1. Introduction
With the development of society, extreme rainfall events occur frequently under the influence of many factors such as climate change, underlying surface conditions, and human activities [
1,
2,
3]. In recent years, extreme heavy rain events have occurred frequently in China, especially in the arid and semi-arid areas in the north, causing serious losses [
4]; for example, from 28 July to 1 August 2023, the flood that occurred in the Haihe river region (named “23 · 7” flood by the Ministry of Water Resources, Beijing, China) [
5], and on 20 July 2021, the heavy rain [
6] that occurred in Zhengzhou, Henan Province, resulting in serious flood disaster losses (namely “7 · 20” flood). In hilly areas, floods are characterized by rapid onset, short response time, steep rise and fall, and high destructive potential. Additionally, many water conservancy projects such as reservoirs, locks, and dams have been usually constructed in river basins for the development of water resources. The construction of reservoirs changes the natural hydrological characteristics of rivers, resulting in changes in the production and aggregation mechanism [
7,
8,
9,
10,
11], especially for extreme rainstorm events. In addition, most small reservoirs and pond dams in China have no operation facilities and lack a long series of observation data. However, in the process of reservoir flood prediction, not only the precipitation and runoff process but also the influence of the storage and release of upstream reservoirs should be considered. Therefore, in order to improve flood disaster prevention abilities, it is crucial to study flood simulation methods for hilly reservoirs that take into account the impact of upstream water conservancy projects. This will greatly enhance flood forecasting and control efforts while reducing losses.
For flood simulation and prediction, after years of research, domestic and foreign scholars have proposed lumped models such as the HBV model, Tank model, and Xin’anjiang model [
12]. With the development of information and computer technology, scholars put forward distributed hydrological models such as Top model, VIC model, SWAT model, and HEC-HMS [
13,
14,
15,
16,
17,
18]. According to different climate zones and watershed characteristics of our country, Chinese scholars established multiple distributed hydrological models adapting to different watershed characteristics [
19,
20].
As for the impact of human activities, in foreign countries, hydrological models are mainly used to study runoff changes through land use/cover change [
21,
22,
23]. In the study and application of conceptual hydrological models, many domestic scholars considered the impact of small and medium-sized reservoirs on production and confluence. For example, Guo Shenglian et al. [
19] converted the interception of small and medium-sized reservoirs in the basin into the water storage capacity of Xin’an River model. Ling Minhua et al. [
24] put forward the Xin’an River model considering pond storage to simulate runoff and analyze the impact of pond storage on river runoff. Chen Jian et al. [
25] converted small and medium-sized reservoirs in the basin into virtual reservoirs and proposed an improved Xin’an River model.
In practical application, the flood forecasting method considering the influence of human activities mainly adds the influence factors of human activities into the lumped model. For example, Cheng Chuntian and Wang Bende [
26] estimated the cumulative net rainfall value of each unit at different time periods, as well as the intercepting capacity of the upstream reservoir and paddy field during the growth period based on flood stage and crop irrigation so as to estimate the maximum intercepting capacity and runoff correction coefficient. They then fine-tuned and optimized the parameters through human–computer interaction to obtain the parameters of flow production and the influence of human activities and pointed out that the forecast accuracy was 9% higher than that without considering the influence of human activities for Shitokoumen Reservoir in Jilin Province, reflecting the influence of human activities on the runoff forecast. Guo Shenglian et al. [
19] classified the water conservancy projects in the basin according to their storage capacity, converted the cumulative storage capacity of the same type of reservoir into the maximum storage capacity, and added it into the soil storage capacity of the Xin’an River model to put forward a flood forecasting scheme considering the influence of human activities. Sun Xinguo et al. [
27] aggregated many water projects in the basin into a virtual reservoir, formulated the water storage and release law of the aggregate reservoir, effectively quantified the impact of reservoir storage and discharge on the flooding process of the basin, and increased the Nash efficiency coefficient of flood prediction in Fengman Reservoir basin by 22%. Aiming at the problem of poor simulation accuracy of floods with rainfall centers distributed in the upper reaches, Yao Shiqian [
28] analyzed and evaluated the impact of changes in land use and water conservancy construction on runoff.
The above studies coupled the lumped model with the reservoir module did not fully consider the spatial distribution of water conservancy projects in the basin and did not describe the regulation and storage function of many small and medium-sized reservoirs with insufficient precision. Therefore, some scholars combined the advantages of the lumped model and distributed the hydrological model to establish a grid-based hydrological model considering the influence of water conservancy projects to further improve the accuracy of watershed flood prediction. For example, Yao Cheng et al. [
29] established a grid-based new Anjiang River model (Grid-XAJ) to consider the influence of Kutangdam storage and drainage on the process of production and convergence. The water distribution process along the river course is described well.
