基于人口统计数据的区域震害快速评估方法

在进行大规模城乡震害预测工作中, 需要使用与传统预测方式不同的新模型及新方法, 以便实现震害快速预测. 利用容易得到的人口统计数据中的人口及建筑抽样信息,通过建筑物分类,在已有的城市建筑震害基础上采用类比方法进行建筑物易损性分析,给出了人口数据及灾害损失的关系模型. 利用该模型建立福建省区域范围的建筑物不同结构平均易损性矩阵,按经济条件给出结构不同年代易损性矩阵调整系数,并建立地震灾害快速评估系统. 应用结果表明, 基于人口统计数据方法进行城乡区域尺度的地震震害评估模型, 具有投入少、 数据自动预测、定期更新且易于获取等优点.      

A fast approach to regional earthquake hazard evaluation based on population statistical data

In the prediction process of large-scale earthquake damage occurred in urban and rural regions,new models and approaches,which are different from traditional ones,should be adopted to rapidly predict earthquake damage. This article utilizes sampled population and buildings data that is easily available from the statistical database to conduct vulnerability analysis of buildings on the basis of earthquake damage of existing urban buildings in an analogical way,so as to provide a relation model between population data and disaster losses. In virtue of this model,the average vulnerability matrix of buildings of different structures in Fujian Province is established,the matrix adjustment coefficient of different decades is developed in accordance with the economic conditions,and the rapid evaluation system is set up as well. The result shows: this evaluation model,based on the population statistical data has merits as small investment,automatic data prediction,regular updates,as well as the advantage of easy accessibility.     

Stochastic simulation of monthly streamflow by a multiple regression model utilizing precipitation data

A model for simulation of monthly streamflow series is developed by a multiple regression approach, which includes both precipitation and flow, instead of the simple regression Markovian model, which is based on the antecedent flow alone.     

A stepwise-cluster forecasting approach for monthly streamflows based on climate teleconnections

In this study, a stepwise cluster forecasting (SCF) framework is proposed for monthly streamflow prediction in Xiangxi River, China. The developed SCF method can capture discrete and nonlinear relationships between explanatory and response variables. Cluster trees are generated through the SCF method to reflect complex relationships between independent (i.e. explanatory) and dependent (i.e. response) variables in the hydrologic system without determining specific linear/nonlinear functions. The developed SCF method is applied for monthly streamflow prediction in Xiangxi River based on the local meteorological records as well as some climate index. Comparison among SCF, multiple linear regression, generalized regression neural network, and least square support vector machine methods would be conducted. The results indicate that the SCF method would produce good predictions in both training and testing periods. Besides, the inherent probabilistic characteristics of the SCF predictions are further analyzed. The results obtained by SCF can presented as intervals, formulated by the minimum and maximum predictions as well as the 5 and 95 % percentile values of the predictions, which can reflect the variations in streamflow forecasts. Therefore, the developed SCF method can be applied for monthly streamflow prediction in various watersheds with complicated hydrologic processes.     

Simulating monthly streamflow using a hybrid feature selection approach integrated with an intelligence model

ABSTRACT

Streamflow prediction is useful for robust water resources engineering and management. This paper introduces a new methodology to generate more effective features for streamflow prediction based on the concept of “interaction effect”. The new features (input variables) are derived from the original features in a process called feature generation. It is necessary to select the most efficient input variables for the modelling process. Two feature selection methods, least absolute shrinkage and selection operator (LASSO) and particle swarm optimization-artificial neural networks (PSO-ANN), are used to select the effective features. Principal components analysis (PCA) is used to reduce the dimensions of selected features. Then, optimized support vector regression (SVR) is used for monthly streamflow prediction at the Karaj River in Iran. The proposed method provided accurate prediction results with a root mean square error (RMSE) of 2.79 m³/s and determination coefficient (R² ) of 0.92.     

Complexity measure of regional seasonal precipitation series based on wavelet entropy

The chaos characteristics for system complexity, e.g. randomness and fractality, are generally ignored when researchers study the complex behaviour of precipitation time series, which makes it difficult to elicit adequate information for such series. The main objective of this study was to diagnose the complexity of seasonal precipitation by using wavelet entropy and mean wavelet entropy, and to find the complex system of spatial variation in seasonal precipitation for the sub-areas of Jiansanjiang Administration in China as a case study. The results illustrate that the complexity characteristic of the seasonal precipitation series is greatest in the North sub-area, lowest in the Middle sub-area, and intermediate in the South sub-area; topography and agricultural development are the key driving factors of the complex dynamic variation in the local seasonal precipitation time series. This study provides a basis for analysing the trend in development of complex precipitation time series and realizing the sustainable use of regional water resources.     

Predictions in ungauged basins: an approach for regionalization of hydrological models considering the probability distribution of model parameters

Regionalization of model parameters by developing appropriate functional relationship between the parameters and basin characteristics is one of the potential approaches to employ hydrological models in ungauged basins. While this is a widely accepted procedure, the uniqueness of the watersheds and the equifinality of parameters bring lot of uncertainty in the simulations in ungauged basins. This study proposes a method of regionalization based on the probability distribution function of model parameters, which accounts the variability in the catchment characteristics. It is envisaged that the probability distribution function represents the characteristics of the model parameter, and when regionalized the earlier concerns can be addressed appropriately. The method employs probability distribution of parameters, derived from gauged basins, to regionalize by regressing them against the catchment attributes. These regional functions are used to develop the parameter characteristics in ungauged basins based on the catchment attributes. The proposed method is illustrated using soil water assessment tool model for an ungauged basin prediction. For this numerical exercise, eight different watersheds spanning across different climatic settings in the USA are considered. While all the basins considered in this study were gauged, one of them was assumed to be ungauged (pseudo-ungauged) in order to evaluate the effectiveness of the proposed methodology in ungauged basin simulation. The process was repeated by considering representative basins from different climatic and landuse scenarios as pseudo-ungauged. The results of the study indicated that the ensemble simulations in the ungauged basins were closely matching with the observed streamflow. The simulation efficiency varied between 57 and 61 % in ungauged basins. The regional function was able to generate the parameter characteristics that were closely matching with the original probability distribution derived from observed streamflow data.     

A parameter allocation approach for flow simulation using the WetSpa-Python model

Hydrologic models are simplified representations of natural hydrologic systems. Since these models rely on assumptions and simplifications to capture some aspects of hydrological processes, calibration of parameters is unavoidable. However, utilizing the philosophy of a recent modelling framework proposed by Bahremand (2016), we show how calibration of most model parameters can be avoided by allocating or presetting these parameters utilizing knowledge gained from sensitivity analyses, field observations and a priori specifications as a part of a parameter allocation procedure. This paper details the simulation of daily river flow of the Shemshak-Roudak watershed performed using the Python version of the WetSpa model. The WetSpa-Python model is a distributed model of hydrological processes applied at the watershed scale. The model was applied to the Shemshak-Roudak watershed of Iran with parameter allocation. Model calibration involved only two parameters. Straightforward methods were proposed for allocating model parameters, including three baseflow-related parameters and the determination of maximum active groundwater storage using a mass curve technique. Also, the Budyko curve was used to constrain a correction factor for potential evapotranspiration. The WetSpa-Python model was extended to include the influence of snowmelt. A failure to include snow in the hydrological processes of the WetSpa-Python model creates a significant discrepancy between the observed and simulated hydrographs during the spring. The results of daily simulations for 12 years (2002–2014) are in good agreement with observations of discharge (Kling-Gupta Efficiency = 0.84). These results demonstrate that it is feasible to simulate hydrographs with limited calibration given a knowledge of hydrological processes and an understanding of relationships between catchment characteristics and model parameters.     

Semi-empirical prediction method for monthly precipitation prediction based on environmental factors and comparison with stochastic and machine learning models

ABSTRACT

Precipitation prediction is central in hydrology and water resources planning and management. This paper introduces a semi-empirical predictive model to predict monthly precipitation and compares its predictive skill with those of machine learning (ML) methods. The stochastic method presented herein estimates monthly precipitation with one-step-ahead prediction properties. The ML predictive skill of the algorithms is evaluated by predicting monthly precipitation relying on the statistical association between precipitation and environmental and topographic factors. The semi-empirical predictive model features non-negative matrix factorization (NMF) for investigating the influence of multiple predictor variables on precipitation. The semi-empirical predictive model’s parameters are optimized with the hybrid genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM), or GALMA, yielding a validated model with high predictive skill. The methodologies are illustrated with data from Hubei Province, China, which comprise 27 meteorological station datasets from 1988–2017. The empirical results provide valuable insights for developing semi-empirical rainfall prediction models.     

A comparative study on a simple two-parameter monthly water balance model and the Kajiyama formula for monthly runoff estimation

ABSTRACT

A two-parameter monthly water balance model to simulate runoff can be used for a water resources planning programme and climate impact studies. However, the model estimates two parameters of transformation of time scale (c) and of the field capacity (SC) by a trial-and-error method. This study suggests a modified methodology to estimate the parameters c and SC using the meteorological and geological conditions. The modified model is compared with the Kajiyama formula to simulate the runoff in the Han River and International Hydrological Programme representative basins in South Korea. We show that the estimated c and SC can be used as the initial or optimal values for the monthly runoff simulation study in the model.

EDITOR M.C. Acreman; ASSOCIATE EDITOR S. Kanae     

Heuristic search strategy based on probabilistic and geostatistical simulation approach for simultaneous identification of groundwater contaminant source and simulation model parameters

Stochastic Environmental Research and Risk Assessment - In this study, a heuristic search strategy based on probabilistic and geostatistical simulation approach is developed for simultaneous...     

动力和统计预测信息融合预测方法及对我国夏季降水预测的检验

短期气候预测中如何将气候模式和统计方法的预测结果科学、客观的集成起来,一直是非常重要的问题.本文针对动力模式和统计方法预测结果相结合的问题,引入资料同化中信息融合的思想,采用最优内插同化方法,实现了动力模式和统计季节降水预测结果的融合.检验表明,对1982-2015年我国夏季降水百分率的回报,融合预测结果与观测的平均空间相关系数可达0.44,分别较统计预测和CFSv2模式统计降尺度订正的技巧提高了0.1左右,而均方根误差较两者可以降低5%~20%.可见,该方法可以进一步提升对我国夏季降水的预测技巧,具有显著的业务应用价值.

     

A new two-way nesting technique for ocean modeling based on the smoothed semi-prognostic method

A new two-way nesting technique is presented for a multiple nested-grid ocean modeling system. The new technique uses the smoothed semi-prognostic (SSP) method to exchange information between the different subcomponents of the nested-grid system. Four versions of the new nesting technique are described, together with conventional one-way nesting. The performance of the different nesting techniques is compared, using two independent nested-grid modeling systems, one for the Scotian Shelf of the northwest Atlantic Ocean and the other for the Meso-American Barrier Reef System of the northwestern Caribbean Sea. Nesting using the semi-prognostic method is shown to effectively prevent unrealistic drift of the inner model, while use of the SSP method avoids unnecessary damping of small scales on the inner model grid. Comparison of the annual-mean flow field with the near-surface currents determined by Fratantoni (in J Geophys Res 106:2977–2996, 2001) from observed trajectories of near-surface drifters demonstrates the overall superiority of the nesting technique based on the SSP method.     

基于Markov链模型的储层岩相随机模拟

在油气储层随机建模研究中，基于Markov链模型的方法是一类较受欢迎的技术，同时也是一类不成熟的技术，问题的症结之一在于侧向的转移概率矩阵很难求取，针对这种情况，作者在深入理解Walther相律的基础上，借鉴模拟退火算法的相应思路，提出了一种岩相模拟的新方法，该方法依据不同岩相的百分比进行随机模拟得到一幅初始图象，而后以按岩相组织剖面得到的垂向和侧向的岩相转移概率矩阵的相似性作为判别标准对图象进行扰动，直至得到满意的图象，二维模型试算结果表明了这种岩相随机模拟方法的可行性。     

On the computation of area probabilities based on a spatial stochastic model for precipitation cells and precipitation amounts

A main task of weather services is the issuing of warnings for potentially harmful weather events. Automated warning guidances can be derived, e.g., from statistical post-processing of numerical weather prediction using meteorological observations. These statistical methods commonly estimate the probability of an event (e.g. precipitation) occurring at a fixed location (a point probability). However, there are no operationally applicable techniques for estimating the probability of precipitation occurring anywhere in a geographical region (an area probability). We present an approach to the estimation of area probabilities for the occurrence of precipitation exceeding given thresholds. This approach is based on a spatial stochastic model for precipitation cells and precipitation amounts. The basic modeling component is a non-stationary germ-grain model with circular grains for the representation of precipitation cells. Then, we assign a randomly scaled response function to each precipitation cell and sum these functions up to obtain precipitation amounts. We derive formulas for expectations and variances of point precipitation amounts and use these formulas to compute further model characteristics based on available sequences of point probabilities. Area probabilities for arbitrary areas and thresholds can be estimated by repeated Monte Carlo simulation of the fitted precipitation model. Finally, we verify the proposed model by comparing the generated area probabilities with independent rain gauge adjusted radar data. The novelty of the presented approach is that, for the first time, a widely applicable estimation of area probabilities is possible, which is based solely on predicted point probabilities (i.e., neither precipitation observations nor further input of the forecaster are necessary). Therefore, this method can be applied for operational weather predictions.     

Designing optimal number of receiving traces based on simulation model

Currently, the selection of receiving traces in geometry design is mostly based on the horizontal layered medium hypothesis, which is unable to meet survey requirements in a complex area. This paper estimates the optimal number of receiving traces in field geometry using a numerical simulation based on a field test conducted in previous research (Zhu et al., 2011). A mathematical model is established for total energy and average efficiency energy using fixed trace spacing and optimal receiving traces are estimated. Seismic data acquired in a complex work area are used to verify the correctness of the proposed method. Results of model data calculations and actual data processing show that results are in agreement. This indicates that the proposed method is reasonable, correct, sufficiently scientific, and can be regarded as a novel method for use in seismic geometry design in complex geological regions.     

A smoothed particle hydrodynamics model for droplet and film flow on smooth and rough fracture surfaces

Flow on fracture surfaces has been identified by many authors as an important flow process in unsaturated fractured rock formations. Given the complexity of flow dynamics on such small scales, robust numerical methods have to be employed in order to capture the highly dynamic interfaces and flow intermittency. In this work we use a three-dimensional multiphase Smoothed Particle Hydrodynamics (SPH) model to simulate surface tension dominated flow on smooth fracture surfaces. We model droplet and film flow over a wide range of contact angles and Reynolds numbers encountered in such flows on rock surfaces. We validate our model via comparison with existing empirical and semi-analytical solutions for droplet flow. We use the SPH model to investigate the occurrence of adsorbed trailing films left behind droplets under various flow conditions and its importance for the flow dynamics when films and droplets coexist. It is shown that flow velocities are higher on prewetted surfaces covered by a thin film which is qualitatively attributed to the enhanced dynamic wetting and dewetting at the trailing and advancing contact lines. Finally, we demonstrate that the SPH model can be used to study flow on rough surfaces.