Attribution analysis method of multi-factor changes in water cycle under multiple climate change impact models

Abstract

The present invention discloses a method for attributing and analyzing changes in multiple elements of water cycle under multiple climate change impact models, comprising the following steps: step 1, collecting and collating data of the study area; step 2, determining the response mode and zoning of water cycle elements to climate change; step 3, separating the changes in water cycle elements in each response model area directly caused by climate change and underlying surface change; step 4, separating the changes in underlying surface in each response model area directly caused by climate change and human activity factors and the contribution of each influencing factor; step 5, separating the changes in water cycle elements in each response model area caused by the chain transmission of climate change and human activities and the contribution of each influencing factor. The method described in the present invention fully considers the regional heterogeneity of changes in water cycle elements, expands the attribution of traditional runoff depth changes to the attribution of changes in multiple elements of water cycle, and divides the traditional impact analysis into direct and chain transmission impact analysis, so that the analysis is more comprehensive, has strong applicability, and is easy to promote.

Description

Technical Field

[0001] The present invention belongs to the technical field of hydrology and meteorology, and in particular to a method for attributing and analyzing changes in multiple factors of a water cycle under a multi-climate change impact model. Background Art

[0002] With the multiple influences of climate change, human activities and river basin characteristics, the evolution of the water cycle shows obvious temporal and spatial differences, and the influencing mechanism is complex. A large number of latest observations and studies have found that climate change and human activities do not affect the hydrological process independently, but have a synergistic effect on the elements of the water cycle. Therefore, scientifically revealing the response mechanism and spatial pattern of multiple elements of the water cycle to climate change, and quantitatively separating the impact of climate change and human activities on the changes in water cycle elements, provides a scientific basis for understanding the evolution of the water cycle in the basin, and also has great scientific significance for the safety and sustainable use of water resources in the basin under a changing environment.

[0003] Climate change and human activities are the two major driving factors that affect the evolution of water cycle in river basins / regions. The driving mechanism and impact quantification have become hot issues in the current field of global change research. Domestic and foreign research mainly uses statistical analysis and numerical simulation to detect or quantify the impact of climate change on evapotranspiration, runoff and other factors, focusing on the overall impact analysis at the regional or river basin scale, and cannot reflect the response pattern and spatial differences of multiple elements of the water cycle caused by the spatiotemporal heterogeneity of climate, underlying surface and human activities. Therefore, the spatial differences in the response mechanism of multiple elements of the water cycle in the river basin and the in-depth exploration of the mechanism model need to be enhanced.

[0004] In addition, a large number of studies have been conducted on the attribution of water cycle changes. The important premise is that the impacts of climate and human activities on water cycle elements are independent of each other, ignoring the driving and response relationship between climate and human activities, underlying surface, hydrological and water resources processes, and ecological processes. Common methods can be summarized as empirical comparative analysis methods based on mathematical statistics, analysis methods based on elasticity coefficients, and hydrological simulation methods. The physical mechanisms of the first two methods are simple, focusing on the changes in multi-year average runoff, and unable to consider changes in different stages (such as flood season and non-flood season). They also cannot reveal the transmission law of the climate drive-underlying surface response-water cycle change chain. The hydrological simulation method has a strong physical mechanism, but the results mainly depend on the accuracy of model simulation, which is affected by the uncertainty of model parameters and structure, and the scarcity of observation data. Therefore, the lack of consideration of the impact mechanism of climate change on the underlying surface and the analysis of a single element of the water cycle in previous studies will lead to certain limitations and uncertainties in the attribution results of multi-factor changes in the water cycle system under a changing environment.

[0005] Therefore, in view of the differences in available water cycle elements in different regions, the large number of influencing factors and the complex and diverse action mechanisms, there is an urgent need for a simple and feasible method to identify the impact patterns and spatial distribution of multiple water cycle elements, separate and quantify the direct impact and chain transmission impact of climate change and human activities on the changes of multiple water cycle elements, so as to promote the applicability of the attribution method of changes in multiple water cycle elements and provide a reference for basin / regional water resources management and climate change response. Summary of the invention

[0006] The purpose of the present invention is to provide a method for attributing and analyzing changes in multiple factors of the water cycle under multiple climate change impact models to solve the above technical problems.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The present invention discloses a method for attributing and analyzing changes in multiple factors of water cycle under multiple climate change impact models, the method comprising the following steps:

[0009] Step 1: Collect and organize data in the study area: collect meteorological element data, water cycle element data of each rainfall station in the study area, and social and economic element data of the prefecture-level city-level administrative districts in the region, and organize the collected data into a unified scale; determine the basic unit of analysis, based on the collected and organized data of the study area, use the area weighted method to determine the meteorological element sequence, water cycle element sequence and social and economic element sequence of each basic unit, establish and solve the Budyko water-heat coupling balance framework of the basin considering the change of terrestrial water storage, and determine the characteristic parameter sequence of the Budyko underlying surface of each basic unit;

[0010] Step 2, determine the response mode and zoning of water cycle elements to climate change: According to the water cycle element sequence of each basic unit determined in step 1, determine the time change trend and jump point of each water cycle element, build a water cycle element change index system, identify the response mode and zoning of water cycle elements to climate change, and then use the weighted average method to determine the meteorological elements, water cycle elements, socio-economic elements, Budyko underlying surface characteristic parameters, and water cycle element change index system sequence of each response mode area, determine the spatial distribution characteristics of each response mode area and the differences of the water cycle element change index system in different response modes;

