A method for calculating water consumption of human activities at river section scale

Abstract

The present invention provides a method for calculating water consumption for human activities at a river section scale, and relates to the technical field of water consumption for residents' lives, industrial production, and agricultural irrigation. It includes: using population and temperature spatial distribution data to calculate the water consumption for residents' lives on a monthly time scale in the future. Using GDP spatial distribution data to calculate the water consumption for industrial production on a monthly time scale in the future. Integrate and average the results of agricultural irrigation water consumption simulations using multiple hydrological models to calculate the agricultural irrigation water consumption on a monthly time scale in the future. Considering the spatial distance from human activities to the river, the water consumption for human activities at a grid scale is distributed to each river section, and the sum of the water consumption for human activities on each river section is calculated. The technical solution of the present invention overcomes the problem that the prior art only measures the water consumption for human activities at a grid scale, ignores the distance between human activities and the river, and cannot measure the water consumption for human activities at a river section scale.

Description

Technical Field

[0001] The invention relates to the technical field of water consumption measurement, and in particular to a method for measuring water consumption of human activities at a river section scale. Background Art

[0002] Water use for human activities mainly includes agricultural irrigation, residents' lives, and industrial production. With the increase in global population and social and economic development, water use for human activities has increased significantly. It is necessary to grasp the current and future trends in water use for human activities. It is urgent to integrate multi-source geographic data such as river network vector spatial data and social and economic perception to calculate refined water use for human activities.

[0003] Although there are many models that can simulate agricultural irrigation water consumption, there is still great uncertainty in the simulation results. This is mainly restricted by the following aspects: ① The irrigation parameters of the land surface process model and the hydrological model are difficult to determine. ② The irrigation module principles of the land surface process model and the hydrological model are very different, resulting in great uncertainty in the simulation results of agricultural irrigation water consumption. ③ The actual irrigation process has not yet been reflected in the model, including the irrigation allocation of surface water and groundwater, water infrastructure management, and inter-basin water resources scheduling.

[0004] For the calculation of water consumption for residential life and industrial production, the simulation principles are similar. Most studies use socioeconomic data such as population, GDP, and electricity consumption to model the relationship between water consumption for residential life and industrial production and these socioeconomic indicators. Based on the above methods, existing studies have published multiple sets of global or regional human activity water consumption data products, which can be roughly divided into three categories. The first category is water consumption data obtained based on historical statistical data. However, there are problems such as slow data update, missing data in some underdeveloped countries or regions, and inconsistent statistical caliber of water use data between countries. The second category is human activity water use data sets simulated based on hydrological models or land surface process models. However, different models do not uniformly classify human activity water use categories, and there is great uncertainty in water use simulation results. In addition, human activity water use data simulated using different hydrological models and land surface process models are all at grid scale with low spatial resolution (mostly 0.5°, about 55 km in the equatorial region), which is not enough to carry out detailed research on changes in water consumption for agricultural irrigation, residential life, and industrial production. The third category is human activity water use data sets that integrate multi-source GIS spatial data, remote sensing observation data, historical statistical data, and multi-model simulation. Although the relevant dataset has effectively reduced the uncertainty of water consumption estimation by integrating water use simulation results from multiple hydrological models, the data only focuses on water consumption for human activities in historical periods and has a low spatial resolution (about 55 km).

[0005] Therefore, there is a need for a method that can accurately estimate the future water consumption of human activities in the region, that is, at the river section scale. Summary of the invention

[0006] The main purpose of the present invention is to provide a method for calculating water consumption of human activities at river section scale, so as to solve the problem that the prior art only measures water consumption of human activities at grid scale, ignores the spatial distance of human activities from the river, and cannot realize the refined calculation of water consumption of human activities at river section scale in the future.

[0007] To achieve the above object, the present invention provides a method for calculating water consumption for human activities at a river section scale, which specifically comprises the following steps:

[0008] S1, using population distribution data to spatially downscale the existing residential water consumption dataset, based on the existing residential water consumption dataset, using population and temperature spatial distribution simulation data to generate residential water consumption on a monthly time scale.

