CN117828906A - Drought transmission process simulation method, system and medium based on crop growth model - Google Patents
Drought transmission process simulation method, system and medium based on crop growth model Download PDFInfo
- Publication number
- CN117828906A CN117828906A CN202410248764.6A CN202410248764A CN117828906A CN 117828906 A CN117828906 A CN 117828906A CN 202410248764 A CN202410248764 A CN 202410248764A CN 117828906 A CN117828906 A CN 117828906A
- Authority
- CN
- China
- Prior art keywords
- drought
- crops
- crop
- weather
- conditions
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 89
- 230000005540 biological transmission Effects 0.000 title claims abstract description 34
- 238000004088 simulation Methods 0.000 title claims abstract description 34
- 230000008569 process Effects 0.000 claims abstract description 50
- 238000012546 transfer Methods 0.000 claims abstract description 12
- 230000008859 change Effects 0.000 claims abstract description 10
- 208000005156 Dehydration Diseases 0.000 claims description 11
- 239000002689 soil Substances 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 10
- 238000011161 development Methods 0.000 claims description 8
- 238000001556 precipitation Methods 0.000 claims description 8
- 238000011835 investigation Methods 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 3
- 239000003086 colorant Substances 0.000 claims description 2
- 238000007726 management method Methods 0.000 claims 2
- 238000013468 resource allocation Methods 0.000 claims 1
- 230000007246 mechanism Effects 0.000 abstract description 3
- 240000008042 Zea mays Species 0.000 description 24
- 235000002017 Zea mays subsp mays Nutrition 0.000 description 24
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 description 22
- 235000005822 corn Nutrition 0.000 description 22
- 238000010586 diagram Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 208000003643 Callosities Diseases 0.000 description 2
- 206010020649 Hyperkeratosis Diseases 0.000 description 2
- 235000016383 Zea mays subsp huehuetenangensis Nutrition 0.000 description 2
- 238000003973 irrigation Methods 0.000 description 2
- 230000002262 irrigation Effects 0.000 description 2
- 235000009973 maize Nutrition 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000035558 fertility Effects 0.000 description 1
- 239000003673 groundwater Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000003621 irrigation water Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000002352 surface water Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application relates to a drought transmission process simulation method, a drought transmission process simulation system and a drought transmission process simulation medium based on a crop growth model, wherein the drought transmission process simulation method comprises the following specific steps of: establishing a crop growth model; constructing weather drought indexes ten days after ten days in the growth period of the crops in the past year; for each ten days in the growing period of crops, combining with the weather drought classification principle, respectively selecting five typical ten days under the weather drought conditions of no drought, light drought, medium drought, heavy drought and extreme drought; simulating the potential evapotranspiration and the actual evapotranspiration of the daily crops; judging drought conditions of crops according to a crop drought grade classification principle; and a variation horizontal bar chart is adopted to describe the dynamic change process of the drought condition of the crops under different meteorological drought situations, and the physical process of the transmission of meteorological drought to the drought of the crops is intuitively displayed. The method can simulate and intuitively display the physical process of the weather drought transfer to the crop drought, and can provide support for revealing the weather drought transfer mechanism to the crop drought and the influence factors thereof.
Description
Technical Field
The application belongs to the field of drought resistance and disaster reduction in agriculture, and particularly relates to a drought transmission process simulation method, system and medium based on a crop growth model.
Background
Drought is a slowly occurring phenomenon that directly affects human life. In recent years, the occurrence frequency of drought is remarkably increased in many countries and regions, and drought has become one of important factors restricting sustainable development of economy and society. There are also various drought classification methods, with internationally popular classification being classification of drought into 4 classes, weather drought, hydrologic drought, agricultural drought and socioeconomic drought. Weather drought is considered to occur when precipitation is absent and persists for a period of time; when the rainfall is lack or the human activity causes the surface water or underground water to be abnormally less, the hydrologic drought is considered to occur; when the lack of precipitation or insufficient irrigation water causes unbalanced water in crops and affects the normal growth and development of the crops, the agricultural drought is considered to occur; the socioeconomic drought refers to the phenomenon of abnormal water shortage caused by unbalanced supply and demand of water resources in a natural system and a human socioeconomic system.
