Urban scale rainwater collection system flood elimination efficiency calculation method and system

Abstract

The invention discloses a flood elimination efficiency calculation method and system for an urban-scale rainwater collection system, and the method comprises the steps: obtaining the related rainfall data of a city, and carrying out the calculation to obtain the average water demand per hour in a non-rainfall period, the average water demand per hour in a rainfall period, and the effective collection area of a roof; defining a minimum event interval time MIT, a low rainfall threshold LRT and a rainfall depth threshold RDT, dividing rainfall events according to the MIT, and eliminating rainfall events with rainfall depth lower than the LRT in unit time and rainfall data with rainfall depth lower than the RDT in one rainfall event; modeling random non-rainfall duration, random rainfall depth and random rainfall duration by a probability density function, and determining a positive rate parameter; the rainwater storage unit capacity meeting the reliability is determined through random simulation; and calculating the original outflow Dor and the random rainfall decrement Dre to obtain a flood elimination effect and a macroscopic rainwater collection effect so as to evaluate the flood elimination efficiency. The method has universality, is not complex, and can assist in optimizing an urban rainwater collection system.

Description

Technical Field

[0001] The present invention relates to the technical field of urban rainwater harvesting system assessment, and particularly to a method and system for calculating the flood mitigation efficiency of a rainwater harvesting system at the urban scale. Background Art

[0002] To realize, enhance, or maximize the effectiveness of rainwater harvesting (RWH) in mitigating challenges, promoting green buildings, and achieving Sustainable Development Goals (SDGs), scientific design, assessment, planning, and management of RWH are required. One key foundation is to perform advanced modeling of the hydrological processes of individual RWH systems and evaluate their flood mitigation efficiency. In this regard, many methods have been developed. In large-scale urban rainwater harvesting simulation, the main methods include water balance accounting and physical hydrological simulation and their coupling. Water balance accounting constructs a simple equation to estimate the long-term average water balance of the RWH system. This equation is easy to use in engineering practice, especially over a large area. However, this method oversimplifies the RWH system (e.g., ignores the temporal dynamics of rainwater demand and rainfall characteristics), resulting in low accuracy. Physical hydrological simulation methods have been applied in most RWH modeling studies in the past decade. Specifically, this method establishes a complex model that uses high-quality data (e.g., long-term high-resolution observational data of climate, water demand, water levels in RWSU, and sewer overflows, etc.) to simulate the continuous water balance of the RWH system. Through nonlinear optimization, the system reliability (e.g., the proportion of time that RWSU meets the water demand) is estimated for different RWSU capacities and maximized. This method is very accurate in describing and designing RWH systems. The first method is simple, with low complexity and strong applicability, but low accuracy. The second method is complex, with high complexity and high accuracy, but low applicability. To guide a wide range of rainwater harvesting applications, there is an urgent need for a method and system for calculating the flood mitigation efficiency of a rainwater harvesting system at the urban scale, especially in the case of a lack of ideal data, talent, and hardware facilities. Summary of the Invention

[0003] The object of the present invention is to provide a method and system for calculating the flood mitigation efficiency of a rainwater harvesting system at the urban scale, which is based on stochastic simulation and relatively balanced between high accuracy, high applicability, and low complexity, and is used to model the hydrological processes of the rainwater harvesting system at the urban scale and evaluate its flood mitigation efficiency.

[0004] To achieve the above object, in the first aspect of the present invention, a method for calculating the flood mitigation efficiency of a rainwater harvesting system at the urban scale is provided, including: obtaining population density data, meteorological data, building contour vector data, and water consumption data of a relevant city, and calculating the average hourly water demand during the non-rainy period, the average hourly water demand during the rainy period, and the effective roof collection area;

[0005] Define the minimum event interval time MIT, the low rainfall threshold LRT, and the rainfall depth threshold RDT. Divide the rainfall records with an interval time greater than or equal to MIT into different rainfall events, so as to divide the continuous rainfall records in the meteorological data into discrete rainfall events. Eliminate the rainfall events with a rainfall depth per unit time lower than LRT and eliminate the rainfall data with a rainfall depth lower than RDT in a rainfall event;

[0006] Model the random non-rainfall duration, random rainfall depth, and random rainfall duration in the discrete rainfall events based on the probability density function of the exponential distribution, and determine the positive rate parameters of the three probability density functions by the maximum likelihood estimation method;

[0007] Combined with the positive rate parameters, the average hourly water demand during the non-rainfall period, the average hourly water demand during the rainfall period, and the effective roof collection area, determine the storage capacity of the rainwater storage unit that meets the preset system reliability through random simulation;

[0008] Calculate the original outflow D through the storage capacity or and the random rainfall reduction amount D re , according to D re and D or Calculate the flood control effect, and evaluate the flood control efficiency of the urban-scale rainwater collection system by calculating the macroscopic rainwater collection effect.

