CN113011685A - Simulation prediction method for water level change of inland lake in runoff data-free area - Google Patents
Simulation prediction method for water level change of inland lake in runoff data-free area Download PDFInfo
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Abstract
The invention discloses a simulation prediction method for water level change of inland lakes in areas without runoff data, which comprises the steps of collecting meteorological hydrological data of areas without runoff data, calibrating a reservoir capacity curve of the inland lakes, combining the curve with a digital elevation model to construct a distributed hydrological model of the inland lakes watershed, carrying out rate correction and verification on the model to obtain an optimized distributed hydrological model of the inland lakes watershed, obtaining long series results of the watershed flows into the lakes under different scenes through simulation, and constructing a water balance model of the inland lakes with the meteorological hydrological data; meanwhile, meteorological hydrological data are predicted to obtain water consumption of different development levels of planned horizontal year-supported watersheds, and a water level evolution trend of a future lake is obtained by predicting through an inland lake water balance model.
Description
Technical Field
The invention relates to the field of lake water resource management and water ecological environment protection, in particular to a simulation and prediction method for water level change of inland lakes in areas without runoff data.
Background
The lakes in China are large in quantity, wide in distribution and complete in type, and the area is 1.0km22693 natural lakes above, total area 81414.6km2It accounts for about 0.9% of the national territorial area.
Lakes are sensitive indicators of climate and environmental changes and are important carriers of information to reveal global climate changes and regional responses. The research on the water level change of the lake is the basis of the hydrology of the lake, the change is closely related to factors such as regional rainfall, air temperature, evaporation, humidity and human activities, and the long-time sequence water level can reflect the influence of regional climate change and human activities on the lake.
The lake level is generally obtained by actually measuring through a hydrological site. The mode needs certain economic and human support, and the conventional hydrological observation method cannot provide effective and continuous water level observation values in inland lakes with remote regions and rare people, so that long-time sequence lake water level data are difficult to obtain, and certain difficulty is brought to the study on the lake water level change. In recent years, students acquire lake water level and area data through multi-source satellites such as radar and optics, study lake evolution characteristics and solve the problem of acquisition of lake hydrological characteristic parameters in data-free areas. However, this method has the following disadvantages:
(1) generally, for the characteristic years, the water level and area evolution data of long-series lakes cannot be acquired;
(2) the water exchange relation between regional surface runoff and underground water and a lake cannot be known, and the reason of the water level and area evolution of the lake cannot be effectively judged;
(3) scientific prediction on the evolution trend of the water level and the area of the future lake cannot be carried out.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a simulation and prediction method for water level change of inland lakes in areas without runoff data.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
a simulation and prediction method for water level change of inland lakes in areas without runoff data specifically comprises the following steps:
s1, collecting meteorological hydrological data of the area without runoff data;
s2, calibrating the inland lake reservoir capacity curve according to the meteorological hydrological data obtained in the step S1;
s3, establishing a lake runoff simulation database by combining the meteorological hydrological data obtained in the step S1 and the digital elevation model, and constructing a distributed hydrological model of the inland lake basin;
s4, calibrating and verifying the inland lake basin distributed hydrological model obtained in the step S3 to obtain an optimized inland lake basin distributed hydrological model;
s5, simulating a long series of runoff results of the watershed afflux into the lake under different scenes according to the optimized distributed hydrological model of the inland lake watershed obtained in the step S4, and constructing a water balance model of the inland lake by combining the meteorological hydrological data obtained in the step S1;
s6, forecasting the economic and social development level of the future basin according to the meteorological hydrological data obtained in the step S1, and calculating by adopting a rating method to obtain water consumption for supporting different development levels of the basin in a planned horizontal year;
s7, forecasting future lake volume and water level evolution trend through the inland lake water volume balance model obtained in the step S5 according to the long series runoff results of the catchment lake under different situations simulated in the step S5 and the water consumption of different development levels of the planned horizontal year support catchment basin predicted in the step S6.
The beneficial effect of this scheme does:
combining meteorological hydrological data of the area without the runoff data with a digital elevation model to construct a distributed hydrological model of the inland lake basin, and calibrating and verifying the model by adopting the actually measured lake water level according to the characteristic that the closed basin lake is the lowest point, so that long series of surface runoff entering the lake is quantized, and the problem that the runoff entering the lake in the area without the data is difficult to quantify is solved; on the basis of a domain distributed hydrological model, an inland lake water volume balance model is constructed, accurate simulation of lake water level and area is realized, and the problem that long series of hydrological characteristic data of inland lakes in data-free areas cannot be obtained by the conventional method is solved; on the basis of reasonably configuring the water resources of the drainage basin, the evolution trend of the water level and the area of the lake is predicted according to the future water resource consumption level, so that a decision maker can select reasonable regulation and control measures timely and accurately, scientific configuration of regional water resources and implementation of a water transfer scheme are facilitated, and the ecological environment of the lake can be better improved.
Further, the meteorological and hydrological data obtained in step S1 include landform data, meteorological data, hydrological data, land utilization data, soil data, economic and social data, and water resource development and utilization data.
