A sensitivity and uncertainty analysis of a continental-scale water quality model of pathogen pollution in African rivers

2017 ◽  
Vol 351 ◽  
pp. 129-139 ◽  
Author(s):  
Klara Reder ◽  
Joseph Alcamo ◽  
Martina Flörke
2016 ◽  
Vol 75 (s1) ◽  
Author(s):  
Jesús G. Rangel-Peraza ◽  
José De Anda ◽  
Fernando A. González-Farías ◽  
Michael Rode

<p>The use of water quality models is determined to a great extent by their ability to accurately reproduce observed data series and by their predictive capability without the need to adjust the calibrated parameters. However, the observed abiotic variables involved in a system are measured with a level of uncertainty. The sensitivity analysis concepts and generalized methodology of uncertainty analysis were used via a computer tool called UNCSIM. As a first step, a parametric optimization model of the Aguamilpa reservoir water quality was accomplished. The aforementioned analysis identified that the wind sheltering coefficient (WSC), Chezy bottom friction solution (FRICC), and coefficient of bottom heat exchange (CBHE) were the parameters of the CE-QUAL-W2 model that significantly influenced the behaviour of temperature and dissolved oxygen concentration in the reservoir. Afterwards, the uncertainty of the water quality model was evaluated through the modification of the hydrological and climatological information, which had a major influence on the simulation of the system. This analysis showed the possible changes in hydrodynamic and water quality characteristics of the reservoir, including an increase in the thermocline due to a possible air temperature increase and a rainfall decrease in the region. The innovative coupling routine of the different modules for the sensitivity and uncertainty analysis developed in this research establishes the basis for the future development of a modelling platform to conduct water quality simulations of the Aguamilpa reservoir in real time through continuous meteorological and water discharge information.<strong><br clear="all" /> </strong><strong></strong></p>


2015 ◽  
Vol 524 ◽  
pp. 733-752 ◽  
Author(s):  
K.C. Abbaspour ◽  
E. Rouholahnejad ◽  
S. Vaghefi ◽  
R. Srinivasan ◽  
H. Yang ◽  
...  

Author(s):  
Soobin Kim ◽  
Yong Sung Kwon ◽  
JongChel Pyo ◽  
Mayzonee Ligaray ◽  
Joong-Hyuk Min ◽  
...  

2021 ◽  
Vol 193 (1) ◽  
Author(s):  
Cássia Monteiro da Silva Burigato Costa ◽  
Izabel Rodrigues Leite ◽  
Aleska Kaufmann Almeida ◽  
Isabel Kaufmann de Almeida

2021 ◽  
Vol 13 (1) ◽  
pp. 454-468
Author(s):  
Yumeng Song ◽  
Jing Zhang

Abstract We integrated hyperspectral and field-measured chlorophyll-a (Chl-a) data from the Kristalbad constructed wetland in the Netherlands. We developed a best-fit band ratio empirical algorithm to generate a distribution map of Chl-a concentration (C chla) from SPOT 6 imagery. The C chla retrieved from remote sensing was compared with a water quality model established for a wetland pond system. The retrieved satellite results were combined with a water quality model to simulate and predict the changes in phytoplankton levels. The regression model provides good retrievals for Chl-a. The imagery-derived C chla performed well in calibrating the simulation results. For each pond, the modeled C chla showed a range of values similar to the Chl-a data derived from SPOT 6 imagery (10–25 mg m−3). The imagery-derived and prediction model results could be used as the guiding analytical tools to provide information covering an entire study area and to inform policies.


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