CN117592663B - Drought risk prediction method and system for changing climate - Google Patents
Drought risk prediction method and system for changing climate Download PDFInfo
- Publication number
- CN117592663B CN117592663B CN202410070523.7A CN202410070523A CN117592663B CN 117592663 B CN117592663 B CN 117592663B CN 202410070523 A CN202410070523 A CN 202410070523A CN 117592663 B CN117592663 B CN 117592663B
- Authority
- CN
- China
- Prior art keywords
- drought
- level
- risk
- area
- drc
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000011156 evaluation Methods 0.000 claims abstract description 68
- 238000012544 monitoring process Methods 0.000 claims abstract description 50
- 238000012545 processing Methods 0.000 claims abstract description 16
- 230000008859 change Effects 0.000 claims abstract description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 62
- 238000001556 precipitation Methods 0.000 claims description 42
- 101100533625 Neurospora crassa (strain ATCC 24698 / 74-OR23-1A / CBS 708.71 / DSM 1257 / FGSC 987) drc-4 gene Proteins 0.000 claims description 37
- 238000002372 labelling Methods 0.000 claims description 22
- 101100031387 Neurospora crassa (strain ATCC 24698 / 74-OR23-1A / CBS 708.71 / DSM 1257 / FGSC 987) drc-1 gene Proteins 0.000 claims description 20
- 101100465868 Neurospora crassa (strain ATCC 24698 / 74-OR23-1A / CBS 708.71 / DSM 1257 / FGSC 987) drc-2 gene Proteins 0.000 claims description 20
- 238000004458 analytical method Methods 0.000 claims description 20
- 101100410018 Neurospora crassa (strain ATCC 24698 / 74-OR23-1A / CBS 708.71 / DSM 1257 / FGSC 987) drc-3 gene Proteins 0.000 claims description 16
- 238000000605 extraction Methods 0.000 claims description 8
- 239000002689 soil Substances 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000012010 growth Effects 0.000 claims description 7
- 230000008635 plant growth Effects 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 4
- 238000005315 distribution function Methods 0.000 claims description 4
- 238000013439 planning Methods 0.000 claims description 4
- 238000001704 evaporation Methods 0.000 claims description 3
- 230000008020 evaporation Effects 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 238000011160 research Methods 0.000 claims description 3
- 238000012876 topography Methods 0.000 claims description 3
- 238000013058 risk prediction model Methods 0.000 abstract description 3
- 238000009826 distribution Methods 0.000 description 5
- 241000282414 Homo sapiens Species 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 230000001186 cumulative effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000003621 irrigation water Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Environmental & Geological Engineering (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Educational Administration (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- General Health & Medical Sciences (AREA)
- Pure & Applied Mathematics (AREA)
- Primary Health Care (AREA)
- Algebra (AREA)
- Computational Mathematics (AREA)
- Quality & Reliability (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a drought risk prediction method and a drought risk prediction system for a changed climate, and relates to the field of meteorological early warning, wherein the drought risk prediction method comprises the steps that a data acquisition unit, an evaluation unit and a processing unit acquire vegetation, a river basin area and meteorological data of a monitoring area by utilizing remote sensing satellite images and meteorological station data, an initial data set is established, four levels of drought risk coefficients are generated gradually according to the evaluation method of each module, and areas exceeding threshold values of each module are marked with corresponding risk levels; constructing an integrated learning weather drought risk prediction model to predict a fourth-level drought risk level in a drought high-risk area; if the risk level exceeds the set threshold, drought early warning is sent out in time, and the characteristics of drought events are provided, so that related departments can take measures in advance. According to the method, the accurate prediction of the regional drought risk is realized by constructing the multi-level evaluation system model, and an effective path is provided for regional drought event early warning under the background of future change climate.
Description
Technical Field
The invention relates to the technical field of meteorological early warning, in particular to a drought risk prediction method and a drought risk prediction system for a changed climate.
Background
The role of drought risk prediction for changing climates is versatile, involving various levels of social, economic and ecological systems. Under the background of continuous change of the current climate, the drought risk can be timely and accurately predicted, and an effective decision can be timely made, so that the influence of extreme climate events can be better dealt with.
First, drought risk prediction helps to warn ahead of time, reducing disaster damage. By monitoring weather, hydrologic, and soil data, scientists can identify potential drought risk areas and predict the likelihood of drought over a period of time in the future. The early warning can be used as a signal for taking emergency measures, such as water resource allocation, agricultural management and emergency rescue, so as to reduce disaster loss caused by drought and ensure the life and property safety of people. Second, drought risk prediction is critical to agricultural production. Agriculture is an important component of human life, and drought often results in a lack of farmland moisture, affecting crop growth and yield. Through timely knowing drought risk, farmers can take corresponding agricultural measures, such as selecting crop varieties with stronger adaptability, reasonably utilizing irrigation water sources, adjusting planting periods and the like, so that agricultural losses are reduced to the greatest extent, and grain safety is ensured. In addition, prediction of drought risk is also critical to water resource management. Climate change causes water resource distribution and supply to change, and drought causes water resources to be more scarce. By accurately predicting drought, the water resource management mechanism can take measures to reasonably allocate water resources, ensure normal water use in cities and rural areas and maintain sustainable development of society. Finally, drought risk prediction is also of positive significance for protection and restoration of the ecosystem. Drought not only affects human life, but also causes serious damage to the ecosystem, threatening biodiversity. By knowing the possible occurrence of drought in advance, measures can be taken to protect the fragile ecological system, promote vegetation recovery and maintain ecological balance.
Overall, the role of drought risk prediction for changing climates is multifaceted, involving a number of social, economic and ecological aspects. Through scientific prediction and timely coping, the method can better adapt to climate change, reduce disaster loss and ensure sustainable development of human beings.