Further, the step S2 is specifically:
and (4) adopting a Civil 3D volume panel method, taking the two-dimensional topographic curved surface of the lake bottom in the meteorological and hydrological data obtained in the step S1 as a reference curved surface, taking the water level horizontal planes of lakes in different historical stages as reference curved surfaces, counting corresponding reservoir volumes, and drawing a water level-area-volume curve of the lake by combining the reservoir volumes corresponding to different water levels and the water surface area.
Further, the step S3 specifically includes the following steps:
s31, establishing a lake basin space database by using a digital elevation model, respectively establishing a lake basin attribute database and a land utilization database by using soil data and land utilization data in meteorological hydrological data obtained in the step S1, and establishing a meteorological database by using the meteorological data in the meteorological hydrological data obtained in the step S1 and combining a distributed hydrological model weather generator;
s32, calculating the basin surface runoff by utilizing the SCS curve in the SWAT model, and constructing a distributed hydrological model of the inland lake basin, wherein the distributed hydrological model is expressed as follows:
wherein, SWtAnd SW0The final water content of the soil and the early-stage water content of the soil are respectively; t is the number of days; rdayThe precipitation amount of the ith day; qsurfThe surface runoff on the ith day; eaEvaporation on day i; w is aseepThe soil profile seepage and the lateral flow of the day i are shown; qgwThe water return amount is the i day.
Further, the calculation formula of the surface runoff of the drainage basin is as follows:
wherein R isdayIs the average daily precipitation; i isaThe initial loss is the initial loss; s is a retention parameter.
Further, the step S4 specifically includes the following steps:
s41, performing sensitivity analysis on the inland lake basin distributed hydrological model parameters by utilizing an SUFI-2 algorithm, screening model parameters matched with the inland lake basin distributed hydrological model, and setting initial conditions;
s42, calibrating the inland lake basin distributed hydrological model by using the lake month average runoff length series data calculated by the actually measured lake water level;
s43, judging whether the calibration result obtained in the step S42 meets a preset threshold value, if so, finishing calibration, and entering the step S44, otherwise, re-determining the parameter range of the distributed hydrological model of the inland lake basin, and returning to the step S42;
s44, verifying the calibrated distributed hydrological model result of the inland lake basin by using the lake monthly mean radial flow length series data obtained by actually measuring the water level of the lake again, finishing verification if the set precision requirement is met to obtain the optimized distributed hydrological model of the inland lake basin, and otherwise, re-determining the parameter range of the distributed hydrological model of the inland lake basin and returning to the step S42.
Further, the calculation formula of the lake monthly mean runoff is as follows:
Q＝ACR/(24×3600)
wherein Q is the lake monthly mean runoff; a. theCIs the basin area; r is the runoff depth.
Further, the step S5 specifically includes the following steps:
s51, simulating a long series runoff result of the river basin afflux into the lake under different series of scenes by using the optimized inland lake river basin distributed hydrological model obtained in the step S44;
s52, constructing a inland lake water balance model by using the meteorological hydrological data obtained in the step S1 and the long series runoff simulation results of the rivers converging into the lake under different series of scenes obtained in the step S51, wherein the model expression is as follows:
ΔW(t)＝Wsurface of earth+WUnderground entering+P(t)+WCalling in－D(t)－E(t)－EWater reservoir(t)－CSurface of earth－CUnderground surface
W(t)＝W(t-1)+ΔW(t)
H(t)＝W(t)×dH/dW
Wherein, Δ W (t) is the storage variable of the lake water in the period t; wSurface of earthThe surface diameter formed by precipitation in the watershed flows into the lake volume; wUnderground enteringThe exchange capacity of the groundwater and the lake water in the drainage basin; e (t) is the lake surface evaporation amount in the period t; eWater reservoir(t) the evaporation and closure of the earth surface runoff of the reservoir pool at the time interval t; p (t) is lake surface precipitation in a period of t; cSurface of earthThe water consumption of the earth surface is t time period; cUnderground surfaceInfluencing the surface water resource quantity for underground water mining in a time period t; d (t) is the direct water consumption of the lake water in the period t; wCalling inRegulating water quantity for trans-regional; w (t) is the volume of the last lake in the period t, and W (t-1) is the volume of the last lake in the period t-1; h (t) is the simulated water level of the lake at the end of the period t; dH/dW is the change rate of the lake volume and the water level.
Further, the step S6 is specifically:
forecasting future basin population, industry and ecological environment development indexes obtained by the meteorological hydrological data obtained in the step S1 by adopting a quota method to obtain future economic and social development water demand, and carrying out basin water resource allocation under different scenes by taking the available quantity of basin water resources as rigid constraint to obtain planned horizontal year supporting flow
Further, the step S7 is specifically:
and (4) performing future lake water balance calculation on the water consumption of different development levels of the planned horizontal year-supporting basin obtained in the step S6 and the long series runoff results of the inflow of the basin into the lake under different situations simulated in the step S5 by using the inland lake water balance model constructed in the step S5 to obtain the future lake water change situation under different situations, and calculating the future lake water change situation under different situations by using the lake reservoir capacity curve obtained in the step S2 to obtain the future lake water level and lake surface area change trend.
