Using Orthogonal Array Sampling to Cope with Uncertainty in Ground Water Problems

Uncertainty in ground water hydrology originates from different sources. Neglecting uncertainty in ground water problems can lead to incorrect results and misleading output. Several approaches have been developed to cope with uncertainty in ground water problems. The most widely used methods in uncertainty analysis are Monte Carlo simulation (MCS) and Latin hypercube sampling (LHS), developed from MCS. Despite the simplicity of MCS, many runs are required to achieve a reliable result. This paper presents orthogonal array (OA) sampling as a means to cope with uncertainty in ground water problems. The method was applied to an analytical stream depletion problem. To examine the convergence rate of the OA sampling, the results were compared to MCS and LHS. This study shows that OA can be applied to ground water problems. Results reveal that the convergence rate of the OA sampling is faster than MCS and LHS, with a smaller error of estimate when applied to a stream depletion problem.     

MULTIOBJECTIVE PROGRAMMING MODEL FOR SEDIMENT SLUICING AND POWER GENERATION IN RESERVOIR OPERATION

I. INTRODUCTIONReservoir operation study in planning stage is an important task for the water resourcesdevelopment design. In the past. much attention has been paid to making the most ot' floodcontol. power generation, navigation. and water supply, but less to the unfavorable effectsdue to reservoir sedimentation. According to the survey of 425 reservoirs in Japan with a total storage capacity of 13.2 billion mJ a volume of 825 million m3 of sediment was depositedby the end of 1979. This…     

"5·12"汶川地震房屋建筑震害分析与对策研究报告

2008年5月12日14时28分,四川省汶川县境内发生了里氏8.0级特大地震,地震造成重大人员伤亡和经济损失.地震发生后,在国家建设部等有关部委支持下,在四川省委、省政府的统一部署下,四川省建设系统和地震系统等迅速组织相关专家和工程技术人员前后共计2500余人次,持续时间达2个多月,深入灾区开展地震震害调查、房屋安全应急评估、专项房屋结构技术鉴定和技术人员培训等工作,掌握了较丰富的房屋震害第一手调查评估资料.     

Modeling the Usefulness of Spatial Correlation Analysis on Karst Systems

Cross-correlation analyses on field data collected in karst aquifer systems can be used to develop a conceptual understanding of the aquifer. This includes the use of many data sets from the same aquifer to develop an understanding of how properties vary spatially. We focus on a method for characterizing the distribution of recharge, which is becoming increasingly important in regions where urban development encroaches on these important sources of water. Spatially varying precipitation data and cross-correlation analysis provide a means of spatially characterizing recharge locations on a karst aquifer. Our work expands on the numerical experiments conducted by Padilla and Pulido-Bosch (1995) using the numerical ground water model MODFLOW to introduce spatially varying parameters. The numerical experiments include conduit-controlled, matrix-controlled, and mixed karst systems with more than one precipitation time series input. The results show that spatially varying parameters can be inferred based on the cross-correlation of precipitation data and spring discharge. Simulations were completed using aquifer parameters derived from studies of the Barton Springs segment of the Edwards Aquifer. The simulations indicate that spatial variability within an aquifer can be inferred using cross-correlation analysis. A field study using these methods is summarized for Barton Springs near Austin, Texas.     

Distinguishing Iron-Reducing from Sulfate-Reducing Conditions

Ground water systems dominated by iron‐ or sulfate‐reducing conditions may be distinguished by observing concentrations of dissolved iron (Fe²⁺) and sulfide (sum of H₂S, HS^?, and S⁼ species and denoted here as “H₂S”). This approach is based on the observation that concentrations of Fe²⁺ and H₂S in ground water systems tend to be inversely related according to a hyperbolic function. That is, when Fe²⁺ concentrations are high, H₂S concentrations tend to be low and vice versa. This relation partly reflects the rapid reaction kinetics of Fe²⁺ with H₂S to produce relatively insoluble ferrous sulfides (FeS). This relation also reflects competition for organic substrates between the iron‐ and the sulfate‐reducing microorganisms that catalyze the production of Fe²⁺ and H₂S. These solubility and microbial constraints operate in tandem, resulting in the observed hyperbolic relation between Fe²⁺ and H₂S concentrations. Concentrations of redox indicators, including dissolved hydrogen (H₂) measured in a shallow aquifer in Hanahan, South Carolina, suggest that if the Fe²⁺/H₂S mass ratio (units of mg/L) exceeded 10, the screened interval being tapped was consistently iron reducing (H₂～0.2 to 0.8 nM). Conversely, if the Fe²⁺/H₂S ratio was less than 0.30, consistent sulfate‐reducing (H₂～1 to 5 nM) conditions were observed over time. Concomitantly high Fe²⁺ and H₂S concentrations were associated with H₂ concentrations that varied between 0.2 and 5.0 nM over time, suggesting mixing of water from adjacent iron‐ and sulfate‐reducing zones or concomitant iron and sulfate reduction under nonelectron donor–limited conditions. These observations suggest that Fe²⁺/H₂S mass ratios may provide useful information concerning the occurrence and distribution of iron and sulfate reduction in ground water systems.