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.
Highlights
Droughts are recognized as an environmental disaster, the consequence of a reduction in precipitation over an extended period of time [1], negatively affecting agriculture, domestic water supply and economic growth [2], and in some cases even altering the functioning of natural ecosystems [3]
Heckerman [42] claims that a Bayesian network for a set of variables X = {X1, ..., Xn} consists of: (1) a network structure (NS) that encodes a set of conditional independence assertions about variables in X; and (2) a set P of local probability distributions associated with each variable
Model yielded better results, with the greatest ranked probability skill score (RPSS) value equal to 0.44, followed by the MCSO and BNSO models with RPSS values equal to 0.37 and 0.35. These results indicate that, for the given case study, the MCFO model performed better for the probabilistic forecast of dry and wet periods, while, for the probabilistic forecast of dry periods, the Bayesian network- (BN)-based models are a better option
Summary
Droughts are recognized as an environmental disaster, the consequence of a reduction in precipitation over an extended period of time [1], negatively affecting agriculture, domestic water supply and economic growth [2], and in some cases even altering the functioning of natural ecosystems [3]. 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. 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.
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