[0009] Preferably, the value range of MIT is 3 to 24 hours, and the specific value is adjusted according to the regional climate characteristics. The value range of LRT is 0.001 to 0.005 m / hour, and the value range of RDT is 0.001 to 0.005 m, which is used to eliminate ineffective low-intensity rainfall.

[0010] Preferably, the expression of the probability density function PDF is:

[0011] PDF of random non-rainfall duration T n :

[0012]

[0013] PDF of random rainfall depth D r :

[0014]

[0015] PDF of random rainfall duration T r :

[0016]

[0017] where Pr(*) is the occurrence probability of the random event *, T n is the random non-rainfall duration, Dr is the random rainfall depth, T r is the random rainfall duration, and ψ, ξ, λ are positive rate parameters, t n is the non-rainfall duration, t r is the rainfall duration, d r is the rainfall depth. The expected values of Tn, Dr, and Tr are 1 / ψ, 1 / ξ, and 1 / λ respectively.

[0018] Preferably, the estimation method for the positive rate parameters ψ, ξ, λ is as follows:

[0019] Extract independent rainfall events from historical rainfall data and calculate t n , d r , t r ;

[0020] Solve by the maximum likelihood estimation method:

[0021]

[0022] where are the means of t n , d r , t r in the historical rainfall data respectively.

[0023] Preferably, the relationship between the effective roof collection area a and the storage capacity v of the rainwater storage unit r satisfies:

[0024]

[0025] where is the runoff coefficient, d f is the first flush depth.

[0026] Preferably, the random simulation, i.e., the random rainwater collection system modeling, is achieved through the following formula:

[0027]

[0028] where a is the effective roof collection area, r is the preset system reliability, d1 is the average hourly water demand during the non-rainfall period, and d2 is the average hourly water demand during the rainfall period.

[0029] Preferably, the calculation formula for the flood control effect R is:

[0030] When D or ≥ D re :

[0031]

[0032] When Dor <D re , R=1.

[0033] Preferably, the macro rainwater collection effect R m is the average value of the flood control effect of a building in N random rainfall events:

[0034]

[0035] Where N is the number of rainfall events.

[0036] Preferably, the method for determining the average hourly water demand during the rainy period and the average hourly water demand during the non-rainy period is:

[0037] The average hourly water demand during the rainy season and the average hourly water demand during the non-rainy season are calculated based on population density and water consumption data. The average hourly water demand during the rainy season is a ratio of the average hourly water demand during the non-rainy season less than 1. The specific ratio needs to take into account the city's additional municipal irrigation water demand.

[0038] To achieve the purpose of the present invention, a second aspect provides a system for calculating the flood control efficiency of a city-scale rainwater collection system, which is used to apply the method for calculating the flood control efficiency of a city-scale rainwater collection system described in the above technical solution. The system comprises:

[0039] The early data acquisition and processing module is used to obtain the population density data, meteorological data, building outline vector data and water consumption data of the relevant cities, and calculate the average hourly water demand in the non-rainy period, the average hourly water demand in the rainy period and the effective collection area of ​​the roof;

[0040] The rainfall data processing module is used to divide the continuous rainfall records in the meteorological data into discrete rainfall events, define the minimum event interval time MIT and the low rainfall threshold LRT, and eliminate the rainfall data with an interval time less than MIT or a rainfall depth per unit time lower than LRT;

[0041] A rainfall characteristic random modeling module is used to model the random non-rainfall duration, random rainfall depth and random rainfall duration in the discrete rainfall event based on the probability density function of the exponential distribution, determine the positive rate parameters of the three probability density functions by the maximum likelihood estimation method, and combine the positive rate parameters, the average hourly water demand in the non-rainfall period, the average hourly water demand in the rainy period and the effective collection area of ​​the roof to determine the storage capacity of the rainwater storage unit that meets the preset system reliability through random simulation;