The beneficial effects of the further scheme are as follows:
(1) the method can collect the data of meteorology, hydrology, geology, land utilization, soil type, social and economic development and water resource development and utilization in all directions in areas without runoff data, and provides powerful data support for reasonably drawing a lake water level-area-volume curve and constructing, calibrating and verifying a lake basin hydrological model;
(2) the Civil 3D volume panel method comprises a three-dimensional dynamic model, can dynamically display the lake bottom terrain in a curved surface mode, has high calculation efficiency, and can quickly and effectively establish a lake water level-area-volume curve by utilizing collected data in a region without runoff data;
(3) combining meteorological hydrological data of the area without the runoff data with a digital elevation model to construct a distributed hydrological model of the inland lake basin, and calibrating and verifying the model by adopting the actually measured lake water level according to the characteristic that the closed basin lake is the lowest point, so that long-series surface runoff entering the lake is quantized, and the problem that the runoff entering the lake in the area without the data is difficult to quantify is solved;
(4) on the basis of reasonably configuring the water resources of the drainage basin, the evolution trend of the water level and the area of the lake is predicted according to the future water resource consumption level, so that a decision maker can select reasonable regulation and control measures timely and accurately, scientific configuration of regional water resources and implementation of a water transfer scheme are facilitated, and the ecological environment of the lake can be better improved.
Drawings
FIG. 1 is a flow chart of a simulation and prediction method for water level variation of inland lakes in areas without runoff data according to the present invention;
FIG. 2 is a flowchart illustrating a step S3 according to the present invention;
FIG. 3 is a flowchart illustrating a step S4 according to the present invention;
FIG. 4 is a flowchart illustrating a step S5 according to the present invention;
FIG. 5 is a measured water level-area-volume curve of Daihai according to the present invention;
FIG. 6 is a water level distribution diagram of a groundwater recharge calculation section and groundwater around a lake according to the present invention;
FIG. 7 is a diagram of the evolution trend of the water surface area of a Daihai lake in the future predicted by the present invention (scenario 1);
FIG. 8 is a diagram of the evolution trend of the water surface area of a Daihai lake in the future predicted by the present invention (scenario 2).
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in figure 1, the invention relates to a simulation and prediction method for water level change of inland lakes in areas without runoff data, which specifically comprises the following steps:
s1, collecting meteorological hydrological data of the area without runoff data;
in this embodiment, the meteorological hydrographic data includes topographic and geomorphic data, meteorological data, hydrographic data, land use data, soil data, economic and social data, and water resource development and utilization data.
Wherein the topographic and geomorphic data specifically comprises SRTM data with the precision of 90m of the United States Geological Survey (USGS), a lake region 1:5000 topographic map, a regional underground water system and an aquifer system partition;
the soil data specifically comprises HWSD soil database data constructed by the national agricultural food organization (FAO) Vienna International application systems research institute (IIASA);
the meteorological data specifically comprises temperature, precipitation, evaporation, solar radiation amount, dew point temperature, average wind speed and the like of all stations in the drainage basin;
the hydrological data comprises serial daily monitoring data of lake water level and lake surface area;
the land utilization data comprises the current land utilization situation data of the second land survey and the third land survey in the river basin;
the economic and social data comprise the current situation data of population, GDP, first yield, second yield, third yield, thermal power installation and the like in the river basin;
the water resource development and utilization data comprises the current situation of water conservancy projects in the river basin and the current situation information of water supply, water consumption and water drainage in 5 years.
S2, calibrating the inland lake reservoir capacity curve according to the meteorological hydrological data obtained in the step S1;
in this embodiment, a Civil 3D volume panel method is adopted, actually taking a dai sea lake area as an example, a 1:5000 two-dimensional topographic curved surface of the lake bottom of the dai sea lake area measured in the meteorological hydrological data obtained in step S1 is taken as a reference curved surface, a water level of the dai sea lake at different historical stages is taken as a reference curved surface, a corresponding reservoir capacity is rapidly counted by using the volume panel, and a lake level-area-volume curve is drawn by combining the reservoir capacities and water surface areas corresponding to different water levels, as shown in fig. 2.
S3, establishing a lake runoff simulation database by combining the meteorological hydrological data obtained in the step S1 and the digital elevation model, and constructing a distributed hydrological model of the inland lake basin;
in the embodiment, hydrological information such as a basin boundary, an area, a gradient and a basin water system is extracted by adopting a digital elevation model DME, an inland lake is used as a final convergence point of the model, a database related to runoff simulation is established by combining meteorological hydrological data obtained in the step S1, surface runoff of the basin is calculated by selecting an SCS curve in a SWAT model, and a distributed hydrological model of the inland lake basin is constructed.
