CN113361742A - Hydrologic simulation-based regional comprehensive drought identification method - Google Patents
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Abstract
A hydrologic simulation-based regional comprehensive drought identification method belongs to the technical field of drought identification. The method comprises the steps of collecting and processing long-series regional hydrometeorology observation data, carrying out parameter calibration on the hydrological model to obtain the hydrological model, inputting precipitation and potential evapotranspiration data in a certain time period, and simulating to obtain the soil water content and runoff in the corresponding time period so as to obtain a regional meteorological, agricultural and hydrometeorology drought index sequence, thereby carrying out regional comprehensive drought identification. The method can reveal the internal relation of the occurrence and development of different types of drought by researching the progressive relation of regional meteorological drought, agricultural drought and hydrological drought in time, and has a strong physical mechanism. And moreover, drought scene prediction can be performed by simulating the soil water content and runoff in the region through a hydrological model, and an evaluation and simulation platform is provided for river basin drought identification and drought early warning prediction.
Description
Technical Field
The invention belongs to the technical field of drought identification, and particularly relates to a regional comprehensive drought identification method based on hydrologic simulation.
Background
Drought is an extremely complex natural phenomenon that usually begins with little rain, causing a water shortage that increasingly spreads throughout the hydrological system, further affecting soil water content, groundwater reserves, channel runoff, and reservoir water storage. Drought is generally divided into four categories, depending on the cause and its course of influence: weather drought, agricultural drought, hydrological drought, and socioeconomic drought. There is a progressive relationship in time between the four types of drought. The study on the evolution process and the basic rule among different types of drought is helpful for improving the regional drought early warning capability and reasonably planning regional water resources, and provides scientific basis for relieving the contradiction between water resource supply and demand and improving the drought defense capability.
Many scholars have conducted a great deal of research on various types of drought in different areas, and have achieved relatively abundant research results. At present, a commonly used drought identification and evaluation method is a Standardized drought Index method, wherein a Standardized Precipitation Index (SPI) is commonly used to analyze meteorological drought; standardized Soil Moisture Index (SSMI) is commonly used to evaluate agricultural drought; standardized Runoff Index (SRI) is commonly used to identify hydrodroughts. Because the influence factors of the regional drought are various, most researches only consider the influence of a single factor on the drought, only use a single index to evaluate a certain type of drought in the region, and fail to consider regional comprehensive drought under the common influence of multiple factors, so that the one-sidedness of regional drought evaluation is caused. In addition, there is a close internal relationship between different types of drought, and how to accurately describe and reveal the relationship by a method with a certain physical mechanism is a problem that needs to be continuously and deeply researched.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a hydrologic simulation and standardized drought index coupled regional comprehensive drought identification method.
In order to solve the technical problems, the invention adopts the following technical scheme:
a hydrologic simulation-based regional comprehensive drought identification method comprises the following steps:
step 1: collecting and processing regional long series of hydrological meteorological observation data: collecting and sorting hydrological meteorological observation data in an area, wherein the hydrological meteorological observation data comprise rainfall, potential evapotranspiration and runoff data;
step 2: carrying out parameter calibration on the hydrological model according to the hydrological meteorological observation data in the step 1 to obtain a calibrated hydrological model;
and step 3: inputting precipitation and potential evapotranspiration data of a certain time period according to the calibrated hydrological model obtained in the step 2, and simulating to obtain the soil water content and runoff of the corresponding time period;
and 4, step 4: calculating weather, agricultural and hydrological drought index sequences in the area by adopting a standardized precipitation index, a standardized soil humidity index and a standardized runoff index method according to precipitation, the simulated soil water content and the runoff data in the step 3;
and 5: and 4, carrying out regional comprehensive drought identification according to the weather, agricultural and hydrological drought index sequences obtained in the step 4.
In step 2, the hydrological model is a two-parameter monthly water balance model.
The two-parameter monthly water balance model is referred to as a TMWB model for short, and the TMWB model is a hyperbolic tangent function relation that the monthly runoff Q (t) is assumed to be the soil water content, namely:
Q(t)＝(S(t-1)+P(t)-E(t))×tanh((S(t-1)+P(t)-E(t))/SC) (1)
in the formula: SC is the first parameter of the model and represents the maximum water storage capacity (mm) of the basin;
s (t-1) is the water content of the soil at the beginning of the tth month,
p (t) is the average precipitation on the drainage surface,
e (t) is the actual evapotranspiration at month t, calculated as follows:
E(t)＝C×PET(t)×tanh(P(t)/PET(t)) (2)
in the formula: c is a second parameter of the TMWB model;
calculating the soil water content S (t) at the end of the tth month:
S(t)＝S(t-1)+P(t)-E(t)-Q(t) (3)。
the standardized drought index method of step 4 comprises the following steps: standardized precipitation index, standardized soil humidity index and standardized runoff index.