[0042] The flood control efficiency evaluation module is used to calculate the original outflow D through the storage capacity. or With random rainfall reduction D re , according to D re With Dor The flood control effect is calculated, and the flood control efficiency of the rainwater harvesting system at the urban scale is evaluated by calculating the macroscopic rainwater harvesting effect.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] By expanding the research scope to a broader urban-scale multi-building area, the present invention uses a stochastic rainwater harvesting system model to break away from the limitations of a single building structure or a small area. The storage capacity of the rainwater storage unit that meets the preset system reliability is determined through stochastic simulation, so as to calculate the flood control effect, and the flood control efficiency of the rainwater harvesting system at the urban scale is evaluated by calculating the macroscopic rainwater harvesting effect. It is universal and not complicated, which helps to analyze the sustainability of the rainwater harvesting system, including the balance between long-term water resource supply and demand, assists in optimizing the urban rainwater harvesting system, ultimately promotes the sustainable development of urban water resource management, and assists in the construction of sponge cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a step flow chart of a method for calculating the flood control efficiency of a rainwater harvesting system at the urban scale according to Embodiment 1 of the present application;

[0046] Figure 2 It is a schematic structural diagram of the rainwater storage unit RWSU according to Embodiment 1 of the present application;

[0047] Figure 3 It is a population density distribution map of Guangzhou City at the end of 2019 according to Embodiment 2 of the present application;

[0048] Figure 4 It is a geographical location map of Guangzhou City and a vector distribution map of building outlines at the end of 2019 according to Embodiment 2 of the present application;

[0049] Figure 5 It is a capacity distribution map of the rainwater storage unit calculated according to the method of the present application in Embodiment 2 of the present application;

[0050] Figure 6 It is a flood control effect distribution map of each building calculated according to the method of the present application after implementing the rainwater harvesting system in Embodiment 2 of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] The following combines the drawings and embodiments to further describe in detail the specific embodiments of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0052] Glossary

[0053] RWH: Rainwater harvesting.

[0054] RWSU: Rainwater storage unit.

[0055] MSRHM: Modeling of Stochastic Rainwater Harvesting System

[0056] MIT: Minimum Inter - event Time, which refers to the minimum time between rainfall events to separate rainfall events (unit: hour).

[0057] LRT: Low Rainfall Threshold, used to distinguish low - depth rainfall (unit: m / h).

[0058] RDT: Rain Depth Threshold, which distinguishes low - depth rainfall in a rainfall event (unit: meter).

[0059] D ov : Overflow Discharge, that is, the amount of water lost when the rainwater harvesting system exceeds its storage capacity during a rainfall event (unit: m 3 ).

[0060] D re : Stochastic Rainfall Reduction, that is, the actual reduction in outflow through the rainwater harvesting system (unit: m 3 ).

[0061] D or : Original Outflow, that is, the amount of water lost from a specific area after rainfall without implementing rainwater harvesting measures (unit: m 3 ).

[0062] t n : Non - rainfall Duration, that is, the duration between two rainfall events (unit: hour).

[0063] t r : Rainfall Duration, that is, the duration of a rainfall event (unit: hour)..

[0064] d r : Rainfall Depth, that is, the depth of a rainfall event (unit: meter).

[0065] T n : Random variable representing the stochastic non - rainfall duration (unit: hour).

[0066] D r : Random variable representing the stochastic rainfall depth (unit: meter).

[0067] T r : Random variable representing the stochastic rainfall duration (unit: hour).

[0068] PDF: Probability Density Function of the random variable

[0069] ψ: Positive rate parameter in the probability density function of the stochastic non - rainfall duration t n .

[0070] ξ: The positive rate parameter d in the probability density function of the random rainfall depth r 。

[0071] λ: The random rainfall duration t r The positive rate parameter in the probability density function of

[0072] d1: The average hourly water demand during non - rainfall period (unit: m 3 / h).

[0073] a: The vertical protruding area of the roof, i.e., the effective collection area of the roof in the rainwater collection system (unit: m 2 ).

[0074] The runoff coefficient, representing the ratio of rainfall converted into runoff (unit: 1).

[0075] d f : The initial flushing depth, i.e., the initial runoff depth diverted by the first - flush diverter or debris filter (unit: meter).

[0076] d2: The average hourly water demand during rainfall period (unit: m 3 / h).

[0077] R: The random time fraction of the collected rainwater meeting the water demands during rainfall and non - rainfall periods, relative to the total duration of rainfall and non - rainfall periods, i.e., the flood - attenuation effect (unit: 1).

[0078] r: The expected reliability of the rainwater collection system (unit: 1).

[0079] v r : The size or capacity of the rainwater storage unit designed to achieve the given expected reliability r of the rainwater collection system (unit: m 3 ).

[0080] R m : The macroscopic rainwater collection effect, i.e., the average value of the flood - attenuation effect of a certain building in random N rainfall events.

