CN116167193A - Method for analyzing influence of land utilization change on runoff process based on SWAT model - Google Patents
Method for analyzing influence of land utilization change on runoff process based on SWAT model Download PDFInfo
- Publication number
- CN116167193A CN116167193A CN202111387236.1A CN202111387236A CN116167193A CN 116167193 A CN116167193 A CN 116167193A CN 202111387236 A CN202111387236 A CN 202111387236A CN 116167193 A CN116167193 A CN 116167193A
- Authority
- CN
- China
- Prior art keywords
- runoff
- soil
- data
- swat
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 73
- 230000008569 process Effects 0.000 title claims abstract description 50
- 230000008859 change Effects 0.000 title claims abstract description 27
- 239000002689 soil Substances 0.000 claims abstract description 75
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 50
- 230000004044 response Effects 0.000 claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 5
- 238000004088 simulation Methods 0.000 claims description 38
- 238000012795 verification Methods 0.000 claims description 10
- 239000010410 layer Substances 0.000 claims description 8
- 238000010206 sensitivity analysis Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 4
- 239000003673 groundwater Substances 0.000 claims description 4
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 3
- 238000005349 anion exchange Methods 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 229910052799 carbon Inorganic materials 0.000 claims description 3
- 239000002245 particle Substances 0.000 claims description 3
- 239000002344 surface layer Substances 0.000 claims description 3
- 238000001704 evaporation Methods 0.000 claims description 2
- 230000008020 evaporation Effects 0.000 claims description 2
- 230000008595 infiltration Effects 0.000 claims description 2
- 238000001764 infiltration Methods 0.000 claims description 2
- 230000014759 maintenance of location Effects 0.000 claims description 2
- 230000000737 periodic effect Effects 0.000 claims description 2
- 238000001556 precipitation Methods 0.000 claims description 2
- 229920006395 saturated elastomer Polymers 0.000 claims description 2
- 238000000638 solvent extraction Methods 0.000 claims description 2
- 230000005068 transpiration Effects 0.000 claims description 2
- 238000011160 research Methods 0.000 description 6
- 235000013550 pizza Nutrition 0.000 description 5
- 238000012545 processing Methods 0.000 description 3
- -1 SILT Substances 0.000 description 2
- 239000004927 clay Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000007526 fusion splicing Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 239000011435 rock Substances 0.000 description 2
- 239000004576 sand Substances 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 238000011282 treatment Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2219/00—Indexing scheme relating to application aspects of data processing equipment or methods
- G06F2219/10—Environmental application, e.g. waste reduction, pollution control, compliance with environmental legislation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Educational Administration (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Quality & Reliability (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method for analyzing the influence of land use change on runoff process based on a SWAT model. Next, a SWAT model is built, including dividing the sub-watershed, generating the hydrologic response unit, loading the data and running the model. Finally, land utilization change impact analysis on the runoff process is performed based on the SWAT model. The invention provides scientific basis and basic support for reasonably planning land utilization, effectively managing water and soil resources and building ecological environment.
Description
Technical Field
The invention relates to the technical field of weather and hydrology, in particular to a method for analyzing the influence of land utilization change on a runoff process based on a SWAT model.
Background
In recent years, human activities such as population growth, urban development and the like drive the land utilization of the river basin to change, directly influence the river runoff, and greatly influence the ecological hydrologic process of the river basin, thereby further causing ecological environment problems such as flood and drought disasters, water and soil loss, water resource shortage and the like. Thus, research on runoff processes by land use changes is a currently of great concern.
Currently, hydrologic models provide an effective means for studying the influence of river basin land utilization and change on the runoff process. The hydrologic model mainly comprises a traditional model and a distributed model, wherein the traditional model can only take a river basin as a whole for research and cannot calculate the hydrologic process of the sub-river basin, and the distributed model can extract characteristic parameters of the space scale of the sub-river basin and more accurately simulate the hydrologic cycle process of the river basin. The SWAT (Soil and Water Assessment Tool) model is a distributed hydrological model developed by the United States Department of Agriculture (USDA), and has the advantages of wide research field, high accuracy, convenient parameter acquisition and the like. The SWAT model can simulate various hydrologic processes of a longer period of time, is widely applied to analyzing and predicting runoff changes caused by land utilization changes and the like, provides important support for land utilization management decisions of the river basin, and can be used for evaluating the performance of the SWAT model in simulating hydrologic response of the drought river basin if the SWAT model is applied to the Yarmouk river basin in the about denier region, and finally, the research shows that the SWAT model can simulate the runoff processes of the about denier drought region in a month scale; there are studies on runoff simulation of the mei river basin using the SWAT model, leading to the conclusion of better applicability in long-term continuous runoff simulation. However, there is no method for analyzing the impact of land use changes based on the SWAT model on the runoff process that forms a system.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a method for analyzing the influence of land use change on a runoff process based on a SWAT model, develops the simulation of the runoff process of a river basin under the land use change, and provides scientific basis for developing comprehensive management of water and soil resources of the river basin and reasonable planning of the land use.
