CN104112067A - Drought loss model building method - Google Patents
Drought loss model building method Download PDFInfo
- Publication number
- CN104112067A CN104112067A CN201410313389.5A CN201410313389A CN104112067A CN 104112067 A CN104112067 A CN 104112067A CN 201410313389 A CN201410313389 A CN 201410313389A CN 104112067 A CN104112067 A CN 104112067A
- Authority
- CN
- China
- Prior art keywords
- arid
- loss
- soil
- moisture content
- drought
- 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
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a drought loss model building method. The method comprises the steps that crops are considered as main computing objects of drought loss, the average soil water content of soil layers with the thickness ranging from 0 to 100 cm in a typical region unit is determined as a disaster-inducing factor and introduced into a drought loss function; according to the soil field water capacity, the capillary pipe breakage water content and the wilting coefficient, the soil water content corresponding to inflection points of an S curve of the drought loss function in the typical region unit is calculated; according to field survey data and historical statistic data of the typical unit, actual agricultural drought loss of the typical region unit is obtained, through combination with all critical values of the soil water content, the maximum agricultural drought loss rate of the typical unit is determined, and a drought loss function model is established through extension towards similar units. By means of the method, an existing drought loss assessment method is simplified, and the estimation result is more accurate; according to parameter systems corresponding to different regions in the function, the extendibility of existing parameters in similar regions and renewability of the existing parameters at the same zone time are achieved.
Description
Technical field
The invention belongs to casualty loss metering model and build field, relate in particular to a kind of method that builds arid loss function model.
Background technology
China is a country that bloods and droughts takes place frequently, and the economic loss causing because of all kinds of water damages is every year huge, wherein especially occurs the most frequent with drought.Arid is a kind of meteorological disaster, and its economic loss causing is comparatively serious in meteorological disaster.Therefore set up simple and effective drought loss assessment method casualty loss is carried out to assessment promptly and accurately, very necessary for preventing and reducing natural disasters.
Because occurrence frequency is high, the duration is long, arid is the most remarkable on agriculture impact.The assessment of arid loss, effectively metering method is that corresponding minimum unit (lose quantitative statistics, gather by farmland, a family family) from devastated.In fact, in the time that occurring, arid adopts the method estimation loss of investigation statistics, its poor in timeliness, and operability is low.And existing assessment technology computing method complexity only limits to particular studies district and loses assessment, be difficult to promote to other regions.Up to now, lack a kind of simple, specification, extendible arid loss rapid evaluation technology.
Summary of the invention
The object of the present invention is to provide a kind of method that builds arid loss function model, be intended to solve arid loss appraisal procedure complexity, disunity, the inaccurate problem of assessment result.
The present invention is achieved in that a kind of arid loss model construction method, comprises the following steps:
S1, the main calculating object of consideration arid loss are crops, determine that the soil average moisture content θ of soil thickness 0～100cm place, representative region unit introduces arid loss model as causing the calamity factor;
S2, according to water-retaining quantity among field of soil θ
f, hollow billet fracture water cut θ
b, wilting coefficient θ
scalculate soil moisture content corresponding to arid loss function S type knee point place, representative region unit;
S3, according to the field survey data of typical unit and historical statistics data, obtain the actual arid loss of representative region unit agricultural, in conjunction with the agricultural arid maximum loss rate of each critical definite typical unit of soil moisture content, and to similar units expansion, build arid loss function model.
Preferably, before step S1, also comprise step:
S0, according to the regional distribution situation of the economic situation in region, social life situation, region is divided into some unit, and selects therein typical unit.
Preferably, described typical unit is defined as: under the historical time, overdrying drought occurs, loss data is more perfect, easily carries out investigation on the spot or collects data by statistical data; Monitoring soil moisture website should be set up in representative region unit, and field capacity and wilting coefficient are carried out to Real-Time Monitoring.
Preferably, in step S1, what represent due to soil moisture content θ is the moistening degree of soil, and soil moisture content is larger, and soil is more moistening, and arid loss is just less.Therefore in soil water-containing figureofmerit is introduced to model time, need carry out certain processing, be converted into the index that is positive relationship with arid loss percentage.Introducing soil Deficient Ratio x is the poor of field capacity and soil moisture content, i.e. x=θ
f-θ, described soil Deficient Ratio is the soil moisture content θ that meets crop growth demand completely
fand the difference between actual soil moisture content θ.
