (Hoplostethus atlanticus). The Porcupine Bank located on the continental shelf west of Ireland, comprises a complex system of mounds and flat areas that are broken up by canyons. Orange roughy form spawning aggregations on mounds and flat areas, and were heavily fished until the resource was depleted. By analysing adults in spawning condition and juvenile orange roughy from six mounds and one flat area, shallow but significant genetic population structure was evident (F_ST=0.0031, D_est across loci=0.0306 and G-test). Most of the structure was accounted for by inclusion of a sample from the flats (six of ten significant pairwise F_ST estimates and G-tests, and five of the highest D_est estimates included the flat sample). While the flat sample contributed most to the genetic structure, there was still significant (albeit weaker) structure among mound samples. The observed structure was supported by otolith analyses. Fish caught as late juveniles in either the flat or mound areas showed consistent differences in chemistry at the otolith core throughout the initial 10 years of growth, which could indicate site fidelity. We hypothesise that seafloor topographic structures (mounds and flats) may provide discrete spawning areas for orange roughy and that the limited gene flow between these spawning areas is insufficient to counteract genetic drift.     

Water quality assessment of groundwater in some rock types in parts of the eastern region of Ghana

A baseline study involving analyses of subsurface water samples from the Cape Coast granitoid complex, Lower Birimian, Togo Formation and the Voltaian Group, was carried out to assess their suitability for drinking, domestic and agricultural purposes. Study results show that pH within the range (3.0 ≤ pH ≤ 6.5) constitutes 74% of the boreholes analysed, and 51% have hardness values ranging from 7.89 to 73.24 mg/l as CaCO₃ and are described as soft. Total dissolved solids are less than 1,000 mg/l and generally characterized by low conductivity values, of which 95% are within the range (55 ≤ EC ≤ 1,500 μS/cm). The mean values of the major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺) and anions (SO₄²⁻, Cl⁻, HCO₃⁻) are all within the World Health Organisation (WHO) standards. Five (5) of the boreholes sampled have nitrate (NO₃⁻) contamination. Even though NO₃⁻ contamination and acidic waters exist in some of the boreholes, the majority of the boreholes are excellent for drinking and domestic purposes. Assessment of the groundwaters for agricultural irrigation revealed three main categories. These are low salinity–low sodicity (C1–S1), medium salinity–low sodicity (C2–S1) and high salinity–low sodicity (C3–S1), using the US Salinity Laboratory (USSL) classification scheme. As much as 95% of the samples plotted in the ‘excellent to good’ and ‘good to permissible’ categories on the Wilcox diagram. The groundwater in the study area may therefore be regarded as good for irrigation activities. The major identifiable geochemical processes responsible for the evolution of the various ions are mineral weathering and chemical reactions.