Flood forecasting method for small reservoirs in data-deficient basins based on distributed hydrological model
By adopting distributed hydrological models and multi-objective optimization algorithms in small reservoir basins, a step-by-step river galaxy forecasting network was established, and flood forecasting problems caused by the lack of hydrological measurement data were solved, and accurate flood forecasting and flood control capabilities of small reservoirs were improved.
Patent Information
- Application Number
- CN202410784667.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-18
- Publication Date
- 2024-12-20
- Estimated Expiration
- 2044-06-18
AI Technical Summary
The prior art is difficult to effectively predict floods in small reservoir basins that lack hydrological measurement data, and traditional parameter transplantation methods are low in accuracy and high uncertainty.
Using a distributed hydrological model method, a spatial topology network for forecasting of small watersheds and river network systems was established by dividing small watersheds and river network water systems, combining the Sanshuiyuan Xin'an River hydrological model and the Mastingen method, and using a multi-objective optimization algorithm to determine the model parameters , and the flood process of small reservoirs entering the reservoir is derived through area conversion algorithm and convergence time translation method.
Accurate flood forecasts in data-deficient basins have been achieved, flood control capabilities of small reservoirs have been improved, blind spots in flood reporting in the basin have been eliminated, and flood control and disaster reduction capabilities have been improved in the entire basin.
Smart Images
Figure CN118821363B_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of hydrological forecasting, and in particular relates to a method for flood forecasting of small reservoirs in data-deficient basins based on a distributed hydrological model. Background Art
[0002] There are many small reservoirs in my country and they are widely distributed in space. Flood forecasting is one of the most effective non-engineering measures for flood prevention and disaster reduction. Fast and accurate flood forecasting can effectively improve the flood prevention capacity of small reservoirs. However, the existing commonly used flood forecasting technology in China is highly dependent on measured hydrological data, and most of my country's small reservoirs lack measured hydrological data, making it difficult to build and apply flood forecasting models. Although new rain and water stations have been built in small reservoir basins in recent years, due to the small area of small reservoir basins, the short construction time of rain and water stations, and the short sequence of hydrological data, it is difficult to meet the needs of small reservoir flood forecasting. Therefore, how to realize flood forecasting for small reservoirs with insufficient data is a key technical problem that needs to be solved urgently.
[0003] Traditionally, the method of parameter transplantation can be used to solve the hydrological forecast of basins with insufficient data. However, due to the spatial variation of underlying surface conditions and the lack of hydrological data observation records, the parameter transplantation method has large uncertainty, low accuracy and is difficult to be effectively applied to small reservoir basins with many points and wide areas. With the rapid development of computer technology and the widespread application of natural geographic information data, distributed hydrological models are gradually applied to basin flood forecasting. The distributed modeling idea can obtain the flood forecasting process of different nodes in the basin, and is still applicable under the conditions of drastic changes in the underlying surface and updated hydrological data. Summary of the invention
[0004] The purpose of the present invention is to provide a method for flood forecasting of small reservoirs in data-deficient basins based on a distributed hydrological model to solve the above technical problems.
