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Kirchner, J. W. (2023). Mixing Models With Multiple, Overlapping, or Incomplete End-Members, Quantified Using Time Series of a Single Tracer. Geophysical Research Letters, 50(12), 2023.
Abstract: Abstract Mixing models are used throughout earth and environmental science to quantify the relative contributions of sources to mixtures, based on chemical or isotopic tracers. Often, however, some end-members are missing or their tracer distributions overlap, precluding the use of conventional mixing models. Here I show how these constraints can be overcome by exploiting the information contained in tracer time-series fluctuations. This approach, ensemble end-member mixing analysis (EEMMA), can potentially quantify many sources using a single tracer, even if their mean concentrations are indistinguishable. EEMMA can also quantify source contributions when some sources are unknown, and even infer the tracer time series of a missing source. Benchmark tests with synthetic data verify the reliability of this approach, thus expanding the range of mixing models that can be quantified using tracer time series. An R script is provided for the necessary calculations, including error propagation.
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Ola, I., Drebenstedt, C., Burgess, R. M., Mensah, M., Hoth, N., Okoroafor, P., et al. (2024). Assessing petroleum contamination in parts of the Niger Delta based on a sub-catchment delineated field assessment. Environmental Monitoring and Assessment, 196(6), 585.
Abstract: The Niger Delta in Nigeria is a complex and heavily contaminated area with over 150,000 interconnected contaminated sites. This intricate issue is compounded by the region’s strong hydrological processes and high-energy environment, necessitating a science-based approach for effective contamination assessment and management. This study introduces the concept of sub-catchment contamination assessment and management, providing an overarching perspective rather than addressing each site individually. A description of the sub-catchment delineation process using the digital elevation model data from an impacted area within the Delta is provided. Additionally, the contamination status from the delineated sub-catchment is reported. Sediment, surface water and groundwater samples from the sub-catchment were analyzed for total petroleum hydrocarbons (TPH) and polycyclic aromatic hydrocarbons (PAHs), respectively. Surface sediment TPH concentrations ranged from 129 to 20,600 mg/kg, with subsurface (2-m depth) concentrations from 15.5 to 729 mg/kg. PAHs in surface and subsurface sediment reached 9.55 mg/kg and 0.46 mg/kg, respectively. Surface water exhibited TPH concentrations from 10 to 620 mg/L, while PAHs ranged from below detection limits to 1 mg/L. Groundwater TPH concentrations spanned 3 to 473 mg/L, with total PAHs varying from below detection limits to 0.28 mg/L. These elevated TPH and PAH levels indicate extensive petroleum contamination in the investigated sediment and water environment. Along with severe impacts on large areas of mangroves and wetlands, comparison of TPH and PAH concentrations with sediment and water quality criteria found 54 to 100% of stations demonstrated exceedances, suggesting adverse biological effects on aquatic and sediment biota are likely occurring.
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Zwartendijk, B. W., Ghimire C. P., Ravelona M., Lahitiana J., & van Meerveld H. J. (2023). Hydrometric data and stable isotope data for streamflow and rainfall in the Marolaona catchment, Madagascar, 2015-2016. NERC EDS Environmental Information Data Centre.
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Ollivier, C. C., Carrière, S. D., Heath, T., Olioso, A., Rabefitia, Z., Rakoto, H., et al. (2023). Ensemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across Madagascar. Journal of Hydrology: Regional Studies, 47, 101400.
Abstract: Study region this study focuses on Madagascar. This island is characterized by a great diversity of climate, due to trade winds and the varying topography. This country is also undergoing extreme rainfall events such as droughts and cyclones. Study focus the rain gauge network of Madagascar is limited (about 30 stations). Consequently, we consider relevant satellite-based precipitation datasets to fill gaps in ground-based datasets. We assessed the reliability of 21 satellite-based and reanalysis precipitation products (P-datasets) through a direct comparison with 24 rain gauge station measurements at the monthly time step, using four statistical indicators: Kling-Gupta Efficiency (KGE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Bias. Based on this first analysis, we produced a merged dataset based on a weighted average of the 21 products. New hydrological insights for the region based on the KGE and the CC scores, WFDEI (WATCH Forcing Data methodology applied to ERA-Interim), CMORPH-BLD (Climate Prediction Center MORPHing satellite-gauge merged) and MSWEP (Multi-Source Weighted Ensemble Precipitation) are the most accurate for estimating rainfall at the national scale. Additionally, the results reveal a high discrepancy between bio-climatic regions. The merged dataset reveals higher performance than the other products in all situations. These results demonstrate the usefulness of a merging approach in an area with a deficit of rainfall data and a climatic and topographic diversity.
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Brutsaert, W. (2017). Global land surface evaporation trend during the past half century: Corroboration by Clausius-Clapeyron scaling. Advances in Water Resources, 106, 3–5.
Abstract: Analyses of satellite data mainly over the world’s ocean surfaces have shown that during 1986–2006 global average values of atmospheric water vapor, precipitation and evaporation have increased at a relative rate of 0.0013a−1; this is roughly in accordance with the Clausius-Clapeyron equation for the average temperature trend during this period, and amounts to 0.065K−1 at the average temperature of T=14∘C. Application of this concept over the world’s land surfaces yields an average global evaporation trend during the past half century of around 0.4 to 0.5 mma−2; this confirms the values obtained in previous studies with totally different methods.
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