The determination of soil parameters is important in predicting the simulated surface runoff using either a distributed or a lumped rainfall-runoff model. Soil characteristics can be collected using remote sensing techniques and represented as a digital map. There is no universal agreement with respect to the determination of a representative parameter from a gridded digital map. Two representative methods, i.e., arithmetic and predominant, are introduced and applied to both FLO-2D and HEC-HMS to improve the model’s accuracy. Both methods are implemented in the Yongdam catchment, and the results show that the former seems to be more accurate than the latter in the test site. This is attributed to the high conductivity of the dominant soil class, which is A type.

Two main sources of data, meteorological data and land surface characteristics, are essential to effectively run a distributed rainfall-runoff model. The specification and averaging of the land surface characteristics in a suitable way is crucial to obtaining accurate runoff output. Recent advances in remote sensing techniques are often being used to derive better representations of these land surface characteristics. Due to the mismatch in scale between digital land cover maps and numerical grid sizes, issues related to upscaling or downscaling occur regularly. A specific method is typically selected to average and represent the land surface characteristics. This paper examines the amount of flooding by applying the FLO-2D routing model, where vegetation heterogeneity is manipulated using the Manning’s roughness coefficient. Three different upscaling methods, arithmetic, dominant, and aggregation, were tested. To investigate further, the rainfall-runoff model with FLO-2D was facilitated in Yongdam catchment and heavy rainfall events during wet season were selected. The results show aggregation method provides better results, in terms of the amount of peak flow and the relative time taken to achieve it. These rwsults suggest that the aggregation method, which is a reasonably realistic description of area-averaged vegetation nature and characteristics, is more likely to occur in reality.

In this study a method for filling in missing data of river water temperature using a pre-constructed mathematical relationship between air and water temperatures is presented. A regression between water temperatures at individual stations and ambient air temperatures at nearby weather stations can provide a practical method for representing missing water temperature data for an entire region. Air and water temperature data that were collected from two test sites (one coastal and, one inland) were individually fitted to a nonlinear regression model. To consider seasonal hysteresis effects, separate functions were fitted to the data in the rising and falling limbs. A single-criterion, multi-parameter optimization technique was used to determine the optimal parameter sets. This method minimizes the differences between the time series of the measured and estimated data. The constructed air-water temperature relationship was subsequently applied to represent missing water temperature data. It was found that the RMSEs(MBEs) were in the range of 1.843―1.976℃(-0.329―0.201℃) and the coefficient of determination were in the range of 0.92―0.96. The results demonstrate that the predicted water temperatures using the regression equations were reasonably accurate.

Aquatic plants serve the crucial function of helping to balance water reservoir ecosystem, as they filter and remove major minerals required for algal growth such as nitrogen, ammonia, and nitrates. Aquatic plants provide food, shade, and protection for the aquatic biome in and around the reservoir. Thus, it is important to accurately determine the existence and areal extent of the aquatic plants. In the present study drone-based facilities were used for this purpose. In the Muncheon water reservoir, Gyeongbuk, the Normalized Difference Vegetation Index (NDVI) and Surface Algal Bloom Index (SABI) were used to determine the existence status of the aquatic plants. The data so obtained exhibited reasonable accuracy; drone-based facilities can be used in future to identify the areal extent of aquatic plants.

Drought is a reoccurring worldwide natural hazard that affects not only food production but also economics, health, and infrastructure. Drought monitoring is usually performed with precipitation-based indices without consideration of the actual state and amount of the land surface properties. A drought index based on the actual evapotranspiration can overcome these shortcomings. The severity of a drought can be quantified by making a spatial map. The procedure for estimating actual evapotranspiration is costly and complicated, and requires land surface information. The possibility of utilizing drone-driven remotely sensed data for actual evapotranspiration estimation was analyzed in this study. A drone collected data was used to calculate the normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI). The spatial resolution was 10 m with a grid of 404 x 395. The collected data were applied and parameterized to an actual evapotranspiration estimation. The result shows that drone-based data is useful for estimating actual evapotranspiration and the corresponding drought indices.

A monitoring system for a field magnetometer was configured with assistance of a Raspberry Pi as a data logger. The suggested geomagnetic system uses a semi-real-time data transmission module. The system consists of two parts: a field-observation part and a data-center part. The field-observation part comprises a Raspberry Pi, magnetometer, LTE router, and power source, while the data center part takes samples at the site. The collected magnetometer data are then sent to the data center through the LTE router. The newly designed monitoring system was deployed and checked in Jeju-do island, and found to operate stably. The suggested system is promising in that it is simple and cost saving, providing at least physical insight and knowledge on the complex natural phenomena.

To project the effects of climate-induced change on aquatic environments, it is necessary to determine the thermal constraints affecting different fish species and to acquire time series of the current and projected water temperature (WT). Assuming that a nonlinear regression between the WT at individual stations and the ambient air temperature (AT) at nearby weather stations could represent the best relationship of air-water temperature, This study estimates future WT using a general circulation model (GCM). In addition, assuming that the grid-averaged observations of AT correspond to the AT output from GCM simulation, this study constructed a regression curve between the observations of the local WT and the concurrent GCM-simulated surface AT. Because of its low spatial resolution, downscaling is unavoidable. The projected WT under global warming scenario A2 (B2) shows an increase of about 1.6 (0.9 ) for the period 2080-2100. The maximum/minimum WT shows an amount of change similar to that of the mean values. This study will provide guidelines for decision-makers and engineers in climate-induced river environment and ecosystem management.

Disasters like mountain landslides and collapse in cutting areas claim many a life and cause economic losses, involving much effort and expenses for their recovery. This study has surveyed and analyzed incident of disasters that had occurred on the sloping sides in Korea for the past 13 years in an effort to relieve damage caused by disasters on the sloping sides. The analysis confirms that while the major cause of disasters on the sloping side was storms in the past, frequency of disasters on sloping sides caused by local downpour is steadily on the rise. In addition, while disasters were concentrated in Gangwon Province, a mountainous region of the country, frequency of disasters occurring on the sloping sides is steadily increasing recently on the sloping sides in downtown areas in Seoul, Gyeonggi-do and so forth, attributing a large percentage of disasters to sloping sides. Data surveyed and analyzed in this study are thought to be applicable as basic data for the establishment of effective measures for the prevention of disasters occurring on the sloping sides in the days to come.