CN110852572B - Method, system and device for quantitatively attributing runoff change of any space-time scale - Google Patents
Method, system and device for quantitatively attributing runoff change of any space-time scale Download PDFInfo
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Abstract
The invention discloses a runoff change quantitative attribution technical method, a system and a device of any space-time scale, which utilize collected global meteorological hydrological data to obtain characteristic parameter values of underlying surfaces of different time scales; secondly, coupling the improved hydrothermal coupling balance equation with a runoff change average absolute deviation formula, and obtaining a runoff change attribution equation under any space-time scale after full differential solution calculation; and finally, evaluating the influence of rainfall, potential evaporation, land water storage and the change of the characteristics of the underlying surface on the change of the global runoff by combining the collected meteorological hydrological data, the values of the underlying surface parameters of different time scales and the runoff change attribution equation under any space-time scale. A long-time watershed scale runoff change cause analysis method based on Budyko assumption is used for deducing an arbitrary space-time scale runoff change attribution method, so that a new technology is provided for revealing runoff response research of different time scales, different watersheds or grid scales under a change environment.
Description
Technical Field
The invention relates to the field of hydrological water resources, in particular to an improved hydrothermal coupling equilibrium equation formula and a runoff change average dispersion formula which take water storage change into consideration in a coupling mode, and further popularizes a long-time watershed scale runoff change attribution method to an arbitrary spatiotemporal scale runoff change attribution method and system.
Background
The water resource and water safety problem in the changing environment is a global problem generally regarded at home and abroad. River runoff is a main component of available water resources, and the development and utilization of the water resources are directly influenced by the change of the river runoff. Therefore, the runoff change simulation and attribution are the most basic and core scientific problems in the efficient development and utilization of water resources and the management of water resources. Particularly, the quantitative identification of the main driving factor of any space-time scale runoff change is the leading edge and the difficulty of the international hydrological water resource research.
Currently, there are two main types of methods for assessing runoff change response to changing environments: a hydrologic simulation-based method and a water-heat coupling balance method based on the Budyko assumption. The advantage of the former is that the hydrological model has a certain mechanistic explanation, and the model simulation has obvious advantages. However, the uncertainty of the model structure and parameters, the complexity of the relation among the terrain, soil, vegetation and climate in the river basin and the like influence the response range and the variability of the model; furthermore, model simulation has high requirements on quality and quantity of data, and is especially true for distributed models, but not for all domains. Compared with the traditional mathematical statistical empirical method, the climate elasticity method based on the Budyko hydrothermal coupling balance theory has obvious physical significance, the calculation process is relatively simple, the parameters are easy to obtain, and the climate elasticity method is widely applied to long-time scale runoff change attribution research of a drainage basin. However, the current runoff change attribution technology aims to quantitatively analyze the cause of runoff change of a long-time scale basin, and different spatiotemporal scale runoff change attribution technologies are lacked.
The fine space-time scale runoff change is not only influenced by climate change and underlying surface characteristic change, but also influenced by regional land water storage change. The global climate type and the underlying surface characteristics have obvious spatial difference, so that runoff change causes have obvious difference in different regions and different time scales. Therefore, research and development of a technical method for quantitatively identifying runoff change attribution of different spatiotemporal scales are needed. How to consider the influence of land water storage change in runoff change, and more accurately and quantitatively identify the contribution of each driving factor to runoff change? In addition, how to extend the long-time scale basin runoff change attribution theory to any space-time scale runoff change attribution is a key scientific problem to be solved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method, a system and a device for quantitatively attributing runoff change with any space-time scale aiming at the defects of the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: constructing an arbitrary space-time scale runoff change quantitative attribution method, comprising the following steps of:
s1, collecting meteorological hydrological data, wherein the meteorological hydrological data comprise precipitation, potential evaporation, land water storage change and runoff data;
s2, introducing the land water storage change into a hydrothermal coupling equilibrium equation to obtain an improved hydrothermal coupling equilibrium equation, and calculating the characteristic parameter values of the underlying surface at different time scales by using the improved hydrothermal coupling equilibrium equation;
s3, coupling the improved hydrothermal coupling balance equation obtained in the step S2 with a runoff change average absolute deviation formula, and obtaining a runoff change attribution equation under any space-time scale after full differential solution calculation;
s4, combining the meteorological hydrological data collected in the step S1, the values of the underlying surface parameters of different time scales obtained by calculation in the step S2 and the runoff change attribution equation under any space-time scale obtained in the step S3, and evaluating the influence of rainfall, potential evaporation, land water storage and the characteristic change of the underlying surface on the global runoff change;
the widely applied long-time watershed scale runoff change attribution theory is deduced to any space-time scale runoff change attribution, and a new technical method is provided for fine-scale global runoff change attribution under a changing environment. The method has wider application range and more accurate simulation effect.
