27 results found
Le Vine N, 2016, Combining information from multiple flood projections in a hierarchical Bayesian framework, Water Resources Research, Vol: 52, Pages: 3258-3275, ISSN: 0043-1397
This study demonstrates, in the context of flood frequency analysis, the potential of a recently proposed hierarchical Bayesian approach to combine information from multiple models. The approach explicitly accommodates shared multi-model discrepancy as well as the probabilistic nature of the flood estimates, and treats the available models as a sample from a hypothetical complete (but unobserved) set of models. The methodology is applied to flood estimates from multiple hydrological projections (the Future Flows Hydrology dataset) for 135 catchments in the UK. The advantages of the approach are shown to be: 1) to ensure adequate ‘baseline' with which to compare future changes; 2) to reduce flood estimate uncertainty; 3) to maximise use of statistical information in circumstances where multiple weak predictions individually lack power, but collectively provide meaningful information; 4) to diminish the importance of model consistency when model biases are large; and 5) to explicitly consider the influence of the (model performance) stationarity assumption. Moreover, the analysis indicates that reducing shared model discrepancy is the key to further reduction of uncertainty in the flood frequency analysis. The findings are of value regarding how conclusions about changing exposure to flooding are drawn, and to flood frequency change attribution studies. This article is protected by copyright. All rights reserved.
Le Vine N, Butler A, McIntyre N, et al., 2015, Diagnosing hydrological limitations of a Land Surface Model: Application of JULES to a deep-groundwater chalk basin, Hydrology and Earth System Sciences Discussions, Vol: 12, Pages: 7541-7582, ISSN: 1812-2116
Land Surface Models (LSMs) are prospective starting points to develop a global hyper-resolution model of the terrestrial water, energy and biogeochemical cycles. However, there are some fundamental limitations of LSMs related to how meaningfully hydrological fluxes and stores are represented. A diagnostic approach to model evaluation is taken here that exploits hydrological expert knowledge to detect LSM inadequacies through consideration of the major behavioural functions of a hydrological system: overall water balance, vertical water redistribution in the unsaturated zone, temporal water redistribution and spatial water redistribution over the catchment's groundwater and surface water systems. Three types of information are utilised to improve the model's hydrology: (a) observations, (b) information about expected response from regionalised data, and (c) information from an independent physics-based model. The study considers the JULES (Joint UK Land Environmental Simulator) LSM applied to a deep-groundwater chalk catchment in the UK. The diagnosed hydrological limitations and the proposed ways to address them are indicative of the challenges faced while transitioning to a global high resolution model of the water cycle.
Buytaert W, Almeida S, le vine N, et al., 2015, Accounting for dependencies in regionalized signatures for predictions in ungauged catchments, Hydrology and Earth System Sciences Discussions, Vol: 12, Pages: 5389-5426, ISSN: 1812-2116
A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall-runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information, and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall-runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be disinformative.
Wheater H, Ballard C, Bulygina N, et al., 2014, Modelling environmental change:quantification of impacts of land use and land management change on UK flood risk, System Identification, Environmental Modelling and Control, Publisher: Springer
Almeida S, Bulygina N, McIntyre N, et al., 2013, Improving parameter priors for data-scarce estimation problems, Water Resources Research
Wheater H, McIntyre N, Bulygina N, et al., 2013, Prediction in Ungauged Basins – The Challenge of Catchment Non-Stationarity, Editors: Pomeroy, Whitfield, Spence, ISBN: 978-1-896513-38-6
McIntyre N, Ballard C, Bruen M, et al., 2013, Modelling the hydrological impacts of rural land use change: current state of the science and future challenges
McIntyre N, Ballard C, Bruen M, et al., 2013, Modelling the hydrological impacts of rural land use change, Hydrology Research, ISSN: 0029-1277
Bulygina N, McIntyre N, Wheater H, 2013, A comparison of rainfall-runoff modelling approaches for estimating impacts of rural land management on flood flows, Hydrology Research, Vol: 44, Pages: 467-483
