63 results about "Aridity index" patented technology

The invention belongs to the technical field of drought risk prediction and vegetation influence evaluation, and particularly discloses a vegetation-to-drought resource utilization coupling response strategy evaluation method, which comprises the following steps: acquiring target data; constructing a drought index according to the target data, and extracting a drought event; extracting vegetation total productivity, vegetation net level productivity and evapotranspiration abnormal data before and after the drought event, and calculating response intensity of vegetation variables; calculating the average variation of the light energy utilization efficiency, the carbon utilization efficiency and the moisture utilization efficiency; comprehensively considering the multivariable resistance-recovery state of the vegetation physiological process and the regulating variable of the resource utilization efficiency of the vegetation in response to drought stress, and analyzing the drought event resistance-recovery response capability of the vegetation. The method can more accurately and objectively understand the physiological mechanism difference of the vegetation to the drought stress response.

The invention belongs to the technical field of drought monitoring, and particularly discloses a drought monitoring method and system based on multi-source data and spatio-temporal context embedding, and the method comprises the following steps: obtaining drought index data, multi-source dynamic remote sensing factor data and static geographic data of a meteorological station in a research area, and carrying out the preprocessing, obtaining original remote sensing features; extracting attribute values from the static geographic data according to the longitude and latitude of a site, extracting a space context embedding vector and a time context embedding vector based on a multi-layer perceptron and a long-short-term memory network, generating a modulated space-time context embedding vector, and splicing the modulated space-time context embedding vector with the original remote sensing features to obtain a final feature vector; and inputting the information enhanced final feature vector and the monthly standardized rainfall evapotranspiration index SPEI-3 of each station into a GXGBDM model, and outputting a drought monitoring result. By adopting the technical scheme, the drought monitoring result can be efficiently and accurately generated by fusing the multi-source data and the spatio-temporal context embedding information.

The invention discloses a GIS-based arid and semi-arid region farmland irrigation priority dynamic evaluation and zoning method, system, device and medium, and belongs to the technical field of farmland irrigation priority evaluation and zoning, and the method comprises the steps: collecting various types of remote sensing satellite data of a to-be-irrigated region; performing pretreatment; identifying the boundary and range of the to-be-irrigated area, and extracting a remote sensing image of the to-be-irrigated area; based on the remote sensing image of the to-be-irrigated area and the basic geographic information, parameter calculation is carried out, and normalization processing is carried out; performing superposition calculation on each normalized parameter according to a set weight to obtain an irrigation priority index; partitioning the to-be-irrigated area based on the irrigation priority index to generate an irrigation priority map; and judging whether agricultural irrigation management requirements are met or not according to preset conditions. According to the method, temperature vegetation drought indexes, normalized moisture indexes, water resource accessibility and crop economic value parameters are coupled; the irrigation priority index weight can be automatically adjusted according to a season strategy.

The invention relates to the technical field of intelligent data identification, in particular to a wetland ecological drought condition identification method based on multi-source data. According to the technical scheme, the method comprises the following steps: data acquisition: acquiring a multi-source data set of a target wetland region in a preset time sequence; the multi-source data set at least comprises a remote sensing reflectivity data set, a surface temperature data set, a meteorological reanalysis data set and an on-site monitoring hydrological data set; and data processing: sequentially carrying out space-time registration, denoising and missing value interpolation processing on the multi-source data set to generate a standardized space-time data cube. According to the method, multi-source data can be integrated to generate a standardized spatio-temporal data cube, each ecological drought index threshold value is dynamically determined based on the background of the historical wet season of the wetland, the comprehensive drought intensity is calculated in a weighted mode according to the wetland type, and accurate wetland ecological drought spatio-temporal distribution, grade sequence and early warning information are output after multi-scale verification. And a basis is provided for wetland ecological protection and water resource regulation and control.

The invention discloses an electric power climate risk assessment method for the influence of high temperature and / or drought events on both sides of supply and demand, a medium and a program product, and belongs to the technical field of meteorological disaster risk assessment and energy system safety crossing. Then identifying a high-temperature or drought event based on a temperature percentile threshold value and an SPEI drought index, and constructing a bivariate distribution model by adopting a joint probability density function to identify a high-temperature drought composite event; respectively constructing a power supply side response model and a load side response model, and quantitatively evaluating output changes of different power supplies and load response characteristics of various users in a high-temperature and / or drought scene; key indexes such as a power gap and load risk exposure intensity are calculated through supply-demand coupling offset analysis; and finally, based on the climate mode prediction data, driving the risk model, and outputting a risk evolution trend in a future scene. The method can provide support for climate toughness improvement and scheduling decision making of the power system.

