Industrial, domestic and agricultural water vulnerability coupling analysis method based on drought response

Abstract

The invention relates to the technical field of water vulnerability coupling analysis, in particular to an industrial, domestic and agricultural water vulnerability coupling analysis method based on drought response. Comprising the following steps: constructing a social economic water shortage ratio index to distinguish general drought and extreme drought scenes; quantifying the industrial, domestic and agricultural water vulnerability by respectively adopting an HARA function, a drinking difficulty population proportion, an area method and a yield method; vulnerability response differences under different drought scenes are analyzed, a comprehensive vulnerability index is calculated by combining a regional long-term water consumption structure, a coupling degree and coordination degree model of water consumption vulnerability of multiple departments is constructed, and comprehensive judgment of the regional water resource system state is achieved. According to the method, the problems of unclear distinguishing of drought scenes, inaccurate description of water vulnerability of multiple departments and insufficient system coupling analysis are solved, and refined evaluation and comprehensive identification of the vulnerability of the regional water resource system under the drought condition are realized.

Description

Technical Field

[0001] This invention relates to the field of water vulnerability coupling analysis technology, and in particular to a method for vulnerability coupling analysis of industrial, domestic and agricultural water use based on drought response. Background Technology

[0002] With the intensification of global climate change and the continued increase in the intensity of human activities, the frequency and impact of regional drought events have increased significantly, and the uneven spatial and temporal distribution of water resources has become increasingly prominent. Under drought conditions, water supply capacity declines, while various water demands, such as industrial production, urban residents' lives, and agricultural irrigation, show rigid or phased growth trends, leading to structural tensions and systemic risks in regional water resource systems. Especially in regions where industrialization, urbanization, and agricultural modernization are developing in parallel, there is a significant competitive relationship and linkage effect between industrial water use, domestic water use, and agricultural water use. Any imbalance in supply and demand in any water use subsystem under drought impact can be transmitted and amplified through the water resource allocation chain, adversely affecting overall regional water security.

[0003] To identify and assess the risk characteristics of water resource systems under drought conditions, the concept of "vulnerability" has been gradually introduced into existing research and engineering practices to characterize the sensitivity, exposure, and resilience of water use systems to drought disturbances. Current technologies typically establish separate evaluation index systems for industrial, domestic, or agricultural water vulnerability, and quantitatively assess them using single-system analysis methods, thus providing a reference for industry water management or special regulation. However, most of these methods treat different water use types as independent research objects, neglecting the highly coupled relationships formed between industrial, domestic, and agricultural water systems during actual operation through water source structures, water supply projects, and scheduling mechanisms. This makes it difficult to comprehensively reflect the overall operational status of regional water resource systems under drought conditions.

[0004] Therefore, there is an urgent need for an analytical method that can simultaneously characterize the vulnerability features of industrial, domestic, and agricultural water use under drought scenarios, and further quantify the coupling relationships and coordination levels among these three water use systems, in order to achieve a comprehensive assessment of the state of regional water resource systems. This method should be able to standardize different water use vulnerability indicators within a unified data processing framework, and, based on system coupling theory and coordinated development theory, reveal the co-evolutionary characteristics of multi-water use systems under drought disturbance conditions. This would provide scientific, systematic, and operable technical support for regional water resource risk assessment, drought early warning, and optimal water resource allocation. Summary of the Invention

[0005] This invention provides a drought-response-based method for coupled analysis of vulnerability to industrial, domestic, and agricultural water use. It addresses the problems in existing technologies, such as unclear distinction between drought scenarios, inaccurate characterization of water use vulnerability across multiple sectors, and lack of systematic coupled analysis. This method can provide a refined assessment of the vulnerability of regional water resource systems under drought conditions and offer a scientific basis for drought risk early warning and water resource regulation.

[0006] In a first aspect, the present invention provides a method for coupled analysis of vulnerability to industrial, domestic, and agricultural water use based on drought response, the method comprising:

[0007] Step S1: Obtain annual water supply, socio-economic data, and auxiliary meteorological and hydrological data for regional industrial, domestic, and agricultural water use sectors; decompose the annual water supply and demand into monthly series based on the monthly allocation coefficient, and calculate the socio-economic water shortage ratio index SEWDRI based on the monthly supply-demand difference to distinguish between general drought and extreme drought scenarios.

[0008] Step S2: Use the HARA function to characterize industrial water use efficiency, calculate the industrial water shortage loss rate based on the water shortage amount to characterize industrial water use vulnerability; calculate the proportion of the population with drinking water difficulties based on the per capita domestic water use quota and the actual water supply to characterize domestic water use vulnerability.

[0009] Step S3: Based on the disaster area and yield data, estimate the agricultural yield loss using the area method and yield method, and take the larger value to calculate the loss rate to characterize the vulnerability of agricultural water use;

[0010] Step S4: Use linear regression to fit the relationship curve between the socioeconomic water shortage ratio index SEWDRI and the vulnerability of the three sectors, and compare the differences in response under general drought and extreme drought scenarios;

[0011] Step S5: Determine the weights of the three sectors based on the region's long-term water use structure and calculate the comprehensive vulnerability index; calculate the coupling degree and coordination degree based on the comprehensive vulnerability index and the vulnerability of the three sectors, and classify the status of the regional water resources system according to the classification criteria.

[0012] As a preferred embodiment of the present invention, step S1, calculating the socioeconomic water shortage ratio index SEWDRI based on the monthly supply-demand difference, includes:

[0013] Using the annual water supply as a benchmark, the monthly water supply is obtained by decomposing it according to the monthly allocation coefficient; the annual water demand is estimated based on socioeconomic data and decomposed into monthly water demand according to the seasonal pattern; the difference between the monthly total water demand and the monthly total water supply is calculated to obtain the socioeconomic water shortage ratio index SEWDRI; when the socioeconomic water shortage ratio index SEWDRI reaches the 95th percentile of the historical series, it is judged as extreme drought, and other water shortage situations are judged as general drought.

[0014] As a preferred embodiment of the present invention, the method of using the HARA function to characterize industrial water use efficiency and calculating the industrial water shortage loss rate based on the water shortage amount to characterize industrial water vulnerability includes:

[0015] The HARA function is used to calculate the industrial water use utility under normal water supply conditions. The HARA function is calculated by combining the normal industrial water demand, parameter α equals 1, parameter γ equals 0, parameter δ equals 0.5, and water supply. The water shortage is calculated as the industrial water demand minus the actual water supply. The water shortage loss is calculated as the normal utility minus the actual utility. The industrial water shortage loss rate is calculated as the water shortage loss divided by the normal industrial output.

[0016] As a preferred embodiment of the present invention, the method of characterizing the vulnerability of drinking water by calculating the proportion of the population with drinking water difficulties based on the per capita domestic water consumption quota and the actual water supply includes:

[0017] The number of people with drinking water difficulties is calculated by multiplying the per capita domestic water quota by the population and subtracting the actual domestic water supply; the proportion of people with drinking water difficulties is calculated by dividing the number of people with drinking water difficulties by the population.

