Random wave-induced onshore scour characteristics around submerged breakwaters using a stochastic method

This paper provides a stochastic method by which the two-dimensional onshore scour characteristics along the base of submerged breakwaters exposed to normally incident random waves on both sloping and horizontal sandy seabed can be derived. Here the formulas for the regular wave-induced scour characteristics provided by Young and Testik (2009) are used. These formulas are combined with describing the waves as a stationary Gaussian narrow-band random process to derive the random wave-induced onshore scour characteristics; the maximum scour depth, the scour length, and the distance of the maximum scour depth location from the onshore breakwater face. An example of calculation is also provided.     

A validation concept for cohesive sediment transport model and application on Lianyungang Harbor,China

Morphology evolution and hydro-sedimentological interactions in muddy coastal environments are long term processes. These processes are closely related to suspended sediment transport driven by waves and tidal currents. In the traditional calibration/verification methodology for cohesive sediment transport models, time-series data of suspended sediment concentration (SSC) measured during one or several tidal periods are commonly used as major validation references. But the disadvantage of this approach includes that it cannot filter the noises caused by the stochastic nature of short term hydrodynamics induced by waves and the varying properties of bottom sediments; besides a phase-lag phenomenon is often observed between sediment transport and hydrodynamics in the short term. On the contrary, a stable relationship between sedimentation and hydrodynamics is usually found in the long term. For a specific weather, the SSC values often agree well with local wave heights for muddy coasts. Therefore, in this paper a conceptual quantity defined as “representative SSC” was brought forward, and a model validation concept, including calibration and verification approaches, was proposed, in which calibration is performed against yearly-representative SSC values and the long term transport trend, and verification by using representative SSC values corresponding to different wave conditions. A numerical simulation was set up, and a real-life engineering application, Lianyungang Harbor, China, was executed to elaborate the proposed validation concept. Finally, the characteristics of SSC distribution around Lianyungang Harbor were discussed.     

基于等效框架模型的砌体结构抗震模拟及验证

等效框架模型采用宏观模型来模拟砌体墙在平面内的抗震性能。砌体墙的墙柱和墙梁采用同时考虑轴向弯曲和剪切变形的基于力法的纤维截面进行模拟,且两者的连接视为刚性区域。轴向压缩及弯曲效应在截面纤维模型中考虑,而剪切效应由V-γ剪切恢复力模型表达,弯曲和剪切在单元层面进行耦合。通过统计和分析,确定骨架曲线的计算方法,并基于Ibarra-Krawinkler模型提出剪切恢复力模型。通过算例得出:该模型在单调加载和循环加载下的数值计算结果与试验结果均吻合较好。     

Burial and scour of short cylinders under combined random waves and currents including effects of second order wave asymmetry

This paper provides an approach by which the burial and scour of short cylinders under combined second order random waves and currents can be derived. Here the formulas for burial and scour for regular waves plus currents presented by Catano-Lopera and Garcia [Catano-Lopera, Y.A. and Garcia, M.H. (2006). Burial of short cylinders induced by scour under combined waves and currents. ASCE J. Waterway, Port, Coastal and Ocean Eng. 132(6), 439–449., Catano-Lopera, Y.A. and Garcia, M.H. (2007). Geometry of scour hole around, and the influence of the angle of attack on the burial of finite cylinders under combined flows. Ocean Eng. 34(5, 6), 856–869.] are used together with Stokes second order wave theory by assuming the basic harmonic wave motion to be a stationary Gaussian narrow-band random process. An example of calculation is also presented.     

Comparative study of multilayer perceptron-stochastic gradient descent and gradient boosted trees for predicting daily suspended sediment load: The case study of the Mississippi River,U.S.

Monitoring sediment transport is essential for managing and maintaining rivers.Estimation of the sediment load in rivers is fundamental for the study of sediment movement,erosion,and flood control.In the current study,three machine learning models-multi-layer perceptron(MLP),multi-layer perceptron-stochastic gradient descent(MLP-SGD),and gradient boosted tree(GBT)-were utilized to estimate the suspended sediment load(SSL)at the St.Louis(SL)and Chester(CH)stations on the Mississippi River,U.S.Four evaluation criteria including the Correlation Coefficient(CC),Nash Sutcliffe Efficiency(NSE),Scatter Index(SI),and Willmott’s Index(WI)were utilized to evaluate the performance of the used models.A sensitivity analysis of the models to the input variables revealed that the current day discharge variable had the most effect on the SSL at both stations,but in the absence of current-day discharge data(Qt),a combination of input parameters including SSL_t-3,SSL_t-2,SSL_t-1,Q_t-3,Q_t-2,Q_t-1 can be used to estimate the SSL.The comparative outcomes indicated the high accuracy of MLP-SGD-5 model with a CC of 0.983,SI of 0.254,WI of 0.991,and NSE of 0.967 at station CH and the MLP-SGD-6 model with a CC of 0.933,SI of 0.576,WI of 0.961,and NSE of 0.867,respectively,at station SL.The results of MLP models were improved by SGD optimization.Therefore,the MLP-SGD method is recommended as the most accurate model for SSL estimation.     

