Quantitative verification method for contribution of groundwater remaining nitrogen and phosphorus to river nitrogen and phosphorus loads

Abstract

The invention belongs to the technical field of water environment monitoring and simulation, and discloses a quantitative verification method for contribution of groundwater remaining nitrogen and phosphorus to river nitrogen and phosphorus loads. The method comprises the steps that river discrete water quality monitoring data and continuous daily flow data are obtained, and a time-flow-season weighted regression model is utilized to calculate the contribution of groundwater remaining nitrogen and phosphorus to river nitrogen and phosphorus loads; determining weights according to time, flow and seasonal dimension differences between the to-be-estimated day and historical sampling points, and estimating day-by-day total nitrogen and phosphorus load of the river through local weighted regression; then, the day-by-day total flow and the total nitrogen and phosphorus load are separated into flow and load of base flow and surface runoff through a digital filtering method, and contribution of underground water to the total nitrogen and phosphorus load of the river is calculated according to the base flow nitrogen and phosphorus load and the total nitrogen and phosphorus load; and finally, collecting drainage basin rainfall and river water samples, measuring a water body stable isotope time sequence, comparing signal seasonal cycle characteristics to obtain a young water proportion in the river water, and verifying a base flow proportion by using an old water proportion. The method can adapt to long-term dynamic change, accurately estimate the daily scale load and verify the segmentation result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water environment monitoring and simulation, and particularly relates to a quantitative verification method for contribution of groundwater residual nitrogen and phosphorus to river nitrogen and phosphorus load. BACKGROUND

[0002] River nitrogen and phosphorus pollution is a key factor leading to water eutrophication, which poses a serious threat to the health of aquatic ecosystems. Nitrogen and phosphorus pollutants produced by human activities enter water bodies through various pathways. Among them, the residual nitrogen and phosphorus input to the soil and aquifer in the historical period continuously recharge the river through groundwater runoff (i.e. base flow), which has become a persistent endogenous source of river nitrogen and phosphorus pollution in the current and next few decades. Therefore, accurately quantifying this part of residual nitrogen and phosphorus load brought by groundwater is crucial for developing effective water environment management strategies.

[0003] The prior art has the following defects and deficiencies in solving this problem: First, river nitrogen and phosphorus monitoring is usually carried out at a low frequency (such as monthly or weekly), and discrete data is obtained. Existing load estimation models, such as LOADEST, are mostly based on fixed regression coefficients to construct the relationship between water quality concentration and time, flow and other variables. However, over a long period of time, the pollution sources, land use and climate conditions of the watershed will change, leading to dynamic changes in the relationship between water quality and variables. It is difficult for models with fixed coefficients to accurately capture long-term trends, and the estimation accuracy is limited.

[0004] Second, it is not technically and economically feasible to directly monitor the base flow and the nitrogen and phosphorus concentration carried by the base flow at the watershed scale for a long time. Therefore, it is impossible to obtain the contribution of base flow to river nitrogen and phosphorus load by direct measurement, and existing research relies on model segmentation, but the reliability of the segmentation results cannot be guaranteed.

[0005] Third, existing load estimation methods usually rely on hydrological data at the outlet section of the watershed, ignoring the landscape heterogeneity within the watershed. The hydrological path and water retention time in different areas are different, leading to uneven distribution of water quality in space. The data at a single section cannot reflect this spatial difference, which affects the accuracy of load estimation and lacks effective spatial verification methods.

[0006] Therefore, there is an urgent need for a comprehensive technical solution that can adapt to long-term dynamic changes, accurately estimate daily load, and verify the segmentation results with an independent method. SUMMARY

[0007] The present application aims to solve the problems of inaccurate estimation of river nitrogen and phosphorus load and inability to directly monitor and verify the contribution of base flow nitrogen and phosphorus load in the prior art due to discrete monitoring data and fixed model coefficients. Specifically, the present application provides a quantitative verification method for the contribution of groundwater residual nitrogen and phosphorus to river nitrogen and phosphorus load, which integrates dynamic load estimation, base flow load digital filtering separation and isotope water age verification.