[0005] To achieve the above object, the present invention provides the following technical solutions:
[0006] The present invention proposes a method for flood forecasting of small reservoirs in data-deficient basins based on a distributed hydrological model, the method comprising the following steps:
[0007] Step 1: Small watershed division and river network water system extraction: Based on the national mountain torrent disaster investigation and evaluation results dataset, divide the small watersheds within the study area, extract the river network water system in the study area, and determine the boundaries of small watersheds and river network water systems;
[0008] Step 2, establish a spatial topological network for river system forecasting at each level: determine the forecast nodes according to the small watersheds divided in step 1 and the extracted river network water system, sort out the spatial positions of the river network water system and the forecast nodes to determine their upstream forecast nodes and small watersheds, and configure associated rainfall stations for the small watersheds, thereby establishing a spatial topological network for river system forecasting at each level from the upstream to the downstream of the small watersheds and forecast nodes, and establish a spatial position correspondence between small reservoirs and small watersheds, with each small reservoir associated with a small watershed;
[0009] Step 3: Modeling of distributed hydrological model for river system forecasting at each level: Establish a distributed hydrological model for river system forecasting at each level from upstream to downstream for all forecast nodes. For forecast nodes without upstream inflow, the flood process is the flood forecast result of the small watershed, and the Sanshuiyuan Xin'anjiang hydrological model is used for modeling; for forecast nodes with upstream inflow, the flood process is the confluence of upstream inflow flood and the superposition of interval small watershed flood. The Muskingum method is used to calculate the confluence of upstream inflow flood, and the interval small watershed flood forecast is modeled using the Sanshuiyuan Xin'anjiang hydrological model; determine the model parameters, and obtain the flow process of all forecast nodes and small watersheds;
[0010] Step 4. Derivation of the flood process of small reservoirs: The hydrological model takes rainfall and evaporation data as input, and outputs the simulated flood process after running the model. According to the distributed hydrological model established in step 3, rainfall and evaporation data are input and the model is run to output the flood simulation calculation results of all forecast nodes and small watersheds in the river network. Combined with the relationship between small reservoirs and small watersheds, the flood process of small reservoirs in watersheds with missing data is deduced.
[0011] Furthermore, the configuration of associated rain gauges for the small watershed in step 2 is specifically: using ArcMap spatial connection and neighbor analysis function modules to configure associated rain gauges for the small watershed; the establishment of the spatial position correspondence between the small reservoir and the small watershed is specifically: using ArcMap's spatial connection function module to establish the spatial position correspondence between the small reservoir and the small watershed.
[0012] Furthermore, the specific process of determining the model parameters described in step 3 is as follows: first, the initial value range of the model parameters is determined according to the physical meaning of the model parameters and empirical estimation, and then the forecast nodes with measured hydrological data in the river network are used to optimize the parameters, and the model parameters are determined by a semi-automatic method combining the multi-objective optimization NSGA-II algorithm and manual work, with the peak flow relative error RPE, peak occurrence time error PTE, certainty coefficient DC and flood volume relative error RRD as the multi-objective function for parameter optimization, and the calculation formulas are:
[0013]
[0014] Where N is the number of flood events; and are respectively the predicted value and measured value of the peak flow of the ith flood, in m 3 / s; and are the simulated and measured values of the peak time of the ith flood respectively; q ij,sim and q ij,obs are the simulated flow value and measured flow value of the i-th flood and the j-th period, respectively, in m 3 / s; is the average measured flow rate of the ith flood, in m 3 / s;r i,sim and r i,obs are the simulated runoff depth and measured runoff depth of the ith flood, respectively, in mm;
[0015] Among them, DC is used to evaluate the fitting degree of the flood process. The closer its value is to 1, the better the flood forecast effect is. RPE and PTE are used to evaluate the peak flow. The closer their values are to 0, the better the flood forecast effect is. RRD is used to evaluate the flood runoff. The closer its value is to 0, the better the flood forecast effect is.
[0016] Furthermore, the derivation of the flood process of the small reservoir in the basin with insufficient data in step 4 is specifically: the flood process of the small reservoir is deduced from the flood process of the small basin by using the simplified method of area conversion method and confluence time shift, which specifically includes the following steps:
[0017] S41, Area ratio coefficient reduction: The flood inflow from small reservoirs in small watersheds is reduced by the ratio of the catchment area of the small reservoir to the area of the small watershed where it is located.
[0018] S42. Calculation of confluence time from small reservoir to small watershed outlet:
[0019] Based on the geospatial data cloud, the DEM grid elevation data of the small watershed is obtained, and the confluence time of each grid unit is calculated using the DEM grid elevation data of the small watershed. The calculation formula is:
[0020] V=KS 0.5 (5)
[0021]
[0022] Where V is the flow velocity of each grid unit in the basin, in m / s; S is the ground slope; K is the velocity coefficient, which is determined according to the water flow state of the basin grid; L is the distance that the water drop flows through each grid unit; △t is the lag time of runoff in each grid unit, in s; t is the confluence time of water flow from each grid unit to the outlet of the small basin, in s; m is the number of grids on the confluence path;
[0023] S43. Derivation of small reservoir inflow flood: The inflow flood obtained by reducing the simulated flood process of the small basin in S41 is shifted backward along the time axis as a whole according to the confluence time calculated in S42 to obtain the small reservoir inflow flood process.
