CN111027810A - Mountain torrent disaster distribution driving force evaluation method in super-large area - Google Patents
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Abstract
The invention discloses a spatial-temporal distribution pattern and driving force evaluation method for mountain torrent disasters in an oversized region, which comprises the following steps of 1, dividing a drainage basin, and determining a research region and an analysis time period; step 2, carrying out spatial partitioning on the research area; step 3, constructing a rainfall driving factor, a surface environment driving factor and a human activity driving factor, and analyzing the driving force; step 4, analyzing continuous factors of the rainfall driving factor, the surface environment driving factor and the human activity driving factor by adopting a geographic detector; and 5, converting mountain torrent disaster point data into continuously distributed fishing net surfaces by adopting a hotspot analysis method, and calculating the disaster intensity of each drainage basin by utilizing an area weight method to obtain the space subarea of the mountain torrent disaster. The driving factors of the mountain torrent disaster space-time distribution pattern are quantitatively analyzed by utilizing the geographic detector, so that the influence of each driving factor is more credible, and the evaluation result is more real.
Description
Technical Field
The invention relates to the technical field of emergency disaster prevention evaluation, in particular to a method for evaluating a space-time distribution pattern and a driving force of a mountain torrent disaster in an oversized area.
Background
The mountain torrent disaster is a main natural disaster which obstructs sustainable development of vast hilly areas in China, can cause a great amount of casualties and huge amount of property loss every year, and has the characteristics of strong burstiness and great difficulty in prevention and prediction. In 2013 to 2016, national mountain torrent disaster investigation is carried out, mountain torrent disaster data from 1949 are collected, natural and social information such as population distribution, social and economic conditions and underlying surface characteristics of a hilly area are investigated, and the information can help to make scientific measures for resisting mountain torrents better. The next work of mountain torrent disaster prevention and control is to make preventive measures of corresponding areas according to the space-time distribution and each driving force.
The mountain torrent disaster driving factors have obvious space-time heterogeneity, but the research on the space-time law of natural disasters rarely focuses on the space-time heterogeneity of disaster driving force, and the quantitative research is lacked. Therefore, there is a need to evaluate the spatial and temporal distribution pattern and driving force of mountain torrent disasters.
Disclosure of Invention
The invention aims to provide a driving force evaluation method for mountain torrent disaster distribution in an oversized area, so that analysis and quantitative evaluation of mountain torrent disasters in China are realized, the problem of lack of quantitative research is solved, and the space-time distribution of mountain torrent disasters in China is determined, so that prevention and treatment of mountain torrent disasters are better performed.
The invention discloses a spatial-temporal distribution pattern and driving force evaluation method for mountain torrent disasters in an oversized area, which comprises the following steps of:
step 1, performing river basin division on the whole country, and determining the research area and the analysis time period of the current time;
step 2, performing spatial zoning on the research area, namely analyzing the mountain torrent disaster data by using a geographic detector for 6 alternative zoning schemes with high recognition degree and high association with the mountain torrent disaster, and calculating the explanatory power, wherein the formula is as follows:
wherein q is the explanatory power of each partition scheme on the spatial distribution of the intensity of the torrential flood disaster, and represents the extent of explaining a certain driving factorSpatial distribution of geographic phenomena; h is1, …, L is the number of divisions of the variable factor, NhAnd N are the number of cells of the partition h and the full partition respectively,
and σ2The variances of the mountain torrent disaster intensity values of the subarea h and the whole area are respectively;
obtaining the explanatory force of each subarea scheme on the spatial distribution of the mountain torrent disaster intensity, and finally selecting the ecological subareas with the second highest explanatory force and reasonable subarea number as subarea schemes for mountain torrent disaster driving force analysis;
step 3, constructing a rainfall driving factor, a surface environment driving factor and a human activity driving factor, and performing driving force analysis, wherein the rainfall driving factor is P10, P25, P50, P100 and P250 obtained according to the rainfall intensity grading standard, and the days for all stations to reach different rainfall intensities are calculated, and the calculation formula is as the following formulas (2) and (3):
wherein, Pi-stationThe days of different stations reaching different rain intensity thresholds in years, wherein a is the year, d is the date, m is 65, and n is 365; rdD amount of rainfall in days CdFor determining whether the d-day rainfall reaches the parameters of different rain intensity thresholds, RiThe rainfall threshold value is the corresponding index i, and A is the calculated year number;
the human driving factors are population density, village density, farmland proportion and GDP respectively, and the surface environment driving factors are NDVI, soil type, gradient and elevation difference respectively;
calculating by using a kriging interpolation method of ArcGISI 10.3 software and obtaining continuous data of each rainfall driving factor;
step 4, analyzing continuous factors of the rainfall driving factor, the surface environment driving factor and the human activity driving factor by adopting a geographic detector, namely grading the continuous driving factors, and then analyzing the driving force of each graded continuous factor by adopting a factor detector and an interactive detector, wherein the analysis result of the factor detector is the explanatory power of each driving factor;
and 5, converting mountain torrent disaster point data into continuously distributed fishing net surfaces by adopting a hotspot analysis method, and calculating the disaster intensity of each drainage basin by utilizing an area weight method to obtain the space subarea of the mountain torrent disaster.