In the natural state, weather drought is the only external driving force for agricultural drought formation. Weather drought occurs, resulting in a decrease in soil moisture content, and if soil moisture is not available for effective replenishment or insufficient replenishment of groundwater, agricultural drought is induced. At present, scholars at home and abroad develop a great deal of researches on a mechanism for transmitting meteorological drought to agricultural drought and influence factors thereof, wherein simulating the transmission process of the meteorological drought to the agricultural drought is a relevant research foundation and a serious difficulty. However, there is no standard set of procedures and mature methods for numerical modeling of the process of weather drought transmission to agricultural drought, requiring further investigation.
Disclosure of Invention
The embodiment of the application aims to provide a drought transmission process simulation method, system and medium based on a crop growth model, which can simulate and intuitively display the physical process of weather drought transmission to crop drought and can provide support for revealing the mechanism of weather drought transmission to crop drought and influence factors thereof.
In order to achieve the above purpose, the present application provides the following technical solutions:
in a first aspect, an embodiment of the present application provides a method for simulating a drought transmission process based on a crop growth model, including the following specific steps:
step 1, according to the crop types, crop parameters, soil parameters and field management parameters are obtained through field investigation or literature reference, and a crop growth model is established;
step 2, selecting a standardized precipitation evapotranspiration index SPEI, and constructing weather drought indexes in the period of growing the crops of the past year by ten days by adopting long-sequence weather data of weather stations;
step 3, selecting five typical ten days under drought conditions of no drought, light drought, medium drought, heavy drought and extreme drought respectively for each ten days in the growing period of crops by combining with the weather drought classification principle;
step 4, setting different meteorological drought scenes, taking meteorological data in typical ten days as input, driving a crop growth model, and simulating the potential evapotranspiration and the actual evapotranspiration of crops day by day;
step 5, selecting a water stress index CWSI, constructing a crop drought index ten-day-by-ten-day in the crop growth period under each situation, and judging the drought condition of the crops according to a crop drought grade division principle;
and 6, describing dynamic change processes of drought conditions of crops under different meteorological drought scenes by adopting a variation horizontal bar graph, and intuitively displaying physical processes of the transmission of meteorological drought to the drought of the crops.
In the step 3, typical ten days under different meteorological drought conditions are selected, the method is as follows,
combining with a meteorological drought grade division principle based on a SPEI index, selecting a ten-day period with a SPEI value closest to 0 as a typical ten-day period under drought-free conditions, selecting a ten-day period with a SPEI value closest to-0.75 as a typical ten-day period under drought-free conditions, selecting a ten-day period with a SPEI value closest to-1.25 as a typical ten-day period under drought-free conditions, selecting a ten-day period with a SPEI value closest to-1.75 as a typical ten-day period under drought-conditions, and selecting a ten-day period with a SPEI value closest to-2.25 as a typical ten-day period under drought-conditions according to the meteorological drought index of the ten-day period past year calculated in the step 2.
Different meteorological drought situations are set in the step 4, the method is that,
assuming that the growing stage of the crops involves N ten days, which are respectively expressed as 1 st, 2 nd and N th, in order to simulate the physical process of weather drought transmission to the drought of the crops, two kinds of 2N-2 schemes are provided,
(1) setting a scheme for simulating an evolution process, namely setting N-1 simulation schemes for simulating the evolution processes of occurrence, development, duration, alleviation, relief and the like of drought of crops along with the duration and relief of the drought of the weather under different levels of weather drought conditions, wherein the scheme 1, the 1 st day is a drought condition, and the 2 nd to the N th days are drought-free conditions; scheme 2, drought conditions are 1 st and 2 nd, drought conditions are 3 rd to N th; scheme 3, drought conditions are from 1 st to 3 rd, drought conditions are from 4 th to N th; similarly, scheme N-1, drought conditions from 1 st to N-1 st, drought-free conditions in N th,
(2) the scheme setting of the transmission condition simulation is to explore the drought-weather grade of crops induced by drought in different growth stages for simulating the critical conditions of drought occurrence in different levels of weather drought conditions, and also set N-1 simulation schemes, wherein scheme N, 1 to N-1 are drought-free conditions, and N is drought condition; scheme N+1, 1 st to N-2 th drought conditions, N-1 st and N th drought conditions; scheme n+2, no drought conditions from 1 st to N-3 th, drought conditions from N-2 nd to N th; similarly, scheme 2N-2, no drought conditions in the 1 st, drought conditions in the 2 nd to the N th,
under each scheme, drought conditions comprise four grades of light drought, medium drought, heavy drought and extreme drought, and 8N-8 weather drought scenes are all provided.
