A method for predicting annual water characteristics of a reservoir based on periodicity and number of dry years
By analyzing the periodicity of reservoir flow and the number of dry years, a correspondence between inflow levels was constructed, solving the problem of integrating hydrological periodicity with water abundance and scarcity events in existing technologies, and realizing accurate forecasting of reservoir abundance and scarcity characteristics.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID XINYUAN GRP CO LTD
- Filing Date
- 2026-05-28
- Publication Date
- 2026-07-10
AI Technical Summary
Existing technologies cannot organically integrate the periodicity of hydrology with the indicative role of water abundance and drought events, resulting in insufficient lead time and low forecast reliability for extreme water abundance and drought events.
By collecting annual average flow data measured over many years, significant cycles are identified, a correlation is established between the number of dry years and the level of water inflow, and dynamic corrections are made based on the actual water inflow to form the final forecast conclusion.
It enables deterministic forecasting of future reservoir water abundance and scarcity characteristics, improving the reliability and accuracy of forecasts.
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Figure CN122365014A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of long-term hydrological forecasting technology and relates to a method for forecasting annual water characteristics of reservoirs based on periodicity and the number of dry years. Background Technology
[0002] The development and changes of hydrological phenomena are always closely related to their past conditions, which in turn affect the present and the future; this phenomenon is called hydrological continuity. By analyzing and studying the evolution patterns of flow rates, and finding corresponding forecasting methods, it is possible to predict the future.
[0003] Hydrological phenomena exhibit significant periodicity in their temporal evolution, with wet, dry, and normal water periods often alternating according to a quasi-periodic pattern. This periodicity stems from the long-term modulating effect of the climate system on watershed precipitation and runoff processes. Extreme wet and dry years, as critical vulnerable points in the water resources system, have particularly prominent impacts on watershed flood control, water supply security, ecosystem stability, and energy production, making them a core focus in the field of hydrological forecasting. Current models struggle to organically integrate the periodicity of hydrological patterns with the indicative role of wet and dry water events, resulting in insufficient lead times and low forecast reliability for extreme wet and dry water events. To address these issues, a new forecasting paradigm that can organically integrate periodic patterns with the indicative role of wet and dry water events is urgently needed. Summary of the Invention
[0004] Purpose of the invention: The purpose of this invention is to provide a method for predicting annual water characteristics of reservoirs based on periodicity and the number of dry years, which summarizes the relationship between the number of dry years and the level of water inflow.
[0005] The technical solution of this invention is: a method for predicting annual reservoir water characteristics based on periodicity and the number of dry years, comprising the following steps: Step (1) Collect the annual average flow data of the target reservoir over many years, draw a flow change diagram, analyze the periodic pattern of the reservoir's water inflow changes, and identify multiple significant periods; Step (2) Select multiple cycles from the significant cycles identified in step (1) to form a fixed set. For the target year to be predicted, calculate the corresponding historical reference year under each cycle. Step (3) Mark the wet and dry characteristics of each historical reference year according to the historical hydrological records, and count the number of dry years corresponding to the target year; Step (4) Analyze and construct the correspondence between the number of different dry years and the actual water abundance / dryness level of the target year, and establish a correspondence table between the number of dry years and the water abundance / dryness level; Step (5) Based on the number of dry years in the year to be predicted and the correspondence established in step (4), the preliminary forecast level is determined. Combined with the actual water inflow in the first half of the year to be predicted, the preliminary forecast is revised to form the final forecast conclusion.
[0006] Furthermore, the specific steps of step (1) are as follows: Step (11) Collection The time series length of the annual average flow data, where... The minimum period is 60 years, in order to ensure the reliability of identifying periodic patterns in the data; Step (12) Based on the continuous and intact annual average flow series, draw an annual average flow process line diagram to visually demonstrate the long-term evolution trend of alternating wet and dry seasons; Step (13) identifies several significant cycles of water inflow variation in reservoirs through empirical visual analysis and periodic backtracking verification.
[0007] Furthermore, step (13) specifically involves: Step (1301) Periodic backtracking verification; specifically, starting from the most recent typical wet or dry year, backtracking along the candidate period length, if most of the historical years traced back are also of the same type, then the candidate period is confirmed as a valid period. The number of effective cycles in step (1302) should be controlled between 3 and 6; Step (1303) controls the selected period to remain constant in subsequent studies.
