Inland lake expansion risk assessment method and device and electronic equipment

Abstract

The invention provides an expansion risk assessment method and device for an inland lake and electronic equipment, and relates to the technical field of hydrological analysis, and the method comprises the steps: obtaining a target water level data sequence of a target lake in a historical time period; determining a lake surface area sequence and a water storage capacity sequence corresponding to the target water level data sequence; determining an evaporation capacity sequence in a historical time period based on the lake surface area sequence, and determining a water storage variation sequence in the historical time period; according to the evaporation capacity sequence and the water storage variable quantity sequence, determining a total inflow quantity value and a corresponding probability of the target lake in a specified time period in the future; and evaluating the expansion risk of the target lake in the future time period based on the total inflow value and the corresponding probability. According to the embodiment of the invention, the water inflow and probability of the target lake in the future time period are determined according to the historical water level data of the lake, so that the expansion risk of the target lake is evaluated, corresponding measures can be taken according to the risk, and harm caused by lake expansion is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of hydrological analysis, and in particular to a method, device and electronic equipment for evaluating the expansion risk of inland lakes. Background Art

[0002] Climate warming and humidification have caused glaciers and permafrost to melt, the storage ratio of solid water to liquid water has changed, a large number of lakes have expanded, and the total water storage capacity of inland lakes has increased significantly. The water level change of inland lakes is an important indicator for the study of climate change and ecological environment change. Therefore, how to conduct hydrological risk assessment of inland lakes is an urgent problem to be solved. Summary of the invention

[0003] The embodiments of the present application provide a method, device and electronic equipment for evaluating the expansion risk of inland lakes to solve the problem of how to conduct hydrological risk assessment on inland lakes.

[0004] To solve the above technical problems, this application is implemented as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for assessing the expansion risk of an inland lake, the method comprising:

[0006] Obtaining a target water level data sequence of a target lake within a historical time period, wherein the target lake is an inland lake;

[0007] According to the corresponding relationship between the water level, area and water storage capacity of the target lake, a lake area sequence and a water storage capacity sequence corresponding to the target water level data sequence are determined;

[0008] Determine the evaporation amount sequence of the target lake in the historical time period based on the lake area sequence, and determine the water storage change amount sequence of the target lake in the historical time period based on the water storage amount sequence;

[0009] Determine the water inflow sequence of the target lake in the historical time period according to the evaporation sequence and the water storage change sequence, and determine the probability corresponding to the total water inflow value of the target lake in a future specified time period according to the water inflow sequence;

[0010] Based on the total water inflow value in the future specified time period and the probability corresponding to the total water inflow value, the expansion risk of the target lake in the future specified time period is evaluated.

[0011] In a second aspect, an embodiment of the present application provides an inland lake expansion risk assessment device, the device comprising:

[0012] An acquisition module, used to acquire a target water level data sequence of a target lake within a historical time period, wherein the target lake is an inland lake;

[0013] A first determination module is used to determine a lake area sequence and a water storage sequence corresponding to the target water level data sequence according to the corresponding relationship between the water level, area and water storage of the target lake;

[0014] A second determination module is used to determine the evaporation amount sequence of the target lake in the historical time period based on the lake area sequence, and to determine the water storage change amount sequence of the target lake in the historical time period based on the water storage amount sequence;

[0015] A third determination module is used to determine the water inflow sequence of the target lake in the historical time period according to the evaporation sequence and the water storage change sequence, and determine the probability corresponding to the total water inflow value of the target lake in a future specified time period according to the water inflow sequence;

[0016] An evaluation module is used to evaluate the expansion risk of the target lake within the specified time period in the future based on the total water inflow value within the specified time period in the future and the probability corresponding to the total water inflow value.

[0017] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor, a memory, and a program stored on the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the inland lake expansion risk assessment method described in the first aspect.

[0018] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the inland lake expansion risk assessment method described in the first aspect are implemented.

[0019] In a fifth aspect, a computer program product is provided, comprising computer instructions, which, when executed by a processor, implement the steps of the inland lake expansion risk assessment method as described in the first aspect.

[0020] In an embodiment of the present application, by obtaining the water level data sequence of the target lake in the historical time period, the evaporation sequence and the water storage change sequence of the target lake in the historical time period are determined based on the water level data sequence, and the water inflow and the corresponding probability in the future specified time period are determined based on the evaporation sequence and the water storage change sequence, and the expansion risk of the target lake in the future time period is evaluated based on the water inflow and the corresponding probability. By evaluating the expansion risk of the target lake in the above manner, the expansion risk can be predicted, and corresponding measures can be taken according to the risk, so as to reduce the harm caused by the expansion of the lake. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0022] Figure 1 It is a flow chart of an inland lake expansion risk assessment method provided in an embodiment of the present application;

[0023] Figure 2 It is a structural schematic diagram of an inland lake expansion risk assessment device provided in an embodiment of the present application;

[0024] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] The embodiments of the present application provide a method, device and electronic equipment for evaluating the expansion risk of inland lakes to solve the problem of how to conduct hydrological risk assessment on inland lakes.