The invention discloses a human drink drought prediction method based on an AE-TT model, and the method comprises the steps: constructing a drought index, constructing a standardized human drink drought index SHI, constructing an AE-TT deep learning model for human drink drought prediction, and outputting a human drink drought prediction result through the AE-TT model. According to the method, aiming at the problem of lack of a systematic index system for human drink drought evaluation in the Da-rock mountainous area in Guangxi, a human drink drought evaluation index system suitable for the area is constructed, accurate quantification and scientific grading of the drought-induced water shortage condition of the area without a stable water source are realized, and a standardized tool is provided for drought degree research and judgment; the method breaks through the applicability limitation of an existing drought prediction technology in the field of human drinks in rocky mountainous areas, develops a high-precision human drink drought index prediction technology based on deep learning, improves the prejudgment capability of drought occurrence trends in different time periods, and provides a scientific basis for deploying water supply safeguard measures in advance.

The invention discloses a drought propagation time prediction method based on machine learning. The method comprises the following steps: collecting and preprocessing hydro meteorological data used for drought index calculation; calculating accumulated rainfall, potential evapotranspiration and soil water content under a given target time scale by adopting a day-by-day sliding method; fitting a probability distribution function of the cumulative data, and performing normal distribution inverse transformation on the cumulative probability to obtain meteorological drought and agricultural drought index sequences; identifying meteorological drought events and agricultural drought events, and counting soil water content states and meteorological drought event characteristics when the meteorological drought events occur as potential factors influencing drought propagation time; matching the identified meteorological drought event and agricultural drought event, and calculating the propagation time of each matched drought event pair; training a drought propagation time prediction model based on an extreme gradient lifting algorithm to realize prediction of drought propagation time; and a technical support is provided for agricultural drought prediction and drought-resistant water source optimization configuration.

The application provides a drought propagation analysis method and system considering multi-dimensional characteristics of drought events. The method comprises the following steps: calculating a drought duration propagation index based on a meteorological drought total duration and an agricultural drought total duration; calculating a drought intensity propagation index based on meteorological drought intensities of previous meteorological drought events and agricultural drought intensities of previous agricultural drought events; calculating a drought intensity trend propagation index based on meteorological drought intensity trends of previous meteorological drought events and agricultural drought intensity trends of previous agricultural drought events; calculating a drought fluctuation propagation index based on meteorological drought index fluctuation rates of previous meteorological drought events and agricultural drought index fluctuation rates of previous agricultural drought events; calculating a drought probability propagation index based on a meteorological drought occurrence probability and an agricultural drought occurrence probability of a target region within a given time period; and constructing a comprehensive drought propagation index by using a CRITIC objective weighting method. The application can accurately describe the propagation process between meteorological drought and agricultural drought.

The application discloses a kind of wind power resource drought degree evaluation method and related equipment.The method comprises: obtaining the hourly wind speed data of target area in history and evaluation period, and determining hourly wind power output sequence in combination with wind turbine power curve;Based on historical wind power output sequence sample, determine reliable output reference value and recharge threshold value for each duration t based on the statistical distribution of historical sample of each duration t;Calculate the equivalent energy buffer capacity of system in the evaluation window for resisting wind power underproduction;Based on reliable output reference value and recharge threshold value, calculate power gap and power surplus hour by hour;According to the reliable reference gap power that is not satisfied by energy storage and energy storage energy state, calculate operating deficit;Based on equivalent energy buffer capacity and operating deficit, calculate wind power resource drought index.Can solve the problem of the shortcomings of the existing method for quantifying and analyzing wind power resource drought, including fixed threshold method, relative anomaly method and standardized index method.

The invention discloses a grassland insurance grade evaluation method and system based on a multi-source time sequence. Based on PCA information entropy, fusing long-time sequence grassland insurance indexes (a meteorological drought index, a vegetation drought index, a soil drought index, a soil-vegetation drought index, a fire index, a flood index, a grassland comprehensive vegetation coverage, a grassland grass yield and a grassland greenness index); the grassland vegetation change condition is detected, the grassland insurance index main feature information limiting the grassland vegetation condition in each time period is extracted, the grassland insurance grade is obtained by using FCM clustering, manual ground patrol and verification work is greatly reduced, the grassland insurance grade condition of a target area can be quickly and accurately mastered, and the loss caused by natural disasters is greatly reduced.

