Chapters authored
Sediment Management of Reservoirs in Volcanic Area: Case of Wlingi and Lodoyo Reservoirs in Indonesia
Volcanoes erupt in many parts of the world, producing abundant sediment that is rapidly delivered to deposition sites. Where a reservoir is located near an active volcano, the sedimentation will be very severe. Wlingi and Lodoyo reservoirs are severely affected by eruptions of Kelud volcano, one of the most active volcanoes in Indonesia. After the February 2014 eruption, the capacity of Wlingi and Lodoyo reservoirs decreased dramatically to 2.20 million cubic meter (Mm3) and 1.33 Mm3, respectively, just 46 and 49% of their pre-eruption capacities and 19.42 and 26.60% of their initial capacities. To cope with the extreme sedimentation problems in Wlingi and Lodoyo reservoirs, diverse sediment management strategies have been applied in these reservoirs and their catchments. Construction of many on-stream sediment control facilities (sabo works) and a sediment bypass channel has reduced sediment inflow to the reservoirs. Removal of deposited sediment by dredging and hydraulic flushing in Wlingi and Lodoyo reservoirs has also resulted in storage capacity recovery. These measures are an integral part of the Mt. Kelud Volcanic Disaster Mitigation Plan.
Part of the book: Sedimentation Engineering
Hydrological Drought Index Based on Discharge
Drought is a natural phenomenon causing disasters and its period of occurrence can be predicted in recent times based on several methods using the same or different variables. The prediction is usually associated with the climate interactions in the form of rainfall or discharge patterns which can be analyzed using the return period. Therefore, this research was conducted in four different stages of data acquisition and validation, drought analysis method based on the data, drought prediction method based on hydrology, and sample applications to determine the debit availability in other watersheds. Historical rainfall data converted to dependable rainfall at 80% probability were used as input for the rainfall-discharge analysis while the hydrological drought analysis was conducted using the drought threshold value. Moreover, the drought was predicted using an artificial neural network model while historical data were used to verify the hydrological character of the prediction model. The results of the analysis conducted were further used to predict the water balance in different river areas due to the fact that each area has a different hydrological character. Meanwhile, the watersheds used as case research showed that the model has reliability of up to 80%.
Part of the book: Drought