Assessing Spatial Scale Effects on Hydrometric Network Design Using Entropy and Multi‐objective Methods

In order to facilitate water resources decisions, it is important that accurate and informative hydrometric data are collected. Combining information theory with multi‐objective optimization has led to methods of optimizing the information content provided by hydrometric networks; however, there is no available study on the effects of spatial scale and data limitation on these methods. Herein, a dual entropy multi‐objective optimization (DEMO) and a transinformation (TI) analysis were done to recommend optimal locations for additional hydrometric stations in the Madawaska Watershed. This analysis was designed to be comparative to a similar study conducted on the Ottawa River Basin which encompasses the Madawaska Watershed to allow for an investigation of the spatial scale effects in this type of network design. This study concludes that TI analysis is not adversely affected by scaling; however, the DEMO analysis is sensitive to the placement of potential station locations and the size of the study area. This study also examines the benefit of including nearby stations when the area of interest does not have a sufficient number of existing hydrometric stations for analysis. It is shown that these stations can provide useful information because their inclusion in the analysis increased the average TI in the watershed. Recommendations were made as to the ideal locations of additional stations in the Madawaska Watershed hydrometric network.     

AN APPROACH TO MARGINAL ECONOMIC ANALYSIS OF HYDROMETRIC DATA COLLECTION1

ABSTRACT: A procedure for computing the benefit/cost ratio of a hydrometric network is outlined. It consists of two steps: firstly, establishing a relationship between hydrometric station density and error in hydrologic parameters and, secondly, relating hydrologic eror to changes in project cost. The procedure was applied to both the whole Canadian hydrometric network and the provincial networks.     

A heuristic optimization approach for Air Quality Monitoring Network design with the simultaneous consideration of multiple pollutants

An interactive optimization methodology for allocating the number and configuration of an Air Quality Monitoring Network (AQMN) in a vast area to identify the impact of multiple pollutants is described. A mathematical model based on the multiple cell approach (MCA) was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different emission sources. These spatial temporal patterns were subject to a heuristic optimization algorithm to identify the optimal configuration of a monitoring network. The objective of the optimization is to provide maximum information about multi-pollutants (i.e., CO, NO(x) and SO(2)) emitted from each source within a given area. The model was applied to a network of existing refinery stacks and the results indicate that three stations can provide a total coverage of more than 70%. In addition, the effect of the spatial correlation coefficient (R(C)) on total area coverage was analyzed. The modeling results show that as the cutoff correlation coefficient R(C) is increased from 0.75 to 0.95, the number of monitoring stations required for total coverage is increased. A high R(C) based network may not necessarily cover the entire region, but the covered region will be well represented. A low R(C) based network, on the other hand, would offer more coverage of the region, but the covered region may not be satisfactorily represented.     

公众对手机基站电磁辐射投诉典型问题及解答

随着中国的移动通信网络进入3G时代,手机基站建设掀起了新一轮高潮.手机基站是手机用户之间信息传递的枢纽,而信息传输的承载方式就是电磁波,也就是通常所称的"电磁辐射".由于绝大多数公众对电磁辐射并不了解,因此"谈辐色变",对手机基站的电磁辐射投诉量逐年增加,一定程度上影响了社区和谐与移动通信业的顺利推进.本文将比较有代表...     

Optimal water quality monitoring network design for river systems

Typical tasks of a river monitoring network design include the selection of the water quality parameters, selection of sampling and measurement methods for these parameters, identification of the locations of sampling stations and determination of the sampling frequencies. These primary design considerations may require a variety of objectives, constraints and solutions. In this study we focus on the optimal river water quality monitoring network design aspect of the overall monitoring program and propose a novel methodology for the analysis of this problem. In the proposed analysis, the locations of sampling sites are determined such that the contaminant detection time is minimized for the river network while achieving maximum reliability for the monitoring system performance. Altamaha river system in the State of Georgia, USA is chosen as an example to demonstrate the proposed methodology. The results show that the proposed model can be effectively used for the optimal design of monitoring networks in river systems.     

SPATIAL ANALYSIS FOR MONTHLY RAINFALL IN SOUTH FLORIDA1

ABSTRACT: Several methods have been developed to interpolate point rainfall data and integrate areal rainfall data from any network of stations. From previous studies, it can be concluded that models for spatial analysis of rainfall are dependent on topography, area of analysis, type of rainfall, and density of gauging network. The purpose of this study is to evaluate a set of six appropriate models for point and areal rainfall estimations over a 4000 square mile area in South Florida. In this study, a case of developing spatial continuity model for monthly rainfall from a database that had various lengths of records and missing data is documented. The spatial correlation and variogram models for monthly rainfall were developed. Six methods of spatial interpolation were applied and the results validated with historical observations. The results of the study indicate that the multiquadric, kriging, and optimal interpolation schemes are the best three methods for interpolation of monthly rainfall within the study area. The optimal and kriging methods have the advantage of providing estimates of the error of interpolation. The optimal interpolation method uses the spatial correlation function and the kriging method uses the variogram function. The two spatial functions are related. Either of the two methods provide good estimates of monthly point and areal rainfall in the study area.     

Use of Streamflow Data to Estimate Base Flow/Ground‐Water Recharge For Wisconsin1

Abstract: The average annual base flow/recharge was determined for streamflow‐gaging stations throughout Wisconsin by base‐flow separation. A map of the State was prepared that shows the average annual base flow for the period 1970‐99 for watersheds at 118 gaging stations. Trend analysis was performed on 22 of the 118 streamflow‐gaging stations that had long‐term records, unregulated flow, and provided aerial coverage of the State. The analysis found that a statistically significant increasing trend was occurring for watersheds where the primary land use was agriculture. Most gaging stations where the land cover was forest had no significant trend. A method to estimate the average annual base flow at ungaged sites was developed by multiple‐regression analysis using basin characteristics. The equation with the lowest standard error of estimate, 9.5%, has drainage area, soil infiltration and base flow factor as independent variables. To determine the average annual base flow for smaller watersheds, estimates were made at low‐flow partial‐record stations in 3 of the 12 major river basins in Wisconsin. Regression equations were developed for each of the three major river basins using basin characteristics. Drainage area, soil infiltration, basin storage and base‐flow factor were the independent variables in the regression equations with the lowest standard error of estimate. The standard error of estimate ranged from 17% to 52% for the three river basins.     

Optimal estuarine sediment monitoring network design with simulated annealing

An objective function based on geostatistical variance reduction, constrained to the reproduction of the probability distribution functions of selected physical and chemical sediment variables, is applied to the selection of the best set of compliance monitoring stations in the Sado river estuary in Portugal. These stations were to be selected from a large set of sampling stations from a prior field campaign. Simulated annealing was chosen to solve the optimisation function model. Both the combinatorial problem structure and the resulting candidate sediment monitoring networks are discussed, and the optimal dimension and spatial distribution are proposed. An optimal network of sixty stations was obtained from an original 153-station sampling campaign.     

OPTIMAL MANAGEMENT AND DESIGN OF HYDRAULIC SYSTEMS1

ABSTRACT: A possible methodology is developed to deal with the problem of designing complex pipeline systems, when they are subject to different rates of demand, and when a hypothesis of the flow distribution in different branches is not allowed. The mathematical algorithm used in linear programming. The problem, which is not linear, is dealt with by means of an iterative method; that is, by starting with a possible solution and inserting at each iteration the solution found in the preceding iteration. By taking as variables of the problem the piezometric heads of the ends for each branch of the network, the piezometric gradients and flows, and by thus considering the diameter as a derived variable, it is possible to isolate the nonlinearity in the cost function of the network. The latter is linearized each time close to the solution found in the preceding iteration.     

浅谈行业环境监测站的任务与作用

我国环境问题日趋严重，环境污染和生态破坏已成为社会发展的制约因素。然而，有些企业忽视环境保护工作，取消或合并环境监测站，削弱环境监测力量。针对这种情况，从行业环境监测站的任务、作用和不可取代性方面阐述了强化行业环境监测站建设的必要性。     

Illinois redesigns its ambient water quality monitoring network

Illinois has been operating an ambient water quality network of almost 600 stations for several years. In 1977 changes in program emphasis toward intensive monitoring, the need for improved procedures and quality control in monitoring operations, and the desire to create a single data base of all Illinois State monitoring data, resulted in a redesign of the ambient monitoring program.A unique cooperative program between the Illinois Environmental Protection Agency and the US Geological Survey provides for their monitoring a portion of the network. The Survey provides flow data at most network stations as well as extensive manpower training, equipment, data processing, and program quality control. Informal agreements with other agencies have permitted a great reduction in the monitoring effort required by the Illinois Environmental Protection Agency.     

OPTIMAL CONTROL OF RESERVOIR RELEASES TO MINIMIZE SEDIMENTATION IN RIVERS AND RESERVOIRS1

ABSTRACT: An optimal control methodology and computational model are developed to evaluate multi‐reservoir release schedules that minimize sediment scour and deposition in rivers and reservoirs. The sedimentation problem is formulated within a discrete‐time optimal control framework in which reservoir releases represent control variables and reservoir bed elevations, storage levels, and river bed elevations represent state variables. Constraints imposed on reservoir storage levels and releases are accommodated using a penalty function method. The optimal control model consists of two interfaced components: a one‐dimensional finite‐difference simulation module used to evaluate flow hydraulics and sediment transport dynamics, and a successive approximation linear quadratic regulator (SALQR) optimization algorithm used to update reservoir release policies and solve the augmented control problem. Hypothetical two‐reservoir and five‐reservoir networks are used to demonstrate the methodology and its capabilities, which is a vital phase towards the development of a more robust optimal control model and application to an existing multiple‐reservoir river network.     

Effects of discharge fluctuation and the addition of fine sediment on stream fish and macroinvertebrates below a water-filtration facility

A small, coastal stream in the San Francisco Bay area of California, USA, received the discharges from a drinking-water filtration plant. Two types of discharges were present. Discharges from filter backwashing were 3–4 times base stream flow, occurred 10–60 times per day, contained fine sediments, and each lasted about 10 min. The other discharge was a large, steady flow of relatively sediment-free water from occasional overflow of the delivery aqueduct which generally lasted several hours a day.Samples of invertebrates from natural substrates had significantly fewer taxa and lower density at the two stations below the backwash than at the two above. However, when stable artificial substrates were used, there were no significant differences among all four stations. The aqueduct apparently had no effect because the. invertebrate community at the station upstream of the backwash but downstream of the aqueduct was statistically similar to the station above the aqueduct. To test for acute toxicity, we exposed additional artificial substrates to short-term simulated backwash conditions. These exposures had no effect on invertebrate density or drift. Three-spine stickleback (Gasterosteus aculeatus) populations were also significantly reduced at the two downstream stations and were made up mostly of larger, adult fish. Prickly sculpins (Cottus asper), restricted to the most downstream station, were emaciated and had poor growth, probably as a result of scarce benthic food organisms. Artificial redds with eggs of rainbow trout (Salmo gairdneri) had significantly lower survival at two stations below the plant backwash (30.7% and 41.8%) than at the one above it (61.4%). Hatchery rainbow trout held in cages below the treatment plant from 7 to 37 days survived and continued to feed.Thus, the major effect of the water treatment plant on fish and invertebrates probably was not from acute toxicity in the discharges or the occasionally large discharge of clean water from the aqueduct, but was from the fluctuating backwash flows containing fine sediment that displaced small fish downstream and created unstable benthic substrates for invertebrates.The filter plant that we studied is a direct-feed type (that is, no sedimentation before filtration). These generally require greater frequencies of backwashing than do conventional plants and may therefore have greater biological impacts. Direct-feed plants are becoming increasingly popular throughout the world, for the most part because they are cheaper to build and operate. But if the associated biological problems are mitigated, then the cost savings of direct-feed compared to conventional plants may be lost.     

COMBINATION OF RADAR AND GAUGE DATA IN A RAINFALL ARCHIVE SYSTEM1

ABSTRACT: Near real time daily rainfall estimates for the UK are available from three sources: a sparse network of gauges, radar data, or radar data adjusted by the sparse gauges. The PARAGON rainfall archive system, which has been developed by the UK Meteorological Office, is able to produce these estimates in near real time on a 5 km grid. The ability of these estimates to reproduce the 5 km grid point field derived later from a dense network of gauges is compared using case studies. Five techniques have been used to assess the relative quality of the various estimates. There is general agreement between the results of the various techniques. For the London radar there are examples of days when the rainfall estimate was improved by incorporating radar data; conversely, there are days when the radar data make it worse. Overall little evidence was found to suggest that adjusted radar data are consistently markedly better than gauge estimates. Discriminate use of radar data is recommended.     

A multiobjective optimization framework for multicontaminant industrial water network design

The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view.     

Spatiotemporal Evaluation of Simulated Evapotranspiration and Streamflow over Texas Using the WRF‐Hydro‐RAPID Modeling Framework

This study assesses a large‐scale hydrologic modeling framework (WRF‐Hydro‐RAPID) in terms of its high‐resolution simulation of evapotranspiration (ET) and streamflow over Texas (drainage area: 464,135 km²). The reference observations used include eight‐day ET data from MODIS and FLUXNET, and daily river discharge data from 271 U.S. Geological Survey gauges located across a climate gradient. A recursive digital filter is applied to decompose the river discharge into surface runoff and base flow for comparison with the model counterparts. While the routing component of the model is pre‐calibrated, the land component is uncalibrated. Results show the model performance for ET and runoff is aridity‐dependent. ET is better predicted in a wet year than in a dry year. Streamflow is better predicted in wet regions with the highest efficiency ~0.7. In comparison, streamflow is most poorly predicted in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These results complement previous evaluations by incorporating more spatial details. They also help identify potential processes for future model improvements. Indeed, improving the dry region streamflow simulation would require synergistic enhancements of ET, soil moisture and groundwater parameterizations in the current model configuration. Our assessments are important preliminary steps towards accurate large‐scale hydrologic forecasts.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

城区天然气配气站噪声的产生及防治

城区天然气配气站噪声超标已成为供气部门亟待解决的难题,文章分析了配气站噪声污染现状,指出噪声产生原因主要为天然气流动,供气量增长使设备超负荷运行等7个方面。分别针对现有配气站和新建、改扩建站场提出降噪措施,指出设备在额定范围内运行、气体流速控制、工艺改造等是噪声控制的重要手段。并对城区天然气配气站噪声治理的进一步研究提出了建议。     

Evaluation of CFSR climate data for hydrologic prediction in data‐scarce watersheds: an application in the Blue Nile River Basin

Data scarcity has been a huge problem in modeling the water resources of the Upper Blue Nile basin, Ethiopia. Satellite data and different statistical methods have been used to improve the quality of conventional meteorological data. This study assesses the applicability of the National Centers for Environmental Prediction's Climate Forecast System Reanalysis (CFSR) climate data in modeling the hydrology of the region. The Soil and Water Assessment Tool was set up to compare the performance of CFSR weather with that of conventional weather in simulating observed streamflow at four river gauging stations in the Lake Tana basin — the upper part of the Upper Blue Nile basin. The conventional weather simulation performed satisfactorily (e.g., NSE ≥ 0.5) for three gauging stations, while the CFSR weather simulation performed satisfactorily for two. The simulations with CFSR and conventional weather yielded minor differences in the water balance components in all but one watershed, where the CFSR weather simulation gave much higher average annual rainfall, resulting in higher water balance components. Both weather simulations gave similar annual crop yields in the four administrative zones. Overall the simulation with the conventional weather performed better than the CFSR weather. However, in data‐scarce regions such as remote parts of the Upper Blue Nile basin, CFSR weather could be a valuable option for hydrological predictions where conventional gauges are not available.     

A LUMPED APPROACH TO THE INVERSE PROBLEM IN GROUND WATER HYDROLOGY1

ABSTRACT: A solution procedure to solve the inverse problem in ground water, based on lumped approach, has been proposed. The method has the following advantages: 1) exact determination of the boundary conditions and the physical laws of flow through porous media is not required; 2) all errors of approximation in describing the boundary conditions, physical laws, and the aquifer properties are lumped into the surrogate parameters; and 3) the same mathematical model can be employed both in the identification process and in the subsequent management studies. The optimal values of the surrogate parameters are found by using a multidimensional unconstrained optimization code devised by Powell. The solution procedure and the convergence characteristics of the proposed algorithm have been illustrated by two hypothetical problems.