[0024] The beneficial effects of the present invention are as follows: the method of the present invention first uses the results of mountain torrent disaster investigation and evaluation to divide small watersheds and extract river network water systems, constructs a spatial topological structure network of river network water systems, forecast nodes and small watersheds, and associates them with small reservoirs, and then constructs a step-by-step river system flood forecasting scheme based on a distributed hydrological model, obtains flood forecasting results of all forecast nodes from upstream to downstream of the river system, and on this basis, deduces the flood inflow process of small reservoirs, and realizes flood forecasting modeling and calculation of small reservoirs in data-deficient basins. The method of the present invention solves the problem that it is difficult to carry out flood forecasting for small reservoirs in data-deficient basins, uses a distributed hydrological model to realize step-by-step forecasting of river network water systems, and then deduces the flood inflow process of small reservoirs, eliminates blind spots in flood reporting in the basin, and provides technical support for improving the flood prevention and disaster reduction capabilities of the entire basin.
[0025] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Figure 1 It is a schematic diagram of the process of the method of the present invention;
[0027] Figure 2 Flowchart of NSGA-II algorithm for multi-objective optimization;
[0028] Figure 3 This is a schematic diagram of the division of the Qiantang River watershed and river network.
[0029] Figure 4 This is the topological network diagram of the Qiantang River water system level-by-level forecast;
[0030] Figure 5 to Figure 7 These are the topological relationship diagrams corresponding to the river system forecast schemes for the three forecast nodes of Xinting, Linxi and Yuliang;
[0031] Figure 8 to Figure 10 These are the flood simulation results of different times for the Xinting, Linxi and Yuliang forecast nodes;
[0032] Fig.11 This is the simulation result of flood inflow into representative small reservoirs in the Qiantang River basin. DETAILED DESCRIPTION
[0033] The present invention discloses a method for flood forecasting of small reservoirs in a data-deficient basin based on a distributed hydrological model. Figure 1 As shown, the method comprises the following steps:
[0034] Step 1: Divide small watersheds and extract river network systems.
[0035] The division of small watersheds and the extraction of river networks are the basic data for constructing a distributed hydrological model. This paper uses the existing national mountain torrent disaster investigation and evaluation results data set. 2 The area is divided into about 530,000 small watersheds, which basically covers the flash flood disaster prevention and control areas in my country, and can fully meet the requirements of small watershed division and river network water system extraction in distributed hydrological modeling in different regions. Therefore, based on the results of the national flash flood disaster investigation and evaluation, small watershed division and river network water system extraction are carried out to determine the boundaries of small watersheds and river network water systems within the study area.
[0036] Step 2: Determine the forecast nodes and analyze the spatial topological structure of small reservoirs.
[0037] S21. Forecast node determination
[0038] The small watersheds divided in step 1 and the extracted river network directly give the outlet position of the small watershed. There are cases where two or more small watersheds converge into the same outlet. On this basis, the outlet of the small watershed is used as a forecast node (NODE), and the same forecast node can be the common outlet of multiple small watersheds.
[0039] S22. Establishing a spatial topological network for level-by-level river system forecasting
[0040] The spatial topological structure analysis of river networks, forecast nodes and small watersheds first sorts out the spatial positions of river networks and forecast nodes to directly determine their upstream forecast nodes and small watersheds, and uses ArcMap spatial connection and neighbor analysis function modules to configure associated rainfall stations for small watersheds, thereby establishing a step-by-step river system forecast spatial topological network of small watersheds and forecast nodes from upstream to downstream.