[0018] As a preferred embodiment of the present invention, the step of estimating agricultural yield loss using area and yield methods based on disaster area and yield data, and calculating the loss rate by taking the larger value to characterize agricultural water vulnerability, includes:

[0019] The yield loss is calculated using the area method by multiplying the base yield per unit area by the area of ​​total crop failure plus the area of ​​disaster multiplied by the yield reduction rate of 0.5 plus the affected area multiplied by the yield reduction rate of 0.2. The yield loss is calculated using the yield method by subtracting the actual yield per unit area from the base yield per unit area multiplied by the crop planting area. The larger value between the yield loss from the area method and the yield loss from the yield method is taken as the agricultural yield loss. The agricultural loss rate is calculated by dividing the agricultural yield loss by the actual grain yield. The base yield per unit area is obtained by fitting the annual yield trend.

[0020] As a preferred embodiment of the present invention, the step of fitting the socioeconomic water scarcity ratio index SEWDRI and the three-sector vulnerability relationship curve using linear regression includes:

[0021] Using the socioeconomic water scarcity ratio index SEWDRI as the independent variable and industrial water vulnerability as the dependent variable, linear regression was performed to obtain general drought functions and extreme drought functions; using the socioeconomic water scarcity ratio index SEWDRI as the independent variable and domestic water vulnerability as the dependent variable, linear regression was performed to obtain general drought functions and extreme drought functions; using the socioeconomic water scarcity ratio index SEWDRI as the independent variable and agricultural water vulnerability as the dependent variable, linear regression was performed to obtain general drought functions and extreme drought functions; the slopes, intercepts, and goodness of fit of the general drought functions and extreme drought functions were compared.

[0022] As a preferred embodiment of the present invention, the step of determining the weights of the three sectors based on the regional long-term water use structure and calculating the comprehensive vulnerability index includes:

[0023] The industrial weight is determined as the average proportion of industrial water supply in the total water supply, the domestic weight as the average proportion of domestic water supply in the total water supply, and the agricultural weight as the average proportion of agricultural water supply in the total water supply.

[0024] The comprehensive vulnerability index is calculated by multiplying the industrial water shortage loss rate by the industrial weight, adding the proportion of the population with drinking water difficulties by the living weight, and adding the agricultural loss rate by the agricultural weight.

[0025] As a preferred embodiment of the present invention, step S5 involves calculating the coupling degree and coordination degree, and classifying the regional water resource system status according to classification criteria, including:

[0026] The vulnerability of industrial water use, domestic water use, and agricultural water use is normalized; the coupling degree is calculated as a combination of normalized industrial water use vulnerability, normalized domestic water use vulnerability, and normalized agricultural water use vulnerability functions; and the coordination degree is calculated as the comprehensive vulnerability index multiplied by the coupling degree multiplied by the coordination coefficient; the coupling state and coordination level are classified according to the coupling degree range and the coordination degree range.

[0027] This invention also provides a drought-responsive vulnerability coupling analysis system for industrial, domestic, and agricultural water use, used to implement the above-mentioned method. The system includes:

[0028] The data acquisition unit is used to acquire annual water supply, socio-economic data, and auxiliary meteorological and hydrological data of regional industrial, domestic, and agricultural water use sectors; it decomposes the annual water supply and demand into monthly sequences based on the monthly allocation coefficient, and calculates the socio-economic water shortage ratio index SEWDRI based on the monthly supply and demand difference to distinguish between general drought and extreme drought scenarios.

[0029] The vulnerability calculation unit is used to characterize industrial water use efficiency using the HARA function, calculate the industrial water shortage loss rate based on the water shortage amount to characterize industrial water use vulnerability, calculate the proportion of people with drinking water difficulties based on per capita domestic water use quota and actual water supply to characterize domestic water use vulnerability, and estimate agricultural output loss based on disaster area and yield data using area method and yield method, and take the larger value to calculate the loss rate to characterize agricultural water use vulnerability.

[0030] The regression fitting unit is used to fit the relationship curves between the socioeconomic water shortage ratio index SEWDRI and the vulnerability of the three sectors using linear regression, and to compare the differences in response under general drought and extreme drought scenarios.

[0031] The comprehensive assessment unit is used to determine the weights of the three sectors based on the region's long-term water use structure and to calculate the comprehensive vulnerability index. Based on the comprehensive vulnerability index and the vulnerability of the three sectors, it calculates the coupling degree and coordination degree and classifies the status of the regional water resources system according to the classification criteria.

[0032] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0033] The beneficial effects of this invention are as follows:

[0034] This invention acquires annual water supply, socioeconomic data, and meteorological and hydrological data for industry, domestic use, and agriculture within a region. Combining this with monthly allocation coefficients, the annual data is decomposed into monthly supply and demand data, constructing the Socioeconomic Water Shortage Ratio Index (SEWDRI). This index effectively distinguishes between general drought and extreme drought scenarios, providing timely and accurate data support for vulnerability analysis. Vulnerability models for industrial, domestic, and agricultural water use are constructed separately. The vulnerability of industrial water use is quantified by modeling the benefits of industrial water use using the HARA function. Domestic water use vulnerability is assessed by calculating the proportion of the population facing drinking water difficulties. Agricultural water use vulnerability is estimated by comprehensively using area and yield methods to measure yield loss. These vulnerability models accurately reflect the differences in disaster resilience among various sectors under different drought scenarios. By using linear regression to correlate the socioeconomic water shortage ratio index with the vulnerability of each sector, the response differences of the three sectors under drought scenarios are further revealed. This not only helps to compare the changes in vulnerability under different drought scenarios but also provides a theoretical basis for subsequent calculation of the comprehensive vulnerability index. By introducing the regional long-term water use structure, the weights of each sector are calculated, and a comprehensive vulnerability index is constructed accordingly, quantifying and integrating the vulnerabilities of different sectors and providing a foundation for calculating coupling and coordination. The coupling and coordination model analyzes the interaction and coordination degree among the three sectors, quantifying the overall vulnerability and collaborative development level of the water resource system under drought scenarios. Through the synergy of the above technical solutions, the dynamic changes in regional water supply and demand are fully considered, and the vulnerability of multiple sectors and their interactions are systematically quantified. This solves the problems of single-sector assessment and unclear distinction between drought scenarios in traditional methods, providing scientific, systematic, and operable support for drought risk identification, water resource allocation, and emergency decision-making. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of a method for coupled vulnerability analysis of industrial, domestic, and agricultural water use based on drought response, as described in this embodiment. Figure 2 This is a schematic diagram of the application example study area in the embodiment; Figure 3 The annual results of the socioeconomic water shortage ratio index constructed in the example are shown. Figure 4 The results of the industrial water vulnerability analysis are shown in the examples; Figure 5 The results of the vulnerability analysis of domestic water supply in the examples are shown. Figure 6 The results of the agricultural water vulnerability analysis are shown in the examples; Figure 7 The coupling analysis results are shown in the examples. Figure 8 The results of the coordination analysis in the examples; Figure 9 This is a structural diagram of a drought-response-based coupled analysis system for vulnerability of industrial, domestic, and agricultural water use in an embodiment. Detailed Implementation

