Seung Beom Seo

Properties of fluoridated hydroxyapatite-alumina biological composites densified with addition of CaF2

Abstract Hydroxyapatite [HA, Ca10(PO4)6(OH)2] composites with 20 vol.% alumina (Al2O3) were sintered without pressure by adding CaF2. In the CaF2-free composites, severe decompositions occurred, resulting in poor densification. However, the addition of CaF2 suppressed the decomposition completely through the substitution of F− for OH− and consequent formation of fluor-hydroxyapatite [FHA, Ca10(PO4)6(OH)2−2xF2x]–Al2O3 composite. Hence, the obtained bodies reached full densification possessing enhanced mechanical properties. The hardness, flexural strength and fracture toughness of the CaF2-added composites improved by factors of ∼2–3 compared to CaF2-free composites, having values of 8 GPa, 125 MPa and 1.2 MPa m1/2, respectively. The osteoblast-like cells showed a similar proliferation behavior with respect to pure HA and plastic control for up to 7 days. The alkaline phosphatase (ALP) activity of the proliferated cells on the composites was higher than that on plastic control and slightly lower than that on pure HA, especially after prolonged culture periods.

Identification of dominant source of errors in developing streamflow and groundwater projections under near‐term climate change

Uncertainties in projecting the changes in hydroclimatic variables (i.e., temperature and precipitation) under climate change partly arises from the inability of global circulation models (GCMs) in explaining the observed changes in hydrologic variables. Apart from the unexplained changes by GCMs, the process of customizing GCM projections to watershed scale through a model chain—spatial downscaling, temporal disaggregation, and hydrologic model—also introduces errors, thereby limiting the ability to explain the observed changes in hydrologic variability. Toward this, we first propose metrics for quantifying the errors arising from different steps in the model chain in explaining the observed changes in hydrologic variables (streamflow and groundwater). The proposed metrics are then evaluated using a detailed retrospective analyses in projecting the changes in streamflow and groundwater attributes in four target basins that span across a diverse hydroclimatic regimes over the U.S. Sunbelt. Our analyses focused on quantifying the dominant sources of errors in projecting the changes in eight hydrologic variables—mean and variability of seasonal streamflow, mean and variability of 3 day peak seasonal streamflow, mean and variability of 7 day low seasonal streamflow, and mean and standard deviation of groundwater depth—over four target basins using an Penn state Integrated Hydrologic Model (PIHM) between the period 1956–1980 and 1981–2005. Retrospective analyses show that small/humid (large/arid) basins show increased (reduced) uncertainty in projecting the changes in hydrologic attributes. Further, changes in error due to GCMs primarily account for the unexplained changes in mean and variability of seasonal streamflow. On the other hand, the changes in error due to temporal disaggregation and hydrologic model account for the inability to explain the observed changes in mean and variability of seasonal extremes. Thus, the proposed metrics provide insights on how the error in explaining the observed changes being propagated through the model under different hydroclimatic regimes.

Flood risk assessment using regional regression analysis

This study aimed to create a flood risk map for ungauged regions, which have limited flood damage data and other relevant data. The fact that there is a shortage of data that are critical for the establishment of a flood assessment and mitigation plan is not surprising even in developed countries like South Korea. To address this problem, the regional regression concept in statistical hydrology was introduced to the flood risk assessment field in this study, and it was framed with a series of two regression functions: flood damage and regional coefficients. As the second regression function utilizes the local socioeconomic variables, the resulting flood risk map can reflect the spatial characteristics well. The proposed methodology was applied to create flood risk maps for the three metropolitan areas in South Korea. The comparison of the proposed methodology with the existing methods revealed that only the proposed methodology can produce a statistically meaningful flood risk map based on a recent major flood in 2001.

Water Balance Projection Using Climate Change Scenarios in the Korean Peninsula

This study proposes a new methodology for future water balance projection considering climate change by assigning a weight to each scenario instead of inputting future streamflows based on GCMs into a water balance model directly. K-nearest neighbor algorithm was employed to assign weights and streamflows in non-flood period (October to the following June) was selected as the criterion for assigning weights. GCM-driven precipitation was input to TANK model to simulate future streamflow scenarios and Quantile Mapping was applied to correct bias between GCM hindcast and historical data. Based on these bias-corrected streamflows, different weights were assigned to each streamflow scenarios to calculate water shortage for the projection periods; 2020s (2010~2039), 2050s (2040~2069), and 2080s (2070~2099). As a result by applying the proposed methodology to project water shortage over the Korean Peninsula, average water shortage for 2020s is projected to increase to 10~32% comparing to the basis (1967~2003). In addition, according to getting decreased in streamflows in non-flood period gradually by 2080s, average water shortage for 2080s is projected to increase up to 97% (516.5 million ) as maximum comparing to the basis. While the existing research on climate change gives radical increase in future water shortage, the results projected by the weighting method shows conservative change. This study has significance in the applicability of water balance projection regarding climate change, keeping the existing framework of national water resources planning and this lessens the confusion for decision-makers in water sectors.

