CN117494419A - Multi-model coupling drainage basin soil erosion remote sensing monitoring method - Google Patents
Multi-model coupling drainage basin soil erosion remote sensing monitoring method Download PDFInfo
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Abstract
The invention discloses a multi-model coupled drainage basin soil erosion remote sensing monitoring method, which belongs to the field of soil and water conservation risk assessment and comprises the following steps: quantitatively inverting the soil erosion modulus collected on the underlying surface by adopting a modified general soil loss equation; adopting a water erosion prediction model to simulate the water and sand loss of the surface water and sand which occur between the fine ditches; simulating the spatial distribution of sediment deposition points of channel water sand along with river channel migration of the river basin in the current and predicted future climate scene from a point scale by adopting a distributed hydrologic model through simulating the water sand transportation process of the river basin; finally, comprehensively evaluating the current state of water and soil loss of the river basin and summarizing the water and soil loss risk law according to space-time superposition analysis of erosion results. The invention fully plays the scale advantages of various erosion models, quantitatively inverts through the erosion function modules of the coupled multi-model, and makes up the defects of quantitative inversion of a single model.
Description
Technical Field
The invention relates to the field of water and soil conservation risk assessment, in particular to a multi-model coupled drainage basin water and soil loss remote sensing monitoring method.
Background
The soil erosion model is an important tool for monitoring and forecasting soil loss by completing inversion and calculation of earth surface erosion information through a mathematical algorithm and a physical model and determining regional erosion degree or erosion risk according to space superposition analysis of inversion results. Early erosion model research is to obtain real-time monitoring data of slope or runoff cells by setting up monitoring stations, but simulation accuracy is not easy to control due to dependence on actual measurement hydrologic data and difficulty in calculation of a specific area. Under the background of rapid development of the remote sensing technology, the erosion evaluation dynamically identifies the erosion and deposition processes of the ground surface and the river basin by utilizing the characteristics of large-area repeated observation, spatial analysis, dynamic monitoring and the like of the remote sensing technology, acquires the time sequence change of the underlying soil erosion, and promotes the soil erosion qualitative judgment and quantitative calculation method to present diversified and multi-form development trend.
The erosion model at the present stage is used for simulating the erosion sand production process based on the soil erosion physical mechanism process, and comprehensively considers the influence of the soil water erosion physical process on erosion, deposition and confluence sand, but has the problems of complex internal algorithm, complex and changeable parameters, difficult acquisition of data, regional application limitation and the like, so that the model inversion result is uncertain. Neither the empirical model nor the physical model is based on basic data for current and past erosion condition assessment, and future soil erosion conditions of the basin cannot be obtained. The distributed watershed evaluation model AnnAGNPS (Annualized Agricultural Non-Point Source) upgraded by the AGNPS model comprehensively considers hydrologic processes such as water sand, nutrient and pesticide transfer and conversion of the watershed based on three functional modules such as an erosion and sediment transfer module, a hydrologic module and a chemical substance transfer module, is superior to other erosion models in all aspects, and is widely applied. However, for the problem of space variability of climate and weather and underlying surfaces, the processing refinement degree is not enough, and meanwhile, the simulation of the erosion process of a single scale is limited, the simulation of the dynamic evolution process of the watershed water and sand under different time and space scales cannot be realized, and the comprehensive evaluation of the integrity of the related erosion processes of the watershed water and sand erosion from the earth surface, the fine ditch erosion and the river channel deposition is not realized. And particularly, simulating the confluence sand process of river courses and coastal slopes in the river fields under the influence of terrain and climate, neglecting the spatial heterogeneity of influencing factors, and evaluating the erosion of the river fields under an ideal state. The simulation precision related to erosion has close relation with model parameters, and is difficult to obtain satisfactory simulation precision when the hydrologic model is practically applied in a river basin where hydrologic and meteorological monitoring data are not suitable to obtain.
Disclosure of Invention
The invention aims to solve the problem of insufficient simulation precision of hydrologic models in the prior art, and provides a multi-model coupled drainage basin soil erosion remote sensing monitoring method.
In order to achieve the above object, the present invention has the following technical scheme:
a multi-model coupled drainage basin soil erosion remote sensing monitoring method is characterized by comprising the following steps:
s1, collecting various basic remote sensing data including weather, soil, vegetation and topography of a river basin, extracting factor data required by constructing various hydrologic models by adopting a remote sensing technology, and finishing database construction of a modified general soil loss model, a water erosion prediction model and a river basin water and soil evaluation distributed hydrologic model;
s2, quantitatively inverting the underlying surface soil erosion modulus by adopting a modified general soil erosion equation based on the extracted regional soil erosion factor;
s3, simulating water and sand scouring erosion occurring between the furrows and the fine furrows under the condition that surface water and sand are converged into different slopes by using a water erosion prediction model;
s4, dividing the sub-river basin and the hydrologic response unit through elevation data, surface attribute data and a given threshold value, combining CMADS meteorological data and RCP future climate change scenes, and simulating the water and sand process according to a distributed hydrologic model to simulate the current and forecast the space distribution of sediment deposition points of future channel water and sand along with river channel migration of the river basin;
s5, carrying out space-time superposition analysis on erosion results obtained by water and sand process simulation, accurately simulating the space-time evolution process of runoff sediment in the river basin from the surface, line and point scales, and comprehensively analyzing water and sand migration space-time distribution characteristics and summarizing erosion risk occurrence rules.