[0037] This invention provides a method for coupled vulnerability analysis of industrial, domestic, and agricultural water use based on drought response. The terms "first," "second," "third," "fourth," etc. (if applicable) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0038] For ease of understanding, the specific process of the embodiments of the present invention will be described below, such as... Figure 1 As shown in the embodiment of the present invention, the vulnerability coupling analysis method for industrial, domestic and agricultural water use based on drought response includes:

[0039] Step S1: Obtain annual water supply, socioeconomic data, and auxiliary meteorological and hydrological data for regional industrial, domestic, and agricultural water use sectors; decompose annual water supply and demand into monthly series based on monthly allocation coefficients, and calculate the socioeconomic water shortage ratio index SEWDRI based on the monthly supply-demand difference to distinguish between general drought and extreme drought scenarios; specifically including:

[0040] Using the annual water supply as a benchmark, the monthly water supply is obtained by decomposing it according to the monthly allocation coefficient; the annual water demand is estimated based on socioeconomic data and decomposed into monthly water demand according to the seasonal pattern; the difference between the monthly total water demand and the monthly total water supply is calculated to obtain the socioeconomic water shortage ratio index SEWDRI; when the socioeconomic water shortage ratio index SEWDRI reaches the 95th percentile of the historical series, it is judged as extreme drought, and other water shortage situations are judged as general drought.

[0041] Specifically, such as Figure 2 As shown, the research area selected for this invention is the Beijing-Tianjin-Hebei region, located between 36°00'-42°40' north latitude and 113°27'-120°50' east longitude, including 13 prefecture-level cities: Beijing, Tianjin, and Shijiazhuang, Baoding, Tangshan, Langfang, Qinhuangdao, Zhangjiakou, Chengde, Cangzhou, Handan, Xingtai, and Hengshui in Hebei Province. The data used in this invention include water resources bulletin data and socio-economic data of each city in the research area, as well as auxiliary data such as meteorological and hydrological data. In order to realize the coupled analysis of the vulnerability of industrial, domestic, and agricultural water use under drought response conditions, a basic data system that can reflect the real supply and demand contradictions of the regional socio-economic system is first constructed. Specifically, the study area is selected as an administrative region or watershed unit, and annual water supply data for industrial, domestic and agricultural water use in the same statistical period are obtained. The above data are preferably obtained from water resources bulletins or water conservancy statistical yearbooks. At the same time, socio-economic data directly related to water demand are obtained, including normal industrial output, resident population size, agricultural sown area and grain output, as well as auxiliary meteorological and hydrological data to reflect changes in natural conditions, such as monthly precipitation, evapotranspiration or runoff change indicators, to support the subsequent water demand estimation and seasonal allocation process.

[0042] After completing the unified acquisition of annual-scale data, to overcome the problem that annual data is difficult to reflect the monthly evolution characteristics of drought events, this embodiment further reconstructs the annual water supply and demand on a monthly scale. The annual water supply for industry, domestic use, and agriculture are used as baseline values, and a monthly allocation coefficient matching the study area is introduced. This allocation coefficient is used to characterize the relative proportion of water consumption in different months throughout the year, and it can be determined based on historical water consumption statistics or mature water consumption databases. By multiplying the annual water supply of each sector by the corresponding monthly allocation coefficient, the water supply sequences for industry, domestic use, and agriculture in each month are obtained, thus forming a monthly water supply dataset reflecting seasonal variation characteristics. Simultaneously, based on... The annual water demand is estimated based on socioeconomic data and further broken down into monthly water demand. The specific annual industrial water demand can be calculated based on the normal industrial output value and the water consumption quota per unit of output value. The annual residential water demand can be determined based on the number of permanent residents and the per capita residential water consumption quota. The annual agricultural water demand can be estimated by combining the sown area, crop type, and water demand per unit area. After obtaining the annual water demand of each sector, seasonal model parameters are introduced to decompose it into monthly values. This seasonal model is used to reflect the changes in water demand intensity caused by differences in climate conditions, production activities, and crop growth cycles in different months, thereby obtaining a monthly water demand sequence that corresponds one-to-one with the monthly water supply on the time scale.

[0043] Based on this, the monthly water supply for industry, domestic use, and agriculture within the same month is aggregated to form the total monthly water supply data. The monthly water demand of the three sectors in the corresponding month is then aggregated to form the total monthly water demand data. By calculating the difference between the total monthly water demand and the total monthly water supply, and normalizing the difference to the total monthly water demand, a socioeconomic water shortage ratio index (SEWDRI) is constructed. This index quantifies the water shortage pressure experienced by the regional socioeconomic system at a monthly scale. Its value directly reflects the degree of supply-demand imbalance, providing a basis for subsequent drought scenario identification. To achieve an objective classification of drought intensity, such as... Figure 3 As shown, statistical analysis is performed on the SEWDRI time series formed over a historical period, and the 95th percentile of the series is extracted as the extreme water shortage threshold. When the SEWDRI of a certain month reaches or exceeds the threshold, the drought scenario corresponding to that month is determined to be an extreme drought scenario; when the SEWDRI is below the threshold but still greater than zero, it is determined to be a general drought scenario. The above technical solution realizes the quantitative identification of drought scenarios based on supply and demand, enabling subsequent vulnerability analysis of industrial, domestic and agricultural water use to be carried out separately under different drought intensities, thereby ensuring the consistency and scientific nature of the entire vulnerability coupling analysis method in terms of time scale, data logic and scenario division.

[0044] Step S2: Use the HARA function to characterize industrial water use efficiency, calculate the industrial water shortage loss rate based on the water shortage amount to characterize industrial water use vulnerability; calculate the proportion of the population with drinking water difficulties based on the per capita domestic water use quota and the actual water supply to characterize domestic water use vulnerability.

[0045] In step S2, the industrial water shortage loss rate is calculated based on the amount of water shortage to characterize the vulnerability of industrial water use, including:

[0046] The HARA function is used to calculate industrial water use utility under normal water supply conditions. The HARA function is calculated using a combination of normal industrial water demand, parameters α, γ, and δ, and the water supply. The water shortage is calculated by subtracting the actual water supply from the industrial water demand; the water shortage loss is calculated by subtracting the actual utility from the normal utility; and the industrial water shortage loss rate is calculated by dividing the water shortage loss by the normal industrial output.

[0047] Economic losses caused by industrial water shortage for:

[0048] (1)

[0049] in, For the department The amount of water shortage, For the department Water demand Industrial output under normal water supply conditions;

[0050] Specifically, in order to accurately characterize the sensitivity and disaster resilience of industrial water systems to water shortages, the vulnerability of industrial water use is further quantified based on the mechanism of economic benefit loss. This is based on the dependence of industrial production on water resource input, and by coupling industrial water supply and demand data with economic output data, a utility HARA function model is introduced to continuously characterize the benefits of industrial water use, thereby realizing a logical mapping from water shortage to economic loss and then to vulnerability indicators.

