Hydrological model uncertainty assessment in southern Africa

Hughes, Denis A.; Kapangaziwiri, Evison; Sawunyama, Tendai

doi:10.1016/j.jhydrol.2010.04.010

Cited by 48 publications

(22 citation statements)

References 23 publications

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“…For problems that map relatively well to the GPU's architecture, having such in-depth knowledge of the functioning of the GPU is not a prerequisite for obtaining a satisfactory speedup, as was found with the uncertainty model in Section 3. Consequently, it would be beneficial to be able to classify a problem as belonging to a particular GPU acceleration 6 Sorting more elements on the CPU was not possible due to memory limitations. di culty level, and identify guidance appropriate for that level.…”

Section: Discussionmentioning

confidence: 99%

“…These models are primarily used to increase our understanding of the observed processes and to make hydrological predictions or estimations [10]. A recent trend in hydrological modelling is the use of uncertainty analysis [6]. Using this approach, a model is run thousands of times using di erent input parameters.…”

Section: Hydrological Modelmentioning

confidence: 99%

“…The ability of the model to accurately represent the hydrological response of any given catchment is reliant on the correct specification of the model parameters. The uncertainty version of this model is designed to assist in the estimation of these parameters, and allows the model results for many di erent options within the feasible parameter space to be explored [6]. This is done by running many ensembles (or instances) of the model, each with a di erent set of parameters.…”

Section: The Pitman Modelmentioning

confidence: 99%

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Evaluating the acceleration of typical scientific problems on the GPU

Tristram

Bradshaw

2013

Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference

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General-purpose computation on graphics processing units (GPGPU) has great potential to accelerate many scientific models and algorithms. However, some problems are considerably more di cult to accelerate than others, and it may be di cult for those new to GPGPU to ascertain the di culty of accelerating a particular problem. Additionally, problems of di erent levels of di culty require varying complexities of optimisations to achieve satisfactory results, and currently there is no clear separation between the di erent levels of known optimisations, which would be helpful to new users of GPGPU. Through what was learned in the acceleration of three problems, problem attributes have been identified to assist in evaluating the di culty of accelerating a problem on a GPU. We envisage that with further development, these attributes could form the foundation of a di culty classification system that could be used to determine whether GPU acceleration is practical for a candidate GPU acceleration problem, aid in identifying appropriate techniques and optimisations, and outline the required GPGPU knowledge.

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Section: Discussionmentioning

confidence: 99%

Section: Hydrological Modelmentioning

confidence: 99%

Section: The Pitman Modelmentioning

confidence: 99%

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Evaluating the acceleration of typical scientific problems on the GPU

Tristram

Bradshaw

2013

Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference

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“…Although the energy balance approach best describes the effects of debris cover on melt totals (Hock, 1999(Hock, , 2003, input data availability is a significant constraint. Uncertainty in the hydrological models might arise due to errors in input data, inappropriate parameter selection and modelling approach used (Hughes et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Modified temperature index model for estimating the melt water discharge from debris-covered Lirung Glacier, Nepal

Parajuli¹,

Chand²,

Kayastha³

et al. 2015

Proc. IAHS

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In the Nepalese Himalayas, the complex topography, occurrence of debris covered glaciers, and limited data availability creates substantial difficulties for modelling glacier melt. The proper recognition of melt processes governs the accurate estimation of melt water from glacier dominated systems, even in the presence of debris-covered glaciers. This paper presents a glacier melt model developed for the Lirung subbasin of Langtang valley, which has both a clean glacier area, 5.86 km 2 , and a debris-covered glacier area, 1.13 km 2 . We use a temperature index approach to estimate sub-daily melt water discharge for a two week period at the end of monsoon, and the melt factor is varied according to the aspect and distributed to each grid processed from the digital elevation model. The model uses easily available data and simple extrapolation techniques capable of generating melt with limited data. The result obtained from this method provides accurate estimate with an R 2 value of 0.89, bias of 0.9% and Nash-Sutcliffe efficiency of 0.86, and suitable in Himalaya where data availability is major issue.

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“…In recent years, many studies have focused on uncertainty analysis of water resources, specifically for water resources management and planning [19][20][21], hydrologic modeling [22,23], distributed watershed modeling [24], decision making [25,26], water allocation [27,28], and water quality [29]. Various uncertainty analysis techniques have been developed.…”

Section: Introductionmentioning

confidence: 99%

Predicting Multiple Functions of Sustainable Flood Retention Basins under Uncertainty via Multi-Instance Multi-Label Learning

Yang

Boehm²,

Scholz

et al. 2015

Water

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Abstract:The ambiguity of diverse functions of sustainable flood retention basins (SFRBs) may lead to conflict and risk in water resources planning and management. How can someone provide an intuitive yet efficient strategy to uncover and distinguish the multiple potential functions of SFRBs under uncertainty? In this study, by exploiting both input and output uncertainties of SFRBs, the authors developed a new data-driven framework to automatically predict the multiple functions of SFRBs by using multi-instance multi-label (MIML) learning. A total of 372 sustainable flood retention basins, characterized by 40 variables associated with confidence levels, were surveyed in Scotland, UK. A Gaussian model with Monte Carlo sampling was used to capture the variability of variables (i.e., input OPEN ACCESSWater 2015, 7 1360 uncertainty), and the MIML-support vector machine (SVM) algorithm was subsequently applied to predict the potential functions of SFRBs that have not yet been assessed, allowing for one basin belonging to different types (i.e., output uncertainty). Experiments demonstrated that the proposed approach enables effective automatic prediction of the potential functions of SFRBs (e.g., accuracy >93%). The findings suggest that the functional uncertainty of SFRBs under investigation can be better assessed in a more comprehensive and cost-effective way, and the proposed data-driven approach provides a promising method of doing so for water resources management.

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Hydrological model uncertainty assessment in southern Africa

Cited by 48 publications

References 23 publications

Evaluating the acceleration of typical scientific problems on the GPU

Evaluating the acceleration of typical scientific problems on the GPU

Modified temperature index model for estimating the melt water discharge from debris-covered Lirung Glacier, Nepal

Predicting Multiple Functions of Sustainable Flood Retention Basins under Uncertainty via Multi-Instance Multi-Label Learning

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