Appraisal of river water quality using open-access earth observation data set: a study of river Ganga at Allahabad (India)

2018 ◽  
Vol 5 (2) ◽  
pp. 755-765 ◽  
Author(s):  
Bhrigumani Sharma ◽  
Mukesh Kumar ◽  
Derrick Mario Denis ◽  
Sudhir Kumar Singh
Keyword(s):  
Water Quality ◽  
Open Access ◽  
River Water ◽  
Earth Observation ◽  
River Water Quality ◽  
Observation Data ◽  
Data Set ◽  
River Ganga ◽  
Earth Observation Data

scholarly journals GRQA: Global River Water Quality Archive

2021 ◽  
Vol 13 (12) ◽  
pp. 5483-5507
Author(s):  
Holger Virro ◽  
Giuseppe Amatulli ◽  
Alexander Kmoch ◽  
Longzhu Shen ◽  
Evelyn Uuemaa
Keyword(s):  
Water Quality ◽  
Time Series ◽  
River Water ◽  
Environmental Sustainability ◽  
Large Scale ◽  
Observation Time ◽  
River Water Quality ◽  
Quality Data ◽  
Observation Data ◽  
Water Quality Data

Abstract. Large-scale hydrological studies are often limited by the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing hydrological models. In addition to the observation data themselves, insufficient or poor-quality metadata have also discouraged researchers from integrating the already-available datasets. Therefore, improving both the availability and quality of open water quality data would increase the potential to implement predictive modeling on a global scale. The Global River Water Quality Archive (GRQA) aims to contribute to improving water quality data coverage by aggregating and harmonizing five national, continental and global datasets: CESI (Canadian Environmental Sustainability Indicators program), GEMStat (Global Freshwater Quality Database), GLORICH (GLObal RIver CHemistry), Waterbase and WQP (Water Quality Portal). The GRQA compilation involved converting observation data from the five sources into a common format and harmonizing the corresponding metadata, flagging outliers, calculating time series characteristics and detecting duplicate observations from sources with a spatial overlap. The final dataset extends the spatial and temporal coverage of previously available water quality data and contains 42 parameters and over 17 million measurements around the globe covering the 1898–2020 time period. Metadata in the form of statistical tables, maps and figures are provided along with observation time series. The GRQA dataset, supplementary metadata and figures are available for download on the DataCite- and OpenAIRE-enabled Zenodo repository at https://doi.org/10.5281/zenodo.5097436 (Virro et al., 2021).

scholarly journals A Self-Calibrating Runoff and Streamflow Remote Sensing Model for Ungauged Basins Using Open-Access Earth Observation Data

2017 ◽  
Vol 9 (1) ◽  
pp. 86 ◽  
Author(s):  
Ate Poortinga ◽  
Wim Bastiaanssen ◽  
Gijs Simons ◽  
David Saah ◽  
Gabriel Senay ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Open Access ◽  
Earth Observation ◽  
Observation Data ◽  
Ungauged Basins ◽  
Remote Sensing Model ◽  
Sensing Model ◽  
Earth Observation Data

scholarly journals Assessment of the Border Argun River Water Quality from the Ecological Point of View

2020 ◽  
pp. 36-51
Author(s):  
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Body ◽  
Well Being ◽  
River Water Quality ◽  
Point Of View ◽  
Anthropogenic Load ◽  
Observation Data ◽  
Linear Trends

Based on archival materials and expedition observation data, the water quality of the border Argun River is assessed. Thecharacteristicpollutantsaredetermined. The linear trends of changes in the concentrations of these pollutants for 2016 – 2019 are analyzed, showing changes in concentrations on the border section of the river more than five hundred and fifty kilometers long from its exit from the territory of a neighboring state.The basic indicators of anthropogenic load (PAN6) were calculated for the observation stations, the trends in their changes were considered, and with this in mind, the ecological well-being of the water body in this section was assessed.

GRQA: Global River Water Quality Archive

2021 ◽  
Author(s):  
Holger Virro ◽  
Giuseppe Amatulli ◽  
Alexander Kmoch ◽  
Longzhu Shen ◽  
Evelyn Uuemaa
Keyword(s):  
Water Quality ◽  
River Water ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Quality Data ◽  
Observation Data ◽  
Water Quality Data ◽  
Physically Based

<p>Recent advances in implementing machine learning (ML) methods in hydrology have given rise to a new, data-driven approach to hydrological modeling. Comparison of physically based and ML approaches has shown that ML methods can achieve a similar accuracy to the physically based ones and outperform them when describing nonlinear relationships. Global ML models have been already successfully applied for modeling hydrological phenomena such as discharge.</p><p>However, a major problem related to large-scale  water quality modeling has been the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing models. In addition to the observation data itself, insufficient or poor quality metadata has also discouraged researchers to integrate the already available datasets. Therefore, improving both, the availability, and quality of open water quality data would increase the potential to implement predictive modeling on a global scale.</p><p>We aim to address the aforementioned issues by presenting the new Global River Water Quality Archive (GRQA) by integrating data from five existing global and regional sources:</p><ul><li>Canadian Environmental Sustainability Indicators program (CESI)</li> <li>Global Freshwater Quality Database (GEMStat)</li> <li>GLObal RIver Chemistry database (GLORICH)</li> <li>European Environment Agency (Waterbase)</li> <li>USGS Water Quality Portal (WQP)</li> </ul><p>The resulting dataset contains a total of over 14 million observations for 41 different forms of some of the most important water quality parameters, focusing on nutrients, carbon, oxygen and sediments. Supplementary metadata and statistics are provided with the observation time series to improve the usability of the dataset. We report on developing a harmonized schema and reproducible workflow that can be adapted to integrate and harmonize further data sources. We conclude our study with a call for action to extend this dataset and hope that the provided reproducible method of data integration and metadata provenance shall lead as an example.</p>

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