Evaluation of Markov Chain Based Drought Forecasts in an Andean Regulated River Basin Using the Skill Scores RPS and GMSS

Abstract

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A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies: application to regional drought processes in China

“…To date, much attention has been paid to methodology improvements. Taking advantage of probabilistic and temporal-evolution features of input variables, statistical drought prediction models are primarily forced with probability or machine-learning methods, such as the ensemble streamflow prediction (ESP) method (AghaKouchak, 2014), Markov chain-and Bayesian network-based models (Aviles et al, 2015(Aviles et al, , 2016Shin et al, 2016), neural network, and support vector models (Belayneh et al, 2014). In addition to method improvement, climate indices represent large-scale atmospheric or oceanic drivers of precipitation, partly responsible for effective model performance.…”

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

“…Consequently, there is a need to assess the drought status using indices based on multiple variables monitored during different time windows. The present study uses the drought index (DI) developed by Avilés et al [15], which is based on water-related variables of different window-sizes in the hydrologic year, enabling the capturing of the drought status for short, medium and long periods.…”

“…This aspect can be handled by probabilistic models, which forecast in a quantitative way droughts and the associated uncertainty [32]. A variety of probabilistic models for drought forecasting has been developed [13,15,18,31,[33][34][35][36][37][38][39][40][41], but not that many calculate the conditional probability if there are multiple events, such as the Markov chains and Bayesian networks. Those approaches generate probabilistic forecasts of future droughts in function of earlier drought conditions.…”

“…Those approaches generate probabilistic forecasts of future droughts in function of earlier drought conditions. According to the probability conditional theory are the models based on MC most common for drought forecasting [15,[33][34][35][36], while the BN-based models are more sophisticated. The latter have not been widely used for the probabilistic forecasting of drought events, notwithstanding they seem to have the ability of better forecasting droughts [13,18].…”

“…To enhance the confidentiality of the forecasts, we used the RPSS for the verification of forecasts, which provides a measure of the accuracy of the forecast in terms of assigned probabilities [48,49]. This study uses the Drought Index (DI) developed on the basis of rainfall and streamflow data of the Chulco River basin in Southern Ecuador, as described in Aviles et al [15]. In this study, the first-order Markov chain and the second-order Markov chain stochastic models were used to predict the frequency of monthly droughts.…”