From marginal to conditional probability functions of parameters in a conceptual rainfall–runoff model: an event-based approach

ABSTRACT

A parameter estimation strategy for a conceptual rainfall–runoff (CRR) model applied to a storm sewer system in an urban catchment (Chassieu, Lyon, France) is proposed on the basis of event-by-event Bayesian local calibrations. The marginal distribution formed by locally-estimated parameters is divided into conditional functions, clustering the event-based parameters based on their transferability to similar rainfall events. The conditional functions showed to be consistent with an observed bimodality in the marginal representation, reflecting two different hydrological conditions mainly related to the magnitude of the rainfall intensities (high or low). The improvements achieved by expressing the parameter probability functions into a conditional form are shown in terms of accuracy (Nash-Sutcliffe efficiency criterion), precision (average relative interval length) and reliability (percentage of coverage) for simulating flow rate in 255 and 110 calibration/verification events.     

A Bayesian method for missing rainfall estimation using a conceptual rainfall–runoff model

The estimation of missing rainfall data is an important problem for data analysis and modelling studies in hydrology. This paper develops a Bayesian method to address missing rainfall estimation from runoff measurements based on a pre-calibrated conceptual rainfall–runoff model. The Bayesian method assigns posterior probability of rainfall estimates proportional to the likelihood function of measured runoff flows and prior rainfall information, which is presented by uniform distributions in the absence of rainfall data. The likelihood function of measured runoff can be determined via the test of different residual error models in the calibration phase. The application of this method to a French urban catchment indicates that the proposed Bayesian method is able to assess missing rainfall and its uncertainty based only on runoff measurements, which provides an alternative to the reverse model for missing rainfall estimates.     

A process-based,distributed hydrologic model based on a large-scale method for surface–subsurface coupling

Process-based watershed models are useful tools for understanding the impacts of natural and anthropogenic influences on water resources and for predicting water and solute fluxes exported from watersheds to receiving water bodies. The applicability of process-based hydrologic models has been previously limited to small catchments and short time frames. Computational demands, especially the solution to the three-dimensional subsurface flow domain, continue to pose significant constraints. This paper documents the mathematical development, numerical testing and the initial application of a new distributed hydrologic model PAWS (Process-based Adaptive Watershed Simulator). The model solves the governing equations for the major hydrologic processes efficiently so that large scale applications become relevant. PAWS evaluates the integrated hydrologic response of the surface–subsurface system using a novel non-iterative method that couples runoff and groundwater flow to vadose zone processes approximating the 3D Richards equation. The method is computationally efficient and produces physically consistent solutions. All flow components have been independently verified using analytical solutions and experimental data where applicable. The model is applied to a medium-sized watershed in Michigan (1169 km²) achieving high performance metrics in terms of streamflow prediction at two gages during the calibration and verification periods. PAWS uses public databases as input and possesses full capability to interact with GIS datasets. Future papers will describe applications to other watersheds and the development and application of fate and transport modules.     

Impact of different sea surface roughness on surface gravity waves using a coupled atmosphere–wave model: a case of Hurricane Isaac (2012)

Ocean Dynamics - The complicated pattern of the chaotic ocean surface depends strongly on the interaction between wind and waves. An accurate representation of momentum and energy exchange at...     

An advanced process for evaluating a linear dielectric constant–bulk electrical conductivity model using a capacitance sensor in field conditions

Abstract

The Hilhorst model was used to convert bulk electrical conductivity (σ_b) to pore water electrical conductivity (σ_p) under laboratory conditions by using the linear relationship between the soil dielectric constant (ε_b) and σ_b. In the present study, applying the linear relationship ε_b–σ_b to data obtained from field capacitance sensors resulted in strong positive autocorrelations between the residuals of that regression. We were able to derive an accurate offset of the relationship ε_b–σ_b and to estimate the evolution of σ_p over time by including a stochastic component to the linear model, rearranging it to a time-varying dynamic linear model (DLM), and using Kalman filtering and smoothing. The offset proved to vary for each depth in the same soil profile. A reason for this might be the changes in soil temperature along the soil profile.