In the step 5, the drought index of the crops is constructed ten days by ten days and the drought condition of the crops is judged, the method comprises the following steps,
(1) constructing a crop drought index, summing the actual daily evapotranspiration and the potential evapotranspiration obtained by simulating crop growth models under different meteorological drought scenes by taking ten days as a time scale, and calculating a water stress index CWSI of ten days by adopting the following steps:
，
in the method, in the process of the invention,the water stress index of crops in the ith ten days in the kth weather drought scene is represented; />Actual evapotranspiration of crops in the ith weather drought scene; />Representing potential evapotranspiration of crops in the ith weather drought scene;
(2) the drought condition of the crops is identified, and the crop drought classification principle based on CWSI is as follows: if CWSI is more than 0 and less than or equal to 0.3, the crops are in a drought-free state; cwsi is 0.3< 0.4 or less, indicating that the crop is in a light drought condition; cwsi is 0.4< 0.5, indicating that the crop is in a medium drought condition; cwsi is 0.5< 0.6 or less, indicating that the crop is in heavy drought condition; and 0.6< CWSI, which indicates that the crops are in special drought conditions, and identifying the drought conditions of the crops in each weather drought scene in ten days according to the constructed drought index of the crops and in combination with the drought classification principle of the crops.
The dynamic change process of drought conditions of crops is depicted in the step 5, and the method comprises the following steps of,
drawing drought conditions of crops in ten days in the growing period under different meteorological drought scenes by adopting a variation horizontal bar chart, wherein the horizontal axis represents time, namely, 1 th, 2 nd, 3 rd and N th in the growing period of the crops; the vertical axis represents weather drought scenarios, namely weather scenario 1, scenario 2, scenario 3, scenario 8N-8, each weather drought scenario representing a ten-day crop drought condition with different colors, wherein no drought condition, light drought condition, medium drought condition, heavy drought condition, and extreme drought condition are represented by green, blue, yellow, orange, and red, respectively.
In a second aspect, embodiments of the present application provide a drought transfer process simulation system based on a crop growth model, comprising:
the crop growth model building module is used for obtaining crop parameters, soil parameters and field management parameters through field investigation or literature reference according to crop types to build a crop growth model;
the ten-day-by-ten-day weather drought index construction module is used for selecting a standardized precipitation evapotranspiration index SPEI and constructing the ten-day-by-ten-day weather drought index in the growth period of the crops in the past year by adopting long-sequence weather data of weather stations;
the typical ten-day selection module is used for respectively selecting five typical ten-days under the conditions of no drought, light drought, medium drought, heavy drought and special drought according to the weather drought grade classification principle for each ten-day in the crop growing period;
the evapotranspiration simulation module is used for setting different meteorological drought scenes, taking meteorological data in typical ten days as input, driving a crop growth model, and simulating the potential evapotranspiration and the actual evapotranspiration of crops day by day;
the crop drought condition judging module is used for selecting a water stress index CWSI, constructing a crop drought index ten-day-by-ten-day in the growth period of the crop under each situation, and judging the drought condition of the crop according to a crop drought grade dividing principle;
and the drought transmission process display module is used for describing the dynamic change process of the drought condition of the crops under different meteorological drought scenes by adopting a variation horizontal bar graph, and visually displaying the physical process of the drought transmission of the meteorological drought to the crops.
In a third aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the crop growth model-based drought transfer process simulation method of any one of the above.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention provides a solution for simulating the drought transfer process of the meteorological drought to crops.
(2) The invention can intuitively display the evolution process of drought of crops along with the development of weather drought.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a drought transfer process simulation method based on a crop growth model according to an embodiment of the present invention;
fig. 2 is a system block diagram provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The terms "first," "second," and the like, are used merely to distinguish one entity or action from another entity or action, and are not to be construed as indicating or implying any actual such relationship or order between such entities or actions.
As shown in fig. 1, the drought transmission process simulation method based on the crop growth model in the embodiment of the application comprises the following specific steps:
step 1, according to the crop types, crop parameters, soil parameters and field management parameters are obtained through field investigation or literature reference, and a crop growth model is established;
step 2, selecting a standardized precipitation evapotranspiration index SPEI, and constructing weather drought indexes in the period of growing the crops of the past year by ten days by adopting long-sequence weather data of weather stations;
step 3, selecting five typical ten days under drought conditions of no drought, light drought, medium drought, heavy drought and extreme drought respectively for each ten days in the growing period of crops by combining with the weather drought classification principle;
step 4, setting different meteorological drought scenes, taking meteorological data in typical ten days as input, driving a crop growth model, and simulating the potential evapotranspiration and the actual evapotranspiration of crops day by day;
step 5, selecting a water stress index CWSI, constructing a crop drought index ten-day-by-ten-day in the crop growth period under each situation, and judging the drought condition of the crops according to a crop drought grade division principle;
and 6, describing dynamic change processes of drought conditions of crops under different meteorological drought scenes by adopting a variation horizontal bar graph, and intuitively displaying physical processes of the transmission of meteorological drought to the drought of the crops.