The invention relates to a drought monitoring method and system, and relates to the technical field of hydraulic engineering, and the method comprises the steps: monitoring the growth state of planted crops in a first irrigation region, the drought state and a ground water source through a plurality of monitoring satellites to obtain satellite monitoring data, and determining a first drought index; acquiring unmanned aerial vehicle monitoring data of the second irrigation area by using the plurality of unmanned aerial vehicles and the intelligent machine nest, determining a second drought index of the second irrigation area based on the unmanned aerial vehicle monitoring data, and determining a preliminary irrigation state of the second irrigation area; acquiring actual measurement data of the second irrigation area through ground drought monitoring equipment, and determining a third drought index according to the actual measurement data; and determining the land parcel drought grade of each land parcel in the second irrigation area by combining the first drought index, the second drought index and the third drought index, and determining the irrigation scheme of each land parcel based on the land parcel drought grade, the initial irrigation state and the actually measured data. According to the invention, the irrigation scheme can be timely and accurately determined.

The invention discloses a method for quantitatively evaluating the dynamic hydrological effect of vegetation, and belongs to the technical field of climate change evaluation. Aiming at the defect that vegetation and hydrological effects cannot be independently and quantitatively separated due to the fact that underlying surface parameters confuse vegetation and non-vegetation factors in an existing Budyko hydrothermal balance attribution model, meteorological, hydrological, vegetation and environmental data are collected and processed; inverting underlying surface parameters based on a Budyko framework; calculating elastic coefficients of runoff to rainfall, potential evapotranspiration and parameters; constructing a linear model taking the drought index and the soil water-holding capacity as variables to obtain vegetation sensitivity parameters; finally, the runoff component caused by vegetation change is quantitatively separated in combination with the elastic coefficient and the vegetation sensitivity parameter. According to the method, the influence of vegetation dynamics on the hydrological effect is independently and accurately quantified, and the transparency of runoff attribution and the application value in ecological hydrological management are improved.

The application provides a hydrothermal soil humidity coupling regional agricultural drought monitoring method and system, which comprises the following steps: 1, collecting soil humidity data and meteorological element data of a research area; 2, determining the time scale value with the highest correlation in different seasons by considering the correlation between the accumulated hydrothermal factors and the soil humidity in different time scales by means of the Pearson correlation coefficient method; 3, calculating the lag time of soil moisture to precipitation and temperature by means of the cross wavelet transform method; 4, constructing a hydrothermal soil humidity coupling drought index RADI by means of a binary Copula model; and 5, dividing different drought grades to represent the regional agricultural drought conditions. The application evaluates the reliability and applicability of RADI in representing regional agricultural drought from the time dimension and the space dimension, provides more fine data support for the evolution characteristics of agricultural drought, and has important significance for regional grain production, ecological safety and environmental protection and the like.

The application discloses a kind of drought series-parallel propagation process analytical method, it is related to the data processing technical field of drought propagation.The method includes: according to the correlation coefficient between the drought index of meteorological drought in each week time scale and the drought index of other drought types in corresponding month time scale, obtain the correlation coefficient sequence;First-order forward difference and second-order forward difference of the first maximum in correlation coefficient sequence are obtained;If it meets that first-order and second-order forward difference are greater than or equal to 0, then determine the week time scale corresponding to the position as the propagation time of meteorological drought to other drought types;According to the propagation time of meteorological drought to other different drought types, determine the series-parallel mode of drought propagation;According to different drought propagation mode, determine the drought propagation threshold value of at least one type of drought to another type of drought.The method can accurately describe the propagation process of multiple types of drought.

This invention discloses a method for simulating soil relative humidity based on meteorological indices, relating to the field of soil moisture simulation application technology. The method includes: acquiring daily meteorological data of the target area and calculating the evapotranspiration index of previous precipitation; constructing a logistic function relationship between the previous precipitation evapotranspiration index and soil relative humidity based on the S-shaped variation characteristics of the soil moisture absorption process; fitting the parameters of the logistic function using soil relative humidity observation samples to obtain a soil relative humidity meteorological simulation model for outputting soil relative humidity simulation results. This invention solves the problems of existing drought indices such as SPEI and SAPEI failing to accurately reflect the daily dynamic changes in farmland soil moisture and the inconsistency between drought levels and actual agricultural soil moisture.

