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

Abstract

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.

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

“…Innovative applications of Bayes's theorem to hydrological forecasting have recently successfully emerged [45][46][47][48]. These applications quantify the uncertainty in post-process deterministic streamflow forecasts [49].…”

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

“…To date, a considerable number of studies have focused on predicting discrete drought classes (Aviles et al, 2016;Bonaccorso et al, 2015;Chen et al, 2013;Moreira et al, 2016) and the probability of drought occurrence within certain classes (AghaKouchak, 2014(AghaKouchak, , 2015Hao et al, 2014). Compared with these studies, prediction Figure 12.…”

“…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.…”

Future Meteorological Droughts in Ecuador: Decreasing Trends and Associated Spatio-Temporal Features Derived From CMIP5 Models

“…They developed a drought index using first and second order Markov chains in the Chulco river sub-basin in the Andes. Additionally, Avilés et al (2016) compared Markov chains and Bayesian network models for drought predictions. Domínguez-Castro et al (2018) conducted an interesting study in the highlands in Quito, the capital of Ecuador, using 125 years (period 1891-2015) of precipitation observations from one ground station, and the registry of the rogation ceremonies in the Chapel Acts of Quito, from 1600 to 1822.…”