CN108595814A - A kind of precipitation generator based on day time scale - Google Patents
A kind of precipitation generator based on day time scale Download PDFInfo
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Abstract
For terrestrial ecosystems some areas period of history Precipitation Record data time is shorter, several years shortage of data amount is big problem, invented a kind of can generate using day as the precipitation generator of time scale the present invention.The input of generator based on objective area survey it is long-term it is annual, monthly with daily precipitation data.The characteristic parameter of distribution curve is extracted in the frequency distribution of every monthly total precipitation determining first, monthly day average precipitation, monthly day maximum precipitation and year precipitation number of days, and structure exponential equation simulation generates day precipitation, and adds up and obtain Month And Year precipitation.The time series data of simulation carries out contrast verification in the time scale of year and the moon with measured data.The distribution of extreme precipitation event is considered in precipitation generator after this authentication so that the precipitation generator can preferably reflect that natural conditions decline the rule of water distribution.Contribute to the ecological effect of influence and Prediction of Climate Change of the assessment precipitation to terrestrial ecosystems hydrologic cycle.
Description
Technical field
The invention belongs to terrestrial ecosystems environmental changes to monitor field, be related to a kind of be with the precipitation data for many years of actual measurement
Foundation, simulated using mathematical modeling generate long-term sequence, it is continuous, using day as the method for time scale precipitation.
Background technology
The influence of the research climatic change on terrestrial ecosystem depends greatly on the utilization of time series climatic data,
And on day, the moon and year scale climatic fluctuation analysis.Due to the complexity of land ecological process, interregional difference and it is
Key ecology component lacks for a long time in system and the measurement data of large spatial scale, model are one kind effectively assessment and prediction ecology
The method of system components variation.Terrestrial ecosystem model usually requires the input of long-time climatic data, and then assessment models
Export the uncertainty of result, the accurate difference for measuring analog result and measured result, to ensure the robustness of model.Partly
Regional weather office period of history climatological observation data time is shorter, especially the poor area of physical geography condition, such as desert ecology
System and alpine ecosystems region；Or it is the more backward area of economic development, the ecosystem in these areas is usually by people
Class disturbing influence is larger or the ecosystem has higher fragility.Therefore exploitation is suitable for the weather generator in these areas,
The ecological effect that climate change can effectively be quantified, plays an important role to protecting ecology system function and service.Meanwhile gas
Waiting variation causes extreme weather events to be continuously increased, on Paris United Nations Climate Change Conferences in 2015, national governments' expression pair
The worry that Extreme Weather Events grow in intensity, and it is desirable that exceptional climate events are coped in cooperation.Extreme weather events have sudden
By force, the features such as predictive difficult and destruction is big, the influence to terrestrial ecosystems is various and sufficiently complex, therefore is being simulated
Consider that extreme precipitation event is very necessary, can more preferably react the rainfall distribution under natural conditions when long-term sequence precipitation
Rule.
Recent domestic scholar has carried out compared with in-depth study the simulation of precipitation.The country has scholar to utilize two states
Single order Markov Chain and two parameter GAMMA distributional analysis the Daily rainfall data of Chinese 672 meteorological sites, calculate not
With the Seasonal Distribution feature (Liao Yaoming etc., 2004) of regional precipitation parameter, however different regions natural conditions differ greatly, and one
The precipitation of a little Extreme droughts or humid region not will present apparent Seasonal Analysis；Have scholar by precipitation and other it is meteorological because
The sub gentle pressure of such as temperature, humidity is associated the generalized linear model (Liu Yonghe, 2010) for establishing Daily rainfall amount, however
Relationship between the climatic factor of different regions is sufficiently complex, and relationship may linear, non-linear or onrelevant.Foreign countries,
Cantet et al. (2011) have evaluated precipitation parameter and extreme drop in a random precipitation generator using maximum likelihood method
The changing rule of water event, however the attribute of extreme precipitation event is typically based on measured data, and there is unpredictability, because
This considers that Extreme Precipitation attribute can increase the confidence level of analogue data in generator.Vallam＆Qin (2016) utilizes k neighbours
Algorithm evaluation is suitable for three precipitation generators of tropical urban area, it is indicated that analysis of uncertainty and extreme event are to optimization
The necessity of precipitation generator.
Retrieval finds the patent of invention of rainfall simulator, and application No. is CN201520362925.0, entitled one kind is just
Formula artificially-simulated rainfall device, Publication No. CN204649731U are taken, which is that one kind is simulated certainly by artificial means
The instrument and equipment of right rainfall, it can indoors under field condition for quickly measure Infiltration Processes for Different Vegetation, Rainfall-runoff process,
Soil erosion process and loss of soil nutrient process are agrology, soil and water conservation, the soil erosion and sediment dynamics
Equal subjects carry out the important equipment of scientific research.The related patents about simulated precipitation algorithm are not retrieved.
