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In contemporary hydrological analysis, numerical models have emerged as an alternative solution for water resources planning and management, particularly in forecasting extreme events and mitigating disaster risks. HEC-RAS 2D flow hydrodynamics enables the modeling of rainfall with input from meteorological boundary condition data. Precipitation within the studied watershed can be modeled as point rainfall or area-averaged rainfall in the rain-on-grid feature of HEC-RAS 2D flow hydrodynamics. In this study, the HEC-RAS 2D flow model utilizes the diffusion wave equations (DWE) for simulating unsteady flow routing. When modeling a complex watershed, the 2D hydrodynamics model has become a viable alternative, especially concerning the watershed's physical characteristics. This study aims to assess the reliability of HEC-RAS 2D flow hydrodynamics and compare its results with the hydrograph data from the observed watershed in the Wiroko Sub-Watershed of the upper Wonogiri Dam. In this research, extreme rainfall events of 100 mm in the first hour are simulated and compared with the hydrograph data from the observed watershed. Based on the numerical model results in the Wiroko Sub-Watershed, it was determined that conforming to the land use with a Manning roughness value of 0.12 (designated as developed area-medium intensity) resulted in a peak discharge (Qp) difference of 0.8%. Meanwhile, the time to peak (Tp) value exhibited a discrepancy of 1.93 hours longer between the numerical model and the observed hydrograph. [ABSTRACT FROM AUTHOR]

This study presents a flood hydrograph numerical modeling of the Keser watershed for disaster risk mitigation and water resources management for an emergency action plan (EAP) on the Tugu dam. Several formulations for calculating peak discharge, which are associated with information on flood design in Indonesia are regulated in SNI 2415:2016 regarding the procedure of flood discharge calculation and design. This information is important and related to water structure dimensions, risk, costs, and reliability. The complexity of watershed conditions affects the hydrograph shape and volume. Hence, a performance analysis is needed to identify the best current hydrograph model. In this study, the performance of Nakayasu, Gamma 1, and Snyder SUH was analyzed and compared with the numerical modeling using the HEC-RAS 2D hydrodynamics precipitation on grid and HEC-HMS numerical model in the Keser watershed with one-hour rainfall height of 114.92 mm (R50). Based on the calculation results, the hydrologic numerical model flood discharge calculation using HEC-HMS method was 451.1 m³/s. The peak discharge values obtained using the Nakayasu, Gamma, and Snyder methods were 424.2, 410.4, and 439.4 m³/s, respectively. The results of the numerical model with HEC-RAS 2D v6.3 with uniform precipitation on the grid obtained a peak discharge hydrograph value of 420.98 m³/s. Therefore, the highest peak discharge results were obtained from the HEC-HMS method among the three SUH formulas and HEC-RAS calculations. Hence, the deviation of peak discharge value using three SUH, HEC-HMS, and HEC-RAS was obtained under 10%. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chla) concentration, which serves as a phytoplankton substitute in inland waters, is one of the leading indicators for water quality. Generally, water samples are analyzed in professional laboratories, and Chla concentrations are measured regularly for the purpose of water quality monitoring. However, limited spatial water sampling and the labor-intensive nature of data collection make global and long-term monitoring difficult. The developments of remote-sensing optical sensors and technologies make the long-term monitoring of Chla concentrations for an entire water body more achievable. Many studies based on machine learning techniques, such as regression and artificial neural network (ANN) methods, have recently been proposed for Chla concentration estimation using optical satellite images. The methods based on machine learning can achieve accurate estimation. However, overfitting problems may arise because the in situ Chla dataset is generally insufficient to train a complicated machine learning model, which makes trained models inapplicable. In this study, an ANN model containing three convolutional and two fully connected layers with 4953 unknown parameters is designed. A transfer learning method, consisting of model pretraining, main-training, and fine-tuning stages, is proposed to ease the problem of insufficient in situ samples. In the model pretraining stage, the ANN model is pretrained and initialized using samples derived from an existing Chla concentration model. The pretrained ANN model is then fine-tuned using the proposed transfer learning technique with in situ samples collected in five different campaigns carried out during early 2019 from Laguna Lake, the Philippines. Before the transfer learning, data augmentation and rebalancing methods are conducted to enrich the variability and to near-uniformly distribute the in situ samples in Chla concentration space, respectively. To estimate the alleviation of model overfitting, the trained ANN model, using an in situ dataset from Laguna Lake, was tested using an in situ dataset from Lake Victoria, Uganda, obtained in 2019, which has a similar trophic state as Laguna Lake. The experimental results from Sentinel-3 imagery indicated that the overfitting problem was significantly alleviated and the trained ANN model outperformed related models in terms of the root-mean-squared error of the estimated Chla concentrations. [ABSTRACT FROM AUTHOR]

ITS-2 is one of the latest synthetic unit hydrograph-based flood estimation models in Indonesia. Developed at the Institut Teknologi Sepuluh Nopember (ITS) Surabaya in 2017, the basic parameters of the model were formulated using fractal and morphometric charac- teristics of eight observed river basin in Central Sulawesi, Indonesia. The fundamental concern with developing this model is how to derive a curve equation which illustrates the shape of the hydrograph. This paper aims to formulate this equation using the 2-parameter Gamma distribution, one of the statistical distribution model that is widely used for hydrological modeling especially hydrographs. The derivation of the equation was carried out by simplifying the parameters of the Gamma function which corresponds to the shape of the hydrograph such as the rising limb, peak and, recession limb. This research has succeeded in formulating an equation of dimensionless synthetic unit hydrograph curve expressed by the ratio of time (T) and peak time (Tp) of the hydrograph. A coefficient of C3 states that the hydrograph shape parameters can be optimized to improve model performance, when a series of observation discharge is available for calibration. The performance of the synthetic unit hydrograph equation was assessed using Mean Absolute Percentage Error (MAPE) and the Coefficient of correlation (r ). The analysis results based on a series of verification indicated that the performance of the hydrograph equation was very high with MAPE below 20% and a r above 0.8. [ABSTRACT FROM AUTHOR]