[0008] In a first aspect, the present application provides a quantitative verification method for the contribution of groundwater residual nitrogen and phosphorus to river nitrogen and phosphorus load, comprising the following steps: Obtain discrete water quality monitoring data and continuous daily flow data of the river, and apply a time-flow-season weighted regression model to estimate the daily river total nitrogen and phosphorus load, wherein the time-flow-season weighted regression model determines weights based on the differences in time, flow and season between the to-be-estimated day and the historical sampling points for each to-be-estimated day, and performs local weighted regression to generate the nitrogen and phosphorus concentration of the day; Apply a digital filtering method to process the daily river total nitrogen and phosphorus load and the corresponding daily total flow, separate the daily total flow into low-frequency base flow and high-frequency surface runoff, and separate the daily river total nitrogen and phosphorus load into low-frequency base flow nitrogen and phosphorus load and high-frequency surface runoff nitrogen and phosphorus load; Based on the base flow nitrogen and phosphorus load and the total nitrogen and phosphorus load, calculate the contribution of groundwater to the total nitrogen and phosphorus load of the river; Collect precipitation and river water samples in the river basin, measure the time series of water stable isotopes, compare the seasonal cycle characteristics of precipitation isotope signals and river isotope signals, calculate the proportion of young water in river water, and verify the proportion of base flow in total flow using the complementary old water proportion.

[0009] As an optional implementation manner of the first aspect of the present application, in the step of applying a time-flow-season weighted regression model to estimate the daily river total nitrogen and phosphorus load, the structure of the time-flow-season weighted regression model is: taking the natural logarithm of nitrogen and phosphorus concentration as the dependent variable, and taking the decimal form of time, the natural logarithm of daily average flow, and the sine and cosine time functions representing seasonal cycles as the independent variables.

[0010] As an optional implementation manner of the first aspect of the present application, in the step of applying a digital filtering method to process the daily river total nitrogen and phosphorus load and the corresponding daily total flow, the digital filtering method is Eckhardt two-parameter digital filtering method, which separates the base flow by applying a filtering equation based on flow recession constant and maximum base flow index BFI max and applies a filtering equation based on load recession constantd and the maximum base current load index BLI max The filtering equation separates the base current nitrogen and phosphorus loads.

[0011] As an optional implementation of the first aspect of this application, the flow decay constant The determination method includes: calculating the time N for complete cessation of surface runoff based on the watershed area; selecting baseflow decay processes starting from day N after the runoff peak from the total flow time series; fitting an exponential function to the flow data of each selected baseflow decay process to obtain its respective exponential coefficient; calculating the respective decay constant based on the exponential coefficient, and averaging multiple decay constants to obtain the final flow decay constant. .

[0012] As an optional implementation of the first aspect of this application, the maximum base current index (BFI) max It is an empirical value set based on the geological characteristics of the river aquifer; the load decay constant d and the maximum base current load index BLI max The determination methods include: screening typical load decay events from the total nitrogen and phosphorus load time series, and performing exponential fitting on each load decay event to determine the load decay constant. d Based on the already determined d The maximum base current load index (BLI) is obtained by using an inverse filtering algorithm. max .

[0013] As an optional implementation of the first aspect of this application, the step of calculating the proportion of young water in the river includes: performing sinusoidal regression analysis on the stable isotope time series of precipitation and river water respectively to obtain the amplitude and phase coefficient of their respective seasonal fluctuations; and based on the ratio of the amplitude of the river water isotope fluctuation to the amplitude of the precipitation isotope fluctuation, and the phase difference between the two, calculating the shape parameters of the watershed hydrological transport time distribution through iterative solution. Based on the shape parameters Calculate and obtain scale parameters and young average age ; by using the aforementioned young average age and the scale parameters The proportion of young water F is calculated using the incomplete gamma function under regularization as the input. yw Furthermore, the proportion of young water F is estimated by performing Monte Carlo simulations on the confidence intervals of the amplitude and phase coefficients obtained from the sinusoidal function regression analysis. yw The range of values ​​for .

