Zhangjun Liu

Spatial and temporal analysis of drought using entropy-based standardized precipitation index: a case study in Poyang Lake basin, China

Drought is a frequent and worldwide disaster causing huge losses in agriculture and damages in natural ecosystems every year. Precise assessment and prediction of droughts are important for regional water resources planning and management. An alternative distribution, based on the entropy theory, was used to derive a unified probability distribution function (PDF) for different cumulative precipitation series to calculate the Standardized Precipitation Index (SPI). Thirteen meteorological stations located within the Poyang Lake basin with daily precipitation records from 1958 to 2011 were selected for spatial and temporal analysis of basin-scale droughts. The entropy-based distribution is proved to be flexible enough for modeling aggregated precipitation at different time scales by the Kolmogorov-Smirnov (K-S) test. Most severely and extremely dry months were recorded in spring and winter in the Poyang Lake basin over the study period. Negative trends of the short-term entropy-based SPIs and corresponding aggregated numbers of rainy days in spring and autumn are detected based on the Mann-Kendall test, which implies an upgrade in difficulties to mitigate the agricultural droughts in the Poyang Lake basin. Once droughts occurred, regions with less frequent drought would face severer drought degree. The lower Ganjiang River, lower and middle Fuhe River, as well as the Xinjiang River are identified to be the most vulnerable regions with highest drought intensities. Droughts could occur at any periods and move from region to region in the Poyang Lake basin; thus, well preparation for potential droughts is needed.

Hydrological uncertainty processor based on a copula function

ABSTRACT Quantifying the uncertainty in hydrological forecasting is valuable for water resources management and decision-making processes. The hydrological uncertainty processor (HUP) can quantify hydrological uncertainty and produce probabilistic forecasts under the hypothesis that there is no input uncertainty. This study proposes a HUP based on a copula function, in which the prior density and likelihood function are explicitly expressed, and the posterior density and distribution obtained using Monte Carlo sampling. The copula-based HUP was applied to the Three Gorges Reservoir, and compared with the meta-Gaussian HUP. The Nash-Sutcliffe efficiency and relative error were used as evaluation criteria for deterministic forecasts, while predictive QQ plot, reliability, resolution and continuous rank probability score (CRPS) were used for probabilistic forecasts. The results show that the proposed copula-based HUP is comparable to the meta-Gaussian HUP in terms of the posterior median forecasts, and that its probabilistic forecasts have slightly higher reliability and lower resolution compared to the meta-Gaussian HUP. Based on the CRPS, both HUPs were found superior to deterministic forecasts, highlighting the effectiveness of probabilistic forecasts, with the copula-based HUP marginally better than the meta-Gaussian HUP.

Multiobjective Cascade Reservoir Operation Rules and Uncertainty Analysis Based on PA-DDS Algorithm

AbstractPareto archived dynamically dimensioned search (PA-DDS) is one of the meta-heuristic methods available to solve multiobjective reservoir operation problems. This study uses this method to o...

Bivariate Seasonal Design Flood Estimation Based on Copulas

AbstractSeasonal design floods reflecting seasonal variation information are very important for reservoir operation and management. The seasonal design flood estimation method currently used in Chi...

Deriving Design Flood Hydrograph Based on Conditional Distribution: A Case Study of Danjiangkou Reservoir in Hanjiang Basin

Design flood hydrograph (DFH) for a dam is the flood of suitable probability and magnitude adopted to ensure safety of the dam in accordance with appropriate design standards. Estimated quantiles of peak discharge and flood volumes are necessary for deriving the DFH, which are mutually correlated and need to be described by multivariate analysis methods. The joint probability distributions of peak discharge and flood volumes were established using copula functions. Then the general formulae of conditional most likely composition (CMLC) and conditional expectation composition (CEC) methods that consider the inherent relationship between flood peak and volumes were derived for estimating DFH. The Danjiangkou reservoir in Hanjiang basin was selected as a case study. The design values of flood volumes and 90% confidence intervals with different peak discharges were estimated by the proposed methods. The performance of CMLC and CEC methods was also compared with conventional flood frequency analysis, and the results show that CMLC method performs best for both bivariate and trivariate distributions which has the smallest relative error and root mean square error. The proposed CMLC method has strong statistical basis with unique design flood composition scheme and provides an alternative way for deriving DFH.