The invention provides a variable irrigation prescription map construction method and device, a medium and equipment. The variable irrigation prescription map construction method comprises the steps of obtaining unmanned aerial vehicle image data, Internet of Things soil sensor data and weather forecast rainfall data under an image coordinate system at the same time and in the same space; a normalized vegetation index and a surface temperature are calculated from the image data, and the vegetation fractal coverage is calculated based on the normalized vegetation index. Establishing a temperature-fractal coverage feature space based on the surface temperature and the vegetation fractal coverage, and calculating a temperature vegetation drought index; the internet-of-things soil moisture content data and the temperature and drought vegetation index data serve as input, the function relation between the soil moisture content and the temperature and drought vegetation index is established, and the soil moisture content of the whole region is calculated and obtained; extracting the field moisture capacity in the current crop planting process; a one-time irrigation quota is calculated by utilizing field moisture capacity, whole-region soil moisture content and weather forecast rainfall data, and a final variable irrigation prescription map is obtained by combining agriculture and an irrigation period.

The invention discloses a drought trigger threshold determination method and system considering early-stage soil humidity. Monthly-scale rainfall, temperature and soil humidity data are obtained and preprocessed; calculating a current month meteorological drought index, a current month agricultural drought index and a previous month agricultural drought index; determining propagation time from meteorological drought to agricultural drought according to the two index sequences, and matching an optimal time scale; depicting a dependency relationship between an earlier-stage soil humidity state and a current drought index by using a vine structure three-dimensional joint distribution model, and calculating conditional probabilities of agricultural drought occurring in the current month under different earlier-stage humidity scenes; and trying to calculate the meteorological drought value according to preset probability threshold value decreasing progressively, and setting a result reaching the threshold value for the first time as a trigger threshold value in the early-stage state. According to the invention, fine threshold output of humidity in different earlier stages is realized, the agricultural drought early warning timeliness and precision are improved, and the method is suitable for early recognition and risk management of regional agricultural drought.

This invention discloses a method, medium, and program product for assessing the impact of high-temperature and / or drought events on both the supply and demand sides of the power system. It belongs to the interdisciplinary field of meteorological disaster risk assessment and energy system security. First, multi-source data from the target area is collected and preprocessed. Then, high-temperature or drought events are identified based on temperature percentile thresholds and the SPEI drought index. A bivariate distribution model is constructed using a joint probability density function to identify combined high-temperature and drought events. Next, response models for the power supply side and load side are constructed separately to quantitatively assess the output changes of different power sources and the load response characteristics of various users under high-temperature and / or drought scenarios. Furthermore, key indicators such as power shortages and load risk exposure intensity are calculated through supply-demand coupling offset analysis. Finally, the risk model is driven by climate model prediction data to output the risk evolution trend under future scenarios. This invention can provide support for improving the climate resilience of power systems and for dispatching decisions.

The present application belongs to the technical field of agricultural meteorological disasters, and particularly relates to a vegetation drought disaster index evaluation method and system based on water vapor flux divergence. The evaluation method comprises the following steps: obtaining historical data of water vapor flux divergence of a target region, processing the historical data to obtain source historical data, calculating the occurrence probability of water vapor flux divergence according to the source historical data, obtaining the water vapor flux divergence index from the occurrence probability, obtaining a satellite remote sensing vegetation change index according to a satellite remote sensing vegetation index, calculating the significance region of the water vapor flux divergence index and the satellite remote sensing vegetation change index, and dividing the vegetation drought disaster level of the significance region according to the water vapor flux divergence index. The present application solves the problems that the traditional drought index does not consider all the factors affecting drought disasters, resulting in low accuracy and mutual interference among multiple vegetation drought disaster factors, and that data processing and analysis are difficult, and overcomes the defect that the complex surface does not have drought disaster prediction capability.