The prior art has the following defects: in traditional weather prediction, weather station data and satellite images are generally adopted for monitoring and analysis, but the traditional method aims at drought risk prediction under the climate change background, and has the problems of single data source, insufficient precision, lack of comprehensiveness, inaccurate early warning, poor model generalization capability and the like. A more comprehensive and accurate method is needed in the field of weather prediction to predict drought risk to meet the more effective demand for weather disasters.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a drought risk prediction method and a drought risk prediction system for a changed climate, which are characterized in that through accurately predicting the drought risk of a region under the changed climate background, a multi-level evaluation system model is constructed, various factors such as vegetation, a river basin area, meteorological data and the like are comprehensively considered, the comprehensiveness of prediction is improved, a high-risk region is predicted more accurately by integrating a learning model, and more detailed and timely drought early warning information is provided for related departments, so that an effective prediction path is provided for the drought event of the region under the future changed climate, and measures are taken in advance to cope with potential meteorological disasters, so that the problems in the background technology are solved.
In order to achieve the above object, the present invention provides the following technical solutions: a drought risk prediction system facing to a changing climate comprises a data acquisition unit, an evaluation unit and a processing unit;
the evaluation unit comprises a 1-level evaluation module, a 2-level evaluation module, a 3-level evaluation module and a 4-level evaluation module;
the processing unit comprises a judging module, an on-site survey module, a risk area marking module, a data modeling module and an instruction module;
the method comprises the steps that a data acquisition unit detects and acquires historical satellite remote sensing land utilization type data, vegetation growth data, drainage basin range data and meteorological data of a ground observation station of a monitoring area, an initial data set is established, a 1-level evaluation module of an evaluation unit generates a 1-level drought risk coefficient Drc-1 according to vegetation data of a research area, if the 1-level drought risk coefficient Drc-1 is lower than a preset 1-level risk threshold, the monitoring area is marked as a 1-level risk area, and first early warning information is sent to the outside;
after the area with the first early warning information is obtained, the 1-level evaluation module extracts a water body range Bs of the natural lake based on the dual-polarized radar index SDWI, the 2-level evaluation module calculates the water body area change rate according to the water body range Bs and generates a 2-level drought risk coefficient Drc-2, and if the 2-level drought risk coefficient Drc-2 is lower than a preset 2-level risk threshold, the monitoring area is marked as a 2-level risk area and second early warning information is sent to the outside;
after the area with the second early warning information is acquired, determining ground weather observation stations in a level 2 risk area, acquiring daily precipitation data of each station in an initial data set, generating a level 3 drought risk coefficient Drc-3 by a level 3 evaluation module according to a precipitation level percentage formula, marking the monitoring area as the level 3 risk area if the level 3 drought risk coefficient Drc-3 is lower than a preset level 3 risk threshold, and sending third early warning information to the outside;
after the area with the third early warning information is acquired, determining ground weather observation stations in a 3-level risk area, acquiring daily precipitation data and average air temperature data of each station in an initial data set, generating a 4-level drought risk coefficient Drc-4 by a 4-level evaluation module according to a standardized precipitation evaporation index calculation formula, marking the monitoring area as the 4-level risk area if the 4-level drought risk coefficient Drc-4 is lower than a preset 4-level risk threshold, and sending fourth early warning information to the outside;
after the processing unit acquires the fourth early warning information, a judgment module sends instructions to a field survey module and a risk area labeling module, the field survey module and the risk area labeling module are used for carrying out field survey and map labeling on drought risk points, and a drought high risk point characteristic evolution model is established through a data modeling module for the drought high risk points, and the instruction module is used for generating next control instructions.
Preferably, the initial data set is established in the following manner:
firstly, monitoring land utilization type LU, plant growth condition NDVI and water body range Bs through a remote sensing satellite;
the water body scope Bs constructs a dual-polarized radar index SDWI index through a remote sensing satellite image, wherein the SDWI calculating mode is as follows:wherein, VV and VH are polarized images, k is a gain coefficient, 10, s are taken as initial thresholds, the initial threshold of water surface extraction is calculated by a maximum inter-class variance method, and all natural lake water body ranges Bs in the monitoring area range are calculated by comparing SDWI indexes with the initial threshold of water surface extraction;
secondly, monitoring meteorological data through a ground meteorological observation station to obtain historical data of average air temperature Tm, precipitation amount Pre, maximum air temperature Tmax, minimum air temperature Tmin, average wind speed Win, relative humidity Rhu and sunshine hours Ssd;
and finally, processing the data into data with consistent spatial resolution by using a downscaling method, and establishing an initial data set of the researched area.
Preferably, the generation mode of the 1-level drought risk coefficient Drc-1 is as follows:
generating a level 1 drought risk coefficient Drc-1 by a level 1 evaluation module of the evaluation unit by using the normalized vegetation index NDVI in the initial condition set in the following manner:wherein (1)>NDVI value for the i-th phase of a particular year,/->And->Respectively the maximum value and the minimum value of the NDVI in the i-th period of a plurality of years;
the generation mode of the 2-level drought risk coefficient Drc-2 is as follows: from stage 2 of the evaluation unitThe evaluation module calculates the water body area change rate k according to the water body range Bs and generates a 2-level drought risk coefficient Drc-2 in the following manner:，wherein (1)>The correction coefficient c is a positive constant adjusted according to the type of the water body in the actual river basin;
generating a 3-level drought risk coefficient Drc-3 by a 3-level evaluation module of the evaluation unit according to a precipitation pitch flat percentage formula by using the precipitation amount Pre in the initial condition set, wherein the calculation formula is as follows:wherein (1)>For period precipitation, < >>Average precipitation amount for the same period;
the generation mode of the 4-level drought risk coefficient Drc-4 is as follows:
generating a 4-level drought risk coefficient Drc-4 by a 4-level evaluation module of the evaluation unit by using the average air temperature Tm and the precipitation amount Pre in the initial condition set according to the following formula:wherein, the parameter meaning is: constant item->，，/>，/>Wherein P is the probability value of the normalized distribution function, < ->，/>Is a scale parameter->For shape parameters +.>Is a location parameter.
