Integration of earth observation data in distributed hydrological models: the Senegal River basin

2003 ◽  
Vol 29 (6) ◽  
pp. 701-710 ◽  
Author(s):  
Inge Sandholt ◽  
Jens Andersen ◽  
Gorm Dybkjær ◽  
Lotte Nyborg ◽  
Medou Lô ◽  
...  
Keyword(s):  
River Basin ◽  
Earth Observation ◽  
Observation Data ◽  
Hydrological Models ◽  
Senegal River ◽  
Earth Observation Data ◽  
Distributed Hydrological Models ◽  
Senegal River Basin

Use of remote sensing data in distributed hydrological models: applications in the Senegal River basin

1999 ◽  
Vol 99 (1) ◽  
pp. 47-57 ◽  
Author(s):  
Inge Sandholt ◽  
Jens Andersen ◽  
Gorm Dybkjær ◽  
Medou Lo ◽  
Kjeld Rasmussen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Remote Sensing Data ◽  
Hydrological Models ◽  
Sensing Data ◽  
Senegal River ◽  
Distributed Hydrological Models ◽  
Senegal River Basin

scholarly journals Model Uncertainty Analysis Methods for Semi-Arid Watersheds with Different Characteristics: A Comparative SWAT Case Study

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1177 ◽  
Author(s):  
Lufang Zhang ◽  
Baolin Xue ◽  
Yuhui Yan ◽  
Guoqiang Wang ◽  
Wenchao Sun ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
River Basin ◽  
Model Uncertainty ◽  
Assessment Tool ◽  
Rapid Development ◽  
Swat Model ◽  
Observation Data ◽  
Hydrological Models ◽  
Distributed Hydrological Models ◽  
Different Characteristics

Distributed hydrological models play a vital role in water resources management. With the rapid development of distributed hydrological models, research into model uncertainty has become a very important field. When studying traditional hydrological model uncertainty, it is very common to use multisite observation data to evaluate the performance of the model in the same watershed, but there are few studies on uncertainty in watersheds with different characteristics. This study is based on the Soil and Water Assessment Tool (SWAT) model, and uses two common methods: Sequential Uncertainty Fitting Version 2 (SUFI-2) and Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis. We compared these methods in terms of parameter uncertainty, model prediction uncertainty, and simulation effects. The Xiaoqing River basin and the Xinxue River basin, which have different characteristics, including watershed geography and scale, were used for the study areas. The results show that the GLUE method had better applicability in the Xiaoqing River basin, and that the SUFI-2 method provided more reasonable and accurate analysis results in the Xinxue River basin; thus, the applicability was higher. The uncertainty analysis method is affected to some extent by the characteristics of the watershed.

Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India

2017 ◽  
Vol 33 (11) ◽  
pp. 1202-1222 ◽  
Author(s):  
Sudhir Kumar Singh ◽  
Prosper Basommi Laari ◽  
Sk. Mustak ◽  
Prashant K. Srivastava ◽  
Szilárd Szabó
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Land Use Land Cover ◽  
Earth Observation Data ◽  
Tons River Basin

MERGING EARTH OBSERVATION DATA, WEATHER PREDICTIONS, IN-SITU MEASUREMENTS AND HYDROLOGICAL MODELS FOR WATER INFORMATION SERVICES

2015 ◽  
Vol 14 (9) ◽  
pp. 2031-2042 ◽  
Author(s):  
Isnaeni M. Hartanto ◽  
Carina Almeida ◽  
Thomas K. Alexandridis ◽  
Melanie Weynants ◽  
Gildo Timoteo ◽  
...  
Keyword(s):  
Earth Observation ◽  
Information Services ◽  
In Situ Measurements ◽  
Observation Data ◽  
Hydrological Models ◽  
Earth Observation Data

Contribution of earth observation data to Congo River basin hydrology understanding

2010 ◽  
Author(s):  
Mirco Boschetti ◽  
Francesco Nutini ◽  
Pietro A. Brivio ◽  
Etienne Bartholome ◽  
Daniela Stroppiana
Keyword(s):  
River Basin ◽  
Earth Observation ◽  
Observation Data ◽  
Congo River ◽  
Congo River Basin ◽  
Basin Hydrology ◽  
Earth Observation Data

Prioritisation of sub-watersheds based on earth observation data of agricultural dominated northern river basin of India

2016 ◽  
Vol 33 (4) ◽  
pp. 339-356 ◽  
Author(s):  
Sandeep Kumar Yadav ◽  
Alok Dubey ◽  
Szabo Szilard ◽  
Sudhir Kumar Singh
Keyword(s):  
River Basin ◽  
Earth Observation ◽  
Observation Data ◽  
Northern River ◽  
Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 12-14
Author(s):  
Eicher, A
Keyword(s):  
Earth Observation ◽  
Observation Data ◽  
Business World ◽  
The Earth ◽  
Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

Sign in / Sign up

Export Citation Format

Share Document