[0081] Example 1

[0082] Please refer to Figure 1 , the present invention provides a method for calculating the flood - attenuation efficiency of an urban - scale rainwater collection system, including:

[0083] S1: Obtain the population density data, meteorological data, building contour vector data, and water consumption data of the relevant city, and calculate the average hourly water demand during non - rainfall period, the average hourly water demand during rainfall period, and the effective roof collection area;

[0084] S2: Define the minimum event interval time MIT, the low rainfall threshold LRT, and the rainfall depth threshold RDT. Divide the rainfall records with an interval time greater than or equal to MIT into different rainfall events, thereby dividing the continuous rainfall records in the meteorological data into discrete rainfall events. Eliminate the rainfall events with a rainfall depth per unit time lower than LRT and eliminate the rainfall data with a rainfall depth lower than RDT in a rainfall event.

[0085] S3: Model the random non-rainfall duration, random rainfall depth, and random rainfall duration in the discrete rainfall events based on the probability density function of the exponential distribution, and determine the positive rate parameters of the three probability density functions by the maximum likelihood estimation method.

[0086] S4: Combine the positive rate parameters, the average hourly water demand during the non-rainfall period, the average hourly water demand during the rainfall period, and the effective roof collection area, and determine the storage capacity of the rainwater storage unit that meets the preset system reliability through random simulation.

[0087] S5: Calculate the original outflow D through the storage capacity or and the random rainfall reduction amount D re , and based on D re and D or calculate the flood control effect, and evaluate the flood control efficiency of the urban-scale rainwater collection system by calculating the macroscopic rainwater collection effect.

[0088] In a preferred embodiment, the value range of MIT is 6 to 24 hours, and the specific value is adjusted according to the regional climate characteristics. The value range of LRT is 0.001 to 0.005 m / hour, and the value range of RDT is 0.001 to 0.005 m, which is used to eliminate ineffective low-intensity rainfall because the resulting runoff is often polluted and not collected. The characteristics of each rainfall event are described as the non-rainfall duration (t n ), the rainfall duration (t r ), and the rainfall depth (d r ).

[0089] The probability density function (PDF) is a mathematical description of the probability distribution of a continuous random variable. On a real number interval, the probability density function f(x) represents the probability density that the random variable takes a value at a certain point x, rather than the specific probability value. For a continuous random variable, there is no specific probability value corresponding to a fixed point, but the cumulative probability density on the interval has practical significance. The probability density function must satisfy the following properties: non-negativity: for all possible x, f(x) ≥ 0; normalization: on the entire real number interval (or the domain of the random variable), the integral of the probability density function is 1. The probability density functions (PDFs) of these rainfall characteristics can be expressed as:

[0090] PDF of the random non - rainfall duration T n :

[0091]

[0092] PDF of the random rainfall depth D r :

[0093]

[0094] PDF of the random rainfall duration T r :

[0095]

[0096] where Pr(*) is the occurrence probability of the random event *, T n is the random non - rainfall duration, D r is the random rainfall depth, T r is the random rainfall duration, ψ, ξ, λ are positive rate parameters, t n is the non - rainfall duration, t r is the rainfall duration, d r is the rainfall depth, and the expected values of Tn, Dr and Tr are 1 / ψ, 1 / ξ and 1 / λ respectively.

[0097] In a specific embodiment, the estimation method of the positive rate parameters ψ, ξ, λ is as follows:

[0098] Extract independent rainfall events from historical rainfall data, and calculate t n , d r , t r ;

[0099] Solve by the maximum likelihood estimation method:

[0100]

[0101] where are the means of t n , d r , t r in the historical rainfall data respectively.

[0102] In the design of the rainwater harvesting system (RWH), the capacity design of the rainwater storage unit (RWSU) of each individual building is crucial. This directly relates to whether the system can meet the requirements of users for the reliability of the rainwater harvesting system and the flood control effect under different rainfall conditions and water usage demands. The following are the main considerations and detailed steps in the design:

[0103] The designed capacity v of the rainwater storage unitr It directly depends on the water demand of the building and the supply situation of rainfall. The demand d1 during the non-rainy period needs to be obtained from the rainwater storage unit, while the demand d2 during the rainy period can be partially met by directly collected rainwater. The effective collection area a of the roof represents the vertical projection area of the building top from which rainwater can converge, with the unit of square meters (m 2 ). The initial flushing depth d f is the amount of water discharged through the diverter at the beginning of a rainfall event to remove initial pollutants, which reduces the effective storage capacity and thus needs to be considered when designing the capacity.

