A river reach scale natural runoff simulation method

Abstract

The application provides a river section scale natural runoff simulation method, relates to the runoff calculation technical field, and specifically comprises the following steps: inputting meteorological element data and other auxiliary data into a JULES land surface model to simulate grid scale runoff; inputting vector river network data and the grid scale runoff simulation result into a MizuRoute vector river network confluence model to obtain a river section scale natural runoff simulation result, and predicting the future river section scale natural runoff spatiotemporal distribution. The technical scheme of the application overcomes the problems in the prior art that the grid scale cannot reflect river grades and cannot explore the natural runoff change of different grade rivers.

Description

Technical Field

[0001] This invention relates to the field of runoff measurement, and specifically to a method for simulating natural runoff at the river section scale. Background Technology

[0002] Natural river runoff refers to the river runoff unaffected by human activities (such as dam and reservoir construction, land-use changes, etc.). It reflects the natural hydrological processes under climate change and is an important source of water resources. Analyzing the spatiotemporal variation trends of natural river runoff is crucial for drought and flood disaster early warning and management decisions, exploring the impact of climate change on land surface hydrological processes, understanding global or regional water resources and biochemical cycles, and analyzing carbon emissions in rivers.

[0003] Although river runoff information obtained through field measurements and hydrological stations is the most accurate, acquiring river runoff data is still limited by several factors. First, field measurements are costly in terms of manpower and difficult to access in remote areas. Second, hydrological stations are spatially heterogeneous due to limitations in natural conditions and logistical support. Available river runoff observation data is mainly concentrated in developed North America and Europe, with relatively little data available for underdeveloped and sparsely populated areas. Third, the number of global hydrological stations is decreasing rapidly and the update rate is slow; over 60% of hydrological stations have not updated their river runoff data for more than ten years, severely hindering timely monitoring of changes in river runoff.

[0004] Currently, land surface process models and hydrological models integrating the physical and climatic characteristics of a watershed are constantly being proposed to simulate long-term and continuous river runoff. The basic principle of land surface process models and hydrological models for simulating river runoff is to use meteorological data to drive the model. The model formulates key hydrological processes such as precipitation, vegetation interception, surface infiltration, and runoff generation and confluence, thereby simulating river runoff. Current research using simulation methods is still limited to a kilometer-scale grid. Although continuous model optimization has increased the grid scale from 100 km to 5 km, the spatial resolution remains low, lacking key information on small and medium-sized rivers and failing to reflect the natural runoff variations of rivers of different sizes.

[0005] Natural runoff simulation at the segment scale not only reveals the spatiotemporal characteristics and variation patterns of runoff in small and medium-sized rivers, but also significantly improves computational efficiency. Vector river network spatial data acquired through high spatial resolution remote sensing observations, defining a river segment as the distance between two river nodes, provides river system characteristics such as width and class for any segment of the river network, as well as relatively accurate location information. Integrating this high spatial resolution river network data at the segment scale and improving the grid-scale model will greatly enhance the understanding of the spatiotemporal characteristics and variation patterns of natural runoff in rivers of different classes. Furthermore, the segment-scale river natural runoff simulation scheme can effectively balance the complex principles of river runoff simulation with computational costs, improving model computational efficiency.

[0006] Current modeling methods for natural river runoff are mostly based on grid scales. However, grid scales cannot reflect river classification and therefore cannot explore the changes in natural runoff across different river classifications. To investigate the spatiotemporal distribution of runoff in high-class large rivers and low-class small and medium-sized rivers, it is necessary to adopt a river segment-scale natural runoff modeling method.

[0007] Therefore, there is a need for a method to simulate natural runoff at the river segment scale that can reveal the spatiotemporal characteristics and variation patterns of runoff in small and medium-sized rivers. Summary of the Invention

[0008] The main objective of this invention is to provide a method for simulating natural runoff at the river segment scale, in order to solve the problem that the grid scale in the prior art cannot reflect the river class and cannot explore the changes in natural runoff of rivers of different classes.

[0009] To achieve the above objectives, the present invention provides a method for simulating natural runoff at the river section scale, specifically including the following steps: S1 inputs meteorological data and other auxiliary data into the JULES surface model to simulate grid-scale runoff.

[0010] S2. Input the vector river network data and the runoff simulation results obtained in step S1 into the MizuRoute vector river network confluence model, fuse the vector river network data and the runoff simulation results obtained in step S1 to obtain the simulation result model of natural runoff at the river section scale, and predict the spatiotemporal distribution of natural runoff at the river section scale in the future.

