Sensitivity and uncertainty analysis on water quality modelling of Aguamilpa reservoir

Rangel‐Peraza, Jesús Gabriel; Anda, José de; González‐Farías, F.; Rode, Michael

doi:10.4081/jlimnol.2016.1391

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…The constants affecting the Chl-a were identified in the phytoplankton module of the PCLake model. The sensitivity test determined the effect of changing the constant in the governing equation on the model output, based on the sensitivity coefficient (SC), as shown in Equation 1 (Rangel-Peraza et al, 2016):…”

Section: Sensitivity and Model Performance Testingmentioning

confidence: 99%

Feasible Application of PCLake Model to Predict Water Quality in Tropical Reservoirs

Wongpipun,

Sirivithayapakorn,

Vongthanasunthorn

2024

Environ. Nat. Resour. J.

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The PCLake model has not previously been used for tropical reservoirs. This study attempted to apply the PCLake model to predict the chlorophyll a concentrations (Chl-a) in a tropical reservoir in Thailand. Sensitivity analyses were performed for the constants affecting the prediction of Chl-a in the phytoplankton module. The model calibration was performed by using the adjusted value of the most sensitive constant with the observed data from July to December 2020. The effects of the initial trophic state of the reservoir on the simulated Chl-a were evaluated. The results showed that Chl-a were sensitive to six constants. Among these constants, the value of the specific extinction of detritus (cExtSpDet) was adjusted using the calculated values from the typical limnological parameters of the studied reservoir. Statistical analyses of the results of calibration and the subsequent validation with the observed data from February to September 2022 were listed as follows: NSE=0.55 and 0.37, RSR=0.67 and 0.79, and PBIAS=27% and 9%, respectively. The initial trophic state of the reservoir had no influence on the long-term prediction of Chl-a. This preliminary effort indicates that the PCLake model can be used to predict Chl-a, which is representative of algal biomass in tropical reservoirs and is essential to water quality models, without complex modifications.

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Section: Sensitivity and Model Performance Testingmentioning

confidence: 99%

Feasible Application of PCLake Model to Predict Water Quality in Tropical Reservoirs

Wongpipun,

Sirivithayapakorn,

Vongthanasunthorn

2024

Environ. Nat. Resour. J.

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“…The sensitivity of the model to the input parameters was done using the grid pertubation method which involves the fluctuation of each of the model input parameters within defined percentages, while holding the others constant, and obtaining corresponding output values based on the variations (Kabala and Milly, 1990;Caers and Hoffman, 2006;Zakari et al, 2015;Rangel-Peraza et al, 2016). This is repeated for all the input parameters in turns.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…The CropWat model sensitivity was hence calculated using a sensitivity coefficient (SC). This arises from the differential quotient of the change in the output over that of the input parameters (Rangel-Peraza et al, 2016). That is, = % %…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…Where, ETo = estimated reference evapotranspiration (mm/day), Rn = surface radiation balance (MJ/m 2 /day), G = sensible heat flux in the soil (MJ/m 2 /day), T = average atmospheric temperature ( o C), U2 = wind speed at 2 m height (m/s), es = saturation vapour pressure (KPa), ea = actual vapour pressure (KPa), Δ = slope of the water vapour saturation pressure curve (KPa/ o C). Sensitivity analysis for a multi-variable model such as the FAO-PM which has different variables is aimed at determining how each of the model equation variables affects the output (Afshar and Saadatpour, 2009), thus resulting in the determination of the dominant parameters in the model (Rangel-Peraza et al, 2016). The sensitivity of ETo to its underlying factors at both regional and local levels have shown that the most important driving change variables differ from one climatic region to the other (Ali et al, 2009;Fan and Thomas, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of Reference Evapotranspiration Computed with FAO CropWat 8.0 Model to Weather Input Variables During Two Irrigation Seasons in Kaduna, Nigeria

Onwuegbunam¹,

Onwuegbunam²,

Ameso³

et al. 2020

NJPAS

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The sensitivity analysis of the Food and Agriculture Organization (FAO) CropWat 8.0 model is aimed at determining the dominant variable of the model in the study area. Hence, the sensitivity coefficient and the correlation of the reference evapotranspiration (ETo) in Afaka, Kaduna, Nigeria, with each of the climatic input variables of the model, namely, maximum temperature (Tmax), minimum temperature (Tmin), humidity (H), wind speed (WS) and sunshine hour (SSH), were evaluated. A perturbation method was used in the evaluation and the sensitivity coefficients were obtained in a decreasing order of 0.55, 0.42, 0.22, 0.09 and -0.08 for Tmax, WS, SSH, Tmin and H, respectively. Also, the correlation of ETo with each of the input parameters, irrespective of the others, show the following correlation coefficients, r; SSH (r = 0.8675), Tmax (r = 0.6979), Tmin (r = 0.6226), H (r = 0.19) and WS (r = 0.0742). Hence, radiation and temperature-based estimations of ETo were recommended as useful tools for ETo estimation in the face of limited or scarce data and are adaptable to areas with similar climate characteristics. This would also serve as a theoretical basis for future studies on the response of ETo to climatic change in the study area and other areas with similar climate.

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“…Environmental factors such as wind gusts affect the quality of data acquisition (photographs) during the construction of high resolution Digital Elevation Models (DEM). These models are used to study the terrain elevations and to model the hydrodynamics and water quality of waterbodies [27]. From this point of view, it is important to obtain reliable data to ensure the accuracy of such models.…”

Section: State Of the Art Of Environmental Studies Using Dronesmentioning

confidence: 99%

Discontinuous High-Gain Observer in a Robust Control UAV Quadrotor: Real-Time Application for Watershed Monitoring

Rodríguez-Mata

Flores

Martinez-Vasquez

et al. 2018

Mathematical Problems in Engineering

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A control algorithm that is robust with respect to wind disturbances for a quadrotor UAV attitude dynamics is presented. The proposed approach consists of a high-gain observer based on a discontinuous technique. Such an algorithm is embedded on board the quadrotor. The high-gain observer estimates external disturbances such as wind and parameter uncertainties, and a control algorithm is designed to compensate these undesired effects. The observer design is based on Lyapunov stability theory; simulation results and experiments validate the nonlinear observer performance and robustness of the approach under windy conditions. Also, a photogrammetry survey was carried out to develop Digital Elevation Models in order to experimentally demonstrate the effectiveness of our approach. The accuracy of such models was compared and the performance improvement is demonstrated.

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Sensitivity and uncertainty analysis on water quality modelling of Aguamilpa reservoir

Abstract: The

Cited by 7 publications

References 24 publications

Feasible Application of PCLake Model to Predict Water Quality in Tropical Reservoirs

Feasible Application of PCLake Model to Predict Water Quality in Tropical Reservoirs

Sensitivity of Reference Evapotranspiration Computed with FAO CropWat 8.0 Model to Weather Input Variables During Two Irrigation Seasons in Kaduna, Nigeria

Discontinuous High-Gain Observer in a Robust Control UAV Quadrotor: Real-Time Application for Watershed Monitoring

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