[0008] Furthermore, step (2) specifically involves: Let the selected The periodic set is For the target year The historical reference year set is as follows: In the formula, And periodic value For integer years, For the first Historical reference years for the cycle.
[0009] Furthermore, step (3) specifically involves: based on historical hydrological records, marking the wet and dry characteristics of each historical reference year according to its position in the flow change diagram drawn in step (1) based on the average annual flow. If the flow of a certain year is at the peak of the curve, it is considered a wet year; if the flow of a certain year is at the trough of the curve, it is considered a dry year; if neither of the above applies, it is considered a transitional year, i.e., a normal year. Finally, a set of historical reference years is obtained, and the number of dry years is recorded as... .
[0010] Furthermore, the specific steps of step (4) are as follows: Step (41) Determine the target year to be predicted and its abundance / sparseness characteristics for the following year; Step (42) Pair the wet and dry characteristics of the historical reference year set corresponding to the target year with the actual water inflow level of the current year and the following year to form a statistical sample set; Step (43) Based on the sample set, summarize the most likely water abundance / dampness level corresponding to the target year to be predicted or the following year.
[0011] Furthermore, the quantification correspondence in step (43) specifically includes: Step (4301) when At that time, the target year or the following year is a year with abundant water or a year with slightly abundant water. Step (4302) when At that time, the target year is mainly a year of average water level: Step (4303) when At that time, the target years are mainly dry years or years with relatively low water levels.
[0012] Furthermore, the specific steps of step (5) are as follows: Step (51) In March of each year, the number of dry years is calculated based on the annual cycle backtracking. A preliminary forecast of the characteristics of the annual water inflow; Step (52) In June of each year, the forecast results for the flood season and the whole year are dynamically revised based on the actual water inflow in the first half of the year; Step (53) is to amend the following rules: If the initial forecast is for a year with abundant water, but the water volume in the first half of the year is significantly lower than normal, the forecast conclusion will be amended to a year with normal water or lower; if the initial forecast is for a year without abundant water, but the water volume in the first half of the year is significantly higher than normal, the forecast level can be raised.
[0013] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: By constructing a multi-period mapping between the target forecast year and the historical reference year, and taking the number of dry years in the backtracking nodes as the core criterion, a deterministic correspondence between the dry and wet water levels in the future is established, thereby predicting the future water level characteristics of the reservoir through the number of dry years. Attached Figure Description
[0014] Figure 1 This is a flowchart of the present invention; Figure 2 This is a process curve diagram of the annual average flow of Baishan Reservoir in an embodiment of the present invention; Figure 3This is a 60-year cycle network structure diagram of the Baishan Reservoir during bumper years (flood years) in this embodiment of the invention; the node year in the diagram represents a bumper year (flood year); a - year; the average horizontal cycle is 60 years, 1935-36 represents two consecutive flood years, and so on; (labeled) This is the highest water volume in history; similar figures can be drawn for the rest. Figure 4 This is a 53-year cycle network structure diagram of the Baishan Reservoir during a bumper year (high water year) in an embodiment of the present invention; the node year in the diagram is a bumper year (high water year); a - year; average horizontal cycle = 53 years; Figure 5 This is a 44-year cycle network structure diagram of the Baishan Reservoir during a bumper year (high water year) in an embodiment of the present invention; the node year in the diagram is a bumper year (high water year); a - year; average horizontal cycle = 44 years; Figure 6 This is a 25-year cycle network structure diagram of the Baishan Reservoir during a bumper year (high water year) in an embodiment of the present invention; the node year in the diagram is a bumper year (high water year); a - year; average horizontal cycle = 25 years; Figure 7 This is a 19-year cycle network structure diagram of the Baishan Reservoir during a bumper year (high water year) in an embodiment of the present invention; the node year in the diagram is a bumper year (high water year); a - year; average horizontal cycle = 19 years; Figure 8 This is a 60-year cycle network structure diagram of the Baishan Reservoir during dry years (dry water years) in this embodiment of the invention; the node year in the diagram is a dry year (dry water year); a - year; the average horizontal cycle is 60 years, 1967-68 is two consecutive dry years, and so on; labeled This is the lowest historical water volume; similar calculations can be made for the rest. Figure 9 This is a 5-cycle relationship forecast chart of Baishan Reservoir in an embodiment of the present invention. Detailed Implementation
[0015] The specific technical solution of the present invention will be further described in detail below with reference to specific examples.