[0027] See also Figure 1 , Figure 1 is a flow chart of an inland lake expansion risk assessment method provided in an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0028] Step 101, obtaining a target water level data sequence of a target lake in a historical time period, wherein the target lake is an inland lake;

[0029] Step 102: Determine a lake area sequence and a water storage sequence corresponding to the target water level data sequence according to the corresponding relationship between the water level, area, and water storage capacity of the target lake;

[0030] Step 103: determining the evaporation amount sequence of the target lake in the historical time period based on the lake area sequence, and determining the water storage change amount sequence of the target lake in the historical time period based on the water storage amount sequence;

[0031] Step 104: Determine the water inflow sequence of the target lake in the historical time period according to the evaporation sequence and the water storage change sequence, and determine the probability corresponding to the total water inflow value of the target lake in a future specified time period according to the water inflow sequence;

[0032] Step 105: Based on the total water inflow value in the future specified time period and the probability corresponding to the total water inflow value, evaluate the expansion risk of the target lake in the future specified time period.

[0033] Among them, the target lake can be an inland lake that needs to be risk assessed. By monitoring multiple water level data of the target lake in a historical time period, a target water level data sequence can be obtained. The target water level data sequence can include two, three or more than four water level data, each of which can be collected at different times, and the different times can be different years, different months, different quarters or different dates. Each water level data can be the maximum value or average value obtained in a time period. For example, the maximum water level data of each year for 10 consecutive years is obtained to form a water level data sequence, and the average water level data of each month for 10 consecutive months can also be obtained to form a water level data sequence.

[0034] The above-mentioned target water level data sequence can be obtained through remote sensing products, or measured data obtained from hydrological stations, or data obtained by correcting the data of remote sensing products with measured data, or data calculated using formulas using existing data, which is not limited here.

[0035] Remote sensing products are currently the main means of detecting hydrological changes in large-scale lakes, and can improve people's understanding of changes in plateau lakes, water levels and water volume. Multispectral optical remote sensing images can be used to monitor lake area changes and cover a wide area, including satellites with full coverage and high-quality images, radar satellites that can provide absolute elevation of lake water levels but have a smaller coverage area, and some satellites that can provide more accurate altimetry data and cover a large number of lakes. For lakes without long-term measured data or lack of measured data, remote sensing products can be used to obtain water level data sequences of the target lakes over a historical period of time.

[0036] Using the acquired water level data series, we can calculate the lake area series and water storage series at different times in the historical period.

[0037] For example, using geographic information computing software, the digital elevation model projection is set to Albers equal-area projection to ensure that the coordinate units in the horizontal and vertical directions are consistent and the horizontal area remains unchanged. Then, the lake area indicated by the elevation data is extracted, and the lake area A corresponding to each elevation i meters above the elevation data is calculated. i(i=1,2,3,…), the water storage volume difference between them can be calculated by using the lake surface area corresponding to different altitudes. The calculation formula is:

[0038]

[0039] Among them, V′ i , V′ i-1 They correspond to the lake area A i and A i-1 of lake water storage capacity.

[0040] Through a series of V' i With A i The data can be used to build a regression equation between them. In this way, when calculating the change in lake water storage between two different times, it is only necessary to obtain the lake surface area at different times.

[0041] Assume that the water storage capacity corresponding to a lower elevation of the target lake is 0, and the water storage capacity corresponding to other higher elevations is the water storage volume difference between the two elevations. Based on the above formula, the lake surface area sequence and lake water storage capacity sequence corresponding to the target water level sequence at different times in the historical time period can be calculated. For example, the maximum lake surface area and the corresponding water storage capacity are obtained each year to form a lake surface area sequence and a water storage capacity sequence.

[0042] Evaporation simulation can be performed based on a single-layer evaporation model of lake energy balance, and the lake surface evaporation sequence within the historical time period can be obtained by combining the lake surface area at different times. By obtaining the water storage of the target lake at different times within the historical time period, the water storage change sequence within the historical time period can be obtained. Based on the water storage change sequence and the lake surface evaporation sequence, the lake water inflow of the target lake within the specified time period in the future can be calculated. When calculating the above-mentioned lake water inflow, the estimated lake water inflow in each time period can be calculated to calculate the estimated cumulative water inflow for multiple time periods.

