Rainfall–interception–evaporation–runoff relationships in a semi-arid catchment,northern Limpopo basin,Zimbabwe

Abstract

Two types of monthly water balance models at basin scale are used: PE models use precipitation and potential evapotranspiration (PET) as their observed input data, whereas P models need only precipitation. Calibration proceeds by comparing model runoff and observed runoff. Calibration is entirely automatic with the exclusion of subjective elements. All models differ only by their actual evapotranspiration equations. PE models from previous papers are generalized essentially by replacing the constant evapotranspiration parameter by a periodic one, thus increasing the number of parameters by two (a “parameter” is an unknown constant to be estimated, and which is a characteristic of the river basin to be described). P models use a periodic “driving force”, which is intended to represent periodicity of hydrological phenomena, normally originating in the (unavailable) PET time series. These eight PE models and three P models are then applied to 55 river basins in 10 countries with widely diverging climates and soil conditions. A marked improvement of model performance in about one third of the basins is due to the introduction of the above mentioned periodic functions. Even when PET data are available it is sometimes useful to consider P models. P models scarcely perform less well than PE models. An engineer, wanting to try out as few models as possible on a given river basin, can restrict his attention to the optimization of two or three models. The paper is an extension of a long effort towards monthly water balance models, and is believed to give a solution in most circumstances.     

UNCERTAIN RESISTIVITY SOUNDING AND EQUIVALENT MODELS*

Problems of the resistivity sounding method may be reparametrized by means of Dar Zarrouk—or generalized D.Z.—transformations. A complete set of equivalent models is subdivided into three classes: initial models, intermediate models, and all other models. Equivalent reparametrized models are subdivided into three analogous classes. There is no one-to-one mapping of intermediate primary and intermediate reparametrized models. Non-trivial equivalent models are generated from initial models via intermediate reparametrized models. A method is developed to find equivalent models relative to the uncertainty of resistivity soundings. There are different forms of approximate equivalence laws, apart from those commonly accepted. The equivalence depends considerably on the “flatness” of the sounding curve. For mean flatness, the electric models exhibit the (hp^±2)-equivalence rather than the (hp^±1)-equivalence.     

Finite volume model for reactive transport in fractured porous media with distance- and time-dependent dispersion

Abstract

There are very few studies of fractured porous media that use distance- and time-dependent dispersion models, and, to the best of our knowledge, none which compare these with constant dispersion models. Therefore, in this study, the behaviour of temporal and spatial concentration profiles with distance- and time-dependent dispersion models is investigated. A hybrid finite volume method is used to solve the governing equations for these dispersion models. The developed numerical model is used to study the effects of matrix diffusion coefficient, groundwater velocity and matrix and fracture retardation factor on concentration profiles in the application of constant, distance-dependent and time-dependent dispersion models. In addition, an attempt is made to evaluate the applicability of these dispersion models by using the models to simulate experimental data. It was found that a better fit to the observed data is obtained in the case of distance- and time-dependent dispersion models as compared to the constant dispersion model. Thus, these numerical experiments indicate that distance- and time-dependent dispersion models have better simulation potential than the constant dispersion model.     

How valid are dynamic models of subduction and convection when plate motions are prescribed?

A detailed comparison between fully dynamic and kinematic plate formulations has been made in models of mantle convection. Plate velocity is computed self-consistently from fully dynamic plate models with temperature- and stress-dependent viscosity and preexisting mobile faults. In fully dynamic models, the flow is driven solely by internal buoyancy, while in kinematic models the flow is driven by a combination of the prescribed surface velocity and internal buoyancy. Only a temperature-dependent viscosity, close to the effective viscosity determined from the fully dynamic models, is used in the kinematic models. The two types of models give very similar temperature structures and slab evolutionary histories when the effective viscosity and surface velocity are nearly identical. In kinematic plate models, the additional work introduced by the prescribed velocity boundary condition is apparently dissipated within the lithosphere and has little influence on the convection under the lithosphere. In models with periodic lateral boundary conditions, slabs sink into the lower mantle at an oblique angle and this contrasts with the vertical sinking which occurs with reflecting boundary conditions. Models show that we can simulate fully dynamic models with kinematic models under either periodic boundary conditions or reflecting boundary conditions.     