Further, the basic remote sensing data comprise topography data, hydrologic data, underlying surface data and meteorological data; the topography data are SRTM remote sensing elevation data; the hydrologic data are month scale measured runoff and sediment transport contents of the river course lower section provided by hydrologic stations in the river basin; the underlying surface data are remote sensing macro-scale image data comprising land surface land utilization, soil type, soil attribute and vegetation coverage; the meteorological data are daily rainfall, temperature, wind direction, wind speed and solar radiation monitoring data provided by the meteorological stations in the China.
Further, the method for quantitatively inverting the underlying soil erosion modulus by adopting the corrected general soil erosion equation based on the extracted regional soil erosion factor comprises the following steps:
acquiring a rainfall erosion force factor R according to rainfall data;
acquiring a soil corrosiveness factor K according to the soil attribute data;
extracting a slope length factor L, S based on the elevation data;
extracting a crop coverage factor C according to the vegetation coverage image data;
determining a water and soil conservation measure factor P by referring to an assignment mode of the water and soil measure factor P and combining gradient changes of different land utilization types of the river basin;
and quantitatively inverting the erosion degree of the underlying surface soil of the river basin according to the corrected general soil erosion equation A=R.K.L.S.C.P.
Furthermore, the water erosion prediction model is used for simulating water erosion generated between the fine ditches and the water erosion generated between the fine ditches under the condition that surface water and sand are converged into different slopes, and the water erosion prediction model comprises the following steps:
s31, selecting slope fine grooves with obvious erosion behaviors near the coast of a river channel of a main flow domain according to the spatial distribution of water and soil loss risk areas of the river channel, and setting up slope cells meeting different spatial distribution and different gradient conditions;
s32, establishing a weather data file, a slope length file, a soil parameter file and a crop management file database of a slope community required by the running of the water erosion prediction model;
and S33, describing sediment movement by adopting a steady-state sediment continuous equation, and calling a functional module of a water erosion prediction model to quantitatively simulate the fine furrows and the water and sediment transport quantity G between the fine furrows of the slope surface cell.
Further, in step S33, the water-sand transporting amount G is calculated by the following formula:
wherein G is the sand transportation amount, X is the distance of a certain point along the downhill direction, D i Rate of sediment transport from the fine to the fine D r Is the rate at which sediment is transported between the furrows to the furrows.
Further, the dividing the sub-river basin and the hydrologic response unit by the elevation data, the surface attribute data and the given threshold value, combining the CMADS meteorological data and the RCP future climate change scene, and performing the water-sand process simulation according to the distributed hydrologic model to the spatial distribution of sediment deposition points of the current and predicted future channel water-sand along with the river basin river channel migration comprises:
s41, dividing the river basin into a plurality of sub-river basins according to elevation data, river network data and a given threshold value; dividing a hydrological response unit HRU according to land utilization, soil properties and gradient raster data;
s42, screening meteorological sites according to the space distribution of the river basin, extracting daily rainfall, temperature, wind direction, wind speed and solar radiation data, and integrating CMADS meteorological data and RCP future climate scene data;
s43, simulating the hydrologic process of evaporation, filtration, surface runoff, underground water runoff and sediment erosion of each hydrologic response unit HRU by combining with a water balance principle simulation through a distributed parameter simulation method of a water and soil evaluation distributed hydrologic model.
S44, calculating the flow and sand production amount of each hydrologic response unit based on the climate change and erosion difference, combining to obtain the water and soil loss condition of the outlet section of the whole river basin, simulating the sediment space path along with river basin river channel migration in the current and predicted future climate situations, and judging the space distribution of sediment deposition points according to the river basin water and sand transport amount.
Further, in step S43, the mathematical expression of the water balance equation is:
in SW t SW for final soil moisture content 0 For the initial soil moisture content, t is the simulation time (d), R day For daily precipitation, Q surf For daily surface runoff, E a For daily vapor emission, W seep For the amount of water entering the envelope from the soil profile on a given date, Q gw Is the amount of reflux for a given date.
Further, before carrying out space-time superposition analysis on erosion results obtained by water-sand process simulation, selecting Nash efficiency coefficient NS, relative error RE and decisive coefficient R 2 And (5) performing parameter calibration on the water-sand process simulation result as an accuracy judgment index, and outputting an optimal hydrologic erosion index.
Further, in step S5, the performing space-time superposition analysis on the erosion result refers to performing coupling superposition analysis on the erosion degree of the underlying surface soil, the erosion of the slope fine ditch water sand and the water sand deposition distribution of the river basin, which are obtained by simulating the water sand process; summarizing a water and soil loss risk rule according to a water and soil loss long-term aging prediction result on a time level; and on the space level, identifying a water and soil loss high-risk area according to the current situation of water and soil loss space distribution.
In summary, the invention has the following advantages:
1. the method refers to the erosion evaluation principle of the distributed watershed evaluation model AnnAGNPS, and the functional modules of the models such as RUSLE, SAWT and WEPP are coupled to replace the erosion process simulation of a single model, so that the defects of quantitative inversion of the single model are overcome, the complexity degree and the collection difficulty of parameters required by the operation of the functional modules of the model are reduced, the operation cost is reduced, and meanwhile, the accuracy of water and soil loss risk evaluation under different scales can be ensured.
2. According to the method, the simulation of hydrologic processes such as river basin water and sand transportation and transformation is met, meanwhile, the comprehensive evaluation of the integrity of relevant erosion processes such as surface loss, fine ditch erosion and river channel deposition related to river basin water and sand erosion is realized, and the method is beneficial to comprehensively inverting the space-time dynamic migration process of river basin water and sand transportation from different scales such as a surface, a line and a point; not only can the water and soil loss high-risk areas be identified by evaluating the current situation of water and soil loss, but also the occurrence risk law of water and soil loss can be predicted based on the water and sand amount in the future period.
