Uncertainty in floodplain delineation: expression of flood hazard and risk in a Gulf Coast watershed     

Jason Christian  Leonardo Duenas‐Osorio  Aarin Teague  Zheng Fang  Philip Bedient 《水文研究》2013,27(19):2774-2784

This paper investigates the development of flood hazard and flood risk delineations that account for uncertainty as improvements to standard floodplain maps for coastal watersheds. Current regulatory floodplain maps for the Gulf Coastal United States present 1% flood hazards as polygon features developed using deterministic, steady‐state models that do not consider data uncertainty or natural variability of input parameters. Using the techniques presented here, a standard binary deterministic floodplain delineation is replaced with a flood inundation map showing the underlying flood hazard structure. Additionally, the hazard uncertainty is further transformed to show flood risk as a spatially distributed probable flood depth using concepts familiar to practicing engineers and software tools accepted and understood by regulators. A case study of the proposed hazard and risk assessment methodology is presented for a Gulf Coast watershed, which suggests that storm duration and stage boundary conditions are important variable parameters, whereas rainfall distribution, storm movement, and roughness coefficients contribute less variability. The floodplain with uncertainty for this coastal watershed showed the highest variability in the tidally influenced reaches and showed little variability in the inland riverine reaches. Additionally, comparison of flood hazard maps to flood risk maps shows that they are not directly correlated, as areas of high hazard do not always represent high risk. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

Spatial scale dependency issues in the application of the Modified Universal Soil Loss Equation (MUSLE)     

D. Gwapedza  A. R. Slaughter 《水文科学杂志》2013,58(13-14):1890-1900

ABSTRACT

The MUSLE is used within hydrological models to estimate sediment yields from catchments of various sizes, but the spatial scale dependency issues associated with estimating the MUSLE parameters have not been adequately addressed. In the absence of detailed observed data on both hydrological response and sediment yield, some analytical approaches and hypothetical examples are presented to identify the key issues. The results suggest that methods used to estimate both the erosivity and topographic factors are scale dependent, particularly if a lumped or semi-distributed modelling approach is used. The conclusion is that spatial scale dependencies will add to the uncertainties inherent in the use of the MUSLE if not carefully understood and appropriately addressed. One suggested approach is to apply the erosivity equation to a fixed (small) representative area and then scale up to the total catchment, an approach that recognizes the variability of averaged parameters across different spatial scales.  相似文献   

Possible change in Korean streamflow seasonality based on multi‐model climate projections     

I. W. Jung  D. H. Bae  B. J. Lee 《水文研究》2013,27(7):1033-1045

Seasonality in hydrology is closely related to regional water management and planning. There is a strong consensus that global warming will likely increase streamflow seasonality in snow‐dominated regions due to decreasing snowfall and earlier snowmelt, resulting in wetter winters and drier summers. However, impacts to seasonality remain unclear in rain‐dominated regions with extreme seasonality in streamflow, including South Korea. This study investigated potential changes in seasonal streamflow due to climate change and associated uncertainties based on multi‐model projections. Seasonal flow changes were projected using the combination of 13 atmosphere–ocean general circulation model simulations and three semi‐distributed hydrologic models under three different future greenhouse gas emission scenarios for two future periods (2020s and 2080s). Our results show that streamflow seasonality is likely to be aggravated due to increases in wet season flow (July through September) and decreases in dry season flow (October through March). In South Korea, dry season flow supports water supply and ecosystem services, and wet season flow is related to flood risk. Therefore, these potential changes in streamflow seasonality could bring water management challenges to the Korean water resources system, especially decreases in water availability and increases in flood risk. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

A new analytical solution based on joint relaxation for analyzing symmetrical block stability     

M. Bagheri  H. Stille 《国际地质力学数值与分析法杂志》2013,37(8):771-786

The magnitude of clamping forces has a significant influence on the estimated ultimate pullout force of a block. The Crawford–Bray equation, which is fundamental in considering clamping forces, is only a function of horizontal stress and block height. Further research to incorporate the influence of induced stress in block stability analysis was considered, but all the previous analytical solutions for analyzing block stability assume a continuum medium to estimate clamping forces and do not allow joint deformations to occur before block movement due to gravity. Assuming a continuous medium to estimate clamping forces leads to an overestimation of block stability and therefore unsafe design. In this paper, an attempt has been made to deepen the understanding of the block failure mechanism and correct the estimated magnitude of clamping forces in a discontinuous medium. A conceptual model is proposed based on the loading–unloading of the block from an in‐situ state to failure. Based on this model, an analytical solution has been developed that calculates clamping forces in a discontinuous medium. The validity and model uncertainty of the solution were checked for different conditions. The new analytical solution is both precise and accurate and can be used as a design tool to estimate block stability. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