[0009] S2, the spatial distribution data of electricity consumption are used to spatially downscale the existing industrial production water use data. Based on the existing industrial production water use data set, the GDP spatial distribution simulation data is used to generate industrial production water use on a monthly time scale.

[0010] S3, integrate and average the results of agricultural irrigation water consumption simulated by multiple hydrological models to calculate agricultural irrigation water consumption on a monthly time scale.

[0011] S4, considering the spatial distance between human activities and the river, allocate the water consumption for residents’ life, industrial production, and agricultural irrigation at the grid scale obtained in steps S1 to S3 to each river section, and calculate the total water consumption for human activities on each river section.

[0012] Furthermore, step S1 specifically includes the following steps:

[0013] S1.1, the study area was divided into 5 km × 5 km grids, and the total population in each 5 km grid was counted through regional statistical analysis.

[0014] S1.2, calculate the total population at 0.5° spatial resolution, and obtain the proportion of the population of each 5 km grid to the 0.5° grid to which it belongs.

[0015] S1.3, under the premise of ensuring that the total amount of domestic water consumption in the 0.5° grid remains unchanged, the domestic water consumption in the 0.5° grid is allocated to the 5 km grid according to the population ratio. The specific formula of the method for downscaling domestic water consumption is as follows:

[0016] ;

[0017] In the formula, is the domestic water consumption of residents in the 5 km grid, is the domestic water consumption of residents in the 0.5° grid, Represents the total population within each 5km grid. for The total population within the 0.5° grid.

[0018] S1.4, based on the known per capita domestic water consumption in the base year, combined with the future population distribution data, calculate the future domestic water consumption of residents in the study area. The calculation formula is as follows:

[0019] ;

[0020] In the formula, for Residential water consumption in the year, in mm; is the population, Base period The per capita domestic water consumption of the year, in mm / month / person; It is the slope of residents’ domestic water use, which is affected by the differences in socio-economic development in the study area.

[0021] S1.5, integrate and average the temperature simulation results of the global climate model, and then calculate the residential water consumption of the study area on a monthly time scale and a 5 km grid scale. The calculation formula is as follows:

[0022] ;

[0023] In the formula, represent Year Monthly residential water consumption, unit: mm / month; represent Year in The average temperature of the month, in °C; , , They are Average temperature, maximum temperature and minimum temperature of the year, unit: ℃; is the adjustment coefficient, and its value is set to 0.29.

[0024] Furthermore, step S2 specifically includes the following steps:

[0025] S2.1, the study area is divided into 5 km × 5 km grids, and the total amount of electricity consumption in each 5 km grid is counted through regional statistical analysis.

[0026] S2.2, calculate the total electricity consumption at 0.5° spatial resolution, and obtain the proportion of electricity consumption of each 5 km grid to the 0.5° grid to which it belongs.

[0027] S2.3, under the premise of ensuring that the total amount of industrial production water in the 0.5° grid remains unchanged, the industrial production water in the 0.5° grid is allocated to the 5 km grid according to the proportion of electricity consumption. The specific formula of the industrial production water downscaling method is as follows:

[0028] ;

[0029] In the formula, is the industrial production water consumption within the 5 km grid, is the industrial production water consumption within the 0.5° grid, represents the total electricity consumption within the 5km grid, for The total electricity consumption within the 0.5° grid.

[0030] S2.4, using the future GDP spatial distribution simulation data, based on the known industrial production water intensity in the base year, calculate the future industrial production water consumption in the study area year by year. The calculation formula is as follows:

[0031] ;

[0032] In the formula, is the total industrial water consumption in year t, in mm; GDP is the gross domestic product, in US dollars; is the industrial production water intensity in the base year t0, in units of mm / month / dollar. The base year t0 of the study is 2010; It is the slope of industrial process water use, unitless, and usually set to 1.1%.

[0033] S2.5, the industrial production water consumption in the study area in the future is evenly distributed to each month to generate an industrial production water consumption dataset at a monthly time scale and a 5 km grid scale.

