A dam is one of the water structures that have many benefits for humans. However, flood disasters caused by dam break may have a very bad impact on human life. This study specifically analyzed the impact of flooding caused by the failure of the Karalloe dam in Bone Regency, South Sulawesi Province, Indonesia. For the first time, the selected flood discharge used in the dam break analysis was verified using the Creager graph by comparing the calculated discharge from several synthetic unit hydrograph methods (HSS) with the flood discharge measured on the automatic water level recorder (AWLR) at the monitoring point. The impact of flooding due to dam break was simulated using HEC-RAS 2D combined with ArcGIS for mapping. The calculation results of the design flood discharge based on rainfall data using the methods of HSS Nakayasu, HSS ITB I, HSS ITB II, and HSS SCS (HEC-HMS) as well as the calculation of the designed flood discharge based on the discharge data showed that the design flood discharge value which is closest to the measured discharge value and Q1000 Creager was the HSS SCS method. The flood discharge values obtained based on the HSS SCS method for Tr (return period) 2 years, Tr 5 years, Tr 10 years, Tr 20 years, Tr 25 years, Tr 50 years, Tr 100 years, and Tr 1000 years were 322.70 m 3 /s, 464.10 m3/s, 560.40 m 3 /s, 658.40 m 3 /s, 682.70 m 3 /s, 787.00 m 3 /s, 885.70 m 3 /s, and 1202.60 m 3 /s, respectively. The simulation results showed that 22 villages will be affected by flooding due to the failure of the Karalloe dam and the fastest standby time of the flood is 12 minutes, namely at Lookout Point 7 in Paitana Village. This result suggests that early warning system should be installed at the downstream of the dam and flood disaster mitigation should be adopted and applied to these threatened area.

This study aims to analyze the distribution of floods in the Bila River and its countermeasures in reducing the impact that occurred in Sidrap Regency. The study performs hydrological analysis using the Bila watershed rainfall data, calculates the planned flood discharge using the HSS SCS model, and simulates the flood flow profile using the HEC-RAS 2D numerical model. It is found that the Q20 flood discharge of the Bila and the Bulucenrana Rivers entered the Bila River downstream calculated from the HSS SCS analysis are 738.60 and 779.50 m3/s, respectively. The overflow of the Bila River flood affects 9 villages, namely Kalola Village (0.06 km2), Sogi Village (0.01 km2), Kalosi Alau Village (0.32 km2), Kampale Village (0.11 km2), Salomalori Village (0.42 km2), Tanru Tedong Village (2.12 km2), Kalosi Village (0.91 km2), Salobukkang Village (1.70 km2), and Taccimpo Village (4.01 km2). It is proposed that the best solution to deal with the issue is by introducing an eco-drainage system, namely by constructing a retention pond with a maximum storage volume of 3.81 million m3 or with a normal storage of 2.4 million m3. The existence of a retention pond can reduce the inundated area around 8.28 km2 or 85.71%.

PT. Vale Indonesia plans to mine at the Petea location using the open pit method. If viewed from the watershed boundary, the planned mining location is located in the upstream part of the Lamunto watershed. In connection with the existence of the mining plan, a solution is required to prevent things that can harm and damage the surrounding environment prior to implementation, especially related to soil erosion which is expected to impact the Lamunto watershed which is the outlet of the planned mining location. The method used in this study is to analyse the potential for erosion that occurs using the USLE (Universal Soil Loss Equation) method with the help of the ArcGIS application. Furthermore, the calculation of the sedimentation rate is carried out using the SDR (Sediment Delivery Ratio) equation based on the calculated erosion rate and the area of the mining plan that will be used to plan the sump (sediment storage tank). The results showed that the total sediment potential in sumps A, B, C, D, E, F, G, and H was 550.39 m3/year, 1195.31 m3/year, 1588.43 m3/year, 1908, 27 m3/year, 2423.33 m3/year, 1899.53 m3/year, 1941.76 m3/year, 2169.15 m3/year and 2756.29 m3/year, respectively. From these results it can be seen that the slope factor (LS) is more influential than the catchment area in calculating the sediment potential as evidenced by the total sediment potential in sump D which is greater than sump F.