[0014] In a second aspect, the embodiments of the present application provide a quantitative verification system for contribution of groundwater residual nitrogen and phosphorus to river nitrogen and phosphorus load, comprising: The daily river total nitrogen and phosphorus load estimation module is configured to acquire discrete water quality monitoring data and continuous daily flow data of the river, and estimate daily river total nitrogen and phosphorus load by applying a time-flow-season weighted regression model, wherein the time-flow-season weighted regression model determines weights based on differences in time, flow and season between a to-be-estimated day and historical sampling points for each to-be-estimated day, and performs local weighted regression to generate nitrogen and phosphorus concentrations of the day. The flow and load separation module is configured to apply a digital filtering method to process the daily river total nitrogen and phosphorus load and corresponding daily total flow, separate the daily total flow into low-frequency base flow and high-frequency surface runoff flow, and separate the daily river total nitrogen and phosphorus load into low-frequency base flow nitrogen and phosphorus load and high-frequency surface runoff nitrogen and phosphorus load. The groundwater contribution calculation module is configured to calculate contribution of groundwater to total river nitrogen and phosphorus load based on the base flow nitrogen and phosphorus load and the total nitrogen and phosphorus load. The verification module is configured to collect precipitation and river water samples in the river basin, determine a time series of water body stable isotopes, calculate a young water proportion in the river water by comparing seasonal cycle characteristics of precipitation isotope signals and river water isotope signals, and verify the proportion of the base flow flow to the total flow by using an old water proportion complementary to the young water proportion.

[0015] In a third aspect, the embodiments of the present application provide an electronic device, which comprises a processor, a memory, and a program or instructions stored on the memory and executable on the processor, and the program or instructions are executed by the processor to implement the steps of the method of the first aspect.

[0016] In a fourth aspect, the embodiments of the present application provide a readable storage medium, which stores a program or instructions, and the program or instructions are executed by a processor to implement the steps of the method of the first aspect.

[0017] Compared with the prior art, the present application has the following advantages: 1. Improve the accuracy of long-term load estimation: the time-flow-season weighted regression model (WRTDS model) used in the present application uses weighted regression based on time, season and flow dimensions for nitrogen and phosphorus concentrations of each day in the study period, dynamically adjusts model parameters, rather than using fixed coefficients, and can more truly reflect the influence of changes in basin conditions on water quality, significantly improving the accuracy of estimating daily continuous load from discrete data.

[0018] 2. Reliable quantification of baseflow load is achieved: By coupling the WRTDS model and Eckhardt digital filtering method, the total load is decomposed into high-frequency surface runoff load and low-frequency baseflow load, overcoming the difficulty of direct monitoring of baseflow and its nutrient concentration due to technical limitations, providing a feasible technical path for quantifying groundwater legacy pollution sources.

[0019] 3. Independent verification means are provided: The stable isotope water age analysis method is introduced to calculate the proportion of young water in the river, and the proportion of old water is inversely deduced to independently verify the baseflow proportion separated by the digital filtering method. This method is based on different physical principles and provides strong evidence for the reliability of model segmentation results. At the same time, the isotope method can reflect the comprehensive hydrological process at the watershed scale, effectively considering the influence of landscape heterogeneity on hydrological pathways, making up for the shortcomings of traditional hydrological models in data-scarce areas. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flowchart of a method for quantitatively verifying the contribution of groundwater legacy nitrogen and phosphorus to river nitrogen and phosphorus load according to an embodiment of the present application; Figure 2 is a structural schematic diagram of a system for quantitatively verifying the contribution of groundwater legacy nitrogen and phosphorus to river nitrogen and phosphorus load according to an embodiment of the present application.

[0021] The following specific embodiments will further illustrate the present application in conjunction with the above drawings. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0023] The terms "first", "second", and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / ", generally means that the front and rear associated objects are in an "or" relationship. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise explicitly specified.

[0024] Embodiment 1 Please refer to Figure 1A flowchart of a quantitative verification method of groundwater residual nitrogen and phosphorus contribution to river nitrogen and phosphorus load provided for an embodiment of the present application. The method can include the following steps: S1: Obtain discrete water quality monitoring data and continuous daily flow data of the river, and apply a time-flow-season weighted regression model to estimate the daily river total nitrogen and phosphorus load, wherein the time-flow-season weighted regression model determines weights based on the differences in time, flow and season between the to-be-estimated day and the historical sampling points for each to-be-estimated day, and performs local weighted regression to generate the nitrogen and phosphorus concentration of the day.