[0041] S23. Association between small reservoirs and small watersheds
[0042] The spatial connection function module of ArcMap is used to establish the spatial location correspondence between small reservoirs and small watersheds, and each small reservoir is associated with a small watershed.
[0043] Step 3: Establish river system forecast model for distributed hydrological model at each level and determine model parameters.
[0044] S31. Establish a distributed hydrological model for river system forecasting at each level
[0045] According to the spatial topological network structure determined in step 2, a distributed hydrological model-based river system forecasting scheme for all forecast nodes from upstream to downstream is established. For forecast nodes without upstream inflow, their flood process is the flood forecast result of the small watershed, and the Sanshuiyuan Xin'anjiang hydrological model is used for modeling; for forecast nodes with upstream inflow, their flood process is the confluence of upstream inflow floods and the superposition of interval small watershed floods. The confluence of upstream inflow floods is calculated using the Muskingum method, and the interval small watershed flood forecast is modeled using the Sanshuiyuan Xin'anjiang hydrological model. The spatial differences in the underlying surface such as vegetation coverage and soil type between different small watersheds directly affect the basin rainfall-runoff conversion process. The distributed hydrological model reflects the spatial differences in the underlying surface through its model parameters. The model parameters are determined, and a step-by-step river system forecasting scheme based on the distributed hydrological model is established, and finally the flow process of all forecast nodes and small watersheds is obtained.
[0046] S32. Distributed hydrological model parameter determination and accuracy evaluation
[0047] The step-by-step river system forecasting scheme established by S31 above adopts the Xin'anjiang hydrological model of three water sources and Muskingum method to build a model. First, the preliminary approximate value range of the model parameters is determined according to the physical meaning and empirical estimation of the model parameters. On this basis, the forecast nodes with measured hydrological data in the river network are used to optimize the parameters, and the semi-automatic method combining multi-objective optimization NSGA-II algorithm and manual work is used to determine the parameters of the basin hydrological model. The relative error of peak flow (RPE), peak occurrence time error (PTE), certainty coefficient (DC) and relative error of flood volume (RRD) are used as multi-objective functions for parameter optimization. The specific calculation formula is as follows:
[0048]
[0049] Where N is the number of flood events; and are respectively the predicted value and measured value of the peak flow of the ith flood, in m 3 / s; and are the simulated and measured values of the peak time of the ith flood respectively; q ij,sim and q ij,obs are the simulated flow value and measured flow value of the i-th flood and the j-th period, respectively, in m 3 / s; is the average measured flow rate of the ith flood, in m 3 / s;r i,sim and r i,obs are the simulated runoff depth and measured runoff depth of the ith flood, respectively, in mm.
[0050] The NSGA-II algorithm is a multi-objective optimization algorithm based on the Pareto optimal solution, which is widely used in the optimization of traditional hydrological model parameters. The algorithm involves initializing the population, generating sub-populations, non-dominated sorting, selection, crossover, mutation, parent-child generation merging, and evolutionary algebraic update. The multi-objective optimization NSGA-II algorithm process is as follows: Figure 2 shown.
[0051] DC is mainly used to evaluate the degree of fitting of the flood process. The closer its value is to 1, the better the flood forecast effect is. RPE and PTE focus on evaluating the peak flow. The closer their values are to 0, the better the flood forecast effect is. RRD is mainly used to evaluate flood runoff. The closer its value is to 0, the better the flood forecast effect is.
[0052] Step 4: Derivation of the flood process into small reservoirs.
[0053] The hydrological model takes rainfall and evaporation data as input, and outputs the simulated flood process after running the model. According to the distributed hydrological model established in step 3, after running the model, the flood simulation calculation results of all forecast nodes and small watersheds of the river network are output, and the association between small reservoirs and small watersheds determined in step S23 is combined to deduce the flood process of small reservoirs in watersheds with insufficient data. The simplified method of area conversion method and confluence time shift is used to deduce the flood process of small reservoirs from the simulated flood process of small watersheds.