By using the method, the evolution processes of occurrence, development, duration, alleviation, release and the like of the corn drought are simulated under different levels of weather drought conditions in southwest areas, and the physical process of the transmission of the weather drought to the corn drought is intuitively displayed.
And step 1, establishing a corn growth simulation model.
And a certain area of a certain city where the irrigation test central station of the certain city is to be selected as a representative area, and a certain regional weather station in the area is a national weather observation basic station. The corn variety in the area is mainly Xindan No. 7, corn is planted in the middle ten days of 3 months, and is harvested in the late ten days of 7 months; the soil type of the area is mainly purple soil, and irrigation is basically not carried out in the growth period of the corn. And (3) adopting an APSIM model, and based on an embedded corn growth module, calibrating and verifying model parameters according to the research area corn growth and development condition and actual yield, and constructing a corn growth simulation model.
And 2, constructing weather drought indexes ten days after ten days in the growth period of the corn in the past year.
A daily value data set (V3.0) of the Chinese ground climate data is obtained from a Chinese meteorological data network, and daily meteorological data of a certain district weather station 1981-2016, including rainfall, air temperature, air pressure, relative humidity and the like, are extracted. The daily reference crop evapotranspiration was first calculated by the Penman-Monteth equation (FAO-56) from 1981 to 2026. And then, summing the daily rainfall and the daily reference crop evapotranspiration by taking ten days as a time scale to obtain the daily rainfall and the daily reference crop evapotranspiration in 1981-2016. Finally, through the formulas 1 to 4, the weather drought index can be calculated and obtained ten days after ten days in the growth period of the corn in the past, and the specific formulas are as follows:
（1）
in the method, in the process of the invention,indicate->Age->Air and water deficiency in ten days; />Indicate->Age->Rainfall in ten days; />Indicate->Age->Ten days of reference crop evapotranspiration;
fitting by adopting a logarithmic logic distribution function:
（2）
in the method, in the process of the invention,、/>and->The parameters of scale, shape and position are respectively,
last year of lifeSPEI sequence values in ten days:
（3）
（4）
in the method, in the process of the invention,、/>、/>、/>、/>and->Representing fixed parameters->Is an intermediate parameter.
Table 1 weather drought index ten days by ten days during the maize fertility period of 1981-2016
And 3, selecting typical ten days under different meteorological drought conditions.
For each ten days in the corn growing period, selecting 5 years with the SPEI value closest to 0, -0.75, -1.25, -1.75 and-2.25 from the annual SPEI sequence calculated in the step 2 as typical ten days under 5 weather drought conditions such as no drought, light drought, medium drought, heavy drought and extreme drought in the ten days, and the results are shown in the table 2.
TABLE 2 typical ten days under different meteorological drought conditions during maize growth period
And 4, setting different meteorological drought scenes.
The corn growth period is 3 to 7 months of late middle ten days, and 14 ten days are involved. In order to simulate the physical process of weather drought transmission to corn drought, 26 simulation schemes in total are set as follows. Under each scheme, drought conditions comprise four grades of light drought, medium drought, heavy drought and extreme drought, and 104 weather drought scenes are all obtained.
(1) Scheme setting of evolution process simulation. 13 schemes shown in table 3 are set, under the condition of simulating weather drought of different grades, the evolution processes of occurrence, development, duration, alleviation and release of corn drought and the like are simulated along with the duration and release of weather drought, and the relationship between the corn drought duration and the weather drought duration is explored.
TABLE 3 scheme settings for evolution process simulation
(2) Scheme settings for the transfer condition simulation. 13 schemes shown in Table 4 are set, and the critical conditions of drought of corns in different stages of cultivation are simulated under different levels of weather drought conditions, so that the weather drought level and the duration of drought of corns induced in different stages of cultivation are explored.
Table 4 scheme settings for transfer condition simulation
And 5, constructing a drought index of the crops every ten days and judging drought conditions of the crops.
