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A three-stage indirect multi-step-ahead prediction model, which combines dynamic spline interpolation into multilayer adaptive time-delay neural network (ATNN), is proposed in this study for the long term hydrologic prediction.
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In this study, the index gage method is proposed to develop FDCs using short-term hydrologic records via an information transfer technique from a nearby hydrologically similar index gage.
However, long-term hydrologic data are needed to better evaluate the potential benefits of conservation tillage.
Deriving representative FDC when there are short-term hydrologic records is important.
Long-term hydrologic and sediment inputs can affect the distribution of sediment constituents and further affect the P sorption capacity.
Long-term hydrologic simulations are used to predict the emissions from urban drainage systems during planning of stormwater management measures.
A dependable long-term hydrologic prediction is essential to planning, designing and management activities of water resources.
To more accurately describe bioretention hydrologic response, a long-term hydrologic model that generates a water balance is needed.
It is worth emphasizing here that the concept of soil-moisture-index (SMI) is generally used to identify the ARC condition in long-term hydrologic simulation.
Another example is a case study of a long-term hydrologic prediction which uses the monthly discharges data from the Manwan Hydropower Plant in Yunnan Province of China.
We compare nine flood-return periods across 31 watersheds with widely varying topography and land cover in the southeastern United States, using long-term hydrologic records (≥20 years).
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