In practice, taking the dai lake area as an example, extracting hydrological information such as a basin boundary, an area, a gradient and a basin water system by using a digital elevation model DME, constructing a dai sea basin space database, an attribute database, a land utilization database and a meteorological database by combining land utilization data, soil type data and meteorological data in the meteorological hydrological data obtained in step S1, considering the dai sea as a final sink reservoir point in the hydrological model, namely processing the dai sea according to a final sink point of the basin river, calculating basin surface runoff by using an SCS curve in a SWAT model, and building a basin distributed hydrological model of the inland lake.
As shown in fig. 3, step S3 specifically includes the following steps:
s31, establishing a lake basin space database by using a digital elevation model, respectively establishing a lake basin attribute database and a land utilization database by using soil data and land utilization data in meteorological hydrological data obtained in the step S1, and establishing a meteorological database by using the meteorological data in the meteorological hydrological data obtained in the step S1 and combining a distributed hydrological model weather generator;
in the embodiment, a lake basin space database is established by using a Digital Elevation Model (DEM) with the accuracy of 90m of the lake basin; establishing a lake basin attribute database by adopting second national land survey soil data (the scale is 1:100 ten thousand, the data resolution is 1km, the data format is grid data, and the coordinate system is a 1984 geodetic coordinate system) in the meteorological hydrological data obtained in the step S1; establishing a lake basin land utilization database covering grasslands, forest lands, cultivated lands, water bodies, urban residential land, bare land and the like by adopting the land utilization data in the meteorological hydrological data obtained in the step S1; and (5) establishing a meteorological database through the distributed hydrological model weather generator by adopting precipitation and temperature data in the meteorological hydrological data obtained in the step S1.
S32, calculating the basin surface runoff by utilizing the SCS curve in the SWAT model, and constructing a distributed hydrological model of the inland lake basin, wherein the distributed hydrological model is expressed as follows:
wherein, SWtAnd SW0The final water content of the soil and the early water content (mm) of the soil are respectively; t is the number of days; rdayThe precipitation amount of the ith day; qsurfSurface runoff (mm) on day i; eaEvaporation (mm) on day i; w is aseepThe soil profile penetration and lateral flow (mm) on day i; qgwDay i water return (mm).
In this embodiment, the surface runoff Q of different land utilization and soil type underlying surface conditions is reflected by calculating the surface runoff through the SCS curve in the SWAT modelsurfThe calculation formula is as follows:
wherein R isdayAverage daily precipitation (mm); i isaThe initial loss, namely the loss of precipitation (mm) before surface runoff is generated; s is a retention parameter (mm), the retention parameter S generates difference in time and space along with the change of factors such as soil, land utilization, gradient and soil water content, and the like, and the calculation formula is as follows:
wherein CN is curve number of a certain day and initial loss IaAbout 0.2S, when Rday>IaSurface runoff is generated, and the CN curve number is that the SCS curve contains water according to three different soil moisture of soil dryness, general wetting and wetting in the early stageConditions are classified as CN1、CN2And CN3The calculation formula is as follows:
CN3＝CN2×exp[0.000673×(100-CN2)]。
s4, calibrating and verifying the inland lake basin distributed hydrological model obtained in the step S3 to obtain an optimized inland lake basin distributed hydrological model;
in this embodiment, the parameter sensitivity analysis is performed on the land and lake basin distributed hydrological model obtained in step S3, a parameter having a large influence on the simulation result is screened out, the value range of the model parameter is preliminarily determined, the lake month runoff length series data is calculated by using the runoff resource-free lake month runoff average method based on the actually measured water level of the lake, the land and lake basin distributed hydrological model is calibrated to obtain a model parameter matching the lake month runoff length and the simulation value, the calibrated simulation result is verified by using the calculated lake month runoff length series data again, and the land and lake distributed hydrological model having a reliable simulation result and a reasonable effect is obtained.
As shown in fig. 4, step S4 includes the steps of:
s41, performing sensitivity analysis on the inland lake basin distributed hydrological model parameters by utilizing an SUFI-2 algorithm, screening model parameters matched with the inland lake basin distributed hydrological model, and setting initial conditions;
in the embodiment, the sensitivity intensity is described by the absolute value of t-stat in the sensitivity analysis process, and when the absolute value of the parameter t-stat is larger, the sensitivity is stronger; meanwhile, P-value is adopted to describe the significance of t-stat, when the P-value of the parameter is closer to 0, the stronger the significance is, the SUFI-2 algorithm calculation formula is as follows:
wherein g is an objective function; alpha, betaiIs the coefficient of the regression equation; n is the number of parameters; biAre parameter values.
In practice, the initial conditions include SCS runoff curve number, soil volume weight, snow melting basic temperature, soil saturation hydraulic conductivity, base flow fading coefficient, soil depth, average slope length, soil effective water content, freezing air temperature lag coefficient, main river hydraulic conductivity, main river manning coefficient, soil saturation water electrical conductivity, soil evaporation compensation coefficient, air temperature drop rate, shallow groundwater re-evaporation coefficient, shallow groundwater runoff coefficient, maximum snow melting depth of 6 months and 21 days, groundwater re-evaporation coefficient, minimum snow melting depth of 12 months and 21 days, and groundwater lag coefficient.