Under each weather drought situation, weather data in the typical ten days are taken as input to drive a corn growth simulation model to simulate daily soil moisture content, potential evapotranspiration and actual evapotranspiration of corn, and according to a formulaCalculating a water stress index CWSI value of the crops in ten days, and identifying drought conditions of the crops in ten days according to the following principle: if 0 is<CWSI is less than or equal to 0.3, indicating that the crop is in a drought-free condition; 0.3<CWSI is less than or equal to 0.4, indicating that the crop is in a light drought condition; 0.4<CWSI is less than or equal to 0.5, indicating that the crop is in a medium drought condition; 0.5<CWSI is less than or equal to 0.6, indicating that the crop is in heavy drought condition; 0.6<CWSI indicates that the crop is in a very drought condition.
And 6, depicting the dynamic change process of the drought condition of the crops.
And (3) according to the drought conditions of the corn ten-day-by-ten-day under different meteorological drought situations identified in the step (5), respectively representing the conditions of no drought, light drought, medium drought, heavy drought and special drought of the corn by adopting green, blue, yellow, orange and red, and drawing a variation level bar graph. In this example, 104 meteorological drought scenarios are set up.
A crop growth model-based drought transfer process simulation system, comprising:
the crop growth model building module 1 is used for obtaining crop parameters, soil parameters and field management parameters through field investigation or literature reference according to crop types to build a crop growth model;
the ten-day-by-ten-day weather drought index construction module 2 is used for selecting a standardized precipitation evapotranspiration index SPEI and constructing the ten-day-by-ten-day weather drought index in the growth period of the crops in the past year by adopting long-sequence weather data of weather stations;
the typical ten-day selection module 3 is used for respectively selecting five typical ten-days under the drought conditions of no drought, light drought, medium drought, heavy drought and special drought according to the weather drought grade classification principle for each ten-day period of the crop growing period;
the evapotranspiration simulation module 4 is used for setting different meteorological drought scenes, taking meteorological data in typical ten days as input, driving a crop growth model, and simulating the potential evapotranspiration and the actual evapotranspiration of crops day by day;
the crop drought condition judging module 5 selects the water stress index CWSI, constructs the crop drought index ten-day-by-ten-day in the crop growth period under each scene, and judges the crop drought condition according to the crop drought grade dividing principle;
and the drought transmission process display module 6 is used for describing the dynamic change process of the drought condition of the crops under different meteorological drought scenes by adopting a variation horizontal bar graph, and visually displaying the physical process of the drought transmission of the meteorological drought to the crops.
Embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a drought transfer process simulation method based on a crop growth model as described in any of the above.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application, and various modifications and variations may be suggested to one skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (7)
1. The drought transmission process simulation method based on the crop growth model is characterized by comprising the following specific steps of:
step 1, according to the crop types, crop parameters, soil parameters and field management parameters are obtained through field investigation or literature reference, and a crop growth model is established;
step 2, selecting a standardized precipitation evapotranspiration index SPEI, and constructing weather drought indexes in the period of growing the crops of the past year by ten days by adopting long-sequence weather data of weather stations;
step 3, selecting five typical ten days under drought conditions of no drought, light drought, medium drought, heavy drought and extreme drought respectively for each ten days in the growing period of crops by combining with the weather drought classification principle;
step 4, setting different meteorological drought scenes, taking meteorological data in typical ten days as input, driving a crop growth model, and simulating the potential evapotranspiration and the actual evapotranspiration of crops day by day;
step 5, selecting a water stress index CWSI, constructing a crop drought index ten-day-by-ten-day in the crop growth period under each situation, and judging the drought condition of the crops according to a crop drought grade division principle;
and 6, describing dynamic change processes of drought conditions of crops under different meteorological drought scenes by adopting a variation horizontal bar graph, and intuitively displaying physical processes of the transmission of meteorological drought to the drought of the crops.
2. The method for simulating drought transmission process based on crop growth model according to claim 1, wherein the step 3 is selected from typical ten days under different meteorological drought conditions,
combining with a meteorological drought grade division principle based on a SPEI index, selecting a ten-day period with a SPEI value closest to 0 as a typical ten-day period under drought-free conditions, selecting a ten-day period with a SPEI value closest to-0.75 as a typical ten-day period under drought-free conditions, selecting a ten-day period with a SPEI value closest to-1.25 as a typical ten-day period under drought-free conditions, selecting a ten-day period with a SPEI value closest to-1.75 as a typical ten-day period under drought-conditions, and selecting a ten-day period with a SPEI value closest to-2.25 as a typical ten-day period under drought-conditions according to the meteorological drought index of the ten-day period past year calculated in the step 2.