[0025] To analyze the long-term river nitrogen and phosphorus output dynamics, the present application estimates the daily nitrogen and phosphorus concentration and output load of the river basin based on the monitored discrete water quality monitoring data (long time series monthly nitrogen and phosphorus concentration data) and continuous daily flow data, using the WRTDS model. The WRTDS model uses weighted regression in three dimensions of time, season and flow to assign corresponding weights to each sampling point. The basic regression equation of the WRTDS model (time-flow-season weighted regression model) is represented as: wherein In is the natural logarithm, c is the river nitrogen and phosphorus concentration (mg / L), is the daily nitrogen and phosphorus concentration and output load, t is the date in fractional form, Q is the daily average flow (m 3 / s), 、 、 、 , β4 is the fitting coefficient, and ε represents the unexplained part. The coefficients 、 、 、 , β4 and ε can gradually change throughout the river flow and time variation. The WRTDS model and its analysis steps are completed using the EGRET package in R language. The coefficient of determination (R 2 ) and the bias index (Bias) are used to evaluate the performance of the WRTDS model.

[0026] S2: Apply a digital filtering method to process the daily river total nitrogen and phosphorus load and the corresponding daily total flow, separate the daily total flow into low-frequency base flow and high-frequency surface runoff flow, and separate the daily river total nitrogen and phosphorus load into low-frequency base flow nitrogen and phosphorus load and high-frequency surface runoff nitrogen and phosphorus load.

[0027] This step separates the daily total flow and total nitrogen and phosphorus load time series obtained in step S1 into surface runoff and base flow components.

[0028] In order to further determine the contribution of groundwater to the nitrogen and phosphorus load of the river, the Eckhardt digital filtering method is used to separate the surface runoff and the base flow and the surface runoff nitrogen and phosphorus load and the base flow nitrogen and phosphorus load. Among them, the division of runoff is based on the two water source hypothesis, which is divided into surface runoff and base flow. The surface runoff can quickly respond to rainfall, and the change range is large, which can be regarded as a high-frequency signal. The base flow is regulated by the water body in the underground aquifer, and the change range is small, which can be regarded as a low-frequency signal. The digital filtering technology can well separate the high and low frequency signals by using the filtering function. Therefore, the separation of surface runoff and base flow can be realized. Because the load of nutrients in the river is affected by the nutrient concentration and the river flow, and the surface runoff is affected by rainfall and human activities, the fluctuation is more obvious, so it can be regarded as a high-frequency signal. Considering that the nutrient concentration in the surface runoff and the runoff are high-frequency signals, the load of the surface nutrient obtained by multiplying the two is theoretically also a high-frequency signal. In contrast, the nutrient concentration in the base flow changes relatively stably, and the fluctuation is small, that is, it can be regarded as a low-frequency signal, and the base flow is relatively stable, so the base flow load is relatively stable and can be regarded as a low-frequency signal. The nitrogen and phosphorus load in the river can be regarded as the superposition of high-frequency signals and low-frequency signals. Similarly, the base flow nutrient load and the surface runoff load can also be separated by the digital filtering model. The Eckhardt two-parameter digital filtering method formula is as follows: Constraints: Among them, is the base flow at time k, is the total runoff at time k, is the filtering parameter.

[0029] Then, according to the linear reservoir-outflow assumption, a new maximum base flow index value (BFI max ) is introduced. Although there may be some problems in considering the outflow and storage of the aquifer as a linear relationship, it can also be regarded as a reasonable approximation for a long duration recession. Finally, after a series of deductions, the Eckhardt two-parameter digital filtering method is obtained. The filtering equations of base flow and base flow load are as follows: Among them, is the flow recession constant, BFI max is the maximum base flow index, and (kg d -1 ​) are the runoff and baseflow nitrogen and phosphorus loads at time step i (days), respectively, d is the recession constant of load, BLI max is the maximum baseflow load index. The parameter , d , BFI max and BLI max are described and calculated as follows: ① Recession constant of flow Determination Recession constant of flow is determined by selecting data that conforms to the baseflow recession process from the flow data set and then calculating by fitting. First, the time at which surface runoff completely stops is calculated using the following formula. Since the recession process after the surface runoff stops is dominated by baseflow, this time point is considered to be the starting point of the baseflow recession event. In this study, n (days) is calculated, so the time from the nth day of the recession event to the day before the runoff begins to rise is considered to be a typical recession process. Subsequently, then the water recession process with less than 5 days of the day selected in the previous step is further removed.