[0104] If the capacity v of the storage unit r is too small, heavy rainfall events may cause excessive rainwater to overflow, resulting in waste of water resources. Therefore, when designing, it is necessary to balance the rainfall characteristics and the water demand of the building to ensure that sufficient water can be provided during the non-rainy period while avoiding overflow during the rainy period. In the system design, the reliability r is regarded as an uncertain value, reflecting the proportion of time when the system meets the demand. There is usually a complex non-linear relationship between the reliability of the system and the capacity v of the storage unit r . To reasonably design the capacity v of the rainwater storage unit r , this embodiment uses the Modeling of Stochastic Rainwater Harvesting System (MSRHM). This method combines the water demand of the building and the water utilization efficiency, and conducts a sensitivity analysis on each variable of the system to ensure that the rainwater harvesting system can meet the water demand d1 during the non-rainy period and meet the demand d2 through the collected rainwater during the rainy period.

[0105] The relationship between the effective collection area a of the roof and the storage capacity v of the rainwater storage unit r is satisfied as follows:

[0106]

[0107] wherein, is the runoff coefficient, and d f is the initial flushing depth.

[0108] Stochastic simulation, that is, the Modeling of Stochastic Rainwater Harvesting System, is realized through the following formula:

[0109]

[0110] wherein, a is the effective collection area of the roof, r is the preset system reliability, d1 is the average hourly water demand during the non-rainy period, d2 is the average hourly water demand during the rainy period, and v r is the capacity of the rainwater storage unit under the expected value of r.

[0111] The characterization of the rainwater harvesting (RWH) effect is measured by calculating the ratio of the reduced water volume of a single building to the original outflow volume of the single building. This ratio reflects the overall impact of the rainwater harvesting system on urban water resource management and its contribution to water resource protection and sustainable utilization. The higher the value of this effect characterization, the higher the success degree of the rainwater harvesting system in reducing losses and improving water resource utilization efficiency, thus helping to reduce the urban water resource pressure.

[0112] The MSRHM method simulates the rainwater harvesting system in the form of a rainfall cycle. Each cycle consists of a rainfall or non-rainfall period with a random duration T n (h), and a rainfall period / event with a random depth D r (m) and a random duration T r (h). The capacity of the rainwater storage unit (i.e., the rainwater storage device) is expressed as v r (m 3 ). Considering that rainwater can be collected to the maximum extent, it is assumed that the rainwater tank is emptied in advance before a random rainfall event starts, that is, the initial water volume of the rainwater storage unit RWSU is v = 0 (m 3 ). The average water demand during the non-rainfall period is d1 (unit: m 3 / h). To ensure water use during the non-rainfall time, it is assumed that after a random rainfall event ends, the rainwater storage tank is full, and the random volume of water in RWSU at the end of any non-rainfall period = v r (m 3 ).

[0113] When the rainwater harvesting system is not set up, the original outflow is D or (m 3 )

[0114]

[0115] The random rainfall reduction is D re :

[0116] D re = d f ·a + v r + d2·t r

[0117] If the D or of the random rainfall event is greater than the random rainfall reduction D re , then there will be an overflow volume D ov (m 3 ) in the rainwater collection tank

[0118] D ov = D or - d f ·a - vr -d2·t r

[0119] The flood control effect R(0-1) is the reduction D of random rainfall re and the original outflow D or ratio

[0120]

[0121] If the D of a random rainfall event or is less than the reduction D of random rainfall re , there will be no overflow D in the rainwater collection tank ov , at this time, the flood control effect R = 1.

[0122] To eliminate the influence of spatial heterogeneity of rainfall on the effect estimation, the macroscopic rainwater collection effect R m is set as the average value of the flood control effect in N random rainfall events for a certain building:

[0123]

[0124] where N is the number of rainfall events.

[0125] Example 2

[0126] This Example 2 is based on Example 1, based on the rainfall data, population density, and building distribution data of Guangzhou from 2016 to 2020, and uses the Figure 2 shown rainwater collection system for simulation experiments.

[0127] Guangzhou is a humid and rainy area with a high degree of urbanization. As of the end of 2019, the cumulative investment in the construction of sponge cities in Guangzhou has reached 33 billion yuan, and the sponge city construction compliance area is 311.5 square kilometers, accounting for 20% of the total area of the built-up area of the city. The rainwater resource utilization volume in 2019 reached 42.26 million cubic meters, but the proportion of rainwater resource utilization volume in the average annual rainfall is only 3.1%.

[0128] First, the preliminary data of Guangzhou are processed, including population density data, building outline vector data, rainfall data, water consumption data, etc., and digital data construction is carried out through geographic information system GIS technology.