3. The method for optimizing water resource allocation under double random uncertainty as claimed in claim 1, wherein: different meteorological drought situations are set in the step 4, the method is that,
assuming that the growing stage of the crops involves N ten days, which are respectively expressed as 1 st, 2 nd and N th, in order to simulate the physical process of weather drought transmission to the drought of the crops, two kinds of 2N-2 schemes are provided,
(1) setting a scheme for simulating an evolution process, namely setting N-1 simulation schemes for simulating the evolution processes of occurrence, development, duration, alleviation, relief and the like of drought of crops along with the duration and relief of the drought of the weather under different levels of weather drought conditions, wherein the scheme 1, the 1 st day is a drought condition, and the 2 nd to the N th days are drought-free conditions; scheme 2, drought conditions are 1 st and 2 nd, drought conditions are 3 rd to N th; scheme 3, drought conditions are from 1 st to 3 rd, drought conditions are from 4 th to N th; similarly, scheme N-1, drought conditions from 1 st to N-1 st, drought-free conditions in N th,
(2) the scheme setting of the transmission condition simulation is to explore the drought-weather grade of crops induced by drought in different growth stages for simulating the critical conditions of drought occurrence in different levels of weather drought conditions, and also set N-1 simulation schemes, wherein scheme N, 1 to N-1 are drought-free conditions, and N is drought condition; scheme N+1, 1 st to N-2 th drought conditions, N-1 st and N th drought conditions; scheme n+2, no drought conditions from 1 st to N-3 th, drought conditions from N-2 nd to N th; similarly, scheme 2N-2, no drought conditions in the 1 st, drought conditions in the 2 nd to the N th,
under each scheme, drought conditions comprise four grades of light drought, medium drought, heavy drought and extreme drought, and 8N-8 weather drought scenes are all provided.
4. A method of simulating drought transmission process based on a crop growth model as claimed in claim 1, wherein: in the step 5, the drought index of the crops is constructed ten days by ten days and the drought condition of the crops is judged, the method comprises the following steps,
(1) constructing a crop drought index, summing the actual daily evapotranspiration and the potential evapotranspiration obtained by simulating crop growth models under different meteorological drought scenes by taking ten days as a time scale, and calculating a water stress index CWSI of ten days by adopting the following steps:
，
in the method, in the process of the invention,the water stress index of crops in the ith ten days in the kth weather drought scene is represented; />Actual evapotranspiration of crops in the ith weather drought scene; />Representing potential evapotranspiration of crops in the ith weather drought scene;
(2) the drought condition of the crops is identified, and the crop drought classification principle based on CWSI is as follows: if CWSI is more than 0 and less than or equal to 0.3, the crops are in a drought-free state; cwsi is 0.3< 0.4 or less, indicating that the crop is in a light drought condition; cwsi is 0.4< 0.5, indicating that the crop is in a medium drought condition; cwsi is 0.5< 0.6 or less, indicating that the crop is in heavy drought condition; and 0.6< CWSI, which indicates that the crops are in special drought conditions, and identifying the drought conditions of the crops in each weather drought scene in ten days according to the constructed drought index of the crops and in combination with the drought classification principle of the crops.
5. A method of simulating drought transmission process based on a crop growth model as claimed in claim 1, wherein: the dynamic change process of drought conditions of crops is depicted in the step 5, and the method comprises the following steps of,
drawing drought conditions of crops in ten days in the growing period under different meteorological drought scenes by adopting a variation horizontal bar chart, wherein the horizontal axis represents time, namely, 1 th, 2 nd, 3 rd and N th in the growing period of the crops; the vertical axis represents weather drought scenarios, namely weather scenario 1, scenario 2, scenario 3, scenario 8N-8, each weather drought scenario representing a ten-day crop drought condition with different colors, wherein no drought condition, light drought condition, medium drought condition, heavy drought condition, and extreme drought condition are represented by green, blue, yellow, orange, and red, respectively.