The technical purpose of the invention is realized by the following technical proposal.
A land utilization change on runoff process influence analysis method based on SWAT model is carried out according to the following steps:
(1) A spatial database including Digital Elevation (DEM) data, land use type data, and soil type data;
wherein: the Digital Elevation (DEM) data is ASTER GDEMV digital elevation data (spatial resolution is 30 m) provided by a computer network information center geospatial data cloud platform of the national academy of sciences, and ArcGIS10.5 is applied to perform image fusion splicing, cutting, depression filling, projection and other processing to obtain basin range Digital Elevation (DEM) data;
the land utilization type data are derived from national land utilization type remote sensing monitoring space distribution data (spatial resolution is 30 m) provided by China academy of sciences resource environment science and data center, and comprise 6 primary types and 21 secondary types of cultivated land, woodland, grasslands, water areas, construction land and unused land, and the arcgis10.5 is applied to perform image cutting, projection, reclassification and other processes to obtain land utilization type data with a river basin range conforming to SWAT land utilization classification standards;
the soil type data are derived from a China soil data set (V1.1) (2009) based on a world soil database (HWSD), wherein the data sources in China are 1:100 ten thousand soil data (spatial resolution is 1000 m) provided by Nanjing soil for second full-country land investigation, and ArcGIS10.5 is applied to perform image cutting, projection, attribute association, reclassification and other treatments to obtain the soil type data with the river basin range conforming to SWAT soil classification standards;
(2) Constructing an attribute database which comprises soil attribute data, meteorological data and runoff observation data;
wherein: the soil layering Number (NLAYERS), the soil TEXTURE (TEXTURE), the maximum root depth of soil profile (SOL_ ZMX), the soil particle composition (CLAY, SILT, SAND, ROCK), the ANION exchange porosity (ANION_EXCL), the depth from the soil surface layer to the bottom layer (SOL_Z) and the organic carbon content (SOL_CBN) in the soil attribute data can be obtained through a Chinese soil data set (V1.1) of a world soil database (HWSD), the soil volume weight (SOL_BD), the soil layer effective water holding capacity (SOL_AWC) and the soil saturated water guide rate (SOL_K) can be obtained through SPAW software calculation;
meteorological data are derived from a CMADS (V1.1) data set (the spatial resolution is 1/4 degree) of a national Qinghai-Tibet plateau science data center, daily data of rainfall, relative humidity, air temperature and wind speed are processed into txt.
The runoff observation data is derived from hydrologic bureau of water conservancy department, comprises average runoff quantity for month by month, and is used for calibrating parameters of SWAT model and checking simulation accuracy of model
sub-watershed partitioning: according to Digital Elevation (DEM) data, defining a river network based on a threshold method by the SWAT model, setting a minimum water collection area threshold of sub-drainage basin division, and deleting and adding a tributary water outlet point according to actual conditions;
the hydrologic response unit generates: dividing each sub-basin into a plurality of hydrological response units, discarding the types lower than the set area threshold by setting the threshold of the area proportion of the land utilization class and the soil type, and respectively overlapping the types greater than the threshold according to the spatial distribution to form a plurality of hydrological response units;
data loading and model operation: and integrating the time scale of the meteorological data CMADS (V1.1) dataset driving field and the coincidence interval of the runoff observation data, setting a preheating period, a periodic rate and a verification period, and completing the operation of the SWAT model.
runoff simulation-SWAT model runoff simulation comprises two stages, wherein the first stage utilizes an SCS runoff model to calculate the water yield of each hydrological response unit by taking the processes of infiltration, lateral flow, soil evaporation, groundwater supply and the like into consideration; the second stage estimates the amount of water produced by each sub-basin delivered to the outlet, i.e. the sum of the runoff volumes produced by the hydrological response unit.