The invention discloses a drought monitoring method and system based on coupling of crop types and multi-dimensional drought characteristics. The method comprises the following steps: acquiring and preprocessing multi-source remote sensing data; based on high-resolution remote sensing data, farmland plots are extracted in combination with terrain and road network data; combining optical remote sensing data to extract crop time sequence characteristics of the farmland plot; inputting the crop time sequence characteristics into a pre-trained crop identification model; constructing a specific three-dimensional drought index model of different types of crops based on the spatial distribution of various types of crops; and according to the specific three-dimensional drought index model, fusing a soil moisture dynamic threshold value and remote sensing drought loss inversion data to generate a crop specific drought risk grading map. According to the method, different crop types can be accurately distinguished, the difference of different crops in the aspects of water demand characteristics and drought response is fully reflected, and refined monitoring and evaluation of drought conditions of different crops are realized.

This disclosure provides a three-dimensional dynamic diagnostic method and apparatus for extreme drought events. The method includes acquiring drought index data and historical three-dimensional circulation fields of a target region, constructing a standardized anomaly field matrix, determining extreme drought grid points based on drought index data and performing regional connections to obtain drought event domains and regional extreme drought events; selecting key physical quantity fields in the historical three-dimensional circulation field, constructing a joint anomaly field matrix by combining it with the standardized anomaly field matrix, identifying the first mode and the dominant circulation pattern through singular value decomposition, reconstructing the three-dimensional physical quantity anomaly field of the dominant circulation pattern based on the first mode, and calculating the dynamic and thermal contribution values; tracing the source of the dominant circulation pattern and constructing the physical mechanism chain of the dominant circulation pattern. This achieves a comprehensive understanding of the mechanism from source forcing to local impacts, providing a directly applicable diagnostic framework and physical basis for the physical attribution of drought, the verification of prediction models, and extended-range climate prediction.

The invention provides a drought multi-dimensional risk zoning method and system based on a clustering algorithm, and the method comprises the steps: obtaining drought index data with a continuous time sequence in a research region, and obtaining a drought index spatio-temporal data set through data quality control; based on the drought index spatio-temporal data set, calculating three drought characteristic indexes of drought duration, drought intensity and drought frequency, and constructing drought multi-dimensional characteristics; inputting the drought multidimensional features into a self-organizing mapping network for preliminary clustering to obtain a neuron weight vector, performing secondary clustering by taking the neuron weight vector as input, and determining an optimal clustering number in combination with a clustering performance evaluation index; and according to a clustering result, sorting, dividing and visually displaying the drought risk levels. According to the method, automatic division of drought risk levels is realized, a visual spatial distribution diagram is generated, and technical support is provided for identifying high-risk areas and formulating differentiated drought control and resource management strategies.

The invention relates to the technical field of hydrology and meteorology, and discloses a summer corn drought detection method and system.The method comprises the steps that day-by-day precipitation and evaporation capacity data in a certain area are obtained, and the effective precipitation and the actual water demand of summer corn are obtained; obtaining a difference value between the effective precipitation amount and the actual water demand amount of the summer corn within a certain period of time; performing trend fitting on the difference value D < t > by using a smooth spline so as to replace a position parameter of Log-logistic distribution, and obtaining a time-varying position parameter; acquiring Log-logistic distribution of a time-varying time sequence according to the time-varying position parameters, estimating the parameters by using a probability weighted distance method PWMs based on empirical frequency, and standardizing drought detection indexes; the invention provides a new non-stationary drought index of the summer maize, which considers the requirements of different crops in growth stages on moisture and the difference of drought tolerance, and can realize day-by-day fine detection of the drought of the summer maize.

The application discloses a soil drought risk early warning system and method based on machine learning, belongs to the field of soil drought early warning, and comprises a data acquisition module, a data repair and fusion module, a space-time drought coupling prediction model construction module and a real-time early warning and model updating module; wherein the data acquisition module is used for acquiring wind cloud weather data and MODIS optical image data; the data repair and fusion module is used for generating all-weather soil humidity maps; the space-time drought coupling prediction model construction module is used for constructing a space-time drought coupling prediction model; and the real-time early warning and model updating module is used for obtaining a dynamic drought index and dynamically updating. The soil drought risk early warning system and method based on machine learning have the advantages that through multi-source data fusion, a space-time coupling prediction model and a dynamic grading early warning mechanism, high-precision drought risk prediction and decision support are realized.