Field determination of the spatial variation of resistance to flow along a steep Alpine stream     

S. Orlandini  C. Boaretti  V. Guidi  G. Sfondrini 《水文研究》2006,20(18):3897-3913

Accurate field data have been collected along the Febbraro River (central Italian Alps) during quasi‐steady, low‐flow conditions to investigate the spatial variations of hydraulic and geomorphologic quantities potentially affecting resistance to flow. Detailed uncertainty analysis and weighted least‐squares fitting of simple power function relationships to field‐derived data are carried out to identify possible interdependencies between observed variables. Mean flow velocity is found to depend on water‐surface slope, bed material particle size, and upstream drainage area, whereas its dependence on hydraulic depth appears less susceptible to quantification. Upstream drainage area is found to explain the variations of hydraulic depth, water‐surface slope, Gauckler–Strickler conductance coefficient, and (although less significantly) flow discharge. Specifically, a highly significant positive dependence of the Gauckler–Strickler conductance coefficient on the upstream drainage area is found to exist, although anomalies in the variations of hydraulic depth and flow discharge are observed along the stream. The combined use of uncertainty analysis, hydraulic equations, and geomorphological relationships allows a possible characterization of resistance to flow along a steep Alpine stream and, perhaps more importantly, provides useful guidelines for future investigative efforts. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

Uncertainties in DRAINMOD predictions of subsurface drain flow for an Indiana silt loam using the GLUE methodology     

X. Wang  J. R. Frankenberger  E. J. Kladivko 《水文研究》2006,20(14):3069-3084

Good modelling practice requires the incorporation of uncertainty analysis into hydrologic/water quality models. The generalized likelihood uncertainty estimation procedure was used to evaluate the uncertainty in DRAINMOD predictions of daily, monthly, and yearly subsurface drain flow. A variance‐based sensitivity analysis technique, the extended Fourier amplitude sensitivity test, was used to identify the main sources of prediction uncertainty. The analysis was conducted for the experimental drainage field at the Southeast Purdue Agricultural Center in Indiana. Six years of data were used and the uncertainties in eight model parameters were considered to analyse how uncertainties in input parameters propagate to model outputs. The width of 90% confidence interval bands of drain flow ranged from 0 to 0·6 cm day^?1 for daily predictions, from 0 to 3·1 cm month^?1 for the monthly predictions, and from 7·6 to 12·4 cm year^?1 for yearly predictions. Annual drain flow predicted by DRAINMOD fell well within the 90% confidence bounds. Model results were most sensitive to the vertical saturated hydraulic conductivity of the restrictive layer and the lateral hydraulic conductivity of the deepest soil layer, followed by the lateral hydraulic conductivity of the top soil layer and surface micro‐storage. Parameter interactions also contributed to the prediction uncertainty. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

Uncertainty and sensitivity analysis of runoff and sediment yield in a small agricultural watershed with KINEROS2 / Analyse d'incertitude et de sensibilité des simulations d'écoulement et de transport solide de KINEROS2 dans un petit bassin versant agricole     

《水文科学杂志》2013,58(6)

Abstract

Using the Monte Carlo (MC) method, this paper derives arithmetic and geometric means and associated variances of the net capillary drive parameter, G, that appears in the Parlange infiltration model, as a function of soil texture and antecedent soil moisture content. Approximate expressions for the arithmetic and geometric statistics of G are also obtained, which compare favourably with MC generated ones. This paper also applies the MC method to evaluate parameter sensitivity and predictive uncertainty of the distributed runoff and erosion model KINEROS2 in a small experimental watershed. The MC simulations of flow and sediment related variables show that those parameters which impart the greatest uncertainty to KINEROS2 model outputs are not necessarily the most sensitive ones. Soil hydraulic conductivity and wetting front net capillary drive, followed by initial effective relative saturation, dominated uncertainties of flow and sediment discharge model outputs at the watershed outlet. Model predictive uncertainty measured by the coefficient of variation decreased with rainfall intensity, thus implying improved model reliability for larger rainfall events. The antecedent relative saturation was the most sensitive parameter in all but the peak arrival times, followed by the overland plane roughness coefficient. Among the sediment related parameters, the median particle size and hydraulic erosion parameters dominated sediment model output uncertainty and sensitivity. Effect of rain splash erosion coefficient was negligible. Comparison of medians from MC simulations and simulations by direct substitution of average parameters with observed flow rates and sediment discharges indicates that KINEROS2 can be applied to ungauged watersheds and still produce runoff and sediment yield predictions within order of magnitude of accuracy.  相似文献   

