scholarly journals Diversity II water quality parameters for 300 lakes worldwide from ENVISAT (2002–2012)

2018 ◽  
Author(s):  
Daniel Odermatt ◽  
Olaf Danne ◽  
Petra Philipson ◽  
Carsten Brockmann
Keyword(s):  
Water Quality ◽  
Large Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Wide Range ◽  
Floating Vegetation ◽  
Vegetation Maps ◽  
Spatio Temporal ◽  
Reference Measurements ◽  
Data Layers

Abstract. The use of ground sampled water quality information for global studies is limited due to practical and financial constraints. Remote sensing is a valuable means to overcome such limitations and to provide synoptic views of ambient water quality at appropriate spatio-temporal scales. In past years several large data processing efforts were initiated to provide corresponding data sources. The Diversity II water quality dataset consists of several monthly, yearly and 9-year averaged water quality parameters for 340 lakes worldwide and is based on data from the full ENVISAT MERIS operation period (2002–2012). Existing retrieval methods and datasets were selected after an extensive algorithm intercomparison exercise using in situ reference measurements for more than 40 lakes representing a wide range of bio-optical conditions. Chlorophyll-a, total suspended matter, turbidity, coloured dissolved organic matter, lake surface water temperature, cyanobacteria and floating vegetation maps, as well as several auxiliary data layers, provide a generically specified data basis that can be used for assessing a variety of locally relevant ecosystem properties and environmental problems. We demonstrate the use of the products by illustrating and discussing remotely sensed evidence of lake-specific processes and prominent regime shifts documented in literature. The Diversity II data are available from https://doi.pangaea.de/10.1594/PANGAEA.871462, and Python scripts for their analysis and visualization are provided at https://github.com/odermatt/diversity/.

scholarly journals Spatio-Temporal Analysis of Water Quality Parameters in Machángara River with Nonuniform Interpolation Methods

Water ◽  
2016 ◽  
Vol 8 (11) ◽  
pp. 507 ◽  
Author(s):  
Iván Vizcaíno ◽  
Enrique Carrera ◽  
Margarita Sanromán-Junquera ◽  
Sergio Muñoz-Romero ◽  
José Luis Rojo-Álvarez ◽  
...  
Keyword(s):  
Water Quality ◽  
Temporal Analysis ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Interpolation Methods ◽  
Spatio Temporal

scholarly journals GIS-Based Modelling of Water Quality in Different Hilsha Spawning Grounds and Sanctuaries of Bangladesh

2018 ◽  
pp. 1-11
Author(s):  
Rumana Yasmin ◽  
Mehady Islam
Keyword(s):  
Water Quality ◽  
Present Status ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
Quality Model ◽  
Spawning Grounds ◽  
Temporal Modelling ◽  
Spatio Temporal

The current study was performed to monitor in situ condition and spatio-temporal modelling of the present status of water quality parameters of different spawning grounds and sanctuaries of Hilsha. The study was conducted in nine sites in lower Padma River (Maowa) to lower Meghna River (Bhola, Patuakhali) from 1 August 2015 to 31 January 2016. This study demonstrates surface water temperature, salinity, conductivity and transparency were ranged from 19.00-33.00°C, 0.10-2.90 ppt, 125.60-4720.00 µS/cm and 6.60-74.00 cm respectively. The values of pH, DO, free CO2, total alkalinity, total hardness and free NH3 were varied from 6.00-9.50, 4.50-11.60 mg/L, 3.46-24.00 mg/L, 33.00-172.50 mg/L, 34.20-1291.00 mg/L and 0.20-1.40 mg/L respectively. Moreover, water quality model reveals that the present status of some water quality parameters (free CO2, free NH3, transparency) deviated from optimum condition suitable for the normal physiological process and spawning of Hilsha.

GIS-based Spatio-Temporal and Geostatistical Analysis of Groundwater Parameters of Lahore Region Pakistan and their Source Characterization

2021 ◽  
Author(s):  
Sadia Ismail ◽  
M Farooq Ahmed
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Water Quality Index ◽  
Physicochemical Parameters ◽  
Quality Index ◽  
Large Data ◽  
Quality Parameters ◽  
Source Characterization ◽  
Data Set ◽  
Spatio Temporal

Abstract Assessment of groundwater quality is critical, especially in the areas where it is continuously deteriorating due to unplanned industrial growth. This study utilizes GIS-based spatio-temporal and geostatistics tools to characterize the groundwater quality parameters of Lahore region. For this purpose, a large data set of the groundwater quality parameters (for a period of 2005–2016) was obtained from the deep unconfined aquifers. GIS-based water quality index (WQI) and entropy water quality index (EWQI) models were prepared using 15 water quality parameters pH (power of hydrogen), TDS (Total dissolve solids), EC (Electrical conductivity), TH (Total hardness), Ca2+ (Calcium), Mg2+ (Magnesium), Na+ (Sodium), K+ (Potassium), Cl− (Chloride), As (Arsenic), F (Fluoride), Fe (Iron), HCO3− (Bicarbonate), NO3− (Nitrate), and SO42− (Sulfate). The data analysis exhibits that 12% of the groundwater samples fell within the category of poor quality that helped to identify the permanent epicenters of deteriorating water quality index in the study area. As per the entropy theory, Fe, NO3−, K, F, SO42− and As, are the major physicochemical parameters those influence groundwater quality. The spatio-temporal analysis of the large data set revealed an extreme behavior in pH values along the Hudiara drain, and overall high arsenic concentration levels in most of the study area. The geochemical analysis shows that the groundwater chemistry is strongly influence by subsurface soil water interaction. The research highlights the significance of using GIS-based spatio-temporal and geostatistical tools to analyze the large data sets of physicochemical parameters at regional level for the detailed source characterization studies.