The invention discloses a wind power resource drought degree assessment method and related equipment. The method comprises the following steps: acquiring hourly wind speed data of a target area in historical and evaluation periods, and determining an hourly wind power output sequence in combination with a wind turbine power curve; determining a reliable output reference value and a recharging threshold value for each duration t based on historical wind power output sequence samples and historical sample statistical distribution of each duration t; calculating the equivalent energy buffer capability of the system for resisting the wind power debt in the evaluation window; based on the reliable output reference value and the recharging threshold value, calculating a power gap and a power surplus hour by hour; according to the reliable reference gap power which is not met by the stored energy and the stored energy state, calculating an operation conferring; and calculating a wind power resource drought index based on the equivalent energy buffer capability and the operation femur. The problem that existing methods for quantifying and analyzing the drought of wind power resources comprise a fixed threshold value method, a relative distance flatness method and a standardized index method can be solved.

The invention relates to a novel drought index construction method based on multi-factor coupling, and belongs to the technical field of agricultural drought monitoring. The method comprises the following steps: calculating vegetation coverage FVC, land surface temperature LST, soil water content SMC and enhanced vegetation index EVI by using multi-source remote sensing image data, calculating chaos characteristic parameters of each factor, determining sensitivity weight of each factor to drought according to the chaos characteristic parameters, adopting an improved random forest algorithm as a multi-factor coupling model, inputting a weighted characteristic matrix for training, and determining drought sensitivity of each factor. And optimizing key parameters of the random forest by adopting a particle swarm optimization algorithm, determining nonlinear mapping from a weighted feature to a chaos coupling multi-factor drought index CCMDI, and judging a drought level according to an index value. According to the method, the comprehensive process of drought formation and development is represented more truly, and high-precision inversion of the drought grade is realized.

The invention discloses a vegetation drought resistance index construction method and system, and the method comprises the steps: obtaining multi-source time series data, unifying the space and time resolution, constructing a rainfall comprehensive index, a runoff comprehensive index, a soil humidity index and an underground water drought index, and employing an R-Vine Copula model for fusion to generate a vertical composite drought index VCDI; identifying a drought event and extracting the duration, severity and strength of the event; performing normalization processing on the kernel normalization difference vegetation index, the surface temperature and the VCDI, constructing a three-dimensional feature space, and defining and calculating a vegetation drought resistance index VDRI through an Euclidean distance; and calculating the vegetation drought recovery time and recovery force by using the VDRI abnormal value and the standardized abnormal value, and identifying the time-lag influence of drought on the vegetation ecological function through lagging cross-correlation analysis. According to the method, the drought process and the comprehensive influence of the drought process on vegetation are accurately described, the research blank in the field of drought-vegetation response comprehensive evaluation is filled up, and a scientific basis is provided for ecological restoration and drought adaptation management.

The present invention relates to a method for generating a composite drought index based on artificial intelligence technology and a computer-readable recording medium having a program for performing the same. More specifically, the invention relates to a method for generating a composite drought index based on artificial intelligence technology and a computer-readable recording medium having a program for performing the same, comprising an input weight extraction step in which input weights for input variables, namely a meteorological index, a hydrological index, and an agricultural index, are respectively extracted using an autoencoder, and an index generation step in which a composite drought index reflecting meteorological, hydrological, and agricultural causes is generated using the input weights for each index.

The application provides a regional drought remote sensing comprehensive index construction and monitoring method, and relates to the field of drought remote sensing monitoring, and the method comprises the following steps: based on time series remote sensing data, determining the ground temperature data of a target region, and then obtaining normalized ground temperature data; by determining the normalized vegetation index and the target section where the normalized vegetation index is located, estimating the vegetation coverage data of the target region; using the 1km resolution resampling data of the target region and a soil moisture estimation model, obtaining 1km resolution soil moisture data; based on the normalized ground temperature data, the vegetation coverage data and the 1km resolution soil moisture data, constructing a regional drought index model. The application selects different vegetation coverage estimation methods for different normalized vegetation indexes of different sections, so that the vegetation coverage data estimation is more accurate, and the combination of the 1km resolution soil moisture data makes the performance of the regional drought index model more optimal, and then the estimation accuracy of the drought index is improved.

The present invention discloses a method for quantitatively analyzing the driving factors of meteorological drought. By performing an inconsistency test on the standardized rainfall index (SPI) sequence and combining it with an analysis of the impact of human activities, the watershed type is identified. The entire study period is divided into a baseline period and a human activity period. The meteorological drought index of the two periods is analyzed and calculated to obtain the impact of human activities on meteorological drought and the impact of climate factors on meteorological drought, thereby quantitatively distinguishing the impact of the two factors on meteorological drought.