[0043] For example, select the annual maximum lake water level data series Z after correcting the remote sensing water level data based on the measured water level data t (t=1,2,…,N), the lake surface area and lake water storage corresponding to the sequence data can be calculated according to the above method, which is the annual maximum lake surface area S t (t=1,2,…,N) and the annual maximum lake water storage capacity T t (t=1,2,…,N). Based on the annual maximum lake water storage data, the change range B of the maximum water storage capacity compared with the previous year can be calculated. t =T t -T t-1(t = 2, ..., N). Due to the lack of lake surface evaporation monitoring data for plateau lakes, a single-layer evaporation model based on lake energy balance can be used to simulate evaporation, and the monthly lake surface evaporation volume can be calculated based on the lake surface area. Then, the total lake surface evaporation volume W corresponding to the time of the maximum lake water level in adjacent years can be calculated. t (t=2, ..., N). Based on the above data, the lake water flow data series between the highest lake water levels in the future is calculated as: R t =B t +W t (t=2,…,N).

[0044] After calculating the lake water inflow, the probability corresponding to the lake water inflow is further calculated. The cumulative probability distribution of water inflow can be obtained by performing hydrological frequency analysis on the lake water inflow sequence, and the probability density function can be calculated. The cumulative probability function is calculated based on the probability density function to determine the total water inflow probability in the specified time period in the future.

[0045] According to the total water inflow and the corresponding probability, the expansion risk of the target lake can be evaluated, including the risk of lake expansion caused by lake flooding and lake water overflowing from the lake. When the total water inflow in the future time period is large and the corresponding probability of the total water inflow is large, the expansion risk is high; when the total water inflow is small and the corresponding probability of the total water inflow is large, the expansion risk is small. In the specific implementation, the total water inflow value and the probability threshold can be set. When the total water inflow value exceeds the preset value and the probability is greater than the preset probability, the expansion risk of the target lake is assessed to be large, and an early warning prompt is output. The risk level can also be set according to the probability of the risk and the water inflow, and an early warning prompt is output according to the risk level, and measures corresponding to the risk level are output.

[0046] The embodiment of the present application can calculate the total water inflow and the corresponding probability in a future time period based on the water level data sequence of the target lake in the historical time period, so as to evaluate the expansion risk of the target lake and reduce the harm caused by the expansion of the target lake.

[0047] Optionally, obtaining a target water level data sequence of a target lake within a historical time period includes:

[0048] Obtaining measured water level data and M remote sensing water level data sequences of the target lake, wherein the M remote sensing water level data sequences are respectively obtained through M remote sensing products, where M is an integer greater than 1;

[0049] Among the M remote sensing water level data sequences, obtaining a target remote sensing water level data sequence having the smallest error with the measured water level data;

[0050] The target remote sensing water level data sequence is corrected according to the measured water level data to obtain the target water level data sequence.

[0051] In the case where the target lake has measured water level data, obtain the measured water level data and remote sensing water level data sequence for the target lake in the same time period. For example, obtain the measured water level data and remote sensing water level data collected on the target lake on the same day. When the measured water level data is less, one measured water level data can be obtained, and when the measured water level data is more, multiple measured water level data sequences can be obtained. And determine the target remote sensing water level data sequence with the minimum error with the measured water level data. Among them, the minimum error can be the minimum difference between the two data.

[0052] Generally speaking, the water level data detected by ground stations in inland lakes is short, and there are fewer continuous hydrological data, while the time period of satellite height measurement water level data is long, and the data is relatively more. Therefore, the existing small amount of measured data can be used to correct the remote sensing data, so as to improve the accuracy of the remote sensing data. When correcting the target remote sensing water level data sequence, the measured water level data and the remote sensing water level data collected in the same time period (for example, the same day) can be compared, and the error between the measured water level data and the remote sensing water level data can be calculated, and then the remote sensing data can be corrected. The error can be a median value among multiple errors, or an average value of multiple errors. When correcting, the remote sensing water level data is subtracted or added with the absolute value of the error to obtain the corrected value.

[0053] By correcting the remote sensing data according to the measured data in the above manner, the accuracy of the remote sensing data can be improved.