[0011] Step 3, separating the changes in water cycle elements in each response model area directly caused by climate change and underlying surface changes: based on the basin Budyko hydrothermal coupling balance framework, water balance and elasticity coefficient method considering the change in terrestrial water storage established in step 1, determine the elasticity coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in each response model area; according to the jump points of each water cycle element of each basic unit and each response model area determined in step 2, determine the jump points of each water cycle element in each response model area and the changes in the multi-year average values ​​of each water cycle element in each response model area before and after the jump points, and determine the changes in water cycle elements directly caused by climate change and underlying surface changes in each response model area; according to the meteorological element sequence and water cycle element value of each response model area determined in step 2, establish the quantitative relationship between the water cycle element value before the jump and the annual runoff depth and meteorological element sequence of each response model area;

[0012] Step 4, separate the changes in the underlying surface of each response model area directly caused by climate change and human activities and the contribution of each influencing factor: based on the water cycle elements and Budyko underlying surface characteristic parameter sequences of each response model area determined in step 2, establish the quantitative relationship between the Budyko underlying surface characteristic parameters and water cycle elements of each response model area, and determine the elasticity coefficient of each meteorological element and socio-economic element to the underlying surface characteristic parameters of each response model area; according to the multi-year average changes of each water cycle element in each response model area before and after the jump point determined in step 3, determine the changes in Budyko underlying surface characteristic parameters caused by climate change and human activities in each response model area, and quantify the contribution of each influencing factor to the changes in underlying surface characteristic parameters through the elasticity coefficients of each meteorological element and socio-economic element to the underlying surface characteristic parameters;

[0013] Step 5: Separate the changes in water cycle elements in each response model area and the contribution of each influencing factor caused by the chain transmission of climate change and human activities:

[0014] Based on the elasticity coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in each response model area determined in step 3, and the quantitative relationship between the water cycle element values ​​before the jump and the annual runoff depth and meteorological element sequence established in each response model area, combined with the elasticity coefficients of each meteorological element and socio-economic element to the underlying surface characteristic parameters in each response model area determined in step 4, the changes in water cycle elements in each response model area caused by the chain transmission of climate change and human activities are determined; based on the changes in water cycle elements directly caused by the underlying surface changes in each response model area determined in step 3, the chain transmission effects of climate change and human activities are deducted to determine the changes in water cycle elements in each response model area directly caused by human activities; using the elasticity coefficients of the underlying surface characteristic parameters in each response model area to water cycle elements, and the elasticity coefficients of each meteorological element and socio-economic element to the underlying surface characteristic parameters, the contribution rate of the attribution analysis of the changes in different water cycle elements in each response model area is quantified.

[0015] Furthermore, the meteorological element data in step 1 include the daily precipitation and daily temperature actually observed at the site; the water cycle element data include daily actual evapotranspiration, daily runoff depth and monthly land water storage changes; the socio-economic element data include annual gross domestic product, total population, agricultural population, non-agricultural population, effective irrigation area, cultivated land area, agricultural water use and industrial water use; the collected data are organized into a unified scale, that is, the collected daily precipitation, temperature and actual evapotranspiration data are organized into annual precipitation, annual average temperature and annual actual evapotranspiration data; the collected daily runoff depth data are organized into annual runoff depth, flood season and non-flood season runoff depth data; the collected monthly land water storage change data are organized into annual land water storage change, flood season and non-flood season land water storage change data; the type of the basic unit is a grid or a small watershed.

[0016] Furthermore, the specific process of establishing and solving the Budyko hydrothermal coupled balance framework of the basin considering the change of terrestrial water storage in step 1 and determining the characteristic parameter sequence of the Budyko underlying surface of each basic unit is as follows: introducing the change of terrestrial water storage into the Budyko hydrothermal coupled balance framework, and the calculation formula is:

[0017]

[0018] Where P i,q , PET i,q , R i,q , ΔS i,q 、n i,q are the precipitation, potential evapotranspiration, runoff depth, terrestrial water storage change and Budyko underlying surface characteristic parameter values ​​of the ith basic unit in the qth year, among which the potential evapotranspiration is calculated based on the collected meteorological element data;

[0019] Then, the iterative approximation method is used to solve equation (1) to determine the characteristic parameter sequence of each basic unit Budyko underlying surface {n i,q}.

[0020] Furthermore, the step 2 of determining the temporal variation trend and jump point of each water cycle element is specifically as follows: trend detection, slope estimation and mutation test methods are used to determine the temporal variation trend and jump point of each water cycle element; the step of identifying the response pattern and zoning of water cycle elements to climate change is specifically as follows: correlation analysis, dimensionality reduction and clustering methods are used to identify the response pattern and zoning of water cycle elements to climate change.

[0021] Furthermore, the calculation formula for determining the elasticity coefficient of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in each response mode area in step 3 is:

[0022]

[0023] In the formula, and are the elastic coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in the jth response model area; n j is the underlying surface characteristic parameter of the jth response mode area; PET j and P j are the multi-year averages of potential evapotranspiration and precipitation series in the jth response model area, respectively.

[0024] The calculation formula for determining the changes in water cycle elements directly caused by climate change and underlying surface change in each response model area is:

[0025]

[0026] In the formula, and are the changes in water cycle elements directly caused by climate change and underlying surface change in the jth response model area; W j , P j are the multi-year average water cycle element values ​​and precipitation in the jth response model area; ΔP j , ΔPET j and Δn j are the multi-year average changes of precipitation, potential evapotranspiration and Budyko underlying surface characteristic parameters before and after the jump point in the jth response model area;

[0027] The calculation formula for establishing the quantitative relationship between the water cycle element value before the jump in each response mode area and the annual runoff depth and meteorological element sequence is:

[0028] W j,i =fW j (C j,1 ,C j,2 ,…,C j,k ,R j ) (7)

[0029] Where W j,i and R j are the ith water cycle element and annual runoff depth in the jth response model area; C j,k is the kth meteorological element in the jth response mode area; fW j (·) is the quantitative relationship between the water cycle elements and the annual runoff depth and meteorological element sequences in the jth response model area.