[0054] S41, Area ratio coefficient reduction
[0055] There are many small reservoirs and they are located in many places, so it is difficult to directly calculate their inflow flood process, but the flood process at the outlet of the small watershed can be calculated. Therefore, it is necessary to deduce the inflow flood process of the small reservoirs in the small watershed from the simulated flood process of the small watershed. Since the catchment area of small reservoirs is small and they are usually less affected by human activities, and the spatial distribution of rainfall in small watersheds is uniform, the regulation and storage of small reservoirs under heavy rainfall conditions during the flood season is less affected. Therefore, the inflow flood of small reservoirs in small watersheds can be directly reduced by using the ratio of the catchment area of small reservoirs to the area of the small watershed where they are located to simulate the flood process of the small watershed.
[0056] S42. Calculation of confluence time from small reservoir to small watershed outlet
[0057] Based on the geospatial data cloud, the DEM grid elevation data of the small watershed is obtained, and the confluence time of each grid unit is calculated using the DEM grid elevation data of the small watershed:
[0058] V=KS 0.5 (5)
[0059]
[0060] Where V is the flow velocity of each grid unit in the watershed, in m / s; S is the ground slope; K is the velocity coefficient, which is determined according to the water flow state of the watershed grid; L is the distance that the water droplets flow through each grid unit; △t is the lag time of runoff in each grid unit, in s; t is the confluence time of water flow from each grid unit to the outlet of the small watershed, in s; and m is the number of grids on the confluence path.
[0061] S43. Derivation of flood water into small reservoirs
[0062] The inflow flood obtained by reducing the simulated flood process of the small watershed in S41 is shifted backward along the time axis as a whole according to the confluence time calculated by S42 to obtain the inflow flood process of the small reservoir.
[0063] Embodiment 1
[0064] This embodiment is an application example of the above method.
[0065] This embodiment discloses a method for flood forecasting of small reservoirs in a data-deficient basin based on a distributed hydrological model, comprising the following steps:
[0066] Step 1: Divide small watersheds and extract river network systems.
[0067] Taking the Qiantang River system in Anhui Province as the research object, according to the results of the national mountain torrent disaster investigation and evaluation, the Qiantang River system is divided into 272 small watersheds. Figure 3 shown.
[0068] Step 2: Determine the forecast nodes and analyze the spatial topological structure of small reservoirs.
[0069] S21. Forecast node determination
[0070] The small watersheds divided in step 1 and the extracted river network directly give the outlet position of the small watershed. There are cases where two or more small watersheds converge into the same outlet. On this basis, the outlet of the small watershed is used as a forecast node (NODE), and the same forecast node can be the common outlet of multiple small watersheds.
[0071] S22. Establishing a spatial topological network for level-by-level river system forecasting
[0072] The spatial topological structure of the river network, forecast nodes and small watersheds in the Qiantang River basin was analyzed. First, the spatial positions of the river network and forecast nodes were sorted out to directly determine the upstream forecast nodes and small watersheds. The ArcMap spatial connection and neighbor analysis function modules were used to configure associated rainfall stations for the small watersheds, and a hierarchical river system forecast spatial topological network of small watersheds and forecast nodes from upstream to downstream in the Qiantang River system was established. Figure 4This is the topological network diagram of the Qiantang River water system step-by-step forecast. Tables 1 to 3 show the associated rainfall stations in the basins controlled by the three forecast nodes of Xinting, Linxi and Yuliang in the Qiantang River water system.
[0073] S23. Association between small reservoirs and small watersheds
[0074] The spatial connection function of ArcMap is used to establish the spatial location correspondence between small reservoirs and small watersheds, and each small reservoir is associated with a small watershed.
