Plastic debris in water systems is a major challenge for our ecosystem, because it is extremely persistent in the environment. Apart from the importance of reducing the amount of plastic entering the ocean, clearing the rivers from debris is important for societal concerns, such as flood risks. Plastic waste accumulation at trash racks leads to a rise in upstream water level and may increase urban flood risk. Until now, most studies of riverine debris accumulation predominantly focused on organic accumulations at trash racks and bridge piers. In this study, flume experiments were used to study the behavior of plastic and mixed debris accumulations. One of the key findings from this study is that plastic debris causes a faster blockage than organic matter, as the plastic blockage contains fewer voids and therefore has a higher blockage density. In addition to the flume experiments, field measurements were performed in the Cikapundung River (Indonesia). This river is one of the tributaries of the Citarum River, which is considered one of the world's most heavily polluted rivers. Combining the results of the flume experiments and field measurements demonstrated that a backwater rise of 1 m/h is plausible for a blocked trash rack in the Cikapundung River, illustrating the additional flood risk caused by plastic pollution. Our results emphasize the need for further quantifying riverine (plastic) debris and investigating its relation to changes in the water system behavior, including its influence on urban flood risk.
Abstract. The objective of this study was to present a sophisticated method of developing supporting material for flood control implementation in DKI Jakarta. High flow rates in the Ciliwung River flowing through Jakarta regularly causes extensive flooding in the rainy season. The affected area comprises highly densely populated villages. For developing an efficient early warning system in view of decreasing the vulnerability of the locations a flood index map has to be available. This study analyses the development of a flood risk map of the inundation area based on a two-dimensional modeling using FESWMS. The reference event used for the model was the most recent significant flood in 2007. The resulting solution represents flood characteristics such as inundation area, inundation depth and flow velocity. Model verification was performed by confrontation of the results with survey data. The model solution was overlaid with a street map of Jakarta. Finally, alternatives for flood mitigation measures are discussed.
The accuracy of quantitative precipitation forecast from numerical weather prediction (NWP) grows as higher resolutions are achieved by the computation capacity of supercomputers. Various distributed dataset, globally and locally, with better spatial and temporal resolution has been rapidly developed. The objective of this paper is to have a flood forecast model that utilises these great benefits and gives reliable accuracy.Another asset of a flood forecast model is a conceptual-distributed runoff model; it has been chosen because of its simplicity. Calibration and validation of the model has been met by good agreement for events in 2002 in the case of a 237 km 2 operational scale basin. These results are based on input Grid Point Value (GPV) precipitation of Japan radar observation.Forecasted Precipitation was based on a GPV Mesoscale Model of Japan NWP. It had an 18 hour lead time and updated four times a day. Flood forecasting based on input from forecasted precipitation shows that the accuracy decreases as lead-time increased. It is clear that flood forecasting depends on precipitation forecast accuracy.Observed precipitation and discharge are used for model updating to determine initial data for flood forecasting. The simplicity of the runoff model gives advantage on water content estimation in soil storage. It is necessary because the runoff model might have basic errors and it needs to have better initial data. Updating calculated discharge with observed discharge approximates the estimation. By estimating more correctly, the model shows to be more reliable.
Numerical weather prediction (NWP) is useful in flood prediction using a rainfall-runoff model. Uncertainty occurring in the forecast, however, adversely affects flood prediction accuracy, in addition to uncertainty inherent in the rainfall-runoff model. Clarifying this uncertainty and its magnitude is expected to lead to wider forecast applications. Taking the case of Japan’s Shichikashuku Dam, 6 flood events between 2002 and 2007 were analyzed. NWP was based on short-range forecasts by the Japan Meteorological Agency (JMA). The rainfall-runoff model is based on a distributed tank model. This research calculates uncertainty by identifying and quantifying the relative error of forecasts by a) NWP and b) the runoff model. Results showed that NAP is the main cause of flood forecast uncertainty. They also showed the correlation between forecast lead time and uncertainty. Uncertainty rises with longer lead time, corresponding to the magnitude of observed discharge and precipitation.
Abstract. Increasing population growth has created problems in water resources. Natural water resources become progressively more expensive and difficult to develop. In addition, it is also becoming increasingly polluted and difficult to obtain. Many countries shown a resurgent interest in the use of rainwater harvesting (RWH) technique to overcome these problems. There are several factors that will influence the RWH performance, such as the rainfall, catchment area, storage tank capacity, and water demand. The performance parameter determines by the volumetric reliability, time reliability, and yield. The RWH system used in this study is a simple RWH system that utilizes roof as a catchment area, pipes as a distribution system and tank as a storage. An analysis is carried out to investigate the effect of altering the large of the catchment area and storage tank capacity to the RWH system performance parameters. A suitable behavioral model based on the water balance method is implemented to evaluate the inflow, outflow, and the storage volume. Results demonstrate that with up to 15 years daily rainfall data in 15 cities in Indonesia, the most influential parameters on the performance of RWH system is the time reliability.
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