Domestic flood simulation and forecast studies on the impact of water conservancy projects are mainly in humid or semi-humid areas, whereas studies on arid and semi-arid areas [
9,
11,
28] only consider drought and humid conditions, but do not reflect semi-drought, and do not study flood simulation and forecast under extreme rainstorm scenarios. This paper proposed a flood simulation method for reservoir basins in hilly areas considering the distribution characteristics of upstream reservoirs and their influence on runoff in semi-humid areas. The method was established on the basis of a reservoir flood simulation model, further studied the flood holding capacity and the influence on runoff of many small and medium reservoirs upstream, and simulated the flooding process under different pre-influence rainfall so as to realize the flood forecast of the watershed as a unit and provide the basis for the following flood control scheduling. Firstly, according to the physical geography and hydrological characteristics, the hydrological model of the Moushan Reservoir basin was constructed based on HEC-HMS, in which two different combination methods of runoff generation and confluence, namely, the steady infiltration method with Snyder unit line method (method set 1) and the SCS-CN curve method with SCS unit line method (method set 2), were selected, respectively, in addition to the Muskingum method, which was used for the river flood evolution. Then, the distribution characteristics of the reservoirs in the basin were analyzed, the three medium and 93 small reservoirs in the upper reaches of the basin were generalized into seven aggregated reservoirs and added to the constructed hydrological model using the “Add Reservoirs” module in HEC-HMS. Finally, nine historical floods under different early influence rainfall were simulated and verified, of which six floods were for parameter calibration and three floods were for parameter verification. The peak discharge error, runoff depth error, peak current difference, and certainty coefficient were analyzed; a comparison between the results from different method sets was made with observed floods to verify the applicability and superiority of this approach.
3. Methods
3.1. Flood Simulation Model
The basin of Moushan Reservoir is a semi-arid and semi-humid area with complex underlying surface conditions. There is basically no base flow in the river course, and the flood caused by rainfall is mainly generated by the rainfall of each event, so the influence of base flow is ignored.
In addition, based on the principle of fewer parameters, easy data access, and stable and reasonable simulation results, combined with the topographic conditions, meteorological conditions, soil conditions, and vegetation conditions of the basin of Moushan Reservoir, the methods with better application effects in semi-arid and sub-humid areas [
9,
10,
11] are compared and analyzed, and two sets of methods (shown in
Table 1) are selected for simulation calculation.
In
Table 1, SCS-CN is the Soil Conservation Service Curve Number model proposed by the United States Department of Agriculture based on practical experience in 1972; CN is the number of runoff curves and has no dimension.
For runoff generation, the initial loss and steady infiltration method has strong applicability in simulating runoff in short-term rainfall scenarios and can accurately simulate the runoff process of short-term rainfall events and for long-term or complex rainfall patterns; the simulation accuracy of this method may be limited. However, the SCS-CN method may show higher simulation accuracy in long-term or complex rainfall patterns and may lead to prediction errors in the case of short-duration heavy rainfall due to simplified assumptions of the model. In the study area, the soil type and distribution are complex, the data are relatively lacking, and short-duration heavy rainfall events commonly occur. Therefore, the above two methods are used to calculate and analyze runoff production, respectively.
For confluence, the SCS unit line method has been widely used worldwide, and its parameters can be adjusted according to different basin characteristics. The combination of the SCS unit line method and SCS-CN current production method significantly improves the accuracy of flood prediction. This combination enables the model to better reflect the geomorphic characteristics of the basin, especially for the small watershed in the hilly area where data are lacking. The Snyder unit line method can reflect the characteristics of the basin well, including the influence of the basin area, topography, and other factors on the runoff process, but the parameter determination may be more complicated, and more field investigation and tests are needed to determine the appropriate parameters. The study area is a hilly area and data are lacking, so the above two methods are used for comparative analysis.
3.1.1. Flow Generation with Initial Loss and Steady Permeability Method
The Thiessen polygon method is used to divide the Thiessen polygon according to the distribution of rain measuring stations, as shown in
Figure 5.