And the drought grade of the step 5 is divided according to the meteorological drought grade (GB/T20481-2006).
Compared with the prior art, the regional comprehensive drought identification method based on hydrologic simulation provided by the invention has the following advantages and beneficial effects:
(1) and (3) comprehensive evaluation: in the prior art, single index is generally adopted for drought assessment, only individual factors influencing drought are considered, and the method can comprehensively and objectively assess the whole-course development of regional drought (meteorological drought, agricultural drought and hydrologic drought) by simulating key factors (soil water content and runoff) influencing drought in different stages through a hydrologic model according to the evolution processes and basic rules of different types of drought.
(2) Revealing the intrinsic relationship: the method can faithfully reflect the chain transmission process of meteorological drought, agricultural drought and hydrological drought in the same region, reveal the internal connection of occurrence and development of different types of drought, and have a strong physical mechanism.
(3) And (3) completing drought prediction: the method can be used for predicting the drought scene by simulating the runoff and the soil water content in the area through the hydrological model.
In conclusion, the method can truly depict and research the progressive relation of regional meteorological drought, agricultural drought and hydrographic drought in time, reveal the internal relation of occurrence and development of different types of drought, and have a strong physical mechanism. And moreover, drought scene prediction can be performed by simulating the soil water content and runoff in the area through a hydrological model, and an important evaluation and simulation platform is provided for river basin drought identification and drought early warning prediction.
Drawings
The invention is further illustrated by the following figures and examples.
FIG. 1 is a block diagram of the logical structure of the method of the present invention;
FIG. 2 is a diagram of sequences of meteorological, agricultural and hydrographic drought indexes at different time scales, wherein a, b, c and d represent sequences of hydrographic drought indexes at 1 month, 3 months, 6 months and 12 months, respectively;
FIG. 3 is a statistical representation of weather, agricultural and hydrographic drought levels at different time scales, in which a, b, c, d represent the statistical representation of the drought levels for light, medium, heavy and extra drought, respectively.
Detailed Description
Referring to the logic structure block diagram of the invention in fig. 1, the hydrologic simulation-based regional integrated drought identification method provided by the invention specifically comprises the following steps:
collecting runoff information Q (t) of a drainage basin outlet section controlled hydrological station length series by time period; and (3) obtaining the drainage basin surface average precipitation P (t) and the potential evapotranspiration data PET (t) by time intervals by utilizing the Thisen polygon method to calculate based on the collected drainage basin multiple meteorological site precipitation and potential evapotranspiration data.
Considering that the data required by the standardized drought index method is a month time scale, the collected long-series hydrological meteorological information needs to be month by month or below the month time scale (such as day by day, ten days by day, and the like). For smaller time scale data such as day by day, ten days by ten days and the like, the data can be arranged into month by month data by a time aggregation method.
The Thiessen polygon method is a conventional technique in the art.
And 2, inputting the observation data in the step 1 into a hydrological model, and calibrating the model parameters by adopting an SCE-UA algorithm. The SCE-UA algorithm is a common method in the art.
Generally, the choice of the hydrological model is closely related to the availability of regional hydrological meteorological data and underlying subsurface information. Considering that compared with other conceptual hydrological models, the Two-parameter Monthly Water Balance (TMWB) model has the advantages of simple structure, clear physical concept, less parameters, only Two parameters, low requirement on data and easiness in popularization and application, and adopts the TMWB model in the specific implementation.
The two parameters are specifically: (1) SC, representing the basin maximum water holding capacity (mm); (2) and C, representing the actual evapotranspiration conversion coefficient.
The TMWB model assumes that the monthly runoff q (t) is a hyperbolic tangent function relationship of the soil water content, i.e.:
Q(t)＝(S(t-1)+P(t)-E(t))×tanh((S(t-1)+P(t)-E(t))/SC) (1)
in the formula: SC is the first parameter of the model and represents the maximum water storage capacity (mm) of the basin;
s (t-1) is the water content of the soil at the beginning of the tth month,
p (t) is the average precipitation on the drainage surface,
e (t) is the actual evapotranspiration at month t, calculated as follows:
E(t)＝C×PET(t)×tanh(P(t)/PET(t)) (2)
in the formula: c is a second parameter of the TMWB model, representing the actual evapotranspiration conversion coefficient.
Calculating the soil water content S (t) at the end of the tth month:
S(t)＝S(t-1)+P(t)-E(t)-Q(t) (3)
preferably, as the TMWB model parameters are obtained by historical hydrographic meteorological data calibration, the model simulation effect is reliable, and the future meteorological data predicted by the climate mode still has great uncertainty, the historical meteorological data in a certain period of time is used as the TMWB model input in step 3 of the specific implementation. With the further improvement of the precision of the climate model products, the method can be applied to future drought prediction.
in step 4, the normalized drought Index uses a normalized Precipitation Index (SPI), a normalized Soil Moisture Index (SSMI), and a normalized Runoff Index (SRI).
The SSMI and SRI are similar to the SPI calculation method, and only the precipitation needs to be replaced with the soil moisture content and runoff simulated in step 3. The SPI calculation steps are as follows:
s1: for a monthly precipitation sequence { x (i) } 1,2, … and 12n } with the sample number of n years, when the designated time scale is w months, calculating a w-month accumulated precipitation sequence { x }w(i) I 1,2, …,12n-w +1, in terms of xw(i) The month m is rearranged and combined to obtain 12 subsequences
The embodied time scale w takes 1, 3, 6 and 12.
S2: selecting Pearson III (P-III) type distribution to fit accumulated precipitation sequence of different time scales of each month
The probability density function for a P-III type distribution is shown below:
in the formula: alpha, beta and gamma are shape, scale and position parameters, and are estimated by adopting an L-moment method; Γ (·) is a Gamma function.
The L-moment method is conventional in the art.
The corresponding cumulative probability is further calculated:
(3) converting F (x) into a standard normal distribution function through the following formula to obtain the SPI:
in the formula: p is the probability of exceeding a certain value, P ═ 1-f (x). If P is>0.5, replacing P with 1-P and changing the sign of SPI accordingly. C0、C1、C2、d1、d2、d3Are all constants. They are respectively: c0＝2.515517，C1＝0.802853，C2＝0.010328，d1＝1.432788，d2＝0.189269，d3＝0.001308。
And 5, carrying out regional comprehensive drought identification according to the drought index sequence obtained in the step 4.
In step 5, the drought levels of the drought indexes are divided according to the weather drought level (GB/T20481-2006). The different time scale SPI/SSMI/SRI sequences are shown in FIG. 2.
The SPI/SSMI/SRI based drought grade divisions are shown in Table 1.
TABLE 1 SPI/SSMI/SRI index rating Scale Table
The statistical results of the weather, agricultural and hydrological drought levels at different time scales are shown in fig. 3.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A regional comprehensive drought identification method based on hydrologic simulation is characterized by comprising the following steps: the method comprises the following steps:
step 1: collecting and processing regional long series of hydrological meteorological observation data: collecting and sorting hydrological meteorological observation data in an area, wherein the hydrological meteorological observation data comprise rainfall, potential evapotranspiration and runoff data;
step 2: carrying out parameter calibration on the hydrological model according to the hydrological meteorological observation data in the step 1 to obtain a calibrated hydrological model;
and step 3: inputting precipitation and potential evapotranspiration data of a certain time period according to the calibrated hydrological model obtained in the step 2, and simulating to obtain the soil water content and runoff of the corresponding time period;
and 4, step 4: calculating weather, agricultural and hydrological drought index sequences in the area by adopting a standardized precipitation index, a standardized soil humidity index and a standardized runoff index method according to precipitation, the simulated soil water content and the runoff data in the step 3;
and 5: and 4, carrying out regional comprehensive drought identification according to the weather, agricultural and hydrological drought index sequences obtained in the step 4.
2. The hydrologic simulation-based regional integrated drought evaluation method according to claim 1, characterized in that: in step 2, the hydrological model is a two-parameter monthly water balance model.
3. The hydrologic simulation-based regional integrated drought evaluation method according to claim 2, characterized in that: the two-parameter monthly water balance model is referred to as a TMWB model for short, and the TMWB model is a hyperbolic tangent function relation that the monthly runoff Q (t) is assumed to be the soil water content, namely:
Q(t)＝(S(t-1)+P(t)-E(t))×tanh((S(t-1)+P(t)-E(t))/SC) (1)
in the formula: SC is the first parameter of the model and represents the maximum water storage capacity (mm) of the basin;
s (t-1) is the water content of the soil at the beginning of the tth month,
p (t) is the average precipitation on the drainage surface,
e (t) is the actual evapotranspiration at month t, calculated as follows:
E(t)＝C×PET(t)×tanh(P(t)/PET(t)) (2)
in the formula: c is a second parameter of the TMWB model and represents an actual evapotranspiration conversion coefficient;
calculating the soil water content S (t) at the end of the tth month:
S(t)＝S(t-1)+P(t)-E(t)-Q(t) (3)
4. the hydrologic simulation-based regional integrated drought evaluation method according to claim 1, characterized in that: the standardized drought index method of step 4 comprises the following steps: standardized precipitation index, standardized soil humidity index and standardized runoff index.
5. The hydrologic simulation-based regional integrated drought identification method as claimed in claim 1, wherein: and the drought grade of the step 5 is divided according to the meteorological drought grade (GB/T20481-2006).
6. The hydrologic simulation-based regional integrated drought evaluation method according to claim 4, characterized in that: in the step 4, the index uses a standardized rainfall index SPI, a standardized soil humidity index SSMI and a standardized runoff index SRI, and the calculation method comprises the following steps:
and (3) SPI calculation step:
s1: for a monthly precipitation sequence { x (i) } 1,2, … and 12n } with the sample number of n years, when the designated time scale is w months, calculating a w-month accumulated precipitation sequence { x }w(i) I 1,2, …,12n-w +1, in terms of xw(i) The month m is rearranged and combined to obtain 12 subsequences
S2: selecting Pearson III (P-III) type distribution to fit accumulated precipitation sequence of different time scales of each month
The probability density function for a P-III type distribution is shown below:
in the formula: alpha, beta and gamma are shape, scale and position parameters, and are estimated by adopting an L-moment method; Γ (·) is a Gamma function;
the corresponding cumulative probability is further calculated:
s3: converting F (x) into a standard normal distribution function through the following formula to obtain the SPI:
in the formula: p is the probability of exceeding a certain value, P ═ 1-f (x); if P > 0.5, replacing P with 1-P and changing the sign of SPI accordingly; c0、C1、C2、d1、d2、d3Are all constants, C0＝2.515517，C1＝0.802853，C2＝0.010328，d1＝1.432788，d2＝0.189269，d3＝0.001308；
The SSMI and SRI are similar to the SPI calculation method, and only the precipitation needs to be replaced with the soil moisture content and runoff simulated in step 3.
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| CN114819317A (en) * | 2022-04-18 | 2022-07-29 | 青海省气象科学研究所 | Farmland and grassland drought early warning method and system |
| CN115310796A (en) * | 2022-07-29 | 2022-11-08 | 西安理工大学 | Method and system for determining propagation relationship among different types of drought |
| CN115310796B (en) * | 2022-07-29 | 2023-06-20 | 西安理工大学 | Method and system for determining propagation relationship between different types of drought |
| CN115329610A (en) * | 2022-10-17 | 2022-11-11 | 中山大学 | Method, device and equipment for identifying drought and waterlogging emergency turn based on soil moisture |
| CN115331215A (en) * | 2022-10-18 | 2022-11-11 | 水利部交通运输部国家能源局南京水利科学研究院 | Three-dimensional identification and matching method and device for drought event |
| CN115859583A (en) * | 2022-11-21 | 2023-03-28 | 上海勘测设计研究院有限公司 | Quantitative analysis method and system for influence of precipitation and initial state on drought process |
| CN115859583B (en) * | 2022-11-21 | 2023-06-27 | 上海勘测设计研究院有限公司 | Quantitative analysis method and system for influence of precipitation and initial state on drought process |
| CN116128371A (en) * | 2023-02-27 | 2023-05-16 | 水利部信息中心(水利部水文水资源监测预报中心) | Multi-index comprehensive drought monitoring and early warning system |
| CN116128371B (en) * | 2023-02-27 | 2024-04-12 | 水利部信息中心(水利部水文水资源监测预报中心) | Multi-index comprehensive drought monitoring and early warning system |
| CN117828906A (en) * | 2024-03-05 | 2024-04-05 | 长江水利委员会长江科学院 | Drought transmission process simulation method, system and medium based on crop growth model |
| CN117828906B (en) * | 2024-03-05 | 2024-05-17 | 长江水利委员会长江科学院 | Drought transmission process simulation method, system and medium based on crop growth model |
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