Preferably, the system further comprises a drought characteristic generating unit, wherein the drought characteristic generating unit comprises a duration time analyzing module, a drought intensity analyzing module and an influence range analyzing module, after the early warning unit sends an early warning notice, the drought characteristic generating unit utilizes an established drought high risk point characteristic evolution model to conduct drought characteristic analysis on the predicted value of Drc-4, and duration time information, drought intensity information and influence range information of the drought event are respectively generated through the duration time analyzing module, the drought intensity analyzing module and the influence range analyzing module and are submitted to related departments to serve as references.
Preferably, a labeling map of the monitoring area is obtained, the labeling map is divided into unit areas with the same resolution by a downscaling method according to the spatial resolution of the initial data set, and drought high risk points and corresponding drought grades are labeled on corresponding positions of the map;
planning an optimal path for the unmanned survey plane based on the drought high risk points, so that the unmanned survey plane shoots each 4-level risk area in the field along a preset path, and image information is acquired;
the method comprises the steps of identifying image information by adopting a computer vision technology, acquiring topography and vegetation types from an identification result, detecting the soil water content Smc of the area by an acquisition personnel, inputting the soil water content Smc into a system, and establishing a drought high risk point characteristic evolution model by a data modeling module by utilizing land type data, normalized vegetation index NDVI, water body data Bs and meteorological data of an initial data set and combining extreme climate factor data generated by an Rclimdex model.
Preferably, the system further comprises a drought characteristic generating unit, wherein the drought characteristic generating unit comprises a duration time analyzing module, a drought intensity analyzing module and an influence range analyzing module, after the early warning unit sends an early warning notice, the drought characteristic generating unit utilizes an established drought high risk point characteristic evolution model to conduct drought characteristic analysis on the predicted value of Drc-4, and duration time information, drought intensity information and influence range information of the drought event are respectively generated through the duration time analyzing module, the drought intensity analyzing module and the influence range analyzing module and are submitted to related departments to serve as references.
A drought risk prediction method facing to a changing climate comprises the following steps:
detecting and acquiring historical satellite remote sensing land utilization type data, vegetation growth data and drainage basin range data of a monitoring area and meteorological data of a ground observation station, establishing an initial data set, generating a 1-level drought risk coefficient Drc-1 according to the vegetation data, marking the monitoring area as a 1-level risk area if the 1-level drought risk coefficient Drc-1 is lower than a preset 1-level risk threshold, and sending first early warning information to the outside;
after the area with the first early warning information is acquired, all the drainage basins near the monitored area are determined, the water body range Bs of the natural lake is extracted based on the dual-polarized radar index SDWI, a 2-level drought risk coefficient Drc-2 is generated, if the 2-level drought risk coefficient Drc-2 is lower than a preset 2-level risk threshold, the monitored area is marked as a 2-level risk area, and second early warning information is sent to the outside;
after the area with the second early warning information is acquired, determining ground weather observation stations in a level 2 risk area, acquiring daily precipitation data of each station in an initial data set, generating a level 3 drought risk coefficient Drc-3, marking the monitoring area as the level 3 risk area if the level 3 drought risk coefficient Drc-3 is lower than a preset level 3 risk threshold, and sending third early warning information to the outside;
after the area with the third early warning information is acquired, daily precipitation data and average air temperature data of each station in the initial data set are acquired, a 4-level drought risk coefficient Drc-4 is generated, if the 4-level drought risk coefficient Drc-4 is lower than a preset 4-level risk threshold, the monitoring area is marked as a 4-level risk area, and fourth early warning information is sent to the outside;
after the fourth early warning information is acquired, carrying out field survey and map labeling of drought risk points, and subsequently establishing a drought high risk point characteristic evolution model aiming at the drought high risk points;
and predicting future values of the Drc-4 at the drought high risk point by using a fourth-level drought risk level prediction model, wherein the fourth-level drought risk level prediction model is an integrated learning prediction model, and is obtained by training and testing an integrated learning model by using an initial data set, and if the predicted values of the Drc-4 exceed the risk threshold values of light drought, medium drought, heavy drought or extreme drought, a weather drought early warning notification of the corresponding risk level is sent to related departments by an early warning unit.
Preferably, after the early warning notification is sent, the drought characteristic analysis is carried out on the predicted value of Drc-4 by using the established drought high risk point characteristic evolution model, and the duration, the drought intensity and the influence range information of the drought event are generated and submitted to related departments as references.
In the technical scheme, the invention has the technical effects and advantages that:
according to the method, the vegetation, the river basin area and the meteorological data of the monitoring area are comprehensively obtained by utilizing the remote sensing satellite image and the meteorological station data, an initial data set is established, drought risk coefficients of different levels are generated through a multi-module evaluation method, the comprehensiveness and the accuracy of prediction are improved, and finally, the integrated learning meteorological drought risk prediction model is adopted to predict the fourth-level drought risk level of a high-risk area so as to more comprehensively predict the potential drought risk.
According to the method, the drought risk of the area under the changed climate background is accurately predicted, a multi-level assessment system model is constructed, various factors such as vegetation, a river basin area and meteorological data are comprehensively considered, the comprehensiveness of prediction is improved, a fourth-level drought risk level is more accurately predicted in a high-risk area through an integrated learning model, more detailed and timely drought early warning information is provided for related departments, an effective prediction path is provided for the drought event of the area under the future changed climate, and measures are taken in advance to cope with potential meteorological disasters.
Drawings
For a clearer description of embodiments of the present application or of the solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present invention, and that other drawings may be obtained according to these drawings for a person skilled in the art.
FIG. 1 is a schematic block diagram of a method and system for drought risk prediction for changing climates according to the present invention.