[0016] Taking the measured annual average flow data of Baishan Reservoir from 1939 to 2024 as an example, such as... Figure 1 As shown, the reservoir annual water characteristics forecasting method based on periodicity and the number of dry years of the present invention includes the following steps: Step 1: In this embodiment, the annual average flow data of the target reservoir over many years are collected, and the flow variation diagram is plotted as follows. Figure 2 As shown, the periodic patterns of reservoir inflow variations are analyzed, and several significant periods are identified. Step 11: In this embodiment, the annual average flow data of Baishan Reservoir from 1939 to 2024 were collected; Step 12: In this embodiment, based on a continuous and complete annual average flow series, an annual average flow process curve is plotted as follows. Figure 2 As shown, this is used to visually demonstrate the long-term evolution trend of alternating periods of abundance and scarcity; Step 13: This embodiment, through empirical visual analysis and periodic backtracking verification, identifies the following patterns in the reservoir inflow samples: High-water years exhibit five cycles on average: 60 years, 53 years, 44 years, 25 years, and 19 years; low-water years exhibit a 60-year cycle. See details... Figures 3-8 ; Step 2: In this embodiment, multiple cycles are selected from the significant cycles identified in Step 1 to form a fixed set. For the target year to be predicted, the corresponding historical reference year under each cycle is calculated. Select five cycles (60 years, 53 years, 44 years, 25 years, and 19 years) from step 13 to form a fixed set, and mark the wet / dry characteristics of the target year corresponding to each cycle; use red boxes to represent dry years, green boxes to represent wet years, and white boxes to represent normal years; create a vertical list of the five cycles, starting from 1933 (dry year) to 2024 (wet year), and mark them sequentially with boxes, as detailed in the diagram. Figure 9 As shown.
[0017] The vertical column shows a 60-year cycle from 1933 to 2024; 1933 + 60 years = 1993, which was the first forecast year, and so on. The second column shows a 53-year cycle from 1940 (1993 - 53 years = 1940) to 2024. The third column shows a 44-year cycle from 1949 (1993 - 44 years = 1949) to 2024. The fourth column shows a 25-year cycle from 1968 (1993 - 25 years = 1968) to 2024. The fifth column shows a 19-year cycle from 1974 (1993 - 19 years = 1974) to 2024. Step 3: In this embodiment, the abundant and dry characteristics of each historical reference year are marked according to historical hydrological records, and the number of dry years corresponding to the target year is counted. Based on the abundant and scarce characteristics of each historical reference year marked by historical hydrological records, analyze the set of historical reference years. The characteristics of dry years, normal years, and wet years; the number of dry years corresponding to the target year in each cycle; see details. Figure 9 The 6th column is the number of dry years, which is the number of dry years counted in the first 5 columns of the same row; Step 4: In this embodiment, we analyze and construct the correspondence between the number of dry years and the actual water abundance / dryness level of the target year, and establish a correspondence table between the number of dry years and the water abundance / dryness level. Step 41: In this embodiment, the target year to be predicted and the abundance / sparseness characteristics of the following year are determined, such as... Figure 9 As shown; Step 42: In this embodiment, the wet and dry characteristics of the historical reference year set corresponding to the target year are paired with the actual water inflow level of the current year and the following year to form a statistical sample set; Step 43: Based on this sample set, this embodiment summarizes the most likely water abundance / dryness level corresponding to the target year or the following year to be predicted. The relationship between the level and the water abundance / dryness characteristics of the reservoir in the target year is analyzed according to the number of dry years = 0, 1, 2, 3,... as shown in Table 1: Table 1. Statistics on the number of dry years and annual water volume levels (1993-2024, a total of 32 years) ; Step 4301: No dry years (number of dry years is 0). Count the number of dry years in Table 1. From the 32 years from 1993 to 2024 when forecasts can begin, there are 6 years without dry years (1994, 2004, 2010, 2013, 2019, and 2021). Among these, 2 years were wet years (2010 and 2013), and the remaining 4 years were not wet years (2 were normal water years, 1 was slightly dry year, and 1 was dry year; all 4 years were normal water years or below). Among the 6 years without dry years, 4 years had a wet year in the second year (1994, 2004, 2019, and 2021), and the remaining 2 years were dry years. A year without a dry year will inevitably be a year with abundant water (or a year with slightly abundant water). Among