For the sub-basin inside the Thiessen polygon, the area rainfall is the rainfall of the rainfall station. For sub-basins spanning multiple Thiessen polygons, the surface rainfall is calculated with the polygon area as the weight listed in
Table 2. The calculation formula is as follows:
where
is the area mean rainfall of sub catchment
i, mm.
n is the number of rain measuring stations.
Ai is the area controlled by the
ith rain measuring station in the sub-watershed, km
2.
F is the area of the sub-watershed, km
2.
Pi is the amount of rainfall at the
i station, mm.
The initial loss steady infiltration method has strong practicability in simulating runoff in short-term rainfall scenarios, and the runoff production process of rainfall can be divided into two stages: initial loss and post-loss. The calculation formula is as follows:
where
is the net rainfall, mm.
is the average rainfall over the surface from time t to time
t + Δ, mm, where Δ is the time step.
is the initial loss of rainfall, mm.
is the soil infiltration rate, mm/h.
3.1.2. Flow Generation with SCS-CN Curve Method
The SCS-CN curve method contains only one dimensionless parameter
CN value, with few parameters, and the calculation process is relatively simple. However, this value is related to soil moisture, land use type, and soil type in the previous period, and can reflect the comprehensive characteristics of the underlying surface in the study area in a more comprehensive way. The theoretical value range of the
CN value is 0–100, and the specific initial value is generally calculated according to the National Engineering Manual of the United States [
15]. In actual simulation calculation, the common value range is 40–98 [
6,
30]. When there are multiple land use types and soil types in the basin, the compound
value of the basin is calculated according to the equation as follows:
where
j is the number of land use types and soil types in the basin.
is the area of land use type and soil type of mass
j.
is the
CN value of land use type and soil type of mass
j.
3.1.3. Confluence Calculation Method—Snyder Unit Line Method
Under ideal conditions, there is the following relationship between the flood peak lag time
Tp and the precipitation duration
Tr of the unit line:
Under standard conditions, the relationship between peak flow
Up and peak time
Tp of the unit line is as follows:
where
Up is peak flow, m
3/s.
Tp is the peak time, h.
A is the catchment area, km
2.
C is the conversion coefficient; for SI units, the value is 2.75.
Cp is the unit line peak coefficient with values ranging from 0.4 to 0.8.
When the actual basin is very different from the ideal condition, the following changes should be made for the peak duration of the unit line:
where
is the expected peak duration of the unit line.
is the expected unit line period; that is, the set time step.
In 1992, Bedient and Huber [
31] proposed the calculation formula of expected peak duration of unit line as follows:
where
L is the length of the main stream of a basin,
Lc is the distance from the basin center to the basin outlet,
C is the conversion constant, which is 0.75 in international units, and
Ct is the stagnation of the basin coefficient, usually taking 1.8 to 2.2.
3.1.4. Confluence Calculation Method—Unit Line Method
The relationship between peak flow and peak occurrence time of unit line is as follows:
where
is the conversion coefficient; for SI units, the value is 2.08.
is net rainfall duration.
is the lag time of the basin; that is, the time difference between the peak of the unit line and the location of the net rain center.
can be obtained by formula as follows:
where
l is the length of channel, m.
S is the maximum possible amount of stagnant water in the basin, mm.
y is watershed slope, whose value can be extracted by HEC-GeoHMS tool.
3.1.5. Confluence Calculation Method—The Muskingum Method
The Muskingum method is a channel confluence algorithm based on tank storage equation and water balance equation and widely used in basin routing because of its simplicity, high efficiency, and relatively low data requirement [
32]. The segmental Muskingen method, Muskingum–Cunge method with the nonlinear calculus [
33,
34,
35], and the meshing calculus, as well as the Muskingum–Cunge–Todini with variable parameter method [
36,
37] were proposed successively.
There are two important model parameters in the Muskingum method: slope
K of the tank storage curve and specific gravity factor of discharge
X. The estimation formulas are as follows [
19]:
where
is the length of river, m.
n is the roughness coefficient.
is the conversion coefficient.
is the gradient of the riverbed.
is steady flow, m
3/s.
3.2. Simulation Method for Regulation and Storage of the Reservoirs in the Upper Reaches
There is 1 large-sized reservoir which is Gaoya reservoir, 3 middle-sized reservoirs including Daguan Reservoir, Yishan Reservoir, and Shangzhuang Reservoir, and 93 small-sized reservoirs in the upper reaches. The projects greatly increase the difficulty of the flood forecast.
