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Scientists release recommendations for building land in coastal Louisiana Sediment diversions should be operated on a pulse that mimics the natural flood cycle of the Mississippi River, which includes taking full advantage of winter flood peaks from November through February when the greatest concentration of sediment is available in the river to sustain the coastal wetlands, as well as operating in the spring when sand needed for building land is at its highest. Operations plans should include robust monitoring and flexibility for adjustments based on rapidly changing conditions, such as hurricanes and other events.
Mixed response: Floodwaters return to the Colorado River but can release greenhouse gases Deliberately flooding riverbeds left parched by dams has great potential to restore wetlands but may also have a significant unintended consequence: the release of greenhouse gases. When a large dry riverbed is flooded, these greenhouse gases are released into the water, and presumably to the atmosphere. However, re-wetting the delta may support the growth of native plants, which typically are better able to absorb and store carbon than invasive species, and may offset the carbon dioxide and methane released by flooding.
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Mitigating floods with an electronic brain Artificial neural networks (ANNs) are a biologically-inspired method of computing that can receive large amounts of data, find patterns, learn from them and then develop predictions for future events. They have been proposed as a useful tool to process the complex relationships between large amounts of data related to the transformation of rainfall into runoff. This relationship is one of the most difficult hydrological problems faced by water resource managers. Researchers at Universiti Putra Malaysia "taught" an ANN to predict daily runoff for the Bertam River into the Ringlet Reservoir 200 km north of Kuala Lumpur. They collected daily rainfall and stream flow data from the Bertam River catchment area over a ten-year period, from 2003 to 2012, and estimated daily water evaporation using temperature data collected from the nearest station to the reservoir. Seventy percent of this data was input into the model to "train" it while the remaining 30% of the data was used to test the model's accuracy using statistical evaluation measurements. The ANN was developed to map the relationship between rainfall and runoff. The more factors used, the more accurate the results. The ANN was able to predict river stream flow into the reservoir with 76% accuracy.