the six years without a dry year (1994, 2004, 2010, 2013, 2019, and 2021), five years with abundant water (1995, 2005, 2010, 2013, and 2022) and one year with slightly abundant water (2020) are included. Step 4302: There is 1 dry year. There are 11 dry years out of 32 years. Excluding the 2 wet years that are the second year without a dry year, there are 9 wet years. Among them, there is 1 wet year, 1 slightly wet year, 5 normal years (most of the 55.6%), and 2 slightly dry years. Step 4303: The number of dry years is 2 or 3. There are 14 dry years out of 32 years. Excluding the 1 year of abundant water in the second year without a dry year, there are 13 years. Among them, there are 2 years of normal water, 2 years of slightly dry water, and 9 years of dry water (the majority, accounting for 69.2%). The number of dry years is 4 or 5; if the number of dry years is 4, there is only 1 year of abundant water (2022, which is the second year without a dry year); if the number of dry years is 5, it has never occurred before. Step 5: In this embodiment, based on the number of dry years in the year to be predicted and the correspondence established in Step 4, a preliminary forecast level is determined. Combined with the actual water inflow in the first half of the year to be predicted, the preliminary forecast is revised to form the final forecast conclusion. Step 51: On a large time scale, based on the number of dry years, make spring flood, flood season, and annual forecasts in March. Specifically: A. If there are 0 dry years, forecasts are made for the current year and the following year, and the forecast in March is for a year with abundant water. B. If there is 1 dry year, there are 4 levels: abundant water, slightly abundant water, normal water, and slightly dry water, with normal water being the most common. The forecast in March is for a year with normal water. C. If there are 2 or 3 dry years, there are normal water, slightly dry water, and dry water, with dry water being the most common. The forecast in March is for a year with dry water. Step 52: Every June, based on the actual water inflow in the first half of the year, dynamically revise the flood season and annual forecasts. Specifically: A. If there are 0 dry years, and the water inflow in the first half of the year is large, the June revised forecast will reconfirm it as a wet year; if the water inflow in the first half of the year is small, the June revised forecast will be a normal year or less. B. If there is 1 dry year, and the water inflow in the first half of the year is large, the June revised forecast will be a slightly wet year; if the water inflow in the first half of the year is small, the June revised forecast will be a slightly dry year or less. C. If there are 2 or 3 dry years, and the water inflow in the first half of the year is large, the June revised forecast will be a slightly dry year; if the water inflow in the first half of the year is small, the June revised forecast will be a dry year. Step 53: The correction rule is as follows: If the initial forecast for the year is a wet year, but the water volume in the first half of the year is significantly lower than normal, the forecast conclusion will be revised to a normal year or lower. If the initial forecast for the year is a non-wet year, but the water volume in the first half of the year is significantly higher than normal, the forecast level can be upgraded. It should be noted that if there are no dry years and the year is a wet year, the forecast for the following year will be based on the number of dry years. If there are no dry years and the year is not a wet year, the forecast will be made in March of the following year and then revised and confirmed again in June.
Claims
1. A method for predicting annual water characteristics of reservoirs based on periodicity and the number of dry years, characterized in that, Includes the following steps: Step (1) Collect the annual average flow data of the target reservoir over many years, draw a flow change diagram, analyze the periodic pattern of the reservoir's water inflow changes, and identify multiple significant periods; Step (2) Select multiple cycles from the significant cycles identified in step (1) to form a fixed set. For the target year to be predicted, calculate the corresponding historical reference year under each cycle. Step (3) Mark the wet and dry characteristics of each historical reference year according to the historical hydrological records, and count the number of dry years corresponding to the target year; Step (4) Analyze and construct the correspondence between the number of different dry years and the actual water abundance / dryness level of the target year, and establish a correspondence table between the number of dry years and the water abundance / dryness level; Step (5) Based on the number of dry years in the year to be predicted and the correspondence established in step (4), the preliminary forecast level is determined. Combined with the actual water inflow in the first half of the year, the preliminary forecast is revised to form the final forecast conclusion.