[0030] where N is the time (days) at which surface runoff completely stops after the peak runoff, A is the area of the watershed (km 2 ).

[0031] Brutsaert (2005) proposed that a linear recession process can be described by the following formula. Integrating the formula gives the recession of baseflow in the form of an exponential function, Q b = Q0e -t / k . Where Q0 is the initial baseflow flow, the recession constant of flow = e 1 / k . In summary, the present invention takes the total runoff at time t as the dependent variable and time as the independent variable, and performs exponential fitting in the form of y = be cx , e c is the recession constant of flow. After obtaining the recession constants of multiple recession processes, the average value is finally taken.

[0032] where t is the length of time, and k is the length of time required for the watershed to completely drain at the time of a drainage event, in days.

[0033] According to the above-mentioned criteria for selecting recession processes, determine the processes that meet the conditions from the runoff data during the monitoring period. The longest duration of the recession event is only x days. Perform exponential fitting on the recession process curve to obtain the recession coefficient. Use the two-parameter digital filter model to separate the baseflow to obtain the daily baseflow during the monitoring period.

[0034] 2. Maximum baseflow index BFI max determination BFI max Index is usually given according to the river and its aquifer, and it is recommended to be set as 0.80, 0.50 and 0.25 for perennial rivers with porous aquifer, intermittent rivers with porous aquifer and perennial streams with hard rock aquifer, respectively.

[0035] 3. Load recession constant d and maximum baseflow load index BLI max determination determination d and BLI max The process of determining is similar to that of determining d . First, typical load recession events are selected from the daily total nitrogen and phosphorus load time series obtained in step S1 according to similar criteria. Subsequently, the load recession constant d is determined by exponential fitting for each load recession process curve. Finally, based on the determined value, the BLI max value is obtained by iterative solution through the inverse filtering algorithm.

[0036] S3: Based on the baseflow nitrogen and phosphorus load and the total nitrogen and phosphorus load, the contribution of groundwater to the total nitrogen and phosphorus load of the river is calculated.

[0037] Specifically, the daily baseflow nitrogen and phosphorus load obtained in step S2 is accumulated on a certain time scale (such as year, month) to obtain the total baseflow nitrogen and phosphorus load in the period. At the same time, the daily total nitrogen and phosphorus load of the river obtained in step S1 is accumulated. By calculating the ratio of the two, the contribution rate of groundwater residual nitrogen and phosphorus to the nitrogen and phosphorus load of the river is obtained.

[0038] S4: Collecting precipitation and river water samples in the river basin, measuring the time series of water stable isotopes, comparing the seasonal cycle characteristics of precipitation isotope signals and river isotope signals, calculating the proportion of young water in river water, and using the old water proportion complementary to the young water proportion to verify the proportion of baseflow to total flow.

[0039] This step provides an independent, physically based verification for the aforementioned model segmentation results. The core idea is that baseflow is mainly composed of "old water" with long residence time in the underground, while surface runoff is mainly composed of "young water" formed by recent precipitation. Therefore, the old water proportion measured by the isotope method should be comparable to the baseflow proportion separated by the digital filtering method.

[0040] ywThe proportion of young water is defined as the percentage of water below a specific age threshold (approximately 0.2 years) in the transport time distribution. Considering that permeability typically decreases sharply with depth, the separated young water likely originates from shallow currents near the channel. In contrast, the proportion of old water is comparable to the proportion of baseflow. The method proposed by Kirchner is used to calculate the proportion of young water (F0). yw ).

