Mapping climate change-caused health risk for integrated city resilience modeling     

Amin M. Owrangi  Robert Lannigan  Slobodan P. Simonovic 《Natural Hazards》2015,77(1):67-88

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Quantification of river water infiltration in shallow aquifers using acesulfame and anthropogenic gadolinium          下载免费PDF全文

Andrea Bichler  Christian Muellegger  Robert Brünjes  Thilo Hofmann 《水文研究》2016,30(11):1742-1756

This study has investigated the use of the artificial sweetener acesulfame and the magnetic resonance imaging contrast agent gadolinium as quantitative tracers for river water infiltration into shallow groundwater. The influence of a river on alluvial groundwater in a subalpine catchment in western Europe has been assessed using the ‘classical’ hydrochemical tracer chloride and the trace contaminants acesulfame and anthropogenic gadolinium. Mixing ratios for riverine bank filtrate with ambient groundwater and the uncertainties associated with the temporal and spatial tracer variability were calculated using acesulfame and gadolinium and compared with those obtained using chloride. The temporal variability of tracer concentrations in river water of gadolinium (standard deviation SD: 63%) and acesulfame (SD: 71%) both exceeded that of chloride (SD: 27%), and this was identified as the main source of uncertainty in the mixing analysis. Similar spatial distributions were detected in the groundwater for chloride and gadolinium, but not for acesulfame. Mixing analyses using acesulfame resulted in calculated mixing ratios that differed from those obtained using gadolinium and chloride by up to 83% and 92%, respectively. At the investigated site, which had oxic conditions and moderate temperatures, acesulfame was found to be a less reliable tracer than either gadolinium or chloride, probably because of natural attenuation and input from other sources. There was no statistically significant difference between the mixing ratios obtained using chloride or gadolinium, the mixing ratios obtained using gadolinium were 40–50% lower than those obtained using chloride. This is mainly due to a bias of the mean gadolinium concentration in river water towards higher values. In view of the uncertainties of the two tracers, neither could be preferred over the other for the quantification of bank filtrate in groundwater. At this specific site gadolinium was able to reliably identify river water infiltration and was a more precise tracer than chloride at low mixing ratios (<20%), because of the exclusive occurrence of gadolinium in river water and its high dynamic range. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

Including spatial distribution in a data‐driven rainfall‐runoff model to improve reservoir inflow forecasting in Taiwan          下载免费PDF全文

Meng‐Jung Tsai  Robert J. Abrahart  Nick J. Mount  Fi‐John Chang 《水文研究》2014,28(3):1055-1070

Multi‐step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3‐h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real‐time rainfall‐runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3 h. In this paper, we develop a novel semi‐distributed, data‐driven, rainfall‐runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network‐based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point‐based rain gauge predictors. Different levels of spatially aggregated radar‐derived rainfall data are used to generate 4, 8 and 12 sub‐catchment input drivers. In general, the semi‐distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3 h. Performance is found to be optimal when spatial aggregation is restricted to four sub‐catchments, with up to 30% improvements in the performance over lumped and point‐based models being evident at 5‐h lead times. The potential benefits of applying semi‐distributed, data‐driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

Increasing Singapore's resilience to drought          下载免费PDF全文

Alan D. Ziegler  James P. Terry  Grahame J.H. Oliver  Daniel A. Friess  Chong Joon Chuah  Winston T.L. Chow  Robert J. Wasson 《水文研究》2014,28(15):4543-4548

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Evaluating an Analytical Model to Predict Subsurface LNAPL Distributions and Transmissivity from Current and Historic Fluid Levels in Groundwater Wells: Comparing Results to Numerical Simulations          下载免费PDF全文

Robert J. Lenhard  Kaveh Sookhak Lari  John L. Rayner  Greg B. Davis 《Ground Water Monitoring & Remediation》2018,38(1):75-84

A recent analytical model predicts free, entrapped, and residual LNAPL saturations and the LNAPL transmissivity in the subsurface from current and historic fluid levels in groundwater wells. As such, the model accounts for effects of fluid level fluctuations in a well. The model was developed to predict LNAPL specific volumes and transmissivities from current fluid level measurements in wells and either recorded historic fluid level fluctuations in wells or estimates. An assumption is made in the model that the predictions are not dependent on whether the historic highest or lowest fluid level elevations in a well occur first. To test the assumption, we conduct two simulations with a modified multiphase flow numerical code TMVOC that incorporates relative permeability‐saturation‐capillary head relations employed in the model. In one simulation, the initial condition is for fluid levels in a well at the historic highest elevations. In the other simulation, the initial condition is for fluid levels in a well at the historic lowest elevations. We change the boundary conditions so both historical conditions occur followed by generating the current condition. Results from the numerical simulations are compared to model predictions and show the assumption in the analytical model is reasonable. The analytical model can be used to develop/refine conceptual site models and for assessing potential LNAPL recovery endpoints, especially on sites with fluctuating fluid levels in wells.  相似文献   