[0051] Based on the above monthly water supply and demand reconstruction results, the normal industrial water demand in the study area under drought-free conditions is determined. This normal industrial water demand can be estimated from the normal industrial output and water consumption per unit of output, and corresponds one-to-one with the monthly water supply data in the time dimension. Furthermore, a hyperbolic absolute risk aversion utility function (HARA function) is introduced to characterize industrial water use efficiency. This function uses the normal industrial water demand and the normal water demand of each sector as the baseline input variables. Parameter α is set to 1 to ensure the linear scaling consistency of the utility function under water supply changes, parameter γ is set to 0 to eliminate the influence of additional shift terms, and parameter δ is set to 0.5 to reflect the diminishing marginal utility of industrial water use. This allows the HARA function to reasonably reflect the nonlinear response of industrial production to water supply changes. The specific water use utility function is then described. for:

[0052] (2)

[0053] Wherein: For the department That is, the normal water demand of the industrial sector. =1、 =0、 =0.5 is a constant value used to reflect the diminishing marginal utility of industrial water use; under normal water supply conditions, the normal industrial water demand is substituted into the above HARA function to obtain the corresponding industrial normal water use utility value, which serves as a benchmark for measuring the output level of the industrial system under ideal water supply conditions; subsequently, under actual drought conditions, the industrial water shortage is calculated based on the difference between the monthly water supply and the industrial water demand, where the industrial water shortage is obtained by subtracting the actual industrial water supply in the same period from the industrial water demand, thereby reflecting the degree of water shortage faced by the industrial system at the monthly scale; on this basis, the actual water supply of the industrial sector is substituted into the same HARA function, i.e., the above formula (2), to calculate the industrial water shortage under the actual industrial water demand in the month. The actual water use utility value under water scarcity conditions is calculated by comparing the industrial water use utility value under normal water supply conditions with that under water scarcity conditions. The difference between the two yields the industrial economic loss caused by water shortage, reflecting the direct impact of drought-induced water shortage on industrial production efficiency. Furthermore, to eliminate the influence of differences in industrial scale between different years and cities, and to ensure the comparability of industrial vulnerability indicators across regions and time dimensions, the aforementioned industrial economic loss is normalized to the corresponding period's normal industrial output. Specifically, the industrial water shortage loss is divided by normal industrial output to obtain the industrial water shortage loss rate. The specific calculation formula is as follows:

[0054] (3)

[0055] in, The rate of industrial water loss due to drought. The amount of industrial economic losses caused by water shortages The above-mentioned industrial water shortage loss rate serves as a quantitative expression of industrial water vulnerability. Its numerical value can intuitively reflect the sensitivity of the industrial system to water shortage shocks and is consistent with the above-mentioned socioeconomic water shortage ratio index (SEWDRI) on a time scale. Therefore, it can be used as a direct input indicator for fitting industrial vulnerability response relationships and coordinating multi-sector water vulnerability coupling under subsequent drought scenarios. Through the above technical solution, a systematic correlation between changes in industrial water supply, changes in economic benefits, and vulnerability characterization is realized. This makes industrial water vulnerability assessment not only have a clear physical meaning but also a clear economic interpretation basis, further enhancing the scientificity and practicality of this invention in drought response analysis and regional water resource risk assessment.

[0056] Furthermore, in step S2, the calculation of vulnerability to domestic water use includes:

[0057] The per capita domestic water consumption quota is multiplied by the population to obtain the total domestic water demand. The domestic water demand is then subtracted from the actual domestic water supply to obtain the domestic water shortage. The ratio of the domestic water shortage to the total domestic water demand is taken as the proportion of residents with drinking water difficulties.

[0058] Specifically, to characterize the resilience of residential water supply systems to water shortages under drought scenarios, a vulnerability calculation method for residential water supply, centered on ensuring basic water security for residents, is constructed. This method takes the difference between residential water demand and actual supply as its starting point, and reflects the vulnerability level of residential water supply systems under socio-economic drought conditions by quantifying the degree of difficulty residents face in accessing drinking water.

[0059] Data on the resident population of the study area during the statistical period was obtained. Combined with the applicable per capita domestic water quota standard for the area, the per capita domestic water quota was multiplied by the corresponding population size to obtain the total domestic water demand required to meet the basic living needs of residents in the area under ideal water supply conditions. The per capita domestic water quota is preferably determined based on relevant national or local standards to reflect the rigid demand for water resources under normal living conditions. Simultaneously, data on the actual domestic water supply in the area during the same period was obtained. This actual domestic water supply reflects the amount of water resources that the domestic water system can actually provide under drought or water-limited conditions. After completing the above data preparation, the domestic water gap was calculated by subtracting the actual domestic water supply from the total domestic water demand. This gap represents the portion of domestic water needs not being met due to drought or water scarcity. To eliminate the impact of population size differences in different areas on the results and to achieve comparability of domestic water vulnerability across time and space scales, the ratio of the domestic water gap to the corresponding total domestic water demand was calculated and defined as the proportion of residents facing drinking water difficulties. The specific expression is as follows:

[0060] (4)

[0061] in, For population size, The per capita domestic water consumption quota, This refers to the actual supply of domestic water.

[0062] The aforementioned proportion of residents facing drinking water difficulties serves as a quantitative indicator of vulnerability to domestic water use. Its magnitude directly reflects the degree to which residents' basic domestic water needs are restricted, and it remains consistent with the aforementioned socioeconomic water shortage ratio index on a time scale. Therefore, it can be directly involved in subsequent multi-sectoral water vulnerability response analysis and coupling coordination degree calculation. Through the above technical solution, a clear correspondence between domestic water demand, supply, and vulnerability characterization is achieved, enabling domestic water vulnerability assessment to have clear practical significance and to be carried out collaboratively with industrial and agricultural water vulnerability analysis within a unified framework.

[0063] Step S3: Based on the affected area and yield data, estimate agricultural yield loss using the area method and yield method, and take the larger value to calculate the loss rate to characterize agricultural water vulnerability; specifically including:

[0064] The yield loss is calculated by multiplying the baseline yield per unit area by the affected area using the area method. The affected area is the sum of the area with no harvest, the area affected by the disaster, and the yield reduction rate. The product of the affected area and the rate of production reduction, plus the product of the affected area and the rate of production reduction. The sum of the products; the yield loss is calculated using the yield method as the benchmark yield per unit area minus the actual yield per unit area multiplied by the crop planting area; the larger of the yield loss by the area method and the yield method is taken as the agricultural yield loss; the agricultural yield loss is divided by the actual grain yield as the agricultural loss rate; the benchmark yield per unit area is obtained by fitting the annual yield trend.

[0065] Specifically, in order to accurately characterize the production risks caused by insufficient water supply in agricultural water systems under drought scenarios, a method for calculating agricultural water vulnerability based on agricultural yield loss is constructed. This method takes the high dependence of agricultural production on water resources as its starting point, and by comprehensively considering the information on crop disaster area and actual yield changes, it describes the agricultural yield reduction from different data dimensions, and on this basis, forms a robust vulnerability quantification result.