[0034] Furthermore, step S3 specifically includes the following steps:

[0035] S3.1, calculate the average of the results of agricultural irrigation water consumption simulated by multiple hydrological models, and measure the regional agricultural irrigation water consumption on a monthly time scale and at a spatial resolution of 0.5°.

[0036] S3.2, the agricultural irrigation water consumption obtained in step S3.1 is evenly distributed within the 0.5° grid, and the original 0.5° grid is resampled to 5 km, and finally an agricultural irrigation water consumption dataset with a regional monthly time scale and a 5 km grid scale is obtained.

[0037] Furthermore, step S4 specifically includes the following steps:

[0038] S4.1, obtain the center point of each 5 km grid, use the center point to reflect the 5 km grid to which the center point belongs, use the river network location information in the river network vector spatial data, and through proximity analysis, calculate the spatial distance between the center point of each 5 km grid i and the river section closest to the center point, and obtain the ID of the closest river section to grid i.

[0039] S4.2, taking the river section as the basic spatial analysis unit, calculate the total water consumption of human activities on each river section j, where human activity water consumption = residential water consumption + industrial production water consumption + agricultural irrigation water consumption.

[0040] The present invention has the following beneficial effects:

[0041] By utilizing multi-source geographic spatial data such as river network vector spatial data, model simulation data, and socio-economic perceptions, and considering the spatial distance of human activities from the river, and using GIS spatial proximity analysis technology, the water consumption of human activities is converted from the grid scale to the river section scale, thereby realizing the calculation of future human activity water consumption at the river section scale and revealing the refined spatiotemporal distribution pattern of water consumption from human activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the following will briefly introduce the drawings required for the specific implementation or the prior art description. Obviously, the drawings described below are some implementations of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0043] Figure 1 A flow chart of a method for calculating water consumption for human activities at a river section scale according to the present invention is shown.

[0044] Figure 2 A schematic diagram showing the relationship between the study area and the river section is shown.

[0045] Figure 3 A conceptual diagram of water consumption grid-river reach scale conversion is shown.

[0046] Figure 4The water consumption distribution map obtained by calculating the average water consumption for human activities in South Asia and Southeast Asia from 1990 to 2050 using the method provided by the present invention is shown.

[0047] Figure 5 Shows Figure 4 A magnified detail of point A.

[0048] Figure 6 The water consumption distribution map obtained by calculating the average agricultural irrigation water consumption in South Asia and Southeast Asia from 1990 to 2050 using the method provided by the present invention is shown.

[0049] Figure 7 Shows Figure 6 Enlarged detail of point B.

[0050] Figure 8 The water consumption distribution map obtained by calculating the average domestic water consumption of residents in South Asia and Southeast Asia from 1990 to 2050 using the method provided by the present invention is shown.

[0051] Fig. 9 Shows Figure 8 Enlarged detail of point C.

[0052] Fig.10 The water consumption distribution map obtained by calculating the average industrial production water consumption in South Asia and Southeast Asia from 1990 to 2050 using the method provided by the present invention is shown.

[0053] Fig.11 Shows Fig.10 Enlarged detail of point D. DETAILED DESCRIPTION

[0054] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] like Figure 1 The method for calculating water consumption of human activities at the river section scale shown in FIG. 1 includes the following steps:

[0056] S1, using population distribution data to spatially downscale the existing residential water consumption dataset, based on the existing residential water consumption dataset, using population and temperature spatial distribution simulation data to generate residential water consumption on a monthly time scale.

[0057] S2, the spatial distribution data of electricity consumption are used to spatially downscale the existing industrial production water use data. Based on the existing industrial production water use data set, the GDP spatial distribution simulation data is used to generate industrial production water use on a monthly time scale.

[0058] S3, integrate and average the results of agricultural irrigation water consumption simulated by multiple hydrological models to calculate agricultural irrigation water consumption on a monthly time scale.

[0059] S4, considering the spatial distance between human activities and the river, distribute the grid-scale water consumption for residential life, industrial production, and agricultural irrigation obtained in steps S1 to S3 to each river section, and calculate the total water consumption for human activities on each river section. Water consumption for human activities = water consumption for residential life + industrial production + agricultural irrigation.

