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>

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 STATUS MUTU DAERAH PENAMBANGAN PASIR DI PERAIRAN SUNGAI SERAYU DENGAN MENGGUNAKAN METODE STORET

INFO-TEKNIK ◽  
2018 ◽  
Vol 19 (2) ◽  
pp. 155
Author(s):  
Nurlinda Ayu Triwuri ◽  
Murni Handayani ◽  
Rosita Dwityaningsih
Keyword(s):  
Water Quality ◽  
River Water ◽  
Good Condition ◽  
River Water Quality ◽  
Quality Data ◽  
Quality Level ◽  
Sand Mining ◽  
Water Quality Data ◽  
Group D

The quality of river water is strongly related to human activities in it. Changesin the condition of water quality in the river flow are the effects of the dischargefrom existing land use. One of them is sand mining activities along the SerayuRiver, especially around Tumiyang, Kebasen, Banyumas Regency. Activitiesfrom sand mining will cause a decrease in river water quality. From this activity,it is necessary to study the status of water quality using the STORET method todetermine the quality of river water so that the river can be utilized in accordancewith the applicable designation.The STORET method is one method for determining water quality data withwater quality standards in accordance with the appointment of Minister ofEnvironment Decree No.115 2003. This research is a quantitative descriptivestudy to determine the water quality of the Serayu river in the sand of miningareas precisely in Banyumas Regency. The parameters measured in this studywere measurements of Total Disolved Solid (TDS), temperature, pH, andElectrical Conductivity. Determining the location of taking water using apurposive sampling method.Based on the results of data analysis using the Storet method and refers to thequality standards of Government Regulation No.20 of 1990 Group D. Waterquality in Serayu River has a total score of 9 after sand mining. This shows thequality status of the lightly polluted Serayu river (-1 to -10). But still in class Band the river water quality level is still in good condition. There are temperatureparameters that exceed the threshold of 25 - 32oC, but the TDS, DHL and pHparameters are still within the threshold of designation in Group D.

scholarly journals Investigation of the Soan River Water Quality Using Multivariate Statistical Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zakaullah ◽  
Naeem Ejaz
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Management ◽  
Oxygen Demand ◽  
River Water Quality ◽  
Quality Data ◽  
Spatiotemporal Pattern ◽  
Multivariate Statistical ◽  
Water Quality Data

Evaluating the quality of river water is a critical process due to pollution and variations of natural or anthropogenic origin. For the Soan River (Pakistan), seven sampling sites were selected in the urban area of Rawalpindi/Islamabad, and 18 major chemical parameters were examined over two seasons, i.e., premonsoon and postmonsoon 2019. Multivariate statistical approaches such as the Spearman correlation coefficient, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the water quality of the Soan River based on temporal and spatial patterns. Analytical results obtained by PCA show that 92.46% of the total variation in the premonsoon season and 93.11% in the postmonsoon season were observed by only two loading factors in both seasons. The PCA and CA made it possible to extract and recognize the origins of the factors responsible for water quality variations during the year 2019. The sampling stations were grouped into specific clusters on the basis of the spatiotemporal pattern of water quality data. The parameters dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, and total suspended solids (TSS) are among the prominent contributing variations in water quality, indicating that the water quality of the Soan River deteriorates gradually as it passes through the urban areas, receiving domestic and industrial wastewater from the outfalls. This study indicates that the adopted methodology can be utilized effectively for effective river water quality management.

Novel bacterial ratio for predicting faecal age

2003 ◽  
Vol 47 (3) ◽  
pp. 45-49 ◽  
Author(s):  
J. Nieman ◽  
G.M. Brion
Keyword(s):  
Water Quality ◽  
River Water ◽  
Total Coliform ◽  
River Water Quality ◽  
Low Flow ◽  
Quality Data ◽  
Water Quality Data ◽  
Ongoing Research ◽  
Kentucky River ◽  
Survival Studies

This study presents an extension of ongoing research into the utility of the ratio of colonies isolated on membrane filters during the total coliform test using m-Endo broth media. Investigations into the relative shifts in concentrations of indicator bacterial populations over time, in laboratory-based survival studies conducted with filtered river water, were undertaken. Also, analysis of Kentucky River water quality data collected from the inlet of a local water treatment plant was carried out. Survival studies found that the ratio between the raw concentrations of atypical colonies (AC) and total coliform colonies (TC) was directly related to the amount of time coliform spiked river water had been held in open jars in the laboratory. The AC/TC ratio in the jars would rise from <1 at the time of coliform spiking to >200 within 4d. The rise in AC/TC ratio with time in river water was confirmed in the analysis of two years of Kentucky River water quality data where the average AC/TC ratio during months with high river flow (rain) was 3.37 and rose to an average of 27.58 during months with low flow. The average AC/TC ratio during high flow months compared to that of raw human sewage (3.9) and the ratio increased to values associated with animal impacted urban runoff (18.9) during low flow months.

scholarly journals Classification and Risk-Mapping of River Water Quality in Surabaya with Semantic Visualitzation

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Taufan Radias Miko ◽  
Tri Harsono ◽  
Aliridho Barakbah
Keyword(s):  
Water Quality ◽  
River Water ◽  
River Water Quality ◽  
Risk Mapping ◽  
Quality Data ◽  
Household Waste ◽  
Polluted Water ◽  
Measuring Instruments ◽  
Water Quality Data

River water pollution is one of the environmental problems that occur in Surabaya. The amount of industrial waste and household waste makes Surabaya River water easily polluted every day, besides that there are also many people who are not aware about the quality of river water in Surabaya. In this paper, we present a new system to classify water quality of river in surabaya. The system involve a semantic visualization of risk-mapping for the river, so that the people of Surabaya are easier to get information about the quality of Surabaya River water. In this paper, we measured the water quality of Surabaya River using Horiba sensor measuring instruments using 5 parameters, namely temperature, PH, DO, Turbidity, TDS. These five parameters are input variables for calculating water quality with the methods applied in this research. We use the Storet Method to determine the quality of Surabaya River water. The results of the Storet Method explained that there were 0.03% of the data on lightly polluted water quality and there were 37.41% of the data being moderately polluted and there were 59.29% of the data heavily polluted. The results of the calculation using the Storet method concluded that the condition of Surabaya River water quality was not good. We also apply the rule of the Storet Method to the Neural Network by using Surabaya River water quality data as learning data and gave performance 70.02% accuracy.

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