[0075] Table 1. Xinting forecast node associated rainfall stations
[0076] Station Name Station Code Site Station Class Management Unit Xinting 70113100 Haiyang Town, Xiuning County, Huangshan City ZQ Huangshan Hydrology Fang Tian 70136900 Hongcun Town, Yi County, Huangshan City PP Huangshan Hydrology Rucun 70137100 Lantian Town, Xiuning County, Huangshan City PP Huangshan Hydrology
[0077] Table 2 Linxi forecast node associated rainfall stations
[0078] Station Name Station Code Site Station Class Management Unit Linxi 70111350 Qishan Town, Qimen County, Huangshan City ZQ Huangshan Hydrology Yangxi 70138400 Yangxi Town, Jixi County, Xuancheng City PP Huangshan Hydrology
[0079] Table 3. Yuliang forecast node associated rainfall stations
[0080] Station Name Station Code Site Station Class Management Unit Fishing Beam 70113600 Huicheng Town, She County, Huangshan City ZQ Huangshan Hydrology Xitou 70139000 Xitou Town, She County, Huangshan City PP Huangshan Hydrology Jixi 70111350 Qishan Town, Qimen County, Huangshan City ZQ Huangshan Hydrology Xucun 70139300 Xucun Town, She County, Huangshan City PP Huangshan Hydrology Yangxi 70138400 Yangxi Town, Jixi County, Xuancheng City PP Huangshan Hydrology
[0081] Step 3: Establish river system forecast model for distributed hydrological model at each level and determine model parameters.
[0082] S31. Establish a distributed hydrological model for river system forecasting at each level
[0083] According to the spatial topological network determined in step S22, a distributed hydrological model-by-level river system forecasting scheme for all forecast nodes in the Qiantang River system from upstream to downstream is established. For forecast nodes without upstream inflow, their flood process is the flood forecast result of the small watershed, and the Sanshuiyuan Xin'anjiang hydrological model is used for modeling; for forecast nodes with upstream inflow, their flood process is the flood process of the small watershed superimposed by the upstream inflow flood. The upstream inflow flood is calculated by the Muskingum method, and the interval small watershed flood forecast is modeled by the Sanshuiyuan Xin'anjiang hydrological model. The spatial differences in the underlying surface such as vegetation coverage and soil type between different small watersheds directly affect the basin rainfall-runoff conversion process. The distributed hydrological model reflects the spatial differences in the underlying surface through its model parameters, thereby establishing a step-by-step river system forecasting scheme based on the distributed hydrological model, and finally obtaining the simulated flood process of all forecast nodes and small watersheds.
[0084] Taking the three river stations (forecast nodes) of Xinting, Linxi and Yuliang in the Qiantang River system with relatively good hydrological data conditions as examples, according to the spatial distribution of forecast nodes and river network water systems, the three-water source Xin'anjiang hydrological model and Muskingum method were used to construct a forecast node-by-level river system forecasting scheme. Figure 5 to Figure 7 These are the topological relationship diagrams corresponding to the river system forecast schemes for the three forecast nodes of Xinting, Linxi and Yuliang.
[0085] S32. Distributed hydrological model parameter determination and accuracy evaluation
[0086] The step-by-step river system forecasting scheme established by S31 above adopts the Xin'anjiang hydrological model of three water sources and the Muskingum method to build a model. First, the approximate value range of the parameters is determined according to the physical meaning and empirical estimation of the model parameters. On this basis, the historical measured flood data of the control basins of the Xinting, Linxi and Yuliang forecast nodes in the river system are used to optimize the calibration of the model parameters. The flood information of the control basins of the Xinting, Linxi and Yuliang forecast nodes are shown in Tables 4 to 6 respectively. The parameters of the Xin'anjiang hydrological model and the Muskingum river flood evolution are determined by repeated debugging using a semi-automatic method combining multi-objective optimization NSGA-II algorithm and manual work. The relative error of peak flow (RPE), peak occurrence time error (PTE), coefficient of certainty (DC) and relative error of flood volume (RRD) are used as multi-objective functions for parameter optimization. After calculation, the flood simulation results of all forecast nodes in the Qiantang River system are finally obtained. Figure 8 to Figure 10 These are the simulation results of different floods selected for the Xinting, Linxi and Yuliang forecast nodes. As can be seen from the figure, the simulated flow process of different floods is highly consistent with the measured sequence, and the measured and simulated flood peaks are also relatively close, indicating that the simulation results of the established distributed hydrological model at these three forecast nodes are good, and the selected model parameters can reflect the characteristics of rainfall and runoff in the Qiantang River basin.
