Extreme events such as rainstorms and floods are likely to increase in frequency due to the influence of global warming, which is expected to put considerable pressure on water resources. This paper presents a regional frequency analysis of precipitation extremes and its spatio-temporal pattern characteristics based on well-known index-flood L-moments methods and the application of advanced statistical tests and spatial analysis techniques. The results indicate the following conclusions. First, during the period between 1969 and 2015, the annual precipitation extremes at Fengjie station show a decreasing trend, but the Wuhan station shows an increasing trend, and the other 24 stations have no significant trend at a 5% confidence level. Secondly, the Hanjiang River Basin can be categorized into three homogenous regions by hierarchical clustering analysis with the consideration of topography and mean precipitation in these areas. The GEV, GNO, GPA and P III distributions fit better for most of the basin and MARE values range from 3.19% to 6.41% demonstrating that the best-fit distributions for each homogenous region is adequate in predicting the quantiles estimates. Thirdly, quantile estimates are reliable enough when the return period is less than 100 years, however estimates for a higher return period (e.g., 1000 years) become unreliable. Further, the uncertainty of quantiles estimations is growing with the growing return periods and the estimates based on R95P series have a smaller uncertainty to describe the extreme precipitation in the Hanjiang river basin (HJRB). Furthermore, In the HJRB, most of the extreme precipitation events (more than 90%) occur during the rainy season between May and October, and more than 30% of these extreme events concentrate in July, which is mainly impacted by the sub-tropical monsoon climate. Finally, precipitation extremes are mainly concentrated in the areas of Du River, South River and Daba Mountain in region I and Tianmen, Wuhan and Zhongxiang stations in region III, located in the upstream of Danjiangkou Reservoir and Jianghan Plain respectively. There areas provide sufficient climate conditions (e.g., humidity and precipitation) responsible for the occurring floods and will increase the risk of natural hazards to these areas.
The extreme precipitation regimes have been changing as the climate system has warmed. Investigating the non-stationarity and better estimating the changes of the extreme precipitation are valuable for informing policy decisions. In this study, two precipitation indices are employed to describe the extreme events, including maximum 5-day precipitation amount (RX5day) and the number of very heavy precipitation days (R20). The generalized additive models for location, scale and shape (GAMLSS) is employed to characterize non-stationarities in extreme precipitation events and related climate indices in 13 stations in the Hanjiang River basin (HJRB). Three models including stationary model without change (M0), nonstationary models over time (M1) and non-stationarity models with large-scale climate indices (M2) as predictors, respectively, are considered to analyse occurrence rates of extreme precipitation. The optimal model and the significant predictors were selected by the Akaike information criterion (AIC). To investigate the main predictors at regional scale, the homogeneous subregions for precipitation extremes are identified by clustering analysis. Results indicate that: (a) the non-stationarities of RX5day series and R20 series at all stations are identified in the HJRB; (b) extreme precipitation behaviour is significantly influenced by climate indices and non-stationary model 2 to describe the changes of extreme precipitation is better than non-stationary model 1, indicating the impact of large-scale climate forcing on the changes of extreme precipitation regimes; (c) the HJRB can be categorized into three homogenous regions. The optimal distributions and the main predictors of extreme precipitation events in most stations of each subregion are basically the same; (d) the dominated climate indices influencing the extreme precipitation events are different in different regions and have regional patterns. The results highlight the modelling of extreme precipitation events under non-stationarity conditions and provide information for developing strategies of mitigation and adaptation to climate change impacts on extreme precipitation.
In order to study the ecological water environment in Erhai Lake, different monitoring sections were set to research the change of hydrodynamics and water quality. According to the measured data, MIKE21 Ecolab, the water quality simulation software developed by DHI, is applied to simulate the water quality in Erhai Lake. The hydrodynamics model coupled with water quality is established by MIKE21FM software to simulate the current situation of Erhai Lake. Then through the comparison with the monitoring data, the model parameters are calibrated and the simulation results are verified. Based on this, water quality is simulated by the two-dimensional hydrodynamics and water quality coupled model. The results indicate that the level of water quality in the north and south of lake is level III, while in the center of lake, the water quality is level II. Finally, the water environment capacity and total emmision reduction of pollutants are filtered to give some guidance for the water resources management and effective utilization in the Erhai Lake.
In order to experimentally investigate the effects of rigid vegetation on the characteristics of flow, the vegetations were modeled by rigid cylindrical rod. Flow field is measured under the conditions of submerged rigid rod in flume with single layer and double layer vegetations. Experiments were performed for various spacings of the rigid rods. The vegetation models were aligned with the approaching flow in a rectangular channel. Vertical distributions of time-averaged velocity at various streamwise distances were evaluated using an acoustic Doppler velocimeter (ADV). The results indicate that, in submerged conditions, it is difficult to described velocity distribution along the entire depth using unified function. The characteristic of vertical distribution of longitudinal velocity is the presence of inflection. Under the inflection, the line is convex and groove above inflection. The interaction of high and low momentum fluids causes the flow to fold and creates strong vortices within each mixing layer. Understanding the flow phenomena in the area surrounding the tall vegetation, especially in the downstream region, is very important when modeling or studying the riparian environment. ADV measures of rigid vegetation distribution of the flow velocity field can give people a new understanding.
In view of the deficiency of the traditional methods, based on the grey theory, GM (1, 1) model is established to predict the groundwater level. The proposed model was applied to predict the groundwater level in Xiaonanhai Spring. The prediction result was compared with that of the traditional method and the reported results in the Xiaonanhai Spring. It is indicated that the performance of the proposed model is practically feasible in the application of prediction of groundwater level and its application is simple.
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