This study estimates soil moisture content (SMC) using Sentinel-1A/B C-band synthetic aperture radar (SAR) images and an artificial neural network (ANN) over a 40 × 50-km² area located in the Geum River basin in South Korea. The hydrological components characterized by the antecedent precipitation index (API) and dry days were used as input data as well as SAR (cross-polarization (VH) and copolarization (VV) backscattering coefficients and local incidence angle), topographic (elevation and slope), and soil (percentage of clay and sand)-related data in the ANN simulations. A simple logarithmic transformation was useful in establishing the linear relationship between the observed SMC and the API. In the dry period without rainfall, API did not decrease below 0, thus the Dry days were applied to express the decreasing SMC. The optimal ANN architecture was constructed in terms of the number of hidden layers, hidden neurons, and activation function. The comparison of the estimated SMC with the observed SMC showed that the Pearson’s correlation coefficient (R) and the root mean square error (RMSE) were 0.85 and 4.59%, respectively.

Small-scale reservoirs located in river estuaries are a significant water resource supporting agricultural and industrial activities; however, they face annual challenges of eutrophication and algal bloom occurrences due to excessive nutrient accumulation and watershed characteristics. Efficient management of algal blooms necessitates a comprehensive analysis of their spatiotemporal distribution characteristics. Therefore, this study aims to develop a chlorophyll-a (Chl-a) estimation model based on high-resolution satellite remote sensing data from Sentinel-2 multispectral sensors and multiple linear regression. The multiple linear regression (MLR) models were constructed using multiple reflectance-based variables that were collected over 2 years (2021–2022) in an estuarine reservoir. A total of 21 significant input variables were selected by backward elimination from the 2–4 band algorithms as employed in previous Chl-a estimation studies, along with the Sentinel-2 B1-B8A wavelength ratio. The developed algorithm exhibited a coefficient of determination of 0.65. Spatiotemporal variations in Chl-a concentration generated by the algorithm reflected the movement of high Chl-a concentration zones within the body of water. Through this analysis, it turned out that Sentinel-2-based spectral images were applicable to a small-scale reservoir which is relatively long and narrow, and the algorithm estimated changes in concentration levels over the seasons, revealing the dynamic nature of Chl-a distributions. The model developed in this study is expected to support effective algal bloom management and water quality improvement in a small-scale reservoir or similar complex water quality water bodies.

This study aims to develop a reservoir operation rule adding downstream environmental flow release (EFR) to the exclusive use of irrigation water supply (IWS) from agricultural reservoirs through canals to rice paddy areas. A reservoir operation option was added in the Soil and Water Assessment Tool (SWAT) to handle both EFR and IWS. For a 366.5 km² watershed including three agricultural reservoirs and a rice paddy irrigation area of 4744.7 ha, the SWAT was calibrated and validated using 21 years (1998–2018) of daily reservoir water levels and downstream flow data at Gongdo (GD) station. For reservoir water level and streamflow, the average root means square error (RMSE) ranged from 19.70 mm to 19.54 mm, and the coefficient of determination (R²) and Nash–Sutcliffe efficiency (NSE) had no effect on the improved SWAT. By applying the new reservoir option, the EFR amount for a day was controlled by keeping the reservoir water level up in order to ensure that the IWS was definitely satisfied in any case. The downstream mean wet streamflow (Q95) decreased to 5.70 m³/sec from 5.71 m³/sec and the mean minimum flow (Q355) increased to 1.05 m³/sec from 0.94 m³/sec. Through the development of a SWAT reservoir operation module that satisfies multiple water supply needs such as IWR and EFR, it is possible to manage agricultural water in the irrigation period and control the environmental flow in non-irrigation periods. This study provides useful information to evaluate and understand the future impacts of various changes in climate and environmental flows at other sites.

This study assessed the water supply stability for Boryeong multipurpose dam by applying future dry climate change scenarios and Soil and Water Assessment Tool (SWAT). CMCC-CM, INM-CM4, and IPSL-CM5A-MR RCP 4.5 and 8.5 scenarios were selected as the future dry conditions using Runs theory and Standardized Precipitation Index (SPI). For historical (1980–1999), present (2000–2019), and future periods (2030s, 2050s, 2070s, and 2090s) of the 6 scenarios, SWAT model was used to simulate the future dam water supply stability. The stability was evaluated in terms of reliability (R_T), resilience (R_S), and vulnerability (V) based on the monthly target storage. The results showed that the future R_T can be decreased to 0.803 in 2050s IPSL-CM5A-MR RCP 8.5 scenario from present 0.955. The future R_S and V showed the minimum value of 0.003 and the biggest value of 3567.6 × 10⁶ m³ in 2070s IPSL-CM5A-MR RCP 4.5 scenario. The future R_T, R_S, and V showed that the dam has low resilience and is vulnerable to future drought scenarios.

Chromophoric dissolved organic matter (CDOM) is a mixture of various types of organic matter and a useful parameter for monitoring complex inland surface waters. Remote sensing has been widely utilized to detect CDOM in various studies; however, in many cases, the dataset is relatively imbalanced in a single region. To address these concerns, data were acquired from hyperspectral images, field reflection spectra, and field monitoring data, and the imbalance problem was solved using a synthetic minority oversampling technique (SMOTE). Using the on-site reflectance ratio of the hyperspectral images, the input variables R_rs (452/497), R_rs (497/580), R_rs (497/618), and R_rs (684/618), which had the highest correlation with the CDOM absorption coefficient a_CDOM (355), were extracted. Random forest and light gradient boosting machine algorithms were applied to create a CDOM prediction algorithm via machine learning, and to apply SMOTE, low-concentration and high-concentration datasets of CDOM were distinguished by 5 m⁻¹. The training and testing datasets were distinguished at a 75%:25% ratio at low and high concentrations, and SMOTE was applied to generate synthetic data based on the training dataset, which is a sub-dataset of the original dataset. Datasets using SMOTE resulted in an overall improvement in the algorithmic accuracy of the training and test step. The random forest model was selected as the optimal model for CDOM prediction. In the best-case scenario of the random forest model, the SMOTE algorithm showed superior performance, with testing R², absolute error (MAE), and root mean square error (RMSE) values of 0.838, 0.566, and 0.777 m⁻¹, respectively, compared to the original algorithm’s test values of 0.722, 0.493, and 0.802 m⁻¹. This study is anticipated to resolve imbalance problems using SMOTE when predicting remote sensing-based CDOM. It is expected to produce and implement a machine learning model with improved reliable performance.