FIG. 2 is a flow chart of a method and system for drought risk prediction for changing climates according to the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The invention provides a drought risk prediction system facing to a changing climate, which is shown in figure 1, and comprises a data acquisition unit, an evaluation unit and a processing unit;
the evaluation unit comprises a 1-level evaluation module, a 2-level evaluation module, a 3-level evaluation module and a 4-level evaluation module;
the processing unit comprises a judging module, an on-site survey module, a risk area marking module, a data modeling module and an instruction module;
the method comprises the steps that a data acquisition unit detects and acquires historical satellite remote sensing land utilization type data, vegetation growth data, drainage basin range data and meteorological data of a ground observation station of a monitoring area, an initial data set is established, a 1-level evaluation module of an evaluation unit generates a 1-level drought risk coefficient Drc-1 according to vegetation data of a research area, if the 1-level drought risk coefficient Drc-1 is lower than a preset 1-level risk threshold, the monitoring area is marked as a 1-level risk area, and first early warning information is sent to the outside;
after the area with the first early warning information is obtained, the 1-level evaluation module extracts a water body range Bs of the natural lake based on the dual-polarized radar index SDWI, the 2-level evaluation module calculates the water body area change rate according to the water body range Bs and generates a 2-level drought risk coefficient Drc-2, and if the 2-level drought risk coefficient Drc-2 is lower than a preset 2-level risk threshold, the monitoring area is marked as a 2-level risk area and second early warning information is sent to the outside;
after the area with the second early warning information is acquired, determining ground weather observation stations in a level 2 risk area, acquiring daily precipitation data of each station in an initial data set, generating a level 3 drought risk coefficient Drc-3 by a level 3 evaluation module according to a precipitation level percentage formula, marking the monitoring area as the level 3 risk area if the level 3 drought risk coefficient Drc-3 is lower than a preset level 3 risk threshold, and sending third early warning information to the outside;
after the area with the third early warning information is acquired, determining ground weather observation stations in a 3-level risk area, acquiring daily precipitation data and average air temperature data of each station in an initial data set, generating a 4-level drought risk coefficient Drc-4 by a 4-level evaluation module according to a standardized precipitation evaporation index calculation formula, marking the monitoring area as the 4-level risk area if the 4-level drought risk coefficient Drc-4 is lower than a preset 4-level risk threshold, and sending fourth early warning information to the outside;
after the processing unit acquires the fourth early warning information, a judgment module sends instructions to a field survey module and a risk area labeling module, the field survey module and the risk area labeling module are used for carrying out field survey and map labeling on drought risk points, and a drought high risk point characteristic evolution model is established through a data modeling module for the drought high risk points, and the instruction module is used for generating next control instructions.
The initial data set is established in the following manner:
firstly, monitoring land utilization type LU, plant growth condition NDVI and water body range Bs through a remote sensing satellite;
the land utilization type LU and the plant growth condition NDVI are obtained by using remote sensing monitoring space distribution historical data and satellite remote sensing vegetation index historical data of the national land;
the water body scope Bs constructs a dual-polarized radar index SDWI index through a remote sensing satellite image, wherein the SDWI calculating mode is as follows:wherein, VV and VH are polarized images, k is a gain coefficient, 10, s are taken as initial thresholds, the initial threshold of water surface extraction is calculated by a maximum inter-class variance method, and all natural lake water body ranges Bs in the monitoring area range are calculated by comparing SDWI indexes with the initial threshold of water surface extraction;
secondly, monitoring meteorological data through a ground meteorological observation station to obtain historical data of average air temperature Tm, precipitation amount Pre, maximum air temperature Tmax, minimum air temperature Tmin, average wind speed Win, relative humidity Rhu and sunshine hours Ssd;
and finally, processing the data into data with consistent spatial resolution by using a downscaling method, and establishing an initial data set of the researched area.
The generation mode of the 1-level drought risk coefficient Drc-1 is as follows:
generating a level 1 drought risk coefficient Drc-1 by a level 1 evaluation module of the evaluation unit by using the normalized vegetation index NDVI in the initial condition set in the following manner:wherein (1)>NDVI value for the i-th phase of a particular year,/->And->Respectively the maximum value and the minimum value of the NDVI in the i-th period of a plurality of years;
the generation mode of the 2-level drought risk coefficient Drc-2 is as follows: the 2-level evaluation module of the evaluation unit calculates the water body area change rate k according to the water body range Bs and generates a 2-level drought risk coefficient Drc-2 in the following manner:，wherein (1)>The correction coefficient c is a positive constant adjusted according to the type of the water body in the actual river basin;
generating a 3-level drought risk coefficient Drc-3 by a 3-level evaluation module of the evaluation unit according to a precipitation pitch flat percentage formula by using the precipitation amount Pre in the initial condition set, wherein the calculation formula is as follows:wherein (1)>For the period precipitation (mm), +.>Average precipitation (mm) for the period of time;
the generation mode of the 4-level drought risk coefficient Drc-4 is as follows:
generating a 4-level drought risk coefficient Drc-4 by a 4-level evaluation module of the evaluation unit by using the average air temperature Tm and the precipitation amount Pre in the initial condition set according to the following formula:wherein, the parameter meaning is: constant item->，，/>，/>Wherein, P is the probability value of the standardized distribution function, and can be calculated by the log-logistic probability distribution cumulative function of 3 parameters:，/>is a scale parameter->For shape parameters +.>The position parameter can be obtained by fitting by a linear moment method.
The system also comprises a prediction unit and an early warning unit, wherein the prediction unit predicts the future value of the Drc-4 of the drought high risk point by using a fourth-level drought risk level prediction model after receiving the control instruction generated by the processing unit, the fourth-level drought risk level prediction model is an integrated learning prediction model, the model is obtained by training and testing an integrated learning model by using an initial data set, and if the predicted value of the Drc-4 exceeds the risk threshold of light drought, medium drought, heavy drought or special drought, the early warning unit sends weather drought early warning notification of the corresponding risk level to related departments.
Acquiring a labeling map of a monitoring area, dividing the labeling map into unit areas with the same resolution by a downscaling method according to the spatial resolution of an initial data set, and labeling drought high risk points and corresponding drought grades on corresponding positions of the map;
planning an optimal path for the unmanned survey plane based on the drought high risk points, so that the unmanned survey plane shoots each 4-level risk area in the field along a preset path, and image information is acquired;
the method comprises the steps of identifying image information by adopting a computer vision technology, acquiring topography and vegetation types from an identification result, detecting the soil water content Smc of the area by an acquisition personnel, inputting the soil water content Smc into a system, and establishing a drought high risk point characteristic evolution model by a data modeling module by utilizing land type data, normalized vegetation index NDVI, water body data Bs and meteorological data of an initial data set and combining extreme climate factor data generated by an Rclimdex model.