The water balance expression used by the SWAT model is:
in SW t Final water content (mm) for soil; SW is the initial water content (mm) of the soil on day i; t is time (d); r is R i Precipitation (mm) for day i; q (Q) i Surface runoff (mm) on day i; ET (electric T) i The amount of transpiration (mm) on day i; p (P) i Lateral seepage and seepage (mm) for the soil profile on day i; QR (quick response) i Is the groundwater content (mm) on day i.
Based on the SWAT water circulation theory, the water yield is calculated as follows:
WYLD＝SURQ+LATQ+GWQ-TLOSS-PA
wherein WYLD is total water yield, and refers to total water yield (mm) entering a main river in a time step; the SURQ is the contribution (mm) of the surface runoff to the total runoff of the main river channel in the time step; LATQ refers to the contribution (mm) of lateral flow to river runoff in time steps; GWQ is the contribution (mm) of the underground runoff to the total runoff of the main river in the time step; TLOSS is the amount of water lost (mm) transported by the riverbed; PA is pond retention (mm);
analysis of land use changes on runoff process: constructing different land utilization scenes by fixing meteorological data, simulating a basin runoff process, and comparing and researching the influence of land utilization change on runoff from a sub-basin scale, wherein the method comprises the following steps:
parameter sensitivity analysis: and performing sensitivity analysis by utilizing a SUFI-2 algorithm built in SWAT-CUP software provided by a SWAT functional network, and performing iterative operation for a plurality of times to obtain the optimal value of the parameter for enabling the runoff simulation result to reach the accuracy.
Runoff simulation calibration and verification: model calibration and verification are carried out by using measured runoff data of hydrologic stations for years month by month, and Nash-Sutcliffe efficiency coefficient (NSE) and decision coefficient (R) are adopted 2 ) The two indexes evaluate the verification result of the model, and the corresponding calculation formula is as follows:
in which Q s，i For the runoff simulation value of SWAT model, Q m，i In order to represent the actual measured value of the runoff,
the average value of the measured value of runoff is obtained, and n is the length of the measured flow.
In which Q s，i For the runoff simulation value of SWAT model, Q m，i In order to represent the actual measured value of the runoff,
is the average value of the runoff measured value, < > and->
And n is the length of the measured flow.
NSE is used for evaluating the fitting degree between the simulated runoff process and the observed runoff process, and the value range is (-infinity, 1)]The larger this value is indicative of a higher degree of matching of the simulated value with the observed value, and generally considered to be unacceptable for NSE < 0, acceptable for NSE > 0.5, and identical for nse=1. R is R 2 Used for representing the coincidence degree of the analog value and the observed value, and the value is 0,1]It is generally considered that R 2 The simulation result is acceptable when the simulation result is more than or equal to 0.5, R 2 The simulation results were very identical when=1.
According to the technical scheme, a SWAT model database is firstly constructed, and the SWAT model database comprises spatial data such as digital elevation data, land utilization type data and soil type data, and attribute data such as soil attribute data, meteorological data and runoff observation data. Next, a SWAT model is built, including dividing the sub-watershed, generating the hydrologic response unit, loading the data and running the model. Finally, land utilization change impact analysis on the runoff process is performed based on the SWAT model. The invention provides scientific basis and basic support for reasonably planning land utilization, effectively managing water and soil resources and building ecological environment. Compared with the prior art, the technical scheme of the invention has the following beneficial effects: according to the method for analyzing the influence of the land utilization change on the runoff process based on the SWAT model, the SWAT model is taken as a platform, the applicability of the SWAT model in a river basin is improved through calibration, the influence of the land utilization change on the runoff process is analyzed, and scientific basis and basic support are provided for reasonably planning the land utilization of the river basin, effectively managing water and soil resources and building ecological environment.
Drawings
FIG. 1 is a flow chart of a method of analyzing the impact of land use variation on a runoff process based on the SWAT model of the present invention.
FIG. 2 is a graph showing simulated values of the water yield in a pizza river basin under different land use scenarios in accordance with the embodiments of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and the specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As can be seen from fig. 1, the present invention provides a method for analyzing the influence of land use variation on runoff process based on a SWAT model, which comprises the following steps:
step 11, constructing a spatial database: including Digital Elevation (DEM) data, land use type data, and soil type data, as shown in table 1.
