According to the statistics of the United Nations Environment Program, compared to the past 100 years, the global annual water resources per capita have reduced from 40,000 m3
to 6840 m3
. Additionally, it is expected that by 2030, nearly 50% of the world’s population will have less than 1000 m3
of annual per capita water resources and will be in a state of severe water shortage [1
]. With the development of society and growing populations, water shortages are becoming more and more prominent. However, large amounts of water have been discharged during the flood season, resulting in a huge waste of water resources. Nowadays, the use of floods has become more and more important in most areas [2
]. Reservoir scheduling is an effective way to utilize flood resources and has been studied by a large number of scholars [3
]. In most of the countries, floods have seasonal patterns of change, and it is necessary to study the flood season and stage it rationally to raise the FLWL (flood limit water level) of the reservoir appropriately. In this way, flood resources can be used to a greater degree, which is one of the important issues that needs to be studied and solved today.
There have been many studies on the seasonal patterns and staging of floods during the flood season [7
]. Different staging methods have been used to segment the flood season, such as the probabilistic change-point analysis technique [13
], the vector statistic and relative frequency method [14
], and the fuzzy set method [18
]. As for the selection of indicator factors for staging, most previous studies have used a single factor to stage the flood season. For example, peak flow [22
] or average daily maximum flow [23
] are used as a single indicator factor for flood staging studies, causing the staging results to not be mutually verified. A reasonable determination of the FLWL is the key to coordinate flood risk and reservoir benefit [24
]. Therefore, many scholars have carried out extensive research on the optimization of FLWL. An FLWL model dynamic control was applied to reservoirs with indeterminate flood process lines, which effectively improved hydropower generation and flood utilization [25
]. Liu et al. [26
] optimized the design of staged flood limit levels, and a framework for optimal reservoir scheduling based on flood staging results was proposed [27
In summary, most previous studies have used a single indicator factor for flood staging leading to uncertainty in the results. In addition, there is less involvement in the calculation of FLWLs for each phase and the evaluation of the benefits of the flood staging. Therefore, the objective of this study is to stage the flood season by selecting multiple indicator factors and then evaluate the benefits of the staging results. The Chengbi River reservoir is selected as the object of this study. Multi-year average daily rainfall time series, multi-year maximum rainfall time series, multi-year average daily runoff time series, and multi-year maximum daily runoff time series are used as index factors to divide flood season by fractal method. The benefit-risk theory is applied to evaluate the effects of staged dispatching.
3. Study Area and Data
The Chengbi River Reservoir is located in Baise City, Guangxi Province, downstream of Chengbi River, (106°21′ E–106°48′ E, and 23°50′ N–24°45′ N) (Figure 1
). It is the second-largest earth-rock fill dam reservoir project in China, with a total storage capacity of 1.15 billion m3
and a normal storage level of 185 m. The engineering characteristics parameters of the reservoir are shown in Table 1
. It operates under the rule of a single FLWL for the entire flood season, resulting in a low storage rate after floods and a large waste of flood resources. The average precipitation of the watershed over the years is 1560 mm, and the rainfall is unevenly distributed during the year, mostly concentrated in April to September, accounting for more than 85% of the annual rainfall. The flood season of the Chengbi River is from 13 April to 31 October with a low storage rate after flood season. The data selected in this paper are the daily precipitation and daily measured runoff from Ba Shou Station (BSS) from 1963 to 2016, and the four index factors (average daily rainfall, maximum daily rainfall, average daily runoff, and maximum daily runoff) which reflect the characteristics of the flooding period are used as the basic data of staging.
5. Discussion and Conclusions
Research on reservoir flood staging and FLWL is significant for improving the utilization of water resources. The study selects multi-year average daily rainfall time series, multi-year maximum rainfall time series, multi-year average daily runoff volume time series, and multi-year maximum daily runoff volume time series as index factors, and uses the fractal method to stage flood season. Finally, the effect of phased dispatching is evaluated in relation to the Benefit-risk theory. The results are as follows:
The pre-flood season of Chengbi River Reservoir is from 13 April to 6 June, the main flood season is from 7 June to 9 September, and the post-flood season is from 10 September to 31 October. Considering the irrigation water demand and flood control risk around the reservoir area, the FLWLs in each stage was finally determined to be 185 m in the pre-flood season, 185 m in the main flood season, and 185~187.5 m in the post-flood season. It is found that when the FLWL in the post-flood season is set at 186 m, the probability of exceedance after reservoir operation by stages in the flood season increases by 0.13 × 10−5, the average annual expected risk is 0.2264 million RMB. However, the average annual increase in benefits is 0.88 to 1.62 million RMB.
Compared with the research results of existing scholars [34
], the present study classifies the flood season to the daily scale with improved accuracy. When the average daily rainfall was used to stage the flooding of the Chengbi River reservoir, the multi-year average series differed from the multi-year maximum series by about five days. Using daily runoff for flood staging, the maximum deviation of the multi-year average series and multi-year maximum series results is about 10 days. The difference between the calculated results of the average daily rainfall time series and the average daily runoff time series is 5 to 10 days. It suggests that the selection of different factor indicators can have an impact on flood staging.
In this study, the staging and scheduling of reservoir floods and the determination of FLWL are investigated. The innovation of the paper is that fractal methods and multiple index factors are used to divide the flood season into daily scales, which improves the staging accuracy. However, there are still some shortcomings in the study. Improvements are needed in the following areas. Firstly, in terms of flood staging, it is recommended that multiple methods of staging should be used and then mutually validated because of the uncertainty and complexity of hydrology. Secondly, the indirect benefits of tourism and aquaculture due to increased storage capacity have not been calculated because of limited information. Finally, this study is based on long-term rainfall and runoff data, but one direction of research on reservoir scheduling is to forecast the future based on short-term data [37
], and how to combine long-term and short-term data needs to be studied further in the future.