[0054] Optionally, the measured water level data is a measured water level data sequence; and obtaining a target remote sensing water level data sequence having the smallest error with the measured water level data sequence among the M remote sensing water level data sequences comprises:

[0055] Calculating an error value sequence between each remote sensing water level data sequence in the M remote sensing water level data sequences and the measured water level data sequence, wherein each remote sensing water level data sequence includes a comparison water level data sequence collected in the same time period as the measured water level data sequence, and the error value sequence is obtained based on the M comparison water level data sequences and the measured water level data sequence;

[0056] Obtaining an average value of the error value sequence;

[0057] Determine the deviation between the error value sequence and the average value to obtain a deviation sequence, and determine the average value of the deviation sequence;

[0058] Obtaining a remote sensing water level data sequence corresponding to a minimum average value from the average values ​​of the deviation sequence, and obtaining a plurality of maximum water level data within a plurality of time periods from the remote sensing water level data sequence corresponding to the minimum average value to form the target remote sensing water level data sequence;

[0059] The step of correcting the target remote sensing water level data sequence according to the measured water level data to obtain the target water level data sequence comprises:

[0060] The target water level data sequence is obtained by subtracting the average value of the error value sequence from each water level data in the target remote sensing water level data sequence.

[0061] After obtaining the measured water level data sequence, the measured water level data sequence of the target lake in the same time period is compared with multiple remote sensing water level data sequences, and the error value sequence between each two groups of data is calculated. In the case where the remote sensing water level data sequence is more than the measured water level data sequence, some remote sensing water level data collected in the same time period as the measured water level data are obtained, and the remote sensing water level data and the measured water level data in the same time period are compared to calculate the error value sequence. The error value sequence is averaged, and the deviation (i.e., the difference) between each error value in the error value sequence and the average value is calculated to obtain the deviation sequence, and the remote sensing water level data sequence corresponding to the minimum average value in the average value of the deviation sequence is obtained. The data corresponding to the remote sensing water level data sequence has good stability and high accuracy. In the remote sensing water level data sequence, the maximum water level data in multiple time periods can be further obtained to form the target remote sensing water level data sequence. For example, the maximum water level data of each year or each month is obtained to form the target remote sensing water level data sequence.

[0062] When correcting the target remote sensing water level data sequence, the average value of the error value sequence is subtracted from each water level data in the target remote sensing water level data sequence to obtain the corrected data, that is, the target water level data sequence.

[0063] For example, the existing inland lake ground station daily monitoring lake water level data series, the inland lake alternative satellite altimetry water level data X i (i=1,2,…,M), i is the product serial number of satellite altimetry water level data, representing the i-th set of data products, and M is the number of alternative satellite altimetry water level data products. Usually, the ground station monitoring water level data of inland lakes has a short period of time, while the satellite altimetry water level data has a long period of time. However, satellite products do not have monitoring data every day, and there is a time interval between data. Select two monitoring data from the i-th set of satellite altimetry data products and the ground station monitoring water level data series on the same day for comparison, and calculate the error value E of the two data. i，j (i=1,2,…,M; j=1,2,…,D i ), where i is the year number, D iis the number of days in the i-th set of alternative satellite data products that are on the same day as the water level data series monitored by ground stations, and j is the day serial number. is the mean error between the i-th set of candidate data products and the water level data series monitored by ground stations, is the error value E in the i-th set of candidate data products i，j The average error The average of the absolute values ​​of the differences. The candidate satellite altimetry water level data corresponding to the minimum value in is used as the selected satellite altimetry water level data. The original annual maximum lake water level data of each year is selected from the selected satellite altimetry water level data to form a series of data, and then the error mean is subtracted from the original annual maximum lake water level data series. That is, the corrected annual maximum lake water level data series Z is obtained. t (t = 1, 2, ..., N), N is the number of years of the selected satellite altimetry water level data series. N It is the maximum lake level data of the historical year closest to the current year.

[0064] By correcting the remote sensing water level data sequence based on the error between the measured data and the remote sensing data, the accuracy of the remote sensing data can be improved, and the comprehensiveness of the data can be improved.

[0065] Optionally, determining the probability corresponding to the total water inflow value of the target lake in a future specified time period according to the water inflow sequence includes:

[0066] Determining the cumulative probability distribution of the water inflow sequence for the historical time period;

[0067] determining a first probability density function based on the cumulative probability distribution;

[0068] Calculate, based on the first probability density function, a second probability density function of the total water inflow to the target lake within the future specified time period;

[0069] Based on the second probability density function, the cumulative probability function of the total water inflow of the target lake in the future specified time period is determined, and based on the cumulative probability function, the probability corresponding to the total water inflow value in the future specified time period is determined.

[0070] In this implementation, the water flow sequence in each time interval within the historical time period is obtained, the cumulative probability distribution of the water flow sequence is calculated, and the probability density function is determined based on the cumulative probability distribution.

