LE TRITIUM DANS LES PRÉCIPITATIONS DE L'ASIE DU SUD-EST ET LE PHÉNOMÉNE DE LA MOUSSON EN 1966 ET EN 1967

Abstract

The problem of non-steady flow of water in a soil-plant system can be described by adding a sink term to the continuity equation for soil water flow. In this paper the sink term is defined in two different ways. Firstly it is considered to be dependent on the hydraulic conductivity of the soil, on the difference in pressure head between the soil and the root-soil interface and some root effectiveness function. Secondly the sink is taken to be a prescribed function of the soil water content. The partial differential equation applying to the first problem is solved by both a finite difference (FD 1) and a finite element (FE 1) technique, that applying to the second problem by a finite difference approach (FD 2). The purpose of this paper is to verify the numerical models against field measurements, to compare the results obtained by the three numerical methods and to show how the finite element method can be applied to complex but realistic two-dimensional flow situations. Two examples are given. The first concerns one-dimensional flow and it compares numerical results with those obtained experimentally in the field from water balance studies on red cabbage (Brassica oleracea L. ‘Rode Herfst’) grown on a clay soil in the presence of a water table. The second example describes two-dimensional flow in a complex field situation in the Netherlands where flow takes place under cropped field conditions through five anisotropic layers. Water is supplied to the system by infiltration from two unlined ditches and is withdrawn from the system by evapotranspiration and by leakage to an underlying pumped aquifer.     

Identification of aquifer parameters from residual drawdowns: an optimization approach

Abstract

An optimization method based on nonlinear least squares is proposed for the identification of aquifer transmissivity and two storage coefficients, one for the pumping phase and the other for the recovery phase, using only residual drawdowns at an observation well. The method is applicable irrespective of whether pumping and recovery storage coefficients are the same or are different and the argument of well function during recovery is less than or greater than 0.01. The method can check for the changed value of storage coefficient during the recovery phase and can quantify this change. Use of the method is illustrated through examples, which show that the parameters are estimated reliably.     

Visual analytics clarify the scalability and effectiveness of massively parallel many-objective optimization: A groundwater monitoring design example

In this study, we contribute a comprehensive framework for simultaneously assessing solution quality and scalability for massively parallel multiobjective evolutionary algorithm (MOEA)-based search using a highly challenging optimization—assimilation application. Visual analytics are used to evaluate how changes in search metric performance relate to actual decision relevant changes in the Pareto approximate set. The application focuses on a four objective groundwater monitoring application in which parallel scalability is tested across compute core counts ranging from 64 to a maximum of 8192. This study demonstrates that parallel search performance must be assessed in terms of how well speedup is exploited to improve the quality of search results and that solely focusing on differences in computational time can be deceptive. Our results demonstrate how visualization can clarify when an MOEA’s search shifts from “translating” the approximation set to “diversifying” its coverage over the extent of the objectives. This is an important observation. If shorter parallel run durations are required, the rapid early translation of the set may yield a reasonable approximation of the Pareto approximate set where further search is unnecessary. Although a groundwater application is used to demonstrate our parallelization, the visual analytics and metrics utilized to characterize the parallel scalability of MOEA-based search are broadly applicable in water resources and beyond.     

重庆测震台网运行率提高措施

开展稳定的电力保障、台站避雷到位保护、双链路互备传输数据及科学合理的运行维护等措施,可有效提高重庆测震台网运行率,具有较高的应用价值.     

前兆远程子台CDMA传输故障诊断及排除

本文简要介绍了CDMA传输技术及其在地震前兆远程子台的应用,之后以天津市地震局宝坻地震台的远程子台为实例,对地震前兆远程子台CDMA传输中的故障原因进行了研究,指出导致CDMA传输故障的原因主要有:仪器通讯部分故障、资费余额不足、路由器"假死"、路由器死机、路由器内部故障(特别是SIM卡接触不良)、CDMA信号强度不够、交换机故障、传输硬件连接断路等。在此基础上提出了一套行之有效的故障诊断流程,并对常见故障原因的判定和排除方法做了归纳整理。本文对使用CDMA传输方式或其他无线传输方式的地震前兆远程子台的维护管理人员有一定的参考意义。     

Boundary-based analysis for the assessment of coastal dune landscape integrity over time

Coastal dunes are transitional systems which offer manifold ecosystem services, including material and intangible benefits, whose relevance for human society has been little investigated to date. In order to plan a sustainable use of these threatened ecosystems, proper indicators to quantify ecological services and functions over time should be provided. Since the functionality of coastal dunes is affected by the specific contiguity among different dune habitat patches, in this paper we propose the use of landscape integrity, expressed by the number and length of boundary types among the aforementioned habitats, as an ecosystem functionality indicator. In particular, we aim at properly expressing the trend of coastal dune mosaic integrity in two Italian LTER sites over the last sixty years by applying a generalized diversity function on the number and length of boundary types. Such functions, unlike a traditional diversity index, allow a complete summarization of landscape diversity and structure. The proposed method has proved to be effective in the considered context, because it has adequately underlined the different landscape integrity trend recorded in the two sites. In particular, generalized diversity functions showed to efficiently express both rough and moderate anthropogenic transformations, which affected spatial heterogeneity and functionality of the considered coastal dune mosaic. Further applications at different scales and across different ecosystems are encouraged.     

A spatial approach to forest‐management optimization: linking GIS and multiple objective genetic algorithms

Forest‐management decision‐support systems are largely monolithic structures. Spatial details are left out during the optimization process and are elaborated during the operational planning. This might produce misleading results and plans that are impossible to implement. In this paper, a forest‐management spatial decision‐support systems is presented, in which spatial formulation needed for wildlife models is included during the optimization process. To this end, a multiple‐objective genetic algorithm is combined with a geographical information system. An online spatial evaluation of the objective functions is made possible. This is illustrated by a pilot study in Kirkhill forest, Aberdeen.     

An integrated approach for addressing geographic uncertainty in spatial optimization

There exist many facets of error and uncertainty in digital spatial information. As error or uncertainty will not likely ever be completely eliminated, a better understanding of its impacts is necessary. Spatial analytical approaches, in particular, must somehow address data-quality issues. This can range from evaluating impacts of potential data uncertainty in planning processes that make use of methods to devising methods that explicitly account for error/uncertainty. To date, little has been done to structure methods accounting for error. This article develops an integrated approach to address data uncertainty in spatial optimization. We demonstrate that it is possible to characterize uncertainty impacts by constructing and solving a new multi-objective model that explicitly incorporates facets of data uncertainty. Empirical findings indicate that the proposed approaches can be applied to evaluate the impacts of data uncertainty with statistical confidence, which moves beyond popular practices of simulating errors in data.     

A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas

Optimizing land use allocation is a challenging task, as it involves multiple stakeholders with conflicting objectives. In addition, the solution space of the optimization grows exponentially as the size of the region and the resolution increase. This article presents a new ant colony optimization algorithm by incorporating multiple types of ants for solving complex multiple land use allocation problems. A spatial exchange mechanism is used to deal with competition between different types of land use allocation. This multi-type ant colony optimization optimal multiple land allocation (MACO-MLA) model was successfully applied to a case study in Panyu, Guangdong, China, a large region with an area of 1,454,285 cells. The proposed model took only about 25 minutes to find near-optimal solution in terms of overall suitability, compactness, and cost. Comparison indicates that MACO-MLA can yield better performances than the simulated annealing (SA) and the genetic algorithm (GA) methods. It is found that MACO-MLA has an improvement of the total utility value over SA and GA methods by 4.5% and 1.3%, respectively. The computation time of this proposed model amounts to only 2.6% and 12.3%, respectively, of that of the SA and GA methods. The experiments have demonstrated that the proposed model was an efficient and effective optimization technique for generating optimal land use patterns.     

Allocation of urban land uses by Multi-Objective Particle Swarm Optimization algorithm

Considering the ever-increasing urban population, it appears that land management is of major importance. Land uses must be properly arranged so that they do not interfere with one another and can meet each other's needs as much as possible; this goal is a challenge of urban land-use planning. The main objective of this research is to use Multi-Objective Particle Swarm Optimization algorithm to find the optimum arrangement of urban land uses in parcel level, considering multiple objectives and constraints simultaneously. Geospatial Information System is used to prepare the data and to study different spatial scenarios when developing the model. To optimize the land-use arrangement, four objectives are defined: maximizing compatibility, maximizing dependency, maximizing suitability, and maximizing compactness of land uses. These objectives are characterized based on the requirements of planners. As a result of optimization, the user is provided with a set of optimum land-use arrangements, the Pareto-front solutions. The user can select the most appropriate solutions according to his/her priorities. The method was tested using the data of region 7, district 1 of Tehran. The results showed an acceptable level of repeatability and stability for the optimization algorithm. The model uses parcel instead of urban blocks, as the spatial unit. Moreover, it considers a variety of land uses and tries to optimize several objectives simultaneously.