Compared with the prior art, the method has the advantages that:
(1) the driving factors of the space-time distribution pattern of the mountain torrent disasters are quantitatively analyzed by utilizing a geographic detector, so that the influence of each driving factor is more credible;
(2) when the driving force is considered, the method makes up the defects of the traditional method on the time-space heterogeneity, and considers the time-space heterogeneity of the disaster driving force, so that the research is more accurate and close to the reality;
(3) the selected driving factors are more and cover the influence of multiple aspects, so that the method is more comprehensively considered, and the evaluation result is more real.
Drawings
FIG. 1 is a flow chart of a method for evaluating mountain torrent disaster distribution driving force in a super-large area according to the present invention;
FIG. 2 is a flow chart of geographic probe analysis.
Detailed Description
The invention is further described below with reference to the figures and examples.
The invention discloses a method for evaluating the spatial and temporal distribution pattern and the driving force of mountain torrent disasters in an oversized area, which is realized by the following technical scheme:
step 1, performing river basin division on the whole country, and determining the research area and the analysis time period of the current time;
comprehensively considering the research viewpoints at home and abroad, selecting a 'secondary watershed' provided by a Chinese academy resource environment data center as a basic space analysis unit, dividing the whole country into 39279 watersheds, and taking 23167 residential hill watersheds excluding an unmanned area and a plain area as research areas;
the five-year time from 2000 to 2015 is selected as a spatial distribution pattern and driving force analysis period based on two reasons of continuous data and high times of major disasters.
And 2, carrying out spatial partitioning on the research area, and scientifically and accurately analyzing main driving force of the mountain torrent disasters in each area. 6 alternative zoning schemes with high general acceptance and high relevance to the mountain torrent disaster are digitized, coordinate correction and boundary processing are carried out, the mountain torrent disaster data causing casualties or missing occurring between 1951 and 2015 are analyzed by a geographic detector, an explanatory force q is calculated, the formula of the explanatory force q is as the following formula (1), the explanatory force of each zoning scheme on the mountain torrent disaster intensity spatial distribution is obtained, and the results are shown in table 1.
Wherein q is the explanatory power of each partition scheme on the spatial distribution of the intensity of the mountain torrent disasters, and represents the extent to which a certain driving factor explains the spatial distribution of the geographic phenomena; h is1, …, L is the number of partitions of the variable factor, NhAnd N are the number of cells of the partition h and the full partition respectively,
and σ2The variances of the mountain torrent disaster intensity values of the subarea h and the whole area are respectively. q has a value range of [0, 1]The larger the q value is, the larger the influence of the driving factor on the spatial distribution of the intensity of the torrential flood disaster is.
TABLE 1 mountain torrent disaster distribution explanatory power of each partition plan
And finally, selecting ecological subareas with the second highest explanatory power and reasonable subarea number as subarea schemes for mountain torrent disaster driving force analysis.
Step 3, constructing driving factors of rainfall, surface environment and human activities, and analyzing driving force, namely analyzing the explanatory power of each driving factor by using a geographic detector, wherein the driving factor with larger explanatory power has larger influence or more important influence on the mountain torrent disaster;
the main disaster-causing factor of the mountain torrent disaster is short-time strong rainfall, 5 rainfall factors are established according to the classification standard of the rainfall intensity, namely P10, P25, P50, P100 and P250, the days for all stations to reach different rainfall intensities are calculated, and the calculation formula is as follows (2) (3):
wherein, Pi-stationThe days of different stations reaching different rain intensity thresholds in years, wherein a is the year, d is the date, m is 65, and n is 365; rdD amount of rainfall in days CdJudging whether the rainfall reaches the parameters of different rainfall intensity thresholds or not in the day d; riAnd A is the rainfall threshold value of the corresponding index i, and A is the calculated year.
