scholarly journals Determinants of spatio-temporal variability of water quality in the Barotse Floodplain, western Zambia

2018 ◽  
Vol 6 ◽  
pp. 96-105
Author(s):  
Imasiku Nyambe ◽  
Anthony Chabala ◽  
Kawawa Banda ◽  
Henry Zimba ◽  
Wilson Phiri
Keyword(s):  
Water Quality ◽  
Temporal Variability ◽  
Spatio Temporal

scholarly journals A bayesian hierarchical model to predict spatio-temporal variability in river water quality at 102 catchments

2020 ◽  
Author(s):  
Danlu Guo ◽  
Anna Lintern ◽  
Angus Webb ◽  
Dongryeol Ryu ◽  
Ulrike Bende-Michl ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Model ◽  
Temporal Variability ◽  
Stream Water ◽  
Spatial Scales ◽  
Water Quality Monitoring ◽  
Catchment Management ◽  
Stream Water Quality ◽  
Bayesian Hierarchical ◽  
Spatio Temporal

<div> <div> <div> <div>Our current capacity to model stream water quality is limited particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years, across 102 catchments, which span over 130,000 km<sup>2</sup>. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NO<sub>x</sub>), and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which had been identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explainable (19.9%), the model explains 38.2% (NO<sub>x</sub>) to 88.6% (EC) of total spatio-temporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; temporal variability remains largely unexplained across all catchments, while long-term trends are well captured. The model is best used to predict proportional changes in water quality in a Box-Cox transformed scale, but can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot-spots and hot moments for waterway pollution; (2) predicting effects of catchment changes on water quality e.g. urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on: (1) alternative statistical model structures to improve fitting for truncated data, for constituents where a large amount of data below the detection-limit; and (2) better representation of non-conservative constituents (e.g. FRP) by accounting for important biogeochemical processes.</div> </div> </div> </div>

scholarly journals Spatio-temporal variability in hydro-chemical characteristics of coastal waters of Salimpur, Chittagong along the Bay of Bengal

2016 ◽  
Vol 4 (1) ◽  
pp. 335 ◽  
Author(s):  
Avijit Talukder ◽  
Debbrota Mallick ◽  
Tasnuba Hasin ◽  
Ishrat Zahan Anka ◽  
Md Mehedi Hasan
Keyword(s):  
Water Quality ◽  
Coastal Zone ◽  
Coastal Waters ◽  
Temporal Variability ◽  
Seasonal Characteristics ◽  
Ship Breaking ◽  
Investigation Period ◽  
Spatio Temporal ◽  
Negative Impacts ◽  
Freshwater Inputs

Diverse seasonal characteristics of hydro-chemical parameters in the coastal zone are significantly related to aquaculture development. In this paper, general water quality condition derived from laboratory analysis from the coastal waters of Salimpur, Chittagong is presented. Samples were collected from onshore and offshore site of two adjacent coastal locations named as North Salimpur (experimental location) and South Kattoli (control) during a monsoon and a dry season spanning 2013-14. The spatio-temporal variability of studied parameters were found as air temperature 26.5-32.5 ˚C, water temperature 23-33 °C, pH 7.1-7.9, DO 4.29-7.11 mg/L, BOD 1.10-3.25 mg/L, salinity 1.6-21 ppt, EC 3.40-35.68 mS/cm, TDS 2.02-21.99 g/L, TSS 0.62-2.76 g/L, transparency 4.5-14 cm, precipitation 64-1992 mm, NO2-N 1.94-2.58 µg/L, PO4-P 0.45-1.84 µg/L, SiO3-Si 130.46-956.31 µg/L during investigation period. Average values of physicochemical parameters were found to be in compliance with standard guidelines. The ship breaking activities near experimental location possess negative impacts on local geomorphology, freshwater inputs, precipitation and aquatic environment as well. Moreover, wind driven forces, tidal action, wave characteristics and changes in monsoon pattern regulate the coastal processes. This research suggests the importance of regular monitoring to assess present status of water quality and future prospect of aquaculture in the Chittagong coastal zone.

scholarly journals A predictive model for spatio-temporal variability in stream water quality

2019 ◽  
Author(s):  
Danlu Guo ◽  
Anna Lintern ◽  
J. Angus Webb ◽  
Dongryeol Ryu ◽  
Ulrike Bende-Michl ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Model ◽  
Temporal Variability ◽  
Stream Water ◽  
Model Performance ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Stream Water Quality ◽  
Spatio Temporal

Abstract. Degraded water quality in rivers and streams can have large economic, societal and ecological impacts. Stream water quality can be highly variable both over space and time. To develop effective management strategies for riverine water quality, it is critical to be able to predict these spatio-temporal variabilities. However, our current capacity to model stream water quality is limited, particularly at large spatial scales across multiple catchments. This is due to a lack of understanding of the key controls that drive spatio-temporal variabilities of stream water quality. To address this, we developed a Bayesian hierarchical statistical model to analyse the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed based on monthly water quality monitoring data collected at 102 sites over 21 years. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NOx), and electrical conductivity (EC). Among the six constituents, the models explained varying proportions of variation in water quality. EC was the most predictable constituent (88.6 % variability explained) and FRP had the lowest predictive performance (19.9 % variability explained). The models were validated for multiple sets of calibration/validation sites and showed robust performance. Temporal validation revealed a systematic change in the TSS model performance across most catchments since an extended drought period in the study region, highlighting potential shifts in TSS dynamics over the drought. Further improvements in model performance need to focus on: (1) alternative statistical model structures to improve fitting for the low concentration data, especially records below the detection limit; and (2) better representation of non-conservative constituents by accounting for important biogeochemical processes. We also recommend future improvements in water quality monitoring programs which can potentially enhance the model capacity, via: (1) improving the monitoring and assimilation of high-frequency water quality data; and (2) improving the availability of data to capture land use and management changes over time.

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