Sensitivity analysis of prior model probabilities and the value of prior knowledge in the assessment of conceptual model uncertainty in groundwater modelling     

Rodrigo Rojas  Luc Feyen  Alain Dassargues 《水文研究》2009,23(8):1131-1146

A key point in the application of multi‐model Bayesian averaging techniques to assess the predictive uncertainty in groundwater modelling applications is the definition of prior model probabilities, which reflect the prior perception about the plausibility of alternative models. In this work the influence of prior knowledge and prior model probabilities on posterior model probabilities, multi‐model predictions, and conceptual model uncertainty estimations is analysed. The sensitivity to prior model probabilities is assessed using an extensive numerical analysis in which the prior probability space of a set of plausible conceptualizations is discretized to obtain a large ensemble of possible combinations of prior model probabilities. Additionally, the value of prior knowledge about alternative models in reducing conceptual model uncertainty is assessed by considering three example knowledge states, expressed as quantitative relations among the alternative models. A constrained maximum entropy approach is used to find the set of prior model probabilities that correspond to the different prior knowledge states. For illustrative purposes, a three‐dimensional hypothetical setup approximated by seven alternative conceptual models is employed. Results show that posterior model probabilities, leading moments of the predictive distributions and estimations of conceptual model uncertainty are very sensitive to prior model probabilities, indicating the relevance of selecting proper prior probabilities. Additionally, including proper prior knowledge improves the predictive performance of the multi‐model approach, expressed by reductions of the multi‐model prediction variances by up to 60% compared with a non‐informative case. However, the ratio between‐model to total variance does not substantially decrease. This suggests that the contribution of conceptual model uncertainty to the total variance cannot be further reduced based only on prior knowledge about the plausibility of alternative models. These results advocate including proper prior knowledge about alternative conceptualizations in combination with extra conditioning data to further reduce conceptual model uncertainty in groundwater modelling predictions. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

Observational uncertainties in hypothesis testing: investigating the hydrological functioning of a tropical catchment          下载免费PDF全文

Ida K. Westerberg  Christian Birkel 《水文研究》2015,29(23):4863-4879

Hypothesis testing about catchment functioning with conceptual hydrological models is affected by uncertainties in the model representation of reality as well as in the observed data used to drive and evaluate the model. We formulated a learning framework to investigate the role of observational uncertainties in hypothesis testing using conceptual models and applied it to the relatively data‐scarce tropical Sarapiqui catchment in Costa Rica. Observational uncertainties were accounted for throughout the framework that incorporated different choices of model structures to test process hypotheses, analyses of parametric uncertainties and effects of likelihood choice, a posterior performance analysis and (iteratively) formulation of new hypotheses. Estimated uncertainties in precipitation and discharge were linked to likely non‐linear near‐surface runoff generation and the potentially important role of soils in mediating the hydrological response. Some model‐structural inadequacies could be identified in the posterior analyses (supporting the need for an explicit soil‐moisture routine to match streamflow dynamics), but the available information about the observational uncertainties prevented conclusions about other process representations. The importance of epistemic data errors, the difficulty in quantifying them and their effect on model simulations was illustrated by an inconsistent event with long‐term effects. Finally we discuss the need for new data, new process hypotheses related to deep groundwater losses, and conclude that observational uncertainties need to be accounted for in hypothesis testing to reduce the risk of drawing incorrect conclusions. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

Identification of rainfall–runoff models for operational applications / Identification de modèles pluie–débit pour des applications opérationnelles     

《水文科学杂志》2013,58(5)

Abstract

The problem of selecting appropriate objective functions for the identification of a lumped conceptual rainfall–runoff model is investigated, focusing on the value of the model in an operational setting. A probability-distributed soil moisture model is coupled with a linear parallel routing scheme, and conditioned on rainfall–runoff observations from three catchments in the southeast of England. Using an abstraction control problem, which requires accurate simulation of the intermediate flow range, it is shown that using the traditional RMSE fit criterion, produces operationally sub-optimal predictions. This is true in the identification period, when applied to a testing period, and to proxy catchment data. Using a second case study of the Leaf River in Mississippi (USA), where the focus changes to predicting flood peaks over a specified threshold, also suggests that the relevant flood threshold should govern the objective function choice. It is concluded that, due to limitations in the structure of the employed model, it would be counter-productive to try to achieve a good all-round representation of the rainfall–runoff processes, and that a more empirical approach to identification may be preferred for specific forecasting problems. This leaves us with the question of how far hydrological realism should be sacrificed in favour of purpose-driven objective functions.  相似文献