Editor D. Koutsoyiannis; Associate editor M.D. Fidelibus     

Simulating temporal variability in catchment response using a monthly rainfall–runoff model

Abstract

Temporal variability can result from shifts in climate, or from changes in the runoff response due to land- or water-use changes, and represents a potential source of uncertainty in calibrating hydrological models. Parameter values were determined using Monte Carlo parameter sampling methods for a monthly rainfall–runoff model (Pitman model) for different sub-periods on four catchments, with different types and degrees of temporal variability, in Australia and Africa. For some catchments, parameters were not dependent upon the sub-period used and fell within expected ranges given the relatively high degree of model equifinality. In other catchments, dependencies can be identified that are associated with signals contained within the sub-periods. While the Pitman model is relatively robust in the face of temporal variability, it is concluded that better simulations will always be obtained from calibration data that include signals representing the total variability in climate, land-use change and catchment responses.     

Modelling of fluid–solid interactions using an adaptive mesh fluid model coupled with a combined finite–discrete element model

Fluid–structure interactions are modelled by coupling the finite element fluid/ocean model ‘Fluidity-ICOM’ with a combined finite–discrete element solid model ‘Y3D’. Because separate meshes are used for the fluids and solids, the present method is flexible in terms of discretisation schemes used for each material. Also, it can tackle multiple solids impacting on one another, without having ill-posed problems in the resolution of the fluid’s equations. Importantly, the proposed approach ensures that Newton’s third law is satisfied at the discrete level. This is done by first computing the action–reaction force on a supermesh, i.e. a function superspace of the fluid and solid meshes, and then projecting it to both meshes to use it as a source term in the fluid and solid equations. This paper demonstrates the properties of spatial conservation and accuracy of the method for a sphere immersed in a fluid, with prescribed fluid and solid velocities. While spatial conservation is shown to be independent of the mesh resolutions, accuracy requires fine resolutions in both fluid and solid meshes. It is further highlighted that unstructured meshes adapted to the solid concentration field reduce the numerical errors, in comparison with uniformly structured meshes with the same number of elements. The method is verified on flow past a falling sphere. Its potential for ocean applications is further shown through the simulation of vortex-induced vibrations of two cylinders and the flow past two flexible fibres.

     

From rainfall to spring discharge: Coupling conduit flow,subsurface matrix flow and surface flow in karst systems using a discrete–continuum model

Physics-based distributed models for simulating flow in karst systems are generally based on the discrete–continuum approach in which the flow in the three-dimensional fractured limestone matrix continuum is coupled with the flow in discrete one-dimensional conduits. In this study we present a newly designed discrete–continuum model for simulating flow in karst systems. We use a flexible spatial discretization such that complicated conduit networks can be incorporated. Turbulent conduit flow and turbulent surface flow are described by the diffusion wave equation whereas laminar variably saturated flow in the matrix is described by the Richards equation. Transients between free-surface and pressurized conduit flow are handled by changing the capacity term of the conduit flow equation. This new approach has the advantage that the transients in mixed conduit flow regimes can be handled without the Preissmann slot approach. Conduit–matrix coupling is based on the Peaceman’s well-index such that simulated exchange fluxes across the conduit–matrix interface are less sensitive to the spatial discretization. Coupling with the surface flow domain is based on numerical techniques commonly used in surface–subsurface models and storm water drainage models. Robust algorithms are used to simulate the non-linear flow processes in a coupled fashion. The model is verified and illustrated with simulation examples.     

A model for the seismic analysis of a periodic viaduct when considering the pile–soil–structure interaction

In this study, a new model is developed for the aseismic design of a periodic viaduct when the pile–soil–structure interaction is considered. To account for the influence of the pile–soil–structure interaction, a wavenumber domain boundary element method (WDBEM) model for the periodic pile row supporting the viaduct is developed using the sequence Fourier transform as well as the boundary element method for the elastic medium. By using the WDBEM model for the pile row, the transfer matrices for the beams and piers, the joint conditions at the beam–beam–pier (BBP) junction as well as the periodicity condition for the viaduct, the wavenumber domain response of the periodic viaduct to spatially harmonic waves is determined. Based on the wavenumber domain response of the viaduct, the space-domain response of the viaduct to an arbitrary seismic wave can be obtained by invoking the inverse sequence Fourier transform method. Numerical results show that when the periodic viaduct is exposed to the spatially harmonic wave, resonances may occur at the bounding frequencies of the passbands of the characteristic waves of the viaduct. Also, it is found that the coincidence between the traveling seismic wave and characteristic waves of the viaduct will generate additional resonant frequencies located in passbands of the characteristic waves.     

Development of a predictive model for <Emphasis Type="Italic">Clostridium difficile</Emphasis> infection incidence in hospitals using Gaussian mixture model and Dempster–Shafer theory

Clostridium difficile infection is one of the major patient safety concerns in hospitals worldwide. Clostridium difficile infection can have high economic burden to patients, hospitals, and government. Limited work has been done in the area of predictive modeling. In this article, A new predictive model based on Gaussian mixture model and Dempster–Shafter theory is proposed to predict Clostridium difficile infection incidence in hospitals. First, the Gaussian mixture model and expectation–maximization algorithms are used to generate explicit probability criteria of risk factors based on the given data. Second, Dempster–Shafter theory is used to predict the Clostridium difficile infection incidence based on the generated probability criteria that have different beliefs attributing to their different credits. The main procedure includes (1) generate the probability criteria model using Gaussian mixture model and expectation–maximization algorithm; (2) determine the credit of the probability criteria; (3) generate the basic probability assignment; (4) discount the evidences; (5) aggregate the evidences using Dempster combining rule; (6) predict Clostridium difficile infection incidence using pignistic probability transformation. Results show that the model has a higher accuracy than an existing model. The proposed model can generate the criteria ratings of risk factors automatically, which would potentially prevent the imprecision caused by the subjective judgement of experts. The proposed model can assist risk managers and hospital administrators in the prediction and control of Clostridium difficile infection incidence with optimizing their resources.     

A potential experiment for in-situ measurement of atmospheric temperature and atomic oxygen density in the 90–150 km altitude range by a Raman Lidar

The Raman backscatter cross sections for a 355 nm light source for the three fine-structure components are calculated. The signal-to-noise considerations show that the determination of the densities of the three fine-structure components separately is a feasible experiment. Since these fine-structure components are calculated to be in local thermodynamic equilibrium up to at least 350 km altitude, this experiment also gives atmospheric temperature. It is pointed out that this experiment does not suffer from the drawbacks of the previous efforts to determine atomic oxygen density and should yield reliable results for this density as well as temperature.     

The construction of a reliable absolute chronology for the last two millennia in an anthropogenically disturbed peat bog: Limitations and advantages of using a radio-isotopic proxy and age–depth modelling

The main aim of this paper is to present the pitfalls connected with the construction of reliable chronologies for anthropogenically disturbed peatlands over the last two millennia based on ²¹⁰Pb and ¹⁴C dating, i.e. the period of the strongest human impact on these ecosystems. The following hypotheses have been formulated: i) parts of peatlands suspected to be affected by peat extraction may possess traces of mechanical disturbances undetectable using different analyses based on biota proxy; ii) failure to consider information included in radionuclide date inversions may contribute to the establishment of misleading chronologies. To test these hypotheses, different scenarios of chronology based on high resolution ²¹⁰Pb and ¹⁴C dating from a peat core retrieved from the Puścizna Krauszowska bog (southern Poland) have been analysed. Nowadays, this mire is intensively exploited by humans; however, it contains remains of dome considered undisturbed, from which the core presented in this paper was collected. The set of dates revealed the presence of marked ¹⁴C date inversions (mechanical disturbances) which, if inappropriately interpreted before the age–depth modelling process, may lead to the establishment of misleading chronologies, and thus an incorrect interpretation of biota proxy records, e.g. pollen. Those sections of peat profiles with prominent age inversions and/or strong discrepancies between the peat accumulation rate and bulk density should be rejected from age–depth modelling, even if interpretable chronologies can be obtained.     

Estimation of extreme floods of the River Meuse using a stochastic weather generator and a rainfall–runoff model / Estimation des crues extrêmes de la Meuse à l'aide d'un générateur stochastique de variables météorologiques et d'un modèle pluie–débit

Abstract

A stochastic weather generator has been developed to simulate long daily sequences of areal rainfall and station temperature for the Belgian and French sub-basins of the River Meuse. The weather generator is based on the principle of nearest-neighbour resampling. In this method rainfall and temperature data are sampled simultaneously from multiple historical records with replacement such that the temporal and spatial correlations are well preserved. Particular emphasis is given to the use of a small number of long station records in the resampling algorithm. The distribution of the 10-day winter maxima of basin-average rainfall is quite well reproduced. The generated sequences were used as input for hydrological simulations with the semi-distributed HBV rainfall–runoff model. Though this model is capable of reproducing the flood peaks of December 1993 and January 1995, it tends to underestimate the less extreme daily peak discharges. This underestimation does not show up in the 10-day average discharges. The hydrological simulations with the generated daily rainfall and temperature data reproduce the distribution of the winter maxima of the 10-day average discharges well. Resampling based on long station records leads to lower rainfall and discharge extremes than resampling from the data over a shorter period for which areal rainfall was available.