Simulation method of hydrological process in cold regions based on dynamic soil water storage capacity parameters

Abstract

The present invention discloses a method for simulating the hydrological process in cold regions based on dynamic soil water storage capacity parameters, which replaces the static soil water storage capacity parameter S in the RCCC-WBM model max with a temperature-related dynamic soil water storage capacity parameter DS maxi . The present invention is a method for simulating the hydrological process in cold regions based on dynamic soil water storage capacity parameters, which is based on the RCCC-WBM model and the physical mechanism of the influence of air temperature change on the soil water storage capacity parameters in cold regions. It can simulate the runoff process in cold regions under frozen soil change more objectively and accurately; using the method of the present invention, the change of soil water storage capacity under frozen soil change can be quantified, effectively supporting the environmental impact assessment work of climate change; using the method of the present invention, the runoff trend in cold regions can be well simulated, effectively supporting the basin water resources evaluation and planning management work.

Description

Technical Field

[0001] The invention relates to the technical field of hydrological simulation, and in particular to a cold region hydrological process simulation method based on dynamic soil water storage capacity parameters. Background Art

[0002] The RCCC-WBM model is a river basin hydrological model developed and gradually improved by the team of the Climate Change Research Center of the Ministry of Water Resources. The river basin hydrological model has currently played an important role in flood prevention and disaster reduction, water resources management, and ecological and environmental protection.

[0003] At present, some studies have shown that the thinning of the permafrost layer under global warming increases the water storage capacity and infiltration capacity of the soil layer, which in turn affects the generation of runoff to a certain extent, and has a certain impact on the evapotranspiration process of the basin and the amount of regional water resources. The hydrological cycle in cold regions has changed significantly under the changing environment. Carrying out the simulation of hydrological processes in cold regions is an important basic work for the evaluation of water resources in cold regions and the impact assessment of environmental changes.

[0004] Quantitative monitoring of frozen soil changes in cold regions is often carried out by remote sensing, lidar and other methods, and direct measurement can also be performed. Usually, appropriate methods are selected based on comprehensive consideration of different monitoring needs, monitoring scope, monitoring accuracy and other factors. However, monitoring methods are still very limited, which to a certain extent limits the quantitative understanding of the impact of frozen soil changes on hydrological processes at the basin scale. At present, most basin hydrological models, including the RCCC-WBM model, only consider the rainfall runoff process and the ice and snow melt runoff process in the simulation of cold region hydrological processes. Due to the lack of frozen soil data, the quantitative impact of frozen soil changes on hydrological processes is relatively lacking, which in turn affects the accuracy of basin hydrological simulation and brings certain challenges to effectively supporting basin water resources evaluation and ecological impact assessment. Summary of the invention

[0005] In order to solve the technical problem that the existing technology does not consider the impact of permafrost changes on hydrological processes, a cold region hydrological process simulation method based on dynamic soil water storage capacity parameters is provided.

[0006] The present invention adopts the following technical solution:

[0007] The simulation method of cold region hydrological process based on dynamic soil water storage capacity parameters specifically includes the following steps:

[0008] Step (1) Collect a long series of monthly hydrological and meteorological data of the basin, including runoff, precipitation, evaporation and temperature;

[0009] Step (2) Analyze and study the interdecadal process and annual distribution characteristics of basin temperature, precipitation, water surface evaporation and runoff;

[0010] Step (3) proposes a calculation formula for the dynamic soil water storage capacity parameter related to air temperature:

[0011]

[0012] Where: DS maxi is the maximum dynamic soil water storage capacity, which is i Function of time period temperature; T i For the i The basin temperature during the period; S max is the maximum soil water storage capacity of the basin under average temperature conditions, S max It is obtained by optimizing and calibrating the RCCC-WBM model using genetic algorithm;

[0013] Step (4) calculates the surface runoff based on the dynamic soil water storage capacity parameters:

[0014]