Preferably, in step S2, arid loss function S type curve is specially:
When slight arid, the higher θ of soil moisture content, water deficit is less, does not substantially affect crop growthing development demand, loses less; When degree of drought's aggravation, soil moisture content is lower than hollow billet fracture water cut θ
btime, crop growth is subject to obvious impact, affects output, arid loss fast rise; During this period, due to the carrying out of drought-relief measure, making to lose speed and slow down, there is flex point in centre; Serious all the more when degree of drought, soil moisture content is lower than wilting coefficient θ
stime, crops withered death, loss approaches maximum, is tending towards afterwards constant, and the whole process reaction of arid loss goes out soil moisture content affects S-type tracing pattern to agricultural losses.
Preferably, in step S3, the mathematic(al) representation of described arid loss function is:
Hollow billet fracture water cut is generally field capacity θ
f65%, judge with reference to hollow billet fracture water cut whether damage caused by a drought occurs.Crop is occurred to the corresponding wilting coefficient of permanent wilting phenomenon is as the loss corresponding state value that causes the calamity factor that reaches capacity.Field capacity θ
fwith hollow billet fracture water cut θ
bthe corresponding x of difference
a, i.e. x
a=θ
f-θ
b; Field capacity θ
fwith wilting coefficient θ
sdifference corresponding to x
c, i.e. x
c=θ
f-θ
s; According to the centre symmetry of S type curve, can try to achieve corresponding x
d; Vulnerability parameter α, maximum loss rate S can obtain according to the on-site inspection to typical unit.
Than the shortcoming and defect of prior art, the present invention has following beneficial effect: the present invention is by building arid loss function and corresponding parameter system thereof, form the arid loss calculation method of a set of simple, Yi Hang, specification, for the assessment of arid loss, the appraisal procedure of having simplified existing arid loss, estimation result is more accurate; Parameter system corresponding to zones of different in function, has realized extensibility and the same area temporal updatability of existing parameter at similar area.
Brief description of the drawings
Fig. 1 is the flow chart of steps that the present invention builds arid loss function model construction;
Fig. 2 is arid loss function S type curve map in the embodiment of the present invention.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.
A kind of arid loss model construction method, as shown in Figure 1, comprises the following steps:
S0, according to the regional distribution situation of the economic situation in region, social life situation, region is divided into some unit, and selects therein typical unit
In step S0, there is overdrying drought in representative region unit under the historical time, and loss data is more perfect, easily carries out investigation on the spot or collect data by statistical data; Monitoring soil moisture website should be set up in representative region unit, and field capacity and wilting coefficient are implemented to monitoring.
S1, the main calculating object of consideration arid loss are crops, determine that the average soil moisture θ of soil thickness 0～100cm place, representative region unit introduces arid loss model as causing the calamity factor
In step S1, in water damage loss function, between water disaster intensity and loss percentage, be positive relationship, the larger loss percentage of water disaster intensity is just larger.Be the moistening degree of soil and soil moisture content θ represents, soil moisture content is larger, and soil is more moistening, and arid loss is just less.Therefore in soil water-containing figureofmerit is introduced to model time, need carry out certain processing, be converted into the index that is positive relationship with loss percentage.Introducing soil Deficient Ratio x is the poor of field capacity and soil moisture content, i.e. x=θ
f-θ.Described soil Deficient Ratio is the soil moisture content θ that meets crop growth demand completely
fand the difference between actual soil moisture content θ.
S2, according to water-retaining quantity among field of soil θ
f, hollow billet fracture water cut θ
b, wilting coefficient θ
scalculate soil moisture content corresponding to arid loss function S type knee point place, representative region unit
In step S2, described arid loss function S type curve as shown in Figure 2, is specially:
When slight arid, the higher θ of soil moisture content, water deficit is less, does not substantially affect crop growthing development demand, loses less; When degree of drought's aggravation, soil moisture content is lower than hollow billet fracture water cut θ
bwhen (65% field capacity), crop growth is subject to obvious impact, affects output, arid loss fast rise; During this period, due to the carrying out of drought-relief measure, making to lose speed and slow down, there is flex point in centre; Serious all the more when degree of drought, soil moisture content is lower than wilting coefficient θ
stime, crops withered death, loss approaches maximum, is tending towards afterwards constant.The whole process reaction of arid loss goes out soil moisture content affects S-type tracing pattern (Fig. 2) to agricultural losses.