[0087] Table 4 Flood information of Xinting Basin
[0088]
[0089] Table 5 Flood information of Linxi River Basin
[0090]
[0091] Table 6 Flood information of Yuliang River Basin
[0092]
[0093] Step 4: Derivation of the flood process into small reservoirs.
[0094] According to the distributed hydrological model established in step 3, after inputting rainfall and evaporation data to run the model, the flood simulation calculation results of all forecast nodes and small watersheds in the river network are output, and the association between small reservoirs and small watersheds determined in step S23 is combined to deduce the flood process of small reservoirs in the watershed with insufficient data. The simplified method of area conversion method and confluence time shift is used to deduce the flood process of small reservoirs from the flood process of small watersheds.
[0095] S41, Area ratio coefficient reduction
[0096] Since small reservoirs have a small catchment area and are usually less affected by human activities, and rainfall in small watersheds is evenly distributed in space, the storage and regulation of small reservoirs is less affected by heavy rainfall conditions during the flood season. Therefore, floods entering small reservoirs in small watersheds are directly reduced by the ratio of the catchment area of the small reservoir to the area of the small watershed in which they are located.
[0097] S42. Calculation of confluence time from small reservoir to small watershed outlet
[0098] The DEM grid elevation data of small watershed is used to infer the confluence time of each grid unit.
[0099] S43. Derivation of flood water into small reservoirs
[0100] The inflow flood obtained by reducing the simulated flood process of the small watershed in S41 is shifted backward along the time axis as a whole according to the confluence time calculated by S42 to obtain the inflow flood process of the small reservoir.
[0101] Table 7 shows the statistical results of the catchment area and cumulative confluence time of small reservoirs in the Qiantang River system. Fig.11 This is a simulation result of flood inflows into representative small reservoirs in the Qiantang River Basin. Fig.11 It can be seen that the rise and fall trends of the simulated flood inflow process of each small reservoir are consistent with the rainfall time distribution, indicating that the method proposed in the present invention has a good application effect in flood forecasting of small reservoirs in basins with insufficient data.
[0102] Table 7 Information on the catchment area and confluence accumulation time of small reservoirs in the Qiantang River system
[0103]
[0104] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred arrangement scheme, a person skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.
Claims
1. A method for flood forecasting of small reservoirs in data-deficient basins based on a distributed hydrological model, characterized in that: The method comprises the following steps: Step 1: Small watershed division and river network water system extraction: Based on the national mountain torrent disaster investigation and evaluation results dataset, divide the small watersheds within the study area, extract the river network water system in the study area, and determine the boundaries of small watersheds and river network water systems; Step 2, establish a spatial topological network for river system forecasting at each level: determine the forecast nodes according to the small watersheds divided in step 1 and the extracted river network water system, sort out the spatial positions of the river network water system and the forecast nodes to determine their upstream forecast nodes and small watersheds, and configure associated rainfall stations for the small watersheds, thereby establishing a spatial topological network for river system forecasting at each level from the upstream to the downstream of the small watersheds and forecast nodes, and establish a spatial position correspondence between small reservoirs and small watersheds, with each small reservoir associated with a small watershed; Step 3: Modeling of distributed hydrological model for river system forecasting at each level: Establish a distributed hydrological model for river system forecasting at each level from upstream to downstream for all forecast nodes. For forecast nodes without upstream inflow, the flood process is the flood forecast result of the small watershed, and the Sanshuiyuan Xin'anjiang hydrological model is used for modeling; for forecast nodes with upstream inflow, the flood process is the confluence of upstream inflow flood and the superposition of interval small watershed flood. The Muskingum method is used to calculate the confluence of upstream inflow flood, and the interval small watershed flood forecast is modeled using the Sanshuiyuan Xin'anjiang hydrological model; determine the model parameters, and obtain the flow process of all forecast nodes and small watersheds; Step 4. Derivation of the flood process of small reservoirs: The hydrological model takes rainfall and evaporation data as input, and outputs the simulated flood process after running the model. According to the distributed hydrological model established in step 3, rainfall and evaporation data are input and the model is run to output the flood simulation calculation results of all forecast nodes and small watersheds in the river network. Combined with the relationship between small reservoirs and small watersheds, the flood process of small reservoirs in watersheds with missing data is deduced.