[0066] Based on agricultural statistics, data on crop planting area, actual yield per unit area, and disaster situation such as area of ​​total crop failure, area of ​​disaster, and area of ​​general disaster were obtained for the study area during the statistical period. At the same time, data on grain yield per unit area in the region over the years were also obtained. By performing trend fitting on multi-year yield per unit area series, a benchmark yield per unit area reflecting the agricultural production level under non-drought or normal climatic conditions is obtained. This benchmark yield serves as a control reference for agricultural production under water stress conditions, thus avoiding interference from abnormal climate in a single year on the loss estimation results. Based on this, the area method is first used to estimate agricultural yield loss, where the benchmark yield per unit area is coupled with the affected area. Specifically, the area with no harvest is considered as a completely unproductive area, and its corresponding yield loss is equal to the product of the benchmark yield per unit area and the area with no harvest. At the same time, the disaster-stricken area is considered as a moderately disaster-stricken area, and its yield loss is converted by introducing a yield reduction rate θ, such as 0.5. The generally disaster-stricken area is considered as a slightly disaster-stricken area, and its yield loss is estimated by a yield reduction rate φ, such as 0.2. By summing the above three parts of the loss, the agricultural yield loss value calculated based on the area method is obtained. This result can reflect the impact of the spatial distribution of crop disasters on the total yield loss.

[0067] Meanwhile, to supplement the area method's shortcomings in identifying hidden yield reductions, the yield method is further employed to estimate agricultural yield loss. Specifically, the difference between the benchmark yield per unit area and the actual yield per unit area is calculated to obtain the yield reduction per unit area. This yield reduction is then multiplied by the corresponding crop planting area to obtain the agricultural yield loss value estimated based on the yield method. This allows for a reflection of the extent to which drought erodes agricultural productivity from an overall output perspective. After calculating yield loss using the above two methods, to avoid underestimating agricultural risk due to a single method, the larger of the yield loss from the area method and the yield method is taken as the final agricultural yield loss, thus forming a more conservative estimate of the impact of drought. Furthermore, robust estimation results are obtained. The agricultural loss rate is obtained by dividing the aforementioned agricultural yield loss by the actual total grain output during the same period, and this agricultural loss rate is used as a quantitative indicator of agricultural water vulnerability. Through the above technical solution, under a unified time scale and data logic, disaster area information, yield change information, and baseline production levels are organically combined, realizing a multi-faceted characterization of the impact of agricultural water shortages on yield. This allows agricultural water vulnerability to have both clear physical and production meanings, and to be used in conjunction with industrial and domestic water vulnerability indicators within the same coupled analysis framework. This provides a reliable data foundation and methodological support for coupled analysis of multi-sectoral water vulnerability under drought response conditions.

[0068] Step S4: Use linear regression to fit the socioeconomic water scarcity ratio index (SEWDRI) and the vulnerability curves of the three sectors, and compare the differences in response under general drought and extreme drought scenarios; specifically including:

[0069] Using the socioeconomic water scarcity ratio index SEWDRI as the independent variable and industrial water vulnerability as the dependent variable, a linear regression was performed to obtain the first vulnerability relationship curve; using the socioeconomic water scarcity ratio index SEWDRI as the independent variable and domestic water vulnerability as the dependent variable, a linear regression was performed to obtain the second vulnerability relationship curve; using the socioeconomic water scarcity ratio index SEWDRI as the independent variable and agricultural water vulnerability as the dependent variable, a linear regression was performed to obtain the third vulnerability relationship curve; the slopes, intercepts, and goodness of fit of the first, second, and third vulnerability relationship curves were compared.

[0070] Specifically, to reveal the response patterns of socioeconomic water shortage pressure to the water vulnerability of the industrial, residential, and agricultural sectors, and to achieve a quantifiable comparison of response differences under general drought and extreme drought scenarios, after constructing the socioeconomic water shortage ratio index SEWDRI and calculating the vulnerability indicators of the three sectors, a statistical response relationship between SEWDRI and the vulnerability of the three sectors is further established. Specifically, a monthly scale is used as a unified analytical scale. The obtained monthly time series of SEWDRI is time-aligned with the aforementioned industrial water shortage loss rate, the proportion of residents with drinking water difficulties, and the agricultural yield loss rate, i.e., the vulnerability indicators of the three sectors. Data pairs are formed in the same city, the same month, and the same year. SEWDRI is used as the independent variable to reflect the intensity of supply and demand imbalance in the current month, while the vulnerability indicators of the three sectors are used as the dependent variable to reflect the degree of loss or shortage of each sector under the current water shortage intensity in the current month. This ensures that the samples used for regression fitting are consistent in terms of data source, time scale, and spatial unit.

[0071] After completing the sample construction, to avoid smoothing the response relationship due to the mixing of different drought intensities, this embodiment divides the samples into a general drought sample set and an extreme drought sample set based on the 95th percentile threshold of the SEWDRI historical sequence. Months with SEWDRI reaching or exceeding this threshold are assigned to the extreme drought sample set, while other months with water shortage pressure but not reaching the threshold are assigned to the general drought sample set. This allows the vulnerability response of the same sector to be fitted and compared under the two scenarios separately. Subsequently, within the general drought sample set, a linear model is constructed with SEWDRI as the independent variable and industrial water shortage loss rate as the dependent variable, and the regression coefficients are solved using the least squares method. The first industrial water scarcity vulnerability curve under general drought conditions was obtained; and the corresponding second industrial water scarcity vulnerability curve under extreme drought conditions was obtained using the same variable definition and solution method in the extreme drought sample set, thus forming two directly comparable industrial response functions. Using the same processing logic, SEWDRI was linearly regressed with the domestic water vulnerability index and the agricultural water vulnerability index, respectively, to obtain the first vulnerability relationship curve for the domestic sector and the third vulnerability relationship curve for the agricultural sector. Corresponding scenario response functions were then formed in the general drought and extreme drought sample sets, respectively, with the specific expressions as follows:

[0072]

[0073] in, , , These are vulnerability indicators for industrial water use, domestic water use, and agricultural water use. , , These are the slopes of the corresponding response functions. , , This is the intercept of the corresponding response curve. The socioeconomic water scarcity ratio index (SEWDRI) is used; taking the study area as an example, the vulnerability analysis of industrial water use is as follows: Figure 4 As shown, the vulnerability analysis of domestic water supply is as follows: Figure 5 As shown, the vulnerability analysis of agricultural water use is as follows: Figure 6 As shown.

[0074] Furthermore, to characterize cross-sectoral and cross-scenario response differences, this embodiment compares and analyzes the key regression parameters of the first, second, and third vulnerability relationship curves. The slope characterizes the sensitivity of vulnerability to increasing water shortage pressure, the intercept characterizes the baseline vulnerability starting point under low water shortage pressure levels, and the goodness of fit characterizes the degree to which the linear model explains sample variation. By comparing the slope differences of the same sector under general drought and extreme drought conditions, it can be identified whether extreme drought leads to an accelerated amplification of vulnerability response. Furthermore, by comparing the differences in slope and goodness of fit among different sectors, the relative sensitivity ranking and stability of industrial, domestic, and agricultural systems to water shortage pressure can be identified, thus providing statistically significant response basis for subsequent construction of a comprehensive vulnerability index and coupling coordination analysis. Through the above regression fitting and parameter comparison process, a functional expression from "monthly-scale water shortage pressure" to "sectoral vulnerability performance" is achieved, and the differences in vulnerability responses among the three sectors under general drought and extreme drought scenarios are quantified within the same evaluation framework. This enhances the explanatory power and application value of this invention for drought risk identification and multi-sectoral coordinated regulation.