scholarly journals Extracting the spatio-temporal linkages between land use/land cover and water quality parameters using spatio-temporal weighted regression

2018 ◽  
Vol 53 (4) ◽  
pp. 205-218
Author(s):  
Farid Karimipour ◽  
Arash Madadi ◽  
Mohammad Hosein Bashough
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Cover ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatial And Temporal Variations ◽  
Weighted Regression ◽  
Land Use Land Cover ◽  
Spatio Temporal

Abstract Studies in water quality management have indicated significant relationships between land use/land cover (LULC) variables and water quality parameters. Thus, understanding this linkage is essential in protecting and developing water resources. This article extends the conventional geographical weighted regression (GWR) to a temporal version in order to take both spatial and temporal variations of such linkages into account, which has been ignored by many of the previous efforts. The approach has been evaluated for total nitrates and nitrites' concentration as the case study. For this, observations of 45 water quality sampling stations were examined in a time interval of 20 years (1992–2011), and the linkages between LULC variables and NO2 + NO3 concentration were extracted through Pearson correlation coefficient as a global regression model, the conventional geographic weighted regression, and the proposed spatio-temporal weighted regression (STWR). Comparing the results based on two global criteria of goodness-of-fitness (R2) and residual sum of squares (RSS) verifies that the simultaneous consideration of spatial and temporal variations by STWR substantially improves the results.

scholarly journals Diversity II water quality parameters from ENVISAT (2002–2012): a new global information source for lakes

2018 ◽  
Vol 10 (3) ◽  
pp. 1527-1549 ◽  
Author(s):  
Daniel Odermatt ◽  
Olaf Danne ◽  
Petra Philipson ◽  
Carsten Brockmann
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Suspended Matter ◽  
Performance Metrics ◽  
Accuracy Assessment ◽  
Information Source ◽  
Quality Parameters ◽  
Total Suspended Matter ◽  
Water Quality Parameters ◽  
Wide Range

Abstract. The use of ground sampled water quality information for global studies is limited due to practical and financial constraints. Remote sensing is a valuable means to overcome such limitations and to provide synoptic views of ambient water quality at appropriate spatio-temporal scales. In past years several large data processing efforts were initiated to provide corresponding data sources. The Diversity II water quality dataset consists of several monthly, yearly and 9-year averaged water quality parameters for 340 lakes worldwide and is based on data from the full ENVISAT MERIS operation period (2002–2012). Existing retrieval methods and datasets were selected after an extensive algorithm intercomparison exercise. Chlorophyll-a, total suspended matter, turbidity, coloured dissolved organic matter, lake surface water temperature, cyanobacteria and floating vegetation maps, as well as several auxiliary data layers, provide a generically specified database that can be used for assessing a variety of locally relevant ecosystem properties and environmental problems. For validation and accuracy assessment, we provide matchup comparisons for 24 lakes and a group of reservoirs representing a wide range of bio-optical conditions. Matchup comparisons for chlorophyll-a concentrations indicate mean absolute errors and bias in the order of median concentrations for individual lakes, while total suspended matter and turbidity retrieval achieve significantly better performance metrics across several lake-specific datasets. We demonstrate the use of the products by illustrating and discussing remotely sensed evidence of lake-specific processes and prominent regime shifts documented in the literature. The Diversity II data are available from https://doi.pangaea.de/10.1594/PANGAEA.871462, and Python scripts for their analysis and visualization are provided at https://github.com/odermatt/diversity/.

Spatio-Temporal Distribution of Water Quality Parameters in Ramsar Site-Kolleru Lake

2020 ◽  
Author(s):  
A Shivakrishna ◽  
Karankumar Ramteke ◽  
M Dhanya ◽  
R Charitha ◽  
Sahina Aktar ◽  
...  
Keyword(s):  
Water Quality ◽  
Monsoon Season ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
Freshwater Ecosystem ◽  
Distribution Maps ◽  
Post Monsoon ◽  
Spatio Temporal

Kolleru lake is one of Asia’s largest freshwater lakes, which has undergone tremendous changes in the water quality due to the sewage, pollution and development of aquaculture in its surrounding area. This study is undertaken to evaluate the present water quality scenario existing in Kolleru lake, which has been affected seriously due to the anthropogenic disturbances since long. Water samples were collected from ten sampling locations within the lake during pre and post-monsoon seasons of 2017-18. A total of 11 water quality parameters were analysed such as pH, temperature, EC, TDS, TSS, total alkalinity, total hardness, dissolved oxygen, salinity, COD, and nitrates. Parameters were estimated by using a standard protocol of APHA 2012. The spatial distribution maps of water quality were generated from pre and post monsoon data using Arc GIS software. Spatio-temporal variation of all parameters indicated that the water quality found was unsatisfactory within the Kolleru lake. The present study shows the better water quality in the post-monsoon season. The Inverse Distance Weighting (IDW) interpolation spatial mapping was also used for water quality mapping to observe the environmental variation for protecting the important freshwater ecosystem-Kolleru lake. The outcome of GIS analysis demonstrated the spatial visualization of the lodging evolution and geographical distribution trends of water quality parameters within the study area.

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