Journal ArticleDOI
River flow forecasting through conceptual models part I — A discussion of principles☆
J.E. Nash,J.V. Sutcliffe +1 more
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In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.About:
This article is published in Journal of Hydrology.The article was published on 1970-04-01. It has received 19601 citations till now. The article focuses on the topics: Conceptual model & Flood forecasting.read more
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Assessment of climate change impacts on Alpine discharge regimes with climate model uncertainty
TL;DR: In this paper, the authors analyzed the uncertainty induced by the use of different state-of-the-art climate models on the prediction of climate change impacts on the runoff regimes of 11 mountainous catchments in the Swiss Alps having current glaciation rates between 0 and 50 %.
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Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method
TL;DR: In this paper, the authors compared three widely used satellite precipitation products with a dense rain gauge network in the Mishui basin (9972 km 2 ) in South China and then optimally merged their simulated hydrologic flows with the semi-distributed Xinanjiang model using the Bayesian model averaging method.
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Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model
TL;DR: In this paper, the authors carried out a number of combined high-resolution one-way driven model experiments to generate runoff hydrographs for seven extreme flood events which occurred in the Lago Maggiore basin between 1993 and 2000.
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Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning
TL;DR: The results showed that networks trained with pre-processed data performed better than networks trained on undecomposed, noisy raw signals, and the best results were obtained using the data partitioning technique.
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Predicting daily flows in ungauged catchments: model regionalization from catchment descriptors at the Coweeta Hydrologic Laboratory, North Carolina
TL;DR: In this paper, the authors investigated regionalization approaches to daily streamflow prediction for 13 catchments in the Coweeta Hydrologic Laboratory using a conceptual rainfall-runoff model of low complexity (six parameters).