(1) Digital Elevation (DEM) model: according to the embodiment, the public downloaded ASTER GDEMV digital elevation data (spatial resolution is 30 m) is provided by using a geospatial data cloud platform of a computer network information center of China academy of sciences, the data is generated based on advanced satellite-borne heat emission and anti-radiometer (ASTER) data calculation, algorithm improvement is carried out on the basis of GDEMV1 data published in 2009, and the spatial resolution precision and the elevation precision of the data are improved. And carrying out image fusion splicing, drainage basin boundary extraction, depression filling, projection and other treatments on the research region by using ArcGIS10.5 to obtain the DEM data of the research region.
(2) Land use type data: the embodiment uses national land utilization type remote sensing monitoring space distribution data (spatial resolution is 30 m) provided by China academy of sciences resource environment science and data center, and comprises 6 primary types and 21 secondary types of cultivated land, woodland, grasslands, water areas, construction land and unused land, and uses ArcGIS10.5 to perform image cutting, projection, reclassification and other processes to obtain land utilization type data with a river basin range conforming to SWAT land utilization classification standards.
(3) Soil type data: in this embodiment, a chinese soil data set (V1.1) (2009) based on a world soil database (HWSD) is used, and a data source in china is 1:100 ten thousand soil data (spatial resolution 1000 m) provided by the second full-country land survey of the nanjing soil, and arcgis10.5 is applied to perform image cropping, projection, attribute association, reclassification and other processes to obtain soil type data with a river basin range conforming to the SWAT soil classification standard.
TABLE 1 SWAT model space database
Step 12, constructing an attribute database: including soil attribute data, meteorological data, runoff observations, as shown in table 2.
(1) Soil attribute data: in this example, the number of soil layers (NLAYERS), the soil TEXTURE (text), the maximum root depth of soil profile (sol_ ZMX), the soil particle composition (CLAY, SILT, SAND, ROCK), the ANION exchange porosity (anion_excl), the depth of soil surface layer to bottom layer (sol_z) and the organic carbon content (sol_cbn) can be obtained from the chinese soil data set (V1.1) of the world soil database (HWSD), the soil volume weight (sol_bd), the soil layer effective water holding capacity (sol_awc) and the soil saturation water holding capacity (sol_k) can be obtained by calculation by the SPAW software.
(2) Weather data: according to the embodiment, a national Qinghai-Tibet plateau science data center CMADS (V1.1) data set (with a spatial resolution of 1/4 DEG) is used for processing daily data of rainfall, relative humidity, air temperature and air speed into txt.
(3) Runoff observation data: the embodiment uses the month-by-month runoff observation data of the hydrologic bureau of the water conservancy department for calibrating parameters of the SWAT model and checking the simulation precision of the model.
Table 2 SWAT model properties database
step 21, sub-drainage basin division: the SWAT model defines river networks by a threshold-based method according to Digital Elevation (DEM) data, and sets 18000hm 2 As a minimum water collection area threshold value for sub-river basin division, in order to enable the extracted river network and the divided sub-river basin to be closer to the actual situation, the operation speed of a model is improved, water outlet points of a plurality of tributaries are deleted and added according to the actual situation, and finally the pizza river basin is divided into 92 sub-river basins.
Step 22, the hydrologic response unit generates: loading land utilization data and soil data by using land utilization/soil/gradient definition, and establishing a corresponding relation with an index table; calculating the gradient of the slope length according to LS-TOOL, and dividing the slope length into four grades of 0-5%, 5-10%, 10-25% and > 25%; and using the HRU definition, selecting a Multiple HRUs dividing method, setting the threshold values of land utilization, soil and gradient to be 10%, and finally generating 593 hydrologic response units.
Step 23, data loading and model operation: and loading a CMADS (V1.1) data set, processing daily data of rainfall, relative humidity, air temperature and air speed into txt format documents, and simultaneously creating index files corresponding to the documents so as to be convenient for reference when writing a model. In order to avoid errors caused by directly increasing the initial model operation parameters from 0 to analog values, and meanwhile, the overlapping intervals of the CMADS (V1.1) dataset driving field time scale (2008-2016) and the runoff observation data (2008-2016) are integrated, the study sets 2008 as a preheating period, 2009-2012 as a rate period and 2013-2016 as a verification period, and the operation of the SWAT model in the Lsa river basin is completed.