3.2.1. Large-Sized Reservoir
For the large upstream Gaoya reservoir, it can be seen from the water level information after the beginning and end of each flood that the maximum water level datum is about 153.0 m. The water storage and discharge rules are simulated by using the top overflow model on a horizontal dam. The water storage capacity of the reservoir is represented by the relation between water level and storage capacity (seen in
Table 3 and
Figure 6).
The initial storage capacity is obtained according to the input initial water level conditions of the reservoir. The dam top elevation is set at 153.0 m; when the water level is lower than 153.0 m, all the incoming water is intercepted, and when the water level exceeds 153.0 m, the excess water is discharged and the overflow length of the dam top is set to 40 m.
3.2.2. Small and Medium-Sized Reservoirs
In order to assess the impact of small and medium-sized reservoirs, the concept of virtual aggregation is employed to simulate the influence of water conservancy projects on production and confluence in the basin by creating aggregate reservoirs. The specific steps are as follows:
(1) Aggregation of small and medium-sized reservoirs. The study area is divided into 22 sub-basins; the small and medium-sized reservoirs located in the same sub-basin are aggregated into a virtual reservoir by analyzing the distribution. Those reservoirs upstream are then aggregated into seven virtual reservoirs (AR1, AR2, AR3, AR4, AR5, AR6, and AR7), as shown in
Figure 7.
(2) Determination of water level–storage capacity relationship for aggregate reservoir. The “Add Reservoir” module in HEC-HMS is utilized to add those virtual reservoirs using a dam top overflow mode to replace their storage and discharge processes. Since an aggregate reservoir consists of several smaller ones, it can be assumed as a circular platform with its total volume representing the total storage capacity. By establishing this relationship based on a circular platform model (narrow at the bottom and wide at the top), we can obtain the water level–storage capacity curve for the aggregate reservoir, as depicted in
Figure 8.
(3) Determine the moisture of the land according to the previous rainfall. If the initial water level is known, the initial storage capacity and interception capacity of the aggregate reservoir can be obtained from
Figure 8.
The initial water level is closely related to the previous rainfall amount, evaporation, and water consumption, and the previous rainfall amount is an important factor. Firstly, according to the catchment area and the available storage capacity [
11] of small and medium-sized reservoirs, the interception capacity
Pt of the aggregate reservoir is calculated, as well as the cumulative rainfall
P1f,
P2f, and
P3f of the 15 days, 30 days, and 60 days before the occurrence of the flood, respectively, are calculated, and the rainfall is divided into three categories including arid, semi-arid, and humid [
12] according to the formula as follows:
where
k is the weight coefficient,
k ≥ 2.
indicates that the rainfall is mainly concentrated in the beginning of the flood season to the previous month.
indicates that the rainfall mainly occurred within one month before the occurrence of rainfall.
(4) Determine the initial water level of the aggregate reservoir. When under arid conditions, assuming the aggregate reservoir water level is zero, the reservoir intercept ability is the strongest. When it is wet, it is assumed that the water level is located at the elevation H of the wide-top weir, and the reservoir is in the full state; at this time, the reservoir has almost no interception effect on the runoff generated by rainfall. In the case of semi-drought, assuming that the level of the aggregate reservoir is H/2, the retention capacity is half of the maximum retention capacity.
The total catchment area of the small and medium-sized reservoirs is 329 km
2 and the total storage capacity is 43.61 million m
3. The interception capacity of the aggregate reservoirs can be calculated to be 132.6 mm. According to the above classification methods, the nine floods selected for simulation research are classified as shown in
Table 4.
3.3. Parameter Calibration
Two methods are used to automatically adjust the model parameters. The first is the Nelder–Mead algorithm, proposed by Nelder and Mead, for finding the extreme value of a multidimensional function, and the second is the Percent Error Peak function, one of the seven optimization objective functions provided in HEC-HMS. The automatically obtained parameter values would often ignore the actual physical meaning of the parameters, resulting in unreasonable values. Then, the unreasonable values are fine-tuned according to the actual meaning of parameters to obtain the calibration values of model parameters. The parameter calibration process is shown in
Figure 9, the optimization results of flow-producing and confluence parameters in the method set 1 and set 2 are shown in
Table 5, and the optimization results of river confluence are shown in
Table 6.
4. Results and Discussions
4.1. Simulation Results
Nine flooding processes were selected for simulation, among which six floods were used to determine model parameters and three floods were verified and analyzed. The simulation results of six floods used to calibrate the model parameters are shown in
Figure 10,
Figure 11,
Figure 12,
Figure 13,
Figure 14 and
Figure 15. The model parameters obtained by the above calibration are used to verify the three floods, and the simulation results are shown in
Figure 16,
Figure 17 and
Figure 18.