S42, utilizing the actually measured lake water level to calculate the lake monthly mean diameter flow length series data to calibrate the inland lake basin distributed hydrological model;
in this embodiment, the lake monthly mean radial flow length series data is calculated by using the actually measured lake water level, and the calculation formula is as follows:
Q＝ACR/(24×3600)
wherein Q is the lake monthly mean runoff; a. theCIs the basin area, km2(ii) a R is runoff depth, mm, ACThe formula for R is:
ACR＝ΔV-ALP+ALE
wherein, the delta V is the change volume of the lake in the average month, km2；ALIs lake area, km2(ii) a P is the average monthly rainfall, mm; e is the monthly average evaporation capacity, mm; q is the monthly mean radial flow rate, m3/s。
In practice, the measured water level in Dai lake area is used to calculate the lake monthly mean diameter flow length coefficient data.
S43, judging whether the calibration result obtained in the step S42 meets a preset threshold value, if so, finishing calibration, and entering the step S44, otherwise, re-determining the parameter range of the distributed hydrological model of the inland lake basin, and returning to the step S42;
in this embodiment, the calibration result obtained in step S42 is determined, and if the nash efficiency coefficient is >0.6 and the decision coefficient is >0.6, the calibration is ended, and step S44 is performed, otherwise, the parameter range of the inland lake basin distributed hydrological model is determined again, and step S42 is returned;
s44, verifying the calibrated distributed hydrological model result of the inland lake basin by using the lake monthly mean radial flow length series data obtained by actually measuring the water level of the lake again, judging whether the verification result meets the precision requirement, if so, finishing the verification to obtain the optimized distributed hydrological model of the inland lake basin, otherwise, re-determining the parameter range of the distributed hydrological model of the inland lake basin, and returning to the step S42.
In this embodiment, the long-series data of the lake monthly mean radial flow obtained by actually measuring the water level of the lake is used again to verify the calibrated distributed hydrological model result of the inland lake basin, whether the verification result meets the Nash efficiency coefficient of >0.6 and the decision coefficient of >0.6 is judged, if yes, the verification is finished, the optimized distributed hydrological model of the inland lake basin is obtained, otherwise, the parameter range of the distributed hydrological model of the inland lake basin is determined again, and the step S42 is returned.
S5, simulating a long series of runoff results of the watershed afflux into the lake under different scenes according to the optimized distributed hydrological model of the inland lake watershed obtained in the step S4, and constructing a water balance model of the inland lake by combining the meteorological hydrological data obtained in the step S1;
in this embodiment, according to the optimized distributed hydrological model of the inland lake watershed obtained in step S4, long series runoff results of the watershed rivers flowing into the lake under different situations are simulated, and income and consumption such as lake surface rainfall, evaporation, consumption and water intake amount are calculated by combining the meteorological hydrological data obtained in step S1, so as to construct an inland lake water balance model.
As shown in fig. 5, step S5 specifically includes the following steps:
s51, simulating a long series runoff result of the river basin afflux into the lake under different series of scenes by using the optimized inland lake river basin distributed hydrological model obtained in the step S44;
s52, constructing a inland lake water balance model by using the meteorological hydrological data obtained in the step S1 and the long series runoff simulation results of the rivers converging into the lake under different series of scenes obtained in the step S51, wherein the model expression is as follows:
ΔW(t)＝Wsurface of earth+WUnderground entering+P(t)+WCalling in－D(t)－E(t)－EWater reservoir(t)－CSurface of earth－CUnderground surface
W(t)＝W(t-1)+ΔW(t)
H(t)＝W(t)×dH/dW
Wherein, Δ W (t) is the storage variable of the lake water in the period of t, the water storage is increased to positive and decreased to negative, ten thousand m3；WSurface of earthThe surface diameter formed by precipitation in the drainage basin flows into the lake by ten thousand meters3；WUnderground enteringTen thousand meters is the exchange capacity of groundwater and lake water in a drainage basin3(ii) a E (t) is the evaporation capacity of the lake surface in the period of t, ten thousand meters3；EWater reservoir(t) ten thousand meters of surface runoff for evaporation and closure of reservoir pit and pond in t time period3(ii) a P (t) is the lake surface precipitation amount in the period of t, ten thousand m3；CSurface of earthTen thousand meters of water consumption on the earth surface in the period of t3；CUnderground surfaceAffects the surface water resource quantity for underground water mining in t time period, ten thousand meters3(ii) a D (t) is the direct water consumption of the lake water in the period of t, ten thousand meters3；WCalling inFor regulating water quantity for transregional use, ten thousand meters3(ii) a W (t) is the volume of the last lake in the period of t, W (t-1) is the volume of the last lake in the period of t-1, ten thousand meters3(ii) a The six parameters are obtained from meteorological data and water resource development and utilization data in the meteorological hydrological data obtained in the step S1; h (t) is the simulated lake water level m at the end of the period t; and dH/dW is the change rate of the volume and the water level of the lake.