The drought characteristic generation unit comprises a duration analysis module, a drought intensity analysis module and an influence range analysis module, and after the early warning unit sends an early warning notice, the drought characteristic generation unit utilizes an established drought high risk point characteristic evolution model to conduct drought characteristic analysis on a predicted value of Drc-4, and duration information, drought intensity information and influence range information of the drought event are respectively generated through the duration analysis module, the drought intensity analysis module and the influence range analysis module and are submitted to related departments as references.
A drought risk prediction method facing to a changing climate comprises the following steps:
detecting and acquiring historical satellite remote sensing land utilization type data, vegetation growth data and drainage basin range data of a monitoring area and meteorological data of a ground observation station, establishing an initial data set, generating a 1-level drought risk coefficient Drc-1 according to the vegetation data, marking the monitoring area as a 1-level risk area if the 1-level drought risk coefficient Drc-1 is lower than a preset 1-level risk threshold, and sending first early warning information to the outside;
after the area with the first early warning information is acquired, all the drainage basins near the monitored area are determined, the water body range Bs of the natural lake is extracted based on the dual-polarized radar index SDWI, a 2-level drought risk coefficient Drc-2 is generated, if the 2-level drought risk coefficient Drc-2 is lower than a preset 2-level risk threshold, the monitored area is marked as a 2-level risk area, and second early warning information is sent to the outside;
after the area with the second early warning information is acquired, determining ground weather observation stations in a level 2 risk area, acquiring daily precipitation data of each station in an initial data set, generating a level 3 drought risk coefficient Drc-3, marking the monitoring area as the level 3 risk area if the level 3 drought risk coefficient Drc-3 is lower than a preset level 3 risk threshold, and sending third early warning information to the outside;
after the area with the third early warning information is acquired, daily precipitation data and average air temperature data of each station in the initial data set are acquired, a 4-level drought risk coefficient Drc-4 is generated, if the 4-level drought risk coefficient Drc-4 is lower than a preset 4-level risk threshold, the monitoring area is marked as a 4-level risk area, and fourth early warning information is sent to the outside;
after the fourth early warning information is acquired, carrying out field survey and map labeling of drought risk points, and subsequently establishing a drought high risk point characteristic evolution model aiming at the drought high risk points;
predicting future values of Drc-4 of drought high risk points by using a fourth-level drought risk level prediction model, wherein the fourth-level drought risk level prediction model is an integrated learning prediction model, the model is obtained by training and testing an integrated learning model by using an initial data set, and if the predicted values of Drc-4 exceed the risk threshold values of light drought, medium drought, heavy drought or extreme drought, a weather drought early warning notification of corresponding risk levels is sent to related departments by an early warning unit;
the initial data set is established in the following manner:
firstly, monitoring land utilization type LU, plant growth condition NDVI and water body range Bs through a remote sensing satellite;
the land utilization type LU and the plant growth condition NDVI are obtained by using remote sensing monitoring space distribution historical data and satellite remote sensing vegetation index historical data of the national land;
the water body scope Bs constructs a dual-polarized radar index SDWI index through a remote sensing satellite image, wherein the SDWI calculating mode is as follows:wherein, VV and VH are polarized images, k is a gain coefficient, 10, s are taken as initial thresholds, the initial threshold of water surface extraction is calculated by a maximum inter-class variance method, and all natural lake water body ranges Bs in the monitoring area range are calculated by comparing SDWI indexes with the initial threshold of water surface extraction;
secondly, monitoring meteorological data through a ground meteorological observation station to obtain historical data of average air temperature Tm, precipitation amount Pre, maximum air temperature Tmax, minimum air temperature Tmin, average wind speed Win, relative humidity Rhu and sunshine hours Ssd;
finally, processing the data into data with consistent spatial resolution by using a downscaling method, and establishing an initial data set of the researched area;
the generation mode of the 1-level drought risk coefficient Drc-1 is as follows:
generating a level 1 drought risk coefficient Drc-1 by a level 1 evaluation module of the evaluation unit by using the normalized vegetation index NDVI in the initial condition set in the following manner:wherein (1)>NDVI value for the i-th phase of a particular year,/->And->Respectively the maximum value and the minimum value of the NDVI in the i-th period of a plurality of years;
the generation mode of the 2-level drought risk coefficient Drc-2 is as follows: calculating the water area change rate k according to the water range Bs and generating a 2-level drought risk coefficient Drc-2 by the following steps:，/>wherein (1)>The correction coefficient c is a positive constant adjusted according to the type of the water body in the actual river basin;
generating a 3-level drought risk coefficient Drc-3 according to a precipitation distance flat percentage formula by using precipitation amount Pre in an initial condition set, wherein the calculation formula is as follows:wherein (1)>For the period precipitation (mm), +.>Average precipitation (mm) for the period of time;
the generation mode of the 4-level drought risk coefficient Drc-4 is as follows:
generating a 4-level drought risk coefficient Drc-4 by using the average air temperature Tm and the precipitation amount Pre in the initial condition set, wherein the method is generated according to the following formula:wherein, the parameter meaning is: constant item->，，/>，/>Wherein, P is the probability value of the standardized distribution function, and can be calculated by the log-logistic probability distribution cumulative function of 3 parameters:alpha is a scale parameter, beta is a shape parameter, gamma is a position parameter, and the shape parameter and the position parameter can be obtained through fitting by a linear moment method.
And after receiving a control instruction generated by the processing unit, predicting the future value of the Drc-4 of the drought high risk point by using a fourth-level drought risk level prediction model, wherein the fourth-level drought risk level prediction model is an integrated learning prediction model, the model is obtained by training and testing an integrated learning model by using an initial data set, and if the predicted value of the Drc-4 exceeds the risk threshold of light drought, medium drought, heavy drought or extreme drought, the early warning unit sends weather drought early warning notification of the corresponding risk level to related departments.