[0041] First, for δ 18 Sine regression analysis was performed on the O(‰) time series to determine the cosine and sine coefficients of precipitation and runoff: in and The tracer signals (δ) for precipitation and runoff at time t are respectively represented. 18 O), f is the annual fluctuation frequency (set to 1 / 365 days). and Represents the vertical offset of the sine wave. , , , To determine the amplitude of the seasonal cycle ( , ) and phase shift ( , The parameters of ).

[0042] Parameters can be estimated based on tracer concentrations in precipitation observations using iterative least squares (IRLS) regression. , , This method can effectively suppress the influence of outliers in the observed data. Because there is a watershed transmission delay in the tracer concentration signal from rainfall to runoff, Must be greater than In the interannual tracer cycle, The value range should be [0, 2π). By iteratively solving the formula, the phase difference between the precipitation and runoff sinusoidal waves can be utilized. and amplitude ratio / Calculate shape parameters : Furthermore, based on Calculate scale parameters and the average age of young people (τ) yw ): Subsequently, the young water proportion F is calculated by the regularized incomplete gamma function yw : In addition, the 95% confidence limit of the sine wave regression coefficient (i.e. , , or ) is taken as the parameter boundary, and the value range of F yw is estimated by 10,000 times of Monte Carlo simulation.

[0043] Embodiment 2 Please refer to Figure 2 , which shows the structure diagram of a quantitative verification system for groundwater residual nitrogen and phosphorus contribution to river nitrogen and phosphorus load according to the second embodiment of the present application. The system includes the following key modules: The daily river total nitrogen and phosphorus load estimation module 100 is configured to obtain discrete water quality monitoring data and continuous daily flow data of the river, and apply a time-flow-season weighted regression model to estimate the daily river total nitrogen and phosphorus load, wherein the time-flow-season weighted regression model determines weights based on the differences in time, flow and season between the to-be-estimated day and historical sampling points for each to-be-estimated day, and performs local weighted regression to generate the nitrogen and phosphorus concentration of the day; The flow and load separation module 200 is configured to apply a digital filtering method to process the daily river total nitrogen and phosphorus load and the corresponding daily total flow, separate the daily total flow into low-frequency base flow and high-frequency surface runoff flow, and separate the daily river total nitrogen and phosphorus load into low-frequency base flow nitrogen and phosphorus load and high-frequency surface runoff nitrogen and phosphorus load; The groundwater contribution calculation module 300 is configured to calculate the contribution of groundwater to the total nitrogen and phosphorus load of the river based on the base flow nitrogen and phosphorus load and the total nitrogen and phosphorus load; The verification module 400 is configured to collect precipitation and river water samples in the river basin, measure the time series of water stable isotopes, calculate the young water proportion in the river water by comparing the seasonal cycle characteristics of the precipitation isotope signal and the river water isotope signal, and verify the proportion of the base flow flow to the total flow using the old water proportion complementary to the young water proportion.

[0044] The system for quantitatively verifying contribution of groundwater residual nitrogen and phosphorus to river nitrogen and phosphorus load in the embodiment of the application can be a device, or a component, an integrated circuit, or a chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a wearable device, an Ultra-mobile Personal Computer (UMPC), a netbook, or a Personal Digital Assistant (PDA), etc., and the non-mobile electronic device can be a server, a Network Attached Storage (NAS), a Personal Computer (PC), etc., which are not limited in the embodiment of the application.

[0045] The system for quantitatively verifying contribution of groundwater residual nitrogen and phosphorus to river nitrogen and phosphorus load in the embodiment of the application can be a device with an operating system. The operating system can be an Android operating system, an IOS operating system, or other possible operating systems, which are not limited in the embodiment of the application.

[0046] The system for quantitatively verifying contribution of groundwater residual nitrogen and phosphorus to river nitrogen and phosphorus load provided in the embodiment of the application can realize the method for quantitatively verifying contribution of groundwater residual nitrogen and phosphorus to river nitrogen and phosphorus load. Figure 1 The processes of the method for quantitatively verifying contribution of groundwater residual nitrogen and phosphorus to river nitrogen and phosphorus load realized by the method embodiment are not repeated here.

