Bayesian Uncertainty Analysis of the Distributed Hydrological Model HYDROTEL

Bouda, Médard; Rousseau, Alain N.; Konan, Brou; Gagnon, Patrick; Gumière, Silvio José

doi:10.1061/(asce)he.1943-5584.0000550

Cited by 46 publications

(15 citation statements)

References 54 publications

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“…HYDROTEL, a distributed hydrologic model (Bouda et al, 2014;Bouda et al, 2012;Fortin et al, 2001;Turcotte et al, 2007;Turcotte et al, 2003), is a continuous watershed model compatible with remote sensing and GIS data that operates on a 3-h or daily time step. Required hydrometeorological data include daily precipitation, maximum and minimum temperatures, and for calibration, daily stream flows at the outlet or at any other river segment.…”

Section: Physitel/hydrotel Modelling Platformmentioning

confidence: 99%

Assessment of the impact of spatio-temporal attributes of wetlands on stream flows using a hydrological modelling framework: a theoretical case study of a watershed under temperate climatic conditions

2015

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Wetlands play a significant role on the hydrological cycle, reducing flood peaks through water storage functions and sustaining low flows through slow water release ability. However, their impacts on water resources availability and flood control are mainly driven by wetland type (e.g. isolated wetland—IW—and riparian wetland—RW) and location within a watershed. Consequently, assessing the qualitative and quantitative impact of wetlands on hydrological regimes has become a relevant issue for scientists as well as stakeholders and decision‐makers. In this study, the distributed hydrological model, HYDROTEL, was used to investigate the role and impact of the geographic distribution of isolated and RWs on stream flows of the Becancour River watershed of the St Lawrence Lowlands, Quebec, Canada. The model was set up and calibrated using available datasets (i.e. DEM, soil, wetland distribution, climate, land cover, and hydrometeorological data for the 1969–2010 period). Different wetland theoretical location tests (WTLT) were simulated. Results were used to determine whether stream flow parameters, related to peak flows and low flows, were related to: (i) geographic location of wetlands, (ii) typology of wetlands, and (iii) seasonality. The contribution of a particular wetland was assessed using intrinsic characteristics (e.g. surface area, typology) and extrinsic factors (e.g. location in the watershed landscape and seasonality). Through these investigations, the results suggest, to some extent, that both IWs and RWs impact landscape hydrology. The more IWs are located in the upper part of the watershed, the greater their effect on both on high flow damping and low flow support seems to be. The more RWs are connected to a main stream, the greater their effect is. Our modelling results indicate that local landscape conditions may influence the wetland effect; promoting or limiting their efficiency, and thus their impacts on stream flows depend on a combined effect of wetland and landscape attributes. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Physitel/hydrotel Modelling Platformmentioning

confidence: 99%

Assessment of the impact of spatio-temporal attributes of wetlands on stream flows using a hydrological modelling framework: a theoretical case study of a watershed under temperate climatic conditions

2015

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“…CC-BY 3.0 License. Beven, 2006;Blasone et al, 2008;Xu et al, 2010;Bouda et al, 2011;Delsman et al, 2013;Cheng et al, 2014a;2014b;. However, these methods actually only test the uncertainty from parameterization and they have the limitation of to be affected greatly by the prior probability.…”

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Uncertainty analysis of hydrological return period estimation, taking the upper Yangtze River as an example

Sun

Jiang

Cheng

et al. 2017

Preprint

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Abstract. Return period estimation plays an important role in the engineering practices of water resources and disaster 10 management, but uncertainties accompany the calculation process. Based on the daily discharge records at two gauging stations (Cuntan and Pingshan) on the upper Yangtze River, three sampling methods (SMs; (annual maximum, peak over threshold, and decadal peak over threshold), five distribution functions (DFs; gamma, Gumbel, lognormal, Pearson III, and general extreme value), and three parameterization methods (PMs; maximum likelihood, L-Moment, and method of moment) were applied to analyze the uncertainties in return period estimation. The estimated return levels based on the different 15 approaches were found to differ considerably at each station. The range of discharge for a 20-year return period was 63,800.8-74,024.1 m3 s-1 for Cuntan and 23,097.8-25,595.3 m3 s-1 for Pingshan, when using the 45 combinations of SMs, DFs, and PMs. For a 1000-year event, the estimated discharge ranges increased to 74,492.5-125,658.0 and 27,339.2-41,718.1 m3 s-1 for Cuntan and Pingshan, respectively. Application of the analysis of variance method showed that the total sum of the squares of the estimated return levels increased with the widening of the return periods, suggestive of increased 20 uncertainties. However, the contributions of the different sources to the uncertainties were different. For Cuntan, where the discharge changed significantly, the SM appeared to be the largest source of uncertainty. For Pingshan, where the discharge series remained almost stable, the DF contributed most to the uncertainty. Therefore, multiple uncertainty sources in estimating return periods should be considered to meet the demands of different planning purposes. The research results also suggest that uncertainties of return level estimation could be reduced if an optimized DF were used, or if the decadal peak 25 over threshold SM were used, which is capable of representing temporal changes of hydrological series.

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“…In the case where the risk evaluation model varies in time or space, it is possible to use several approaches to quantify the impact of uncertainty in the model. One can assess the consequences of this variation through the different simulation models, compare different values of the input variables in different models and use different Bayesian approaches to analyze the model uncertainties [4,8,9].…”

Section: Uncertainty Analysismentioning

confidence: 99%

Response Surface Methodology: A Review of Applications to Risk Assessment

Oliveira

Leal

Oliveira

2015

Springer Proceedings in Mathematics &Amp; Statistics

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Risk Analysis has assumed a crucial relevance over the past few years, particularly in dynamical systems with increasing complexity. Thanks to recent technological advances, the use of simulation techniques to estimate models has become the norm rather than the exception. These simulated models are used to predict the behavior of a system, to compute the probability of occurrence of a specific event and to predict the consequence of the said event. Uncertainty associated with the simulation, either in model parameters or in experimental data, requires its quantification as a prerequisite in probabilistic risk assessment. The computational costs of numerical simulations are often very high, thus the use of metamodels arises as a pressing necessity. Response Surface Methodology is known to be a suitable tool, both for the estimation of metamodels for the behaviors of systems and risk assessment, and for the quantification of uncertainty. A review of applications and of various aspects on the use of Response Surface Methodology in Risk Assessment Systems will be presented.

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Bayesian Uncertainty Analysis of the Distributed Hydrological Model HYDROTEL

Cited by 46 publications

References 54 publications

Assessment of the impact of spatio-temporal attributes of wetlands on stream flows using a hydrological modelling framework: a theoretical case study of a watershed under temperate climatic conditions

Assessment of the impact of spatio-temporal attributes of wetlands on stream flows using a hydrological modelling framework: a theoretical case study of a watershed under temperate climatic conditions

Uncertainty analysis of hydrological return period estimation, taking the upper Yangtze River as an example

Response Surface Methodology: A Review of Applications to Risk Assessment

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