[0030] Furthermore, the calculation formula for determining the elastic coefficient of each meteorological element and socio-economic element in each response mode area to the underlying surface characteristic parameters in step 4 is:

[0031]

[0032]

[0033] In the formula, and are the kth meteorological element C in the jth response mode area j,k and the elasticity coefficient of the pth socio-economic factor Hj,p to the underlying surface characteristic parameters; n j =f j (·) is the quantitative relationship between the Budyko underlying surface characteristic parameters and water cycle elements in the jth response model area; ΔC j,k and ΔH j,p are the multi-year mean changes of the kth meteorological element sliding series and the pth socio-economic element sliding series in the jth response model area, respectively;

[0034] The calculation formula for determining the changes in Budyko underlying surface characteristic parameters caused by climate change and human activities in each response model area is:

[0035]

[0036] In the formula, Δn j,C and Δn j,H are the changes in the characteristic parameters of the Budyko underlying surface caused by climate change and human activities in the jth response model area; M j and N j are the number of meteorological elements and the number of socio-economic elements in the jth response model area, respectively.

[0037] Furthermore, the calculation formula for determining the changes in water cycle elements in each response model area caused by the chain transmission of climate change and human activities described in step 5 is:

[0038]

[0039] In the formula, and are the changes in water cycle elements in the jth response model area caused by the chain transmission of climate change and human activities; F j (C j ,R j ) is the water cycle element W in the jth response model area j The annual runoff depth R j 、Meteorological elements C j The quantitative relationship between W is the elastic coefficient of the underlying surface characteristic parameters of the jth response mode area to the water cycle elements; j is the multi-year average water cycle element value of the j-th response model area;

[0040] The calculation formula for determining the changes in water cycle elements in each response model area directly caused by human activities is:

[0041]

[0042] In the formula, is the change of water cycle elements directly caused by human activities in the jth response model area; is the change of water cycle elements directly caused by the change of underlying surface in the jth response model area.

[0043] The beneficial effects of the present invention are:

[0044] (1) The method of the present invention constructs an indicator system for water cycle changes, objectively divides the response zones of the water cycle system to climate change, fully considers the regional heterogeneity of changes in water cycle elements, and has strong applicability.

[0045] (2) The method described in the present invention introduces the change of terrestrial water storage into the Budyko hydrothermal coupling balance framework, which solves the problem that the traditional Budyko framework is only applicable to multi-year average states and cannot analyze the attribution of runoff depth changes during flood season and non-flood season. The elasticity coefficient method and quantitative relationship identification are used to derive the impact of climate change and human activities on the underlying surface and their impact on the changes in water cycle elements in different response mode areas, and separate and quantify the chain transmission contribution of climate change-underlying surface change-water cycle element change.

[0046] (3) The method described in the present invention expands the traditional attribution analysis of runoff depth changes to attribution analysis of changes in multiple water cycle elements such as runoff depth and terrestrial water storage changes at different time stages, making the analysis more comprehensive and more applicable.

[0047] (4) The method described in the present invention provides water cycle element changes directly caused by climate change, water cycle element changes caused by the chain transmission of climate change, water cycle element changes directly caused by human activities, and water cycle element changes caused by the chain transmission of human activities in different response mode areas. It can be used by decision makers to formulate targeted water cycle management strategies based on the needs of water cycle management in the river basin. It is easy to use, highly practical, and easy to promote.

[0048] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of the method of the present invention;

[0050] Figure 2 The clustering effect evaluation index distribution diagram under different clustering numbers in Example 1;

[0051] Figure 3 The n-sequence distribution diagram of the Budyko underlying surface characteristic parameters in each response mode area in the first embodiment;

[0052] Figure 4 It is a distribution diagram of the contribution of climate change and human activities to the changes of underlying surface characteristic parameters in each response model area in Example 1, taking annual runoff depth as an example;

[0053] Figure 5 It is a pie chart of the attribution analysis of the changes in different water cycle elements in each response mode area in Example 1;

[0054] Figure 6 This is a distribution diagram of the contribution of climate change and human activities to the underlying surface characteristic parameters in different water cycle elements in each response model area in Example 1. DETAILED DESCRIPTION

[0055] The present invention discloses a method for attributing and analyzing changes in multiple factors of water cycle under multiple climate change impact models, such as Figure 1 As shown, the method comprises the following steps:

[0056] Step 1: Collect and organize data on the study area.

[0057] First, the meteorological element data and water cycle element data of each rain gauge station in the study area, as well as the socio-economic element data of the prefecture-level city administrative district in the region, were collected and sorted. The meteorological element data included the daily precipitation and daily temperature actually observed at the station; the water cycle element data included the daily actual evapotranspiration, daily runoff depth and monthly land water storage changes; the socio-economic element data included the annual gross domestic product, total population, agricultural population, non-agricultural population, effective irrigation area, cultivated land area, agricultural water use and industrial water use, etc. Among them, in order to unify the time scale, the collected daily precipitation, temperature and actual evapotranspiration data were sorted into annual precipitation, annual average temperature and annual actual evapotranspiration data; the collected daily runoff depth data were sorted into annual runoff depth, flood season and non-flood season runoff depth data; the collected monthly land water storage change data were sorted into annual land water storage change, flood season and non-flood season land water storage change data. The spatial scale of the water cycle element data is the station, and the spatial scale of the socio-economic element data is the prefecture-level city administrative district.

[0058] Then the size of the basic unit of analysis is determined, and the types of basic units include grids or small watersheds. Based on the collected and collated data of the study area, the area weighted method is used to determine the meteorological element sequence, water cycle element sequence and socio-economic element sequence of each basic unit.