2. The method for predicting annual reservoir water characteristics based on periodicity and the number of dry years according to claim 1, characterized in that, The specific steps of step (1) are as follows: Step (11) Collection The time series length of the annual average flow data, where... Not less than 60 years; Step (12) Based on the continuous and intact annual average flow series, draw an annual average flow process line diagram to visually demonstrate the long-term evolution trend of alternating wet and dry seasons; Step (13) identifies several significant cycles of water inflow variation in reservoirs through empirical visual analysis and periodic backtracking verification.
3. The method for predicting annual reservoir water characteristics based on periodicity and the number of dry years according to claim 2, characterized in that, The specific steps of step (13) are as follows: Step (1301) Periodic backtracking verification; The number of effective cycles in step (1302) should be controlled between 3 and 6; Step (1303) controls the selected period to remain constant in subsequent studies.
4. The method for predicting annual reservoir water characteristics based on periodicity and the number of dry years according to claim 1, characterized in that, In step (1301), the periodic backtracking verification means that, starting from the most recent typical wet or dry year, backtracking along the length of the candidate period, if most of the historical years backtracked are also of the same type, then the candidate period is confirmed as a valid period.
5. The method for predicting annual reservoir water characteristics based on periodicity and the number of dry years according to claim 1, characterized in that, The specific steps (2) are as follows: Let the selected The periodic set is For the target year The historical reference year set is as follows: In the formula, and periodic value For integer years, For the first Historical reference years for the cycle.
6. The method for predicting annual reservoir water characteristics based on periodicity and the number of dry years according to claim 1, characterized in that, Step (3) specifically involves: based on historical hydrological records, marking the wet and dry characteristics of each historical reference year according to its position in the flow change diagram drawn in step (1) based on the average annual flow. If the flow of a certain year is at the peak of the curve, it is considered a wet year; if the flow of a certain year is at the trough of the curve, it is considered a dry year; if neither of the above applies, it is considered a transitional year, i.e., a normal year. Finally, a set of historical reference years is obtained. The number of dry years is recorded as .
7. The method for predicting annual reservoir water characteristics based on periodicity and the number of dry years according to claim 1, characterized in that, The specific steps of step (4) are as follows: Step (41) Determine the target year to be predicted and its abundance / sparseness characteristics for the following year; Step (42) Pair the wet and dry characteristics of the historical reference year set corresponding to the target year with the actual water inflow level of the current year and the following year to form a statistical sample set; Step (43) Based on the sample set, summarize the most likely water abundance / dampness level corresponding to the target year to be predicted or the following year.
8. The method for predicting annual reservoir water characteristics based on periodicity and the number of dry years according to claim 7, characterized in that, The quantification correspondence in step (43) specifically includes: Step (4301) when At that time, the target year or the following year is a year with abundant water or a year with slightly abundant water. Step (4302) when At that time, the target year is mainly a year of average water level: Step (4303) when At that time, the target years are mainly dry years or years with relatively low water levels.
9. The method for predicting annual reservoir water characteristics based on periodicity and the number of dry years according to claim 1, characterized in that, The specific steps of step (5) are as follows: Step (51) In March of each year, the number of dry years is calculated based on the annual cycle backtracking. A preliminary forecast of the characteristics of the annual water inflow; Step (52) In June of each year, the forecast results for the flood season and the whole year are dynamically revised based on the actual water inflow in the first half of the year; Step (53) Modify the rules.
10. The method for predicting annual reservoir water characteristics based on periodicity and the number of dry years according to claim 9, characterized in that, The correction rule in step (53) is as follows: if the initial forecast is for a year with abundant water, but the water volume in the first half of the year is low, the forecast conclusion will be revised to a year with normal water or lower; if the initial forecast is for a year without abundant water, but the water volume in the first half of the year is high, the forecast level will be raised.