 |
| Titel |
Snow glacier melt estimation in tropical Andean glaciers using artificial neural networks |
| VerfasserIn |
V. Moya Quiroga, A. Mano, Y. Asaoka, S. Kure, K. Udo, J. Mendoza |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 4 ; Nr. 17, no. 4 (2013-04-02), S.1265-1280 |
| Datensatznummer |
250018837
|
| Publikation (Nr.) |
 copernicus.org/hess-17-1265-2013.pdf |
|
|
|
|
|
| Zusammenfassung |
| Snow and glacier melt (SGM) estimation plays an important role in water
resources management. Although melting process can be modelled by energy
balance methods, such studies require detailed data, which is rarely
available. Hence, new and simpler approaches are needed for SGM estimations.
The present study aims at developing an artificial neural networks (ANN)
based technique for estimating the energy available for melt (EAM) and SGM
rates using available and easy to obtain data such as temperature, short-wave
radiation and relative humidity. Several ANN and multiple linear
regression models (MLR) were developed to represent the energy fluxes and
estimate the EAM. The models were trained using measured data from the Zongo
glacier located in the outer tropics and validated against measured data
from the Antizana glacier located in the inner tropics. It was found that
ANN models provide a better generalisation when applied to other data sets.
The performance of the models was improved by including Antizana data into
the training set, as it was proved to provide better results than other
techniques like the use of a prior logarithmic transformation. The final
model was validated against measured data from the Alpine glaciers
Argentière and Saint-Sorlin. Then, the models were applied for the
estimation of SGM at Condoriri glacier. The estimated SGM was compared with
SGM estimated by an enhanced temperature method and proved to have the same
behaviour considering temperature sensibility. Moreover, the ANN models have
the advantage of direct application, while the temperature method requires
calibration of empirical coefficients. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|