Ewen J, O'Donnell G, Bulygina N, et al., 2013, Towards understanding links between rural land management and the catchment flood hydrograph, QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Vol: 139, Pages: 350-357, ISSN: 0035-9009
McIntyre N, Ballard C, Park JS, et al., 2012, Land use management effects on flood flows – guidance on prediction
McIntyre N, Ballard C, Bulygina N, et al., 2012, The potential for reducing flood risk through changes to rural land management: outcomes from the Flood Risk Management Research Consortium
Bakopoulou C, Bulygina N, Butler A, et al., 2012, Sensitivity Analysis and Parameter Identifiability of the Land Surface Model JULES at the point scale in permeable catchments
Almeida S, Bulygina N, McIntyre N, et al., 2012, Predicting flows in ungauged catchments using correlated information sources, BHS International Hydrology Symposium
McIntyre N, Ballard C, Park J-S, et al., 2012, Land use management effects on flood flows - guidance for predictions, Publisher: CIRIA
Bulygina N, Ballard C, McIntyre N, et al., 2012, Integrating different types of information into hydrological model parameter estimation: application to ungauged catchments and land use change scenario analysis, WRR
Bulygina N, Ballard C, McIntyre N, et al., 2011, Bayesian conditioning of a rainfall-runoff modelfor predicting flows in ungauged catchments and under land use changes, BHS Third International Hydrology Symposium
Bulygina N, McIntyre N, Wheater H, 2011, Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes, WATER RESOURCES RESEARCH, Vol: 47, ISSN: 0043-1397
Wheater HS, McIntyre N, Jackson B, et al., 2011, Multiscale impacts of land management on flooding, Flood Risk Science and Management, Editors: Pender, Faulkner, Pender, Faulkner, Oxford, UK, Publisher: Wiley-Blackwell, Pages: 39-59, ISBN: 978-1405186575
Bulygina N, Gupta H, 2011, Correcting the Mathematical Structure of a Hydrological Model via BayesianData Assimilation, Water Resources Research
Wheater H, McIntyre N, Jackson B, et al., 2010, Multi-scale impacts of land management on flooding, Flood Management Handbook
Bulygina N, Gupta H, 2010, How Bayesian Data Assimilation can be used to estimate mathematical structure of a model, SERRA, Vol: 477
In previous work, we presented a method forestimation and correction of non-linear mathematical modelstructures, within a Bayesian framework, by merginguncertain knowledge about process physics with uncertainand incomplete observations of dynamical input-stateoutputbehavior. The resulting uncertainty in the modelinput-state-output mapping is expressed as a weightedcombination of an uncertain conceptual model prior and adata-derived probability density function, with weightsdepending on the conditional data density. Our algorithm isbased on the use of iterative data assimilation to update aconceptual model prior using observed system data, andthereby construct a posterior estimate of the model structure(the mathematical form of the equation itself, not just itsparameters) that is consistent with both physically basedprior knowledge and with the information in the data. Animportant aspect of the approach is that it facilitates a cleardifferentiation between the influences of different typesof uncertainties (initial condition, input, and mappingstructure) on the model prediction. Further, if some priorassumptions regarding the structural (mathematical) formsof the model equations exist, the procedure can help revealerrors in those forms and how they should be corrected. Thispaper examines the properties of the approach by investigatingtwo case studies in considerable detail. The resultsshow how, and to what degree, the structure of a dynamicalhydrological model can be estimated without little or no priorknowledge (or under conditions of incorrect prior information)regarding the functional forms of the storage–streamflowand storage–evapotranspiration relationships. Theimportance and implications of careful specification of themodel prior are illustrated and discussed.
Bulygina N, Gupta H, 2009, Estimating the uncertain mathematical structure of a water balance model via Bayesian data assimilation, WATER RESOURCES RESEARCH, Vol: 45, ISSN: 0043-1397
Bulygina N, McIntyre N, Wheater H, 2009, Conditioning rainfall-runoff model parameters for ungauged catchments and land management impacts analysis, HESS, Vol: 13, Pages: 893-904
Bulygina N, 2007, Model Structure Estimation and Correction through Data Assimilation
Bulygina NS, Nearing MA, Stone JJ, et al., 2007, DWEPP: a dynamic soil erosion model based on WEPP source terms, EARTH SURFACE PROCESSES AND LANDFORMS, Vol: 7, Pages: 998-1012
Bulygina N, 2003, Eigen vectors and vectors for electro-magnetic field in a resonator
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.