In this embodiment, the surface area formed by precipitation in the watershed is measured by the inflow amount W of the lakeSurface of earthSimulating and generating the watershed distributed hydrological model obtained in the step S4; as the lake can receive the replenishment of the groundwater of the drainage basin, the lateral replenishment of the groundwater is a main replenishment mode, and the replenishment amount of the groundwater of the drainage basin and the water amount of the lake can be obtained by calculating the lateral runoff replenishment amount of the groundwater to the lake water, and the replenishment flow rate Q of the lateral runoffUnderground (underground)The calculation formula of (2) is as follows:
Qunderground (underground)＝K×A×J
Wherein Q isUnderground (underground)Supply flow of lateral runoff of groundwater to lake water, m3D; k is the permeability coefficient of the aquifer, m/d; a is the cross-sectional area of water passing, m2(ii) a J is groundwater hydraulic gradient and is dimensionless.
In practice, according to the water resource evaluation result, the surface water resource quantity C is influenced by the underground water exploitation in the time period tUnderground surfaceThe total discharge amount of underground water in hilly and plateau areas of Dai ocean current areas from 2001 to 2017 is 11011.65 km3Wherein the amount of the rainfall infiltration and supply forms a river discharge amount of 1068.69 ten thousand meters3Lateral outflow of 1085.66 km in plain area3The average river discharge and the lateral outflow of the plain area occupy 19.6% of the total sewage discharge over the years.
In practice, as shown in FIG. 6, according to the ground water level monitoring data of Dai basin, the water level of lake in the equilibrium period is decreased significantly, the average water level is 1216.57m in 2014, the average water level is 1214.48m in 2018, and the average water level is decreased by about 2.09 m. Because the water level is low, the terrain around the lake is slow, and the perimeter of the lake is obviously shortened after the water level is reduced, the supply amount of the lateral radial flow direction of the groundwater to the lake water is respectively calculated by adopting the groundwater flow fields at the beginning and the end of the equilibrium period.
The selection of the calculation sections of the surrounding lake considers the equal water level lines, the shapes of lake areas, river watersheds and the like, the sections basically mainly take the vertical flow direction, and 5 calculation sections of I-II, II-III, III-IV, IV-V and V-I are selected. The permeability parameter value is referenced to the existing achievement value of 2.85-7.66 m/d. The results of calculation of the amount of groundwater supply to dai sea lake in 2013 and 2018 are shown in tables 1 and 2.
Table 12013 amount of groundwater in Daihai lake
Table 22018 amount of groundwater in Daihai lake
The hydraulic gradient between the groundwater level and the surface of the Daihai lake cannot be accurately determined due to the lack of measured underground water level data in other years, and the average annual discharge of underground water in 2014-2017 is analogized according to the proportion distribution of annual rainfall according to the area and the perimeter of the lake in the Daihai lake, and the average annual discharge of underground water in years is about 462 ten thousand m for many years3And (4) a year. See table 3 for details.
In Table 32013-2018, the amount of groundwater in Daihai lake is calculated
The constructed inland lake water balance model is adopted to carry out annual water balance analysis between the Daihai lakes from 2001 to 2017, and the result shows that the annual average storage variable of the Daihai lakes from 2001 to 2017 is-2089.9 ten thousand meters3The annual average calculated balance is-2149.5 ten thousand meters3The difference between the lake water storage variable and the calculated balance is 59.7 km3. The simulation errors are calculated by comparing the calculated balance quantity with the lake water storage variable, the average error is 2.9%, the maximum error is 178% in 2012, the minimum error is 5% in 2008, and most of the simulation errors are below 30% except for simulation errors of 136% and 94% in 2002 and 2005 in other years. The results are detailed in Table 4.
Table 42001 year-2017 Daihai lake water quantity year-by-year balance result
S6, forecasting the economic and social development level of the future basin according to the meteorological hydrological data obtained in the step S1, and calculating by adopting a rating method to obtain water consumption for supporting different development levels of the basin in a planned horizontal year;
in this embodiment, a quota method is used to predict future population, industry and ecological environment development indexes of the drainage basin obtained from the meteorological and hydrological data obtained in step S1 to obtain future economic and social development water demand, and drainage basin water resource allocation under different situations is performed with the available drainage basin water resource amount as a rigid constraint to obtain water consumption amounts for supporting different development levels of the drainage basin in a planned horizontal year.
In practice, development indexes such as future basin population, industry and ecological environment and the like are predicted by using a rating method for land utilization data, economic social data, water resource development utilization data and the like in the meteorological hydrological data obtained in the step S1 to obtain future economic social development water demand, basin water resource allocation under different scenes is developed by taking the available water resource amount of the basin as rigid constraint, and water consumption amounts supporting different development levels of the basin in a planned horizontal year are obtained and detailed in a table 5.
