Abstract
On behalf of the decision-makers of Andean regulated river basins a drought index was developed to predict the occurrence and extent of drought events. Two stochastic models, the Markov Chain First Order (MCFO) and the Markov Chain Second Order (MCSO) model, predicting the frequency of monthly droughts were applied and the performance checked using two skill scores, respectively the ranked probability score (RPS) and the Gandin-Murphy skill score (GMSS). Data of the Chulco River basin (3200–4300 m.a.s.l.), situated in the Ecuadorian southern Andes, were employed to test the performance of both models. Results indicate that events with greater drought severity were more accurately predicted. The study also revealed the importance of verifying the quality of the forecasts and to have an assessment of the likely performance of the forecasting models before adopting any model and accepting the resulting information for decision-making.
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Acknowledgments
The research was conducted within the frame of the projects “Meteorological Cycles and Evapotranspiration along the Altitudinal Gradient of the Cajas National Park” and “Identification of hydro-meteorological processes that trigger extreme floods in the city of Cuenca using precipitation radar”. Both projects were funded by the University of Cuenca and the Public Municipal Company of Water Supply from Cuenca (ETAPA). Thanks are due to INAMHI and CBRM for providing the information of the Chulco river basin.
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Avilés, A., Célleri, R., Paredes, J. et al. Evaluation of Markov Chain Based Drought Forecasts in an Andean Regulated River Basin Using the Skill Scores RPS and GMSS. Water Resour Manage 29, 1949–1963 (2015). https://doi.org/10.1007/s11269-015-0921-2
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DOI: https://doi.org/10.1007/s11269-015-0921-2