[0129] In this Example 2, the population density data is from https: / / www.worldpop.org / , and the population raster data with a resolution of 100m×100m is used, such as Figure 3As shown. WorldPop was launched in October 2013, integrating the AfriPop, AsiaPop, and AmeriPop population mapping projects, aiming to provide an open archive of spatial demographic datasets for Central America, South America, Africa, and Asia. The world population density maps published by WorldPop are widely cited as the most accurate and reliable long-term time series data currently, providing important data support for numerous studies.

[0130] The observed data of the hourly rainfall in this Embodiment 2 is sourced from the Guangzhou Public Data Open Platform (https: / / data.gz.gov.cn / dataDetail.html?sid=49772005), specifically the regional meteorological station observation live dataset released by the Guangzhou Meteorological Bureau. These observed data are divided into rainfall cycles, and each cycle consists of a non-rainy period and a rainy period. Time series of the non-rainy duration, rainfall depth, and rainfall duration for all cycles are generated. In this embodiment, the method for handling outliers in hourly rainfall is: replacing the outliers with the mean value of that time period to maintain the continuity and integrity of the data. This method ensures the availability of the data while reducing the impact of outliers on the analysis results. In Embodiment 2, the water consumption data comes from the official website of the Guangzhou Water Bureau. The determination methods for the average hourly water demand during the rainy period and the average hourly water demand during the non-rainy period are:

[0131] The average hourly water demand during the rainy period and the average hourly water demand during the non-rainy period are calculated based on the population density and water consumption data. The average hourly water demand during the rainy period is a proportion less than 1 of the average hourly water demand during the non-rainy period. The specific proportion needs to consider the additional municipal irrigation water demand in Guangzhou.

[0132] The building outline vector data of this embodiment is sourced from the Bigmap GIS Office software, as Figure 4 shown. During the data processing, the ArcGIS software is used to extract the vertical projection area of the roof, which is the effective collection area of the roof for the rainwater harvesting system (RWH), denoted as a (unit: m 2) It is represented as follows. To facilitate subsequent data matching, the building vector surface is converted into point features. The specific steps are as follows: Select the "ArcToolbox" button in the toolbar to open the ArcToolbox toolbox. Then, sequentially select the "Features" category in the "Data Management Tools" and finally select the "Feature to Point" tool. The principle of this tool is to create a new feature class that contains points generated from the centroids of the input features or points located within the input features. The original data includes 9,964,58 buildings. After mask extraction using ArcGIS software, the buildings located within the scope of Guangzhou City are selected, and the buildings with a population density of 0 are excluded. Finally, point features of 982,304 buildings are obtained. Subsequently, the matching of building point features, meteorological observation stations, and population density raster values is carried out. First, use the "Extract Multi Values to Points" tool in GIS to match the building features with the population density raster to obtain the population density information corresponding to each building. Then, during the matching process with meteorological observation stations, the neighborhood analysis method is adopted to determine the nearest meteorological observation station for each building. Selecting the nearest observation station is to ensure that the meteorological data used can most accurately reflect the meteorological conditions at the location of the building, thereby improving the accuracy and reliability of subsequent calculations.

[0133] The first flushing depth d f is the amount of water discharged through the diverter at the beginning of a rainfall event to remove initial pollutants, which reduces the effective storage capacity and thus needs to be considered when designing the capacity. According to the "Technical Code for Rainwater Control and Utilization Engineering in Buildings and Residential Areas (GB50400 - 2016)", the roof runoff thickness for initial flush can be 2mm - 3mm. In this Example 2, the first flushing depth of the rainwater collection system is set to 0.003m, the reliability r is set to 0.9, and the runoff coefficient represents the proportion of rainfall converted into runoff, ranging from 0 to 1, which is affected by roof materials and design. According to the "Technical Code for Rainwater Control and Utilization Engineering in Buildings and Residential Areas (GB 50400 - 2016)", the runoff coefficient range for hard roofs, flat roofs without gravel, and asphalt roofs is 0.80 - 0.90. In this Example 2, the runoff coefficient value is selected as 0.9.

[0134] Such as Figure 5 shown Figure 5 is the capacity distribution map of the rainwater storage unit calculated according to the method of this application.

[0135] The drainage flow rate before implementing the rainwater collection system, that is, the original discharge flow D or has an average value of 66.88m 3 . And after introducing the rainwater collection system, the average overflow flow D ov is 61.76m 3 , that is, through the operation of the system, the overall overflow flow has decreased by approximately 5.12m3 This indicates that the rainwater harvesting system effectively reduces the overflow volume in the watershed, reduces water resource waste and potential flood risks, and relieves the pressure on the municipal drainage system from individual buildings, thus contributing to the improvement of urban rainwater management and resource utilization.

