Uncertainty in the parameterization of sediment build-up and wash-off processes in the simulation of sediment transport in urban areas

Gorgoglione, Angela; Bombardelli, Fabián A.; Pitton, Bruno J.L.; Oki, Lorence R.; Haver, Darren; Young, Thomas M.

doi:10.1016/j.envsoft.2018.09.022

Cited by 50 publications

(30 citation statements)

References 31 publications

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“…Despite continuous expensive research and efforts to gather hydrologic data, there are still some areas of the world with scarce hydrometric gauging stations [2,3]. These areas have been representing a motivating and challenging topic for researchers and decision-makers.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Narbondo

Gorgoglione

Crisci

et al. 2020

Water

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Regionalization techniques have been comprehensively discussed as the solution for runoff predictions in ungauged basins (PUB). Several types of regionalization approach have been proposed during the years. Among these, the physical similarity one was demonstrated to be one of the most robust. However, this method cannot be applied in large regions characterized by highly variable climatic conditions, such as sub-tropical areas. Therefore, this study aims to develop a new regionalization approach based on an enhanced concept of physical similarity to improve the runoff prediction of ungauged basins at country scale, under highly variable-weather conditions. A clustering method assured that watersheds with different hydrologic and physical characteristics were considered. The novelty of the proposed approach is based on the relationships found between rainfall-runoff model parameters and watershed-physiographic factors. These relationships were successively exported and validated at the ungauged basins. From the overall results, it can be concluded that the runoff prediction in the ungauged basins was very satisfactory. Therefore, the proposed approach can be adopted as an alternative method for runoff prediction in ungauged basins characterized by highly variable-climatic conditions.

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Section: Introductionmentioning

confidence: 99%

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Narbondo

Gorgoglione

Crisci

et al. 2020

Water

Self Cite

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“…It is important to assess how much uncertainties are involved in hydrologic modeling because there are many different sources of uncertainty including model structures, parameters, and input and output data [1][2][3][4]. Model structural uncertainty is due to the fact that we cannot perfectly represent the natural processes involved in hydrologic modeling [4].…”

Section: Introductionmentioning

confidence: 99%

“…Model structural uncertainty is due to the fact that we cannot perfectly represent the natural processes involved in hydrologic modeling [4]. Parameter uncertainty indicates that many model parameters are not directly measurable (such as conceptual parameters) or can only be obtained with unknown errors (such as physical parameters) [3]. Measurement uncertainty in input and output data can be caused by unknown measurement errors, incommensurability issues, etc., [2,4].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Four GLUE Likelihood Measures and Behavior of Large Parameter Samples in ISPSO-GLUE for TOPMODEL

Cho

Park

Kim

2019

Water

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We tested four likelihood measures including two limits of acceptability and two absolute model residual methods within the generalized likelihood uncertainty estimation (GLUE) framework using the topography model (TOPMODEL). All these methods take the worst performance of all time steps as the likelihood of a model and none of these methods were successful in finding any behavioral models. We believe that reporting this failure is important because it shifted our attention from which likelihood measure to choose to why these four methods failed and how to improve these methods. We also observed how large parameter samples impact the performance of a hybrid uncertainty estimation method, isolated-speciation-based particle swarm optimization (ISPSO)-GLUE using the Nash–Sutcliffe (NS) coefficient. Unlike GLUE with random sampling, ISPSO-GLUE provides traditional calibrated parameters as well as uncertainty analysis, so over-conditioning the model parameters on the calibration data can affect its uncertainty analysis results. ISPSO-GLUE showed similar performance to GLUE with a lot less model runs, but its uncertainty bounds enclosed less observed flows. However, both methods failed in validation. These findings suggest that ISPSO-GLUE can be affected by over-calibration after a long evolution of samples and imply that there is a need for a likelihood measure that can better explain uncertainties from different sources without making statistical assumptions.

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“…These mathematical models heavily rely on the data collected by direct field observations. However, a functional and complete dataset of any environmental variable is difficult to collect because of two main reasons: (i) the low reliability in the measurements (e.g., due to issues related to the equipment location or occurrences of equipment malfunctions); and (ii) the high cost of the monitoring campaigns [2,3]. The lack of an adequate amount of Earth-science data may induce an unsatisfactory and not reliable representation of the response's complexity of an environmental system to any input/change, both natural and human-induced.…”

Section: Introductionmentioning

confidence: 99%

Overcoming Data Scarcity in Earth Science

Gorgoglione

Castro

Chreties

et al. 2020

Data

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The Data Scarcity problem is repeatedly encountered in environmental research. This may induce an inadequate representation of the response’s complexity in any environmental system to any input/change (natural and human-induced). In such a case, before getting engaged with new expensive studies to gather and analyze additional data, it is reasonable first to understand what enhancement in estimates of system performance would result if all the available data could be well exploited. The purpose of this Special Issue, “Overcoming Data Scarcity in Earth Science” in the Data journal, is to draw attention to the body of knowledge that leads at improving the capacity of exploiting the available data to better represent, understand, predict, and manage the behavior of environmental systems at meaningful space-time scales. This Special Issue contains six publications (three research articles, one review, and two data descriptors) covering a wide range of environmental fields: geophysics, meteorology/climatology, ecology, water quality, and hydrology.

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Uncertainty in the parameterization of sediment build-up and wash-off processes in the simulation of sediment transport in urban areas

Cited by 50 publications

References 31 publications

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Evaluation of Four GLUE Likelihood Measures and Behavior of Large Parameter Samples in ISPSO-GLUE for TOPMODEL

Overcoming Data Scarcity in Earth Science

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