step 31, runoff simulation, including:
step 311, parameter sensitivity analysis: and selecting 23 parameters with larger correlation with runoffs for sensitivity analysis, and obtaining a sensitivity analysis result of the parameters through 500 iterative computations. The optimal parameter range given by each iteration of the SWAT-CUP is utilized to continuously narrow the range, the optimal values of 11 important parameters enabling the simulation result of the runoff of the pizza river basin to reach a certain precision are obtained through multiple iterative operations, the five parameters before the sensitivity priority ordering are respectively the surface soil wet volume weight (SOL_BD), the SCS runoff curve value (CN 2), the soil saturation hydraulic conductivity coefficient (SOL_K), the river channel regulation coefficient (ALPHA_BNK) and the average gradient (HRU_SLP), and the detailed results are shown in the table 3.
TABLE 3 parameter sensitivity ordering and best calibration values
Note that: the parameter adjustment method R represents replacement with the optimum parameter value, and V represents the original parameter× (1+optimum parameter value).
Step 312, calibrating and verifying runoff simulation: using the Nash-Suttcliffe efficiency coefficient (NSE) and the decision coefficient (R) 2 ) The two indexes are calibrated by using measured runoff data of 2009-2012 Tang Jia and the Lhasa hydrologic station, and the model is verified by using measured runoff data of 2013-2016 Tang Jia and the Lhasa hydrologic station, so that NSE coefficients of Tang Jia and Lhasa hydrologic station rate at regular intervals are respectively 0.74 and 0.75, and R 2 Are all 0.75, NSE coefficients of 0.66 and 0.67 in the verification period respectively, R 2 The simulation results are 0.69 and 0.68, so that the simulation effect is good, and the process of the runoff of the river basin of the pizza can be basically and accurately described.
Step 32, analyzing the influence of land utilization change on the runoff process: different land utilization scenes are constructed through fixed meteorological data, the runoff process of the river basin is simulated, and the influence of land utilization changes on the runoff process is compared and researched from the dimension of the sub-river basin. The influence of cultivated land, woodland and grassland on the runoff process of the pizza river basin is simulated by using a SWAT model respectively. The simulation results of the water yield in the Lasa river basin 2009-2016 are shown in figure 2. The simulation value of the average water yield for many years is as follows from big to small in turn: tilling scene (T1) > 2015 land use scene (T0) > grassland scene (T3) > woodland scene (T2). The simulation result of the annual average water yield of the cultivated land scene (T1) is 343.90mm, 9.52mm is added compared with T0, and the change rate is 2.85%. The water yield simulation result of the forest land scene (T2) is 328.69mm, which is reduced by 5.69mm compared with T0, and the change rate is-1.70%. The water yield simulation result of the grassland scene (T3) is 332.28mm, which is reduced by 2.10mm compared with the grassland scene T0, and the change rate is-0.63%.
According to the method for analyzing the influence of land utilization change on the runoff process based on the SWAT model, the runoff process under different land utilization situations is simulated by using the measured month-by-month runoff data of the hydrologic station of the specific area for years based on the SWAT model, so that the influence of the land utilization change on the runoff process is analyzed. It should be noted that any simple variations, modifications or other equivalent which does not take the inventive effort by a person skilled in the art can fall within the protection scope of the present invention without departing from the core of the present invention.