[0136] As Figure 6 shown, Figure 6 is the distribution map of the flood control effect of each building calculated according to the method of this application after the implementation of the rainwater harvesting system.

[0137] The implementation of the rainwater harvesting system not only reduces the overflow volume but also provides better water resource utilization efficiency for buildings. This reduced overflow volume can be further used for irrigation, cleaning, or other non-potable water needs, with significant environmental and economic benefits. In the future, further improvement of this effect can be considered by optimizing the system design and enhancing the collection efficiency.

[0138] Embodiment 3

[0139] Based on Embodiment 1, this Embodiment 3 provides a flood control efficiency calculation system for a rainwater harvesting system at the urban scale, which is used to apply the flood control efficiency calculation method for a rainwater harvesting system at the urban scale described in Embodiment 1. This system includes:

[0140] A pre - data acquisition and processing module, which is used to obtain the population density data, meteorological data, building contour vector data, and water consumption data of the relevant city, and calculate the average hourly water demand during the non - rainfall period, the average hourly water demand during the rainfall period, and the effective roof collection area;

[0141] A rainfall record processing module, which is used to define the minimum event interval time MIT, the low rainfall threshold LRT, and the rainfall depth threshold RDT, divide the rainfall records with an interval time greater than or equal to MIT into different rainfall events to divide the continuous rainfall records in the meteorological data into discrete rainfall events, and eliminate the rainfall events with a rainfall depth per unit time lower than LRT and eliminate the rainfall data with a rainfall depth lower than RDT in a rainfall event;

[0142] A rainfall characteristic random modeling module, which is used to model the random non - rainfall duration, random rainfall depth, and random rainfall duration in the discrete rainfall events based on the probability density function of the exponential distribution, determine the positive rate parameters of the three probability density functions through the maximum likelihood estimation method, and combine the positive rate parameters, the average hourly water demand during the non - rainfall period, the average hourly water demand during the rainfall period, and the effective roof collection area, and determine the storage capacity of the rainwater storage unit that meets the preset system reliability through random simulation;

[0143] A flood control efficiency evaluation module, which is used to calculate the original outflow D or and the random rainfall reduction amount D re, according to D re and D or The flood control effect is calculated, and the flood control efficiency of the urban-scale rainwater harvesting system is evaluated by calculating the macro rainwater harvesting effect.

[0144] As described in the above embodiments, the solution of this application extends the research scope to a wider urban-scale multi-building, adopts a stochastic rainwater harvesting system modeling, gets rid of the limitations of a single building structure or a small area, determines the storage capacity of the rainwater storage unit that meets the preset system reliability through stochastic simulation, calculates the flood control effect therefrom, and evaluates the flood control efficiency of the urban-scale rainwater harvesting system by calculating the macro rainwater harvesting effect. It is universal and not complicated, helps to analyze the sustainability of the rainwater harvesting system, including the balance between long-term water resource supply and demand, assists in optimizing the urban rainwater harvesting system, ultimately promotes the sustainable development of urban water resource management, assists in the construction of sponge cities, provides a scientific basis for the optimal design of urban rainwater harvesting systems, and provides a practical reference for coping with climate change and urban flood management. However, there are also certain limitations. Future research can further consider more complex rainfall processes and multi-level data sources to improve the reliability and accuracy of simulation results.

[0145] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle described in the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for calculating the flood control efficiency of an urban-scale rainwater collection system, characterized in that, It includes the following steps: Obtain the population density data, meteorological data, building outline vector data and water consumption data of relevant cities, and calculate the average hourly water demand during the non-rainy period, the average hourly water demand during the rainy period and the effective roof collection area; Define the minimum event interval time MIT, the low rainfall threshold LRT and the rainfall depth threshold RDT. Divide the rainfall records with an interval time greater than or equal to MIT into different rainfall events, so as to divide the continuous rainfall records in the meteorological data into discrete rainfall events. Eliminate the rainfall events with rainfall depth per unit time lower than LRT and eliminate the rainfall data with rainfall depth lower than RDT in a rainfall event; Model the random non-rainy duration, random rainfall depth and random rainfall duration in the discrete rainfall events using the probability density function based on the exponential distribution, and determine the positive rate parameters of the three probability density functions by the maximum likelihood estimation method; Combine the positive rate parameters, the average hourly water demand during the non-rainy period, the average hourly water demand during the rainy period and the effective roof collection area, and determine the storage capacity of the rainwater storage unit that meets the preset system reliability through random simulation; The original outflow D is calculated through the storage capacity or and the random rainfall reduction amount D re , based on D re and D or The flood control effect is calculated, and the flood control efficiency of the urban-scale rainwater collection system is evaluated by calculating the macroscopic rainwater collection effect 2. The method according to claim 1, wherein The value range of MIT is 3 to 24 hours, and the specific value is adjusted according to the regional climate characteristics. The value range of LRT is 0.001 to 0.005 m / hour, and the value range of RDT is 0.001 to 0.005 m, which is used to eliminate ineffective low-intensity rainfall.