[0011] Furthermore, the meteorological element data in step S1 includes: air temperature, maximum temperature, minimum temperature, precipitation, snowfall, easterly wind, northerly wind, longwave radiation, shortwave radiation, air pressure, and specific humidity; other auxiliary data include: land cover, soil properties, and hydrological characteristics.

[0012] Further, step S2 includes the following steps: S2.1, using the sub-basin area weighting method, the grid-scale runoff data is converted into runoff data based on hydrological response units;

[0013] S2.2, calculates slope runoff and river runoff based on gamma distribution of time and shape parameters;

[0014] S2.3 uses the unit line-based impulse response mechanism (IRF-UH confluence mechanism) to calculate the natural runoff of each river segment in the river network.

[0015] Furthermore, the vector river network data includes: river segment ID, upstream and downstream river segment ID, river length, river segment slope, hydrological response unit ID, and hydrological response unit area.

[0016] Furthermore, step S2.2 specifically includes: Gamma distribution Represented as: ; in, For time, For shape parameters, For scale parameters, This is the Gamma function.

[0017] The convolution of the gamma distribution with runoff is used to calculate the runoff distribution at the current time. The formula for calculating the runoff of the corresponding river segment at each future time period is as follows: ; in, For runoff, For abortion, Let be the maximum duration of the gamma distribution, and s be the integral variable.

[0018] Furthermore, step S2.3, which uses the IRF-UH river network confluence mechanism to calculate the natural runoff of each river segment in the river network, specifically involves: ; in, The runoff at a specific location within a specific time period. and These represent wave velocity and diffusion coefficient, respectively.

[0019] After convolution integration, the formula for calculating runoff is obtained: ; ; in, for The flow of time, It is a natural exponential function. This is a bus function.

[0020] The present invention has the following beneficial effects: This invention improves the operational efficiency of natural runoff simulation; the method provided by this invention can obtain the spatiotemporal differentiation patterns of natural runoff in rivers of different grades, revealing refined changes in natural runoff. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart of a method for simulating natural runoff at the river section scale according to the present invention is shown.

[0022] Figure 2 A schematic diagram of the natural runoff of a river simulated using the method provided by the present invention is shown. Detailed Implementation

[0023] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] like Figure 1 The method for simulating natural runoff at the river section scale, as shown, specifically includes the following steps: S1 inputs meteorological data and other auxiliary data into the JULES surface model to simulate grid-scale runoff.

[0025] S2. Input the vector river network data and the runoff simulation results obtained in step S1 into the MizuRoute vector river network confluence model, fuse the vector river network data and the runoff simulation results obtained in step S1 to obtain the simulation result model of natural runoff at the river section scale, and predict the spatiotemporal distribution of natural runoff at the river section scale in the future.

[0026] Specifically, the meteorological data in step S1 include: air temperature, maximum temperature, minimum temperature, precipitation, snowfall, easterly wind, northerly wind, longwave radiation, shortwave radiation, air pressure, and specific humidity; other auxiliary data include: land cover, soil properties (soil thermal conductivity, saturated water content, etc.) and hydrological characteristics (slope, topographic index, etc.).

[0027] The input data for the JULES surface model also includes: configuration files, which contain information such as the parameters and data paths required for the model to run.

[0028] The reasons for choosing the JULES surface model are: 1. The JULES surface model can realize hourly land surface interaction processes, and its time resolution is better than the daily time scale of most current hydrological models. 2. The land surface interaction processes in the JULES surface model include land-atmosphere energy exchange, water cycle, carbon cycle, etc.

[0029] Specifically, step S2 includes the following steps: S2.1, using the sub-basin area weighting method, transforms grid-scale runoff data into runoff data based on hydrological response units.

[0030] S2.2, based on the gamma distribution of time and shape parameters, calculates slope runoff and channel runoff.

[0031] S2.3 The IRF-UH (Impulse Response Function-Unit-Hydrograph) confluence mechanism is used to calculate the natural runoff of each river segment in the river network.

[0032] Specifically, the vector river network data includes: river segment ID, upstream and downstream river segment ID, river length, river segment slope, hydrological response unit ID, and hydrological response unit area.

[0033] The MizuRoute vector river network confluence model receives two types of input data: 1. Runoff simulation results, obtained from the JULES surface model; 2. Vector river network data, including information such as river segment ID, length, and slope. Before calculating the river network confluence process, the MizuRoute vector river network confluence model requires data preprocessing. Using the area-weighted method, the grid-scale runoff data is converted into runoff data based on hydrological response units.