[0060] The present invention considers the spatial proximity relationship between human activities and rivers, integrates multi-hydrological model integration and multi-source geographic data of social and economic development, and constructs a method for simulating water consumption of three types of human activities, namely agricultural irrigation, industrial production and residents' lives, at the river section scale. First, the agricultural irrigation water consumption simulation results of multiple hydrological models are integrated to obtain the agricultural irrigation water consumption at the 5 km grid scale, and the social and economic perception data such as population, electricity consumption, and GDP are used to calculate the industrial production and residents' lives at the 5 km grid scale. Secondly, the GIS spatial proximity analysis technology is used to calculate the spatial distance from human activities to the river, and the calculation results of human activity water consumption at the grid scale are assigned to the nearest river section, realizing the conversion of human activity water consumption from the grid scale to the vector river section scale, and calculating the monthly river section scale agricultural irrigation, industrial production and residents' lives water consumption data.

[0061] Specifically, the amount of domestic water used by residents is closely related to population distribution. In densely populated areas, the demand for domestic water is large. Therefore, the present invention uses population distribution data to spatially downscale the existing domestic water use data set, and downscales the existing domestic water use data from a spatial resolution of 0.5° to 5 km. Step S1 specifically includes the following steps:

[0062] S1.1, the study area was divided into 5 km × 5 km grids, and the total population in each 5 km grid was counted through regional statistical analysis;

[0063] S1.2, calculate the total population at 0.5° spatial resolution and obtain the proportion of the population of each 5 km grid to the 0.5° grid to which it belongs;

[0064] S1.3. Most of the existing residential water use data sets are 0.5°. Under the premise of ensuring that the total amount of residential water use in the 0.5° grid remains unchanged, the residential water use in the 0.5° grid is allocated to the 5 km grid according to the population ratio. The specific formula of the residential water use downscaling method is as follows:

[0065] ;

[0066] In the formula, is the domestic water consumption of residents within the 5 km grid, is the domestic water consumption of residents within the 0.5° grid, Represents the total population within each 5km grid. for The total population within the 0.5° grid.

[0067] S1.4, based on the known base year per capita residential water consumption (obtained from statistical data or existing residential water consumption data), combined with future population distribution data, calculate the future residential water consumption in the study area. The calculation formula is as follows:

[0068] ;

[0069] In the formula, for Residential water consumption in the year, in mm; is the population, Base period The per capita domestic water consumption of the year, in mm / month / person; is the slope of residential water use, which is affected by the differences in socio-economic development in the study area;

[0070] S1.5, integrate and average the temperature simulation results of the global climate model, and then calculate the residential water consumption of the study area on a monthly time scale and a 5 km grid scale. The calculation formula is as follows:

[0071] ;

[0072] In the formula, represent Year Monthly residential water consumption, unit: mm / month; represent Year in The average temperature of the month, in °C; , , They are Average temperature, maximum temperature and minimum temperature of the year, unit: ℃; is the adjustment coefficient, and its value is set to 0.29.

[0073] Specifically, step S2 includes the following steps:

[0074] S2.1, the study area is divided into 5 km × 5 km grids, and the total amount of electricity consumption in each 5 km grid is counted through regional statistical analysis;

[0075] S2.2, calculate the total power consumption at 0.5° spatial resolution, and obtain the proportion of power consumption of each 5 km grid to the 0.5° grid to which it belongs;

[0076] S2.3, the existing industrial production water use data sets are mostly 0.5°. Under the premise of ensuring that the total amount of industrial production water in the 0.5° grid remains unchanged, the industrial production water in the 0.5° grid is allocated to the 5 km grid according to the proportion of electricity consumption. The specific formula of the industrial production water downscaling method is as follows:

[0077] ;

[0078] In the formula, is the industrial production water consumption within the 5 km grid, is the industrial production water consumption within the 0.5° grid, represents the total electricity consumption within the 5km grid, for The total power consumption within the 0.5° grid;