Drawings
FIG. 1 is a basic flow chart provided by an embodiment of the present invention;
FIG. 2 is a flow chart of a coupling method of RUSLE, SWAT and WEPP according to an embodiment of the present invention;
FIG. 3 is a graph showing soil erosion factor distribution provided by an embodiment of the present invention;
FIG. 4 is a graph showing soil erosion level distribution provided by an embodiment of the present invention;
fig. 5 is a WEPP model slope confluence sand simulation diagram provided by an embodiment of the present invention;
FIG. 6 is a graph of a simulation result of a SWAT model month-scale runoff provided by an embodiment of the present invention;
FIG. 7 is a graph showing simulated results of a SWAT model month scale sediment in accordance with an embodiment of the present invention;
FIG. 8 is a predicted amount of runoff for future climate scenarios provided by an embodiment of the present invention;
FIG. 9 is a graph showing sediment prediction in future climatic conditions according to an embodiment of the present invention;
fig. 10 is a coupling diagram of soil erosion simulation results of RUSLE, SWAT and WEPP models provided in an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the present invention, the present invention will be further described with reference to preferred embodiments and the accompanying drawings. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and that this invention is not limited to the details given herein.
The method comprehensively evaluates the space-time evolution dynamic process of runoff sediment of the river basin from the surface, line and point scales, meets the simulation of the hydrologic process such as the water and sand transportation and conversion of the river basin, and simultaneously has the comprehensive evaluation of the integrity of the related erosion processes such as the earth surface loss, the fine ditch erosion, the river channel deposition and the like related to the water and sand erosion of the river basin, not only can identify the high risk area of the water and soil loss by evaluating the current situation of the water and soil loss, but also can predict the occurrence risk of the water and soil loss based on the water and sand quantity of the future period. The method for remotely sensing and monitoring the soil erosion of the river basin by the multi-model coupling comprises the following specific implementation steps:
collecting various basic remote sensing data such as meteorology, soil, vegetation, topography and the like of a river basin, extracting factor data required by constructing various hydrological model databases by adopting a remote sensing technology, and finishing the database construction of a modified general soil loss model, a water erosion prediction model and a river basin water and soil evaluation distributed hydrological model;
step two, quantitatively inverting the underlying soil erosion modulus by adopting a corrected general soil erosion equation based on the extracted river basin soil erosion factors;
setting slope surface cells of the river basin main river course coast, constructing a slope surface model by considering a climate file, a slope length file, a soil parameter file and a crop management file, and quantitatively simulating water and sand scouring erosion occurring between fine ditches under the condition that surface water and sand are gathered into different slopes according to a steady-state sediment continuous equation of a water erosion prediction model;
dividing the sub-river basin and the hydrologic response unit by the elevation data, the surface attribute data and the given threshold value, combining CMADS meteorological data and RCP future climate change scenes, and simulating the current water-sand process and predicting the spatial distribution of sediment deposition points of future channel water-sand along with river channel migration according to the hydrologic module of the distributed hydrologic model;
step five, using Nash efficiency coefficient NS, relative error RE and decisive coefficient R 2 As an accuracy judgment index, parameter calibration is carried out on the simulation result of the water-sand process, and the optimal hydrologic erosion index is outputAnd (3) performing space-time superposition analysis on the erosion result, accurately simulating the space-time evolution process of runoff sediment in the river basin from the scales of a surface, a line, a point and the like, comprehensively analyzing the space-time distribution characteristics of water and sand migration and summarizing the erosion risk occurrence law.
Example 1
The invention relates to a multi-model coupled drainage basin soil erosion remote sensing monitoring method which is described by a specific application example.
The landform type of a certain river basin is complex and various, and the valleys are alternate. The shape of the river basin is dendritic, the water system develops, the branches are more, and the drop height of the river valley and the stream ratio of the river valley are reduced greatly. The vertical climate has obvious characteristics, is influenced by the monsoon and the topography in summer, often has heavy rain or storm to occur, and a storm center is often formed on a windward slope. So that the river section is obviously affected by natural disasters such as flood, landslide, mud-rock flow and the like, and the erosion conditions are different due to the differences of topography, geology and topography of the river bank. Meanwhile, the produced sand on the peripheral slope surface can be scattered and converged into the main river channel, so that the water and soil loss of the river basin is difficult to accurately evaluate on the space-time scale. Therefore, taking the area as an example, the current state of water and soil loss of the river basin is accurately estimated by using a dynamic monitoring method coupled with multiple erosion models, and the space-time evolution process of runoff sediment of the river basin is accurately simulated from the surface, line and point scales.
Taking a water-sand process prediction model study of a certain river basin as an example, and adopting a multi-model coupled river basin water and soil loss remote sensing monitoring method to complete water and soil loss risk assessment of the river basin. Firstly, evaluating the water and soil loss condition of the underlying surface from the related erosion processes of surface water and sand loss, fine ditch slope flushing, river sediment deposition and the like based on a RUSLE, SWAT, WEPP model; secondly, carrying out space superposition analysis on the earth surface soil erosion degree of the river basin, the water and sand transfer quantity among slope fine furrows, the river basin runoff quantity and the sediment deposition quantity, and inverting the space-time dynamic migration process of the water and sand transfer of the river basin; and finally, evaluating the current state of water and soil loss of the river basin and predicting the water and soil loss risk law according to the comprehensive evaluation result of the sand production time and space in the river basin. The specific operation flow is as follows:
fig. 1 is a basic flowchart provided in an embodiment of the present invention.
Fig. 2 is a flowchart of a coupling method of an rui, an satt, and a WEPP according to an embodiment of the present invention.
As shown in fig. 1 and fig. 2, a flowchart of an implementation of the coupling method of the rui, WEPP and SWAT provided in the present invention is shown, and the details are as follows:
s1, collecting various basic remote sensing data such as meteorology, soil, vegetation and topography of a river basin, extracting factor data required by construction of various hydrological model databases by adopting a remote sensing technology, and finishing construction of the databases of a corrected general soil loss model, a water erosion prediction model and a river basin water and soil evaluation distributed hydrological model;
s2, quantitatively inverting the underlying soil erosion modulus by adopting a modified general soil erosion equation based on the extracted river basin soil erosion factors;
s3, setting slope surface cells of the river basin main river course coast, constructing a slope surface model by considering a climate file, a slope length file, a soil parameter file and a crop management file, and quantitatively simulating water and sand scouring erosion occurring between the fine ditches under the condition that surface water and sand are gathered into different slopes according to a steady-state sediment continuous equation of the water erosion prediction model;
s4, dividing the sub-river basin and the hydrologic response unit through elevation data, surface attribute data and a given threshold value, combining CMADS meteorological data and RCP future climate change scenes, and simulating the current water-sand process and predicting the spatial distribution of sediment deposition points of future channel water-sand along with river basin river channel migration according to hydrologic modules of the distributed hydrologic model;
s5, using Nash efficiency coefficient NS, relative error RE and decisive coefficient R 2 And (3) taking the water and sand process simulation result as an accuracy judgment index, carrying out parameter calibration on the water and sand process simulation result, outputting an optimal hydrologic erosion index, carrying out space-time superposition analysis on the erosion result, accurately simulating the space-time evolution process of runoff sediment in the river basin from the scales of a surface, a line, a point and the like, and comprehensively analyzing the water and sand migration space-time distribution characteristics and summarizing the erosion risk occurrence law.
In summary, the method of the present invention mainly includes three parts: basic data collection, model construction and verification, and water sand simulation process coupling and time-space scale analysis.
The following description is made for these three parts:
1. basic data collection
Step S1 is to collect and preprocess underlying foundation multisource data required for model construction and verification, wherein the foundation data mainly comprises:
s11, topographic and geomorphic data: the topography data are SRTM remote sensing elevation data;
s12, hydrologic data: the hydrologic data are month scale measured runoff and sediment transport contents of the river course lower section provided by hydrologic stations in the river basin;
s13, underlying surface data: the underlying surface data are remote sensing macro-scale image data such as land surface land utilization, soil type, soil attribute, vegetation coverage and the like;
s14, meteorological data: the meteorological data are daily rainfall, temperature, wind direction, wind speed and solar radiation monitoring data provided by the meteorological stations in the China.
2. Model construction and verification
As shown in fig. 2, the part is divided into three models of ruie, SWAT and WEPP, and specifically includes the following contents:
the construction of the RUSLE model corresponds to the step S2, and the main steps comprise:
rainfall erosion factor: the rainfall erosion force R is obtained based on the average rainfall capacity of the month scale and the year scale provided by hydrologic stations in the river basin, the space distribution of the rainfall erosion force of the research area is obtained by adopting a kriging interpolation method, and the calculation formula is as follows:
in the formula (1), R is a rainfall erosion factor; p is p i Rainfall (mm) for month; p is annual rainfall (mm).
Soil corrosiveness factor: based on the distribution content of soil organic carbon and sand grains, powder particles and clay grains in the soil texture in the HWSD world large scale soil space distribution data set, acquiring a soil corrodibility factor K by adopting an EPIC model algorithm:
in the formula (2), K is a soil corrosiveness factor, sa is sand, si is powder, cl is a cosmid, and C is the organic carbon content of the soil.
Slope factor: identifying the terrain gradient of the river basin based on SRTM digital terrain model data, wherein a McCool gradient formula is adopted for calculating a gentle slope (theta is more than or equal to 0 DEG and less than or equal to 10 DEG), a Liu Baoyuan gradient formula is adopted for calculating a steep slope (theta is more than or equal to 10 DEG), and the calculation formula is as follows:
in the formula (3), L is a gradient factor, S is a gradient factor, and θ is a gradient.
Slope length factor: the calculation formula is as follows by applying the McCool improved slope length factor algorithm:
L＝(λ/22.13) α
α＝β/(β+1)
β＝(sinθ/0.0896/[3.0(sinθ 0.8 )+0.56] (4)
in the formula (4), L is a slope length factor, lambda is a slope length, alpha is a slope length coefficient, theta is a slope, and beta is a slope correction value.
Crop coverage factor: based on the large scale Landsat remote sensing image, extracting the annual maximum vegetation coverage index of the river basin, and establishing a relation between a factor value and vegetation coverage c:
in the formula (5), C is a crop coverage factor, and C is vegetation coverage;
soil and water conservation measure factors: based on the large-scale land cover data set of the China area, the water and soil measure factor P is consulted, the cultivated land in the convection area is assigned to 0.4, moss is assigned to 0.5, woodland, grassland, shrubs and bare land are assigned to 1, and water and artificial ground surface are assigned to 0.
Fig. 3 is a graph showing soil erosion factor distribution provided by an embodiment of the present invention. As shown in fig. 3, soil erosion factors of the watershed were extracted according to the above steps.
The underlying soil erosion modulus of the basin was quantitatively inverted according to the modified general soil erosion equation a=r·k·l·s·c·p.
Fig. 4 is a graph showing soil erosion degree distribution provided by an embodiment of the present invention.
As shown in fig. 4, according to the classification and classification standard of soil erosion, the soil erosion modules obtained by superposition analysis of erosion factors are classified into six erosion levels of micro erosion, light erosion, medium erosion, strong erosion, extremely strong erosion and severe erosion, and the spatial distribution of the soil erosion degree in the flow area is intuitively analyzed.
