Xingjun Hong

Uncertainties in assessing hydrological drought using streamflow drought index for the upper Yangtze River basin

Drought is an environmental disaster which is frequently and world-widely occurred in recent years. Precisely assessment and prediction of drought is important for water resources planning and management. Sampling uncertainty commonly exists in frequency analysis-based hydrological drought assessment due to the limited length of observed data series. Based on the daily streamflow data of the Yichang hydrological station from 1882 to 2009, the streamflow drought index (SDI) series with 12-month time scale was calculated and the hydrological drought of the upper Yangtze River was assessed. By employing the bootstrap method, the impact of sample size on the sampling uncertainty of the SDI was analyzed. The longer record is used to derive the SDI, the narrower the shifting ranges of the parameters of the streamflow volume probability distribution functions and corresponding interval estimators of SDI are. The upper Yangtze River basin has experienced successive alternation of wet and dry years, and the spring seems to be the driest season within a year. The current difficulty in fighting against increasing droughts in upper Yangtze River basin is upgrading. Considering the possible misjudgment of drought degree results from the sampling uncertainty, attention should be paid to the preparation of drought relief strategies in order to reduce the potential losses.

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.

Projected hydrologic regime changes in the Poyang Lake Basin due to climate change

Poyang Lake, the largest freshwater lake in China, and its surrounding sub-basins have suffered frequent floods and droughts in recent decades. To better understand and quantitatively assess hydrological impacts of climate change in the region, this study adopted the Statistical Downscaling Model (SDSM) to downscale the outputs of a Global Climate Model (GCM) under three scenarios (RCP2.6, RCP4.5 and RCP8.5) as recommended by the fifth phase of the Coupled Model Inter-comparison Project (CMIP5) during future periods (2010‒2099) in the Poyang Lake Basin. A semi-distributed two-parameter monthly water balance model was also used to simulate and predict projected changes of runoff in the Ganjiang sub-basin. Results indicate that: 1) SDSM can simulate monthly mean precipitation reasonably well, while a bias correction procedure should be applied to downscaled extreme precipitation indices (EPI) before being employed to simulate future precipitation; 2) for annual mean precipitation, a mixed pattern of positive or negative changes are detected in the entire basin, with a slightly higher or lower trend in the 2020s and 2050s, with a consistent increase in the 2080s; 3) all six EPI show a general increase under RCP4.5 and RCP8.5 scenarios, while a mixed pattern of positive and negative changes is detected for most indices under the RCP2.6 scenario; and 4) the future runoff in the Ganjiang sub-basin shows an overall decreasing trend for all periods but the 2080s under the RCP8.5 scenario when runoff is more sensitive to changes in precipitation than evaporation.

Rational Function Method for Allocating Water Resources in the Coupled Natural-Human Systems

Allocating water resources in coupled natural-human systems is largely determined by available water (W), water demand (D), water demand and the regional characteristics of water resources management (m). As the interactions among these factors have evolved with hydrological and societal changes in the environment, water resources allocation models based on optimization and simulation techniques become more complicated and are challenged to meet the requirements of generating detailed but simple simulations that yield practical allocation results. Unlike the simulation-optimization model, we have proposed a rational function method for allocating water resources based on the physical mechanism of water use. The validity of the proposed method has been examined through the comparison of results from the Mike Basin optimal water resources allocation model. The sensitivity and the controlling factors of the rational function method are analyzed theoretically and applied in our case study. The result of the absolute value of mean percentage error (MPE) in every study unit is less than 2%, which indicates that the estimated the amounts of water resources allocation from proposed model agree well with the performance of the MIKE BASIN model. We have also identified two critical values at W / Du2009=u20091 and mu2009=u20092. The index of water richness (W / D) plays more important role than m when mu2009>u20092 and a lesser role when mu2009<u20092. Additionally, it has been demonstrated that aside from the water richness index, water demand, reservoir operation, and water management level are also significant factors for water resources allocation.