[0059] The Budyko hydrothermal coupled balance framework of the basin considering the change of terrestrial water storage is established and solved. Specifically, the change of terrestrial water storage is introduced into the Budyko hydrothermal coupled balance framework, as shown in formula (1), to solve the problem that the original Budyko framework is only applicable to the multi-year average state and cannot analyze the attribution of runoff depth changes in flood season and non-flood season;

[0060]

[0061] Where P i,q , PET i,q , R i,q , ΔS i,q 、n i,q are the precipitation, potential evapotranspiration, runoff depth, terrestrial water storage change and Budyko underlying surface characteristic parameter values ​​of the ith basic unit in the qth year, among which the potential evapotranspiration is calculated based on the collected meteorological element data.

[0062] Then, the iterative approximation method is used to solve equation (1) to determine the characteristic parameter sequence of each basic unit Budyko underlying surface {n i,q}.

[0063] Step 2: Determine the response patterns and zoning of water cycle elements to climate change.

[0064] According to the water cycle element sequence of each basic unit determined in step 1, multivariate statistical analysis methods such as trend detection, slope estimation and mutation test are used to determine the time change trend and jump point of each water cycle element, and to construct a water cycle element change index system, as shown in Table 1;

[0065] Table 1 Water cycle element change indicator system

[0066]

[0067]

[0068] Based on the determined water cycle element change index system, multivariate statistical analysis methods such as correlation analysis, dimensionality reduction and clustering are used to identify the main response modes and partitions to climate change. Different response modes correspond to different partitions, namely response mode areas. According to the meteorological elements, water cycle elements, and socio-economic element sequences of each basic unit determined in step 1, as well as the Budyko underlying surface characteristic parameter series of each basic unit, combined with the determined water cycle element change index system and the identified main response modes and partitions, the weighted average method is used to determine the meteorological elements, water cycle elements, socio-economic elements, Budyko underlying surface characteristic parameters, and water cycle element change index system sequences of each response mode area, and determine the spatial distribution characteristics of each response mode area and the differences in the water cycle element change index system in different response modes.

[0069] Step 3: Separate the changes in water cycle elements in each response model area that are directly caused by climate change and underlying surface changes.

[0070] Based on the Budyko hydrothermal coupled balance framework, water balance and elasticity coefficient method considering the change of terrestrial water storage, the elasticity coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in each response model area are determined. The calculation formulas are:

[0071]

[0072]

[0073] In the formula, and are the elastic coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in the jth response model area; n j is the underlying surface characteristic parameter of the jth response mode area; PET j and P j They are the multi-year average values ​​of potential evapotranspiration and precipitation series in the j-th response model area (multi-year generally refers to more than 5 years).

[0074] According to the jump points of each water cycle element in each basic unit and each response mode area determined in step 2, determine the jump points of each water cycle element in each response mode area, as well as the changes in the multi-year average values ​​of each water cycle element in each response mode area before and after the jump points; determine the changes in water cycle elements directly caused by climate change and underlying surface changes in each response mode area, and the calculation formula is:

[0075]

[0076] In the formula, and are the changes in water cycle elements directly caused by climate change and underlying surface change in the jth response model area; W j , P j are the multi-year average water cycle element values ​​and precipitation in the jth response model area; ΔP j , ΔPET j and Δn j They are the multi-year average changes of precipitation, potential evapotranspiration and Budyko underlying surface characteristic parameters before and after the jump point in the jth response model area.

[0077] According to the determined meteorological element sequence and water cycle element value of each response mode area, the quantitative relationship between the water cycle element value before the jump and the annual runoff depth and meteorological element sequence of each response mode area is established. The calculation formula is:

[0078] Wj,i =fW j (C j,1 ,C j,2 ,…,C j,k ,R j ) (7)

[0079] Where W j,i and R j are the ith water cycle element and annual runoff depth in the jth response model area; C j,k is the kth meteorological element in the jth response mode area; fW j (·) is the quantitative relationship between the water cycle elements and the annual runoff depth and meteorological element sequences in the jth response model area.

[0080] Step 4: Separate the underlying surface changes in each response model area directly caused by climate change and human activities and the contribution of each influencing factor.

[0081] Based on the water cycle element sequence and Budyko underlying surface characteristic parameter sequence of each response mode area determined in step 2, the quantitative relationship between Budyko underlying surface characteristic parameters and water cycle elements in each response mode area is established, and the elasticity coefficient of each meteorological element and socio-economic element in each response mode area to the underlying surface characteristic parameters is determined. The calculation formula is:

[0082]

[0083] In the formula, and are the kth meteorological element C in the jth response mode area j,k and the elasticity coefficient of the pth socio-economic factor Hj,p to the underlying surface characteristic parameters; n j =f j (·) is the quantitative relationship between the Budyko underlying surface characteristic parameters and water cycle elements in the jth response model area; ΔC j,k and ΔH j,p They are the multi-year average changes of the kth meteorological element sliding series and the pth socio-economic element sliding series in the jth response model area, respectively.

[0084] According to the multi-year average changes of each water cycle element in each response model area before and after the jump point determined in step 3, the changes in Budyko underlying surface characteristic parameters caused by climate change and human activities in each response model area are determined. The calculation formulas are shown in equations (10) and (11). The contribution of each influencing factor (climate change and human activity factor) to the change of underlying surface characteristic parameters is quantified through the elasticity coefficients of each meteorological element and socio-economic element on the underlying surface characteristic parameters.

[0085]

[0086] In the formula, Δn j,C and Δn j,H are the changes in the characteristic parameters of the Budyko underlying surface caused by climate change and human activities in the jth response model area; M j and N j are the number of meteorological elements and the number of socio-economic elements in the jth response model area, respectively.