In step S3, choose the initial function of water damage loss universal model function as arid loss, mathematic(al) representation:
(1)；
(2)；
In theory by field capacity θ
fas the soil moisture content that meets crop growth demand completely, the available moisture of soil is defined as from wilting coefficient θ
sto field capacity θ
fbetween water cut, but in fact due to wilting coefficient θ
sto hollow billet fracture water cut θ
bthis part water yield moves slowly, is difficult to by crop utilization, conventionally not using wilting coefficient as the lower limit of whether irrigating, but determines with reference to hollow billet fracture water cut.Hollow billet fracture water cut is generally field capacity θ
f65%, and whether occur as standard determination damage caused by a drought.Crop is occurred to the corresponding wilting coefficient of permanent wilting phenomenon is as the loss corresponding state value that causes the calamity factor that reaches capacity.Field capacity θ
fwith hollow billet fracture water cut θ
bthe corresponding x of difference
a, i.e. x
a=θ
f-θ
b; Field capacity θ
fwith wilting coefficient θ
sdifference corresponding to x
c, i.e. x
c=θ
f-θ
s.According to the centre symmetry of S type curve, can try to achieve corresponding x
d; Vulnerability parameter α, maximum loss rate S can obtain according to the on-site inspection to typical unit.
The field capacity θ of soil
f, capillary fracture water cut θ
b, wilting coefficient θ
sin close relations with soil types, the crop species in region, need measure by test.Taking Taihu Lake basin as illustrating meaning computation process.In Taihu Lake basin, Main Agricultural anthropogenic soil type is rice soil, Taihu Lake basin crops are taking rice wheat as main, and there are a certain proportion of cotton, oil and coarse cereals, carry out the crop rotation of rice wheat, the cotton crop rotation of rice wheat and paddy crop rotation, crop is for yielding two crops a year, arable soil type is rice soil, is at special soil that constantly floods and droughts forms under replacing tillage condition.The soil water constant of all kinds of rice soil of Taihu Lake basin and distribution situation are in table 1.
The soil water constant (weight moisture capacity, %) of table 1 Taihu Lake basin Different Types of Rice soil
X
dwith α can be according to aforementioned relational expression:
Calculating is tried to achieve, and result is as shown in table 2.
The table 2 rice soil parameter value of classifying
S3, according to the field survey data of typical unit and historical statistics data, obtain the actual arid loss of representative region unit agricultural, in conjunction with the agricultural arid maximum loss rate of each critical definite typical unit of soil moisture content, and to similar units expansion, build arid loss function model
In step S3, determining of arid maximum loss rate S need to be by the field survey data to representative region unit and historical statistics data, obtains the actual arid loss of typical unit and is determined.Still taking Taihu Lake basin as illustrating meaning computation process.
Taihu Lake basin agricultural planting structure is similar, and choosing the good Jiangsu Province of data condition Changzhou is Typical Areas.According to statistics, Changzhou in 2011 causes 4.57 hundred million yuan of direct economic losses, wherein 2.39 hundred million yuan of agricultural losses, 69.6 hundred million yuan of plant husbandry added values then, 34.8 hundred million yuan of the average single cropping output values, loss percentage 6.88% because of drought; Changzhou is in Taihu Plain district, and rice soil type is bleached paddy soil, field capacity θ
fbe that 32.39%, α is 0.275, during suffering from drought, soil average moisture content θ is 20.08%.Above-mentioned numerical value is brought in formula 1, calculated maximum loss rate S=0.22.Taihu Lake basin each province and city the model of social economy, humanistic environment are similar, the maximum loss coefficient S of Typical Areas can be expanded to other regions, build Taihu Lake basin agricultural arid loss function, suc as formula 3:
According to formula (3) and rice soil parameter corresponding to table 2 zones of different, the loss of watershed agricultural arid is calculated.
The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any amendments of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.