[0079] S2.4, using the future GDP spatial distribution simulation data, based on the known industrial production water intensity in the base year (obtained from statistical data or existing industrial production water collection), the future industrial production water consumption in the study area is calculated year by year. The calculation formula is as follows:

[0080] ;

[0081] In the formula, is the total industrial water consumption in year t, in mm; GDP is the gross domestic product, in US dollars; is the industrial production water intensity in the base year t0, in units of mm / month / dollar. The base year t0 of the study is 2010; is the slope of industrial process water use, unitless, usually set to 1.1%;

[0082] S2.5, the industrial production water consumption in the study area in the future is evenly distributed to each month to generate an industrial production water consumption dataset at a monthly time scale and a 5 km grid scale.

[0083] Specifically, step S3 includes the following steps:

[0084] S3.1, the simulation results of agricultural irrigation water consumption driven by multiple hydrological models are used, namely CWatM, H08 and hydroPy hydrological models. The agricultural irrigation water consumption data are obtained by simulating hydrological models driven by different meteorological data, with a spatial resolution of 0.5°. In order to effectively reduce the uncertainty of different hydrological models in simulating agricultural irrigation water consumption, the average value of agricultural irrigation water consumption simulated by multiple hydrological models is calculated to measure the agricultural irrigation water consumption in the region on a monthly time scale and with a spatial resolution of 0.5°.

[0085] S3.2, the agricultural irrigation water consumption obtained in step S3.1 is evenly distributed within the 0.5° grid, and the original 0.5° grid is resampled to 5 km, and finally an agricultural irrigation water consumption dataset with a regional monthly time scale and a 5 km grid scale is obtained.

[0086] Specifically, step S4 includes the following steps:

[0087] S4.1, obtain the center point of each 5 km grid, use the center point to reflect the 5 km grid to which the center point belongs, use the river network location information in the river network vector spatial data, calculate the spatial distance between the center point of each 5 km grid i and the river section closest to the center point through proximity analysis, and obtain the ID of the closest river section to grid i;

[0088] S4.2, taking the river section (the section between the confluence of two adjacent rivers) as the basic spatial analysis unit, calculate the total water consumption of human activities on each river section j, where human activity water consumption = residential water consumption + industrial production water consumption + agricultural irrigation water consumption.

[0089] Figure 2 The relationship between the study area and the river section is shown. The river water resources on a river section may be used by human activities in multiple 5 km grids, or they may not be used. For example, Figure 3 The runoff at the middle river section j is taken by human activities in n 5 km grids, W j is the total water consumption of human activities on river section j, W1, W2, ...W n They are the water consumption of river section j caused by human activities in grid 1, grid 2, ... grid n:

[0090] W j =W1+W2+ W3+ W4+…+ W n .

[0091] like Figure 4-Figure 11As shown in the figure, using the river section scale human activity water consumption calculation method, the study obtained the average water consumption of three types of human activities in South Asia and Southeast Asia from 1990 to 2050, namely agricultural irrigation, residential life and industrial water use, and analyzed the spatiotemporal distribution pattern of human activity water consumption in South Asia and Southeast Asia in the future.