[0087] Step 5: Separate the changes in water cycle elements in each response model area and the contribution of each influencing factor caused by the chain transmission of climate change and human activities (caused by human activities through the underlying surface).

[0088] Based on the elasticity coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters of each response model area to water cycle elements determined in step 3, and the quantitative relationship between the water cycle element values ​​before the jump in each response model area and the annual runoff depth and meteorological element sequence established, combined with the elasticity coefficients of each meteorological element and socio-economic element to the underlying surface characteristic parameters of each response model area determined in step 4, the changes in water cycle elements in each response model area caused by climate change and chain transmission of human activities are determined. The calculation formula is:

[0089]

[0090] In the formula, and are the changes in water cycle elements in the jth response model area caused by the chain transmission of climate change and human activities; F j (C j ,R j ) is the water cycle element W in the jth response model area j The annual runoff depth R j 、Meteorological elements C j The quantitative relationship between W is the elastic coefficient of the underlying surface characteristic parameters of the jth response mode area to the water cycle elements; j is the multi-year average water cycle element value of the j-th response model area.

[0091] Based on the changes in water cycle elements directly caused by the underlying surface changes in each response model area determined in step 3, the changes in water cycle elements in each response model area directly caused by human activities can be determined by deducting the chain transmission effects of climate change and human activities. The calculation formula is:

[0092]

[0093] In the formula, is the change of water cycle elements directly caused by human activities in the jth response model area; is the change of water cycle elements directly caused by the change of underlying surface in the jth response model area.

[0094] The contribution rate of the attribution analysis of the changes in different water cycle elements in each response model area is quantified by using the elasticity coefficients of the underlying surface characteristic parameters of each response model area to water cycle elements, and the elasticity coefficients of various meteorological elements and socio-economic elements to the underlying surface characteristic parameters.

[0095] Embodiment 1

[0096] This embodiment is an application example of the above method.

[0097] This embodiment discloses a method for attributing and analyzing changes in multiple factors of water cycle under multiple climate change impact models, the method comprising the following steps:

[0098] Step 1: Collect and organize data on the study area.

[0099] This embodiment selects the upper reaches of the Yellow River as the study area, and collects and organizes the meteorological element data and water cycle element data of each rainfall station in the region, as well as the socio-economic element data of the prefecture-level city-level administrative district in the region. The meteorological element data collected in this embodiment include daily precipitation and daily temperature (including daily average temperature, daily maximum temperature and daily minimum temperature, etc.) of 93 monitoring stations from 1980 to 2020; the water cycle element data include daily actual evapotranspiration, daily runoff depth, and monthly land water storage changes of 31 monitoring stations from 1980 to 2020; the socio-economic element data include the annual gross domestic product, annual total population, annual agricultural population, annual non-agricultural population, annual agricultural water consumption, annual industrial water consumption, etc. of 26 prefecture-level cities from 1980 to 2020. Among them, in order to unify the time scale, the collected daily precipitation, temperature and actual evapotranspiration data are organized into annual precipitation, annual average temperature and annual actual evapotranspiration data; the collected daily runoff depth data are organized into annual runoff depth, flood season and non-flood season runoff depth data; the collected monthly land water storage change data are organized into annual land water storage change, flood season and non-flood season land water storage change data.

[0100] The basic unit type determined in this embodiment is a small watershed. Based on the collected and collated research area data, the meteorological element sequence, water cycle element sequence and socio-economic element sequence of each basic unit are determined using the area weighting method.

[0101] The Budyko water-heat coupled balance framework of the basin considering the change of terrestrial water storage is established and solved. Specifically, the change of terrestrial water storage is introduced into the Budyko water-heat coupled balance framework, as shown in formula (1):

[0102]

[0103] Where P i,q , PET i,q , R i,q , ΔSi,q 、n i,q are the precipitation, potential evapotranspiration, runoff depth, terrestrial water storage change and Budyko underlying surface characteristic parameter values ​​of the ith basic unit in the qth year, among which the potential evapotranspiration is calculated based on the collected meteorological element data.

[0104] Then, the iterative approximation method is used to solve equation (1) to determine the characteristic parameter sequence of each basic unit Budyko underlying surface {n i,q}.

[0105] Step 2: Determine the response patterns and zoning of water cycle elements to climate change.

[0106] This embodiment adopts the Mann-Kendall trend detection method to determine the time variation trend of each water cycle element, and adopts the Pettitt mutation test method to determine whether each water cycle element has a mutation and the corresponding jump point. The corresponding jump point is approximately between 1986 and 1993.

[0107] This example uses the Pearson correlation analysis method to analyze the correlation of the above indicators, uses the principal component analysis method for dimensionality reduction, uses the hierarchical clustering method to identify the main response patterns of the three water cycle elements to climate change, and uses the Dunn index to evaluate the clustering effect. When the number of clusters is 2 to 10, the distribution of Dunn index values ​​is as follows: Figure 2 As shown. The results show that there are three main response patterns identified, corresponding to three different sub-regions, that is, three different response pattern areas are identified. Among them, in the Yiluo River area, climate change has no significant impact on runoff depth, but the water storage has increased significantly; in the Weihe River area, the water storage has increased significantly, but the runoff depth has no obvious impact; in the Yellow River source area, both the runoff depth and water storage have increased significantly. The Budyko parameter n sequence distribution of the three response pattern areas determined in this example is as follows Figure 3 shown.

[0108] According to the meteorological elements, water cycle source elements, and socio-economic elements of each basic unit determined in step 1, as well as the Budyko underlying surface characteristic parameter series of each basic unit, combined with the determined water cycle element change indicator system and the identified main response modes and zoning, the weighted average method is used to determine the meteorological elements, water cycle elements, socio-economic elements, Budyko underlying surface characteristic parameters, and water cycle element change indicator system sequence of each response mode area, and determine the spatial distribution characteristics of each response mode area and the differences in the water cycle element change indicator system in different response modes.

