Global database of diffuse riverine nitrogen and phosphorus loads and yields

McDowell, R. W.; Noble, Alasdair; Pletnyakov, Peter; Mosley, Luke M.

doi:10.1002/gdj3.111

Cited by 11 publications

(5 citation statements)

References 61 publications

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“…However, the validity of their model might be questionable (and cannot be verified) because only a single train-test split was implemented in Damania et al (2019) , whereas we conducted the 10-fold cross-validation (repeated 3 times) as well as out-of-sample testing to validate our model performance. Another example is McDowell et al (2021) who used GEMStat database to build a multiple linear regression model for predicting NOx—N values globally (for 7 years; centered around 2008). Multiple linear regression achieved R 2 of 0.57, which is 36.7 % lower than our model performance.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding the time dimension, we relied on a commonly used approach introduced by Cohn et al (1989) (also known as L7 model), where the constituent concentration is related to three explanatory variables: discharge, time, and season. Similar approach was implemented in some regression-based WQ models and it has been shown that this scheme can reasonably explain temporal variability in constituent concentrations ( Hirsch et al, 2010 ; McDowell et al, 2021 ). Therefore, we added two new auxiliary inputs, namely Cumulative Month since the beginning of the simulation (CM) and Month of the Year (MOY) to represent distance in the time domain, thereby better capturing dynamics of nitrogen levels.…”

Section: Methodsmentioning

confidence: 99%

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Global patterns and key drivers of stream nitrogen concentration: A machine learning approach

Sheikholeslami

Hall

2023

Science of The Total Environment

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Global patterns and key drivers of stream nitrogen concentration: A machine learning approach

Sheikholeslami

Hall

2023

Science of The Total Environment

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“…(a) A table from Detrital zircon U–Pb geochronology and geochemistry of the Riachuelos and Palma Sola beach sediments, Veracruz State, Gulf of Mexico: a new insight on palaeoenvironment (Armstrong‐Altrin, 2020). (b) A table from Global database of diffuse riverine nitrogen and phosphorus loads and yields (McDowell et al, 2021).…”

Section: Challenges To Support Data Extraction Workflow For Scientifi...mentioning

confidence: 99%

GeoDeepShovel: A platform for building scientific database from geoscience literature with AI assistance

Shao

Jia

et al. 2023

Geoscience Data Journal

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With the rapid development of big data science, the research paradigm in the field of geosciences has also begun to shift to big data‐driven scientific discovery. Researchers need to read a huge amount of literature to locate, extract and aggregate relevant results and data that are published and stored in PDF format for building a scientific database to support the big data‐driven discovery. In this paper, based on the findings of a study about how geoscientists annotate literature and extract and aggregate data, we proposed GeoDeepShovel, a publicly available AI‐assisted data extraction system to support their needs. GeoDeepShovel leverages state‐of‐the‐art neural network models to support researcher(s) easily and accurately annotate papers (in the PDF format) and extract data from tables, figures, maps, etc., in a human–AI collaboration manner. As a part of the Deep‐Time Digital Earth (DDE) program, GeoDeepShovel has been deployed for 8 months, and there are already 400 users from 44 geoscience research teams within the DDE program using it to construct scientific databases on a daily basis, and more than 240 projects and 50,000 documents have been processed for building scientific databases.

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“…When instantaneous water discharge and/or suspended sediment concentration and/or solution properties (T , pH, alkalinity, Si(OH) 4 , DIC, DOC, HCO − 3 , SO 2− 4 , Cl − , Ca 2+ , Mg 2+ Na + , K + , calcite saturation) were reported in the same study, these were also added for comparison. However, the focus of data collection remains on the composition of solid phases, and detailed information on water chemistry is available from other sources (Hartmann et al, 2014a;McDowell et al, 2020a;Virro et al, 2021). Doublings with entries of other databases were not checked.…”

Section: Data Collectionmentioning

confidence: 99%

Introducing GloRiSe – a global database on river sediment composition

Müller

Middelburg

Sluijs

2021

Earth Syst. Sci. Data

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Abstract. Rivers transport dissolved and solid loads from terrestrial realms to the oceans and between inland reservoirs, representing major mass fluxes on Earth's surface. The composition of river water and sediment provides clues to a plethora of Earth and environmental processes, including weathering, erosion, nutrient and carbon cycling, environmental pollution, reservoir exchange, and tectonic cycles. While there are documented, publicly available databases for riverine dissolved and suspended nutrients, there is no openly accessible, georeferenced database for riverine suspended sediment composition. Here, we present a globally representative set of 2828 suspended and bed sediment compositional measurements from 1683 locations around the globe. This database, named Global River Sediments (GloRiSe) version 1.1, includes major, minor and trace elements, along with mineralogical data, and provides time series for some sites. Each observation is complemented by metadata describing geographic location, sampling date and time, sample treatment, and measurement details, which allows for grouping and selection of observations, as well as for interoperability with external data sources, and improves interpretability. Information on references, unit conversion and references makes the database comprehensible. Notably, the close to globe-spanning extent of this compilation allows the derivation of data-driven, spatially resolved global-scale conclusions about the role of rivers and processes related to them within the Earth system. GloRiSe version 1.1 can be downloaded from Zenodo (https://doi.org/10.5281/zenodo.4485795, Müller et al., 2021) and GitHub (https://github.com/GerritMuller/GloRiSe, last access: 26 May 2021), where updates with adapted version numbers will become available, along with a technical documentation and an example calculation in the form of MATLAB scripts, which calculate the sediment-flux-weighted major element composition of the annual riverine suspended sediment export to the ocean and related uncertainties.

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Global database of diffuse riverine nitrogen and phosphorus loads and yields

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 11 publications

References 61 publications

Global patterns and key drivers of stream nitrogen concentration: A machine learning approach

Global patterns and key drivers of stream nitrogen concentration: A machine learning approach

GeoDeepShovel: A platform for building scientific database from geoscience literature with AI assistance

Introducing GloRiSe – a global database on river sediment composition

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