The geodynamo as a bistable oscillator

Abstract

Our intent is to provide a simple and quantitative understanding of the variability of the axial dipole component of the geomagnetic field on both short and long time scales. To this end we study the statistical properties of a prototype nonlinear mean field model. An azimuthal average is employed, so that (1) we address only the axisymmetric component of the field, and (2) the dynamo parameters have a random component that fluctuates on the (fast) eddy turnover time scale. Numerical solutions with a rapidly fluctuating α reproduce several features of the geomagnetic field: (1) a variable, dominantly dipolar field with additional fine structure due to excited overtones, and sudden reversals during which the field becomes almost quadrupolar, (2) aborted reversals and excursions, (3) intervals between reversals having a Poisson distribution. These properties are robust, and appear regardless of the type of nonlinearity and the model parameters. A technique is presented for analysing the statistical properties of dynamo models of this type. The Fokker-Planck equation for the amplitude a of the fundamental dipole mode shows that a behaves as the position of a heavily damped particle in a bistable potential ∝(1 ? a ²)², subject to random forcing. The dipole amplitude oscillates near the bottom of one well and makes occasional jumps to the other. These reversals are induced solely by the overtones. Theoretical expressions are derived for the statistical distribution of the dipole amplitude, the variance of the dipole amplitude between reversals, and the mean reversal rate. The model explains why the reversal rate increases with increasing secular variation, as observed. Moreover, the present reversal rate of the geodynamo, once per (2?3) × 10⁵ year, is shown to imply a secular variation of the axial dipole moment of ～ 15% (about the current value). The theoretical dipole amplitude distribution agrees well with the Sint-800 data.     

Building more realistic reservoir optimization models using data mining - A case study of Shelbyville Reservoir

In this paper, we promote a novel approach to develop reservoir operation routines by learning from historical hydrologic information and reservoir operations. The proposed framework involves a knowledge discovery step to learn the real drivers of reservoir decision making and to subsequently build a more realistic (enhanced) model formulation using stochastic dynamic programming (SDP). The enhanced SDP model is compared to two classic SDP formulations using Lake Shelbyville, a reservoir on the Kaskaskia River in Illinois, as a case study. From a data mining procedure with monthly data, the past month’s inflow (Q_t−1), current month’s inflow (Q_t), past month’s release (R_t−1), and past month’s Palmer drought severity index (PDSI_t−1) are identified as important state variables in the enhanced SDP model for Shelbyville Reservoir. When compared to a weekly enhanced SDP model of the same case study, a different set of state variables and constraints are extracted. Thus different time scales for the model require different information. We demonstrate that adding additional state variables improves the solution by shifting the Pareto front as expected while using new constraints and the correct objective function can significantly reduce the difference between derived policies and historical practices. The study indicates that the monthly enhanced SDP model resembles historical records more closely and yet provides lower expected average annual costs than either of the two classic formulations (25.4% and 4.5% reductions, respectively). The weekly enhanced SDP model is compared to the monthly enhanced SDP, and it shows that acquiring the correct temporal scale is crucial to model reservoir operation for particular objectives.     

A stochastic model describing the impact of daily rainfall depth distribution on the soil water balance

Simplified, vertically-averaged soil moisture models have been widely used to describe and study eco-hydrological processes in water-limited ecosystems. The principal aim of these models is to understand how the main physical and biological processes linking soil, vegetation, and climate impact on the statistical properties of soil moisture. A key component of these models is the stochastic nature of daily rainfall, which is mathematically described as a compound Poisson process with daily rainfall amounts drawn from an exponential distribution. Since measurements show that the exponential distribution is often not the best candidate to fit daily rainfall, we compare the soil moisture probability density functions obtained from a soil water balance model with daily rainfall depths assumed to be distributed as exponential, mixed-exponential, and gamma. This model with different daily rainfall distributions is applied to a catchment in New South Wales, Australia, in order to show that the estimation of the seasonal statistics of soil moisture might be improved when using the distribution that better fits daily rainfall data. This study also shows that the choice of the daily rainfall distributions might considerably affect the estimation of vegetation water-stress, leakage and runoff occurrence, and the whole water balance.     

Simulation of future climate scenarios with a weather generator

Numerous studies across multiple disciplines search for insights on the effects of climate change at local spatial scales and at fine time resolutions. This study presents an overall methodology of using a weather generator for downscaling an ensemble of climate model outputs. The downscaled predictions can explicitly include climate model uncertainty, which offers valuable information for making probabilistic inferences about climate impacts. The hourly weather generator that serves as the downscaling tool is briefly presented. The generator is designed to reproduce a set of meteorological variables that can serve as input to hydrological, ecological, geomorphological, and agricultural models. The generator is capable of reproducing a wide set of climate statistics over a range of temporal scales, from extremes, to low-frequency interannual variability; its performance for many climate variables and their statistics over different aggregation periods is highly satisfactory. The use of the weather generator in simulations of future climate scenarios, as inferred from climate models, is described in detail. Using a previously developed methodology based on a Bayesian approach, the stochastic downscaling procedure derives the frequency distribution functions of factors of change for several climate statistics from a multi-model ensemble of outputs of General Circulation Models. The factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. Using embedded causal and statistical relationships, the generator simulates future realizations of climate for a specific point location at the hourly scale. Uncertainties present in the climate model realizations and the multi-model ensemble predictions are discussed. An application of the weather generator in reproducing present (1961-2000) and forecasting future (2081-2100) climate conditions is illustrated for the location of Tucson (AZ). The stochastic downscaling is carried out using simulations of eight General Circulation Models adopted in the IPCC 4AR, A1B emission scenario.