[0109] Step 3: Separate the changes in water cycle elements in each response model area that are directly caused by climate change and underlying surface changes.

[0110] Based on the basin hydrothermal coupling balance framework, water balance and elasticity coefficient method considering the change of terrestrial water storage, the elasticity coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in each response model area are determined. The calculation formulas are:

[0111]

[0112] In the formula, and are the elastic coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in the jth response model area; n j is the underlying surface characteristic parameter of the jth response mode area; PET j and P j are the multi-year averages of potential evapotranspiration and precipitation series in the jth response model area, respectively.

[0113] According to the jump points of each water cycle element in each basic unit and each response mode area determined in step 2, determine the jump points of each water cycle element in each response mode area, as well as the changes in the multi-year average values ​​of each water cycle element in each response mode area before and after the jump points; determine the changes in water cycle elements directly caused by climate change and underlying surface changes in each response mode area, and the calculation formula is:

[0114]

[0115] In the formula, and are the changes in water cycle elements directly caused by climate change and underlying surface change in the jth response model area; W j , P j are the multi-year average water cycle element values ​​and precipitation in the jth response model area; ΔP j , ΔPET j and Δn j They are the multi-year average changes of precipitation, potential evapotranspiration and Budyko underlying surface characteristic parameters before and after the jump point in the jth response model area.

[0116] The multi-year average changes of runoff depth before and after the jumping point in the Yiluo River area, runoff depth in flood season and non-flood season, change of land water storage, and change of land water storage in flood season and non-flood season determined in this embodiment are -79.7mm, -49.0mm, -30.7mm, 1.6mm, 1.7mm and -0.1mm respectively; the multi-year average changes of runoff depth before and after the jumping point in the Weihe River area, runoff depth in flood season and non-flood season, change of land water storage, and change of land water storage in flood season and non-flood season are -34.2mm, -20.2mm, -14.0, 2.4mm, 1.5mm and 0.9mm respectively; the multi-year average changes of runoff depth before and after the jumping point in the Yellow River source area, runoff depth in flood season and non-flood season, change of land water storage, and change of land water storage in flood season and non-flood season are -32.2mm, -26.2mm, -6.0mm, 0.9mm, 1.1mm and -0.2mm respectively.

[0117] Taking the Yiluo River area as an example, the changes in water cycle elements directly caused by climate change and underlying surface change are shown in Table 2.

[0118] Table 2 Changes in water cycle elements directly caused by climate change and underlying surface changes in the Yiluo River region

[0119]

[0120] According to the determined meteorological element sequence and water cycle element value of each response mode area, the quantitative relationship between the water cycle element value before the jump and the annual runoff depth and meteorological element sequence of each response mode area is established. The calculation formula is:

[0121] W j,i =fW j (C j,1 ,C j,2 ,…,C j,k ,R j ) (7)

[0122] Where W j,i and R j are the ith water cycle element and annual runoff depth in the jth response model area; C j,k is the kth meteorological element in the jth response mode area; fW j (·) is the quantitative relationship between the water cycle elements and the annual runoff depth and meteorological element sequences in the jth response model area.

[0123] Step 4: Separate the underlying surface changes in each response model area directly caused by climate change and human activities and the contribution of each influencing factor.

[0124] Based on the water cycle element sequence and Budyko underlying surface characteristic parameter sequence of each response mode area determined in step 2, the quantitative relationship between Budyko underlying surface characteristic parameters and water cycle elements in each response mode area is established, and the elasticity coefficient of each meteorological element and socio-economic element in each response mode area to the underlying surface characteristic parameters is determined. The calculation formula is:

[0125]

[0126]

[0127] In the formula, and are the kth meteorological element C in the jth response mode area j,k and the elasticity coefficient of the pth socio-economic factor Hj,p to the underlying surface characteristic parameters; n j =f j (·) is the quantitative relationship between the Budyko underlying surface characteristic parameters and water cycle elements in the jth response model area; ΔC j,k and ΔH j,p They are the multi-year average changes of the kth meteorological element sliding series and the pth socio-economic element sliding series in the jth response model area, respectively.

[0128] According to the changes in the multi-year average values ​​of each water cycle element in each response model area before and after the jump point determined in step 3, the changes in the Budyko underlying surface characteristic parameters caused by climate change and human activities in each response model area are determined. The calculation formulas are shown in equations (10) and (11). The contribution of each influencing factor to the change in the underlying surface characteristic parameters is quantified through the elasticity coefficients of each meteorological element and socio-economic element on the underlying surface characteristic parameters.

[0129]

[0130] In the formula, Δn j,C and Δn j,H are the changes in the characteristic parameters of the Budyko underlying surface caused by climate change and human activities in the jth response model area; M j and N j are the number of meteorological elements and the number of socio-economic elements in the jth response model area, respectively.

[0131] In this example, the quantitative relationship between the Budyko underlying surface characteristic parameters and indicators such as precipitation, temperature, potential evapotranspiration, agricultural population, non-agricultural population, gross domestic product, agricultural water consumption, and industrial water consumption was constructed in the Yiluo River area, Weihe River area, and the Yellow River source area. Taking the change in annual runoff depth as an example, the contribution of climate change and human activities to the change in Budyko underlying surface characteristic parameters is as follows: Figure 4 shown.

[0132] Step 5: Separate the changes in water cycle elements in each response model area and the contribution of each influencing factor caused by the chain transmission of climate change and human activities.