Claims (5)
1. The method for analyzing the influence of land utilization change on the runoff process based on the SWAT model is characterized by comprising the following steps of:
step 1, constructing a SWAT model database, which comprises the following steps:
(1) The space database comprises digital elevation data, land utilization type data and soil type data;
(2) Constructing an attribute database which comprises soil attribute data, meteorological data and runoff observation data;
step 2, establishing a SWAT model, which comprises the following steps:
(1) Sub-watershed partitioning: according to the digital elevation data, defining a river network by the SWAT model based on a threshold method, setting a minimum water collection area threshold of sub-drainage basin division, and deleting and adding a tributary water outlet point according to actual conditions;
(2) The hydrologic response unit generates: dividing each sub-basin into a plurality of hydrological response units, discarding the types lower than the set area threshold by setting the threshold of the area proportion of the land utilization class and the soil type, and respectively overlapping the types greater than the threshold according to the spatial distribution to form a plurality of hydrological response units;
(3) Data loading and model operation: integrating the time scale of the meteorological data set driving field and the coincidence interval of the runoff observation data, setting a preheating period, a periodic rate and a verification period, and completing the operation of the SWAT model;
step 3, analyzing the influence of land utilization change on the runoff process based on the SWAT model, comprising the following steps:
(1) Runoff simulation-SWAT model runoff simulation comprises two stages, wherein the first stage utilizes an SCS runoff model to calculate the water yield of each hydrological response unit by taking the processes of infiltration, lateral flow, soil evaporation, groundwater supply and the like into consideration; estimating the water yield of each sub-basin conveyed to the outlet, namely the sum of runoff generated by the hydrological response unit;
the water balance expression used by the SWAT model is:
in SW t Final water content (mm) for soil; SW is the initial water content (mm) of the soil on day i; t is time (d); r is R i Precipitation (mm) for day i; q (Q) i Surface runoff (mm) on day i; ET (electric T) i The amount of transpiration (mm) on day i; p (P) i Lateral seepage and seepage (mm) for the soil profile on day i; QR (quick response) i The groundwater content (mm) on day i;
based on the SWAT water circulation theory, the water yield is calculated as follows:
WYLD＝SURQ+LATQ+GWQ-TLOSS-PA
wherein WYLD is total water yield, and refers to total water yield (mm) entering a main river in a time step; the SURQ is the contribution (mm) of the surface runoff to the total runoff of the main river channel in the time step; LATQ refers to the contribution (mm) of lateral flow to river runoff in time steps; GWQ is the contribution (mm) of the underground runoff to the total runoff of the main river in the time step; TLOSS is the amount of water lost (mm) transported by the riverbed; PA is pond retention (mm);
(2) Analysis of land use changes on runoff process: constructing different land utilization scenes by fixing meteorological data, simulating a basin runoff process, and comparing and researching the influence of land utilization change on runoff from a sub-basin scale, wherein the method comprises the following steps:
and (3) parameter sensitivity analysis, namely obtaining the optimal value of the parameter which enables the runoff simulation result to reach the precision through repeated iterative operation.
And (3) runoff simulation calibration and verification: using the Nash-Sutcliffe efficiency coefficient (NSE) and the decision coefficient (R 2 ) The two indexes evaluate the verification result of the model, and the corresponding calculation formula is as follows:
in which Q s，i For the runoff simulation value of SWAT model, Q m，i In order to represent the actual measured value of the runoff,
the average value of the runoff measured values is n, and the length of the measured flow is n;
in which Q s，i For the runoff simulation value of SWAT model, Q m，i In order to represent the actual measured value of the runoff,
is the average value of the runoff measured value, < > and->
The average value of the runoff simulation values is n, and the length of the measured flow is n;
the NSE is used for evaluating the fitting degree between the simulated runoff process and the observed runoff process, the NSE is smaller than 0, the simulation result is not acceptable, when the NSE is larger than 0.5, the simulation result is acceptable, and when the NSE=1, the simulation value is completely the same as the observed value; r is R 2 Used for representing the coincidence degree of the simulation value and the observed value, R is considered as 2 The simulation result is acceptable when the simulation result is more than or equal to 0.5, R 2 The simulation results were very identical when=1.
2. The method for analyzing the influence of land use variation on runoff processes based on a SWAT model according to claim 1, wherein the spatial resolution of the digital elevation data is 30m, the spatial resolution of the land use type data is 30m, the method comprises 6 primary types and 21 secondary types of cultivated land, woodland, grassland, water area, construction land and unused land, and the spatial resolution of the soil type data is 1000m.
3. The method for analyzing the influence of land use variation on runoff processes based on a SWAT model according to claim 1, wherein the soil attribute data comprises the number of soil layers, soil texture, maximum root depth of soil profile, soil particle composition, anion exchange porosity, depth from soil surface layer to bottom layer and organic carbon content, soil volume weight, soil layer effective water holding capacity and soil saturated water conductivity; the meteorological data has a spatial resolution of 1/4 DEG, and the runoff observation data comprises a month-by-month average runoff amount.
4. The method for analyzing the influence of land use change on runoff processes based on a SWAT model as claimed in claim 1, wherein when parameter sensitivity analysis is carried out, sensitivity analysis is carried out by utilizing a SUFI-2 algorithm built in SWAT-CUP software provided by a SWAT functional network, and a plurality of iterative operations are carried out to obtain the optimal value of the parameter which enables the runoff simulation result to reach the accuracy.