By utilizing a kriging interpolation method in ArcGISI 10.3 software, due to the spatial correlation of rainfall intensity, the linear unbiased optimal estimation can be carried out on unknown sampling points through the data of known points, and the continuous data of each rainfall factor is obtained through calculation.
The driving factors of 4 human activities were selected to represent the human effects, respectively: population density, which represents regional differences of disaster-bearing objects; village density, which reflects the distribution characteristics of population; the cultivated land proportion reflects the influence of human on the underlying surface condition; GDP represents the social and economic development degree of the region. Since the effects of human activities have hysteresis, population density, arable land proportion, and GDP in 2000 were chosen as representative of the study period between 2000 and 2015. And (4) integrating the human activity indexes into the drainage basin attribute field by adopting an area weight method and an average value method according to the secondary drainage basin boundary.
Selecting NDVI, soil type, gradient and elevation difference as driving factors of the earth surface environment, wherein the NDVI represents the earth surface vegetation coverage condition; the elevation difference represents the relief degree of the terrain; the soil type affects the production convergence process of rainfall; the grade represents the steepness of the surface.
And 4, analyzing the explanatory power of each driving factor by adopting a geographic detector, and respectively using a factor detector and an interaction detector. As shown in fig. 2, a flow chart of the geographic probe analysis is shown.
Grading the continuous factors, and classifying the 5 rainfall factors into 10 grades according to a natural breakpoint classification method; dividing human activity factors into 10 grades according to an equal numerical spacing method; the gradient is divided into 6 grades (0-3 degrees, 3-8 degrees, 8-15 degrees, 15-25 degrees, 25-35 degrees and >35 degrees) according to the general rule of water and soil conservation comprehensive treatment planning; the elevation and the NDVI are classified into 10 levels according to a natural breakpoint classification method. And then, a factor detector and an interaction detector are adopted to carry out driving force analysis on each graded continuous factor.
The factor detector analysis results are for each driving factor explanatory force, as shown in table 2.
TABLE 2
As can be seen from table 2, the interpretability of the ecological partition in the driving factor reaches 0.644, which is the highest, and this means that the ecological partition can interpret 64.4% of the spatial variation of the historical mountain torrent disaster, which indicates that the spatial distribution of the historical mountain torrent disaster has a significant regional difference. Sorting the factors according to explanatory power, wherein after ecological zoning, rainfall factors (0.317-0.440), human activity factors (0.175-0.299) and surface environment factors (0.066-0.193) are adopted, which are consistent with the time variation scale sorting of three factors: rainfall factor (daily change) > human activity factor (annual change) > surface environment factor (year-to-year change). The interpretative force of the 5 rainfall factors is gradually increased along with the increase of the rainfall intensity, which shows that the interpretative force of different rainfall intensities on the space distribution of the mountain torrent disaster is changed. Although the gradient and the elevation difference are generally considered as one of the main conditions for the occurrence of the mountain torrent disaster, the two factors in the analysis result have the lowest explanatory power, and the reason is summarized that the terrain condition is the necessary condition for the occurrence of the mountain torrent disaster, human beings tend to benefit and avoid the disaster, the distribution of disaster-bearing bodies in a high-risk area is necessarily sparse, and the disaster prevention capability and consciousness are relatively high.
By analyzing the factors by using the interactive detector, the interpretative force can be obviously improved after other factors are superposed with the ecological subarea, and particularly the interpretative force of P50 after the interaction with the ecological subarea reaches the highest 0.721, which shows that the relation between rainfall larger than 50mm and mountain torrent disaster space patterns is strongest. The single factors of the slope and the elevation difference have the lowest interpretative force, and after the single factors are superposed with three human activity factors of population density, arable land proportion and GDP, the interpretative force can be increased in a nonlinear mode, which shows that the interpretation of the mountain torrent disaster space pattern by the terrain condition and the human activity factors is interactively enhanced, and the violent human activity can lead mountain torrent disaster events to be increased in a nonlinear mode in areas with poor natural conditions.