[0015] Where: Q si For the i Surface runoff during the period; P raini For the i The average precipitation of the watershed area during the period is obtained by dividing snow and rain according to temperature and precipitation in the RCCC-WBM model; S i-1 For the i -1 period soil moisture content is the state variable of the watershed, which is calculated based on the water balance in the RCCC-WBM model; DS maxi is the dynamic soil water storage capacity, calculated according to formula (1); k s is the surface runoff coefficient, obtained by optimizing the RCCC-WBM model;

[0016] Step (5) calculates evapotranspiration based on the dynamic soil water storage capacity parameter:

[0017]

[0018] Where: E ai For the i Actual evapotranspiration of the basin during the period; EP i For the i The evaporation capacity of the watershed during a period of time is measured using the E601 evaporation pan or calculated using the Penman formula; Si-1 is the soil moisture content in the i-1th period of the basin, which is the state variable of the basin and is calculated based on the water balance in the RCCC-WBM model; DS maxi is the dynamic soil water storage capacity, calculated according to formula (1); k e is the evapotranspiration coefficient, which is determined by model optimization;

[0019] Step (6) Combination E ai and Q si , the RCCC-DWBM model based on dynamic soil water storage capacity parameters was established.

[0020] Furthermore, the runoff simulated by the RCCC-DWBM model established in step (6) is:

[0021]

[0022] Where: Q SIMi For the i River runoff during the period; Q si For the i The surface runoff during the period is calculated by formula (2); Q sni and Q gi Respectively i The snowmelt runoff and underground runoff during the period were simulated by the RCCC-WBM model; P sni For the i The average rainfall in the watershed area during the period is obtained by dividing snow and rain according to temperature and precipitation in the RCCC-WBM model; Sn i and Sn i-1 is the snow depth for this period and the previous period; K g is the underground runoff coefficient, which is optimized and calibrated by the RCCC-WBM model; S i-1 It is the soil water content in the previous period.

[0023] Furthermore, the annual distribution dynamic process is obtained based on the simulated runoff, the annual average soil water storage capacity is calculated, and the dynamic changes of soil water storage capacity parameters are analyzed.

[0024] The beneficial effects of the present invention are:

[0025] The present invention is based on the RCCC-WBM model and the physical mechanism of the influence of temperature changes on soil water storage capacity parameters in cold regions, and proposes a cold region hydrological process simulation method based on dynamic soil water storage capacity parameters, which can more objectively and accurately simulate the cold region runoff process under frozen soil changes; the method of the present invention can be used to quantify the changes in soil water storage capacity under frozen soil changes, effectively supporting the environmental impact assessment of climate change; the method of the present invention can be used to better simulate the runoff trend in cold regions, effectively supporting the evaluation and planning management of water resources in the basin. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 For Example 1, the decadal processes of temperature, precipitation, evaporation capacity and runoff in the basin from 1990 to 2018 were studied;

[0027] Figure 2 For Example 1, the annual distribution process of basin temperature, precipitation, evaporation capacity and runoff is studied;

[0028] Figure 3 It is a schematic diagram of the RCCC-DWBM model structure based on dynamic soil water storage capacity parameters;

[0029] Figure 4 This is the monthly measured and simulated runoff process of Tangke Station from 1990 to 2018 in Example 1;

[0030] Figure 5 For Example 1, the interdecadal changes of soil water storage capacity parameters in the basin from 1990 to 2018 were studied;

[0031] Figure 6 For Example 1, the dynamic process of annual distribution of soil water storage capacity parameters in the basin from 1990 to 2018 was studied. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical scheme and technical effect of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0033] Example 1

[0034] The simulation method of cold region hydrological process based on dynamic soil water storage capacity parameters is taken as an example. Baihe River is a first-level tributary of the Yellow River source area. It has high altitude and low temperature. It is a typical high-cold area located in the Qinghai-Tibet Plateau. The basin area above Tangke Hydrological Station is 5374km 2 The specific calculation process includes the following steps:

[0035] Step (1) Collect a long series of monthly hydrological and meteorological data from 1990 to 2018 in the study basin, including runoff, precipitation, water surface evaporation and temperature, and then calculate the monthly average precipitation, temperature and water surface evaporation.