[0075] Step S5: Determine the weights of the three sectors based on the region's long-term water use structure and calculate the comprehensive vulnerability index; calculate the coupling degree and coordination degree based on the comprehensive vulnerability index and the vulnerability of the three sectors, and classify the status of the regional water resources system according to the classification criteria;

[0076] In step S5, the comprehensive vulnerability index is calculated, including:

[0077] The industrial weight is determined as the average proportion of industrial water supply in the total water supply, the domestic weight as the average proportion of domestic water supply in the total water supply, and the agricultural weight as the average proportion of agricultural water supply in the total water supply. The comprehensive vulnerability index is calculated by multiplying the industrial water shortage loss rate by the industrial weight, adding the proportion of the population with drinking water difficulties by the domestic weight, and adding the agricultural loss rate by the agricultural weight.

[0078] Specifically, in this embodiment, in order to reflect the comprehensive effect of the vulnerability of water use in multiple sectors of regional industry, domestic and agriculture under drought scenarios from the system level, a comprehensive vulnerability index is further constructed to characterize the overall resilience of the regional water resource system to water shortage shocks. This comprehensive vulnerability index maintains the vulnerability differences among various sectors while achieving unified integration of vulnerability results across multiple sectors through objective weight allocation. Specifically, based on acquired and reconstructed multi-year, multi-sector water supply data, the water supply of the industrial, residential, and agricultural sectors in the study area within the statistical period is organized, and the proportion of each sector's annual water supply in the total water supply for that year is calculated. To avoid interference from fluctuations in the water supply structure in a single year on the determination of weights, the above proportion results are averaged over the entire study period to obtain industrial, residential, and agricultural weights that reflect the long-term water use structure characteristics of the region. Among them, the industrial weight is defined as the multi-year average of the proportion of industrial water supply in the total water supply, the residential weight is defined as the multi-year average of the proportion of residential water supply in the total water supply, and the agricultural weight is defined as the multi-year average of the proportion of agricultural water supply in the total water supply, and the sum of the three is 1, thereby ensuring the standardization and comparability of the weight system.

[0079] After determining the weights, the calculated vulnerability indicators for the three sectors are introduced into a comprehensive calculation framework. Industrial water vulnerability is represented by the industrial water shortage loss rate, domestic water vulnerability by the proportion of residents facing drinking water difficulties, and agricultural water vulnerability by the agricultural loss rate. To ensure consistency across time scales and spatial units, the results for the same month and region are used as input data for all three vulnerability indicators. Subsequently, the industrial water shortage loss rate is multiplied by its corresponding industrial weight, the proportion of residents facing drinking water difficulties is multiplied by its corresponding domestic weight, and the agricultural loss rate is multiplied by its corresponding agricultural weight. The weighted results of these three parts are then summed to obtain the final result. The comprehensive vulnerability index for this month and region; the comprehensive vulnerability index constructed in the above manner not only retains the impact of the differences in vulnerability of the industrial, residential and agricultural sectors under drought conditions, but also avoids the uncertainty brought about by subjective weighting by setting weights based on long-term water use structure. This allows the comprehensive index to truly reflect the relative contribution of each sector in the regional water resources system to the risk of water shortage. The above comprehensive vulnerability index can be used as a system development level indicator in the subsequent coupling coordination degree analysis, and participate in the calculation of coupling degree and coordination degree together with multi-sector vulnerability indicators, thereby providing a quantitative basis for the overall risk identification and control decision-making of the regional water resources system under drought scenarios.

[0080] Further, in step S5, the coupling degree and coordination degree are calculated, and the regional water resource system status is classified according to the classification criteria, including:

[0081] The vulnerability of industrial water use, domestic water use, and agricultural water use is normalized; the coupling degree is calculated as a combination of normalized industrial water use vulnerability, normalized domestic water use vulnerability, and normalized agricultural water use vulnerability functions; and the coordination degree is calculated as the comprehensive vulnerability index multiplied by the coupling degree multiplied by the coordination coefficient; the coupling state and coordination level are classified according to the coupling degree range and the coordination degree range.

[0082] Specifically, in regional water resource systems analyzed using administrative regions or watershed units, these regions simultaneously contain three main water use subsystems: industrial water use, domestic water use, and agricultural water use. Furthermore, these regions are affected by varying degrees of drought scenarios during historical or predicted periods. The corresponding industrial water use vulnerability, domestic water use vulnerability, and agricultural water use vulnerability are calculated. Since these three vulnerability indices differ in dimensions, value ranges, and magnitudes of change, to ensure comparability and mathematical consistency between different subsystems in subsequent coupled analysis, the industrial water use vulnerability, domestic water use vulnerability, and agricultural water use vulnerability are normalized. Ideally, extreme value standardization or interval mapping methods are used to linearly map each vulnerability value to the [0,1] interval, thereby obtaining normalized industrial water use vulnerability indices, normalized domestic water use vulnerability indices, and normalized agricultural water use vulnerability indices, respectively. The larger the normalized value, the higher the vulnerability of the corresponding subsystem under drought response conditions.

[0083] After completing the above normalization process, the industrial water system, domestic water system, and agricultural water system are considered as three interacting subsystems. Based on multi-system coupling theory, a water resource vulnerability coupling degree model is constructed. Specifically, the coupling degree C is calculated by a functional combination of normalized industrial water vulnerability indices, normalized domestic water vulnerability indices, and normalized agricultural water vulnerability indices. The number of subsystems, n, is set to 3. Product and equilibrium terms are introduced to characterize the interdependence and mutual constraints of the three types of water vulnerability during drought response. This ensures that significantly higher or lower vulnerability in any subsystem will affect the overall coupling level. This coupling degree calculation method can quantitatively reflect the structural correlation strength and synergistic change characteristics of the industrial, domestic, and agricultural water systems under drought conditions. The expression for the coupling degree C is:

[0084] Where n represents the number of subsystems, 3. For the first Each subsystem represents the vulnerability index corresponding to industrial, domestic, and agricultural water systems.

[0085] Furthermore, to avoid neglecting the overall development level of the system by relying solely on coupling degree indicators, a coordination degree model is constructed by introducing a comprehensive vulnerability index and a coordination coefficient based on the coupling degree calculation results. The comprehensive vulnerability index is obtained by weighted summation of normalized industrial water use vulnerability indicators, normalized domestic water use vulnerability indicators, and normalized agricultural water use vulnerability indicators. The weights can be set according to regional water use structure characteristics, water use proportions, or policy guidance, thereby comprehensively reflecting the overall vulnerability of the regional water resources system under drought response conditions. The specific expression is as follows:

[0086] ,and ;

[0087] in, , , These are the normalized industrial water use vulnerability index, the normalized domestic water use vulnerability index, and the normalized agricultural water use vulnerability index. , , The weights corresponding to the vulnerability indicators;

[0088] The aforementioned coordination degree D is calculated jointly by the comprehensive vulnerability index, the coupling degree C, and the preset coordination coefficient, and is expressed as follows:

[0089]

[0090] Wherein, C is the coupling degree and C∈[0,1], reflecting the strength of interaction between the vulnerabilities of the three sectors; while the coupling coordination degree D∈[0,1] reflects the degree of coordinated development, and T is the comprehensive vulnerability index. The higher the C value, the stronger the interaction; the higher the D value, the better the coordination and the greater the potential for sustainable development.