5. The method for analyzing the influence of land use change on runoff processes based on a SWAT model according to claim 1, wherein the runoff simulation calibration and verification is performed by using measured runoff data of a hydrologic station for years and month.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111387236.1A CN116167193A (en) | 2021-11-22 | 2021-11-22 | Method for analyzing influence of land utilization change on runoff process based on SWAT model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111387236.1A CN116167193A (en) | 2021-11-22 | 2021-11-22 | Method for analyzing influence of land utilization change on runoff process based on SWAT model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116167193A true CN116167193A (en) | 2023-05-26 |
Family
ID=86418682
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111387236.1A Pending CN116167193A (en) | 2021-11-22 | 2021-11-22 | Method for analyzing influence of land utilization change on runoff process based on SWAT model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116167193A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117669392A (en) * | 2024-02-01 | 2024-03-08 | 自然资源部第二海洋研究所 | Remote sensing monitoring method for flux of nutrient salts of river |
-
2021
- 2021-11-22 CN CN202111387236.1A patent/CN116167193A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117669392A (en) * | 2024-02-01 | 2024-03-08 | 自然资源部第二海洋研究所 | Remote sensing monitoring method for flux of nutrient salts of river |
| CN117669392B (en) * | 2024-02-01 | 2024-06-18 | 自然资源部第二海洋研究所 | Remote sensing monitoring method for flux of nutrient salts of river |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Himanshu et al. | Application of SWAT in an Indian river basin for modeling runoff, sediment and water balance | |
| Deb et al. | Multi-GCMs approach for assessing climate change impact on water resources in Thailand | |
| Batelaan et al. | GIS-based recharge estimation by coupling surface–subsurface water balances | |
| Merritt et al. | Hydrologic response to scenarios of climate change in sub watersheds of the Okanagan basin, British Columbia | |
| Charlton et al. | Assessing the impact of climate change on water supply and flood hazard in Ireland using statistical downscaling and hydrological modelling techniques | |
| Aerts et al. | STREAM (Spatial Tools for River Basins and Environment and Analysis of Management Options):‘set up and requirements’ | |
| Ghazavi et al. | Predicting the impacts of climate change on groundwater recharge in an arid environment using modeling approach | |
| Usman et al. | Estimation of distributed seasonal net recharge by modern satellite data in irrigated agricultural regions of Pakistan | |
| Zhang et al. | Using hydrologic simulation to explore the impacts of climate change on runoff in the Huaihe River basin of China | |
| Nie et al. | Hydrological impacts of mesquite encroachment in the upper San Pedro watershed | |
| CN110674467A (en) | Method for monitoring response of hydrological process to climate change based on SWAT model | |
| Boufala et al. | Hydrological modeling of water and soil resources in the basin upstream of the Allal El Fassi dam (Upper Sebou watershed, Morocco) | |
| Wang et al. | Regional calibration of a water balance model for estimating stream flow in ungauged areas of the Yellow River Basin | |
| Azizi et al. | Evaluating the effects of climate change on groundwater level in the Varamin plain | |
| Beyene et al. | Impact of land use/land cover change on watershed hydrology: a case study of Upper Awash Basin, Ethiopia | |
| CN116167193A (en) | Method for analyzing influence of land utilization change on runoff process based on SWAT model | |
| Croley et al. | Distributed-parameter large basin runoff model. II: Application | |
| Noor | Comparison of single-site and multi-site based calibrations of SWAT in Taleghan Watershed, Iran | |
| Nourani et al. | Multi-station calibration strategy for evaluation and sensitivity analysis of the snowmelt runoff model using MODIS satellite images | |
| Reddy et al. | FEM–GIS based channel network model for runoff simulation in agricultural watersheds using remotely sensed data | |
| Choi et al. | The effect of climate change on Byeongseong stream's water quantity and quality | |
| Ignatius et al. | High resolution water body mapping for SWAT evaporative modelling in the Upper Oconee watershed of Georgia, USA | |
| Flohr et al. | An ENVI-met Simulation Data Pipeline for Evaluating Urban Tree Patterns Impact on Urban Micro-climate | |
| Mo et al. | Runoff sensitivity to climate and land-use changes: A case study in the Longtan basin, Southwestern China | |
| Seisia et al. | Water Reliability Analysis in Industrial and Residential Area, Based on NRECA Method Using ArcGIS Software |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |