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Hydrology 2014, 1(1), 89-111; doi:10.3390/hydrology1010089

Remote Sensing and Ground-Based Weather Forcing Data Analysis for Streamflow Simulation

1
NOAA-Cooperative Remote Sensing Science and Technology (NOAA-CREST Center), City College of New York, New York, NY 10031, USA
2
CUNY Institute of Sustainable Cities/New York City Department of Environmental Protection (NYC-DEP), Kingston, NY 12401, USA
*
Author to whom correspondence should be addressed.
Received: 23 July 2014 / Revised: 20 October 2014 / Accepted: 24 October 2014 / Published: 31 October 2014
(This article belongs to the Special Issue Hydrological Modeling: Beyond Runoff Calibration)
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Abstract

Hydrological simulation, based on weather inputs and the physical characterization of the watershed, is a suitable approach to predict the corresponding streamflow. This work, carried out on four different watersheds, analyzed the impacts of using three different meteorological data inputs in the same model to compare the model’s accuracy when simulated and observed streamflow are compared. Meteorological data from the Daily Global Historical Climatology Network (GHCN-D), National Land Data Assimilation Systems (NLDAS) and the National Operation Hydrological Remote Sensing Center’s Interactive Snow Information (NOHRSC-ISI) were used as an input into the Soil and Water Assessment Tool (SWAT) hydrological model and compared as three different scenarios on each watershed. The results showed that meteorological data from an assimilation system like NLDAS achieved better results than simulations performed with ground-based meteorological data, such as GHCN-D. However, further work needs to be done to improve both the datasets and model capabilities, in order to better predict streamflow. View Full-Text
Keywords: NLDAS; NOHRSC-ISI; GHCN-D; SWAT; meteorological data; streamflow simulation NLDAS; NOHRSC-ISI; GHCN-D; SWAT; meteorological data; streamflow simulation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Corona, J.A.I.; Lakhankar, T.; Pradhanang, S.; Khanbilvardi, R. Remote Sensing and Ground-Based Weather Forcing Data Analysis for Streamflow Simulation. Hydrology 2014, 1, 89-111.

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