Further, the improved hydrothermal coupling equilibrium equation obtained in step S2 is:
wherein R, P, TWSC, E0And n respectively represents characteristic parameters of runoff, precipitation, land water storage change, potential evaporation and underlying surface; i is time, and when i is month, the above formula is a hydro-thermal coupling balance equation of the intra-year scale; when i is year, the above formula is the hydrothermal coupling balance equation of the annual scale; when i is years, the equation is a multi-year scale hydrothermal coupling equilibrium equation.
The traditional budhko hydrothermal coupling equilibrium equation takes parameters of precipitation, latent evaporation and underlying surface characteristics as explanatory variables. To achieve water balance, this equation can only be used in long-term, watershed-scale water balance studies. If the land water storage change is integrated into a hydrothermal coupling balance equation, the hydrothermal coupling balance of any time scale can be realized, and further, the possibility of attributing runoff change of any time scale is provided.
Further, in step S3, after the improved hydrothermal coupling balance formula and the average absolute deviation formula are coupled and differentiated, the radial flow change attribution equation under any space-time scale is obtained as follows:
wherein, P, TWSC, E0And n represents characteristic parameters of runoff, precipitation, land water storage change, potential evaporation and underlying surface respectively; i is time; delta Pi，
TWSCiAnd Δ niRespectively corresponding parameter data under corresponding time i; rv is the runoff change value, N is the number of time samples taken,
and
is the partial derivative of runoff change to precipitation, latent evaporation, land water storage change and the characteristic parameters of the underlying surface.
Currently, the academic world has a annual-scale actual evaporation change variance decomposition attribution method based on a Budyko framework (proposed by the Chua Ximing Ministry of civil and environmental engineering college of champagne university of Illinois USA) for evaluating the influence of climate change and water storage change on actual evaporation change. However, this method ignores the hydrological effect of changes in the underlying surface features, resulting in large errors in the simulation effect (see fig. 4). In addition, the factor contribution rates evaluated by the method are directed to the variance of the actual evaporation change amount, not the actual change amount thereof. In other words, the calculated contribution is the contribution of each factor change to the square of the actual evaporation change amplitude (standard deviation). This approach will therefore lead to the phenomenon that a certain factor contribution squared (variance) and two factor contributions cross-overlapped (covariance) will occur in the attributed decomposition. The invention is based on the improved hydrothermal coupling balance formula and the average absolute deviation formula, avoids the defects of the prior art method, can quantitatively separate the independent influence of each factor on the runoff change, has smaller simulated attribution error and more reasonable attribution result due to fully considering the hydrological effect of the characteristic change of the underlying surface (refer to the attached figure 4)
Further, in step S3, a partial derivative calculation is performed on the partial derivatives of the runoff change to the precipitation, the potential evaporation, the land water storage change, and the underlying surface characteristic parameters by using the basis function deriv included in the R language programming.