[0036] Step (2) Analyze and study the decadal process and annual distribution characteristics of the monthly average temperature, precipitation, water surface evaporation and runoff in the basin to understand the hydrological and meteorological conditions in the basin and whether it is necessary to consider the frozen soil process; the decadal process and annual distribution characteristics are as follows: Figure 1 , 2 As shown, Figure 2 It is the multi-year average from 1990 to 2018.

[0037] Step (3) When the temperature rises to a certain level, the permafrost layer disappears completely and the soil water storage capacity reaches its maximum. On the contrary, when the temperature continues to drop, the permafrost layer thickens and the soil water storage capacity reaches a minimum. Therefore, the dynamic soil water storage capacity is in a dynamic range that changes with temperature, and the inverse tangent function takes a value between ±π / 2. Combined with limited monitoring data, a calculation formula for the dynamic soil water storage capacity parameter related to temperature is proposed:

[0038]

[0039] Where: DS maxi is the maximum dynamic soil water storage capacity, which is i Function of time period temperature; T i For the i The basin temperature during the period, i The basin temperature during a period is the average temperature for that month; S max is the RCCC-WBM model parameter, which represents the maximum soil water storage capacity of the basin under the average temperature of the basin. S max The RCCC-WBM model is obtained by optimizing and calibrating using a genetic algorithm. The process and method of optimizing and calibrating the model parameters can be seen in Example 2.

[0040] Soil water storage capacity S max Characterizes the water storage capacity of the soil saturated zone, and is a model parameter that is related to soil structure and has a clear physical meaning. For cold regions, temperature changes will cause changes in the permafrost layer. The soil water storage capacity characterizes the water storage capacity of the vadose zone above the permafrost layer. Temperature changes will cause changes in the soil water storage capacity, and the monitoring data on the thickness of the permafrost layer is very scarce. Therefore, the present invention adopts an alternative solution of temperature characterization. Since the permafrost layer becomes thinner with increasing temperature, the soil water storage capacity in cold regions is a dynamic parameter that changes with temperature and is limited by the physical structure of the soil. When the temperature is high enough, the permafrost layer completely degenerates and disappears, and the soil water storage capacity reaches the upper limit, that is, the maximum soil water storage capacity. DS maxAccording to formula (1), the dynamic soil water storage capacity is constructed DS maxi Varies between under extreme temperature conditions. S max Characterizes the soil water storage capacity under normal conditions in the basin.

[0041] Step (4) calculates the surface runoff based on the dynamic soil water storage capacity parameters:

[0042]

[0043] Where: Q si For calculation i Surface runoff during the period; P raini For the i The average rainfall in the watershed area during the period is obtained by dividing snow and rain according to temperature and precipitation in the RCCC-WBM model; S i-1 For the early stage of the basin ( i -1 period) soil moisture content, which is the state variable of the watershed, is calculated based on the water balance in the RCCC-WBM model; DS maxi is the dynamic soil water storage capacity, calculated according to formula (1); k s is the surface runoff coefficient, which is obtained by model optimization calibration.

[0044] Step (5) calculates evapotranspiration based on the dynamic soil water storage capacity parameter:

[0045]

[0046] Where: E ai For calculation i Actual evapotranspiration in the watershed during the period; EP i For the i The evaporation capacity of the watershed during a period of time is measured using the E601 evaporation pan or calculated using the Penman formula; S i-1 For the early stage of the basin ( i -1 period) soil moisture content, which is the state variable of the watershed, is calculated based on the water balance in the RCCC-WBM model; DS maxi is the dynamic soil water storage capacity, calculated according to formula (1); k e is the evapotranspiration coefficient, which is determined by model optimization.

[0047] Step (6) Based on the RCCC-WBM model, combined E ai and Q si , the RCCC-DWBM model based on dynamic soil water storage capacity parameters is established, such as Figure 3 shown.

[0048] The RCCC-WBM model is a watershed hydrological model developed and gradually improved by the team of the Climate Change Research Center of the Ministry of Water Resources. The model has a simple structure and few parameters (five parameters, namely soil water storage capacity S max , surface runoff coefficient K s , underground runoff coefficient K g , snowmelt runoff coefficient K sn , evaporation coefficient K e ), easy to understand, etc. The RCCC-WBM model input includes time period temperature, precipitation, water surface evaporation and measured runoff at the basin outlet control station.