6. A crop growth model-based drought transfer process simulation system, comprising:
the crop growth model building module is used for obtaining crop parameters, soil parameters and field management parameters through field investigation or literature reference according to crop types to build a crop growth model;
the ten-day-by-ten-day weather drought index construction module is used for selecting a standardized precipitation evapotranspiration index SPEI and constructing the ten-day-by-ten-day weather drought index in the growth period of the crops in the past year by adopting long-sequence weather data of weather stations;
the typical ten-day selection module is used for respectively selecting five typical ten-days under the conditions of no drought, light drought, medium drought, heavy drought and special drought according to the weather drought grade classification principle for each ten-day in the crop growing period;
the evapotranspiration simulation module is used for setting different meteorological drought scenes, taking meteorological data in typical ten days as input, driving a crop growth model, and simulating the potential evapotranspiration and the actual evapotranspiration of crops day by day;
the crop drought condition judging module is used for selecting a water stress index CWSI, constructing a crop drought index ten-day-by-ten-day in the growth period of the crop under each situation, and judging the drought condition of the crop according to a crop drought grade dividing principle;
and the drought transmission process display module is used for describing the dynamic change process of the drought condition of the crops under different meteorological drought scenes by adopting a variation horizontal bar graph, and visually displaying the physical process of the drought transmission of the meteorological drought to the crops.
7. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the crop growth model-based drought transfer process simulation method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410248764.6A CN117828906B (en) | 2024-03-05 | 2024-03-05 | Drought transmission process simulation method, system and medium based on crop growth model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410248764.6A CN117828906B (en) | 2024-03-05 | 2024-03-05 | Drought transmission process simulation method, system and medium based on crop growth model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117828906A true CN117828906A (en) | 2024-04-05 |
| CN117828906B CN117828906B (en) | 2024-05-17 |
Family
ID=90523166
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410248764.6A Active CN117828906B (en) | 2024-03-05 | 2024-03-05 | Drought transmission process simulation method, system and medium based on crop growth model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117828906B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107782701A (en) * | 2017-09-20 | 2018-03-09 | 北京师范大学 | A kind of agricultural arid monitoring method of multi- source Remote Sensing Data data |
| CN107918135A (en) * | 2017-11-13 | 2018-04-17 | 中国科学院寒区旱区环境与工程研究所 | Water stress state monitoring method, device and electronic equipment |
| CN109523175A (en) * | 2018-11-21 | 2019-03-26 | 兰州大学 | A kind of Multiple Time Scales damage caused by a drought appraisal procedure considering product merging into nature |
| CN111160680A (en) * | 2019-03-25 | 2020-05-15 | 水利部交通运输部国家能源局南京水利科学研究院 | Agricultural drought assessment method based on information assimilation and fusion |
| CN111445064A (en) * | 2020-03-18 | 2020-07-24 | 广东省气候中心 | Weather drought day-by-day dynamic monitoring index |
| US20200323156A1 (en) * | 2019-04-11 | 2020-10-15 | Nrg Holdings, Llc | Irrigation system |
| CN113361742A (en) * | 2020-12-28 | 2021-09-07 | 中国长江三峡集团有限公司 | Hydrologic simulation-based regional comprehensive drought identification method |
| CN113837666A (en) * | 2021-11-23 | 2021-12-24 | 长江水利委员会长江科学院 | Agricultural drought assessment method based on improved CMI index |
| CN113919146A (en) * | 2021-09-28 | 2022-01-11 | 河北地质大学 | Agricultural drought index construction method based on soil quick-acting water |
| CN114936765A (en) * | 2022-05-16 | 2022-08-23 | 中国科学院大学 | Agricultural drought index construction method considering spatial heterogeneity |
| CN116628978A (en) * | 2023-05-23 | 2023-08-22 | 郑州大学 | Method for determining drought vulnerability curve of meshed crop based on improved moisture sensitivity |
| KR20230164782A (en) * | 2022-05-25 | 2023-12-05 | 고려대학교 산학협력단 | Diagnostic drought prediction method and system |
-
2024
- 2024-03-05 CN CN202410248764.6A patent/CN117828906B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107782701A (en) * | 2017-09-20 | 2018-03-09 | 北京师范大学 | A kind of agricultural arid monitoring method of multi- source Remote Sensing Data data |
| CN107918135A (en) * | 2017-11-13 | 2018-04-17 | 中国科学院寒区旱区环境与工程研究所 | Water stress state monitoring method, device and electronic equipment |