3. The method according to claim 1, wherein The expression of the probability density function PDF is: Random non-rainfall duration T n PDF of: Random rainfall depth D r PDF of: PDF of the random rainfall duration T r : where Pr(*) is the occurrence probability of random event *, T n is the random non - rainfall duration, D r is the random rainfall depth, T r is the random rainfall duration, ψ, ξ, λ are positive rate parameters, t n is the non - rainfall duration, t r is the rainfall duration, d r is the rainfall depth, and the expected values of Tn, Dr, and Tr are 1 / ψ, 1 / ξ, and 1 / λ, respectively.

4. The method according to claim 3, characterized in that The estimation methods of the positive rate parameters ψ, ξ, λ are: Extract independent rainfall events from historical rainfall data and calculate t for each event n 、d r 、t r ; Solve by the maximum likelihood estimation method: Among them, are the mean values of t n , d r , and t r in the historical rainfall data respectively.

5. The method according to claim 3, characterized in that, The effective collection area a of the roof and the storage capacity v of the rainwater storage unit r satisfy the following relationship: Among them, is the runoff coefficient, d f is the first flushing depth.

6. The method according to claim 5, characterized in that, The random simulation is the random rainwater collection system modeling, which is realized by the following formula: Among them, a is the effective roof collection area, r is the preset system reliability, d1 is the average hourly water demand during the non-rainy period, and d2 is the average hourly water demand during the rainy period.

7. The method according to claim 1, wherein The calculation formula of the flood control effect R is: When D or ≥ D re : When D or <D re is true, R = 1.

8. The method according to claim 7, wherein The macroscopic rainwater collection effect R m is the average value of the flood control effect of a certain building in random N rainfall events: Among them, N is the number of rainfall events.

9. The method according to claim 1, wherein The determination methods of the average hourly water demand during the rainy period and the average hourly water demand during the non-rainy period are: The average hourly water demand during the rainy period and the average hourly water demand during the non-rainy period are calculated according to the population density and water consumption data. The average hourly water demand during the rainy period is a ratio less than 1 of the average hourly water demand during the non-rainy period. The specific ratio needs to consider the additional municipal irrigation water demand of the city.

10. A flood control effectiveness calculation system for an urban-scale rainwater collection system, which is used to apply the flood control effectiveness calculation method for an urban-scale rainwater collection system described in claims 1-9, and is characterized in that, The system includes: The pre-data acquisition and processing module is used to obtain the population density data, meteorological data, building outline vector data and water consumption data of relevant cities, and calculate the average hourly water demand during the non-rainy period, the average hourly water demand during the rainy period and the effective roof collection area; The rainfall record processing module is used to define the minimum event interval time MIT, the low rainfall threshold LRT and the rainfall depth threshold RDT, and divide the rainfall records with an interval time greater than or equal to MIT into different rainfall events, so as to divide the continuous rainfall records in the meteorological data into discrete rainfall events. Eliminate the rainfall events with rainfall depth per unit time lower than LRT and eliminate the rainfall data with rainfall depth lower than RDT in a rainfall event; A rainfall characteristic stochastic modeling module is used to model the random non-rainfall duration, random rainfall depth, and random rainfall duration in the discrete rainfall events based on the probability density function of the exponential distribution, determine the positive rate parameters of the three probability density functions by the maximum likelihood estimation method, and combine the positive rate parameters, the average hourly water demand during the non-rainfall period, the average hourly water demand during the rainfall period, and the effective roof collection area, and determine the storage capacity of the rainwater storage unit that meets the preset system reliability through stochastic simulation; The flood discharge reduction efficiency evaluation module is used to calculate the original discharge D through the storage capacity or and the random rainfall reduction amount D re , and based on D re and D or calculate the flood discharge reduction effect, and evaluate the flood discharge reduction efficiency of the urban-scale rainwater harvesting system by calculating the macroscopic rainwater harvesting effect.

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