[0049] like Figure 3 As shown in the figure, the RCCC-DWBM model based on the dynamic soil water storage capacity parameter established in the present invention does not change the structure of the RCCC-WBM model, but only replaces the static soil water storage capacity parameter S in the RCCC-WBM model with the static soil water storage capacity parameter S in the RCCC-WBM model. max Develops into a temperature-dependent dynamic parameter DS maxi , in order to describe the quantitative impact of changes in the permafrost layer in cold regions on the soil water storage capacity parameters, and on this basis, to improve the surface runoff and evapotranspiration calculation formulas related to this parameter. The runoff simulated by the established RCCC-DWBM model is:

[0050]

[0051] Where: Q SIMi For the i River runoff during the period; Q si For the i The surface runoff during the period is calculated by formula (2); Q sni and Q gi Respectively i The snowmelt runoff and underground runoff during the period were simulated by the RCCC-WBM model; P sni For the i The average rainfall in the watershed area during the period is obtained by dividing snow and rain according to temperature and precipitation in the RCCC-WBM model; Sn i and Sn i-1 is the snow depth for this period and the previous period; K g is the underground runoff coefficient, which is determined by model optimization; S i-1 It is the soil water content in the previous period.

[0052] The model's input and output data and state variables for typical years are shown in Table 1. Due to the large amount of data, Table 1 only gives the data for two years.

[0053] Table 1 Model input and output data and state variables for typical years

[0054]

[0055] like Figure 5 , 6 As shown, it is drawn based on the monthly data from 1990 to 2018. The dynamic changes of soil water storage capacity parameters are analyzed, which shows that the soil water storage capacity has an increasing trend, which is consistent with the changing trend of temperature.

[0056] Example 2

[0057] The genetic algorithm is used to calibrate the model parameters in Example 1.

[0058] Taking the Nash-Sutcliffe model efficiency coefficient NSE and relative error RE as the objective function, the parameters of the RCCC-DWBM model were calibrated to simulate the hydrological process of the cold region basin, and the adaptability of the RCCC-DWBM model to the study basin was analyzed; if NSE is greater than 60% and the relative error RE is less than 5%, it is considered that the model has good adaptability to the study basin.

[0059] The RCCC-DWBM model has 5 parameters, namely, the maximum soil water storage capacity Smax, the surface runoff coefficient Ks, the underground runoff coefficient Kg, the snowmelt runoff coefficient Ksn and the evaporation coefficient Ke. The genetic algorithm is used to calibrate the model for these five parameters. The steps are as follows: First, use the rand function in matlab to randomly generate the initial parameter population according to the upper and lower limits of each parameter, put it into the model for calculation, and use the Nash-Sutcliffe model efficiency coefficient corresponding to each parameter as the fitness of each individual, sort it, and then generate the offspring population through crossover mutation and calculate its fitness. The individuals with high fitness of the initial population and the self-contained population will be inherited as the new initial population to the next generation. After reaching the target evolutionary generation, the individual with the maximum fitness obtained in the evolution process is output as the optimal solution, and the calculation is terminated. If NSE is greater than 60% and the relative error RE is less than 5%, it is considered that the model has good adaptability to the study basin.

[0060]

[0061] Where: MAR sim is the simulated average runoff, MAR obs is the measured average runoff, Q SIMi is the simulated runoff volume, Q REi is the measured runoff, N is the total length of the runoff series, i.e. the total number of time periods, is the mean of the measured runoff.

[0062] The present invention uses the data from 1990 to 2018 to calibrate the parameters of the RCCC-DWBM model. The NSE coefficient of the monthly runoff simulation of the Tangke hydrological station is 66.7%, the relative error RE is 1.4%, and the measured and simulated runoffs are generally well matched. Figure 4 As shown in the figure, it shows that the RCCC-DWBM model taking into account the dynamic maximum soil water storage capacity parameters has good adaptability to the Baihe River Basin in the alpine area.

[0063] Comparative Example 1

[0064] This comparative example does not consider the effect of temperature change on soil water storage capacity parameters, that is, the static soil water storage capacity S max The model parameters were calibrated using the method described in Example 2, and the NSE coefficient of the monthly runoff simulation at the Baihe Tangke Station was 62.2%, and the relative error RE was 2.4%.

[0065] Comparative Example 2 and Comparative Example 1 show that the static soil water storage capacity S max The NSE coefficient of the monthly runoff simulation at the Tangke Station of the Baihe River is relatively low, and the relative error RE is relatively high, which shows the effectiveness of considering the dynamic maximum soil water storage capacity parameters for basin hydrological simulation.

[0066] It should be understood that parts not described in detail in this specification belong to the prior art.

[0067] The above combined with the accompanying drawings are only specific implementation methods and processes of the present invention. Parts not described here can be implemented by existing technologies; however, the protection scope of the present invention is not limited thereto. Any technician familiar with the art should understand that this is only an example, and various changes and substitutions can be made to this implementation method without departing from the essence of the present invention. The scope of the present invention is limited only by the attached claims.

Claims

1. A method for simulating hydrological processes in cold regions based on dynamic soil water storage capacity parameters, characterized in that: The specific steps include: Step (1) Collect a long series of monthly hydrological and meteorological data of the basin, including runoff, precipitation, evaporation and temperature; Step (2) Analyze and study the interdecadal process and annual distribution characteristics of basin temperature, precipitation, water surface evaporation and runoff; Step (3) proposes a calculation formula for the dynamic soil water storage capacity parameter related to air temperature: Where: DS maxi is the maximum dynamic soil water storage capacity, which is i Function of time period temperature; T i For the i The basin temperature during the period; S max is the maximum soil water storage capacity of the basin under average temperature conditions, S max It is obtained by optimizing and calibrating the RCCC-WBM model using genetic algorithm; Step (4) calculates the surface runoff based on the dynamic soil water storage capacity parameters: Where: Q si For the i Surface runoff during the period; P raini For the i The average rainfall in the watershed area during the period is obtained by dividing snow and rain according to temperature and precipitation in the RCCC-WBM model; S i-1 For the basin i The soil moisture content in the -1 period is the state variable of the watershed and is calculated based on the water balance in the RCCC-WBM model; DS maxi is the dynamic soil water storage capacity, calculated according to formula (1); k s is the surface runoff coefficient, obtained by optimizing the RCCC-WBM model; Step (5) calculates evapotranspiration based on the dynamic soil water storage capacity parameter: Where: E ai For the i Actual evapotranspiration of the basin during the period; EP i For the i The evaporation capacity of the watershed during a period of time is measured using the E601 evaporation pan or calculated using the Penman formula; S i-1 is the soil moisture content in the i-1 period of the basin, which is the state variable of the basin and is calculated based on the water balance in the RCCC-WBM model; DS maxi is the dynamic soil water storage capacity, calculated according to formula (1); k e is the evapotranspiration coefficient, which is calibrated by the RCCC-WBM model; Step (6) Combination E ai and Q si , the RCCC-DWBM model based on dynamic soil water storage capacity parameters was established.

2. The cold region hydrological process simulation method based on dynamic soil water storage capacity parameters according to claim 1 is characterized in that: The runoff simulated by the RCCC-DWBM model established in step (6) is: Where: Q SIMi For the i River runoff during the period; Q si For the i The surface runoff during the period is calculated by formula (2); Q sni and Q gi Respectively i The snowmelt runoff and underground runoff during the period were simulated by the RCCC-WBM model; P sni For the i The average rainfall in the watershed area during the period is obtained by dividing snow and rain according to temperature and precipitation in the RCCC-WBM model; Sn i and Sn i-1 is the snow depth for this period and the previous period; K g is the underground runoff coefficient, which is optimized and calibrated by the RCCC-WBM model; S i-1 It is the soil water content in the previous period.

3. The cold region hydrological process simulation method based on dynamic soil water storage capacity parameters according to claim 2 is characterized in that: Based on the simulated runoff, the annual distribution dynamic process is obtained, the annual average soil water storage capacity is calculated, and the dynamic changes of soil water storage capacity parameters are analyzed.

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