| CN109523175A (en) * | 2018-11-21 | 2019-03-26 | 兰州大学 | A kind of Multiple Time Scales damage caused by a drought appraisal procedure considering product merging into nature |
| CN111160680A (en) * | 2019-03-25 | 2020-05-15 | 水利部交通运输部国家能源局南京水利科学研究院 | Agricultural drought assessment method based on information assimilation and fusion |
| US20200323156A1 (en) * | 2019-04-11 | 2020-10-15 | Nrg Holdings, Llc | Irrigation system |
| CN111445064A (en) * | 2020-03-18 | 2020-07-24 | 广东省气候中心 | Weather drought day-by-day dynamic monitoring index |
| CN113361742A (en) * | 2020-12-28 | 2021-09-07 | 中国长江三峡集团有限公司 | Hydrologic simulation-based regional comprehensive drought identification method |
| CN113919146A (en) * | 2021-09-28 | 2022-01-11 | 河北地质大学 | Agricultural drought index construction method based on soil quick-acting water |
| CN113837666A (en) * | 2021-11-23 | 2021-12-24 | 长江水利委员会长江科学院 | Agricultural drought assessment method based on improved CMI index |
| CN114936765A (en) * | 2022-05-16 | 2022-08-23 | 中国科学院大学 | Agricultural drought index construction method considering spatial heterogeneity |
| KR20230164782A (en) * | 2022-05-25 | 2023-12-05 | 고려대학교 산학협력단 | Diagnostic drought prediction method and system |
| CN116628978A (en) * | 2023-05-23 | 2023-08-22 | 郑州大学 | Method for determining drought vulnerability curve of meshed crop based on improved moisture sensitivity |
Non-Patent Citations (2)
| Title |
|---|
| 宋廷强等: "基于作物缺水指数的农业干旱监测模型构建", 《农业工程学报》, 31 December 2021 (2021-12-31), pages 65 - 72 * |
| 郭建平;: "农业气象灾害监测预测技术研究进展", 应用气象学报, no. 05, 15 September 2016 (2016-09-15), pages 620 - 630 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117828906B (en) | 2024-05-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Moeletsi et al. | Rainy season characteristics of the Free State Province of South Africa with reference to rain-fed maize production | |
| CN108510107B (en) | Agricultural planting guidance method, electronic equipment and storage medium | |
| Salazar et al. | Estimating irrigation water use for maize in the Southeastern USA: A modeling approach | |
| CN110909973B (en) | Comprehensive drought monitoring and evaluating method considering underlying surface condition | |
| CN105389740A (en) | Agricultural drought risk assessment method based on crop growth model | |
| Andrade et al. | Impact of urbanization trends on production of key staple crops | |
| CN110059915A (en) | A kind of winter wheat meteorological disaster integrated risk dynamic evaluation method and device | |
| Rahman et al. | Hydrological impacts of climate change on rice cultivated riparian wetlands in the Upper Meghna River Basin (Bangladesh and India) | |
| Hossain et al. | Response of crop water requirement and yield of irrigated rice to elevated temperature in Bangladesh | |
| CN117828906B (en) | Drought transmission process simulation method, system and medium based on crop growth model | |
| Mantel et al. | Assessment of the impact of water erosion on productivity of maize in Kenya: an integrated modelling approach | |
| Asseng et al. | Rainfall–human–spatial interactions in a salinity-prone agricultural region of the Western Australian wheat-belt | |
| Büchi et al. | Integrating simulation data from a crop model in the development of an agri-environmental indicator for soil cover in Switzerland | |
| CN107085643A (en) | A kind of acquisition methods of saline Irrigation node | |
| Blanco et al. | CAPRI Water 2.0: an upgraded and updated CAPRI water module | |
| CN111563824B (en) | Artificial forest land research system and method based on Chinese pine | |
| Chen et al. | Modelling the comparative growth, water use and productivity of the perennial legumes, tedera (Bituminaria bituminosa var. albomarginata) and lucerne (Medicago sativa) in dryland mixed farming systems | |
| CN113762768B (en) | Agricultural drought dynamic risk assessment method based on natural gas generator and crop model | |
| DANIEL | Suitability assessment and projected impact of climate change on rice yield in two agro-ecological zones in south-Kivu, Democratic Republic of Congo | |
| Kishan et al. | Estimate the irrigation water requirements of existing and other high value crops in the appapuram channel command using'aquacrop'model. | |
| CN117436710B (en) | Drought assessment method based on crop model | |
| Sishah | Suitability Analysis for Moringa Oleifera Tree Production in Ethiopia-A Spatial Modelling Approach | |
| Balderama et al. | Assessment of potential yield and climate change sensitivity of selected dryland crops in Cagayan Valley, Philippines using Simulation Mode LS | |
| Lacombe et al. | Modeling a biophysical environment to better understand the decision-making rules for water use in the rainfed lowland rice ecosystem | |
| Chitragar | Measurement of agricultural productivity and levels of development in the Malaprabha river basin, Karnataka, India |
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 |