[0133] Based on the elasticity coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters of each response model area to water cycle elements determined in step 3, and the quantitative relationship between the water cycle element values ​​before the jump in each response model area and the annual runoff depth and meteorological element sequence established, combined with the elasticity coefficients of each meteorological element and socio-economic element to the underlying surface characteristic parameters of each response model area determined in step 4, the changes in water cycle elements in each response model area caused by climate change and chain transmission of human activities are determined. The calculation formula is:

[0134]

[0135] In the formula, and are the changes in water cycle elements in the jth response model area caused by the chain transmission of climate change and human activities; F j (C j ,R j ) is the water cycle element W in the jth response model area j The annual runoff depth R j 、Meteorological elements C j The quantitative relationship between W is the elastic coefficient of the underlying surface characteristic parameters of the jth response mode area to the water cycle elements; j is the multi-year average water cycle element value of the j-th response model area.

[0136] Based on the changes in water cycle elements directly caused by the underlying surface changes in each response model area determined in step 3, the changes in water cycle elements in each response model area directly caused by human activities can be determined by deducting the chain transmission effects of climate change and human activities. The calculation formula is:

[0137]

[0138] In the formula, is the change of water cycle elements directly caused by human activities in the jth response model area; is the change of water cycle elements directly caused by the change of underlying surface in the jth response model area.

[0139] The changes in water cycle elements caused by the chain transmission of climate change and human activities in each response model area in this embodiment are shown in Table 3.

[0140] Table 3 Changes in water cycle elements caused by climate change and human activities in each response model area

[0141]

[0142] The changes in water cycle elements directly caused by human activities in each response model area in this embodiment are shown in Table 4.

[0143] Table 4 Changes in water cycle elements directly caused by human activities in each response model area

[0144]

[0145] This embodiment uses the elasticity coefficients of the underlying surface characteristic parameters of each response model area to the water cycle elements, the elasticity coefficients of various meteorological elements and socio-economic elements to the underlying surface characteristic parameters, to quantify the contribution rate of the attribution analysis of the changes in different water cycle elements in each response model area, such as Figure 5 and Figure 6 shown.

[0146] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred arrangement scheme, a person skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for attributing and analyzing changes in multiple factors of water cycle under multiple climate change impact models, characterized in that: The method comprises the following steps: Step 1: Collect and organize data in the study area: collect meteorological element data, water cycle element data of each rainfall station in the study area, and social and economic element data of the prefecture-level city-level administrative districts in the study area, and organize the collected data into a unified scale; determine the basic unit of analysis, based on the collected and organized data of the study area, use the area weighted method to determine the meteorological element sequence, water cycle element sequence and social and economic element sequence of each basic unit, establish and solve the Budyko water-heat coupling balance framework of the basin considering the change of terrestrial water storage, and determine the characteristic parameter sequence of the Budyko underlying surface of each basic unit; The specific process of establishing and solving the Budyko hydrothermal coupling balance framework of the basin considering the change of terrestrial water storage and determining the characteristic parameter sequence of the Budyko underlying surface of each basic unit is as follows: introducing the change of terrestrial water storage into the Budyko hydrothermal coupling balance framework, the calculation formula is: Where P i,q , PET i,q , R i,q , ΔS i,q 、n i,q are the precipitation, potential evapotranspiration, runoff depth, terrestrial water storage change and Budyko underlying surface characteristic parameter values ​​of the ith basic unit in the qth year, among which the potential evapotranspiration is calculated based on the collected meteorological element data; Then, the iterative approximation method is used to solve equation (1) to determine the characteristic parameter sequence of each basic unit Budyko underlying surface {n i,q }; Step 2, determine the response mode and zoning of water cycle elements to climate change: According to the water cycle element sequence of each basic unit determined in step 1, determine the time change trend and jump point of each water cycle element, build a water cycle element change index system, identify the response mode and zoning of water cycle elements to climate change, and then use the weighted average method to determine the meteorological elements, water cycle elements, socio-economic elements, Budyko underlying surface characteristic parameters, and water cycle element change index system sequence of each response mode area, determine the spatial distribution characteristics of each response mode area and the differences of the water cycle element change index system in different response modes; Step 3, separating the changes in water cycle elements in each response model area directly caused by climate change and underlying surface changes: based on the basin Budyko hydrothermal coupling balance framework, water balance and elasticity coefficient method considering the change in terrestrial water storage established in step 1, determine the elasticity coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in each response model area; according to the jump points of each water cycle element of each basic unit and each response model area determined in step 2, determine the jump points of each water cycle element in each response model area and the changes in the multi-year average values ​​of each water cycle element in each response model area before and after the jump points, and determine the changes in water cycle elements directly caused by climate change and underlying surface changes in each response model area; according to the meteorological element sequence and water cycle element value of each response model area determined in step 2, establish the quantitative relationship between the water cycle element value before the jump and the annual runoff depth and meteorological element sequence of each response model area; Step 4, separate the changes in the underlying surface of each response model area directly caused by climate change and human activities and the contribution of each influencing factor: based on the water cycle elements and Budyko underlying surface characteristic parameter sequences of each response model area determined in step 2, establish the quantitative relationship between the Budyko underlying surface characteristic parameters and water cycle elements of each response model area, and determine the elasticity coefficient of each meteorological element and socio-economic element to the underlying surface characteristic parameters of each response model area; according to the multi-year average changes of each water cycle element in each response model area before and after the jump point determined in step 3, determine the changes in Budyko underlying surface characteristic parameters caused by climate change and human activities in each response model area, and quantify the contribution of each influencing factor to the changes in underlying surface characteristic parameters through the elasticity coefficients of each meteorological element and socio-economic element to the underlying surface characteristic parameters; Step 5: Separate the changes in water cycle elements in each response model area and the contribution of each influencing factor caused by the chain transmission of climate change and human activities: Based on the elasticity coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in each response model area determined in step 3, and the quantitative relationship between the water cycle element values ​​before the jump and the annual runoff depth and meteorological element sequence established in each response model area, combined with the elasticity coefficients of each meteorological element and socio-economic element to the underlying surface characteristic parameters in each response model area determined in step 4, the changes in water cycle elements in each response model area caused by the chain transmission of climate change and human activities are determined; based on the changes in water cycle elements directly caused by the underlying surface changes in each response model area determined in step 3, the chain transmission effects of climate change and human activities are deducted to determine the changes in water cycle elements in each response model area directly caused by human activities; using the elasticity coefficients of the underlying surface characteristic parameters in each response model area to water cycle elements, and the elasticity coefficients of each meteorological element and socio-economic element to the underlying surface characteristic parameters, the contribution rate of the attribution analysis of the changes in different water cycle elements in each response model area is quantified.