Acquiring a labeling map of a monitoring area, dividing the labeling map into unit areas with the same resolution by a downscaling method according to the spatial resolution of an initial data set, and labeling drought high risk points and corresponding drought grades on corresponding positions of the map;
planning an optimal path for the unmanned survey plane based on the drought high risk points, so that the unmanned survey plane shoots each 4-level risk area in the field along a preset path, and image information is acquired;
the image information is identified by adopting a computer vision technology, the terrain and vegetation types are obtained from the identification result, the soil water content Smc of the area is detected by an acquisition personnel and is input into the system, and a drought high risk point characteristic evolution model is established by utilizing land type data, normalized vegetation index NDVI, water body data Bs and meteorological data of an initial data set and combining extreme climate factor data generated by an Rclimdex model.
After the early warning notice is sent, the drought characteristic analysis is carried out on the predicted value of Drc-4 by utilizing the established drought high risk point characteristic evolution model, and the duration, the drought intensity and the influence range information of the drought event are generated and submitted to related departments as references.
According to the method, the vegetation, the river basin area and the meteorological data of the monitoring area are comprehensively obtained by utilizing the remote sensing satellite image and the meteorological station data, an initial data set is established, drought risk coefficients of different levels are generated through a multi-module evaluation method, the comprehensiveness and the accuracy of prediction are improved, and finally, the integrated learning meteorological drought risk prediction model is adopted to predict the fourth-level drought risk level of a high-risk area so as to more comprehensively predict the potential drought risk.
According to the method, the drought risk of the area under the changed climate background is accurately predicted, a multi-level assessment system model is constructed, various factors such as vegetation, a river basin area and meteorological data are comprehensively considered, the comprehensiveness of prediction is improved, a fourth-level drought risk level is more accurately predicted in a high-risk area through an integrated learning model, more detailed and timely drought early warning information is provided for related departments, an effective prediction path is provided for the drought event of the area under the future changed climate, and measures are taken in advance to cope with potential meteorological disasters.
While certain exemplary embodiments of the present invention have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the invention, which is defined by the appended claims.
Claims (4)
1. The drought risk prediction system for the changed climate is characterized by comprising a data acquisition unit, an evaluation unit and a processing unit;
the evaluation unit comprises a 1-level evaluation module, a 2-level evaluation module, a 3-level evaluation module and a 4-level evaluation module;
the processing unit comprises a judging module, an on-site survey module, a risk area marking module, a data modeling module and an instruction module;
the method comprises the steps that a data acquisition unit detects and acquires historical satellite remote sensing land utilization type data, vegetation growth data, drainage basin range data and meteorological data of a ground observation station of a monitoring area, an initial data set is established, a 1-level evaluation module of an evaluation unit generates a 1-level drought risk coefficient Drc-1 according to vegetation data of a research area, if the 1-level drought risk coefficient Drc-1 is lower than a preset 1-level risk threshold, the monitoring area is marked as a 1-level risk area, and first early warning information is sent to the outside;
after the area with the first early warning information is obtained, the 1-level evaluation module extracts a water body range Bs of the natural lake based on the dual-polarized radar index SDWI, the 2-level evaluation module calculates the water body area change rate according to the water body range Bs and generates a 2-level drought risk coefficient Drc-2, and if the 2-level drought risk coefficient Drc-2 is lower than a preset 2-level risk threshold, the monitoring area is marked as a 2-level risk area and second early warning information is sent to the outside;
after the area with the second early warning information is acquired, determining ground weather observation stations in a level 2 risk area, acquiring daily precipitation data of each station in an initial data set, generating a level 3 drought risk coefficient Drc-3 by a level 3 evaluation module according to a precipitation level percentage formula, marking the monitoring area as the level 3 risk area if the level 3 drought risk coefficient Drc-3 is lower than a preset level 3 risk threshold, and sending third early warning information to the outside;
after the area with the third early warning information is acquired, determining ground weather observation stations in a 3-level risk area, acquiring daily precipitation data and average air temperature data of each station in an initial data set, generating a 4-level drought risk coefficient Drc-4 by a 4-level evaluation module according to a standardized precipitation evaporation index calculation formula, marking the monitoring area as the 4-level risk area if the 4-level drought risk coefficient Drc-4 is lower than a preset 4-level risk threshold, and sending fourth early warning information to the outside;
the generation mode of the 1-level drought risk coefficient Drc-1 is as follows:
generating a level 1 drought risk coefficient Drc-1 by a level 1 evaluation module of the evaluation unit by using the normalized vegetation index NDVI in the initial condition set in the following manner:
wherein (1)>NDVI value for the i-th phase of a particular year,/->Andrespectively the maximum value and the minimum value of the NDVI in the i-th period of a plurality of years;
the generation mode of the 2-level drought risk coefficient Drc-2 is as follows: the 2-level evaluation module of the evaluation unit calculates the water body area change rate k according to the water body range Bs and generates a 2-level drought risk coefficient Drc-2 in the following manner:
，/>
wherein,the correction coefficient c is a positive constant adjusted according to the type of the water body in the actual river basin;
generating a 3-level drought risk coefficient Drc-3 by a 3-level evaluation module of the evaluation unit according to a precipitation pitch flat percentage formula by using the precipitation amount Pre in the initial condition set, wherein the calculation formula is as follows:
wherein (1)>For period precipitation, < >>For period synchronizationAverage precipitation;
the generation mode of the 4-level drought risk coefficient Drc-4 is as follows:
generating a 4-level drought risk coefficient Drc-4 by a 4-level evaluation module of the evaluation unit by using the average air temperature Tm and the precipitation amount Pre in the initial condition set according to the following formula:
wherein, the parameter meaning is: constant item->，，/>，/>Wherein P is the probability value of the normalized distribution function, < ->，/>Is a scale parameter->As a function of the shape parameter(s),is a location parameter;
after the processing unit acquires the fourth early warning information, a judgment module sends instructions to a field survey module and a risk area labeling module, the field survey module and the risk area labeling module are used for carrying out field survey and map labeling on drought risk points, a drought high risk point characteristic evolution model is established for the drought high risk points through a data modeling module, and a next control instruction is generated by the instruction module;
the drought characteristic generation unit comprises a duration analysis module, a drought intensity analysis module and an influence range analysis module, and after the early warning unit sends an early warning notice, the drought characteristic generation unit utilizes an established drought high risk point characteristic evolution model to conduct drought characteristic analysis on the predicted value of Drc-4, and duration information, drought intensity information and influence range information of a drought event are respectively generated through the duration analysis module, the drought intensity analysis module and the influence range analysis module and are submitted to related departments as references.