As can be seen from
Figure 10,
Figure 11,
Figure 12,
Figure 13,
Figure 14,
Figure 15,
Figure 16,
Figure 17 and
Figure 18, the flooding processes simulated by the two method sets are consistent with the measured ones. The errors in flood peak discharge
Q and runoff depth
H, difference in peak time
, and certainty coefficient
C of the simulation results were compared and analyzed, as shown in
Table 7,
Table 8 and
Table 9.
(1) The flood peak errors simulated by method set 1 are between 2.4% and 15%, with an average error of 5.63%, and those simulated by method set 2 are between 2.3% and 12.3%, with an average error of 5.72%. Except the floods on 11 October 2003 and 10 August 2019, the flood peak error of the other seven fields simulated by method set 1 is smaller than those simulated by method set 2. The simulated runoff depth errors of method set 1 are between 0.1% and 16.1%, with an average value of 5.87% and those of method set 2 are between 3.0% and 19.6%, with an average value of 12.33%. It can be seen that the accuracy of the simulation results of the two method sets meets the requirements of Class B accuracy of the “Water forecast Code” [
38]; that is, the two simulation method sets can be used for the flood forecast of the Moushan Reservoir basin, and method set 1 shows better simulation results compared to method set 2.
(2) The average simulation certainty coefficient of method set 1 and set 2 are both greater than 0.7, which meets the accuracy requirements of Class B and can be used for flood forecasting. The average simulation certainty coefficient of method set 1 is 0.89 and that of method set 2 is 0.85, which also shows that the simulation effect of method set 1 is better than that of method set 2.
(3) According to
Figure 10,
Figure 11,
Figure 12,
Figure 13,
Figure 14,
Figure 15,
Figure 16,
Figure 17 and
Figure 18, considering the regulation and storage function of small and medium-sized reservoirs, both methods have better simulation effects, and the simulated flooding process is more consistent with the actual flooding process, which solves the difficult problem of flood simulation and provides a reference for other river basin flood simulations.
(4) By using the reservoir module of HEC-HMS, the initial water level of the aggregate reservoir is determined by considering the three initial states of “drought, semi-drought and wet”, ensuring accurate flood holding capacity to accommodate incoming flooding process curves and improve flood forecasting accuracy.
4.2. Discussions
To test the validity of the model proposed, some discussions about the model are presented below.
(1) The generalization of rainfall data results in differences between simulated and measured values. The distribution of rainfall stations in the study area are not uniform, and the areal rainfall of each sub-watershed is generalized by the Thiessen polygon method, but the areal rainfall calculated by the weight of the control area of each rainfall station in the sub-watershed is too average, ignoring the influence of the rainstorm center. The rainstorm center has an obvious peaking effect, which has a great influence on the flood peak and the confluence time, so there are differences between the simulated values and the measured values.
(2) The setting of the aggregation reservoir causes the difference between the simulated values and the measured values. In this paper, there are some shortcomings in considering the influence of small and medium-sized reservoirs. The uneven spatial distribution of small and medium-sized reservoirs is not taken into account in the model, and the storage capacity of small reservoirs is only a quantitative estimate. Therefore, with the reinforcement of small reservoirs and the increase in monitoring stations in the basin overcoming the lack of data at present, a distributed hydrological model should be established to simulate the influence of a large number of upstream water conservancy projects on the downstream hydrological process, and the accuracy of watershed flood simulation could improve.
(3) The difference in runoff depth between simulated results and measured values. The flood control standards of small reservoirs in the basin are not high, and this paper does not consider the phenomenon of a sudden increase in water intake in the reservoir caused by dam break and dam collapse under extreme circumstances. In addition, there are a large number of small reservoirs in the study area. This paper generalizes these reservoirs and greatly improves the accuracy of flood simulation. However, the generalized treatment results are different from the actual situation, especially the small water storage buildings such as ponds and dams, which will partially intercept flood waters, resulting in the simulated runoff depth generally being slightly larger than the actual situation.
(4) Calculation accuracy of storage capacity of small and medium-sized reservoirs. The model of the aggregation reservoir established in this paper only considers the influence of small and medium-sized reservoirs on flooding, and the storage capacity of aggregation reservoirs is mainly calculated according to the soil water content in the early stage. However, actually, the influencing factors of hydraulic engineering should generally be considered in addition to the upstream small and medium-sized reservoirs, as well as ponds and dams, farmland, and water blocking projects on rivers, etc., and the influencing factors for the impaction of reservoirs are not only the soil water storage in the early stage. So, in future studies, the influence of other hydraulic projects and other indicators affecting storage capacity can be added to further improve the model.