Further, in step S4, the multi-year average value of each meteorological hydrological element, the variation value of each meteorological hydrological element under different time scales, and the characteristic parameter value of the underlying surface are substituted into the runoff variation attribution equation under any space-time scale, and the influence of four driving factors of precipitation, latent evaporation, land water storage variation, and underlying surface variation on the global runoff variation under different time scales is evaluated, wherein the mathematical expression of the influence of the corresponding driving factors on the runoff variation is as follows:
wherein i is time and x denotes each driving factor; n is the number of time samples taken, sign (Δ R)i) For radial flow changing direction, dxiFor the variation of each driving factor, IXInfluence quantity of corresponding driving factors on runoff change; sign (dR)i) And (5) the runoff changing direction corresponds to the time i.
The invention provides a system for quantitatively attributing runoff change in any space-time scale, which comprises the following modules:
the system comprises a data collection module, a data acquisition module and a data processing module, wherein the data collection module is used for collecting meteorological hydrological data, and the meteorological hydrological data comprises precipitation, potential evaporation, land water storage change and runoff data;
the data fusion module is used for introducing the land water storage change data into a hydrothermal coupling equilibrium equation to obtain an improved hydrothermal coupling equilibrium equation, and the improved hydrothermal coupling equilibrium equation calculates the characteristic parameter values of the underlying surface at different time scales;
the equation coupling derivation module is used for coupling the improved hydrothermal coupling balance equation from the data fusion module with a runoff change average absolute deviation formula, and obtaining a runoff change attribution equation under any space-time scale after full differential solution calculation;
and the evaluation influence module is used for evaluating the influence of rainfall, potential evaporation, land water storage and the change of the characteristics of the underlying surface on the change of the global runoff by combining the meteorological hydrological data collected by the data collection module, the underlying surface parameter values of different time scales calculated by the data fusion module and the runoff change attribution equation under any space-time scale obtained by the equation coupling derivation module.
Further, in the equation coupling derivation module, the input data is subjected to a full differential solving operation by using a basic function deriv included in the R language programming.
Further, the influence evaluation module further comprises a meteorological hydrological data processing module, wherein the meteorological hydrological data processing module is used for averaging meteorological hydrological data under a multi-year scale to obtain multi-year average data of the meteorological hydrology; and then subtracting the meteorological hydrological data under different time scales from the average data of the meteorological hydrological data over years to obtain the variation values of the meteorological hydrological data under different time scales.
The device comprises a processor and a memory, wherein the memory is used for storing any one space-time scale runoff change quantitative attribution method and realizing data of the runoff change quantitative attribution method, and the processor is used for acquiring data accessed in the memory and evaluating the influence of rainfall, potential evaporation, land water storage and underlay surface characteristic change on the global runoff change.
In the method, the system and the device for quantitatively attributing the runoff change of any space-time scale, the full differential equation of the runoff change of any space-time scale is deduced by coupling the improved hydrothermal coupling balance formula and the average dispersion formula of the runoff change in consideration of the water storage change, and the method for attributing the runoff change of the watershed scale for a long time is popularized to attributing the runoff change of any space-time scale.
The invention brings the following practical beneficial effects:
1. because the required data information is easy to obtain, the attribution method is simple and effective, and the operation cost is greatly reduced;
2. the method is suitable for attribution of runoff change of watersheds or grid scales in different time scales and different climate areas, so that the method has wider application range compared with the conventional method;
3. because the influence of rainfall, potential evaporation, land water storage and the change of the characteristics of the underlying surface are simultaneously considered, the simulation attribution effect is obviously improved, and the attribution result is more comprehensive and scientific.
Drawings
The invention will be further described with reference to the following drawings and examples, in which:
FIG. 1 is a flow chart of a method for implementing quantitative attribution of runoff changes;
FIG. 2 is a block diagram of a runoff change quantification attribution system;
FIG. 3 is a graph comparing the performance of the hydrothermal coupling equation in hydrothermal constraints with an improved hydrothermal coupling equation that takes into account water storage variations;
figure 4 is a graph comparing runoff change simulation attribution errors with simulation attribution errors of the prior method at different latitudes.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
In this embodiment, an arbitrary spatiotemporal scale runoff change attributing method is disclosed, which includes the following steps (please refer to fig. 1 for a specific method flowchart):
s1, collecting basic data: first, in this embodiment, the latest global meteorological hydrological Data set published by Scientific Data is downloaded by procedure. The data set integrates multi-source data such as site actual measurement, remote sensing inversion, land mode, global reanalysis products and the like, and is obtained by processing through a constraint Kalman filtering data assimilation technology, wherein the data comprises grid-scale precipitation, land water storage and runoff data, the time coverage range of the data is 1958-2017, and the spatial precision is 1/24 degrees (about 4 km). In addition, global potential evaporation data calculated by the university of east angulia climate research group was downloaded.
