Tallo River has the potential to pose a threat of flooding because it is strongly influenced tide. Regulation Ponds are flood control structures built upstream from areas that will be protected from flood hazards. Storage Capacity Analysis of Nipa regulation pond using Ripple method. Flood routing results obtained a maximum of 3.395.116,79 m3, while topographic conditions obtained a maximum of 3.583.270,96 m3 then optimization analysis based on the Ripple Method obtained with volume accommodated during 1980-1984 is 2.508.330,462 m3, during 1985-1989 is 2.158.594,429 m3, during 1990-1994 is 1.721.928,984 m3, during 1995-1999 is 2.072.035,680 m3, the volume accommodated during 2000-2004 is 2.526.941,033 m3, volume accommodated during 2005-2009 is 1.996.205,351 m3 and volume accommodated during 2010-2014 is 1.913.798,405 m3. This shows that storage capacity of regulation Pond can serve to reduce flood in Tallo River. Based on the cumulative storage analysis, a maximum storage of 10.592.032,714 m3 obtained from the inflow and outflow analysis, indicating that the Nipa regulation pond was able to reduce flood in amount of 38,07 % i.e. 4.032.440,62 m3.

One of the efforts to minimize the negative impacts of flooding in the Kelara Watershed is to make model flood-prone areas that can be used as an initial reference for flood disaster mitigation. The analytical method used is hydraulic analysis using 2D HECRAS numerical simulations to determine areas affected by flooding based on flood discharge at a return period of 2 - 100 years. Based on the simulation results, it is found that the discovery area for Q2 is 331.42 Ha, Q5 is 547.21 Ha, Q10 is 798.76 Ha, Q20 is 925.11 Ha, Q25 is 925.79 Ha, Q50 is 1048.48 Ha, and Q100 is 1146.71 Ha. From the results of the flood depth verification, it was also known that the flood that occurred on January 22, 2019 was approaching the 100 year return period flood and June 12, 2020, was approaching the 20 year return period flood. So from the results of mapping and field verification, it is known that the 4 affected districts are Binamu District, Kelara District, Turatea District, and Biringbulu District with the location of the worst flooding occurred in Sapanang Village, Binamu District.

Spatial modelling of flood-prone areas will provide maximum results if it is supported by the accuracy of the data acquired, mainly related to elevation data or the area’s topography. Spatial modelling generated from accurate topographic data can estimate the river’s carrying capacity. This study built a spatial model using data from aerial, terrestrial, and hydrographic surveys. Aerial surveys were conducted using UAV corrected by terrestrial surveys, GCP, and ICP. Testing the accuracy of the spatial model is carried out by comparing the results of current field velocity with the results of 2D Hec-Ras numerical simulations using a variation of the manning coefficient. The combination of aerial, terrestrial, and hydrographic surveys produces a cross-sectional spatial model of the river, which is used in calculating the river’s carrying capacity. The river’s capacity is calculated using a 2D numerical simulation method using Hec-Ras software and verified by a mathematical approach based on the flood hydrograph curve. The results showed that the horizontal accuracy of the GCP was 2.8 cm and the vertical accuracy was 6.5 cm. The results of testing the vertical elevation accuracy of aerial photographs on terrestrial topographic data measured in the field (ICP) have a Mean Absolute Percentage Error (MAPE) value of 5.81%. According to the spatial model, the manning roughness value is 0.06-0.09. The river’s capacity based on numerical simulations is 1.700.766 m3, and the results of the verification using a mathematical approach are 1.683.433 m3 with a difference of 1.02%.