Application of real option analysis for planning under climate change uncertainty: a case study for evaluation of flood mitigation plans in Korea

With concerns regarding global climate change increasing, recent studies on adapting to nonstationary climate change recommended a different planning strategy that could spread risks. Uncertainty in global climate change should be considered in any decision-making processes for flood mitigation strategies, especially in areas within a monsoon climate regime. This study applied a novel planning method called real option analysis (ROA) to an important water resources planning practice in Korea. The proposed method can easily be applied to other watersheds that are threatened by flood risk under climate change. ROA offers flexibility for decision-makers to reflect uncertainty at every stage during the project planning period. We successfully implemented ROA using a binomial tree model, including two real options—delay and abandon—to evaluate flood mitigation alternatives for the Yeongsan River Basin in Korea. The priority ranking of the four alternatives between the traditional discount cash flow (DCF) and ROA remained the same; however, two alternatives that were assessed as economically infeasible using DCF, were economically feasible using ROA. The binomial decision trees generated in this study are expected to be informative for decision-makers to conceptualize their adaptive planning procedure.

Selecting climate change scenarios for regional hydrologic impact studies based on climate extremes indices

When selecting a subset of climate change scenarios (GCM models), the priority is to ensure that the subset reflects the comprehensive range of possible model results for all variables concerned. Though many studies have attempted to improve the scenario selection, there is a lack of studies that discuss methods to ensure that the results from a subset of climate models contain the same range of uncertainty in hydrologic variables as when all models are considered. We applied the Katsavounidis–Kuo–Zhang (KKZ) algorithm to select a subset of climate change scenarios and demonstrated its ability to reduce the number of GCM models in an ensemble, while the ranges of multiple climate extremes indices were preserved. First, we analyzed the role of 27 ETCCDI climate extremes indices for scenario selection and selected the representative climate extreme indices. Before the selection of a subset, we excluded a few deficient GCM models that could not represent the observed climate regime. Subsequently, we discovered that a subset of GCM models selected by the KKZ algorithm with the representative climate extreme indices could not capture the full potential range of changes in hydrologic extremes (e.g., 3-day peak flow and 7-day low flow) in some regional case studies. However, the application of the KKZ algorithm with a different set of climate indices, which are correlated to the hydrologic extremes, enabled the overcoming of this limitation. Key climate indices, dependent on the hydrologic extremes to be projected, must therefore be determined prior to the selection of a subset of GCM models.

Assessing the restoration time of surface water and groundwater systems under groundwater pumping

Since surface water and groundwater systems are fully coupled and integrated, increased groundwater withdrawal during drought may reduce groundwater discharges into the stream, thereby prolonging both systems’ recovery from drought. To analyze watershed response to basin-level groundwater pumping, we propose a modelling framework to understand the resiliency of surface water and groundwater systems using an integrated hydrologic model under transient pumping. The proposed framework incorporates uncertainties in initial conditions to develop robust estimates of restoration times of both surface water and groundwater and quantifies how pumping impacts state variables such as soil moisture. Groundwater pumping impacts over a watershed were also analyzed under different pumping volumes and different potential climate scenarios. Our analyses show that groundwater restoration time is more sensitive to variability in climate forcings as opposed to changes in pumping volumes. After the cessation of pumping, streamflow recovers quickly in comparison to groundwater, which has higher persistence. Pumping impacts on various hydrologic variables were also discussed. Potential for developing optimal conjunctive management plans using seasonal-to-interannual climate forecasts is also discussed.

A dual preamble random access protocol for reducing access congestion in disaster situations

In long term evolution (LTE) systems, the random access (RA) protocol is used for initial access. Since the protocol is designed based on contention, the congestion on physical RA channel (PRACH) can get worse severely as the number of contending user equipments (UEs) increases. On the other hand, when a disaster occurs, we expect that a huge number of access attempts and traffic bursts rush to LTE systems, and these are likely to block each other, which can lead to excessive access delay and packet loss. In this paper, we propose a novel RA scheme for solving the congestion on the PRACH of LTE system. In the scheme, UEs attempt to access the LTE network by using not a single access preamble but two preambles simultaneously. As a result, we get the same effect as the number of preambles is logically increased. Although the congestion can be reduced with the proposed scheme, it can bring about unnecessary resource overhead. We formulate an optimal problem, by which we can maximize the system performance considering both the congestion control and the resource overhead. The simulation results show that the proposed scheme well resolves the congestion while reducing the overhead as much as possible.

Robust and fast device discovery in OFDMA-based cellular networks for disaster environment

Device-to-device (D2D) communication is a salient function of public safety networks for enabling communications, even in a disaster environment where the communication infrastructures are fully or partially destroyed. In order that devices directly communicate, they must first find and identify each other. Especially, in a disaster environment where wireless channel conditions may be very poor, discovery should be robust and fast for quick resume of the blocked communications again. In this paper, considering frequency selective fading which may lead to missing devices even though they exist, we propose a device discovery scheme using a simple physical layer waveform called signature. Typically, in a signature-based discovery under OFDMA-based system, a device advertises its presence by selecting a discovery channel and energizing subcarriers in the channel for a while (one subcarrier at a time). In the proposed scheme, we design discovery channels having well dispersed subcarriers to tolerate the frequency selectivity. By simulation, it is shown that not only successful discovery ratio of the proposed scheme is improved, but also our scheme is more frequency selective fading tolerant in comparison with other approaches.