[0047] Optionally, the embodiment of the application further provides an electronic device, which includes a processor, a memory, a program or instructions stored in the memory and executable on the processor. The program or instructions are executed by the processor to realize the processes of the above-mentioned method embodiment for quantitatively verifying contribution of groundwater residual nitrogen and phosphorus to river nitrogen and phosphorus load, and achieve the same technical effects, which are not repeated here.

[0048] The embodiment of the application further provides a readable storage medium, which stores a program or instructions. The program or instructions are executed by a processor to realize the processes of the above-mentioned method embodiment for quantitatively verifying contribution of groundwater residual nitrogen and phosphorus to river nitrogen and phosphorus load, and achieve the same technical effects, which are not repeated here.

[0049] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0050] It should be noted that, in this document, the terms "comprises", "comprising", or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises... a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that includes the element. In addition, it should be noted that the scope of the methods and apparatus of the present embodiments are not limited by the order of the steps or the order of the functions performed in the steps, and can include performing the functions in different orders, or substantially simultaneously, or in reverse order, such as described, for example, the described methods can be performed in an order different from that described, and various steps can be added, omitted, or combined, in addition, features described with reference to certain examples can be combined in other examples.

[0051] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and the necessary general hardware platform, of course, they can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for making a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application.

[0052] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific embodiments, which are merely illustrative and not limiting, and those skilled in the art can make many forms under the inspiration of the present application without departing from the scope of the present application and the scope of protection of the claims.

Claims

1. A method for quantitatively verifying the contribution of residual nitrogen and phosphorus in groundwater to the nitrogen and phosphorus load in rivers, characterized in that, Includes the following steps: Discrete water quality monitoring data and continuous daily flow data of the river are obtained, and the daily total nitrogen and phosphorus load of the river is estimated by applying a time-flow-seasonal weighted regression model. The time-flow-seasonal weighted regression model determines the weights based on the differences between the day to be estimated and historical sampling points in the three dimensions of time, flow and season for each day to be estimated, and performs local weighted regression to generate the nitrogen and phosphorus concentration of the day. The daily total nitrogen and phosphorus load of the river and the corresponding daily total flow are processed by digital filtering method. The daily total flow is separated into low-frequency baseflow flow and high-frequency surface runoff flow, and the daily total nitrogen and phosphorus load of the river is separated into low-frequency baseflow nitrogen and phosphorus load and high-frequency surface runoff nitrogen and phosphorus load. Based on the base flow nitrogen and phosphorus load and the total nitrogen and phosphorus load, the contribution of groundwater to the total nitrogen and phosphorus load of the river is calculated. Rainfall and river water samples were collected from the river basin, and the time series of stable isotopes in the water were measured. By comparing the seasonal cyclic characteristics of the precipitation isotope signals and the river water isotope signals, the proportion of young water in the river was calculated. The proportion of old water, which is complementary to the proportion of young water, was used to verify the proportion of baseflow to total flow.

2. The quantitative verification method for the contribution of residual nitrogen and phosphorus in groundwater to the nitrogen and phosphorus load of rivers according to claim 1, characterized in that, In the step of estimating the daily total nitrogen and phosphorus load of a river using a time-flow-seasonal weighted regression model, the structure of the time-flow-seasonal weighted regression model is as follows: The natural logarithm of nitrogen and phosphorus concentrations was used as the dependent variable, while the decimal form of time, the natural logarithm of the daily average flow, and the sine and cosine time functions characterizing the seasonal cycle were used as independent variables.

3. The quantitative verification method for the contribution of residual nitrogen and phosphorus in groundwater to the nitrogen and phosphorus load of rivers according to claim 1, characterized in that, In the step of processing the daily total nitrogen and phosphorus load and corresponding daily total flow of the river using a digital filtering method, the digital filtering method is the Eckhardt two-parameter digital filtering method, which is based on the flow decay constant. and the maximum baseflow index BFI max The filtering equations separate the base flow rate and apply a method based on the load decay constant. d and the maximum base current load index BLI max The filtering equation separates the base current nitrogen and phosphorus loads.