The classification result shows that: the unetched area in the flow field accounts for about 55.25%, the eroded area is mainly eroded slightly and moderately, and the surface soil erosion risk in the whole flow field is small.
The construction of the WEPP model corresponds to the step S3, and the main steps comprise:
according to the spatial distribution of water and soil loss risk areas of a research area, selecting channel slopes with obvious erosion behaviors near the coast of a river channel of a main flow area, and setting up 6 slope cells meeting different slopes and different spatial distribution conditions;
the climate, slope length, soil parameters and crop management files of the selected slope plot required by the model operation are read, and the detailed process is as follows:
weather meteorological files: according to the acquired daily sequence meteorological data, obtaining parameters such as daily average minimum air temperature, daily average maximum air temperature, daily average rainfall, single month continuous rainfall probability, single month discontinuous rainfall probability and the like by adopting a calculation formula model, and generating climate parameters by utilizing a climate generator CLIGEN;
slope length file: according to the determined water and soil loss risk areas, selecting slope surface cells with different slope lengths, decomposing a complex slope surface into a plurality of single straight slopes, and respectively establishing a slope length database according to the conditions of the slope and the slope length;
soil parameter file: according to the distribution content of soil organic carbon, sand grains, powder particles and clay grains in the soil texture in the soil space distribution data set, adopting a calculation formula to obtain soil parameters such as soil albedo, initial saturation, soil critical shear force, fine ditch soil corrosiveness, effective hydraulic conductivity and the like required by the model operation, and the soil parameters are shown in table 1;
table 1 basin different soil parameters file:
| soil type | Gray soil for simple cultivation | Lime clay | Jian Yogao active leaching soil |
| Depth (mm) | 300.00 | 300.00 | 300.00 |
| Sand (%) | 25.00 | 36.00 | 41.00 |
| Clay (%) | 21.00 | 21.00 | 22.00 |
| Organic matter content (%) | 1.60 | 0.65 | 0.74 |
| Cation exchange capacity (meq/100 g) | 21.00 | 16.00 | 13.00 |
| Gravel (%) | 10.00 | 6.00 | 4.00 |
| Albedo of | 0.60 | 0.60 | 0.60 |
| Initial saturation | 0.44 | 0.40 | 0.40 |
| Inter-groove erosion factor | 4896270.00 | 3419560.00 | 3515610.00 |
| Factors of fine groove erosion | 0.01 | 0.03 | 0.02 |
| Critical shear force | 1.60 | 1.72 | 1.72 |
| Effective hydraulic conductivity coefficient | 3.73 | 6.61 | 8.29 |
Crop management files: and establishing a crop management file according to the detailed data of crop types, cultivation measures, soil conditions, irrigation conditions, stubble management, crop growth and the like of the slope surface community.
And calling a steady-state sediment continuous equation according to the screened slope surface cells under different gradient conditions, and quantitatively simulating the fine ditches of the slope surface cells and the water and sediment transfer processes among the fine ditches by adopting a water erosion prediction model to obtain the water and sediment transfer amounts of the different slope surface cells.
The water and sand transfer amount between the fine ditches is calculated as follows:
describing the movement of the sediment between the slope fine ditches by adopting a steady-state sediment continuous equation, and when the water flow shearing force is greater than the critical soil shearing force and the sediment conveying capacity is smaller than the sediment conveying capacity, mainly carrying the sediment in the fine ditches; when the sand conveying amount is larger than the sediment conveying capacity, the deposition process is mainly used.
The calculation formula of the steady-state sediment continuous equation is as follows:
in the formula (6), G is the sand transporting amount, X is the distance of a certain point along the downhill direction, D i D for the transfer rate of sediment from the inter-sipe gap to the sipe gap r Is the erosion rate of the fine groove.
In the formula (6), the calculation formula of the rate of sediment transport between the fine furrows to the fine furrows is as follows:
in the formula (7), D i Is the rate of sediment transport from the inter-sipe to the sipe; k (K) i Soil corrodibility between fine furrows;the rainfall intensity is effective; g e A canopy cover adjustment factor; c (C) e Forest canopy adjustment coefficients; s is S f A grade adjustment factor;
in the formula (6), the calculation formula of the fine groove erosion rate is:
D r ＝K r ·(τ 1 -τ 2 ) (8)
in formula (8): d (D) r For the erosion rate of the narrow groove, τ 1 Is the shear stress value of water flow, τ 2 Critical shear stress of soil.
And predicting the runoff and the sand yield of the side slope under the conditions of different gradients and different rainfall intensities by using the WEPP model, and evaluating the prediction result of the WEPP model according to actual measurement data to check the suitability evaluation of the WEPP model on the side slope of the river basin.
Fig. 5 is a diagram of simulation effects of WEPP model slope confluence sand according to an embodiment of the present invention.
According to the water-sand simulation results of the slope surface cells provided in fig. 5, tabulating statistics are performed on the produced flow and sand amounts of different slope surface cells, as shown in table 2:
table 2 water and sand transport statistics for different slope cells:
the statistical analysis is carried out on the table, and the water and sand transfer quantity of the whole slope in the river basin is found to be smaller, the yield of the produced water and sand flowing into the river along with the slope is in a controllable range, and only part of the slope has the water and soil loss risk condition.
Comparing the rainfall erosion measured data of the slope surface cell with the result simulated by using the WEPP model, checking the parameters of each rainfall process, the simulation value and the absolute value of the relative error of the measured value, and predicting the water and soil loss of the slope by using the WEPP model to determine the water and soil loss law of the water and soil loss risk slope surface cell under different conditions, as shown in table 2.