Suspended sediment yield estimation using genetic algorithm-based artificial intelligence models: case study of Mahanadi River,India

The estimation of sediment yield is important in design, planning and management of river systems. Unfortunately, its accurate estimation using traditional methods is difficult as it involves various complex processes and variables. This investigation deals with a hybrid approach which comprises genetic algorithm-based artificial intelligence (GA-AI) models for the prediction of sediment yield in the Mahanadi River basin, India. Artificial neural network (ANN) and support vector machine (SVM) models are developed for sediment yield prediction, where all parameters associated with the models are optimized using genetic algorithms simultaneously. Water discharge, rainfall and temperature are used as input to develop the GA-AI models. The performance of the GA-AI models is compared to that of traditional AI models (ANN and SVM), multiple linear regression (MLR) and sediment rating curve (SRC) method for evaluating the predictive capability of the models. The results suggest that GA-AI models exhibit better performance than other models.     

半湿润流域水文模型比较与集合预报

选择7种水文模型分别在中国北部3个半湿润流域做模拟对比,分析不同水文模型在各流域的适用性,并使用贝叶斯模型平均法对不同模型集合,比较各种集合方法的优势,研究贝叶斯模型平均法的应用效果.研究结果表明,以蓄满产流模式为主的模型在半湿润流域应用效果较好,针对不同流域特点对传统模型进行改进可以提高模拟精度.贝叶斯模型平均法能提供较好的确定性预报结果和概率预报结果,仅对少数模拟效果好的模型进行集合,并不能有效提高预报精度,适当增加参与集合的模型数量能使贝叶斯模型平均法更好地综合各模型优势,提高预报结果的精度.     

火山区基本压力源模型及应用

通过简要介绍火山区不同压力源模型引起的垂直形变与水平形变特征,总结不同模型引起的最大水平位移与最大垂直位移的比值R,得到膨胀模型产生的地表形变多以中心为对称,R小于0.5;腔状模型产生的地表形变多为轴对称,可得到更高的R比值;这些结论为模拟分析火山区压力源参数选用模型时提供依据.最后利用所得结果,分析了长白山天池火山可...     

分块三维速度模型生成及理论地震图的计算

本文提出了在计算机上实现分块三维地壳模型及利用加权最小二乘拟合生成平缓光滑的三维速度函数的方法,给出了适用于分块、块内速度连续变化的三维模型中Cauchy射线追踪的新算法,简介了基于上述方法反射线的基本理论所编制的合成三维理论地震图的程序包RSSGTD.给出的两个盆地状模型的算例表明,所使用的模型生成方法具有模拟复杂地壳结构的能力;与三维样条函数方法比较,最小二乘拟合方法能给出更加适合射线方法合成地震图计算的速度函数,并且内存小、计算速度快;所给出的Cauchy射线追踪算法能够适合块状模型中任何体波射线的追踪.     

A hypoplastic model for site response analysis

Site response analyses must take into account the nonlinear behavior of soils. This is typically achieved using an equivalent linear approach or using numerical analyses with an appropriate constitutive model. In this work a family of hypoplastic models is proposed for use in site response analyses. These nonlinear models use a rate-type tensorial equation and are capable of reproducing plastic deformation of soils for cyclic loading under both drained and undrained conditions. A methodology for the calibration of hypoplastic parameter for dynamic loading is proposed. The hypoplastic constitutive models are implemented in a finite element code and the site response of the Lotung downhole array site is used to validate the use of hypoplastic models for site response analysis. The hypoplastic models reproduce accurately site response at the Lotung site. The advantages and disadvantages of the hypoplastic models compared to other models are discussed.     

An alternative approach to predict human response to blast induced ground vibration

The aim of this study is to create alternative approaches to predict human response to ground vibration.Site measurements were conducted in an urban quarry.The multiple discriminant analysis technique was used to develop classification models.The measured ground vibration values were grouped based on the limit values appearing in the literature.Two classification models were created to predict human response to ground vibration.In addition,classical predictor equations were developed to compare to the classification models.The best equations were chosen considering several error measures.All the models were tested on the independent data.The prediction accuracy of the discriminant analysis models is quite promising.Nearly eighty-nine percent of the test data were correctly classified.The classification models have a combined structure.The results of the models can be directly presented to the neighbors of the studied quarry.The models are not complex.They do not require additional software or information to practice and can be easily used by site engineers.     

Anisotropic viscoelastic models with singular memory

The physical background of singular memory models and in particular the Cole–Cole model is discussed. Three models of anisotropic linear viscoelasticity with frequency-dependent stiffness coefficients are considered. The models are constructed in such a way that anisotropic properties are separated from anelastic effects. Two of these models represent finite-speed wave propagation with singularities at the wavefronts (the exponential relaxation model) and without singularities at the wavefronts (the Cole–Cole model), while a third model called the fractional model is related to the constant Q with unbounded propagation speed. The Cole–Cole and fractional models belong to the class of singular memory models studied earlier because of their applications in polymer rheology, poroelasticity, poroacoustics, seismic wave propagation and other applications. Well-posedness of initial boundary value problems with mixed Dirichlet–Neumann boundary conditions is established for the three models. Regularity properties of the three models are examined.     

A class of hidden Markov models for regional average rainfall

Abstract

Basic hidden Markov models are very useful in stochastic environmental research but their ability to accommodate sufficient dependence between observations is somewhat limited. However, they can be modified in several ways to form a rich class of flexible models that are useful in many environmental applications. We consider a class of hidden Markov models that incorporate additional dependence among observations to model average regional rainfall time series. The focus of the study is on models that introduce additional dependence between the state level and the observation level of the process and also on models that incorporate dependence at observation level. Construction of the likelihood function of the models is described along with the usual second-order properties of the process. The maximum likelihood method is used to estimate the parameters of the models. Application of the proposed class of models is illustrated in an analysis of daily regional average rainfall time series from southeast and southwest England for the winter season during 1931 to 2010. Models incorporating additional dependence between the state level and the observation level of the process captured the distributional properties of the daily rainfall well, while the models that incorporate dependence at the observation level showed their ability to reproduce the autocorrelation structure. Changes in some of the regional rainfall properties during the time period are also studied.

Editor D. Koutsoyiannis     

Understanding the role of hydrologic model structures on evapotranspiration-driven sensitivity

ABSTRACT

Most conceptual hydrological models do not treat vegetation as a dynamic component. This study focuses on understanding the impact of model structural complexity on the sensitivity of hydrologic models to potential evapotranspiration forcing data. To achieve this, two classes of hydrologic models are examined: (1) lumped, conceptual rainfall–runoff models and (2) eco-hydrologic models. A sample of 57 US catchments, covering eight eco-regions, included in the MOPEX dataset is used. While streamflow simulation performance in complex models did not exhibit increased sensitivity to PET, actual evapotranspiration simulation performance showed greater sensitivity in energy-limited catchments. This analysis warns against using over-simplistic PET estimations in energy-limited catchments for eco-hydrologic models and for more complex conceptual hydrologic models. This is particularly true for streamflow-only calibrations that commonly fail to properly constrain physically based parameters. Ultimately, these results have the potential to inform data collection and model selection efforts to yield the greatest benefit.     

A non-linear and non-stationary perspective for downscaling mean monthly temperature: a wavelet coupled second order Volterra model

This study presents a multiscale framework for downscaling of the General Circulation Model (GCM) outputs to the mean monthly temperature at regional scale using a wavelet based Second order Voltera (SoV) model. The models are developed using the reanalysis climatic data from the National Centers for Environmental Prediction (NCEP) and are validated using the simulated climatic dataset from the Can CM4 GCM for five locations in the Krishna river basin, India. K-means clustering, based on the multiscale wavelet entropy of the predictors, is used for obtaining the clusters of the input climatic variables. Principal component analysis (PCA) is used to obtain the representative variables from each cluster. These input variables are then used to develop a wavelet based multiscale model using Second order Volterra approach to simulate observed mean monthly temperature for the selected locations in the basin. These models are called W-P-SoV models in this paper. For the purpose of comparison, linear multi-resolution models are developed using Multiple Linear regression (MLR) and are called W-P MLR models. The performance of the models is further compared with other Wavelet-PCA based models coupled with Multiple linear regression models (P-MLR) and Artificial Neural Networks (P-ANN), and, stand-alone MLR and ANN to establish the superiority of the proposed approach. The results indicate that the performance of the wavelet based models is superior in terms of downscaling accuracy when compared with the other models used.     

The practical use of simplicity in developing ground water models

The advantages of starting with simple models and building complexity slowly can be significant in the development of ground water models. In many circumstances, simpler models are characterized by fewer defined parameters and shorter execution times. In this work, the number of parameters is used as the primary measure of simplicity and complexity; the advantages of shorter execution times also are considered. The ideas are presented in the context of constructing ground water models but are applicable to many fields. Simplicity first is put in perspective as part of the entire modeling process using 14 guidelines for effective model calibration. It is noted that neither very simple nor very complex models generally produce the most accurate predictions and that determining the appropriate level of complexity is an ill-defined process. It is suggested that a thorough evaluation of observation errors is essential to model development. Finally, specific ways are discussed to design useful ground water models that have fewer parameters and shorter execution times.     

Elemental damping formulation: an alternative modelling of inherent damping in nonlinear dynamic analysis

To date, nonlinear dynamic analysis for seismic engineering predominantly employs the classical Rayleigh damping model and its variations. Though earlier studies have identified issues with the use of this model in nonlinear seismic analysis, it still remains the popular choice for engineers as well as for software providers. In this paper a new approach to modelling damping is initiated by formulating the damping matrix at an elemental level. To this regard, two new elemental level discrete damping models adapted from their global counterparts are proposed for its application in nonlinear dynamic analysis. Implementation schemes for these newly proposed models using Newmark incremental method and revised Newmark total equilibrium method is outlined. The performance of these proposed models, compared to existing models, is illustrated by conducting nonlinear dynamic analyses on a four story RC frame designed to Eurocodes. The incremental dynamic analysis study presented in the paper illustrates the fact that both the proposed models seem to produce more reliable results from an engineering perspective in comparison to the global models. It is also shown that the proposed elemental damping models lead to smaller and more realistic damping moments in the plastic hinges. Furthermore, these models could be easily included in existing software frameworks without adding noticeably to the computational effort. The computation time required for these models is approximately equivalent to the one required when using the tangent Rayleigh damping matrix with constant coefficients.     

Petrological models of the oceanic lithosphere: Geophysical and geochemical tests

Petrological models of the oceanic lithosphere are tested to satisfy geophysical and geochemical constraints within the framework of plate tectonics. Quartz eclogite, olivine eclogite, peridotite and dunite are considered as the material of the lithosphere. The temperature at the base of the lithosphere is assumed to be the solidus temperature. This temperature, the thermal conductivity, and the heat flow and topography changes with age are used as the geophysical constraints. The compressional wave velocity-depth profile is used to select preferred models. Among geophysically successful models, high-temperature models are preferred to wet low-temperature models, because the low-temperature models have difficulties in explaining the mechanism of generation of oceanic basalt magmas. A preferred model is a two-layer model 70 km thick consisting of peridotite at the upper lithosphere and olivine eclogite at the lower lithosphere bounded at the base by the dry solidus.     

Modelling hourly and daily open‐water evaporation rates in areas with an equatorial climate

Evaporation rate estimation is important for water resource studies. Previous studies have shown that the radiation‐based models, mass transfer models, temperature‐based models and artificial neural network (ANN) models generally perform well for areas with a temperate climate. This study evaluates the applicability of these models in estimating hourly and daily evaporation rates for an area with an equatorial climate. Unlike in temperate regions, solar radiation was found to correlate best with pan evaporation on both the hourly and daily time‐scales. Relative humidity becomes a significant factor on a daily time‐scale. Among the simplified models, only the radiation‐based models were found to be applicable for modelling the hourly and daily evaporations. ANN models are generally more accurate than the simplified models if an appropriate network architecture is selected and a sufficient number of data points are used for training the network. ANN modelling becomes more relevant when both the energy‐ and aerodynamics‐driven mechanisms dominate, as the radiation and the mass transfer models are incapable of producing reliable evaporation estimates under this circumstance. Copyright © 2006 John Wiley & Sons, Ltd.     

Arma models for earthquake ground motions

This paper outlines the use of discrete, autoregressive/moving-average (ARMA) models for identification and estimation of parameters in models derived from analysis of uniformly digitized earthquake ground motion acceleration data. Such models are of equal generality as compared to continuous-time models and have a number of significant advantages for purposes of digital analysis and simulation. The structure of ARMA models is briefly described, their relation to continuous models noted, and results of their application to a number of recorded accelerograms summarized.     

Systematic lumped‐parameter models for foundations based on polynomial‐fraction approximation

Based on the approximation by polynomial‐fraction, a series of systematic lumped‐parameter models are developed in this paper for efficiently representing the dynamic behaviour of unbounded soil. Concise formulation is first employed to represent the dynamic flexibility function of foundation with a ratio of two polynomials. By defining an appropriate quadratic error function, the optimal coefficients of the polynomials can be directly solved from a system of linear equations. Through performing partial‐fraction expansion on this polynomial‐fraction and designing two basic discrete‐element models corresponding to the partial fractions, systematic lumped‐parameter models can be conveniently established by connecting these basic units in series. Since the systematic lumped‐parameter models are configured without introducing any mass, the foundation input motion can be directly applied to these models for their applications to the analysis of seismic excitation. The effectiveness of these new models is strictly validated by successfully simulating a semi‐infinite bar on an elastic foundation. Subsequently, these models are applied for representing the dynamic stiffness functions for different types of foundation. Comparison of the new models with the other existing lumped‐parameter models is also made to illustrate their advantages in requiring fewer parameters and featuring a more systematic expansion. Copyright © 2002 John Wiley & Sons, Ltd.