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Water 2016, 8(2), 37; doi:10.3390/w8020037

Probabilistic Forecasting of Drought Events Using Markov Chain- and Bayesian Network-Based Models: A Case Study of an Andean Regulated River Basin

1
Departamento de Recursos Hídricos y Ciencias Ambientales & Facultad de Ciencias Químicas, Universidad de Cuenca, Víctor Manuel Albornoz y los Cerezos, Campus Balzay, Cuenca 10207, Ecuador
2
Departamento de Recursos Hídricos y Ciencias Ambientales & Facultad de Ciencias Agropecuarias, Universidad de Cuenca, Víctor Manuel Albornoz y los Cerezos, Campus Balzay, Cuenca 10207, Ecuador
3
Institute of Water and Environmental Engineering, Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Paolo Reggiani and Ezio Todini
Received: 3 October 2015 / Revised: 11 January 2016 / Accepted: 11 January 2016 / Published: 23 January 2016
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
View Full-Text   |   Download PDF [1717 KB, uploaded 23 January 2016]   |  

Abstract

The scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of the water resources in general and of the Andean watersheds in particular. This study compares Markov chain- (MC) and Bayesian network- (BN) based models in drought forecasting using a recently developed drought index with respect to their capability to characterize different drought severity states. The copula functions were used to solve the BNs and the ranked probability skill score (RPSS) to evaluate the performance of the models. Monthly rainfall and streamflow data of the Chulco River basin, located in Southern Ecuador, were used to assess the performance of both approaches. Global evaluation results revealed that the MC-based models predict better wet and dry periods, and BN-based models generate slightly more accurately forecasts of the most severe droughts. However, evaluation of monthly results reveals that, for each month of the hydrological year, either the MC- or BN-based model provides better forecasts. The presented approach could be of assistance to water managers to ensure that timely decision-making on drought response is undertaken. View Full-Text
Keywords: probabilistic drought forecasting; drought index; Markov chains; Bayesian networks; copulas; Andean watersheds probabilistic drought forecasting; drought index; Markov chains; Bayesian networks; copulas; Andean watersheds
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

Avilés, A.; Célleri, R.; Solera, A.; Paredes, J. Probabilistic Forecasting of Drought Events Using Markov Chain- and Bayesian Network-Based Models: A Case Study of an Andean Regulated River Basin. Water 2016, 8, 37.

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