The frequency and magnitude of droughts and floods are increasing due to climate change, necessitating comprehensive water resource management of estuary reservoirs considering both water supply and flood control. Therefore, the purpose of this study is to optimize the seasonal managed water levels of estuary reservoirs using a Robust Decision-Making (RDM) approach. Sustainability indices were used to establish evaluation indicators that consider both water supply and flood control. Climate change data were selected from six Global Climate Models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6), designated as S1 to S6 in order of their precipitation levels, utilizing SSP (Shared Socioeconomic Pathway)1, 2, 3, and 5 scenarios. The Hydrological Simulation Program-Fortran (HSPF) was used to simulate changes in inflow due to climate change, and changes in reservoir water levels were simulated according to sluice gate operations. The evaluation indicators calculated under current managed water level conditions showed very poor performance especially in terms of water supply. The optimized managed water level based on RDM using regret was WL0 to WL5 at 0.0, 0.0, -0.3, -1.1, -1.7, and -2.2 EL. m, respectively. Applying the optimal alternative showed improvements in terms of water supply.

The determination of soil dry unit weight is crucial for assessing the stability of diverse geotechnical structures. While empirical regression methods have been proposed to predict dry unit weight using data from various sounding techniques (e.g., CPT, SPT, and DCPT), these approaches may exhibit uncertainties when applied across different regions or soil types, thus compromising their reliability. This study investigates the efficacy of Bayesian regression analysis in conjunction with outlier processing techniques for the probabilistic prediction of dry unit weight, utilizing Dynamic Cone Penetration Test (DCPT) and in-situ density test data. The findings demonstrate that Bayesian methods, coupled with outlier processing, effectively quantify prediction uncertainty and leverage prior information to yield more robust and accurate predictions. The proposed methodology facilitates reliable and precise probabilistic estimations of dry unit weight using the cost-effective and straightforward DCPT. Moreover, this approach offers the advantage of deriving site-specific prediction models by integrating both the distribution of data from previous studies and a limited amount of field data, as opposed to directly applying generalized formulas. The results underscore the potential of this methodology in enhancing the accuracy and reliability of dry unit weight predictions in geotechnical engineering. Further research exploring the influence of varied soil conditions and the quantity of field data on prediction outcomes may enhance the generality and applicability of these findings.

The development of cavities underneath a culvert in levees lead to levee collapse, and it is crucial to inspect for cavity formation to ensure flood control stability. However, there are currently no methods available domestically to verify these cavities. This study analyzes the characteristics of reflection signals from cavities beneath drainage sluice gates using numerical simulations to verify the potential of detecting these cavities with Ground Penetrating Radar (GPR). To simulate the GPR signal responses numerically, the open-source program gprMax, which solves Maxwell's equations using the Finite-Difference Time-Domain (FDTD) method, is applied. Numerical simulation cases are constructed with variables such as concrete thickness, presence of rebar, and cavity location, considering the structural features of cavities. Three commonly used GPR antenna modules, GSSI 400 MHz, GSSI 1.5 GHz, and MALA 1.2 GHz, are applied. Under reinforced concrete conditions, various scales of hyperbolas overlapped due to reflections and double reflections from the rebar, making the B-Scan complex. The GSSI 1.5 GHz could somewhat distinguish contact cavities up to a concrete thickness of 0.3 m, but have difficulty distinguishing isolated cavities. The MALA 1.2 GHz has difficulty distinguishing between contact and isolated cavities in the presence of rebar. Despite some limitations, the high-frequency GPRs show potential for distinguishing cavities beneath a culvert and characteristics of reflections caused by cavities are presented in the study. However, for practical application, it is deemed necessary to derive signal characteristics considering field conditions, such as inhomogeneity of field media, through GPR field data collection and analysis.

As rice paddies are being converted to upland fields, there is necessary to design agricultural irrigation pipelines for upland crops. The layout of irrigation pipelines needs to consider spatial data, as they tend to be laid on plot boundaries, roads and rivers. The objective of this study was to design irrigation pipeline layouts for stable water supply considering spatial information. Two reservoir irrigation districts (Geoyeo and Shinheung) with significant conversion from rice paddies to upland fields were selected as study sites. The optimal number and location of water storages were designed using a genetic algorithm. The branched pipeline layouts that minimized water supply failure areas were designed using Dijkstra algorithm and taking into account pipe break probability. To ensure a stable water supply, the reduction in water supply areas was calculated after adding extra pipelines connecting branch lines. The results of branched pipeline layout, the Geoyeo site needed 3.2 km of mainline and 15.4 km of branch line, while the Shinheung site needed 5.2 km of mainline and 11.2 km of branch line. The water supply failure areas were 2.96 ha in Geoyeo and 2.33 ha in Shinheung. The water supply failure areas could be reduced by up to 9% in Geoyeo and by up to 15% in Shinheung by adding extra pipelines. This study can be applied to determine the layout of agricultural irrigation pipeline, ensuring a stable water supply for upland crops.

Erratic rainfall due to climate change in the Kenya's Arid and Semi-Arid Lands, particularly Makueni County, threatens rain-fed maize production and food security. This study explores deficit irrigation to optimize water use, aiming to improve water productivity, stabilize yields and enhance resilience, aligning with Sustainable Development Goals on water management and climate action. The Soil-Water-Atmosphere-Plant (SWAP) model is used for estimating soil moisture/transpiration and managing irrigation and drainage across diverse climatic and environmental contexts from 2013 to 2022. We derived the Soil Moisture Deficit Index (SMDI) and Crop Water Stress Index CWSI to assess drought conditions and optimize irrigation schedules. Several numerical experiments were conducted to validate and test SWAP in balancing water use and productivity at the field-scale. The SMDI-based irrigation interval (7-day) consistently outperformed in achieving water productivity up to 25.2 kg ha^-1 mm^-1 compared to the results (22.5 kg ha^-1 mm^-1) of CWSI. SMDI showed better crop yields and water savings, especially in the dry years, while CWSI performed better in the wetter years but consumed more water. The 7-day interval with SMDI offered the best balance between water efficiency and productivity. Although SMDI requires advanced soil moisture monitoring, it proved more efficient than CWSI, which may be suitable for less-resourced areas.

This study employed a coupled HSPF-EFDC model to examine the three-dimensional salinity distribution within the Ganwol estuarine reservoir and assess the suitability of its water for field crops and rice cultivation. The HSPF model was used to calculate river inflows from the upstream watershed, which subsequently served as boundary conditions for the EFDC model to simulate water levels and spatial salinity distributions. Model calibration and validation indicated generally satisfactory performance, although some discrepancies were observed during low-flow periods. Results showed that the reservoir's salinity was primarily affected by river inflows, seasonal variations, and potential seepage through the barrage or floodgates. According to FAO salinity thresholds, median salinity levels at various pumping stations generally fell into the "Slight to Moderate" restriction category for field crops, with outliers occasionally reaching "Severe" levels. For rice, median salinity levels in most stations were sufficient to maintain 75-100% yield potential, yet extreme salinity events could reduce yields by over 50%. These findings underscore the necessity for comprehensive management strategies-such as monitoring seawater intrusion and investigating crop varieties more resilient to salinity-to optimize agricultural water use. Overall, this study provides foundational information for effective salinity management in estuarine reservoirs, contributing to improved agricultural productivity and sustainable water resource utilization in reclaimed areas.