[0071] For example, for the lake water flow data series R between the highest lake water levels each year t(t=2,…,N) to perform hydrological frequency analysis, and obtain the cumulative probability distribution y(x) of annual water flow based on the Pearson III type curve. The probability density function of annual water flow can be obtained by taking the derivative of the cumulative probability distribution. Considering that the water flow in each year of K years is independent of each other, the sum of water flow in K years is the sum of K independent and identically distributed random variables, and its probability density function can be calculated by convolution. Then the probability density function Y(t) of the total water flow in K years can be expressed as:

[0072] Y(t)=y(x)*y(x)*…*y(x) (K y(x) in total)

[0073] Among them, * represents the convolution symbol, and the calculation formula of the first step of convolution is as follows, and the subsequent K-1 step convolution method is the same.

[0074]

[0075] By integrating Y(t), we can obtain the cumulative probability function F(t) of the total water inflow for K consecutive years, thereby determining the probability corresponding to the total water inflow value of the lake for K consecutive years. K can be determined based on actual conditions.

[0076] By using the above method, the cumulative probability function is calculated for the water inflow according to the probability density function, so as to determine the probability of the total water inflow in multiple time intervals, which can improve the calculation accuracy and the accuracy of risk assessment.

[0077] Optionally, the historical time period includes T time intervals, the evaporation amount sequence, the water storage change amount sequence and the water inflow amount sequence are respectively obtained in the T time intervals, wherein each of the time intervals includes a start time and an end time, the target water level data sequence includes first water level data corresponding to the start time and second water level data corresponding to the end time, the first water level data and the second water level data are respectively the maximum water levels in two adjacent time periods, and T is an integer greater than 1;

[0078] Each evaporation amount in the evaporation amount sequence and each water storage amount in the water storage change amount sequence are calculated based on the first water level data and the second water level data.

[0079] The time interval may be divided by year, month or quarter. Taking the year division as an example, for example, the highest water level data of each year in 10 consecutive years is obtained, wherein the highest water level data of two consecutive years are the first water level data and the second water level data, respectively, and the time corresponding to the highest water level data of two consecutive years is the start time and the end time, such as May 20 of the first year and May 28 of the second year are the start time and the end time of a time interval, respectively.

[0080] By obtaining the water inflow sequence in the time interval corresponding to the maximum water level in adjacent time periods, the maximum water level data in each time period can be obtained, thereby improving the accuracy of lake expansion risk assessment.

[0081] Optionally, the step of evaluating the expansion risk of the target lake within the future specified time period based on the total water inflow value within the future specified time period and the probability corresponding to the total water inflow value includes:

[0082] Obtaining the lowest elevation of the mountain pass area surrounding the target lake;

[0083] Obtaining a starting water level of the target lake, where the starting water level is determined based on a maximum water level value of the target lake in a historical time period;

[0084] Based on the total water inflow value and the probability corresponding to the total water inflow value, the probability of flooding of adjacent areas within the target elevation range and the probability of overflow expansion from the lowest elevation are determined, wherein the target elevation range is the range between the lowest elevation and the elevation corresponding to the starting water level.

[0085] Among them, there is a location with the lowest elevation in the surrounding area of ​​the target lake, that is, the surrounding mountain pass area. When the water level of the target lake is high and overflows and expands, it usually overflows and expands first from the location with the lowest elevation.

[0086] According to the flood control or water storage needs of the target lake, the highest water level within the historical time period can be used as the starting water level of the target lake.

[0087] The elevation of the lowest elevation position and the elevation corresponding to the starting water level of the target lake are obtained, and the probability of flooding of the adjacent areas within the elevation range between the two elevations and the probability of overflow expansion at the lowest elevation are calculated. In the above manner, the expansion risk of the lake can be evaluated, thereby reducing the impact of the lake expansion on the adjacent areas. When the probability of the lake expansion risk is high according to the calculated evaluation, an early warning prompt can be output to take corresponding preventive measures in advance. For example, the risk level is determined based on the probability of the expansion risk and the degree of expansion, and the preventive measures corresponding to the risk level are output.

[0088] In order to facilitate understanding of the above embodiments, specific application scenarios are used as examples below.

[0089] The maximum lake level value Z in the history N As the starting water level, the lake surface evaporation is taken into account to calculate the water balance of the lake in the next K years.

