Surface current variability in the Keum River Estuary (South Korea) during summer 2002 as observed by high-frequency radar and coastal monitoring buoy

High-frequency (HF) radar observations of surface currents were conducted for 3 months during summer 2002 in the Keum River estuary. A comparison between HF radar-derived currents and directly measured ones form a buoy showed that the regression slope is close to 1 and the correlation coefficient greater than 0.86, with an RMS difference less than 13 cm/s which is less than 17% of the tidal current. This fairly good agreement allows us to use HF radar observation in investigating the surface flow and circulation in this tidal-current-dominant coastal-plume area. To examine the spatial variation in tidal current characteristics, as well as currents associated with non-tidal forcing, the HF radar-derived currents were separated into tidal and sub-tidal frequency currents. The overall pattern of M₂-current ellipse distribution in the study area showed a counterclockwise rotation, with the offshore maximum current direction to the northeast. Eccentricity, the direction of maximum current, and the phase of net motion of the ellipse changed near the estuary mouth and near the gap of the Saemangeum reclamation tide dyke due to the complex coastal geometry and the out-flowing jet during the ebb period.     

Developing a high-resolution climatology for the Central California coastal region

This work presents a procedure for developing a high-resolution, regional climatology estimate, named RClimo, off the coast of central California. This high-resolution climatology may provide an alternative way to initialize numerical nowcast/forecast exercises in coastal regions. The methodology includes two primary steps: (1) averaging available data on a high-resolution grid and (2) objective interpolating the resulting average profiles onto a regular grid. The first step involves the computation of averages over density layers in the vertical and allowing for data gaps in the horizontal if data are unavailable at a high resolution. The OA in the second step uses anisotropic correlation length scales derived from the data themselves and an averaging radius to preserve the scales and variability of the synoptic fields.     

TBT toxicity on the marine microalga Nannochloropsis oculata

Commercial antifouling formulations containing TBT are the major source of organotin contamination in coastal waters. In view of the persisting TBT residues (13 ng Sn l⁻¹) in the coastal waters of South Korea, an attempt has been made to evaluate the growth response and biochemical composition of laboratory-cultured Nannochloropsis oculata to TBT toxicity. It is evident that the persisting concentration level of TBT is high enough to cause adverse effect on the microalgal species. The EC₅₀ (24 h) was found to be at 0.89 nM level of TBT for this marine eustigmatophyte N. oculata. Photosynthetic pigment content was significantly affected. At elevated TBT concentrations of 1.0 nM, especially pronounced changes in biochemical composition was found. TBT tolerance of N. oculata and its growth as well as biochemical responses are discussed.     

Spatial rainfall model using a pattern classifier for estimating missing daily rainfall data

Missing data in daily rainfall records are very common in water engineering practice. However, they must be replaced by proper estimates to be reliably used in hydrologic models. Presented herein is an effort to develop a new spatial daily rainfall model that is specifically intended to fill in gaps in a daily rainfall dataset. The proposed model is different from a convectional daily rainfall generation scheme in that it takes advantage of concurrent measurements at the nearby sites to increase the accuracy of estimation. The model is based on a two-step approach to handle the occurrence and the amount of daily rainfalls separately. This study tested four neural network classifiers for a rainfall occurrence processor, and two regression techniques for a rainfall amount processor. The test results revealed that a probabilistic neural network approach is preferred for determining the occurrence of daily rainfalls, and a stepwise regression with a log-transformation is recommended for estimating daily rainfall amounts.     

Performance Analysis of ANN Prediction for Groundwater Level Considering Regional-Specific Influence Components

Groundwater level (GWL) varies periodically or non-periodically with various factors including precipitation, river stage (RS) change, sea level, and dewatering activities. In this study, the effect of influence components on the prediction of GWL using an artificial neural network (ANN) was investigated. Six regions with different hydrologic and geologic conditions were collected and adopted in the investigation using various input combinations. In urban areas with a high surface paved ratio, GWL was mainly affected by RS. In rural areas, the permeability of ground showed a significant impact on GWL. For such cases, the moving average (MA) was a suitable component as it could reflect both time lag and the effect of preceding precipitation. It was shown that site-specific influence component should be firstly identified and introduced into input for more enhanced and reliable prediction of GWL using ANN. The effect of learning data length (LDL) was less significant. In urban and rural areas, the introduction of RS and MA into ANN input significantly improved the prediction performance, respectively, which was consistent with the correlation analysis of GWL influence components.     