By combining the analysis, the influence of human activities on historical mountain torrent disasters is greater than that of rainfall factors through chronological scale analysis; in the specific time period of 2000 to 2015, rainfall has a greater impact on historical mountain torrent disasters than human activities, and the interpretation of the spatial pattern of the mountain torrent disasters by the surface environmental factors is minimal. The reason for this is that, on the chronological scale, human activities change significantly, which causes significant changes in disaster-bearing objects and underlying surface conditions, which can cause changes in rainfall disaster-causing forces, and thus human activities become a major influencing factor; in a specific time period, human activities change slightly, and the spatial pattern of the mountain torrent disaster is mainly determined by the spatial distribution change of rainfall. The terrain conditions are necessary conditions for the occurrence of mountain torrent disasters, but the influence is not as good as the difference between human activities and rainfall, and the explanation of the terrain conditions is statistically shown to be low.
And 5, converting mountain torrent disaster point data of 1579 mountain torrent disaster events which cause casualties or missing in 2000 to 2015 into continuously distributed fishing net surfaces by adopting a hot spot analysis method, and calculating disaster intensity of each drainage basin by using an area weight method so as to perform space zoning of the mountain torrent disasters.
Claims (1)
1. A spatial-temporal distribution pattern and driving force evaluation method for mountain torrent disasters in an oversized area is characterized by comprising the following steps:
step 1, performing river basin division on the whole country, and determining the research area and the analysis time period of the current time;
step 2, performing spatial zoning on the research area, namely analyzing the mountain torrent disaster data by using a geographic detector for 6 alternative zoning schemes with high recognition degree and high association with the mountain torrent disaster, and calculating the explanatory power, wherein the formula is as follows:
wherein q is the explanatory power of each partition scheme on the spatial distribution of the intensity of the mountain torrent disasters, and represents the extent to which a certain driving factor explains the spatial distribution of the geographic phenomena; h is1, …, L is the number of divisions of the variable factor, NhAnd N are the number of cells of the partition h and the full partition respectively,
and σ2The variances of the mountain torrent disaster intensity values of the subarea h and the whole area are respectively;
obtaining the explanatory force of each subarea scheme on the spatial distribution of the mountain torrent disaster intensity, and finally selecting the ecological subareas with the second highest explanatory force and reasonable subarea number as subarea schemes for mountain torrent disaster driving force analysis;
step 3, constructing a rainfall driving factor, a surface environment driving factor and a human activity driving factor, and performing driving force analysis, wherein the rainfall driving factor is P10, P25, P50, P100 and P250 obtained according to the rainfall intensity grading standard, and the days for all stations to reach different rainfall intensities are calculated, and the calculation formula is as the following formulas (2) and (3):
wherein, Pi-stationThe days of different stations reaching different rain intensity thresholds in years, wherein a is the year, d is the date, m is 65, and n is 365; rdD amount of rainfall in days CdFor determining whether the d-day rainfall reaches the parameters of different rain intensity thresholds, RiThe rainfall threshold value is the corresponding index i, and A is the calculated year number;
the human driving factors are population density, village density, farmland proportion and GDP respectively, and the surface environment driving factors are NDVI, soil type, gradient and elevation difference respectively;
calculating by using a kriging interpolation method of ArcGISI 10.3 software and obtaining continuous data of each rainfall driving factor;
step 4, analyzing continuous factors of the rainfall driving factor, the surface environment driving factor and the human activity driving factor by adopting a geographic detector, namely grading the continuous driving factors, and then analyzing the driving force of each graded continuous factor by adopting a factor detector and an interactive detector, wherein the analysis result of the factor detector is the explanatory power of each driving factor;
and 5, converting mountain torrent disaster point data into continuously distributed fishing net surfaces by adopting a hotspot analysis method, and calculating the disaster intensity of each drainage basin by utilizing an area weight method to obtain the space subarea of the mountain torrent disaster.
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| CN114332631A (en) * | 2022-01-12 | 2022-04-12 | 中铁二院工程集团有限责任公司 | LiDAR point cloud data extraction method suitable for mountainous area dangerous rockfall |
| CN114332631B (en) * | 2022-01-12 | 2023-04-18 | 中铁二院工程集团有限责任公司 | LiDAR point cloud data extraction method suitable for mountainous area dangerous rockfall |
| CN115330284A (en) * | 2022-10-17 | 2022-11-11 | 国家林业和草原局林草调查规划院 | Wetland change driving factor analysis method and system and readable storage medium |
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