2. The drought risk prediction system for a changing climate according to claim 1, wherein the initial data set is established by:
firstly, monitoring land utilization type LU, plant growth condition NDVI and water body range Bs through a remote sensing satellite;
the water body scope Bs constructs a dual-polarized radar index SDWI index through a remote sensing satellite image, wherein the SDWI calculating mode is as follows:
wherein, VV and VH are polarized images, k is a gain coefficient, 10, s are taken as initial thresholds, the initial threshold of water surface extraction is calculated by a maximum inter-class variance method, and all natural lake water body ranges Bs in the monitoring area range are calculated by comparing SDWI indexes with the initial threshold of water surface extraction;
secondly, monitoring meteorological data through a ground meteorological observation station to obtain historical data of average air temperature Tm, precipitation amount Pre, maximum air temperature Tmax, minimum air temperature Tmin, average wind speed Win, relative humidity Rhu and sunshine hours Ssd;
and finally, processing the data into data with consistent spatial resolution by using a downscaling method, and establishing an initial data set of the researched area.
3. The drought risk prediction system for the changing climate according to claim 1, wherein a labeling map of a monitoring area is obtained, the labeling map is divided into unit areas with the same resolution by a downscaling method according to the spatial resolution of an initial data set, and drought high risk points and corresponding drought grades thereof are labeled on corresponding positions of the map;
planning an optimal path for the unmanned survey plane based on the drought high risk points, so that the unmanned survey plane shoots each 4-level risk area in the field along a preset path, and image information is acquired;
the method comprises the steps of identifying image information by adopting a computer vision technology, acquiring topography and vegetation types from an identification result, detecting the soil water content Smc of the area by an acquisition personnel, inputting the soil water content Smc into a system, and establishing a drought high risk point characteristic evolution model by a data modeling module by utilizing land type data, normalized vegetation index NDVI, water body data Bs and meteorological data of an initial data set and combining extreme climate factor data generated by an Rclimdex model.
4. A method of drought risk prediction for changing climates, implemented by a drought risk prediction system for changing climates according to any one of the preceding claims 1 to 3, comprising the steps of:
detecting and acquiring historical satellite remote sensing land utilization type data, vegetation growth data and drainage basin range data of a monitoring area and meteorological data of a ground observation station, establishing an initial data set, generating a 1-level drought risk coefficient Drc-1 according to the vegetation data, marking the monitoring area as a 1-level risk area if the 1-level drought risk coefficient Drc-1 is lower than a preset 1-level risk threshold, and sending first early warning information to the outside;
after the area with the first early warning information is acquired, all the drainage basins near the monitored area are determined, the water body range Bs of the natural lake is extracted based on the dual-polarized radar index SDWI, a 2-level drought risk coefficient Drc-2 is generated, if the 2-level drought risk coefficient Drc-2 is lower than a preset 2-level risk threshold, the monitored area is marked as a 2-level risk area, and second early warning information is sent to the outside;
after the area with the second early warning information is acquired, determining ground weather observation stations in a level 2 risk area, acquiring daily precipitation data of each station in an initial data set, generating a level 3 drought risk coefficient Drc-3, marking the monitoring area as the level 3 risk area if the level 3 drought risk coefficient Drc-3 is lower than a preset level 3 risk threshold, and sending third early warning information to the outside;
after the area with the third early warning information is acquired, daily precipitation data and average air temperature data of each station in the initial data set are acquired, a 4-level drought risk coefficient Drc-4 is generated, if the 4-level drought risk coefficient Drc-4 is lower than a preset 4-level risk threshold, the monitoring area is marked as a 4-level risk area, and fourth early warning information is sent to the outside;
after the fourth early warning information is acquired, carrying out field survey and map labeling of drought risk points, and subsequently establishing a drought high risk point characteristic evolution model aiming at the drought high risk points;
predicting future values of Drc-4 of drought high risk points by using a fourth-level drought risk level prediction model, wherein the fourth-level drought risk level prediction model is an integrated learning prediction model, the model is obtained by training and testing an integrated learning model by using an initial data set, and if the predicted values of Drc-4 exceed the risk threshold values of light drought, medium drought, heavy drought or extreme drought, a weather drought early warning notification of corresponding risk levels is sent to related departments by an early warning unit;
after the early warning notice is sent, the drought characteristic analysis is carried out on the predicted value of Drc-4 by utilizing the established drought high risk point characteristic evolution model, and the duration, the drought intensity and the influence range information of the drought event are generated and submitted to related departments as references.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410070523.7A CN117592663B (en) | 2024-01-18 | 2024-01-18 | Drought risk prediction method and system for changing climate |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410070523.7A CN117592663B (en) | 2024-01-18 | 2024-01-18 | Drought risk prediction method and system for changing climate |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117592663A CN117592663A (en) | 2024-02-23 |
| CN117592663B true CN117592663B (en) | 2024-04-05 |
Family
ID=89913702