Claims (6)
1. an arid loss model construction method, is characterized in that, comprises the following steps:
S1, the main calculating object of consideration arid loss are crops, determine that the soil average moisture content θ of soil thickness 0～100cm place, representative region unit introduces arid loss model as causing the calamity factor;
S2, according to water-retaining quantity among field of soil θ
f,, hollow billet fracture water cut θ
b, wilting coefficient θ
scalculate soil moisture content corresponding to arid loss function S type knee point place, representative region unit;
S3, according to the field survey data of typical unit and historical statistics data, obtain the actual arid loss of representative region unit agricultural, in conjunction with the agricultural arid maximum loss rate of each critical definite typical unit of soil moisture content, and to similar units expansion, build arid loss function model.
2. arid loss model construction method as claimed in claim 1, is characterized in that, also comprises step before step S1:
S0, according to the regional distribution situation of the economic situation in region, social life situation, region is divided into some unit, and selects therein typical unit.
3. arid loss model construction method as claimed in claim 1, is characterized in that, described typical unit is defined as: under the historical time, overdrying drought occurs, loss data is more perfect, easily carries out investigation on the spot or collects data by statistical data; Monitoring soil moisture website should be set up in representative region unit, and field capacity and wilting coefficient are carried out to Real-Time Monitoring.
4. arid loss model construction method as claimed in claim 1, it is characterized in that, in step S1, described soil average moisture content θ needs to be converted into the index that is positive relationship with arid loss percentage before the arid loss model of introducing, introducing soil Deficient Ratio x is the poor of field capacity and soil moisture content, i.e. x=θ
f-θ, described soil Deficient Ratio is the soil moisture content θ that meets crop growth demand completely
fand the difference between actual soil moisture content θ.
5. arid loss model construction method as claimed in claim 1, is characterized in that, in step S2, arid loss function S type curve is specially:
When slight arid, soil moisture content θ is higher, and water deficit is less, does not substantially affect crop growthing development demand, loses less; When degree of drought's aggravation, soil moisture content is lower than hollow billet fracture water cut θ
btime, crop growth is subject to obvious impact, affects output, arid loss fast rise; During this period, due to the carrying out of drought-relief measure, making to lose speed and slow down, there is flex point in centre; Serious all the more when degree of drought, soil moisture content is lower than wilting coefficient θ
stime, crops withered death, loss approaches maximum, is tending towards afterwards constant, and the whole process reaction of arid loss goes out soil moisture content affects S-type tracing pattern to agricultural losses.
6. arid loss model construction method as claimed in claim 1, is characterized in that, in step S3, the mathematic(al) representation of described arid loss function is:
Hollow billet fracture water cut is generally field capacity θ
f65%, judge with reference to hollow billet fracture water cut whether damage caused by a drought occurs, crop is occurred to the corresponding wilting coefficient of permanent wilting phenomenon is as the loss corresponding state value that causes the calamity factor that reaches capacity, field capacity θ
fwith hollow billet fracture water cut θ
bthe corresponding x of difference
a, i.e. x
a=θ
f-θ
b; Field capacity θ
fwith wilting coefficient θ
sdifference corresponding to x
c, i.e. x
c=θ
f-θ
s; According to the centre symmetry of S type curve, can try to achieve corresponding x
d; Vulnerability parameter α, maximum loss rate S can obtain according to the on-site inspection to typical unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410313389.5A CN104112067B (en) | 2014-07-02 | 2014-07-02 | A kind of arid loss model construction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410313389.5A CN104112067B (en) | 2014-07-02 | 2014-07-02 | A kind of arid loss model construction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104112067A true CN104112067A (en) | 2014-10-22 |
| CN104112067B CN104112067B (en) | 2017-09-26 |
Family
ID=51708854
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410313389.5A Expired - Fee Related CN104112067B (en) | 2014-07-02 | 2014-07-02 | A kind of arid loss model construction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104112067B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794539A (en) * | 2015-04-20 | 2015-07-22 | 中国水利水电科学研究院 | Method for building flood loss models |
