Mineralogical and geochemical characterization of precipitates on stream receiving acid mine water, Korea

Seasonal variations of water chemistry occurred in acid mine drainage receiving mine and leachate water. Sulfate and metal concentrations were low in winter but high in spring and summer. Mine waters were highly acidic (up to pH 3.4) in nature with high concentrations of manganese, copper and zinc but high electrical conductivity and sulfate in leachate. The blue and brownish yellow precipitates were formed under different chemical environments of acid mine drainage. Brownish yellow (Munsell color 7.5YR 8/12), blue (Munsell color 2.5B 9/7) and light blue (Munsell color 2.5B 9/3) precipitates deposited on the stream bottom receiving acid mine water. The brownish yellow precipitates formed in the acid mine water, whereas the blue and light blue precipitates formed in the leachate water. The brownish yellow precipitates consisted mainly of ferrihydrite, whereas the blue and light blue precipitates consisted of glaucocerinite and/or woodwardite.     

Modeling of coupled tide–wave–surge process in the Yellow Sea

A coupled wave–tide–surge model has been developed in this study in order to investigate the effect of the interactions among tides, storm surges, and wind waves. The coupled model is based on the synchronous dynamic coupling of a third-generation wave model, WAM cycle 4, and the two-dimensional tide–surge model. The surface stress, which is generated by interactions between wind and wave, is calculated by using the WAM model directly based on an analytical approximation of the results using the quasi-linear theory of wave generation. The changes in bottom friction are created by the interactions between waves and currents and calculated by using simplified bottom boundary layer model. In consequence, the combined wave–current-induced bottom velocity and effective bottom drag coefficient were increased in the shallow waters during the strong storm conditions.     

Intercomparison of multiple statistical downscaling methods: multi-criteria model selection for South Korea

A number of statistical downscaling methodologies have been introduced to bridge the gap in scale between outputs of climate models and climate information needed to assess potential impacts at local and regional scales. Four statistical downscaling methods [bias-correction/spatial disaggregation (BCSD), bias-correction/constructed analogue (BCCA), multivariate adaptive constructed analogs (MACA), and bias-correction/climate imprint (BCCI)] are applied to downscale the latest climate forecast system reanalysis (CFSR) data to stations for precipitation, maximum temperature, and minimum temperature over South Korea. All methods are calibrated with observational station data for 19 years from 1973 to 1991 and validated for the more recent 19-year period from 1992 to 2010. We construct a comprehensive suite of performance metrics to inter-compare methods, which is comprised of five criteria related to time-series, distribution, multi-day persistence, extremes, and spatial structure. Based on the performance metrics, we employ technique for order of preference by similarity to ideal solution (TOPSIS) and apply 10,000 different weighting combinations to the criteria of performance metrics to identify a robust statistical downscaling method and important criteria. The results show that MACA and BCSD have comparable skill in the time-series related criterion and BCSD outperforms other methods in distribution and extremes related criteria. In addition, MACA and BCCA, which incorporate spatial patterns, show higher skill in the multi-day persistence criterion for temperature, while BCSD shows the highest skill for precipitation. For the spatial structure related criterion, BCCA and MACA outperformed BCSD and BCCI. From the TOPSIS analysis, we found that MACA is the most robust method for all variables in South Korea, and BCCA and BCSD are the second for temperature and precipitation, respectively. We also found that the contribution of the multi-day persistence and spatial structure related criteria are crucial to ranking the skill of statistical downscaling methods.     

Geospatial spreadsheets with microscale air quality visualization and synchronization for supporting multiple-scenario visual collaboration

A major obstacle of the interdisciplinary communications for decision-making is that each participant prefers the solution from their own area because current collaboration systems cannot effectively mediate among many alternatives. If a visual collaboration system could display multiple alternatives in a single visual presentation, users would be able to compare alternatives with only a few variables, where all other parameters are fixed, at least from a visual perspective. In this study, we developed a visualization tool to support a microscale air quality (MSAQ) management group, the members of which often need to compare the effects of many alternative control methods. The main achievements of this study are: (1) three-dimensional (3D) visualization of the MSAQ using a 3D city model and (2) development of a geospatial spreadsheet (GS) to facilitate visual comparisons, thereby improving decision-making by making it easy to compare differences based on realistic visual display items. A questionnaire-based survey showed that use of the GS could generate a shared perspective and provide a spatiotemporal context for the participants, thereby making it easy to compare, mediate and reach decisions when confronted with many alternatives.     

