Sensitivity analysis and auto-calibration of an integral dynamic model for river water quality

Griensven, Ann van; Francos, A.; Bauwens, Willy

doi:10.2166/wst.2002.0271

Cited by 89 publications

(41 citation statements)

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“…Additionally, to examine the relative performance across all months between each Mode we generated Taylor diagrams for each of the simulated flux terms which employ three statistics: the centred RMSE (E 0 ), the correlation coefficient (R), and the standard deviation (r) (Taylor, 2001): To test the sensitivity of SUEWS to differences in meteorological forcing data (for example differences which might arise between off-site and on-site stations) we employed a one-factor-a-time (OFAT) approach (Griensven et al, 2002). First, we generated highly typified data i.e.…”

Section: Model Evaluation and Sensitivitymentioning

confidence: 99%

Using LCZ data to run an urban energy balance model

2015

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a b s t r a c tIn recent years a number of models have been developed that describe the urban surface and simulate its climatic effects. Their great advantage is that they can be applied in environments outside the cities in which they have been developed and evaluated. Thus, they may be applied to cities in the economically developing world, which are growing rapidly, and where the results of such models may have greatest impact with respect to informing planning decisions. However, data requirements, particularly for the more complex urban models, represent a major obstacle to their employment. Here, we examine the potential for running the Surface Urban Energy and Water Balance model (SUEWS) using readily obtained data. SUEWS was designed to simulate energy and water balance terms at a neighbourhood scale (P1 km 2 ) and requires site-specific meteorological data and a detailed description of the surface. Here, its simulations are evaluated by comparison with measurements made over a seven month (approximately 3 seasons) period (April-October) at two flux tower sites (representing urban and suburban landscapes) in Dublin, Ireland. However, the main purpose of this work is to test the performance of the model under 'ideal' and 'imperfect' circumstances in relation to the input data required to run SUEWS. The ideal case uses detailed urban land cover data and meteorological data from the tower sites. The imperfect cases use parameters derived from the Local Climate Zone (LCZ) classification scheme and meteorological data from a standard weather station located beyond the urban area. For the period of record examined, the simulations show good E-mail address: paul.alexander@nuim.ie (P.J. Alexander).Urban Climate 13 (2015) 14-37 Contents lists available at ScienceDirectUrban Climate journ al homepage: www.elsevier.com/locate/uclim agreement with the observations in both ideal and imperfect cases, suggesting that the model can be used with data that is more easily derived. The comparison also shows the importance of including vegetative cover and of the initial moisture state in simulating the urban energy budget.

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Section: Model Evaluation and Sensitivitymentioning

confidence: 99%

Using LCZ data to run an urban energy balance model

2015

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“…The Salsola was divided into 18 sub-basins, and the Celone into 9 sub-basins. Prior to calibration, the sensitivity analysis (SA) developed by van Griensven et al (2002) was conducted for 27 parameters to assess the most sensitive hydrological parameters that can influence river flow. The SA was then carried out using streamflow simulation at the Salsola P.te FG (gauge 4, in Fig.…”

Section: Modeling Streamflowmentioning

confidence: 99%

An eco-hydrological assessment method for temporary rivers. The Celone and Salsola rivers case study (SE, Italy)

Girolamo

Gallart

Pappagallo

et al. 2015

Ann. Limnol. - Int. J. Lim.

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-This paper presents the results of a study which characterizes the hydrological regime of a temporary river at reach scale and analyzes the hydrological alterations due to anthropogenic pressures using the SWAT model to estimate the daily streamflows and the periods without flow which would occur in natural conditions. The study area is the Salsola and Celone river basins, located in SE Italy. In a first part, we identified six classes of flow, the so-called Aquatic States (ASs), which play a major role in determining the available mesohabitats and subsequently the characteristics of the aquatic life. The monthly occurrence of the different ASs over a long period has been evaluated in order to provide useful information to design biological samplings. The results show that dry bed conditions can occur from May to January and arheic state from April to January in the reaches located in the upper part of the basin. The impacted river reaches showed a different behavior depending on the anthropogenic impacts. In a second part, two indicators based on the statistics of the periods without flows were used, monthly flow permanence and dry season predictability, for describing the flow regime components which may have been altered by anthropogenic pressures. The indexes, which were computed in impacted and natural conditions, were used as coordinates in a plot to obtain a graphical vision of the regimes. The distance between the points representing the actual (impacted) and natural states in the plot were used to classify the hydrological alterations.

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“…A parameter sensitivity analysis method is embedded in SWAT to determine the relative ranking of those parameters that most affect the output variance due to input variability (van Griensven et al, 2002). The SWAT model, version 2005, also has an autocalibration procedure embedded that is used to obtain an optimal fit of process parameters.…”

Section: Model Backgroundmentioning

confidence: 99%

“…The SWAT model, version 2005, also has an autocalibration procedure embedded that is used to obtain an optimal fit of process parameters. This procedure incorporates the shuffled complex evolution method, which uses a global optimization standard for calibration in which multiple output parameters can be integrated concurrently (van Griensven et al, 2002). A statistical method uses the fit of the observed series to its related simulated series and translates the normalized values of the objective functions (van Griensven and Bauwens, 2003) per variable.…”

Section: Model Backgroundmentioning

confidence: 99%