S2, substituting the land water storage change into a Choudhury-Yang hydrothermal coupling equilibrium equation to obtain an improved hydrothermal coupling equilibrium equation of any time scale, and determining the characteristic parameter values of the underlying surface under different time scales; wherein:
the Choudhury-Yang hydrothermal coupling equilibrium equation is as follows:
wherein n is the characteristic parameter of the underlying surface of the Choudhury-Yang hydrothermal coupling equilibrium formula. P, E and E0 represent precipitation value, actual evaporation value, potential evaporation value, respectively; wherein, the characteristic parameter n of the underlying surface can be calculated by using the minimum root mean square error;
although the rainfall in the flow area finally forms runoff and evaporation, the evaporation is often not equal to the difference between the rainfall and the runoff on a short time scale (such as an annual or annual scale) under the influence of the change of land water storage. Research has shown that water storage changes have a significant impact on the annual hydrothermal coupling balance. In this embodiment, according to the basin perennial average water balance equation (R ═ P-E), the water storage change data is substituted into the Choudhury-Yang hydrothermal coupling equilibrium formula, so as to obtain an improved hydrothermal coupling equilibrium equation of any space-time scale:
wherein R, P, TWSC, E0And n represents characteristic parameters of runoff, precipitation, land water storage change, potential evaporation and underlying surface respectively; i is time, and when i is month, the above formula is a hydro-thermal coupling balance equation of the intra-year scale; when i is year, the above formula is the hydrothermal coupling balance equation of the annual scale; when i is years, the equation is a multi-year scale hydrothermal coupling equilibrium equation.
S3, carrying out full differential calculation on the hydro-thermal coupling balance equation formula and the average absolute deviation formula which are subjected to coupling improvement in the step S2, and outputting a runoff change attribution equation under any space-time scale; wherein:
the full differential equation of any space-time scale runoff change is as follows:
in the above formula, the first and second carbon atoms are,
and
the partial derivatives of the runoff change to precipitation, potential evaporation, land water storage change and the controllability parameter change to the runoff change are obtained, wherein the partial derivatives can be calculated by utilizing an R language basis function deriv.
In this embodiment, referring to the related research, since the mean dispersion is a statistical method for describing the sample change more simply than the difference, the method is more suitable for the real situation, and the phenomenon that the contribution rate squared or the contribution rates of the two influencing elements overlap with each other does not occur in the attribution decomposition, and therefore, the invention adopts the mean dispersion instead of the variance to describe the change of the runoff based on the conclusion. Wherein, under different time scales (such as the annual and intra-annual scales), the runoff change value is expressed by the average dispersion as:
where i is time and N is the number of time samples taken.
S4, evaluating the contribution rate of the characteristics of precipitation, potential evaporation, land water storage and underlying surface to runoff change according to the elasticity coefficient, the annual average value of meteorological hydrological data and meteorological hydrological changes of different time scales to obtain:
in the formula IP，
IΔSAnd InRespectively represent P, E0The influence of TWSC and n on the runoff change can be expressed as:
wherein x represents various factors affecting the amount of runoff change, including P, E0TWSC and n. Relative contribution rate (δ R) of each factor change to runoff changex) The following can be used for calculation:
in a similar manner, a calculation of the relative contribution of each factor to the actual change in evaporation may also be derived.
Based on the formula, the meteorological hydrological data of any space-time scale is input, and then the cause of runoff change of the corresponding space-time scale can be evaluated.
The invention discloses a runoff change attribution system under any space-time scale, which has a system structure diagram, please refer to fig. 2, and the device comprises a data collection module L1, a data fusion module L2, an equation coupling derivation module L3, an evaluation influence module L4 and a meteorological hydrology data processing module L41, wherein:
the data collection module L1 is used for collecting meteorological hydrological data, wherein the meteorological hydrological data comprises precipitation, potential evaporation, land water storage change and runoff data;
the data fusion module L2 is used for introducing the land water storage variation data into a hydrothermal coupling equilibrium equation to obtain an improved hydrothermal coupling equilibrium equation, and the improved hydrothermal coupling equilibrium equation calculates the characteristic parameter values of the underlying surface at different time scales;
the equation coupling derivation module L3 is used for coupling the improved hydrothermal coupling balance equation from the data fusion module with a runoff change average absolute deviation formula, and obtaining a runoff change attribution equation under any space-time scale after full differential solution calculation; wherein, in the equation coupling derivation module, the input data is subjected to full differential solving operation by using a basic function deriv included in the R language programming.
The evaluation influence module L4 is used for evaluating the influence of rainfall, potential evaporation, land water storage and the change of the characteristics of the underlying surface on the change of the global runoff by combining meteorological hydrological data collected by the data collection module, the underlying surface parameter values of different time scales calculated by the data fusion module and the runoff change attribution equation under any space-time scale obtained by the equation coupling derivation module; the evaluation influence module L4 further includes a meteorological hydrological data processing module L41, where the meteorological hydrological data processing module is configured to average meteorological hydrological data in a multi-year scale to obtain multi-year average meteorological hydrological data; and then subtracting the meteorological hydrological data under different time scales from the average data of the meteorological hydrological data over multiple years to obtain the variation values of the meteorological hydrological data under different time scales.
Fig. 3 is a diagram of the hydrothermal coupling equilibrium relationship, wherein the hydrothermal coupling equation in the diagram labeled (a) on the left side of fig. 3 represents a comparison with the improved hydrothermal coupling equation in the diagram labeled (b) on the right side, taking into account the water storage variation, in the hydrothermal limiting conditions. In the figure, scattered points represent global grids, and the color depth of the scattered points represents the number of overlapped grids; each curve shown in the figure is a Budyko hydrothermal coupling curve, and represents the characteristic parameters of the underlying surface from top to bottom, namely n ═ infinity, n ═ 5, n ═ 2, n ═ 1, n ═ 0.6, and n ═ 0.4. As can be seen from the figure, the evaporation rate of more grids in the global range is greater than 1 and exceeds the water quantity limit line, which indicates that the existing Choudhury-Yang hydrothermal coupling balance formula is not suitable for the hydrothermal coupling balance research of the annual scale; after using the hydro-thermal coupling equilibrium equation with any time scale considering the change of the land water storage, please refer to the diagram marked as (b) in fig. 3, which hardly occurs the situation that the water limit line is exceeded. The results show that the short-time scale runoff attribution research must consider the change of land water storage, and the improved hydrothermal coupling equilibrium equation better meets the requirements of the short-time scale runoff attribution research.
Fig. 4 is a comparison graph of runoff change simulation attribution errors in the invention and simulation attribution errors in the existing method at different latitudes, and it can be known from the graph that the simulation attribution effect of any time scale runoff change attribution method provided by the invention on runoff change is better than that of the existing method (refer to the graphs marked as (b) and (d) in fig. 4) on both the annual scale (refer to the graph marked as (a) in fig. 4) and the annual scale (refer to the graph marked as (c) in fig. 4).
The invention is based on an improved hydrothermal coupling balance equation and a runoff change average dispersion equation, deduces any space-time scale runoff change attribution method comprehensively considering the influences of climate change (precipitation and potential evaporation change), water storage change and underlying surface characteristics, and provides a new mode for disclosing the runoff response research of different time scales, different watersheds or grids in a changing environment. The method enables the runoff change attribution analysis to be more comprehensive and accurate, and has important significance in the aspects of regional and global water resource evolution cause identification, water resource planning management, water resource safety and the like in a change environment.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (5)
1. An arbitrary space-time scale runoff change quantitative attribution method is characterized by comprising the following steps:
s1, collecting meteorological hydrological data, wherein the meteorological hydrological data comprise precipitation, potential evaporation, land water storage change and runoff data;
s2, introducing the land water storage change data into a hydrothermal coupling equilibrium equation to obtain an improved hydrothermal coupling equilibrium equation, and calculating the characteristic parameter values of the underlying surface at different time scales by using the improved hydrothermal coupling equilibrium equation;
the hydrothermal coupling equilibrium equation is as follows:
wherein n is the underlying surface characteristic parameter, P, E and E0Respectively representing a precipitation value, an actual evaporation value and a potential evaporation value; wherein, the characteristic parameter n of the underlying surface can be calculated by using the minimum root mean square error;
the improved hydrothermal coupling equilibrium equation is:
wherein R and TWSC respectively represent a runoff value and a land water storage change value; i is time, and when i is month, the above formula is a hydro-thermal coupling balance equation of the intra-year scale; when i is year, the above formula is the hydrothermal coupling balance equation of the annual scale; when i is years, the equation is a multi-year scale hydrothermal coupling equilibrium equation;
s3, coupling the improved hydrothermal coupling balance equation obtained in the step S2 with a runoff change average absolute deviation formula, and obtaining a runoff change attribution equation under any space-time scale after full differential solution calculation:
wherein, Δ Pi，
TWSCiAnd Δ niRespectively corresponding parameter data under corresponding time i; rv is the runoff change value, N is the number of time samples taken,
and
partial derivatives of runoff change on precipitation, potential evaporation, land water storage change and underlying surface characteristic parameters are obtained;
s4, quantitatively evaluating the influence of rainfall, potential evaporation, land water storage and underlay surface characteristic change on different global space-time scale runoff change by combining the meteorological hydrological data collected in the step S1, the underlay surface parameter values of different time scales obtained by calculation in the step S2 and the runoff change attribution equation under any space-time scale obtained in the step S3;
the specific process implemented in step S4 is as follows:
firstly, averaging meteorological hydrological data under a multi-year scale to obtain multi-year average data of the meteorological hydrological data; then subtracting the meteorological hydrological data under different time scales from the average data of the meteorological hydrological data over years to obtain the variation values of the meteorological hydrological data under different time scales;
and finally substituting the annual average value of the meteorological hydrology, the variation value of the meteorological hydrology under different time scales and the characteristic parameter value of the underlying surface into a runoff variation attribution equation under any space-time scale, and evaluating the influence of four driving factors of precipitation, potential evaporation, land water storage variation and underlying surface variation on runoff variation, wherein the mathematical expression of the influence of the corresponding driving factors on the runoff variation is as follows:
wherein sign (dR)i) Runoff changing direction at corresponding time i; x denotes the respective driving factor; n is the number of time samples taken, dxiFor the variation of each driving factor, IXThe influence quantity of the corresponding driving factors on the runoff change.
2. The quantitative attribution method of runoff change as claimed in claim 1, wherein in step S3, partial derivatives of runoff change to precipitation, latent evaporation, land water storage change and underlying surface characteristic parameters are calculated using a deriv function in R language programming.
3. An arbitrary spatiotemporal scale runoff change quantitative attribution system is characterized by comprising the following modules:
the system comprises a data collection module, a data acquisition module and a data processing module, wherein the data collection module is used for collecting meteorological hydrological data, and the meteorological hydrological data comprises precipitation, potential evaporation, land water storage change and runoff data;
the data fusion module is used for introducing the land water storage change data into a hydrothermal coupling equilibrium equation to obtain an improved hydrothermal coupling equilibrium equation, and the improved hydrothermal coupling equilibrium equation calculates the characteristic parameter values of the underlying surface at different time scales;
the hydrothermal coupling equilibrium equation is as follows:
wherein n is the underlying surface characteristic parameter, P, E and E0Respectively representing a precipitation value, an actual evaporation value and a potential evaporation value; wherein, the characteristic parameter n of the underlying surface can be calculated by using the minimum root mean square error;
the improved hydrothermal coupling equilibrium equation is:
wherein R and TWSC respectively represent a runoff value and a land water storage change value; i is time, and when i is month, the above formula is a hydro-thermal coupling balance equation of the intra-year scale; when i is year, the above formula is the hydrothermal coupling balance equation of the annual scale; when i is years, the equation is a multi-year scale hydrothermal coupling equilibrium equation;
the equation coupling derivation module is used for coupling the improved hydrothermal coupling balance equation from the data fusion module with a runoff change average absolute deviation formula, and obtaining a runoff change attribution equation under any space-time scale after full differential solution calculation:
wherein, Δ Pi，
TWSCiAnd Δ niRespectively corresponding parameter data under corresponding time i; rv is the runoff change value, N is the number of time samples taken,
and
partial derivatives of runoff change to rainfall value, potential evaporation value, land water storage change value and characteristic parameters of the underlying surface;
the evaluation influence module is used for evaluating the influence of rainfall, potential evaporation, land water storage and the change of the characteristics of the underlying surface on the change of the global runoff by combining the meteorological hydrological data collected by the data collection module, the underlying surface parameter values of different time scales calculated by the data fusion module and the runoff change attribution equation under any space-time scale obtained by the equation coupling derivation module;
the influence evaluation module also comprises a meteorological hydrological data processing module, and the meteorological hydrological data processing module is used for averaging meteorological hydrological data under a multi-year scale to obtain multi-year average data of the meteorological hydrology; then subtracting the meteorological hydrological data under different time scales from the average data of the meteorological hydrological data over years to obtain the variation values of the meteorological hydrological data under different time scales;
substituting the multi-year average value of the meteorological hydrology, the variation value of the meteorological hydrology under different time scales and the characteristic parameter value of the underlying surface into a runoff variation attribution equation under any space-time scale, and evaluating the influence of four driving factors of precipitation, potential evaporation, land water storage variation and underlying surface variation on runoff variation, wherein the mathematical expression of the runoff variation attribution equation is as follows:
wherein sign (dR)i) Flow change direction is performed at corresponding time i; x denotes the respective driving factor; n is the number of time samples taken, dxiFor the variation of each driving factor, IXThe influence quantity of the corresponding driving factors on the runoff change.
4. The quantitative attribution system for runoff change as claimed in claim 3, wherein in the equation coupling derivation module, the full differential solution operation is performed on the input data by using a basis function deriv included in the R language programming.
5. An apparatus using any one of the spatiotemporal scale runoff change quantitative attribution methods of any one of claims 1-2, wherein the apparatus comprises a processor and a memory, wherein the memory is used for storing the any one of the spatiotemporal scale runoff change quantitative attribution methods of any one of claims 1-2 and realizing data of the runoff change quantitative attribution method, and the processor is used for acquiring data accessed in the memory and realizing evaluation of influence of precipitation, latent evaporation, land water storage and subsurface feature changes on global runoff change.
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| RU2667745C1 (en) * | 2017-08-07 | 2018-09-24 | Государственное Унитарное Предприятие "Водоканал Санкт-Петербурга" | Method of optimization of the wastewater streams |
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| CN107818238A (en) * | 2017-09-28 | 2018-03-20 | 河海大学 | A kind of method for determining coupling between evapotranspiration change main cause and differentiation factor |
Non-Patent Citations (3)
| Title |
|---|
| Budyko框架下黄土高原流域蒸散时空变化及其归因分析;宁婷婷;《中国博士学位论文全文数据库 (基础科学辑)》;20170815;全文 * |
| 人类用水活动对大尺度陆地水循环的影响;汤秋鸿等;《地球科学进展》;20151010(第10期);全文 * |
| 基于Budyko假设的渭河径流变化归因识别;张丽梅 等;《生态学报》;20180808;全文 * |
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