[0091] The aforementioned coordination degree D is used to adjust the intensity of the influence of coupling relationship on system coordination, so that the coordination degree result reflects both the tightness of coupling between subsystems and the constraint effect of the overall vulnerability level on the system state. After obtaining the coupling degree and coordination degree values, the state of the regional water resources system is determined according to the pre-established classification criteria. Specifically, the value range of coupling degree is divided into different coupling state levels such as low-level coupling state, antagonistic state, running-in state, moderate coupling state, and high coupling state, and the value range of coordination degree is divided into different coordination levels such as imbalance, near imbalance, primary coordination, intermediate coordination, and high coordination, thereby constructing a correspondence matrix between coupling state and coordination level. As shown in the table below, by mapping the coupling degree value and coordination degree value calculated in the above region under a specific drought scenario to the above classification interval, the comprehensive operating status of the regional water resources system in terms of industrial, domestic, and agricultural water use can be determined, providing quantitative basis and technical support for subsequent drought risk early warning, water resources regulation strategy formulation, and water use structure optimization.

[0092] range Coupling state describe Coordination status describe (0, 0.2) Low-level coupling state The systems are poorly interconnected and are basically developing independently. disorder System imbalance and uncoordinated development [0.2, 0.4) Conflict status The systems are somewhat interconnected, but are still in an unstable interaction phase. On the verge of disorder The coordination is at a low level and needs to be optimized. [0.4, 0.6) break-in period The interaction between systems is enhanced, and they are entering an adaptive stage of coordinated development. Primary Coordination The system is well-coordinated and sustainable. [0.6, 0.8) Moderately coupled state The systems are highly interconnected, develop relatively synchronously, and have significant positive impacts. Intermediate Coordination In a state of high-level coordination [0.8, 1.0] High-level coupling state The systems are closely interconnected, achieving a high degree of coordination and tending towards dynamic equilibrium. Highly coordinated The system is developing well and is becoming more stable.

[0093] The aforementioned technical solution not only achieves a unified scale conversion of multi-source water vulnerability indicators at the data processing level, but also clarifies the coupling logic between different water use subsystems and their impact mechanism on system coordination at the model construction level. This avoids the one-sidedness of single-indicator analysis and effectively improves the scientific rigor and operability of the comprehensive analysis of regional industrial, domestic, and agricultural water vulnerability under drought response conditions. Specifically, the coupling degree analysis of the study area is as follows: Figure 7 As shown, the coordination analysis is as follows: Figure 8 As shown.

[0094] This invention also provides a drought-responsive coupled vulnerability analysis system for industrial, domestic, and agricultural water use, used to implement the above-mentioned methods, such as... Figure 9 As shown, the system includes:

[0095] The data acquisition unit is used to acquire annual water supply, socio-economic data, and auxiliary meteorological and hydrological data of regional industrial, domestic, and agricultural water use sectors; it decomposes the annual water supply and demand into monthly sequences based on the monthly allocation coefficient, and calculates the socio-economic water shortage ratio index SEWDRI based on the monthly supply and demand difference to distinguish between general drought and extreme drought scenarios.

[0096] The vulnerability calculation unit is used to characterize industrial water use efficiency using the HARA function, calculate the industrial water shortage loss rate based on the water shortage amount to characterize industrial water use vulnerability, calculate the proportion of people with drinking water difficulties based on per capita domestic water use quota and actual water supply to characterize domestic water use vulnerability, and estimate agricultural output loss based on disaster area and yield data using area method and yield method, and take the larger value to calculate the loss rate to characterize agricultural water use vulnerability.

[0097] The regression fitting unit is used to fit the relationship curves between the socioeconomic water shortage ratio index SEWDRI and the vulnerability of the three sectors using linear regression, and to compare the differences in response under general drought and extreme drought scenarios.

[0098] The comprehensive assessment unit is used to determine the weights of the three sectors based on the region's long-term water use structure and to calculate the comprehensive vulnerability index. Based on the comprehensive vulnerability index and the vulnerability of the three sectors, it calculates the coupling degree and coordination degree and classifies the status of the regional water resources system according to the classification criteria.

[0099] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0100] In summary, this invention acquires annual water supply, socioeconomic data, and meteorological and hydrological data for industry, domestic use, and agriculture within a region. By combining this data with monthly allocation coefficients, the annual data is decomposed into monthly supply and demand data, thus constructing the Socioeconomic Water Shortage Ratio Index (SEWDRI). This index effectively distinguishes between general drought and extreme drought scenarios, providing timely and accurate data support for vulnerability analysis. Vulnerability models for industrial, domestic, and agricultural water use are constructed separately. The vulnerability of industrial water use is quantified by modeling the benefits of industrial water use using the HARA function. Domestic water use vulnerability is assessed by calculating the proportion of the population facing drinking water difficulties. Agricultural water use vulnerability is estimated by comprehensively using the area method and the yield method to measure yield loss. These vulnerability models accurately reflect the differences in carrying capacity among sectors under different drought scenarios. By using linear regression to correlate the socioeconomic water shortage ratio index with the vulnerability of each sector, the response differences of the three sectors under drought scenarios are further revealed. This not only helps to compare the changes in vulnerability under different drought scenarios but also provides a theoretical basis for subsequent calculation of the comprehensive vulnerability index. By introducing the regional long-term water use structure, the weights of each sector are calculated, and a comprehensive vulnerability index is constructed accordingly, quantifying and integrating the vulnerabilities of different sectors and providing a foundation for calculating coupling and coordination. The coupling and coordination model analyzes the interaction and coordination degree among the three sectors, quantifying the overall vulnerability and collaborative development level of the water resource system under drought scenarios. Through the synergy of the above technical solutions, the dynamic changes in water supply and demand within the region are fully considered, and the vulnerability of multiple sectors and their interactions are systematically quantified. This solves the problems of single-sector assessment and unclear distinction between drought scenarios in traditional methods, providing scientific, systematic, and operable support for drought risk identification, water resource allocation, and emergency decision-making.

