Journal ArticleDOI
River flow forecasting through conceptual models part I — A discussion of principles☆
J.E. Nash,J.V. Sutcliffe +1 more
TLDR
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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Future high-mountain hydrology: a new parameterization of glacier retreat
TL;DR: In this article, a simple parameterization for calculating the change in glacier surface elevation and area, which is mass conserving and suited for hydrological modelling, is proposed, which can be easily applied to large samples of glaciers.
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Modelling evaporation using an artificial neural network algorithm
TL;DR: The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique, and would be of much use in instances where data availability is limited.
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Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau
TL;DR: In this paper, the authors evaluate four widely used global high-resolution satellite precipitation products against gauge observations over the Tibetan Plateau (TP) and investigate the capability of the satellite products in streamflow simulations using the VIC hydrological model.
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Estimation of small reservoir storage capacities in a semi-arid environment: A case study in the Upper East Region of Ghana
TL;DR: In this article, the authors developed a simple method that allows the estimation of reservoir storage volumes as a function of their surface areas, based on an extensive bathymetrical survey that was conducted in the Upper East Region of Ghana.
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Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks
TL;DR: In this article, the authors presented a methodology to forecast diurnal cooling load energy consumption for institutional buildings using data driven techniques using Artificial Neural Networks (ANN) and showed that the ANN is able to predict the next day energy use based on five previous days' data with good accuracy.