Despite the analysis of the above shortcomings, the simulation results of the established model still meet the accuracy requirements and can provide more accurate forecast information for defense decisions such as flood control scheduling, according to the result analysis in 4.1. Of course, aiming at the above shortcomings, the following research plan will further improve accuracy.
(5) Further research plans for flash flood simulation methods. The above analysis and discussion results show that the rainfall center and the setting of the aggregate reservoir have an important influence on the flood simulation accuracy. Therefore, it is planned to carry out studies on the regulating effect of rainfall distribution and reservoir aggregation methods on the confluence process of small and medium-sized reservoirs; for example, calculating the water storage capacity of both upper and lower soil layers in the basin by developing a model to simulate and rate various types of floods and then determine the soil water content over the first 30 days using the Thiessen polygon method to weight the rainfall of each sub-region and calculate the preliminary filling rate and storage capacity of the aggregate reservoir to improve the calculation accuracy of flood peak discharge and peak time.
(6) The focus of this paper is how to consider the impact of upstream water conservancy projects and simulate the flooding of hilly reservoirs under extreme rainstorm conditions. The impact of climate change on rainfall and flood simulation is not taken into account. Although climate change is recognized as a key factor affecting the frequency and intensity of extreme weather events [
39], studies have shown that there is a significant correlation between temperature and rainfall for the Northeastern Himalayas [
40], and climate change affects the spatial distribution of precipitation and heavy precipitation [
41], potentially increasing the risk of flash floods in the basin [
42,
43]. Therefore, research on predicting changes in climate and environmental factors and their impact on rainfall is beneficial for simulating floods in hilly area reservoirs.
5. Conclusions
This paper takes the basin of Moushan Reservoir as the research object, considers the influence of small and medium-sized reservoirs in the upstream of the basin, and summarizes them as aggregate reservoirs. HEC-HMS is used to construct the hydrological model of the basin, and two kinds of flow generation and confluence simulation methods are developed. The effects of the two method sets in the flood simulation of the basin of Moushan Reservoir are compared in terms of peak flow, runoff depth, and peak occurrence time and certainty coefficient. The main conclusions are as follows:
(1) Based on HEC-HMS, the hydrology model of the Moushan Reservoir basin is constructed by using two different production–confluence combination method sets. Both methods are suitable for flood simulation and forecast of the basin. The simulated flood peak errors of method set 1 are between 2.4% and 15%, and those of method set 2 are between 2.3% and 12.3%. The errors of simulated runoff depth in method set 1 are between 0.1% and 16.1%, and those in method set 2 are between 3.0% and 19.6%. The accuracy of the simulation results of the two method sets can meet the requirements of Class B accuracy of the “Water Forecast Code”.
(2) Of the models established by the two method sets, method set 1 can better simulate the flood in the basin of Moushan Reservoir. The average flood peak error simulated by method set 1 is 5.63%, and that simulated by method set 2 is 5.72%. The average errors of runoff depth simulated by method set 1 and method set 2 are 5.87% and 12.33%, respectively. The simulated average certainty coefficient of method set 1 is 0.89, and that of method set 2 is 0.85. Therefore, the simulation effect of the first method is better than that of the second method in the Moushan Reservoir basin in terms of the certainty coefficient, the precision of the peak discharge, and the precision of the runoff depth.
(3) Many small and medium-sized reservoirs in the basin are summarized as aggregate reservoirs and dynamic parameters are used to reflect the regulation and storage functions under the three scenarios of drought, semi-drought, and humid, which can better reflect the influence of small water conservancy projects on confluence, solve the problem of flood simulation difficulties caused by small and medium-sized reservoirs, and facilitate the realization of flood prediction for the basin. It provides the basis for the next flood control operation and has certain reference significance for other related river basins.
(4) In order to enhance the accuracy of basin flood simulation and forecasting, further research on the impact of climate change on rainfall distribution and intensity is crucial for addressing the precision issue caused by above-average area rainfall. As well, for calculating the aggregation and storage capacity of small and medium-sized reservoirs, more factors should be taken into account. It is necessary to improve the model or establish a watershed-distributed hydrological model so as to better reflect the impact of small and medium-sized reservoirs on the basin flooding process.