Assessment on variability of extreme climate events for the Upper Thames River basin in Canada

Climate change may affect magnitude and frequency of regional extreme events with possibility of serious impacts on the existing infrastructure systems. This study investigates how the current spatial and temporal variations of extreme events are affected by climate change in the Upper Thames River basin, Ontario, Canada. A weather generator model is implemented to obtain daily time series of three climate variables for two future climate scenarios. The daily time series are disaggregated into hourly to capture characteristics of intense and rapidly changing storms. The maximum annual precipitation events for five short durations, 6‐, 12‐, 24‐, 48‐, and 72‐h durations, at each station are extracted from the generated hourly data. The frequency and seasonality analyses are conducted to investigate the temporal and spatial variability of extreme precipitation events corresponding to each duration. In addition, this study investigates the impacts of increase in temperature using reliability, resilience, and vulnerability. The results indicate that the extreme precipitation events under climate change will occur earlier than in the past. In addition, episodes of extremely high temperature may last longer up to 19·7% than under the no‐change climate scenario. This study points out that the revision of the design storms (e.g. 100‐ or 250‐year return period) is warranted for the west and the south east region of the basin. Copyright © 2011 John Wiley & Sons, Ltd.     

The value of updating ensemble streamflow prediction in reservoir operations

This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the meteorological and hydrological variations during that month. The reservoir operating policies derived from a sampling stochastic dynamic programming model using ESP scenarios updated three times a month were applied to the Geum River basin to measure the value of updated ESP for 21 years with 100 initial storage combinations. The results clearly demonstrate that updating the ESP scenario improves the accuracy of the forecasts and consequently their operational benefit. This study also proves that the accuracy of the ESP scenario, particularly when high flows occur, has a considerable effect on the reservoir operations. Copyright © 2010 John Wiley & Sons, Ltd.     

Stabilization of arsenic-contaminated mine tailings using natural and calcined oyster shells

Natural oyster shells (NOS) and calcined oyster shells (COS) were used to immobilize arsenic (As) from contaminated mine tailings. In addition, a blend of Portland cement (PC)/cement kiln dust (CKD) was used as a stabilizing agent. The Korean Standard Test (KST) method (1 N HCl extraction) was used to evaluate the effectiveness of the treatment. The experimental results showed that COS effectively immobilized As in treated mine tailings. Specifically, an As concentration less than 1 mg L⁻¹ was obtained following COS treatment at 25 and 30 wt%. However, all the samples subjected to NOS treatments failed the Korean warning standard of 1.2 mg L⁻¹ after 28 days of curing. All of the COS-PC treatments were successful meeting the Korean warning standard after 7 days of curing. However, the PC-only treatment failed to meet the Korean warning standard. Similarly, the CKD-only treatment was failed to meet the Korean warning standard after 7 days of curing. However, the COS-CKD treatment showed that when the COS content was greater than 20 wt%, less than 1 mg L⁻¹ of As leachability was obtained. Scanning electron microscopy–energy dispersive X-ray spectroscopy (SEM–EDX) showed needle-like and torpedo-like Ca–As phases in the COS-treated samples suggesting that As was strongly associated with Ca and O. X-ray absorption near edge structure (XANES) analyses confirmed that As(V) was prevalent in the tailings and that there were no changes in As speciation following NOS or COS treatment.     