TABLE 5 scheme for water resource allocation in Daisy ocean area under different scenes
S7, forecasting future lake volume and water level evolution trend through the inland lake water volume balance model obtained in the step S5 according to the long series runoff results of the catchment lake under different situations simulated in the step S5 and the water consumption of different development levels of the planned horizontal year support catchment basin predicted in the step S6.
In this embodiment, the inland lake water balance model constructed in step S5 is used to perform future lake water balance calculation on the water consumption of different development levels of the planned horizontal year-supported basin obtained in step S6 and the long series runoff results of the basin converging into the lake under different situations simulated in step S5 to obtain future lake water change conditions under different situations, and the lake capacity curve obtained in step S2 is used to calculate the future lake water change conditions under different situations to obtain the future lake water level and lake surface area change trends.
1) Scene 1 deep water-saving and water-controlling mode
In practice, as shown in FIG. 7, the changes in Dai seawater level and lake surface area will undergo two fluctuations in the future 17 years, the water level and lake surface area first passing through 1214.51m and 54.71km in the 1 st year2Slowly recovering the lifting and recovering until reaching the peak value in the 5 th year, and respectively reaching 1215.85m and 59.60km for the water level and the lake surface area2(ii) a The water level and lake surface area reach 1213.13m and 44.59km respectively by the first valley in the 12 th year2(ii) a Gradually recovering the lake surface area to 55.03km after the 14 th year2Then continuously shrinking again until the lake surface area shrinks again to 50km in the 17 th year2The water level is reduced to 1213.81m, and the lake surface area is shrunk to 49.25km2。
2) Scenario 2 high quality development mode
In practice, as shown in FIG. 8, the changes in Dai seawater level and lake surface area will undergo two fluctuations in the future 17 years, the water level and lake surface area first passing through 1214.51m and 54.71km in the 1 st year2Slowly recovering the lifting and recovering until reaching the peak value in the 5 th year, and respectively reaching 1215.64m and 58.96km for the water level and the lake surface area2(ii) a The water level and lake surface area reach 1212.36m and 39.72km respectively after 7 years and reach the minimum value by 12 years2(ii) a Gradually recovering the lake surface area to 49.93km after the 14 th year2Then continuing to shrink again until the water level drops to 1213.01m in the 17 th year and the lake surface area shrinks to 43.85km2。
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (10)
1. A simulation and prediction method for water level change of inland lakes in areas without runoff data is characterized by comprising the following steps:
s1, collecting meteorological hydrological data of the area without runoff data;
s2, calibrating the inland lake reservoir capacity curve according to the meteorological hydrological data obtained in the step S1;
s3, establishing a lake runoff simulation database by combining the meteorological hydrological data obtained in the step S1 and the digital elevation model, and constructing a distributed hydrological model of the inland lake basin;
s4, calibrating and verifying the inland lake basin distributed hydrological model obtained in the step S3 to obtain an optimized inland lake basin distributed hydrological model;
s5, simulating a long series of runoff results of the watershed afflux into the lake under different scenes according to the optimized distributed hydrological model of the inland lake watershed obtained in the step S4, and constructing a water balance model of the inland lake by combining the meteorological hydrological data obtained in the step S1;
s6, forecasting the economic and social development level of the future basin according to the meteorological hydrological data obtained in the step S1, and calculating by adopting a rating method to obtain water consumption for supporting different development levels of the basin in a planned horizontal year;
s7, forecasting future lake volume and water level evolution trend through the inland lake water volume balance model obtained in the step S5 according to the long series runoff results of the catchment lake under different situations simulated in the step S5 and the water consumption of different development levels of the planned horizontal year support catchment basin predicted in the step S6.
2. The inland lake water level variation simulation prediction method of claim 1, wherein the meteorological hydrological data obtained in step S1 comprises landform data, meteorological data, hydrological data, land utilization data, soil data, economic and social data, and water resource development and utilization data.
3. The inland lake water level variation simulation prediction method according to claim 1, wherein the step S2 is specifically:
and (4) adopting a Civil 3D volume panel method, taking the two-dimensional topographic curved surface of the lake bottom in the meteorological and hydrological data obtained in the step S1 as a reference curved surface, taking the water level horizontal planes of lakes in different historical stages as reference curved surfaces, counting corresponding reservoir volumes, and drawing a water level-area-volume curve of the lake by combining the reservoir volumes corresponding to different water levels and the water surface area.
4. The inland lake water level variation simulation prediction method of claim 1, wherein the step S3 specifically comprises the steps of:
s31, establishing a lake basin space database by using a digital elevation model, respectively establishing a lake basin attribute database and a land utilization database by using soil data and land utilization data in meteorological hydrological data obtained in the step S1, and establishing a meteorological database by using the meteorological data in the meteorological hydrological data obtained in the step S1 and combining a distributed hydrological model weather generator;
s32, calculating the basin surface runoff by utilizing the SCS curve in the SWAT model, and constructing a distributed hydrological model of the inland lake basin, wherein the distributed hydrological model is expressed as follows:
wherein, SWtAnd SW0The final water content of the soil and the early-stage water content of the soil are respectively; t is the number of days; rdayThe precipitation amount of the ith day; qsurfThe surface runoff on the ith day; eaEvaporation on day i; w is aseepThe soil profile seepage and the lateral flow of the day i are shown; qgwThe water return amount is the i day.