2. The method for attribution analysis of changes in multiple factors of water cycle under multiple climate change impact models according to claim 1 is characterized in that: The meteorological element data in step 1 include the daily precipitation and daily temperature actually observed at the site; the water cycle element data include daily actual evapotranspiration, daily runoff depth and monthly land water storage changes; the socio-economic element data include annual gross domestic product, total population, agricultural population, non-agricultural population, effective irrigation area, cultivated land area, agricultural water use and industrial water use; the collected data are organized into a unified scale, that is, the collected daily precipitation, temperature and actual evapotranspiration data are organized into annual precipitation, annual average temperature and annual actual evapotranspiration data; the collected daily runoff depth data are organized into annual runoff depth, flood season and non-flood season runoff depth data; the collected monthly land water storage change data are organized into annual land water storage change, flood season and non-flood season land water storage change data; the type of the basic unit is a grid or a small watershed.

3. The method for attribution analysis of changes in multiple factors of water cycle under multiple climate change impact models according to claim 1 is characterized in that: The method of determining the time change trend and jump point of each water cycle element in step 2 is specifically: using trend detection, slope estimation and mutation test methods to determine the time change trend and jump point of each water cycle element; the method of identifying the response pattern and zoning of water cycle elements to climate change is specifically: using correlation analysis, dimensionality reduction and clustering methods to identify the response pattern and zoning of water cycle elements to climate change.

4. The method for attribution analysis of changes in multiple factors of water cycle under multiple climate change impact models according to claim 1 is characterized in that: The calculation formula for determining the elasticity coefficient of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in each response model area described in step 3 is: In the formula, and are the elastic coefficients of precipitation, potential evapotranspiration and underlying surface characteristic parameters to water cycle elements in the jth response model area; n j is the underlying surface characteristic parameter of the jth response mode area; PET j and P j are the multi-year averages of potential evapotranspiration and precipitation series in the jth response model area; The calculation formula for determining the changes in water cycle elements directly caused by climate change and underlying surface change in each response model area is: In the formula, and are the changes in water cycle elements directly caused by climate change and underlying surface change in the jth response model area; W j , P j are the multi-year average water cycle element values ​​and precipitation in the jth response model area; ΔP j , ΔPET j and Δn j are the multi-year average changes of precipitation, potential evapotranspiration and Budyko underlying surface characteristic parameters before and after the jump point in the jth response model area; The calculation formula for establishing the quantitative relationship between the water cycle element value before the jump in each response mode area and the annual runoff depth and meteorological element sequence is: W j,i =fW j (C j,1 ,C j,2 ,…,C j,k ,R j ) (7) Where W j,i and R j are the ith water cycle element and annual runoff depth in the jth response model area; C j,k is the kth meteorological element in the jth response mode area; fW j (·) is the quantitative relationship between the water cycle elements and the annual runoff depth and meteorological element sequences in the jth response model area.

5. The method for attribution analysis of changes in multiple factors of water cycle under multiple climate change impact models according to claim 1 is characterized in that: The calculation formula for determining the elastic coefficient of each meteorological element and socio-economic element in each response mode area to the underlying surface characteristic parameters in step 4 is: In the formula, and are the kth meteorological element C in the jth response mode area j,k and the elasticity coefficient of the pth socio-economic factor Hj,p to the underlying surface characteristic parameters; n j =f j (·) is the quantitative relationship between the Budyko underlying surface characteristic parameters and water cycle elements in the jth response model area; ΔC j,k and ΔH j,p are the multi-year mean changes of the kth meteorological element sliding series and the pth socio-economic element sliding series in the jth response model area, respectively; The calculation formula for determining the changes in Budyko underlying surface characteristic parameters caused by climate change and human activities in each response model area is: In the formula, Δn j,C and Δn j,H are the changes in Budyko underlying surface characteristic parameters caused by climate change and human activities in the jth response model area; M j and N j are the number of meteorological elements and the number of socio-economic elements in the jth response model area, respectively.

6. The method for attribution analysis of changes in multiple factors of water cycle under multiple climate change impact models according to claim 1 is characterized in that: The calculation formula for determining the changes in water cycle elements in each response model area caused by the chain transmission of climate change and human activities described in step 5 is: In the formula, and are the changes in water cycle elements in the jth response model area caused by the chain transmission of climate change and human activities; F j (C j ,R j ) is the water cycle element W in the jth response model area j The annual runoff depth R j 、Meteorological elements C j The quantitative relationship between W is the elastic coefficient of the underlying surface characteristic parameters of the jth response mode area to the water cycle elements; j is the multi-year average water cycle element value of the j-th response model area; The calculation formula for determining the changes in water cycle elements in each response model area directly caused by human activities is: In the formula, is the change of water cycle elements directly caused by human activities in the jth response model area; is the change of water cycle elements directly caused by the change of underlying surface in the jth response model area.

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