It can be seen that by mathematical model generate long-term sequence precipitation data, improve analogue data stability and
Reliability is very necessary, while the changing rule that extreme precipitation event is incorporated in model can preferably reflect natural conditions
Under rainfall distribution, for assess and Prediction of Climate Change to terrestrial ecosystems dynamic and process influence have important meaning
Justice.
Invention content
The foundation of precipitation generator includes four parts：Analysis part, modeled segments, verification portion and optimization part.Analysis
Part is to carry out statistical analysis to surveying precipitation data for many years, is analyzed in different time scales year, month and day precipitation parameter
Changing rule；Modeled segments are that precipitation daily during math equation is simulated 1 year is established based on different precipitation parameters；Verification
Part is the accuracy for analyzing dewatering model, the difference of comparison actual measurement and simulated precipitation data；Optimization part is based on actual measurement number
According to the changing rule of analysis extreme precipitation event, Extreme Precipitation attribute is dissolved into generator.
Description of the drawings
The simulation of Fig. 1 Daily rainfalls amount (1 year)
The comparison of actual measurement and analogue data under Fig. 2 annual time scales
The comparison of actual measurement and analogue data under Fig. 3 months scale
Fig. 4 incorporates the Daily rainfall amount distribution of Extreme Precipitation attribute
Specific implementation mode
1) actual measurement precipitation data analysis for many years
Targeted sites Daily rainfall data for many years are obtained from Chinese meteorological data net (data.cma.cn) first, according to drop
Aqueous condition carries out Nature District subregion to website.Because moisture is the key factor of regulation ecological systematic procedure, with annual precipitation
It can be divided into moistening, semi-moist, semiarid, arid and extremely arid five regional categories for leading indicator.For different classes of,
Precipitation parameter can be variant in model, for example, semi-moist and the seasonal characteristics of semiarid zone precipitation it is obvious, and dry
Drought and humid region precipitation are smaller in annual distributional difference.Since rising for data is established and observed in the corresponding weather station of each website
Beginning the time may be different, this research association carries out edit to the precipitation data of acquisition, such as the conversion of data format, target data
Extraction and missing values processing.Data analysis need to meet following three conditions：(1) data record must not be less than 30 years；(2) every
Missing data must not exceed 10% within 1 year；(3) missing data is filled up in a manner of long-time average annual value.Subsequent statistical analysis items drop
Water index：Annual precipitation, year precipitation number of days, average daily precipitation, per monthly total precipitation, monthly precipitation number of days and monthly it is maximum
Precipitation.The change of Mann-Kendall non-parametric test (equation (1)-(4)) analysis time sequence is used annual precipitation later
Change trend.If Annual Precipitation, which is presented, significantly increases or decreases trend, the precipitation data for many years of permutatation simulation makes it be in
Existing identical variation tendency.The rule of permutatation is the average value of the precipitation data for many years of calculating simulation first, and (1) is surveyed for many years
The trend dramatically increased is presented in precipitation：Precipitation be less than average value time position it is constant, be more than average value time according to
Sequence from small to large rearranges；(2) reduced trend is presented in actual measurement Annual Precipitation：Precipitation is more than the time of average value
Position is constant, and the time less than average value rearranges according to sequence from big to small；
The step of Mann-Kendall non-parametric tests, is as follows：
For variable X=(x of time series1,x2,…,xn), n is the length of variable, and it is as follows to define statistic S：
X in formulajAnd xiThe respectively corresponding actual measurement precipitation guide line value of jth, i；
Work as n>When 8, random sequence Si(i=1,2 ..., n) approximation Normal Distribution, variance calculation formula are：
S is standardized, statistical check value Z is obtainedMK：
In formula, ZMKIt is the statistic of a normal distribution, ZMK>Show that the trend risen, Z is presented in time series when 0MK<0
Indicate that time series is in downward trend, ZMKAbsolute value bigger expression variation it is more notable.This research gives significance p<
0.05, corresponding ZMK>1.96 or ZMK<-1.96；
2) simulation of Daily rainfall amount
It is first depending on annual precipitation to classify, 0-1000mm is divided into 10 grades by interval of 100mm, in conjunction with for many years
Actual measurement precipitation data estimates the probability that each grade precipitation occurs.The curve of frequency distribution of each moon precipitation, base in drawing 1 year
It is inferred to the possibility of each moon day precipitation in the curve, and calculates the degree of bias of opposite normal curve, using exponential distribution equation (5)
Daily precipitation is generated in conjunction with last point of precipitation guide line simulation；
In formula, daypre is the daily precipitation of simulation, and maxpre is actual measurement each moon day maximum precipitation, rand (0,1)
For the random number in generation (0,1) section, monthrateFor specify month curve of frequency distribution characteristic parameter, ra be correction because
Son, prob are the possibility of each moon day precipitation；
3) precipitation data of verification simulation
The Daily rainfall data accumulation of simulation is obtained into each monthly total precipitation and annual precipitation, and with the Month And Year precipitation of actual measurement
Amount is compared (equation (6)-(7)), if a certain monthly total precipitation error is more than to drop in required standard (± 5mm) or a certain year
Water error is more than required standard (± 10mm), refuses the Precipitation Time Series generated and adjustment index equation parameter, produces again
Raw each day precipitation meets standard until error；
errorM=| PPM-PSM| (6)
errorY=| PPY-PSY| (7)
In formula, errorMTo simulate the error of monthly total precipitation, PPMTo survey monthly total precipitation, PSMTo simulate monthly total precipitation,
errorYTo simulate the error of annual precipitation, PPYTo survey annual precipitation, PSYTo simulate annual precipitation；
4) extreme precipitation event attribute is incorporated
Based on the Extreme Precipitation index that extreme weather events index expert group (ETCCDI) recommends, we therefrom choose
3 indexs are dissolved into precipitation generator.These indexs from intensity, frequency and duration metric precipitation event it is extreme
Degree.Index includes moderate strength precipitation number of days (day precipitation>10mm), number of days and maximum day precipitation are continuously moistened.Substantially
It is assumed that being to maintain, gross precipitation is constant, and the involvement process of 3 Extreme Precipitation indexs is without sequencing, until the precipitation data of simulation
Meet above-mentioned 3 indexs simultaneously：
(1) moderate strength precipitation number of days：The moderate strength precipitation number of days that the precipitation most big moon is actual and simulates is calculated,
If the number of days of simulation is more, actual number of days is subtracted with the number of days of simulation, it is right according to the sequence of day precipitation from small to large later
The moderate strength precipitation Day of simulation is arranged, and the part by the precipitation of the smaller difference number of days of precipitation more than 10mm sums it up,
Non- moderate strength precipitation Day is averagely arrived into the part, if actual number of days is more, the number of days of simulation is subtracted with actual number of days, it
After choose several with the immediate precipitation Day of moderate strength, number is difference number of days, calculates other non-moderate strength precipitation successively
The part of day n% sum it up and be averaged to the immediate precipitation Day of moderate strength, until the precipitation of these precipitation Days is not less than
10mm；
(2) maximum continuous moistening number of days：The continuous moistening number of days that the precipitation most big moon is actual and simulates is calculated, if mould
Quasi- number of days is more, then subtracts actual number of days with the number of days of simulation, the difference number of days of day side will be continuously moistened in analogue data
Precipitation sums it up and averagely arrives other precipitation Days；If actual number of days is more, the number of days of simulation is subtracted with actual number of days, is calculated
The part of precipitation Day (discontinuous moistening precipitation Day) 5% and the average difference day to continuous moistening day side of adduction in analogue data
Number；
(3) maximum day precipitation：The maximum day precipitation that the precipitation most big moon is actual and simulates is calculated, if the analogue value
Greatly, then actual value is subtracted with the analogue value, by difference averagely to other precipitation Days in analogue data；If actual value is big, with reality
Value subtracts the analogue value, the part adduction of precipitation Day in analogue data (non-maximum rainfall day) n% is attached to maximum rainfall day, directly
Maximum day precipitation to simulation is equal to actual maximum day precipitation.
Embodiment
The precipitation generator of this paper is saved applied to NW China, which is arid, semiarid, semi-moist type
1. the province covers arid, semiarid, Semi-humid area chooses a website from three regional classifications respectively,
The precipitation data for many years of three websites is analyzed；
The time series variation trend of 1 year precipitation of table
2. Daily rainfall amount is simulated
Daily precipitation in being generated 1 year with exponential equation constantly repeats the process and generates data for many years, as a result sees
Fig. 1；
3. the precipitation data verification simulated
Actual measurement and simulation precipitation data is compared in the time scale of year and the moon, as a result sees Fig. 2 and 3；
4. extreme precipitation event attribute
The maximum moon moderate strength precipitation number of days of precipitation, continuous moistening number of days and maximum day are calculated according to actual measurement precipitation data
Precipitation, statistical result show that the precipitation most big moon is August, and moderate strength precipitation number of days is 5 days, and the continuous number of days that moistens is 6 days,
Maximum precipitation is 23.2
Mm, the precipitation data then simulated according to the algorithm permutatation of setting, to meet three Extreme Precipitation indexs simultaneously,
As a result see Fig. 4.
Claims (1)
1. a kind of method of the simulation Daily rainfall amount of precipitation data for many years based on actual measurement, it is characterised in that：With statistics side
Method calculates the precipitation guide line in measured data year, month and day time scale, by exponential equation, in conjunction with the precipitation guide line mould of calculating
Quasi- Daily rainfall amount, and each monthly total precipitation and annual precipitation simulated, the comparison actual measurement drop in the time scale of Month And Year
Extreme precipitation event attribute is dissolved into precipitation generator after contrast verification so that the drop of simulation by water and Simulated precipitation
Water number is as follows according to the rainfall distribution under preferably matching natural conditions：
1) actual measurement precipitation data analysis for many years
Targeted sites Daily rainfall data for many years are obtained from Chinese meteorological data net (data.cma.cn) first, according to precipitation shape
Condition carries out Nature District subregion to website.Because moisture is the key factor of regulation ecological systematic procedure, based on annual precipitation
Want index that can be divided into moistening, semi-moist, semiarid, arid and extremely arid five regional categories.For different classes of, model
Middle precipitation parameter can be variant, for example, semi-moist and the seasonal characteristics of semiarid zone precipitation it is obvious, and arid and
Humid region precipitation is smaller in annual distributional difference.When establishing due to the corresponding weather station of each website and observe the starting of data
Between may be different, this research association carries out edit to the precipitation data of acquisition, and such as the conversion of data format, target data carries
Take the processing with missing values.Data analysis need to meet following three conditions：(1) data record must not be less than 30 years；(2) each year
Missing data must not exceed 10%；(3) missing data is filled up in a manner of long-time average annual value.Subsequent statistical analysis items precipitation refers to
Mark：Annual precipitation, year precipitation number of days, average daily precipitation, per monthly total precipitation, monthly precipitation number of days and monthly day maximum rainfall
Amount.Become later with the variation of Mann-Kendall non-parametric tests (equation (1)-(4)) analysis time sequence to annual precipitation
Gesture.If Annual Precipitation presents and significantly increases or decreases trend, the precipitation data for many years of permutatation simulation makes it that phase be presented
Same variation tendency.The rule of permutatation is the average value of the precipitation data for many years of calculating simulation first, and (1) surveys precipitation for many years
The trend dramatically increased is presented in amount：The time position that precipitation is less than average value is constant, is more than the time of average value according to from small
It is rearranged to big sequence；(2) reduced trend is presented in actual measurement Annual Precipitation：Precipitation is more than the time position of average value
Constant, the time less than average value rearranges according to sequence from big to small；
The step of Mann-Kendall non-parametric tests, is as follows：
For variable X=(x of time series1,x2,…,xn), n is the length of variable, and it is as follows to define statistic S：
X in formulajAnd xiThe respectively corresponding actual measurement precipitation guide line value of jth, i；
Work as n>When 8, random sequence Si(i=1,2 ..., n) approximation Normal Distribution, variance calculation formula are：
S is standardized, statistical check value Z is obtainedMK：
In formula, ZMKIt is the statistic of a normal distribution, ZMK>Show that the trend risen, Z is presented in time series when 0MK<0 indicates
Time series is in downward trend, ZMKAbsolute value bigger expression variation it is more notable.This research gives significance p<0.05,
Corresponding ZMK>1.96 or ZMK<-1.96；
2) simulation of Daily rainfall amount
It is first depending on annual precipitation to classify, 0-1000mm is divided into 10 grades by interval of 100mm, in conjunction with surveying for many years
Precipitation data estimates the probability that each grade precipitation occurs.The curve of frequency distribution of each moon precipitation in drawing 1 year, being based on should
Curve is inferred to the possibility of each moon day precipitation, and calculates the degree of bias of opposite normal curve, is combined using exponential distribution equation (5)
Last point of precipitation guide line simulation generates daily precipitation；
In formula, daypre is the daily precipitation of simulation, and maxpre is actual measurement each moon day maximum precipitation, and rand (0,1) is production
The random number in raw (0,1) section, monthrateTo specify the characteristic parameter of month curve of frequency distribution, ra is correction factor,
Prob is the possibility of each moon day precipitation；
3) precipitation data of verification simulation
The Daily rainfall data accumulation of simulation is obtained into each monthly total precipitation and annual precipitation, and with the Month And Year precipitation of actual measurement into
Row comparison (equation (6)-(7)), if a certain monthly total precipitation error is more than required standard (± 5mm) or a certain annual precipitation
Error is more than required standard (± 10mm), refuses the Precipitation Time Series generated and adjustment index equation parameter, is regenerated each
Its precipitation meets standard until error；
errorM=| PPM-PSM| (6)
errorY=| PPY-PSY| (7)
In formula, errorMTo simulate the error of monthly total precipitation, PPMTo survey monthly total precipitation, PSMTo simulate monthly total precipitation, errorY
To simulate the error of annual precipitation, PPYTo survey annual precipitation, PSYTo simulate annual precipitation；
4) extreme precipitation event attribute is incorporated
Based on the Extreme Precipitation index that extreme weather events index expert group (ETCCDI) recommends, we therefrom have chosen 3
A index is dissolved into precipitation generator.These indexs are from intensity, frequency and the duration metric extreme journey of precipitation event
Degree.Index includes moderate strength precipitation number of days (day precipitation>10mm), number of days and maximum day precipitation are continuously moistened.Substantially false
Surely it is to maintain that gross precipitation is constant, the involvement process of 3 Extreme Precipitation indexs is without sequencing, until the precipitation data of simulation is same
When meet above-mentioned 3 indexs：
(1) moderate strength precipitation number of days：The moderate strength precipitation number of days that the precipitation most big moon is actual and simulates is calculated, if mould
Quasi- number of days is more, then subtracts actual number of days with the number of days of simulation, later the order of analog according to day precipitation from small to large
Moderate strength precipitation Day arranged, by the precipitation of the smaller difference number of days of precipitation more than 10mm part sum it up, by this
Non- moderate strength precipitation Day is averagely arrived in part, if actual number of days is more, the number of days of simulation, Zhi Houxuan are subtracted with actual number of days
It is difference number of days to take several and immediate precipitation Day of moderate strength, number, calculates other non-moderate strength precipitation Day n% successively
Part sum it up and be averaged to the immediate precipitation Day of moderate strength, until these precipitation Days precipitation be not less than 10mm；
(2) maximum continuous moistening number of days：The continuous moistening number of days that the precipitation most big moon is actual and simulates is calculated, if simulation
Number of days is more, then subtracts actual number of days with the number of days of simulation, the difference number of days precipitation of day side will be continuously moistened in analogue data
Amount sums it up and averagely arrives other precipitation Days；If actual number of days is more, the number of days of simulation, calculating simulation are subtracted with actual number of days
The part of precipitation Day (discontinuous moistening precipitation Day) 5% and the average difference number of days to continuous moistening day side of adduction in data；
(3) maximum day precipitation：The maximum day precipitation that the precipitation most big moon is actual and simulates is calculated, if the analogue value is big,
Actual value then is subtracted with the analogue value, by difference averagely to other precipitation Days in analogue data；If actual value is big, subtracted with actual value
The analogue value is gone, the part adduction of precipitation Day in analogue data (non-maximum rainfall day) n% is attached to maximum rainfall day, Zhi Daomo
Quasi- maximum day precipitation is equal to actual maximum day precipitation.
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| CN109685334A (en) * | 2018-12-10 | 2019-04-26 | 浙江大学 | A kind of new hydrological model simulation evaluation method based on Multiscale Theory |
| CN109871584A (en) * | 2019-01-16 | 2019-06-11 | 中山大学 | A kind of region month precipitation statistic frequency analysis method based on log-sinh transformation |
| CN112394424A (en) * | 2020-09-28 | 2021-02-23 | 南京信息工程大学 | Method for monitoring regional extreme rainfall event |
| CN118095521A (en) * | 2023-12-13 | 2024-05-28 | 兰州大学 | Method for predicting summer extreme precipitation of large river basin |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN109871584A (en) * | 2019-01-16 | 2019-06-11 | 中山大学 | A kind of region month precipitation statistic frequency analysis method based on log-sinh transformation |
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| CN112394424A (en) * | 2020-09-28 | 2021-02-23 | 南京信息工程大学 | Method for monitoring regional extreme rainfall event |
| CN112394424B (en) * | 2020-09-28 | 2022-04-26 | 南京信息工程大学 | Method for monitoring regional extreme rainfall event |
| CN118095521A (en) * | 2023-12-13 | 2024-05-28 | 兰州大学 | Method for predicting summer extreme precipitation of large river basin |
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| CN108595814B (en) | 2021-06-04 |
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