Evaluation of regional climate model simulations versus gridded observed and regional reanalysis products using a combined weighting scheme

This study presents a combined weighting scheme which contains five attributes that reflect accuracy of climate data, i.e. short-term (daily), mid-term (annual), and long-term (decadal) timescales, as well as spatial pattern, and extreme values, as simulated from Regional Climate Models (RCMs) with respect to observed and regional reanalysis products. Southern areas of Quebec and Ontario provinces in Canada are used for the study area. Three series of simulation from two different versions of the Canadian RCM (CRCM4.1.1, and CRCM4.2.3) are employed over 23?years from 1979 to 2001, driven by both NCEP and ERA40 global reanalysis products. One series of regional reanalysis dataset (i.e. NARR) over North America is also used as reference for comparison and validation purpose, as well as gridded historical observed daily data of precipitation and temperatures, both series have been beforehand interpolated on the CRCM 45-km grid resolution. Monthly weighting factors are calculated and then combined into four seasons to reflect seasonal variability of climate data accuracy. In addition, this study generates weight averaged references (WARs) with different weighting factors and ensemble size as new reference climate data set. The simulation results indicate that the NARR is in general superior to the CRCM simulated precipitation values, but the CRCM4.1.1 provides the highest weighting factors during the winter season. For minimum and maximum temperature, both the CRCM4.1.1 and the NARR products provide the highest weighting factors, respectively. The NARR provides more accurate short- and mid-term climate data, but the two versions of the CRCM provide more precise long-term data, spatial pattern and extreme events. Or study confirms also that the global reanalysis data (i.e. NCEP vs. ERA40) used as boundary conditions in the CRCM runs has non-negligible effects on the accuracy of CRCM simulated precipitation and temperature values. In addition, this study demonstrates that the proposed weighting factors reflect well all five attributes and the performances of weighted averaged references are better than that of the best single model. This study also found that the improvement of WARs’ performance is due to the reliability (accuracy) of RCMs rather than the ensemble size.     

Alteration of hydrologic indicators for Korean catchments under CMIP5 climate projections

The change of hydrological regimes may cause impacts on human and natural system. Therefore, investigation of hydrologic alteration induced by climate change is essential for preparing timely proper adaptation to the changes. This study employed 24 climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Pathway (RCP) 4.5 scenario. The climate projections were downscaled at a station‐spacing for seven Korean catchments by a statistical downscaling method that preserves a long‐term trend in climate projections. Using an ensemble of future hydrologic projections simulated by three conceptual rainfall‐runoff models (GR4J, IHACRES, and Sacramento models), we calculated Hydrologic Alteration Factors (HAFs) to investigate degrees of variations in Indicators of Hydrologic Alteration (IHAs) derived from the hydrologic projections. The results showed that the seven catchments had similar trend in terms of the HAFs for the 24 IHAs. Given that more frequent severe floods and droughts were projected over Korean catchments, sound water supply strategies are definitely required to adapt to the alteration of streamflow. A wide range of HAFs between rainfall‐runoff models for each catchment was detected by large variations in the magnitude of HAFs with the hydrologic models and the difference could be the hydrologic prediction uncertainty. There were no‐consistent tendency in the order of HAFs between the hydrologic models. In addition, we found that the alterations of hydrologic regimes by climate change are smaller as the size of catchment is larger. Copyright © 2016 John Wiley & Sons, Ltd.     

Quality of hybrid location data drawn from GPS‐enabled mobile phones: Does it matter?

Despite their increasing popularity in human mobility studies, few studies have investigated the geo‐spatial quality of GPS‐enabled mobile phone data in which phone location is determined by special queries designed to collect location data with predetermined sampling intervals (hereafter “active mobile phone data”). We focus on two key issues in active mobile phone data—systematic gaps in tracking records and positioning uncertainty—and investigate their effects on human mobility pattern analyses. To address gaps in records, we develop an imputation strategy that utilizes local environment information, such as parcel boundaries, and recording time intervals. We evaluate the performance of the proposed imputation strategy by comparing raw versus imputed data with participants’ online survey responses. The results indicate that imputed data are superior to raw data in identifying individuals’ frequently visited places on a weekly basis. To assess the location accuracy of active mobile phone data, we investigate the spatial and temporal patterns of the positional uncertainty of each record and examine via Monte Carlo simulation how inaccurate location information might affect human mobility pattern indicators. Results suggest that the level of uncertainty varies as a function of time of day and the type of land use at which the position was determined, both of which are closely related to the location technology used to determine the location. Our study highlights the importance of understanding and addressing limitations of mobile phone derived positioning data prior to their use in human mobility studies.