Non-stationary frequency analysis of extreme precipitation in South Korea using peaks-over-threshold and annual maxima

The conventional approach to the frequency analysis of extreme precipitation is complicated by non-stationarity resulting from climate variability and change. This study utilized a non-stationary frequency analysis to better understand the time-varying behavior of short-duration (1-, 6-, 12-, and 24-h) precipitation extremes at 65 weather stations scattered across South Korea. Trends in precipitation extremes were diagnosed with respect to both annual maximum precipitation (AMP) and peaks-over-threshold (POT) extremes. Non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with model parameters made a linear function of time were applied to AMP and POT respectively. Trends detected using the Mann–Kendall test revealed that the stations showing an increasing trend in AMP extremes were concentrated in the mountainous areas (the northeast and southwest regions) of South Korea. Trend tests on POT extremes provided fairly different results, with a significantly reduced number of stations showing an increasing trend and with some stations showing a decreasing trend. For most of stations showing a statistically significant trend, non-stationary GEV and GPD models significantly outperformed their stationary counterparts, particularly for precipitation extremes with shorter durations. Due to a significant-increasing trend in the POT frequency found at a considerable number of stations (about 10 stations for each rainfall duration), the performance of modeling POT extremes was further improved with a non-homogeneous Poisson model. The large differences in design storm estimates between stationary and non-stationary models (design storm estimates from stationary models were significantly lower than the estimates of non-stationary models) demonstrated the challenges in relying on the stationary assumption when planning the design and management of water facilities. This study also highlighted the need of caution when quantifying design storms from POT and AMP extremes by showing a large discrepancy between the estimates from those two approaches.     

Variations of the critical frequency <Emphasis Type="Italic">foF</Emphasis>2 at middle latitudes during strong disturbance in the electric field observed in the <Emphasis Type="Italic">F</Emphasis> region over saint-santin

Morphological analysis of variations of the critical frequency foF2 in the midlatitude ionosphere at various sectors of local time is carried out on the basis of data from ground-based stations of vertical sounding of the ionosphere in the period when during use of the incoherent scatter radar at Saint-Santin an anomalously strong increase in the electric field was observed at heights of the ionospheric F region in the period of enhanced geomagnetic activity (4+ < Kp < 6−). The obtained picture of the space-time distribution of disturbances in foF2 makes it possible to assume that they could be caused by penetration to middle latitudes of the large-scale electric field of the magnetospheric convection directed westward in the nighttime and morning hours and eastward in the noon and evening sectors.     

Effect of the inter-annual variability of rainfall statistics on stochastically generated rainfall time series: part 2. Impact on watershed response variables

This study analyzes how the stochastically generated rainfall time series accounting for the inter-annual variability of rainfall statistics can improve the prediction of watershed response variables such as peak flow and runoff depth. The modified Bartlett–Lewis rectangular pulse (MBLRP) rainfall generation model was improved such that it can account for the inter-annual variability of the observed rainfall statistics. Then, the synthetic rainfall time series was generated using the MBLRP model, which was used as input rainfall data for SCS hydrologic models to produce runoff depth and peak flow in a virtual watershed. These values were compared to the ones derived from the synthetic rainfall time series that is generated from the traditional MBLRP rainfall modeling. The result of the comparison indicates that the rainfall time series reflecting the inter-annual variability of rainfall statistics reduces the biasness residing in the predicted peak flow values derived from the synthetic rainfall time series generated using the traditional MBLRP approach by 26–47 %. In addition, it was observed that the overall variability of the peak flow and run off depth distribution was better represented when the inter-annual variability of rainfall statistics are considered.     

Determination of reservoir induced earthquake using support vector machine and gaussian process regression

The prediction of magnitude (M) of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine (SVM) and Gaussian Process Regression (GPR) for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth (H) are considered as inputs to the SVM and GPR. We give an equation for determination of reservoir induced earthquake M. The developed SVM and GPR have been compared with the Artificial Neural Network (ANN) method. The results show that the developed SVM and GPR are efficient tools for prediction of reservoir induced earthquake M.