[0090] Assuming that the lowest elevation of the mountain pass around the lake is ZL, the lake area SL and water storage value TL corresponding to ZL can be calculated based on the relationship between the lake's water level, area, and water storage capacity. N The corresponding lake area S N and water storage value TN, Eh represents the average lake evaporation over many years, and the total lake water inflow corresponding to elevation ZL RL = TL-TN + Eh × (S N +SL) / 2 / 100000*K (where RL, TL, TN are in 100 million m 3 , Eh is in mm, S N , SL is in km 2 ).

[0091] According to the cumulative probability function of the total water inflow in K years, the probability that the total water inflow value of the lake exceeds RL is calculated to be PL. Then the probability that the lake will overflow in the next K years is PL. N For any lake water level Zh between the lowest elevation ZL of the mountain pass around the lake area, the lake surface area Sh and water storage value Th corresponding to Zh can be calculated based on the lake water level-area-water storage relationship. Similarly, the total lake water inflow corresponding to Zh in the next K years can be calculated as Rh=Th-T N +Eh×(S N +Sh) / 2 / 100000*K(where RL, TL, T N The unit is 100 million m 3 , Eh is in mm, S N , SL is in km 2 ).

[0092] According to the cumulative probability function of the total water inflow in K years, the probability Ph of the total water inflow value of the lake exceeding Rh can be calculated. N The probability that the lake area between Zh and Zh is completely flooded is Ph. For the lake area with an elevation greater than the lowest elevation of the mountain pass ZL, there is basically no risk of flooding. Investigate the location and elevation of settlements, grasslands, roads, railways and other important infrastructure in the lake area and adjacent areas, and combine the above probability calculation values ​​to assess the flooding risk faced by the lake area and adjacent areas in the next K years.

[0093] In the embodiment of the present application, the remote sensing water level data of the lake in the same period is compared with the measured water level data, and the historical sequence data is corrected considering the systematic deviation of the remote sensing data, and the annual maximum lake water level data after correction is selected to form a long sequence data;

[0094] According to the digital elevation data of the lake area, the corresponding relationship between lake water level, area and water storage is determined, and the annual maximum lake area sequence data and annual maximum lake water storage sequence data corresponding to the annual highest lake water level sequence data are calculated. Based on the water balance principle of inland lakes, the change range of the annual maximum water storage of the lake is added to the annual evaporation of water from the lake surface, and the water inflow series data between the annual highest lake water levels are obtained (including the sum of the water inflow from the upstream of the lake and the precipitation on the lake surface);

[0095] The hydrological frequency analysis of the water inflow series data between the annual highest lake water levels was carried out, and on this basis, the probability distribution function of the total water inflow of the lake for K consecutive years was determined. Based on the lake's starting water level and the relationship between water level-area-storage capacity, the water balance of the lake area was calculated considering the evaporation of the lake surface, the probability of flooding in different elevation ranges of the lake area in the next K years was determined, the location and probability of overflow and expansion of the lake water were determined, and the flooding risk faced by the social and economic system of the lake area and neighboring areas was assessed.

[0096] See also Figure 2 , Figure 2 is a schematic diagram of the structure of an inland lake expansion risk assessment device provided in an embodiment of the present application, such as Figure 2 As shown, the expansion risk assessment device 200 for inland lakes includes:

[0097] An acquisition module 201 is used to acquire a target water level data sequence of a target lake in a historical time period, wherein the target lake is an inland lake;

[0098] The first determination module 202 is used to determine the lake area sequence and the water storage sequence corresponding to the target water level data sequence according to the corresponding relationship between the water level, area and water storage of the target lake;

[0099] A second determination module 203 is used to determine the evaporation amount sequence of the target lake in the historical time period based on the lake area sequence, and determine the water storage change amount sequence of the target lake in the historical time period based on the water storage amount sequence;

[0100] The third determination module 204 is used to determine the water inflow sequence of the target lake in the historical time period according to the evaporation sequence and the water storage change sequence, and determine the probability corresponding to the total water inflow value of the target lake in a future specified time period according to the water inflow sequence;

[0101] The evaluation module 205 is used to evaluate the expansion risk of the target lake within the specified time period in the future based on the total water inflow value within the specified time period in the future and the probability corresponding to the total water inflow value.

[0102] Optionally, the acquisition module 201 includes:

[0103] The first acquisition submodule is used to acquire the measured water level data of the target lake and M remote sensing water level data sequences, wherein the M remote sensing water level data sequences are acquired through M remote sensing products respectively, and M is an integer greater than 1;

[0104] A second acquisition submodule is used to acquire, from among the M remote sensing water level data sequences, a target remote sensing water level data sequence having the smallest error with the measured water level data;

[0105] The correction submodule is used to correct the target remote sensing water level data sequence according to the measured water level data to obtain the target water level data sequence.