And a confluence sand simulation result distribution diagram output by the WEPP model is used for defining the overall water and soil loss condition of the slope fine ditch of each water and soil loss risk area in the river, so that the large-scale water and soil loss risk area is accurately judged to the slope in the river, and the river risk prediction precision is easy to improve.
The construction of the SWAT model corresponds to the step S4, and the main steps comprise:
sub-watershed partitioning: determining a sub-river basin dividing threshold range according to actual river network conditions based on terrain DEM data and river network data in the river basin, numbering each sub-river basin and corresponding river reach of the river basin, and establishing a corresponding topological relation among discrete elements of the river basin;
given a threshold range of 1000, the basin is divided into 25 sub-basins.
The hydrologic response unit constructs: and reclassifying the land utilization, soil property and slope remote sensing image data set of the river basin according to the recognition rules of the land utilization and the soil property data in the SWAT model, reconstructing the soil property data set according to the recognition rules, setting 10%, 15% and 10% threshold ranges of the land utilization, the soil property and the slope respectively, and defining a hydrological response unit HRU.
The reconstructing soil attribute data set comprises the following detailed steps:
referring to a world soil database HWSD data set, a river basin is mainly divided into simple high-activity leaching soil, lime prototype soil and thin-layer soil, soil corrosiveness, hydrologic grouping, wet volume weight, effective water content and saturated water conservancy conductivity are judged according to a soil pebble, sand grains, powder grains, sticky grain content and soil water characteristic calculation program SPAW, and a soil database is constructed according to SWAT model soil database tabulation rules.
Weather generator formulation: 9 weather stations near the river basin are screened based on the China national-level ground weather stations, a weather generator is constructed according to the daily rainfall, the temperature, the relative humidity, the wind speed and solar radiation data of the weather stations and the formulation rules of the weather index file, and the future climate change situation of the river basin is simulated.
The construction of the weather generator comprises the following detailed steps:
the weather site daily value monitoring data based on screening are converted into a model built-in WXGEN format, and the estimation of the solar radiation quantity and the potential evapotranspiration quantity is simulated by adopting a Hargreaves method, and the equation is expressed as follows:
in the formula (9), T x And T n The highest air temperature and the lowest air temperature in each day are respectively; r is R a For atmospheric top solar radiation, it can be calculated from latitude or looked up from an atmospheric top radiation table provided by FAO.
And (3) water sand process simulation: by a distributed parameter simulation method of a water and soil evaluation distributed hydrologic model, the water balance calculation formula of the water balance simulation method is that the water hydrologic processes such as evapotranspiration, filtration, surface runoff, groundwater runoff, sediment erosion and the like of each hydrologic response unit HRU are simulated by combining water balance principle simulation:
in formula (10), SW t Is the final soil moisture content; SW (switch) 0 For the initial soil moisture content, t is the simulation time (d), R day For daily precipitation, Q surf For daily surface runoff, E a For daily vapor emission, W seep For the amount of water entering the envelope from the soil profile on a given date, Q gw Is the amount of reflux for a given date.
In the above parameter correction step, nash efficiency coefficient NSE, relative error RE and decisive coefficient R are selected 2 And the parameter calibration accuracy is used as an evaluation index for reflecting the correlation and fitting degree between the simulated runoff and the actually measured runoff.
Based on actual observation values and simulation data in the flow field, water and sand simulation results of the past year are selected for parameter calibration, and water and sand simulation results of the current year are used for verification analysis. And (3) carrying out uncertainty analysis by applying a SUFI-2 algorithm, fully considering uncertainty of model input, model structure, input parameters and observed data, and carrying out iterative estimation on unknown parameters through a sequential fitting process to finish final estimation, wherein the uncertainty of the model is evaluated by two factors, namely a P-factor and an r-factor, and the sensitivity is evaluated by adopting a t-test and a P-test method.
FIG. 6 is a graph of the results of a SWAT model month-scale runoff simulation provided by an embodiment of the present invention.
FIG. 7 is a graph showing the results of a SWAT model month scale sediment simulation provided by the embodiment of the invention.
And carrying out visual analysis on river basin main river runoff and sediment simulation results output by the SWAT model, dividing water and sediment erosion risk grades according to river basin water and sediment transfer quantity, comparing spatial distribution of sediment deposition points in the river basin, and determining a high-risk area with water and soil loss risks in the river basin.
The analysis results show that: the runoff of the main river channel in the flow field is 5.0-12.0m 3 In the range of/s, the sediment quantity is in the range of 5000-8000t/ha, sediment in the river basin is mainly collected in the river basin outlet river channel and nearby branches along with the river channel migration, but the water and soil loss quantity is small, so that the risk of water and soil loss of the whole river basin is small.
FIG. 8 is a predicted amount of runoff for future climate scenarios provided by an embodiment of the present invention;
FIG. 9 is a graph showing sediment prediction in future climatic conditions according to an embodiment of the present invention;
and simulating and predicting the runoff change trend in the period of 2023-2043 years in the future of the river basin by using the SWAT model and the RCP future climate scene data, and selecting No. 16 and No. 18 as simulated sub-river basins, wherein the water and sand prediction result can be used for predicting the water and soil loss risk of the river basin.
The prediction result shows that: the water and sand transfer amount of the watershed in the future 20 years is stable as a whole, and for the runoff prediction result, the 16 # sub-watershed is 20000-30000cm as a whole 3 Integral number 18/s basin10000-20000cm 3 And (3) simulating the runoff change trend of the river basin to be wholly similar, and generating the minimum runoff in 2026; the sediment prediction result has larger overall fluctuation, particularly in 2032, the sediment transport amount reaches the peak value in the future period, but the overall sediment amount in the future period is in a lower state, so that the river basin has larger erosion risk except for the specific year with larger water and sediment transport amount, and the rest years are in a low risk state.