[0101] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A coupled vulnerability analysis method for industrial, domestic, and agricultural water use based on drought response, characterized in that, The method includes: Step S1: Obtain annual water supply, socio-economic data, and auxiliary meteorological and hydrological data for regional industrial, domestic, and agricultural water use sectors; decompose the annual water supply and demand into monthly series based on the monthly allocation coefficient, and calculate the socio-economic water shortage ratio index SEWDRI based on the monthly supply-demand difference to distinguish between general drought and extreme drought scenarios. Step S2: Use the HARA function to characterize industrial water use efficiency, calculate the industrial water shortage loss rate based on the water shortage amount to characterize industrial water use vulnerability; calculate the proportion of the population with drinking water difficulties based on the per capita domestic water use quota and the actual water supply to characterize domestic water use vulnerability. Step S3: Based on the disaster area and yield data, estimate the agricultural yield loss using the area method and yield method, and take the larger value to calculate the loss rate to characterize the vulnerability of agricultural water use; Step S4: Use linear regression to fit the relationship curve between the socioeconomic water shortage ratio index SEWDRI and the vulnerability of the three sectors, and compare the differences in response under general drought and extreme drought scenarios; Step S5: Determine the weights of the three sectors based on the region's long-term water use structure and calculate the comprehensive vulnerability index; calculate the coupling degree and coordination degree based on the comprehensive vulnerability index and the vulnerability of the three sectors, and classify the status of the regional water resources system according to the classification criteria.

2. The method as described in claim 1, characterized in that, In step S1, the socioeconomic water shortage ratio (SEWDRI) is calculated based on the monthly supply-demand difference, including: Using the annual water supply as a benchmark, the monthly water supply is obtained by decomposing it according to the monthly allocation coefficient; the annual water demand is estimated based on socioeconomic data and decomposed into monthly water demand according to the seasonal pattern; the difference between the monthly total water demand and the monthly total water supply is calculated to obtain the socioeconomic water shortage ratio index SEWDRI; when the socioeconomic water shortage ratio index SEWDRI reaches the 95th percentile of the historical series, it is judged as extreme drought, and other water shortage situations are judged as general drought.

3. The method as described in claim 1, characterized in that, The method of using the HARA function to characterize industrial water use efficiency and calculating the industrial water shortage loss rate based on the amount of water shortage to characterize industrial water vulnerability includes: The HARA function is used to calculate the industrial water use utility under normal water supply conditions. The HARA function is calculated by combining the normal industrial water demand, parameter α equals 1, parameter γ equals 0, parameter δ equals 0.5, and water supply. The water shortage is calculated as the industrial water demand minus the actual water supply. The water shortage loss is calculated as the normal utility minus the actual utility. The industrial water shortage loss rate is calculated as the water shortage loss divided by the normal industrial output.

4. The method as described in claim 1, characterized in that, The method of calculating the proportion of the population facing drinking water difficulties based on per capita domestic water consumption quota and actual water supply to characterize domestic water vulnerability includes: The number of people with drinking water difficulties is calculated by multiplying the per capita domestic water quota by the population and subtracting the actual domestic water supply; the proportion of people with drinking water difficulties is calculated by dividing the number of people with drinking water difficulties by the population.

5. The method as described in claim 1, characterized in that, The method of estimating agricultural yield loss using area and yield methods based on disaster area and yield data, and calculating the loss rate by taking the larger value to characterize agricultural water vulnerability, includes: The yield loss is calculated using the area method by multiplying the base yield per unit area by the area of ​​total crop failure plus the area of ​​disaster multiplied by the yield reduction rate of 0.5 plus the affected area multiplied by the yield reduction rate of 0.

2. The yield loss is calculated using the yield method by subtracting the actual yield per unit area from the base yield per unit area multiplied by the crop planting area. The larger value between the yield loss from the area method and the yield loss from the yield method is taken as the agricultural yield loss. The agricultural loss rate is calculated by dividing the agricultural yield loss by the actual grain yield. The base yield per unit area is obtained by fitting the annual yield trend.

6. The method as described in claim 1, characterized in that, The method of fitting the socioeconomic water scarcity ratio index SEWDRI and the relationship curve between the three sectors using linear regression includes: Using the socioeconomic water scarcity ratio index SEWDRI as the independent variable and industrial water vulnerability as the dependent variable, linear regression was performed to obtain general drought functions and extreme drought functions; using the socioeconomic water scarcity ratio index SEWDRI as the independent variable and domestic water vulnerability as the dependent variable, linear regression was performed to obtain general drought functions and extreme drought functions; using the socioeconomic water scarcity ratio index SEWDRI as the independent variable and agricultural water vulnerability as the dependent variable, linear regression was performed to obtain general drought functions and extreme drought functions; the slopes, intercepts, and goodness of fit of the general drought functions and extreme drought functions were compared.

7. The method as described in claim 1, characterized in that, The determination of the three sector weights based on the region's long-term water use structure and the calculation of the comprehensive vulnerability index include: The industrial weight is determined as the average proportion of industrial water supply in the total water supply, the domestic weight as the average proportion of domestic water supply in the total water supply, and the agricultural weight as the average proportion of agricultural water supply in the total water supply. The comprehensive vulnerability index is calculated by multiplying the industrial water shortage loss rate by the industrial weight, adding the proportion of the population with drinking water difficulties by the living weight, and adding the agricultural loss rate by the agricultural weight.

8. The method as described in claim 1, characterized in that, In step S5, the coupling degree and coordination degree are calculated, and the regional water resource system status is classified according to the classification criteria, including: The vulnerability of industrial water use, domestic water use, and agricultural water use is normalized; the coupling degree is calculated as a combination of normalized industrial water use vulnerability, normalized domestic water use vulnerability, and normalized agricultural water use vulnerability functions; and the coordination degree is calculated as the comprehensive vulnerability index multiplied by the coupling degree multiplied by the coordination coefficient; the coupling state and coordination level are classified according to the coupling degree range and the coordination degree range.

9. A drought-responsive coupled vulnerability analysis system for industrial, domestic, and agricultural water use, used to implement the method as described in any one of claims 1-8, characterized in that, The system includes: The data acquisition unit is used to acquire annual water supply, socio-economic data, and auxiliary meteorological and hydrological data of regional industrial, domestic, and agricultural water use sectors; it decomposes the annual water supply and demand into monthly sequences based on the monthly allocation coefficient, and calculates the socio-economic water shortage ratio index SEWDRI based on the monthly supply and demand difference to distinguish between general drought and extreme drought scenarios. The vulnerability calculation unit is used to characterize industrial water use efficiency using the HARA function, calculate the industrial water shortage loss rate based on the water shortage amount to characterize industrial water use vulnerability, calculate the proportion of people with drinking water difficulties based on per capita domestic water use quota and actual water supply to characterize domestic water use vulnerability, and estimate agricultural output loss based on disaster area and yield data using area method and yield method, and take the larger value to calculate the loss rate to characterize agricultural water use vulnerability. The regression fitting unit is used to fit the relationship curves between the socioeconomic water shortage ratio index SEWDRI and the vulnerability of the three sectors using linear regression, and to compare the differences in response under general drought and extreme drought scenarios. The comprehensive assessment unit is used to determine the weights of the three sectors based on the region's long-term water use structure and to calculate the comprehensive vulnerability index. Based on the comprehensive vulnerability index and the vulnerability of the three sectors, it calculates the coupling degree and coordination degree and classifies the status of the regional water resources system according to the classification criteria.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the method as described in any one of claims 1-8.

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