Fluorescent Dyes: A New Weapon for Conquering DNAPL Characterization          下载免费PDF全文

John Horst  Nicklaus Welty  Robert Stuetzle  Rick Wenzel  Randy St. Germain 《Ground Water Monitoring & Remediation》2018,38(1):19-25

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1,4‐Dioxane Soil Remediation Using Enhanced Soil Vapor Extraction (XSVE): II. Modeling          下载免费PDF全文

David R. Burris  Paul C. Johnson  Robert E. Hinchee  Paul R. Dahlen 《Ground Water Monitoring & Remediation》2018,38(2):49-56

1,4‐Dioxane is a volatile organic compound that is fully miscible in water, allowing it to sequester in vadose zone pore water and serve as a long‐term source of groundwater contamination. Conventional soil vapor extraction (SVE) removes 1,4‐dioxane; however, substantial 1,4‐dioxane can remain even after other colocated chlorinated solvents have been remediated. A field demonstration of “enhanced SVE” (XSVE) with focused extraction and heated injection was conducted at former McClellan AFB, CA, achieving 94% reduction in soil concentrations. A screening‐level tool, HypeVent XSVE, was created to assist in system design and data reduction and to anticipate how operating factors affect XSVE performance (e.g., cleanup level, remediation time, etc.). It assumes well‐mixed conditions, and combines an energy balance, mass balances for water and contaminant, and a temperature‐dependent 1,4‐dioxane Henry's Law constant. User inputs include the target treatment zone size, initial 1,4‐dioxane and soil moisture concentrations, and ambient site and injection/extraction conditions (temperature, humidity). Projections based on inputs representative of demonstration site conditions adequately anticipated the observed macroscopic field results. Sensitivity analyses show that removal increases with increasing heated air injection temperature and relative humidity and decreasing initial soil moisture content.  相似文献   

Assessing regression‐based statistical approaches for downscaling precipitation over North America          下载免费PDF全文

Jie Chen  François P. Brissette  Robert Leconte 《水文研究》2014,28(9):3482-3504

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Best practices for the use of meteorite names in publications     

Philipp R. Heck  Christopher Herd  Jeffrey N. Grossman  Dmitry Badjukov  Audrey Bouvier  Emma Bullock  Hasnaa Chennaoui‐Aoudjehane  Vinciane Debaille  Tasha L. Dunn  Denton S. Ebel  Ludovic Ferrire  Laurence Garvie  Jrme Gattacceca  Matthieu Gounelle  Richard Herd  Trevor Ireland  Emmanuel Jacquet  Robert J. Macke  Tim McCoy  Francis M. McCubbin  Takashi Mikouchi  Knut Metzler  Mathieu Roskosz  Caroline Smith  Meenakshi Wadhwa  Linda Welzenbach‐Fries  Toru Yada  Akira Yamaguchi  Ryan A. Zeigler  Michael Zolensky 《Meteoritics & planetary science》2019,54(7):1397-1400

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Characterisation of hydroclimatological trends and variability in the Lake Naivasha basin,Kenya          下载免费PDF全文

Vincent Omondi Odongo  Christiaan van der Tol  Pieter R. van Oel  Frank M. Meins  Robert Becht  Japheth Onyando  Zhongbo Su 《水文研究》2015,29(15):3276-3293

Recent hydro‐climatological trends and variability characteristics were investigated for the Lake Naivasha basin with the aim of understanding the changes in water balance components and their evolution over the past 50 years. Using a Bayesian change point analysis and modified Mann–Kendall tests, time series of annual mean, maximum, minimum, and seasonal precipitation and flow, as well as annual mean lake volumes, were analysed for the period 1960–2010 to uncover possible abrupt shifts and gradual trends. Double cumulative curve analysis was used to investigate the changes in hydrological response attributable to either human influence or climatic variability. The results indicate a significant decline in lake volumes at a mean rate of 9.35 × 10⁶ m³ year^?1. Most of the river gauging stations showed no evidence of trends in the annual mean and maximum flows as well as seasonal flows. Annual minimum flows, however, showed abrupt shifts and significant (upward/downward) trends at the main outlet stations. Precipitation in the basin showed no evidence of abrupt shifts, but a few stations showed gradual decline. The observed changes in precipitation could not explain the decline in both minimum flows and lake volumes. The findings show no evidence of any impact of climate change for the Lake Naivasha basin over the past 50 years. This implies that other factors, such as changes in land cover and infrastructure development, have been responsible for the observed changes in streamflow and lake volumes. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献