[0106] Optionally, the measured water level data is a measured water level data sequence; and the second acquisition submodule includes:

[0107] A calculation unit, used for calculating an error value sequence between each remote sensing water level data sequence in the M remote sensing water level data sequences and the measured water level data sequence, wherein each remote sensing water level data sequence includes a comparison water level data sequence collected in the same time period as the measured water level data sequence, and the error value sequence is obtained based on the M comparison water level data sequences and the measured water level data sequence;

[0108] A first acquisition unit, used to acquire an average value of the error value sequence;

[0109] a determination unit, configured to determine a deviation between the error value sequence and the average value, obtain a deviation sequence, and determine an average value of the deviation sequence;

[0110] A second acquisition unit is used to acquire a remote sensing water level data sequence corresponding to a minimum average value in the average values ​​of the deviation sequence, and acquire a plurality of maximum water level data within a plurality of time periods in the remote sensing water level data sequence corresponding to the minimum average value to form the target remote sensing water level data sequence;

[0111] The correction submodule is specifically used for:

[0112] The target water level data sequence is obtained by subtracting the average value of the error value sequence from each water level data in the target remote sensing water level data sequence.

[0113] Optionally, the third determining module 204 includes:

[0114] A first determination submodule is used to determine the cumulative probability distribution of the water flow sequence in the historical time period;

[0115] A second determination submodule, configured to determine a first probability density function based on the cumulative probability distribution;

[0116] A calculation submodule, used for calculating a second probability density function of the total water inflow of the target lake in the future specified time period according to the first probability density function;

[0117] The third determination submodule is used to determine the cumulative probability function of the total water inflow of the target lake in the future specified time period based on the second probability density function, and determine the probability corresponding to the total water inflow value in the future specified time period based on the cumulative probability function.

[0118] Optionally, the historical time period includes T time intervals, the evaporation amount sequence, the water storage change amount sequence and the water inflow amount sequence are respectively obtained in the T time intervals, wherein each of the time intervals includes a start time and an end time, the target water level data sequence includes first water level data corresponding to the start time and second water level data corresponding to the end time, the first water level data and the second water level data are respectively the maximum water levels in two adjacent time periods, and T is an integer greater than 1;

[0119] Each evaporation amount in the evaporation amount sequence and each water storage amount in the water storage change amount sequence are calculated based on the first water level data and the second water level data.

[0120] Optionally, the evaluation module 205 includes:

[0121] The third acquisition submodule is used to obtain the lowest elevation of the mountain pass area around the target lake;

[0122] A fourth acquisition submodule is used to acquire a starting water level of the target lake, where the starting water level is determined according to a maximum water level value of the target lake in a historical time period;

[0123] The fourth determination submodule is used to determine the probability of flooding of an adjacent area whose elevation is within a target elevation range and the probability of overflow expansion from the lowest elevation based on the total water inflow value and the probability corresponding to the total water inflow value, wherein the target elevation range is the range between the lowest elevation and the elevation corresponding to the starting water level.

[0124] The expansion risk assessment device for inland lakes can achieve Figure 1 The various processes implemented in the method embodiment can achieve the same technical effect, and to avoid repetition, they will not be described again here.

[0125] like Figure 3As shown, an embodiment of the present application further provides an electronic device 300, including: a processor 301, a memory 302, and a program stored on the memory 302 and executable on the processor 301. When the program is executed by the processor 301, each process of the above-mentioned inland lake expansion risk assessment method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0126] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned inland lake expansion risk assessment method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0127] The present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of the method embodiment shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0128] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0129] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0130] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for assessing the expansion risk of inland lakes, characterized in that: include: Obtaining a target water level data sequence of a target lake within a historical time period, wherein the target lake is an inland lake; According to the corresponding relationship between the water level, area and water storage capacity of the target lake, a lake area sequence and a water storage capacity sequence corresponding to the target water level data sequence are determined; Determine the evaporation amount sequence of the target lake in the historical time period based on the lake area sequence, and determine the water storage change amount sequence of the target lake in the historical time period based on the water storage amount sequence; Determine the water inflow sequence of the target lake in the historical time period according to the evaporation sequence and the water storage change sequence, and determine the probability corresponding to the total water inflow value of the target lake in a future specified time period according to the water inflow sequence; Based on the total water inflow value in the future specified time period and the probability corresponding to the total water inflow value, the expansion risk of the target lake in the future specified time period is evaluated.