[0092] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for calculating water consumption for human activities at a river section scale, characterized in that: The specific steps include: S1, using population distribution data to spatially downscale the existing residential water consumption dataset, based on the existing residential water consumption dataset, using population and temperature spatial distribution simulation data to generate residential water consumption on a monthly time scale; S2, using the spatial distribution data of electricity consumption to spatially downscale the existing industrial production water use data, and based on the existing industrial production water use data set, using GDP spatial distribution simulation data to generate industrial production water use on a monthly time scale; S3, integrate and average the results of agricultural irrigation water consumption simulated by multiple hydrological models to calculate agricultural irrigation water consumption on a monthly time scale; S4, considering the spatial distance between human activities and the river, the grid-scale water consumption for residential life, industrial production, and agricultural irrigation obtained in steps S1 to S3 is allocated to each river section, and the total water consumption for human activities on each river section is calculated; Step S1 specifically includes the following steps: S1.1, the study area was divided into 5 km × 5 km grids, and the total population in each 5 km grid was counted through regional statistical analysis; S1.2, calculate the total population at 0.5° spatial resolution and obtain the proportion of the population of each 5 km grid to the 0.5° grid to which it belongs; S1.3, under the premise of ensuring that the total amount of domestic water consumption in the 0.5° grid remains unchanged, the domestic water consumption in the 0.5° grid is allocated to the 5 km grid according to the population ratio. The specific formula of the method for downscaling domestic water consumption is as follows: ； In the formula, is the domestic water consumption of residents within the 5 km grid, is the domestic water consumption of residents within the 0.5° grid, Represents the total population within each 5km grid. for The total population within the 0.5° grid; S1.4, based on the known per capita domestic water consumption in the base year, combined with the future population distribution data, calculate the future domestic water consumption of residents in the study area. The calculation formula is as follows: ； In the formula, for Residential water consumption in the year, in mm; is the population, Base period The per capita domestic water consumption of the year, in mm / month / person; is the slope of residential water use, which is affected by the differences in socio-economic development in the study area; S1.5, integrate and average the temperature simulation results of the global climate model, and then calculate the residential water consumption of the study area on a monthly time scale and a 5 km grid scale. The calculation formula is as follows: ； In the formula, represent Year Monthly residential water consumption, unit: mm / month; represent Year in The average temperature of the month, in °C; , , They are Average temperature, maximum temperature and minimum temperature of the year, unit: ℃; is the adjustment coefficient, and its value is set to 0.29; Step S2 specifically includes the following steps: S2.1, the study area is divided into 5 km × 5 km grids, and the total amount of electricity consumption in each 5 km grid is counted through regional statistical analysis; S2.2, calculate the total power consumption at 0.5° spatial resolution, and obtain the proportion of power consumption of each 5 km grid to the 0.5° grid to which it belongs; S2.3, under the premise of ensuring that the total amount of industrial production water in the 0.5° grid remains unchanged, the industrial production water in the 0.5° grid is allocated to the 5 km grid according to the proportion of electricity consumption. The specific formula of the industrial production water downscaling method is as follows: ； In the formula, is the industrial production water consumption within the 5 km grid, is the industrial production water consumption within the 0.5° grid, represents the total electricity consumption within the 5km grid, for The total power consumption within the 0.5° grid; S2.4, using the future GDP spatial distribution simulation data, based on the known industrial production water intensity in the base year, calculate the future industrial production water consumption in the study area year by year. The calculation formula is as follows: ； In the formula, is the total industrial water consumption in year t, in mm; GDP is the gross domestic product, in US dollars; is the industrial production water intensity in the base year t0, in units of mm / month / dollar. The base year t0 of the study is 2010; is the slope of industrial process water use, unitless, usually set to 1.1%; S2.5, evenly distribute the future industrial production water consumption in the study area to each month, and generate an industrial production water consumption dataset at a monthly time scale and a 5km grid scale; Step S3 specifically includes the following steps: S3.1, calculate the average of the results of agricultural irrigation water consumption simulated by multiple hydrological models, and measure the agricultural irrigation water consumption in the region on a monthly time scale and at a spatial resolution of 0.5°; S3.2, evenly distribute the agricultural irrigation water volume obtained in step S3.1 within the 0.5° grid, resample the original 0.5° grid to 5 km, and finally obtain the agricultural irrigation water volume dataset of the region at a monthly time scale and a 5 km grid scale; Step S4 specifically includes the following steps: S4.1, obtain the center point of each 5 km grid, use the center point to reflect the 5 km grid to which the center point belongs, use the river network location information in the river network vector spatial data, calculate the spatial distance between the center point of each 5 km grid i and the river section closest to the center point through proximity analysis, and obtain the ID of the closest river section to grid i; S4.2, taking the river section as the basic spatial analysis unit, calculate the total water consumption of human activities on each river section j, where human activity water consumption = residential water consumption + industrial production water consumption + agricultural irrigation water consumption.

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