3. Water-sand simulation process coupling and time-space scale analysis
Step S5 is coupling and time-space scale analysis of RUSLE, SWAT and WEPP model water sand simulation processes, and comprises the following detailed steps:
first, nash efficiency coefficient NSE, relative error RE and decisive coefficient R are selected 2 As an evaluation index of parameter calibration accuracy, the method is used for reflecting the correlation and fitting degree between the simulated runoff and the actually measured runoff, and the calculation formula is as follows:
nash efficiency coefficient NSE: the fitting degree between the simulated runoff process and the actually measured runoff process of the characterization model is calculated according to the following formula:
the relative error RE reflects the coincidence degree between the runoff total analog value and the actual measurement value, and the calculation formula is as follows:
decisive coefficient R 2 The method is used for reflecting the correlation between the analog value and the measured value, and the calculation formula is as follows:
in the above formulas (11), (12) and (13), Q 1 For actual measurement of the runoff (m 3/s), Q 2 To simulate the runoff amount (m 3/s),for the measured month mean diameter flow (m 3/s),>is the average runoff amount (m 3/s) of the simulation month.
When NSE and R2 are more than or equal to 0.6, the water sand simulation shows that the water sand simulation is qualified, the closer the value is to 1, the higher the coincidence degree between the simulation value and the measured value is, and RE generally has the credibility degree within +/-25%.
And outputting an optimal erosion simulation result according to the calibration result, and if the result does not meet the requirement, correcting the erosion model until the calibration accuracy standard is met.
The reliability of the calculation simulation results using the above formulas (11), (12) and (13) is verified to show that: evaluation index NSE and R 2 The RE is controlled to be +/-5% in the regular rate and verification period of 0.8, which means that the result has higher credibility.
Next, as shown in fig. 10, the simulation results of the water-sand process of the RUSLE, SWAT and WEPP models are coupled, and the indexes obtained by the simulation of the water-sand process are subjected to superposition analysis by carrying out ARCGIS space-time superposition analysis on the soil erosion degree of the sublevel surface of the river basin, the water-sand erosion degree of the fine ditch of the river slope, the migration quantity of the water-sand of the river channel and the deposition space distribution.
Fig. 10 is a coupling diagram of soil erosion simulation results of RUSLE, SWAT and WEPP models provided in an embodiment of the present invention.
And finally, comprehensively analyzing the water and soil loss risk condition of the river basin from the space-time scale.
On a time scale, predicting a water and soil loss risk rule in a river basin according to a water and soil loss long-term aging prediction result, and based on water and sand change trend analysis of the river basin in a future 20-year period, compared with the whole, water and sand transfer amounts in 2028 and 2032 are at a larger erosion risk. The seasonal runoff and the annual average runoff of the river basin are in a remarkable positive correlation, the water and soil loss in the river basin is obviously increased in months with larger rainfall, the water and soil loss risks are larger in rainy seasons with 7 and 8 months, and the erosion risks of other months are smaller.
On the spatial scale, according to the current situation of water and sand deposition spatial distribution of the river basin, determining a water and soil loss high-risk area in the river basin, wherein the erosion risk is concentrated in the northwest part of the river basin and the erosion level is generally lower, and the erosion risk suffered by the southeast part of the river basin is extremely low.
The embodiment of the invention can be used for early warning and forecasting of the water and soil loss risk of the river basin, comprehensively inverting the space-time dynamic migration process of the water and sand transportation of the river basin from different scales such as a surface, a line and a point on the basis of the RUSLE, SWAT, WEPP model according to basic data such as basic remote sensing geographic data and meteorological data of the river basin, realizing the integrated evaluation of the relevant erosion processes such as surface loss, fine ditch flushing and river channel deposition related to the water and sand erosion of the river basin from a macroscopic scale, and ensuring the accuracy of the water and soil loss risk evaluation under different scales while reducing the running cost. Meanwhile, the water and soil loss risk law prediction in the future period of the river basin is easy to determine the distribution of the water and soil loss risk high-risk areas, so that corresponding precautionary measures are formulated to avoid the influence of water and soil loss on the ecological environment.
The foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and any simple modification, equivalent variation, etc. of the above embodiment according to the technical matter of the present invention fall within the scope of the present invention.
Claims (9)
1. A multi-model coupled drainage basin soil erosion remote sensing monitoring method is characterized by comprising the following steps:
s1, collecting various basic remote sensing data including weather, soil, vegetation and topography of a river basin, extracting factor data required by constructing various hydrologic models by adopting a remote sensing technology, and finishing database construction of a modified general soil loss model, a water erosion prediction model and a river basin water and soil evaluation distributed hydrologic model;
s2, quantitatively inverting the underlying surface soil erosion modulus by adopting a modified general soil erosion equation based on the extracted regional soil erosion factor;
s3, simulating water and sand scouring erosion occurring between the furrows and the fine furrows under the condition that surface water and sand are converged into different slopes by using a water erosion prediction model;
s4, dividing the sub-river basin and the hydrologic response unit through elevation data, surface attribute data and a given threshold value, combining CMADS meteorological data and RCP future climate change scenes, and simulating the water and sand process according to a distributed hydrologic model to simulate the current and forecast the space distribution of sediment deposition points of future channel water and sand along with river channel migration of the river basin;
s5, carrying out space-time superposition analysis on erosion results obtained by water and sand process simulation, accurately simulating the space-time evolution process of runoff sediment in the river basin from the surface, line and point scales, and comprehensively analyzing water and sand migration space-time distribution characteristics and summarizing erosion risk occurrence rules.
