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

Authors

José Alberto Infante Corona, CUNY City CollegeFollow
Tarendra Lakhankar, CUNY City CollegeFollow
Soni M. Pradhanang, CUNY Hunter CollegeFollow
Reza Khanbilvardi, CUNY City CollegeFollow

Document Type

Article

Publication Date

10-31-2014

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.

Comments

This article originally appeared in Hydrology, available at DOI: 10.3390/hydrology1010089

© 2014 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).