2. The method for flood forecasting of small reservoirs in data-deficient basins based on a distributed hydrological model according to claim 1 is characterized in that: The configuration of associated rain gauges for the small watershed described in step 2 is specifically: using ArcMap spatial connection and neighbor analysis function modules to configure associated rain gauges for the small watershed; the establishment of the spatial position correspondence between the small reservoir and the small watershed is specifically: using ArcMap's spatial connection function module to establish the spatial position correspondence between the small reservoir and the small watershed.
3. The method for flood forecasting of small reservoirs in data-deficient basins based on a distributed hydrological model according to claim 1 is characterized in that: The specific process of determining the model parameters described in step 3 is as follows: first, determine the initial value range of the model parameters based on the physical meaning of the model parameters and empirical estimation, then use the forecast nodes with measured hydrological data in the river network to optimize the parameters, and use the multi-objective optimization NSGA-II algorithm and a semi-automatic method combining manual work to determine the model parameters. The relative error of peak flow RPE, the error of peak occurrence time PTE, the deterministic coefficient DC, and the relative error of flood volume RRD are used as the multi-objective functions for parameter optimization. The calculation formulas are: Where N is the number of flood events; and are respectively the predicted value and measured value of the peak flow of the ith flood, in m 3 / s; and are the simulated and measured values of the peak time of the ith flood respectively; q ij,sim and q ij,obs are the simulated flow value and measured flow value of the i-th flood and the j-th period, respectively, in m 3 / s; is the average measured flow rate of the ith flood, in m 3 / s;r i,sim and r i,obs are the simulated runoff depth and measured runoff depth of the ith flood, respectively, in mm; Among them, DC is used to evaluate the fitting degree of the flood process. The closer its value is to 1, the better the flood forecast effect is. RPE and PTE are used to evaluate the peak flow. The closer their values are to 0, the better the flood forecast effect is. RRD is used to evaluate the flood runoff. The closer its value is to 0, the better the flood forecast effect is.
4. The method for flood forecasting of small reservoirs in data-deficient basins based on a distributed hydrological model according to claim 1 is characterized in that: The specific method of deducing the flood process of small reservoirs in the basin with insufficient data in step 4 is: using the simplified method of area conversion method and confluence time shift to deduce the flood process of small reservoirs from the flood process of small basins, which specifically includes the following steps: S41, Area ratio coefficient reduction: The flood inflow from small reservoirs in small watersheds is reduced by the ratio of the catchment area of the small reservoir to the area of the small watershed where it is located. S42. Calculation of confluence time from small reservoir to small watershed outlet: Based on the geospatial data cloud, the DEM grid elevation data of the small watershed is obtained, and the confluence time of each grid unit is calculated using the DEM grid elevation data of the small watershed. The calculation formula is: V=KS 0.5 (5) Where V is the flow velocity of each grid unit in the basin, in m / s; S is the ground slope; K is the velocity coefficient, which is determined according to the water flow state of the basin grid; L is the distance that the water drop flows through each grid unit; △t is the lag time of runoff in each grid unit, in s; t is the confluence time of water flow from each grid unit to the outlet of the small basin, in s; m is the number of grids on the confluence path; S43. Derivation of small reservoir inflow flood: The inflow flood obtained by reducing the simulated flood process of the small basin in S41 is shifted backward along the time axis as a whole according to the confluence time calculated in S42 to obtain the small reservoir inflow flood process.