4. The quantitative verification method for the contribution of residual nitrogen and phosphorus in groundwater to the nitrogen and phosphorus load of rivers according to claim 3, characterized in that, The flow decay constant The methods for determining this include: Calculate the time N for complete cessation of surface runoff based on the watershed area; The baseflow decay process, starting from day N after the peak runoff, is selected from the total flow time series. The flow data for each selected base flow decay process are fitted with an exponential function to obtain their respective exponential coefficients; The respective decay constants are calculated based on the exponential coefficients, and the final flow decay constant is obtained by averaging the multiple decay constants. .

5. A quantitative verification method for the contribution of residual nitrogen and phosphorus in groundwater to the nitrogen and phosphorus load of rivers according to claim 3, characterized in that, The maximum baseflow index BFI max It is an empirical value set based on the geological characteristics of the river aquifer; the load decay constant d and the maximum base current load index BLI max The methods for determining this include: Typical load decay events were selected from the total nitrogen and phosphorus load time series, and an exponential fit was performed on each load decay event to determine the load decay constant. d Based on the already determined d The maximum base current load index (BLI) is obtained by using an inverse filtering algorithm. max .

6. A method for quantitatively verifying the contribution of residual nitrogen and phosphorus in groundwater to the nitrogen and phosphorus load of rivers according to claim 1, characterized in that, The steps to calculate the proportion of young water in a river include: Sine function regression analysis was performed on the stable isotope time series of precipitation and river water respectively to obtain the amplitude and phase coefficient of their respective seasonal fluctuations; Based on the ratio of the amplitude of river water isotope fluctuations to the amplitude of precipitation isotope fluctuations, and the phase difference between the two, the shape parameters of the watershed hydrological transport time distribution are obtained through iterative calculation. ; Based on the shape parameters Calculate and obtain scale parameters and young average age ; By using the aforementioned young average age and the scale parameters The proportion of young water F is calculated using the incomplete gamma function under regularization as the input. yw ; Furthermore, the proportion of young water F is estimated by performing Monte Carlo simulations on the confidence intervals of the amplitude and phase coefficients obtained from the sinusoidal function regression analysis. yw The range of values ​​for .

7. A quantitative verification system for the contribution of residual nitrogen and phosphorus in groundwater to the nitrogen and phosphorus load in rivers, characterized in that, include: The daily total nitrogen and phosphorus load estimation module for rivers is configured to acquire discrete water quality monitoring data and continuous daily flow data of rivers, and apply a time-flow-seasonal weighted regression model to estimate the daily total nitrogen and phosphorus load of rivers. The time-flow-seasonal weighted regression model determines the weights for each day to be estimated based on the differences between the day to be estimated and historical sampling points in the three dimensions of time, flow and season, and performs local weighted regression to generate the nitrogen and phosphorus concentrations for that day. The flow and load separation module is configured to apply a digital filtering method to process the daily total nitrogen and phosphorus load of the river and the corresponding daily total flow, separating the daily total flow into low-frequency baseflow flow and high-frequency surface runoff flow, and separating the daily total nitrogen and phosphorus load of the river into low-frequency baseflow nitrogen and phosphorus load and high-frequency surface runoff nitrogen and phosphorus load. The groundwater contribution calculation module is configured to calculate the contribution of groundwater to the total nitrogen and phosphorus load of the river based on the base flow nitrogen and phosphorus load and the total nitrogen and phosphorus load. The verification module is configured to collect precipitation and river water samples from the river basin, measure the time series of stable isotopes in the water body, calculate the proportion of young water in the river by comparing the seasonal cyclic characteristics of precipitation isotope signals and river water isotope signals, and verify the proportion of baseflow to total flow by using the proportion of old water that is complementary to the proportion of young water.

8. An electronic device, characterized in that, The method includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the steps of a method for quantitatively verifying the contribution of residual nitrogen and phosphorus in groundwater to the nitrogen and phosphorus load of rivers as described in any one of claims 1-6.

9. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions, which, when executed by a processor, implement the steps of a method for quantitatively verifying the contribution of residual nitrogen and phosphorus in groundwater to the nitrogen and phosphorus load of rivers as described in any one of claims 1-6.

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