2. The multi-model coupled drainage basin water and soil loss remote sensing monitoring method according to claim 1, wherein the basic remote sensing data comprises topography data, hydrologic data, underlying surface data and meteorological data; the topography data are SRTM remote sensing elevation data; the hydrologic data are month scale measured runoff and sediment transport contents of the river course lower section provided by hydrologic stations in the river basin; the underlying surface data are remote sensing macro-scale image data comprising land surface land utilization, soil type, soil attribute and vegetation coverage; the meteorological data are daily rainfall, temperature, wind direction, wind speed and solar radiation monitoring data provided by the meteorological stations in the China.
3. The method for remotely sensing and monitoring the soil erosion of a river basin by multi-model coupling according to claim 2, wherein the quantitatively inverting the underlying soil erosion modulus by adopting a modified general soil erosion equation based on the extracted regional soil erosion factor comprises:
acquiring a rainfall erosion force factor R according to rainfall data;
acquiring a soil corrosiveness factor K according to the soil attribute data;
extracting a slope length factor L, S based on the elevation data;
extracting a crop coverage factor C according to the vegetation coverage image data;
determining a water and soil conservation measure factor P by referring to an assignment mode of the water and soil measure factor P and combining gradient changes of different land utilization types of the river basin;
and quantitatively inverting the erosion degree of the underlying surface soil of the river basin according to the corrected general soil loss equation.
4. The method for remotely sensing and monitoring the water and soil loss of a river basin with multi-model coupling according to claim 1, wherein the simulation of water and sand erosion occurring between the furrows and the fine furrows under the condition that surface water and sand are converged into different slopes by using a water erosion prediction model comprises the following steps:
s31, selecting slope fine grooves with obvious erosion behaviors near the coast of a river channel of a main flow domain according to the spatial distribution of water and soil loss risk areas of the river channel, and setting up slope cells meeting different spatial distribution and different gradient conditions;
s32, establishing a weather data file, a slope length file, a soil parameter file and a crop management file database of a slope community required by the running of the water erosion prediction model;
and S33, describing sediment movement by adopting a steady-state sediment continuous equation, and calling a functional module of a water erosion prediction model to quantitatively simulate the fine furrows and the water and sediment transport quantity G between the fine furrows of the slope surface cell.
5. The method for remotely sensing and monitoring water and soil loss in a river basin with multi-model coupling according to claim 4, wherein in step S33, the water and sand transportation amount G is calculated by the following formula:
wherein G is the sand transportation amount, X is the distance of a certain point along the downhill direction, D i Rate of sediment transport from the fine to the fine D r Is the rate at which sediment is transported between the furrows to the furrows.
6. The method for remotely sensing and monitoring the water and soil loss of a river basin according to claim 1, wherein the dividing the sub-river basin and the hydrologic response unit by the elevation data, the surface attribute data and the given threshold value, combining the CMADS meteorological data and the RCP future climate change scene, performing the water and sand process simulation according to the distributed hydrologic model to simulate the present and predicted space distribution of the sediment deposition points of the future channel water and sand along with the river basin river channel migration comprises:
s41, dividing the river basin into a plurality of sub-river basins according to elevation data, river network data and a given threshold value; dividing a hydrological response unit HRU according to land utilization, soil properties and gradient raster data;
s42, screening meteorological sites according to the space distribution of the river basin, extracting daily rainfall, temperature, wind direction, wind speed and solar radiation data, and integrating CMADS meteorological data and RCP future climate scene data;
s43, simulating the hydrologic process of evaporation, filtration, surface runoff, underground water runoff and sediment erosion of each hydrologic response unit HRU by combining with a water balance principle simulation through a distributed parameter simulation method of a water and soil evaluation distributed hydrologic model.
S44, calculating the flow and sand production amount of each hydrologic response unit based on the climate change and erosion difference, combining to obtain the water and soil loss condition of the outlet section of the whole river basin, simulating the sediment space path along with river basin river channel migration in the current and predicted future climate situations, and judging the space distribution of sediment deposition points according to the river basin water and sand transport amount.
7. The method for remotely sensing and monitoring water and soil loss in a river basin with multi-model coupling according to claim 6, wherein in step S43, the mathematical expression of the water balance equation is:
in SW t Is the final soil moisture content; SW (switch) 0 For the initial soil moisture content, t is the simulation time (d), R day For daily precipitation, Q surf For daily surface runoff, E a For daily vapor emission, W seep For the amount of water entering the envelope from the soil profile on a given date, Q gw Is the amount of reflux for a given date.
8. The method for remotely sensing and monitoring water and soil loss in a river basin with multi-model coupling according to claim 1, wherein the Nash efficiency coefficient NS, the relative error RE and the decisive coefficient R are selected before carrying out space-time superposition analysis on erosion results obtained by water and sand process simulation 2 And (5) performing parameter calibration on the water-sand process simulation result as an accuracy judgment index, and outputting an optimal hydrologic erosion index.
9. The method for remotely sensing and monitoring the water and soil loss of a river basin with multi-model coupling according to claim 1, wherein in the step S5, the erosion result is subjected to space-time superposition analysis, namely coupling superposition analysis on the soil erosion degree of the underlying surface, the slope fine ditch water and sand erosion and the water and sand deposition distribution of the river basin, which are obtained by simulation of the water and sand process; summarizing a water and soil loss risk rule according to a water and soil loss long-term aging prediction result on a time level; and on the space level, identifying a water and soil loss high-risk area according to the current situation of water and soil loss space distribution.
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