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91.
Temporal and spatial variations in water flow and sediment load in Narmada River Basin, India: natural and man-made factors 总被引：1，自引：0，他引：1
The Narmada River flows through the Deccan volcanics and transports water and sediments to the adjacent Arabian Sea. In a
first-ever attempt, spatial and temporal (annual, seasonal, monthly and daily) variations in water discharge and sediment
loads of Narmada River and its tributaries and the probable causes for these variations are discussed. The study has been
carried out with data from twenty-two years of daily water discharge at nineteen locations and sediment concentrations data
at fourteen locations in the entire Narmada River Basin. Water flow in the river is a major factor influencing sediment loads
in the river. The monsoon season, which accounts for 85 to 95% of total annual rainfall in the basin, is the main source of
water flow in the river. Almost 85 to 98% of annual sediment loads in the river are transported during the monsoon season
(June to November). The average annual sediment flux to the Arabian Sea at Garudeshwar (farthest downstream location) is 34.29×106 t year−1 with a water discharge of 23.57 km3 year−1. These numbers are the latest and revised estimates for Narmada River. Water flow in the river is influenced by rainfall,
catchment area and groundwater inputs, whereas rainfall intensity, geology/soil characteristics of the catchment area and
presence of reservoirs/dams play a major role in sediment discharge. The largest dam in the basin, namely Sardar Sarovar Dam,
traps almost 60–80% of sediments carried by the river before it reaches the Arabian Sea. 相似文献
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Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method 
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下载免费PDF全文 Lei Ye Jianzhong Zhou Hoshin V. Gupta Hairong Zhang Xiaofan Zeng Lu Chen 《水文研究》2016,30(15):2703-2716
Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
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