Low-impact development (LID) methods are an important approach to storm-water mitigation. Modeling the effects of these installations using rainfall-runoff simulations can provide useful data for future design and implementation. In this study, we used the Storm Water Management Model to assess seven types of LID installations (vegetated areas, garden pots, tree filter boxes, permeable pavement, infiltration ditches, rain barrels, and infiltration blocks) at a South Korean industrial site. Using both short- and long-term simulation periods and distinct sub-basins within the study site, we were able to assess LID performance at the combined watershed, as well as at one LID facility. All LID types showed reasonable performance for storm-water runoff reduction, though rain barrels were the least effective. The effect of rainfall runoff reduction on LID facilities is changed according to rainfall depth (annual precipitation, monthly rainfall), the ratio of drainage area and facility capacity. We concluded that SWMM-LID modeling can effectively support the management of LID installations by providing additional design and planning data to better mitigate the effects of storm-water runoff.

Rainwater pumping stations located near urban centers or agricultural areas help prevent flooding by activating an appropriate number of pumps with varying capacities based on real-time rainwater inflow. However, relying solely on rule-based pump operations that monitor only basin water levels is often insufficient for effective control. In addition to maintaining a low maximum water level to prevent flooding, pump operation at rainwater stations also requires minimizing the number of pump on/off switches. Reducing pump switch frequency lowers the likelihood of mechanical failure and thus decreases maintenance costs. This paper proposes a real-time pump operation method for rainwater pumping stations using Deep Reinforcement Learning (DRL) to meet these operational requirements simultaneously, based only on currently observable information such as rainfall, inflow, storage volume, basin water level, and outflow. Simulated rainfall data with various return periods and durations were generated using the Huff method to train the model. The Storm Water Management Model (SWMM), configured to simulate the Gasan rainwater pumping station located in Geumcheon-gu, Seoul, South Korea, was used to conduct experiments. The performance of the proposed DRL model was then compared with that of the rule-based pump operation currently used at the station.

In recent years, the intensity and frequency of droughts have been increasing with the advent of the climate crisis. Agricultural droughts have a significant economic and social impact. Agricultural drought is not only a natural disaster but also leads to food security threats and reduced economic activities, such as decreased productivity. Therefore, it is very important to specify the scale of agricultural drought and quantitatively estimate the economic damage. In this study, we developed an analytical methodology to quantitatively assess the economic damage of agricultural drought and estimated the damage of agricultural drought in 2018 and 2019 for the Republic of Korea. The 2018 agricultural drought was estimated to have caused USD 4.438 million in damage cost and USD 5.180 million in recovery cost. The 2019 drought was less damaging than the previous year, with an estimated damage cost of USD 286,000 and recovery costs of USD 218,000. The results suggest that the economic impact of agricultural drought varies by region depending on the frequency and intensity of the drought and confirm the importance of regional strategies for effective drought management and response. The impacts of agricultural drought go beyond short-term agricultural losses and lead to long-term economic burdens. Therefore, the results of this study are expected to be used as a basis for understanding the impacts of agricultural drought on national economies and for developing policies and strategies to minimize impacts.

Numerical and empirical studies of soil slurry transport and deposition in urban stormwater sewers are few, presumably due to the difficulty of direct observation of soil slurry flow in stormwater pipes. Slurry in a sewer system includes both suspended load and bedload, but few studies have attempted to demarcate these two components. A boundary layer is a crucial determinant of sediment transport capacity. Stormwater runoff enters the sewer in turbulent flow, mostly mixed with soil slurry generated by rainfall. In this paper, we attempt analysis using ANSYS Fluent commercial CFD software. We describe the development of a numerical analytical methodology capable of predicting the flow of soil slurry in stormwater pipes, and propose a method for estimating the sediment–flow boundary layer. Using this model, we simulated stormwater runoff with a large content of soil slurry during a rainfall event. We investigated soil slurry transport and predict the formation of shear boundary layer by varying the inlet conditions (volume of soil slurry entering the stormwater sewer system) and by analyzing the flow velocity field and soil slurry volume fraction in the pipes under various experimental flow conditions. Based on the shear and settling velocity of sediment particles, we propose criteria for the formation of a shear boundary layer in stormwater pipes.

The construction industry, responsible for approximately 30% of global carbon emissions, is closely linked to national development, making carbon reduction challenging. While national development is of paramount importance, it is essential to prioritize individual projects and establish a direction for reducing carbon emissions. The starting point should involve calculating the carbon emissions for each project and comprehending their quantitative impact. In this study, we calculated the carbon emissions for a small river maintenance project aimed at disaster prevention in the construction industry in Yongin-si, Gyeonggi-do, Korea. The total carbon emissions generated by the small river maintenance project in the target area amounted to 2016.6 tonCO₂. By process, the embankment construction was responsible for 789.7 tonCO₂, while the revetment construction contributed 1226.9 tonCO₂. The analysis revealed that the carbon emissions generated by the small river maintenance project equated to 10.2 tonCO₂/km of river length. Additionally, we developed an equation by applying the double-log function model (log–log) to small river length and carbon emissions. The coefficient of determination for the calculation equation is 0.42, which may not yield highly precise results. However, it is believed that this equation will provide a rough estimate of the carbon emissions associated with the small river maintenance project.