2. The method according to claim 1, characterized in that The step of obtaining a target water level data sequence of a target lake within a historical time period includes: Obtaining measured water level data and M remote sensing water level data sequences of the target lake, wherein the M remote sensing water level data sequences are respectively obtained through M remote sensing products, where M is an integer greater than 1; Among the M remote sensing water level data sequences, obtaining a target remote sensing water level data sequence having the smallest error with the measured water level data; The target remote sensing water level data sequence is corrected according to the measured water level data to obtain the target water level data sequence.

3. The method according to claim 2, characterized in that The measured water level data is a measured water level data sequence; and obtaining a target remote sensing water level data sequence having the smallest error with the measured water level data sequence from among the M remote sensing water level data sequences comprises: Calculating an error value sequence between each remote sensing water level data sequence in the M remote sensing water level data sequences and the measured water level data sequence, wherein each remote sensing water level data sequence includes a comparison water level data sequence collected in the same time period as the measured water level data sequence, and the error value sequence is obtained based on the M comparison water level data sequences and the measured water level data sequence; Obtaining an average value of the error value sequence; Determine the deviation between the error value sequence and the average value to obtain a deviation sequence, and determine the average value of the deviation sequence; Obtaining a remote sensing water level data sequence corresponding to a minimum average value from the average values ​​of the deviation sequence, and obtaining a plurality of maximum water level data within a plurality of time periods from the remote sensing water level data sequence corresponding to the minimum average value to form the target remote sensing water level data sequence; The step of correcting the target remote sensing water level data sequence according to the measured water level data to obtain the target water level data sequence comprises: The target water level data sequence is obtained by subtracting the average value of the error value sequence from each water level data in the target remote sensing water level data sequence.

4. The method according to claim 1, characterized in that: Determining the probability corresponding to the total water inflow value of the target lake in a future specified time period according to the water inflow sequence includes: Determining the cumulative probability distribution of the water inflow sequence for the historical time period; determining a first probability density function based on the cumulative probability distribution; Calculate, based on the first probability density function, a second probability density function of the total water inflow to the target lake within the future specified time period; Based on the second probability density function, the cumulative probability function of the total water inflow of the target lake in the future specified time period is determined, and based on the cumulative probability function, the probability corresponding to the total water inflow value in the future specified time period is determined.

5. The method according to claim 4, characterized in that The historical time period includes T time intervals, the evaporation amount sequence, the water storage change amount sequence and the water inflow amount sequence are respectively obtained in the T time intervals, wherein each of the time intervals includes a start time and an end time, the target water level data sequence includes the first water level data corresponding to the start time and the second water level data corresponding to the end time, the first water level data and the second water level data are respectively the maximum water levels in two adjacent time periods, and T is an integer greater than 1; Each evaporation amount in the evaporation amount sequence and each water storage amount in the water storage change amount sequence are calculated based on the first water level data and the second water level data.

6. The method according to any one of claims 1 to 5, characterized in that The step of evaluating the expansion risk of the target lake within the specified future time period based on the total water inflow value within the specified future time period and the probability corresponding to the total water inflow value includes: Obtaining the lowest elevation of the mountain pass area surrounding the target lake; Obtaining a starting water level of the target lake, where the starting water level is determined based on a maximum water level value of the target lake in a historical time period; Based on the total water inflow value and the probability corresponding to the total water inflow value, the probability of flooding of adjacent areas within the target elevation range and the probability of overflow expansion from the lowest elevation are determined, wherein the target elevation range is the range between the lowest elevation and the elevation corresponding to the starting water level.

7. An inland lake expansion risk assessment device, characterized in that: include: An acquisition module, used to acquire a target water level data sequence of a target lake within a historical time period, wherein the target lake is an inland lake; A first determination module is used to determine a lake area sequence and a water storage sequence corresponding to the target water level data sequence according to the corresponding relationship between the water level, area and water storage of the target lake; A second determination module is used to determine the evaporation amount sequence of the target lake in the historical time period based on the lake area sequence, and to determine the water storage change amount sequence of the target lake in the historical time period based on the water storage amount sequence; A third determination module is used to determine the water inflow sequence of the target lake in the historical time period according to the evaporation sequence and the water storage change sequence, and determine the probability corresponding to the total water inflow value of the target lake in a future specified time period according to the water inflow sequence; An evaluation module is used to evaluate the expansion risk of the target lake within the specified time period in the future based on the total water inflow value within the specified time period in the future and the probability corresponding to the total water inflow value.

8. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the inland lake expansion risk assessment method as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the inland lake expansion risk assessment method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises computer instructions which, when executed by a processor, implement the steps of the inland lake expansion risk assessment method as described in any one of claims 1 to 6.

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