Papers

@article{Fraga2019160,

title = {Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models},

author = {Ignacio Fraga and Luis Cea-Gómez and Jerónimo Puertas-Agudo},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057832365&doi=10.1002%2fhyp.13319&partnerID=40&md5=8bbb0e74a81e6b2f7faed33c5596c9e0},

doi = {10.1002/hyp.13319},

issn = {08856087},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Hydrological Processes},

volume = {33},

number = {1},

pages = {160-173},

publisher = {John Wiley and Sons Ltd},

abstract = {This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km2 located in the north-west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase. © 2018 John Wiley & Sons, Ltd.},

note = {cited By 38},

keywords = {Catchments; Glues; Gluing; Rain; Runoff; Storms, Conditional simulations; Fully distributed modeling; Generalized likelihood uncertainty estimation; Hydrological models; Quantitative precipitation estimation; Rainfall uncertainties; Runoff modelling; Transformation process, discharge; estimation method; precipitation assessment; quantitative analysis; rainfall-runoff modeling; uncertainty analysis, Spain, Uncertainty analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Fraga2016,

title = {Global sensitivity and GLUE-based uncertainty analysis of a 2D-1D dual urban drainage model},

author = {Ignacio Fraga and Luis Cea-Gómez and Jerónimo Puertas-Agudo and Joaquín Suárez-López and Vicente Jiménez and Juan Alfredo Jácome-Burgos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964692558&doi=10.1061%2f%28ASCE%29HE.1943-5584.0001335&partnerID=40&md5=66dbee243579b6ca8b79c9ed6a9a3941},

doi = {10.1061/(ASCE)HE.1943-5584.0001335},

issn = {10840699},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {Journal of Hydrologic Engineering},

volume = {21},

number = {5},

publisher = {American Society of Civil Engineers (ASCE)},

abstract = {This paper presents a global sensitivity and uncertainty analysis of a dual drainage model applied to a 0.049km2 (4.9-ha) urban catchment based on generalized likelihood uncertainty estimation (GLUE). The model solves the nonlinear bidimensional (2D) and one-dimensional (1D) Saint-Venant equations to compute respectively the surface runoff and the flow in the sewer network. The sensitivity of the outlet hydrograph to the input parameters of the model was determined by using variance-based Sobol sensitivity indices. These indices highlight the great effect of the overland flow parameters on the model output, in contrast with the limited effect of the sewer-network parameters. The Manning coefficient of the impervious terrains was identified as the most influential model parameter. The effect on model calibration of two subjective factors of the GLUE methodology (the acceptability threshold and the shaping factor of the likelihood function) was analyzed. From this analysis, a NSE acceptability threshold of 0.6 and a shaping factor of 1 were adopted for model calibration. Greater values of these parameters resulted in very narrow uncertainty bounds and therefore in low coverage ratios. The results obtained after calibration and validation show a satisfactory performance of the model, with NSE values within 0.78 and 0.98 and coverage ratios of the 95% confidence intervals in the range 66-91%. The worst performances are obtained in the events with lowest rainfall depths, since in those cases the overland flow in the whole catchment is extremely shallow and the runoff propagation is not well captured by the model. © 2016 American Society of Civil Engineers.},

note = {cited By 41},

keywords = {calibration; hydrological modeling; hydrological response; two-dimensional modeling; uncertainty analysis; urban drainage, Catchments; Glues; Gluing; Nonlinear equations; Rain; Runoff; Sewers, Generalized likelihood uncertainty estimation; Global sensitivity; Model calibration; Model validation; Rainfall runoff; Urban drainage model, Uncertainty analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Cea20111526,

title = {Uncertainty and sensitivity analysis of a depth-averaged water quality model for evaluation of Escherichia Coli concentration in shallow estuaries},

author = {Luis Cea-Gómez and María Bermúdez and Jerónimo Puertas-Agudo},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855511004&doi=10.1016%2fj.envsoft.2011.08.001&partnerID=40&md5=0eb5560b721bac147dd0b5ccc2f9587f},

doi = {10.1016/j.envsoft.2011.08.001},

issn = {13648152},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

journal = {Environmental Modelling and Software},

volume = {26},

number = {12},

pages = {1526-1539},

abstract = {Sensitivity and uncertainty analysis investigate the robustness of numerical model predictions and provide information about the factors that contribute most to the variability of model output, identifying the most important parameters for model calibration. This paper presents a sensitivity and uncertainty analysis of a 2D depth-averaged water quality model applied to a shallow estuary. The model solves the mass transport equation for Escherichia Coli, including the effects of water temperature, salinity, solar radiation, turbulent diffusion and short wave dispersion. The sensitivity of the concentration of E. Coli in the estuary to input parameters and the different sources of uncertainty are studied using Global Sensitivity Analysis based on Monte Carlo simulation methods and sensitivity measures based on linear and non-linear regression analysis, in order to aid modellers in the calibration process and in the interpretation of model output. The extinction coefficient of light in water and the depth of the vertical layer over which the E.coli spread were found to be the most relevant parameters of the model. In the shallowest regions of the estuary errors in the bathymetry are also an important source of uncertainty on model output. Globally, the combination of these three parameters was found to be very effective for calibration purposes in the whole estuary. © 2011 Elsevier Ltd.},

note = {cited By 30},

keywords = {Analysis of variance (ANOVA); Calibration; Computer simulation; Escherichia coli; Estuaries; Monte Carlo methods; Quality assurance; Quality control; Radiation effects; Regression analysis; Sensitivity analysis; Sun; Water quality, Depth-averaged modelling; Monte Carlo Simulation; Sensitivity; Uncertainty; Water quality modelling, Escherichia coli, Uncertainty analysis},

pubstate = {published},

tppubtype = {article}

}