[0034] Specifically, step S2.2 is as follows: Gamma distribution Represented as: ; in, For time, For shape parameters (dimensionless). is a scale parameter (dimensionless). Gamma function, shape parameter and time parameters Both are closely related to the characteristics of hydrological response units, and both affect the mathematical expectation and variance of the gamma distribution.

[0035] The convolution of the gamma distribution with runoff is used to calculate the runoff distribution at the current time. The formula for calculating the runoff of the corresponding river segment at each future time period is as follows: ; in, Runoff (m 3 / s), For flow rate (mm). Let be the maximum duration of the gamma distribution, and s be the integral variable.

[0036] Specifically, step S2.3 uses the IRF-UH river network confluence mechanism to calculate the natural runoff of each river segment in the river network as follows: ; in, For a certain time ( Within a unit (s), at a certain location ( Runoff at a location (in meters). and Representing wave velocity (m / s) and diffusion coefficient (m²) respectively. 2 The wave velocity was set to 1.5 m / s, and the diffusion coefficient was set to 800 m / s. 2 / s.

[0037] After convolution integration, the formula for calculating runoff is obtained: ; ; in, for The flow of time, It is a natural exponential function. This is a bus function.

[0038] Figure 2 This invention illustrates a watershed comprising 17 river segments across 6 Strahler river classifications. The method provided by this invention allows for the acquisition of natural runoff for each of the 17 segments. This invention can precisely characterize the spatiotemporal distribution of natural runoff at the segment scale, overcoming the limitations of traditional hydrological models that can only simulate runoff at the watershed outlet section or grid scale. This provides data support for the refined implementation of river chief system management and cross-border river water allocation agreements. It effectively eliminates anthropogenic noise in highly developed rivers, accurately simulating the natural evolution of river segments without human intervention, providing an objective benchmark for evaluating aquatic ecological health and calculating ecological baseflow. The simulated natural runoff results can directly serve the attribution analysis of water yield in transnational / transprovincial rivers, clarifying the contribution of each river segment to the main stream flow, and providing a scientific and quantitative technical basis for fair and reasonable water resource allocation schemes.

[0039] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for simulating natural runoff at the river section scale, characterized in that, Specifically, the steps include the following: S1 inputs meteorological element data and other auxiliary data into the JULES surface model to simulate grid-scale runoff; S2, input the vector river network data and the runoff simulation results obtained in step S1 into the MizuRoute vector river network confluence model, fuse the vector river network data and the runoff simulation results obtained in step S1 to obtain the simulation result model of natural runoff at the river section scale, and predict the spatiotemporal distribution of natural runoff at the river section scale in the future. The meteorological data in step S1 include: air temperature, maximum temperature, minimum temperature, precipitation, snowfall, easterly wind, northerly wind, longwave radiation, shortwave radiation, air pressure, and specific humidity; other auxiliary data include: land cover, soil properties, and hydrological characteristics. Step S2 includes the following steps: S2.1, using the sub-basin area weighting method, the grid-scale runoff data is converted into runoff data based on hydrological response units; S2.2, calculates slope runoff and river runoff based on gamma distribution of time and shape parameters; S2.3 uses the unit line-based impulse response mechanism (IRF-UH confluence mechanism) to calculate the natural runoff of each river segment in the river network.

2. The method for simulating natural runoff at the river section scale according to claim 1, characterized in that, Vector river network data includes: river segment ID, upstream and downstream river segment ID, river length, river segment slope, hydrological response unit ID, and hydrological response unit area.

3. The method for simulating natural runoff at the river section scale according to claim 1, characterized in that, Step S2.2 specifically includes: Gamma distribution Represented as: ； in, For time, For shape parameters, For scale parameters, It is the Gamma function; The convolution of the gamma distribution with runoff is used to calculate the runoff distribution at the current time. The formula for calculating the runoff of the corresponding river segment at each future time period is as follows: ； in, For runoff, For abortion, Let be the maximum duration of the gamma distribution, and s be the integral variable.

4. The method for simulating natural runoff at the river section scale according to claim 1, characterized in that, Step S2.3 Calculating the natural runoff of each river segment in the river network using the IRF-UH river network confluence mechanism is as follows: ； in, The runoff at a specific location within a specific time period. and These represent wave velocity and diffusion coefficient, respectively. After convolution integration, the formula for calculating runoff is obtained: ； ； in, for The flow of time, It is a natural exponential function. This is a bus function.

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