5. The inland lake water level variation simulation prediction method of claim 4, wherein the calculation formula of the watershed surface runoff is as follows:
wherein R isdayIs the average daily precipitation; i isaThe initial loss is the initial loss; s is a retention parameter.
6. The inland lake water level variation simulation prediction method of claim 1, wherein the step S4 specifically comprises the steps of:
s41, performing sensitivity analysis on the inland lake basin distributed hydrological model parameters by utilizing an SUFI-2 algorithm, screening model parameters matched with the inland lake basin distributed hydrological model, and setting initial conditions;
s42, calibrating the inland lake basin distributed hydrological model by using the lake month average runoff length series data calculated by the actually measured lake water level;
s43, judging whether the calibration result obtained in the step S42 meets a preset threshold value, if so, finishing calibration, and entering the step S44, otherwise, re-determining the parameter range of the distributed hydrological model of the inland lake basin, and returning to the step S42;
s44, verifying the calibrated distributed hydrological model result of the inland lake basin by using the lake monthly mean radial flow length series data obtained by actually measuring the water level of the lake again, finishing verification if the set precision requirement is met to obtain the optimized distributed hydrological model of the inland lake basin, and otherwise, re-determining the parameter range of the distributed hydrological model of the inland lake basin and returning to the step S42.
7. The inland lake water level variation simulation prediction method of claim 6, wherein the formula for calculating the lake monthly mean runoff is as follows:
Q＝ACR/(24×3600)
wherein Q is the lake monthly mean runoff; a. theCIs the basin area; r is the runoff depth.
8. The inland lake water level variation simulation prediction method of claim 1, wherein the step S5 specifically comprises the steps of:
s51, simulating a long series runoff result of the river basin afflux into the lake under different series of scenes by using the optimized inland lake river basin distributed hydrological model obtained in the step S44;
s52, constructing a water balance model of the inland lake according to the meteorological hydrological data obtained in the step S1 and the long series runoff simulation results of the rivers converging into the lake under different series of scenes obtained in the step S51, wherein the long series runoff simulation results are expressed as:
ΔW(t)＝Wsurface of earth+WUnderground entering+P(t)+WCalling in－D(t)－E(t)－EWater reservoir(t)－CSurface of earth－CUnderground surface
W(t)＝W(t-1)+ΔW(t)
H(t)＝W(t)×dH/dW
Wherein, Δ W (t) is the storage variable of the lake water in the period t; wSurface of earthThe surface diameter formed by precipitation in the watershed flows into the lake volume; wUnderground enteringThe exchange capacity of the groundwater and the lake water in the drainage basin; e (t) is the lake surface evaporation amount in the period t; eWater reservoir(t) the evaporation and closure of the earth surface runoff of the reservoir pool at the time interval t; p (t) is lake surface precipitation in a period of t; cSurface of earthThe water consumption of the earth surface is t time period; cUnderground surfaceInfluencing the surface water resource quantity for underground water mining in a time period t; d (t) is the direct water consumption of the lake water in the period t; wCalling inRegulating water quantity for trans-regional; w (t) is the volume of the last lake in the period t, and W (t-1) is the volume of the last lake in the period t-1; h (t) is the simulated water level of the lake at the end of the period t; and dH/dW is the change rate of the volume and the water level of the lake.
9. The inland lake water level variation simulation prediction method according to claim 1, wherein the step S6 is specifically:
forecasting future basin population, industry and ecological environment development indexes obtained by the meteorological hydrological data obtained in the step S1 by using a quota method to obtain future economic and social development water demand, and performing basin water resource allocation under different scenes by using the available quantity of basin water resources as rigid constraint to obtain water consumption for supporting different development levels of the basin in a planned horizontal year.
10. The inland lake water level variation simulation prediction method according to claim 1, wherein the step S7 is specifically:
and (4) performing future lake water balance calculation on the water consumption of different development levels of the planned horizontal year-supporting basin obtained in the step S6 and the long series runoff results of the inflow of the basin into the lake under different situations simulated in the step S5 by using the inland lake water balance model constructed in the step S5 to obtain the future lake water change situation under different situations, and calculating the future lake water change situation under different situations by using the lake reservoir capacity curve obtained in the step S2 to obtain the future lake water level and lake surface area change trend.
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| CN117273313B (en) * | 2023-09-08 | 2024-05-24 | 中关村科学城城市大脑股份有限公司 | Water network regulation method, device, electronic equipment and computer readable medium |
| CN117236668A (en) * | 2023-11-15 | 2023-12-15 | 山东锋士信息技术有限公司 | Regional water resource allocation analysis decision method and system based on supply and consumption |
| CN117236668B (en) * | 2023-11-15 | 2024-03-08 | 山东锋士信息技术有限公司 | Regional water resource allocation analysis decision method and system based on supply and consumption |
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