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410070523.7A Active CN117592663B (en) | 2024-01-18 | 2024-01-18 | Drought risk prediction method and system for changing climate |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117592663B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160057911A (en) * | 2014-11-14 | 2016-05-24 | 사단법인 한국농림기상학회 | System for deducting agricultural drought index and method therefor |
| CN110930048A (en) * | 2019-11-29 | 2020-03-27 | 中国农业科学院农业资源与农业区划研究所 | Crop drought risk assessment system and method based on disaster mechanism process |
| CN112819312A (en) * | 2021-01-25 | 2021-05-18 | 华中科技大学 | Method and system for evaluating drought socioeconomic exposure degree under climate change scene |
| WO2022115938A1 (en) * | 2020-12-03 | 2022-06-09 | Riskthinking.Ai Inc. | Systems and methods with classification standard for computer models to measure and manage radical risk using machine learning and scenario generation |
| CN115099453A (en) * | 2022-05-06 | 2022-09-23 | 河海大学 | Multivariable rasterized satellite remote sensing comprehensive drought risk assessment method |
| CN116451879A (en) * | 2023-06-16 | 2023-07-18 | 武汉大学 | Drought risk prediction method and system and electronic equipment |
| CN116596303A (en) * | 2023-05-08 | 2023-08-15 | 广东省水利水电科学研究院 | Drought risk assessment and zoning method, system, medium, equipment and terminal |
| CN116663885A (en) * | 2023-04-24 | 2023-08-29 | 中国水利水电科学研究院 | Drought risk evaluation system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB201405788D0 (en) * | 2014-03-31 | 2014-05-14 | Imp Innovations Ltd | A computer implemented method of deriving performance from a local model |
-
2024
- 2024-01-18 CN CN202410070523.7A patent/CN117592663B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160057911A (en) * | 2014-11-14 | 2016-05-24 | 사단법인 한국농림기상학회 | System for deducting agricultural drought index and method therefor |
| CN110930048A (en) * | 2019-11-29 | 2020-03-27 | 中国农业科学院农业资源与农业区划研究所 | Crop drought risk assessment system and method based on disaster mechanism process |
| WO2022115938A1 (en) * | 2020-12-03 | 2022-06-09 | Riskthinking.Ai Inc. | Systems and methods with classification standard for computer models to measure and manage radical risk using machine learning and scenario generation |
| CN112819312A (en) * | 2021-01-25 | 2021-05-18 | 华中科技大学 | Method and system for evaluating drought socioeconomic exposure degree under climate change scene |
| CN115099453A (en) * | 2022-05-06 | 2022-09-23 | 河海大学 | Multivariable rasterized satellite remote sensing comprehensive drought risk assessment method |
| CN116663885A (en) * | 2023-04-24 | 2023-08-29 | 中国水利水电科学研究院 | Drought risk evaluation system |
| CN116596303A (en) * | 2023-05-08 | 2023-08-15 | 广东省水利水电科学研究院 | Drought risk assessment and zoning method, system, medium, equipment and terminal |
| CN116451879A (en) * | 2023-06-16 | 2023-07-18 | 武汉大学 | Drought risk prediction method and system and electronic equipment |
Non-Patent Citations (2)
| Title |
|---|
| Prediction of Drought Risk Based on the WRF Model in Yunnan Province of China;Zhao, CH;《Advances in Meteorology》;20131107;全文 * |
| 不同温升情景下中国旱灾风险变化评估;卢冬燕;《干旱区地理》;20231214;正文第1-15页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117592663A (en) | 2024-02-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tsakiris et al. | Regional drought assessment based on the Reconnaissance Drought Index (RDI) | |
| CN111737651B (en) | Multi-source data-based spatial grid drought disaster risk assessment method and system | |
| US20160003792A1 (en) | Functional soil maps | |
| Bai et al. | Spatial-temporal variations of ecological vulnerability in the Tarim River Basin, Northwest China | |
| Dalezios et al. | Water scarcity management: Part 2: Satellite-based composite drought analysis | |
| Tran et al. | Characterising spatiotemporal vegetation variations using LANDSAT time‐series and Hurst exponent index in the Mekong River Delta | |
| Mondino et al. | A GIS tool for the land carrying capacity of large solar plants | |
| Wambua et al. | Drought forecasting using indices and Artificial Neural Networks for upper Tana River basin, Kenya-A review concept | |
| BR112021014654A2 (en) | WEATHER FORECASTS WITH SCALE REDUCTION | |
| CN107403004B (en) | Remote-measuring rainfall site suspicious numerical inspection method based on terrain data | |
| Chen et al. | Risk assessment of maize drought in China based on physical vulnerability | |
| Jalayer et al. | Assessment of spatiotemporal characteristic of droughts using in situ and remote sensing-based drought indices | |
| Ahmad et al. | Statistically downscaled projections of CORDEX South Asia using quantile mapping approach over Pakistan region | |
| Wambua et al. | Spatio-temporal drought characterization for the Upper Tana River Basin, Kenya using the Standardized Precipitation Index (SPI) | |
| CN117592663B (en) | Drought risk prediction method and system for changing climate | |
| Azizi Mobaser et al. | Evaluating the performance of Era-5 Re-analysis data in estimating daily and monthly precipitation, Case Study; Ardabil Province | |
| Zhang et al. | Assessing and mapping human well-being for sustainable development amid drought and flood hazards: Dadu River Basin of China | |
| Minh et al. | Deciphering the relationship between meteorological and hydrological drought in Ben Tre province, Vietnam | |
| Raksapatcharawong et al. | Development of drought risk analysis platform using multiple satellite sensors | |
| Verma et al. | Drought analysis in the seonath river basin using reconnaissance drought index and standardised precipitation index | |
| Roustaei et al. | Investigating Lagged Cross-correlation between Wind Erosion and Drought in Southern Iran’s arid regions Investigating Lagged Cross-correlation between Wind Erosion and Drought in Southern Iran’s arid regions | |
| Wang et al. | Extreme summer precipitation events in China and their changes during 1982~ 2019 | |
| Ainembabazi | Comparison of standardized precipitation index (SPI) and vegetation health index (VHI) in drought monitoring in Isingiro District. | |
| Gana et al. | Monitoring Land Use/Land Cover Dynamics in the Province of Constantine, Algeria using Remote Sensing and GIS | |
| Doun et al. | Temporal and Spatial Evaluation of Drought in Agricultural Stability Zones in Syria between 1992 and 2018 by Using SPI Index |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |