Abstract
Frequency analysis of streamflow is critical for water-resources system planning, water conservancy projects and the mitigation of hydrological extremes events. In this study, a maximum entropy-Archimedean copula-based Bayesian network (MECBN) method has been proposed for frequency analysis of monthly streamflow in the Kaidu River Basin, which integrates the maximum entropy-Archimedean copula (MEAC) and Bayesian network methods into a general framework. MECBN is effective for representing the uncertainties that exist in model representation, preserving the distributional characteristics of streamflow records and addressing the correlation structure between streamflow pairs. Application to the Kaidu River Basin shows a good performance of MECBN in describing the historical data of this basin in China. The results indicate that the interactions between two adjacent monthly streamflow pairs are non-linear. There is upper tail dependence between monthly streamflow pairs. The dependence coefficients including Spearman’s rho, Kendall’s tau, and the upper tail dependence coefficient are in inverse proportion of monthly streamflow values in the Kaidu River Basin, due to the fact that other factors (i.e., rainfall, snow melting, evapotranspiration rate and requirement of water use) provide more contributions to the streamflow in the flooding season. These findings can be used for providing vital information in the prevention and control of hydrological extremes and to further water resources planning in Kaidu River Basin.
Highlights
Many regions especially in developing countries are suffering from severe water stresses resulting from heterogenous precipitation, extreme hydrological events, water shortages, as well as numerous demands from socio-economic and natural systems [1,2,3,4,5]
In order to evaluate the marginal distributions of monthly streamflow in Kaidu River Basin generated by the maximum entropy-Archimedean copula-based Bayesian network (MECBN) method, GOF
The results indicate that observed monthly streamflows in the Kaidu River Basin could be appropriately represented by the marginal distributions generated by MECBN
Summary
Many regions especially in developing countries are suffering from severe water stresses resulting from heterogenous precipitation, extreme hydrological events, water shortages, as well as numerous demands from socio-economic and natural systems [1,2,3,4,5]. The variation of natural circumstances, the lack of historical records, and the limitation of measurement may cause uncertainties in input information, i.e., randomness in the streamflow inputs [11]. The interactive relationship among historical data records of streamflow may lead to uncertainties in hydrological predictions, e.g., sub-optimal parameter values and errors due to incomplete or biased model structures. All these uncertainties may affect the resulting results of frequency analysis for streamflow. It is necessary to develop effective tools to identify and analyze these uncertainties in order to preserve the distributional characteristics of streamflow records, maintain the dependence structure of such records, and quantify the uncertainties existing in the hydrological processes
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