| CN105651968A (en) * | 2016-03-04 | 2016-06-08 | 江苏大学 | Method using water potential instrument to online detect soil water content |
| CN106680462A (en) * | 2017-01-04 | 2017-05-17 | 塔里木大学 | Method for determining water content of crop at optimal growing point |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1616964A (en) * | 2003-11-10 | 2005-05-18 | 尹伟伦 | Quantitatice evaluation technology for plant drought tolerance |
| CN103440420A (en) * | 2013-08-30 | 2013-12-11 | 民政部国家减灾中心 | Agricultural drought disaster-inducing factor risk assessment method |
-
2014
- 2014-07-02 CN CN201410313389.5A patent/CN104112067B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1616964A (en) * | 2003-11-10 | 2005-05-18 | 尹伟伦 | Quantitatice evaluation technology for plant drought tolerance |
| CN103440420A (en) * | 2013-08-30 | 2013-12-11 | 民政部国家减灾中心 | Agricultural drought disaster-inducing factor risk assessment method |
Non-Patent Citations (3)
| Title |
|---|
| 李锦秀等: "流域水污染经济损失计量模型", 《水利学报》 * |
| 金川等: "基于TVDI的作物产量损失评估模型", 《2007环境遥感学术年会-自然灾害专题研讨会论文集》 * |
| 魏胜利: "田间持水量的测定与旱情分析", 《水科学与工程技术》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794539A (en) * | 2015-04-20 | 2015-07-22 | 中国水利水电科学研究院 | Method for building flood loss models |
| CN105651968A (en) * | 2016-03-04 | 2016-06-08 | 江苏大学 | Method using water potential instrument to online detect soil water content |
| CN106680462A (en) * | 2017-01-04 | 2017-05-17 | 塔里木大学 | Method for determining water content of crop at optimal growing point |
| CN106680462B (en) * | 2017-01-04 | 2020-02-21 | 塔里木大学 | Method for measuring water content of optimal growing point of crop |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104112067B (en) | 2017-09-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Study of the temporal and spatial patterns of drought in the Yellow River basin based on SPEI | |
| Xinchun et al. | Water footprint assessment for crop production based on field measurements: A case study of irrigated paddy rice in East China | |
| Zhao et al. | Climate changes and their impacts on water resources in semiarid regions: a case study of the Wei River basin, China | |
| Candogan et al. | Yield, quality and crop water stress index relationships for deficit-irrigated soybean [Glycine max (L.) Merr.] in sub-humid climatic conditions | |
| Wang et al. | Detecting changes in extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China | |
| Huang et al. | Detection of trends in precipitation during 1960–2008 in Jiangxi province, southeast China | |
| Abbas | Analysis of a historical (1981–2010) temperature record of the Punjab province of Pakistan | |
| Zhang et al. | Runoff variation and its response to climate change in the Three Rivers Source Region | |
| CN107944219B (en) | Method and device for representing drought and waterlogging disaster-causing characteristics at different time periods | |
| CN104112067A (en) | Drought loss model building method | |
| Ma et al. | Green water appropriation of the cropland ecosystem in China | |
| Chen et al. | Is the S-shaped curve a general law? An application to evaluate the damage resulting from water-induced disasters | |
| Yang et al. | Indicator-based spatiotemporal characteristics of apple drought in North China | |
| Wu et al. | Analysis of 22‐year Drought Characteristics in Heilongjiang Province Based on Temperature Vegetation Drought Index | |
| Cao et al. | Scarce water resources and priority irrigation schemes from agronomic crops | |
| Yu et al. | Impact of soil textures on agricultural drought evolution and field capacity estimation in humid regions | |
| Zhang et al. | An uncertainty-based multivariate statistical approach to predict crop water footprint under climate change: a case study of Lake Dianchi Basin, China | |
| Li et al. | The key driving factors of irrigation water productivity based on soil spatio-temporal characteristics | |
| Huang et al. | The vulnerability assessment method for Beijing Agricultural Drought | |
| CN105223141A (en) | The detection method of calcium ion in soil | |
| TWI707294B (en) | Field clear water sensing device, field clear water sensing system, and field clear water sensing method | |
| Guo et al. | Spatial-temporal distribution and impact factors of irrigation water use efficiency in the grain production of China | |
| Fu et al. | Impact factors and dynamic simulation of tillage-layer temperature in frozen-thawed soil under different cover conditions | |
| Ling et al. | Drought evolution in the Haihe River basin during 1960–2020 and the correlation with global warming, sunspots, and atmospheric circulation indices | |
| SALAH et al. | Assessing the potential impacts of climate change on droughts in East Africa using CORDEX-CORE regional climate models' simulations: A focus on Tanzania |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170926 Termination date: 20200702 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |