Cold and dry mountain area drainage basin runoff simulation method and system based on MIKE SHE / MIKE 11 coupling model in combination with GIS / RS data

Abstract

The invention belongs to the technical field of hydrology and water resources, and discloses a cold and dry mountainous area drainage basin runoff simulation method based on an MIKE SHE / MIKE 11 coupling model in combination with GIS / RS data. According to the multi-source data fusion technology, a cold and dry mountainous area distributed input data set is constructed through TRMM satellite rainfall data, an MODIS LAI product and an ASTER DEM, and the rainfall space representation precision is improved by 40%. According to the model coupling mechanism, an MIKE SHE / MIKE 11 collaborative simulation framework is provided, earth surface-groundwater flow data interaction is achieved through river channel linkage, and the complex river network flood routing simulation error is reduced to 10% or below. According to the parameter partition calibration method, a drainage basin is divided into five hydrogeological regions, sensitive parameters such as a Manning coefficient and a hydraulic conduction coefficient are optimized in combination with a genetic algorithm, and the basic flow simulation precision is improved by 25%.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of hydrology and water resources, and particularly relates to a runoff simulation method and system for cold and arid mountainous basins based on a MIKE SHE / MIKE 11 coupling model combined with GIS / RS data. BACKGROUND

[0002] The runoff recharge of a cold and arid mountainous basin is highly dependent on precipitation and snowmelt, but the hydro-meteorological observation network is extremely sparse due to high altitudes and poor transportation - in the hinterland of the Tarim River mountainous area, there are only 0.3 hydrological stations per 1000 km 2 , which is less than 1 / 20 of the humid area. The three traditional models of lumped, distributed and data-driven have their own shortcomings in this environment: the lumped model ignores spatial heterogeneity, and the runoff peak error can exceed 40% when the terrain is high; the distributed method requires high-density soil-vegetation parameter observation, and the base flow deviation is often more than 30% in data-scarce areas; the data-driven model is convenient for dimension reduction, but it has insufficient explanatory power and poor extrapolation ability for extreme weather, and the prediction can be underestimated by 42% in storm years.

[0003] The hydrological process in cold and arid mountainous areas has three significant challenges: first, snowmelt contributes a high proportion (65%-75% in the Tizinafu River), which is sensitive to temperature rise and rain-snow conversion; second, the large altitude difference (1575-6234m) causes a sharp water-heat gradient and spatial variation of precipitation; third, the surface and underground water exchange frequently, and the proportion of groundwater in the base flow can reach 70%. However, existing models use linear reservoirs or diffusion wave approximations to simplify three-dimensional saturated flow fields, which cannot accurately depict this interaction, resulting in simulation errors of flood evolution and base flow of more than 25% and 30%, respectively.

[0004] The defects in observation and parameters further amplify the uncertainty of the model: the density of precipitation stations is only 0.18 stations per 1000 km 2 , which is much lower than the WMO recommended value; the error of conventional satellites such as TRMM in estimating precipitation in complex mountainous areas is as high as 30%; the 1:1 million soil map cannot support the distributed model. More importantly, most models are still based on the assumption of climate stability and have not considered the dynamic impact of precipitation interannual variability and temperature rise on the snowmelt degree-day factor, resulting in an increase in annual runoff error to 15% under the background of climate warming, and the simulation of spring flood peak is 3-5 days ahead of time.

[0005] The consequences of technical defects have already emerged: 23% overestimation of reservoir capacity in water resources planning, perennial low water level in reservoirs, 15-hour prediction lag of Tarim River flood in 2020 due to glacier jump meltwater, resulting in tens of millions of economic losses, and wetland water replenishment scheme ignoring evaporation enhancement, with an ecological restoration rate of only 65%. To break out of the dilemma, the following are urgently needed: (1) high-precision distributed input combined with multi-source remote sensing; (2) fine description of river network-groundwater three-dimensional mutual feedback by MIKESHE / MIKE11 coupling; (3) partitioning for high efficiency to alleviate the 'different parameters with the same effect'; (4) establishment of precipitation-temperature threshold determination system to quantify the influence of climate change on runoff, and to provide scientific basis for adaptive management of water resources in cold and arid regions. SUMMARY

[0006] In view of the problems existing in the prior art, the application provides a cold and arid mountainous basin runoff simulation method based on a MIKE SHE / MIKE 11 coupling model combined with GIS / RS data.

[0007] The application is implemented as follows: a cold and arid mountainous basin runoff simulation method based on a MIKE SHE / MIKE 11 coupling model combined with GIS / RS data comprises the following steps:

[0008] Step 1, multi-source heterogeneous data acquisition and preprocessing: remote sensing technology is used to acquire basin precipitation, land surface parameters and terrain data, and combined with limited site observation data to construct an input data set;

[0009] Step 2, coupling model construction: a collaborative simulation framework of the MIKE SHE distributed hydrological model and the MIKE 11 river channel hydrodynamic model is established, and data interaction is realized through river channel linking;

[0010] Step 3, model parameter partitioning calibration: based on the hydrogeological characteristics of the basin, the calibration area is divided, and sensitive parameters such as Manning coefficient and hydraulic conductivity coefficient are optimized;

[0011] Step 4, climate change scenario simulation: set the precipitation and temperature change combination scenario, and analyze the runoff response law.

[0012] Further, the multi-source heterogeneous data acquisition and preprocessing step comprises:

[0013] (1.1) the basin is divided into 16 precipitation zones by using TRMM satellite data, and the precipitation data of each zone is independently input, wherein the precipitation of the area outside the basin is set to 0;

[0014] (1.2) NASA ASTER DEM data is used, resampled to 1km×1km grid, and projected to WGS_1984_UTM_Zone_43N coordinate system;

[0015] (1.3) Leaf area index (LAI) and root depth (RD) time series were retrieved from MODIS data, with a resolution of 500 m and a time step of 1 day;

[0016] The preprocessing of TRMM precipitation data also includes:

[0017] (1.1.1) The 0.25° grid data was downscaled to 1 km x 1 km using IDW interpolation method, considering the terrain elevation correction with a correction coefficient of 0.005 mm / (m·day);

[0018] (1.1.2) Combined with VIC model precipitation data (resolution 1 km), Kalman filter fusion algorithm was adopted, and the weight coefficient was dynamically adjusted according to the observation error of the station.

[0019] Further, the MIKE SHE / MIKE 11 coupling model construction step includes:

[0020] (2.1) In the MIKE SHE model, the Kristensen-Jensen method was used for evapotranspiration calculation, and the input parameters included LAI, RD and soil moisture;

[0021] (2.2) In the MIKE 11 model, the one-dimensional Saint-Venant equation set was used to describe the river flow:

[0022]

[0023] where Q is the flow (m / s), A is the water section area (m2), q is the lateral inflow (m / s·m), α is the momentum correction coefficient, K is the flow modulus, and g is the gravitational acceleration (m / s2); 3 2 3 2

[0024] (2.3) Coupling interface setting: generating river links on the grid boundary of MIKE SHE, matching the coordinates of MIKE 11 river nodes, and exchanging flow and water level data;

[0025] The MIKE SHE saturated flow module uses the three-dimensional Darcy equation to solve:

[0026]

[0027] where K is the horizontal hydraulic conductivity coefficient (m / s), K is the vertical hydraulic conductivity coefficient (m / s), S is the storage coefficient, and h is the water head (m); h v s

[0028] ​​​​​​​Boundary condition setting: zero flux boundary for the basin perimeter, variable head boundary for the outlet, based on the observed data of Yuzimenleke Station.

[0029] Further, the model parameter partition calibration step comprises:

[0030] (3.1) The basin is divided into 5 saturated zones, wherein Zone 5 is set as a permafrost zone without groundwater flow;

[0031] (3.2) The calibrated parameters include: river Manning coefficient (range 20-30 m / s), surface Manning coefficient (range 5-10 m / s), river bed permeability coefficient (range 1x10-1x10 m / s); 1 / 3 1 / 3 -7 -6

[0032] (3.3) Genetic algorithm is used to optimize the parameters, and the objective functions are Nash-Sutcliffe efficiency coefficient (EF2) and correlation coefficient (R), requiring EF2>0.68 and R>0.75 during the calibration period;

[0033] The genetic algorithm parameters are set as:

[0034] (3.3.1) Population size 100, iteration number 200;

[0035] (3.3.2) Crossover probability 0.8, mutation probability 0.1;

[0036] (3.3.3) Fitness function is EF2x0.7+Rx0.3.

[0037] Further, the climate change scenario simulation step comprises:

[0038] (4.1) 5 scenarios are set: ① Precipitation +25%, temperature unchanged; ② Precipitation-25%, temperature unchanged; ③ Precipitation+25%, temperature+1℃; ④ Precipitation-25%, temperature+1℃; ⑤ Temperature+1℃, precipitation unchanged;

[0039] (4.2) The simulation period is 30 years, and the bootstrap resampling method is used to generate the climate sequence;

[0040] (4.3) Quantitative indicators: annual average runoff change rate, runoff annual distribution unevenness coefficient (Cv), extreme flow event frequency;

[0041] The climate change scenario simulation also includes extreme climate event simulation:

[0042] (4.1.1) Set extreme scenario: 3 consecutive years of precipitation less than 40% of the average value, and temperature higher than the average value by 2℃; ​​​​

[0043] (4.1.2) Evaluation index: duration of reservoir storage below dead storage level, number of days of river channel cutoff, ecological water demand satisfaction rate.

[0044] Further, the method further comprises a model uncertainty analysis step:

[0045] (5.1) Latin hypercube sampling method is adopted to generate 100 groups of parameter combinations;

[0046] (5.2) The runoff simulation range under 90% confidence interval is calculated;

[0047] (5.3) The contribution rate of each input parameter is quantified, and it is determined that the influence of precipitation data uncertainty on the simulation result accounts for more than 50%.

[0048] Another object of the present application is to provide a cold and arid mountainous basin runoff simulation system based on a MIKE SHE / MIKE 11 coupling model combined with GIS / RS data, comprising:

[0049] A preprocessing module is used for multi-source heterogeneous data acquisition and preprocessing: remote sensing technology is used to obtain basin precipitation, land surface parameters and terrain data, and input data set is constructed combined with limited site observation data;

[0050] A coupling model construction module is used for establishing a collaborative simulation framework of a MIKE SHE distributed hydrological model and a MIKE 11 river channel hydrodynamic model, and data interaction is realized through river channel linkage;

[0051] A calibration module is used for model parameter partition calibration: calibration regions are divided based on basin hydrogeological characteristics, and sensitive parameters such as Manning coefficient and hydraulic conduction coefficient are optimized;

[0052] A simulation module is used for climate change scenario simulation: precipitation and temperature change combination scenarios are set, and runoff response law is analyzed.

[0053] Another object of the present application is to provide a computer device, which comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the cold and arid mountainous basin runoff simulation method based on the MIKE SHE / MIKE 11 coupling model combined with GIS / RS data.

[0054] Another object of the present application is to provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to make the processor execute the steps of the cold and arid mountainous basin runoff simulation method based on the MIKE SHE / MIKE 11 coupling model combined with GIS / RS data.

[0055] Another object of the present application is to provide an information data processing terminal for realizing the cold and arid mountainous basin runoff simulation system based on the MIKE SHE / MIKE 11 coupling model combined with GIS / RS data.

[0056] In combination with the above technical solutions and solved technical problems, the technical solutions to be protected by the present application have the following advantages and positive effects:

[0057] First, multi-source data fusion technology: using TRMM satellite precipitation data (spatial resolution 0.25°), MODIS LAI product (16-day synthetic data) and ASTER DEM (30m precision), a distributed input data set of cold and arid mountainous area is constructed, so that the spatial representation accuracy of precipitation is improved by 40% (compared with single station observation).

[0058] Model coupling mechanism: a MIKE SHE / MIKE 11 collaborative simulation framework is proposed, and the data interaction of surface and underground water flow is realized through river channel linkage, so that the simulation error of complex river network flood routing is reduced to less than 10% (compared with single MIKE SHE model).

[0059] Parameter partition calibration method: the basin is divided into five hydrogeological zones, and sensitive parameters such as Manning coefficient and hydraulic conductivity coefficient are optimized combined with genetic algorithm, so that the base flow simulation accuracy is improved by 25% (Nash-Sutcliffe coefficient is improved from 0.52 to 0.68).

[0060] Climate change scenario simulation system: quantifying the influence threshold of precipitation and temperature change on runoff, it is found that the change of 25% of precipitation leads to the fluctuation of 2.3% of annual runoff, which provides scientific basis for water resources adaptive management.

[0061] In combination with the field observation data of Tizinafu River Basin, a multi-source data fusion-model coupling-partition calibration technical system is proposed. The key breakthrough points include: ① finding the optimal fusion weight of TRMM precipitation data and VIC model data (0.7:0.3 in mountainous area); ② establishing the quantitative relationship between the river channel linkage density of MIKE SHE / MIKE 11 coupling and the simulation accuracy (the optimal density is 1 / 50km 2 ) per 50km; ③ proposing a five-zone parameter calibration scheme based on terrain humidity index, which improves the base flow simulation accuracy by 25%.

[0062] Secondly, the application shows significant direct economic benefits and water resources scheduling capability improvement in the practical application of the Ti Zhanfu River Basin. Through the construction of a refined scheduling model, the accuracy of reservoir scheduling can be improved by 30%, and the estimated annual power generation income increases by 12 million yuan; the estimated water resource utilization rate increases from 45% to 58%, the estimated additional irrigation area reaches 32,000 mu, and the estimated annual increase in agricultural income reaches 25 million yuan. At the same time, in terms of disaster prevention and reduction and ecological protection, the flood forecasting accuracy can be improved by 20%, and the estimated annual average flood disaster loss is reduced by 5 million yuan; the estimated ecological water conveyance guarantee rate is improved to 90%, effectively supporting the service capability of the ecological system in the basin, and the estimated ecological value is increased by 18 million yuan per year.

[0063] In terms of technical innovation, the application fills a number of technical gaps in the field of hydrological simulation. For the first time, a fusion system of TRMM satellite precipitation and VIC model data is constructed, which improves the accuracy of precipitation estimation in mountainous areas from about 60% to more than 85%, significantly breaking through the bottleneck of large error and poor representation of precipitation in high-altitude areas. At the same time, the one-way coupling limitation of MIKE SHE / MIKE 11 model is broken, and a three-dimensional groundwater and river two-way feedback module is developed, realizing the synchronous simulation of saturated-unsaturated flow and river hydrodynamic process in cold and arid areas, and providing a systematic solution for complex underground-surface water process.

[0064] For the problem of parameter calibration in cold and arid mountainous areas, the application proposes a five-zone parameter partition calibration method based on terrain humidity index, effectively solving the problem of more than 30% bias in base flow simulation caused by traditional global rating in mountainous areas, and reducing the regionalization error to 18%. In the face of the reality of extremely scarce hydrological stations (<0.5 per 10,000 square kilometers), the use of remote sensing and multi-source data fusion technology improves the effective data coverage rate from 15% to 80%, significantly reducing the dependence of the model on ground observations, and realizing the availability and reliability of distributed models in data-scarce areas.

[0065] In terms of industrialization and commercial value, the market size for cold and arid areas, border basins and ecological scheduling needs in the country exceeds 500 million yuan. At the same time, based on a series of practical data and results, the application effectively corrects the industry's general bias towards distributed models, parameter regionalization, TRMM data error and other technical paths, and establishes the feasibility and popularization of high-precision hydrological simulation in complex terrain and data-limited areas, with significant technical leadership and commercial expansion value. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 is a flow chart of a cold and arid mountainous basin runoff simulation method provided by an embodiment of the application based on a MIKE SHE / MIKE 11 coupled model combined with GIS / RS data;

[0067] Figure 2is a cold and arid mountainous basin runoff simulation system structure block diagram based on a MIKE SHE / MIKE 11 coupling model combined with GIS / RS data provided by the embodiment of the present application;

[0068] Figure 3 is a Ti Zhi Na Fu River basin saturated area division map provided by the embodiment of the present application;

[0069] Figure 4 is a MIKE SHE / MIKE 11 coupling model flow chart provided by the embodiment of the present application, which shows the data interaction and calculation process;

[0070] Figure 5 is the runoff simulation result of the calibration period from 2002 to 2004 provided by the embodiment of the present application, which compares the fitting effect of TRMM, VIC and observation data;

[0071] Figure 6 is the runoff simulation result of the calibration period from 2006 to 2007 provided by the embodiment of the present application, which compares the fitting effect of TRMM, VIC and observation data;

[0072] Figure 7 is a column chart of the average annual runoff change rate under the climate change scenario provided by the embodiment of the present application;

[0073] Figure 8 is the parameter sensitivity analysis result provided by the embodiment of the present application, which shows the influence weight of each parameter on EF2. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0075] As shown in Figure 1 , the cold and arid mountainous basin runoff simulation method based on the MIKE SHE / MIKE 11 coupling model combined with GIS / RS data provided by the embodiment of the present application comprises the following steps:

[0076] S101, multi-source heterogeneous data acquisition and preprocessing: using remote sensing technology to acquire basin precipitation, land surface parameters and terrain data, and combining with limited site observation data to construct an input data set;

[0077] S102, coupling model construction: establishing a collaborative simulation framework of MIKE SHE distributed hydrological model and MIKE 11 river channel hydrodynamic model, and realizing data interaction through river channel linkage;

[0078] S103, model parameter partition calibration: based on the hydrogeological characteristics of the basin, the calibration area is divided, and sensitive parameters such as Manning coefficient and hydraulic conductivity coefficient are optimized;

[0079] S104, climate change scenario simulation: set the combination scenario of precipitation and temperature change, and analyze the runoff response law.

[0080] The method first constructs a high-resolution basic geographic and hydrological element database by integrating remote sensing (RS) and geographic information system (GIS) data. Remote sensing data includes TRMM / GPM satellite precipitation, MODIS land surface temperature, vegetation coverage (NDVI) and snow cover index (NDSI); GIS data includes DEM, land use type, soil type and water system distribution, etc. All data are projected to UTM-WGS84 coordinate system and resampled to 500m grid resolution. Precipitation data is corrected by Kalman filter and VIC simulation results, and finally a high-precision and high-accuracy precipitation driving data set is formed, which provides a high-quality signal source for model input.

[0081] In the model building stage, MIKE SHE is used to simulate the surface-aerated zone-saturated zone three-layer water cycle process, and MIKE11 is responsible for simulating the river channel hydrodynamic process. The core signal path of the model is driven by data between modules: precipitation data is first used as a surface source signal to drive surface runoff generation (2D diffusion wave equation), and then transmitted to the aerated zone (1D gravity flow) and saturated zone (3D Darcy flow) through vertical infiltration process; groundwater head affects the base flow response in turn. MIKE 11 receives the output of MIKE SHE and the boundary conditions, and based on the Saint-Venant equation, it performs 1D dynamic river flow simulation to finally form a continuous runoff signal output curve. The two are realized by dynamic link (HD-Coupling) to realize two-way feedback, so that the simulation system has high-resolution response and fine-scale transmission capability.

[0082] The model parameters adopt a five-zone partition strategy guided by terrain humidity index, which divides the area according to slope, soil water storage capacity and surface roughness, and assigns initial permeability coefficient, Manning coefficient, evaporation parameter and other key values to each region. Each region optimizes its parameter set through genetic algorithm, and uses historical runoff observation signal for multi-objective calibration (EF2, NSE, PBIAS, etc.). In the climate scenario simulation stage, a 12-variable climate disturbance field (precipitation, temperature extreme, snowmelt threshold, etc.) is constructed and coupled into the model driving end to form a new round of input signal, output the runoff change rate curve under the scenario, and support risk warning, dispatch optimization and ecological water supplement simulation. This whole process realizes a closed-loop modeling structure from data acquisition-signal modeling-feedback calculation-result prediction, with high timeliness and stability.

[0083] The multi-source heterogeneous data acquisition and preprocessing steps provided by the embodiment of the application comprise:

[0084] (1.1) The basin is divided into 16 precipitation areas by using TRMM satellite data, and precipitation data of each area is independently input, wherein the precipitation of the area outside the basin is set to 0;

[0085] (1.2) The NASA ASTER DEM data is resampled to a 1km*1km grid and projected to the WGS_1984_UTM_Zone_43N coordinate system;

[0086] (1.3) The leaf area index (LAI) and root depth (RD) time series are inversely calculated by using MODIS data, the resolution is 500m, and the time step is 1 day;

[0087] The TRMM precipitation data preprocessing further comprises:

[0088] (1.1.1) The 0.25° grid data is down-scaled to 1km*1km by using the IDW interpolation method, the topographic elevation correction is considered, and the correction coefficient is 0.005mm / (m*day);

[0089] (1.1.2) The Kalman filter fusion algorithm is adopted in combination with the VIC model precipitation data (the resolution is 1km), and the weight coefficient is dynamically adjusted according to the observation error of the station.

[0090] The MIKE SHE / MIKE 11 coupling model construction steps provided by the embodiment of the application comprise:

[0091] (2.1) In the MIKE SHE model, the Kristensen-Jensen method is adopted for evapotranspiration calculation, and the input parameters comprise LAI, RD and soil humidity;

[0092] (2.2) In the MIKE 11 model, the one-dimensional Saint-Venant equation set is adopted for describing the river flow:

[0093]

[0094] Wherein Q is the flow (m / s), A is the water section area (m2), q is the side inflow (m / s*m), a is the momentum correction coefficient, K is the flow modulus, and g is the gravity acceleration (m / s2); 3 2 3 2

[0095] (2.3) Coupling interface setting: the river link is generated on the grid boundary of the MIKE SHE, matched with the node coordinates of the MIKE 11 river, and the flow and water level data are exchanged;

[0096] ​​​​The MIKE SHE saturated flow module solves the three-dimensional Darcy equation:

[0097]

[0098] where K h is the horizontal hydraulic conductivity (m / s), K v is the vertical hydraulic conductivity (m / s), S s is the storage coefficient, and h is the water head (m).

[0099] The boundary conditions are set as follows: the zero flux boundary for the basin periphery, the variable head boundary for the outlet, and the observed data of Yuzimen Lake Station.

[0100] The model parameter partition calibration step provided by the embodiment of the application comprises:

[0101] (3.1) The basin is divided into five saturated zones, wherein Zone 5 is set as a permafrost zone without groundwater flow;

[0102] (3.2) The calibration parameters comprise: the river channel Manning coefficient (range 20-30 m 1 / 3 / s), the surface Manning coefficient (range 5-10 m 1 / 3 / s), and the river bed permeability coefficient (range 1x10 -7 -1x10 -6 m / s);

[0103] (3.3) The genetic algorithm is used to optimize the parameters, the objective functions are the Nash-Sutcliffe efficiency coefficient (EF2) and the correlation coefficient (R), and the requirements are that EF2>0.68 and R>0.75 in the calibration period;

[0104] The genetic algorithm parameters are set as follows:

[0105] (3.3.1) The population size is 100, and the iteration number is 200;

[0106] (3.3.2) The crossover probability is 0.8, and the mutation probability is 0.1;

[0107] (3.3.3) The fitness function is EF2x0.7+Rx0.3.

[0108] The climate change scenario simulation step provided by the embodiment of the application comprises:

[0109] (4.1) Five scenarios are set as follows: ① Precipitation +25%, temperature unchanged; ② Precipitation-25%, temperature unchanged; ③ Precipitation +25%, temperature +1℃; ④ Precipitation-25%, temperature +1℃; and ⑤ Temperature +1℃, precipitation unchanged;

[0110] (4.2) Simulation period is 30 years, and a bootstrap resampling method is used to generate a climate sequence;

[0111] (4.3) Quantitative indicators: annual average runoff change rate, runoff annual distribution uneven coefficient (Cv), and extreme flow event frequency;

[0112] The climate change scenario simulation also includes extreme climate event simulation:

[0113] (4.1.1) Set an extreme scenario: precipitation is less than 40% of the average value for 3 consecutive years, and temperature is higher than the average value by 2 degrees Celsius;

[0114] (4.1.2) Evaluation indicators: duration of reservoir storage volume below dead water level, river channel dry-up days, and ecological water demand satisfaction rate.

[0115] The method provided by the embodiment of the application further includes a model uncertainty analysis step:

[0116] (5.1) A Latin hypercube sampling method is used to generate 100 groups of parameter combinations;

[0117] (5.2) The runoff simulation range under a 90% confidence interval is calculated;

[0118] (5.3) The contribution rate of each input parameter is quantified, and it is determined that the influence of precipitation data uncertainty on the simulation result accounts for more than 50%.

[0119] As shown in Figure 2 The system for simulating runoff in a cold and arid mountainous basin provided by the embodiment of the application based on a MIKE SHE / MIKE 11 coupled model combined with GIS / RS data includes:

[0120] A preprocessing module is configured to acquire and preprocess multi-source heterogeneous data: remote sensing technology is used to acquire basin precipitation, land surface parameters and terrain data, and combined with limited site observation data to construct an input data set;

[0121] A coupled model construction module is configured to establish a collaborative simulation framework of a MIKE SHE distributed hydrological model and a MIKE 11 river channel hydrodynamic model, and realize data interaction through river channel linkage;

[0122] A calibration module is configured to calibrate model parameters in a partitioned manner: calibration regions are divided based on basin hydrogeological characteristics, and sensitive parameters such as Manning coefficient and hydraulic conductivity coefficient are optimized;

[0123] A simulation module is configured to simulate climate change scenarios: set a precipitation and temperature change combination scenario, and analyze the runoff response law.

[0124] Another object of the present application is to provide a computer device comprising a memory and a processor, the memory storing a computer program, the computer program being executed by the processor to enable the processor to perform the steps of the cold and arid mountainous basin runoff simulation method based on the MIKE SHE / MIKE 11 coupled model combined with GIS / RS data.

[0125] Another object of the present application is to provide a computer readable storage medium storing a computer program, the computer program being executed by a processor to enable the processor to perform the steps of the cold and arid mountainous basin runoff simulation method based on the MIKE SHE / MIKE 11 coupled model combined with GIS / RS data.

[0126] Another object of the present application is to provide an information data processing terminal for implementing the cold and arid mountainous basin runoff simulation system based on the MIKE SHE / MIKE 11 coupled model combined with GIS / RS data.

[0127] The present application is embodied as follows:

[0128] The present application provides a cold and arid mountainous basin runoff simulation method based on the MIKE SHE / MIKE 11 coupled model combined with GIS / RS data, the core of the technical solution of which is the organic integration of multi-source data fusion, model collaborative coupling, parameter partition calibration and climate change scenario simulation. Specifically, it includes the following four key steps:

[0129] 1. Multi-source heterogeneous data acquisition and preprocessing

[0130] 1.1 Remote sensing data acquisition and processing

[0131] Precipitation data: TRMM3B42V7 products (2000-2020, 0.25°x0.25°) are obtained, and ANUSPLINE software is used for topographic correction, considering the gradient effect that precipitation increases by 5% for every 100m increase in elevation. The basin is divided into 16 precipitation zones (Zone1-16), of which Zone1 is located outside the basin, and the precipitation is set to 0. At the same time, VIC model precipitation data (1kmx1km, 2000-2020) from the Institute of Geography, Chinese Academy of Sciences are obtained, and 700 ground sites are used for interpolation.

[0132] Land surface parameters: MYD15A2H products (LAI and FPAR, 500m, 16-day synthesis) are downloaded from the MODIS website, and a daily LAI time series is generated by fitting a double logistic function. The surface temperature is retrieved from FY-2C satellite data, and the soil moisture is estimated using the Ts-NDVI characteristic space method of Sandholt et al. (2002), with a spatial resolution of 1km and a time step of 1 day.

[0133] Terrain data: Download NASA ASTER GDE MV3 data (30m x 30m), resample to 1 km x 1 km, project to WGS_1984_UTM_Zone_43N coordinate system, used to extract basin slope, aspect and flow path.

[0134] 1.2 Ground observation data integration

[0135] Hydrological data: Collect daily runoff data of Yuzhimenleke station from 2002 to 2007 (calibration period 2002-2004, validation period 2006-2007), and daily evaporation data of Qaqun station from 2002 to 2007. Due to the scarcity of evaporation data, through sensitivity analysis, it is found that the contribution rate of evaporation to runoff simulation is less than 5%, so the data of Qaqun station is used to represent the whole basin.

[0136] Meteorological data: Obtain daily temperature data of Yuzhimenleke station from 2002 to 2007, based on the temperature gradient of 6.5℃ / km, divide the basin into 23 temperature zones (one zone per 200m altitude), generate a distributed temperature field.

[0137] 1.3 Data fusion algorithm

[0138] Design Kalman filter fusion model to optimize the combination of TRMM and VIC precipitation data:

[0139] State equation: X t = X t-1 + ω t , where Xt is the true precipitation at time t, ω t is the process noise.

[0140] Observation equation: Z t = H t X t + v t , where Z t = [Z TRMM，t , Z VIC，T ] T , H t = [h TRMM，t , h VIC，t ], h TRMM，t , h VIC,t and h TRMM,t are observation weights, dynamically adjusted by site observation error.

[0141] Weight update: where σ 2 TRMM and σ 2 VIC are the historical mean square errors of the two kinds of data.

[0142] 2. MIKE SHE / MIKE 11 coupling model construction

[0143] 2.1 MIKE SHE model setup

[0144] Evapotranspiration module: Kristensen-Jensen method was adopted, and the formula is:

[0145] ET = K c x ET0

[0146] where ET0is the reference crop evapotranspiration (calculated by Penman-Monteith formula), K c is the crop coefficient, which is related to LAI and RD:

[0147]

[0148] where a1=0.2, a2=0.5, b1=0.3, c1=0.1, c2=0.2 are empirical coefficients, RD max = 2.5 m is the maximum root depth.

[0149] Surface runoff module: based on the diffusion wave approximation of the two-dimensional Saint-Venant equations, the finite difference method was used to solve:

[0150]

[0151] where h is the surface water depth, qx, qyare the x, y direction unit width flow, i is the rainfall intensity, and f is the infiltration rate (Horton model was adopted).

[0152] Unsaturated flow module: based on the Richards equation, the one-dimensional finite difference method was used to solve:

[0153]

[0154] where θ is the soil moisture content, K(θ) is the unsaturated permeability coefficient (Van Genuchten model was adopted), and S is the root water uptake rate.

[0155] Saturated flow module: the three-dimensional Darcy equation was adopted, and the iterative implicit finite difference method was used to solve:

[0156]

[0157] The watershed was divided into 5 saturated zones (Zone 1-5), and Zone 5 was set as the permanent frozen soil zone, Kv=1 x 10 -10 m / s, and the parameters of the remaining zones were preliminarily set according to the soil type (refer to the Chinese soil database) as shown in Table 1.

[0158] Table 1 5 preliminary setting table of saturated zone parameters

[0159]

[0160]

[0161] 2.2 MIKE 11 model construction

[0162] River network: Based on DEM, the main river and tributaries of Ti Zinafu River Basin were extracted, and the MIKE 11 river network was constructed, with a total length of about 970 km, divided into 1200 calculation units, with an average unit length of 800 m.

[0163] Hydrodynamic equation: One-dimensional Saint-Venant equation set was adopted, including continuity equation and motion equation:

[0164]

[0165] Where Q is the flow, A is the cross-sectional area, q is the lateral inflow, a is the momentum correction coefficient (1.05), K is the flow modulus K = AR 2 / 3 / n, R is the hydraulic radius, and n is the Manning coefficient).

[0166] Boundary conditions: The flow process output by MIKE SHE was used at the upstream, the water level boundary was set at the downstream (based on historical observation data), and the flow boundary was set at the tributary confluence.

[0167] 2.3 Coupling mechanism design

[0168] Data exchange interface: River Link was generated in MIKE SHE, with the position matching the MIKE 11 river node coordinates. Each MIKE SHE grid cell was coupled with at most one river link, and the link spacing needed to be greater than 1 / 2 of the grid size (i.e. 500 m), to avoid link conflicts.

[0169] Coupling algorithm: Time step synchronous coupling mode was adopted, MIKE SHE calculated every 1 hour, and output the grid cell outflow to MIKE 11, MIKE 11 calculated the river hydrodynamic process, and returned the river water level to MIKE SHE as the boundary condition. The specific process is as follows:

[0170] (1) MIKE SHE calculates the surface runoff and underground outflow at the current time step, and sums up the grid cell outflow Q SHE ;

[0171] (2) Q SHE is taken as the inflow boundary of the upstream of MIKE 11 river, and the river water level h MIKE11 is calculated.

[0172] (3) h MIKE11 As the boundary condition of MIKE SHE saturated flow module, the groundwater head is updated;

[0173] (4) Go to the next time step, repeat the above process.

[0174] 3. Model parameter zonation calibration

[0175] 3.1 Calibration zone division

[0176] Based on the topographic wetness index (TWI) and soil type, the watershed is divided into 5 calibration zones (Zone 1-5), as shown in Figure 1

[0177] Zone 1: High-altitude mountain area (altitude > 4500m), mainly bare rock and glaciers, with permafrost soil as the main soil type;

[0178] Zone 2: Mid-high altitude mountain area (4000-4500m), with alpine meadow as the vegetation and alpine meadow soil as the soil;

[0179] Zone 3: Mid-altitude mountain area (3500-4000m), with shrub forest as the vegetation and mountain chestnut calcareous soil as the soil;

[0180] Zone 4: Low-altitude mountain area (<3500m), with oasis agriculture as the vegetation and irrigated soil as the soil;

[0181] Zone 5: Alluvial plain in the lower reaches of the watershed, set as permafrost area with no groundwater flow.

[0182] 3.2 Sensitivity parameter identification

[0183] Through global sensitivity analysis (using Sobol method), the parameters that have significant impact on runoff simulation and their impact weights are determined:

[0184] Channel Manning coefficient (weight 25%): affects the speed of flood evolution, range 20-30 m 1 / 3 / s;

[0185] Surface Manning coefficient (weight 20%): affects the surface runoff concentration time, range 5-10 m 1 / 3 / s;

[0186] River bed permeability coefficient (weight 15%): affects the exchange between surface water and groundwater, range 1 x 10 -7 -1 x 10 -6 m / s;

[0187] Vertical hydraulic conductivity coefficient (Zone 1-4, weight 18%): affects the vertical movement of groundwater, range 1 x 10 -5 -5 x 10 -4 ​m / s;

[0188] Horizontal hydraulic conductivity (Zone 1-4, weight 22%): affects lateral groundwater movement, range 5 x 10 -6 -3 x 10 -4 m / s.

[0189] 3.3 Genetic algorithm optimization

[0190] Real-coded genetic algorithm was used for parameter optimization, with the following settings:

[0191] Population size: 100 individuals, each individual is a combination of 5-zone parameters;

[0192] Number of iterations: 200 generations, fitness function is:

[0193] Fitness = 0.7 x EF2 + 0.3 x R

[0194] Where EF2 is the Nash-Sutcliffe efficiency coefficient, and R is the correlation coefficient, the calculation formula is:

[0195]

[0196] Crossover operation: arithmetic crossover, crossover probability 0.8, generate offspring individuals:

[0197] X1' = aX1 + (1-a)X2

[0198] X2' = (1-a)X1 + aX2

[0199] Where a is a random number between [0, 1];

[0200] Mutation operation: Gaussian mutation, mutation probability 0.1, mutation amount is: X' = X + σ x N(0, 1), where σ is the mutation step, taking 10% of the parameter range.

[0201] 3.4 Calibration process

[0202] 1. Initialization: based on soil database and literature, set initial values for 5-zone parameters;

[0203] 2. Sensitivity analysis: determine key parameters and their weights;

[0204] 3. Genetic algorithm optimization: run 200 generations to obtain the optimal parameter combination;

[0205] 4. Verification: use 2006-2007 data to verify the model, if EF2 < 0.68, return to step 3 to adjust algorithm parameters;

[0206] 5. Final parameter determination: the main parameters after calibration are shown in Table 2.

[0207] Table 2 Main parameters after calibration

[0208] Parameter Calibration value Initial value Adjustment amplitude Channel Manning coefficient (m 1 / 3 / s) 25 20 +25％ Surface Manning coefficient (m 1 / 3 / s) 8 5 +60％ River bed permeability coefficient (m / s) 5.3 x 10 -7 ]]> 1 x 10 -7 ]]> +430％ Zone 1 vertical K_v (m / s) 4.3 x 10 -4 ]]> 2 x 10 -4 ]]> +115％ Zone 1 horizontal K_h (m / s) 2.5 x 10 -4 ]]> 1 x 10 -4 ]]> +150％ Zone 2 vertical K_v (m / s) 9.0 x 10 -5 ]]> 5 x 10 -5 ]]> +80％ Zone 2 horizontal K_h (m / s) 2.0 x 10 -5 ]]> 1 x 10 -5 ]]> +100％ Zone 3 vertical K_v (m / s) 1.6 x 10 -4 ]]> 1 x 10 -4 ]]> +60％ Zone 3 horizontal K_h (m / s) 1.4 x 10 -4 ]]> 8 x 10 -5 ]]> +75％ Zone 4 vertical K_v (m / s) 1.8 x 10 -4 ]]> 1 x 10 -4 ]]> +80％ Zone 4 horizontal K_h (m / s) 1.6 x 10 -4 ]]> 1 x 10 -4 ]]> +60％

[0209] 4. Climate change scenario simulation

[0210] 4.1 Scenario design

[0211] Based on the RCP4.5 and RCP8.5 scenarios of the IPCC AR6 report, combined with the climate characteristics of the study area, five climate change scenarios are designed:

[0212] 1. Scenario 1: Precipitation +25%, temperature unchanged;

[0213] 2. Scenario 2: Precipitation -25%, temperature unchanged;

[0214] 3. Scenario 3: Precipitation +25%, temperature +1℃;

[0215] 4. Scenario 4: Precipitation -25%, temperature +1℃;

[0216] 5. Scenario 5: Precipitation unchanged, temperature +1℃.

[0217] 4.2 Climate sequence generation

[0218] Statistical downscaling method is used to generate scenario climate sequences:

[0219] 1. Collect daily precipitation and temperature data from 5 meteorological stations around the watershed from 1961 to 2020;

[0220] 2. Establish statistical relationship between regional climate model (RCM) and global climate model (GCM) for precipitation and temperature;

[0221] 3. Based on the RCP8.5 scenario output of HadGEM3-GC3.1 model, generate daily precipitation and temperature sequences from 2021 to 2050;

[0222] 4. Bias-correct the generated sequences to ensure consistency with historical observations;

[0223] 5. According to the scenario design, adjust the corrected sequences: precipitation ±25% is realized by multiplication factor, temperature ±1℃ is realized by additive offset.

[0224] 4.3 Runoff response analysis

[0225] Annual scale indicators: Calculate the annual average runoff change rate, runoff depth, runoff coefficient under each scenario;

[0226] Monthly scale indicators: Analyze runoff uneven distribution coefficient (Cv) and continuous maximum 4-month runoff proportion;

[0227] Extreme indices: frequency of extreme precipitation events (daily precipitation > 50 mm), number of extreme high temperature days (daily air temperature > 25℃), number of river dry days;

[0228] Sensitivity analysis: Establish the sensitivity coefficient of runoff to precipitation and temperature:

[0229]

[0230] Where Q0, P0, T0 are the baseline values, and AQ, AP, AT are the change amounts.

[0231] 4.4 Uncertainty analysis

[0232] Bayesian Model Averaging (BMA) method is used to quantify the uncertainty of simulation:

[0233] 1. Generate 100 sets of climate sequences (considering the differences between GCM models);

[0234] 2. Run the model for each sequence to obtain the runoff simulation results;

[0235] 3. Calculate the runoff prediction range under 90% confidence interval;

[0236] 4. Decompose the sources of uncertainty: climate model uncertainty (35%), downscaling method uncertainty (25%), model structure uncertainty (20%), and parameter uncertainty (20%).

[0237] Beneficial effects

[0238] Improved simulation accuracy in data-scarce areas: Through the fusion of TRMM and VIC precipitation data, the spatial representation accuracy of precipitation in the Tizinafu River Basin is improved by 40% compared to single-site observation. The EF2 during the calibration period is 0.68-0.77, and the EF2 during the validation period is 0.59-0.62, which is 15%-20% higher than the traditional single data input (as shown in Table 2). The simulation effect is better in the non-flood season, with EF2 reaching 0.93 in October-December 2003, indicating that the model's ability to simulate the base flow process has been significantly improved.

[0239] Improved simulation accuracy of surface-groundwater interaction: Compared with the MIKE SHE model alone, the MIKE SHE / MIKE 11 coupled model reduces the simulation error of river flood evolution from 25% to less than 10%, and the simulation bias of groundwater and surface water exchange from 30% to 15%. As shown in Figure 4 , the upstream groundwater continuously recharges the river, the midstream river recharges the groundwater during the flood period, and the downstream river and groundwater interact frequently. The simulation results are consistent with the isotope tracing results of Andermann et al. (2012), proving that the model can accurately depict the water-groundwater interaction characteristics in cold and arid mountainous areas.

[0240] Parameter calibration efficiency improvement: The five-zone partition calibration strategy improves the parameter optimization efficiency by 30% compared to the global unified calibration, reducing the number of calibration iterations from an average of 500 to 200. The sensitive parameter identification results show that the partition optimization of the hydraulic conductivity coefficient contributes the most to the improvement of the base flow simulation accuracy (25%), followed by the river bed permeability coefficient (15%). After calibration, the model's adaptability to different climate types is enhanced, with EF2 = 0.77 in the wet year of 2003, EF2 = 0.67 in the normal year of 2004, and EF2 = 0.59 in the dry year of 2006, all meeting the hydrological model accuracy requirements (EF2 > 0.5).

[0241] Quantification of climate change impact: Scenario simulation results show that a 25% change in precipitation leads to a 2.3% change in annual runoff (+25% precipitation corresponds to +1.82% runoff, -25% precipitation corresponds to -1.88% runoff), and a 1°C change in temperature leads to a 0.1% change in annual runoff (+1°C corresponds to +0.07% runoff). In the extreme scenario (3 consecutive years of -40% precipitation and +2°C temperature), annual runoff can decrease by 12.5%, and the number of river dry days increases from 5 days in the baseline period to 22 days, providing quantitative basis for water resources emergency management in the basin.

[0242] Technical application range expansion: The method has been successfully applied in the Tizinafu River Basin and has been promoted to other tributaries of the upper reaches of the Tarim River (such as the Hotan River and the Yarkant River), with simulation accuracy meeting the requirements. Compared with traditional methods, this technology does not require a dense observation network and can quickly build a runoff simulation system in data-scarce mountainous areas, reducing construction costs by more than 60%, with significant economic and social benefits.

[0243] Example: Runoff simulation and climate change impact assessment in the Tizinafu River Basin

[0244] 1. Overview of the study area

[0245] The Tizinafu River Basin is located in the southwest of Xinjiang Uygur Autonomous Region and is a tributary of the Yarkant River, with a basin area of about 5500km 2 , an average elevation of 3600m. The basin has complex terrain, with an elevation ranging from 1575m to 6234m, and the main land cover is bare land (51%), shrubs (35%), grassland (9%), and forest land (5%). The climate is dry and rainy, with an annual precipitation of about 50mm, and the runoff mainly depends on the snowmelt of the Kunlun Mountains. There is only one hydrological station, Yuzimenlek, in the basin, located at the outlet of the basin, with observation data including daily runoff, temperature, and precipitation from 2002 to 2007.

[0246] 2. Data acquisition and preprocessing

[0247] · Remote sensing data acquisition:

[0248] TRMM precipitation data: 2000-2020, 0.25°x0.25°, downloaded from NASA website;

[0249] MODIS LAI data: MYD15A2H product, 2002-2007, 500m, 16-day composite;

[0250] ASTER DEM: 30m x 30m, 2000, downloaded from the Geospatial Data Cloud Platform.

[0251] • Data preprocessing:

[0252] TRMM precipitation data: terrain correction using ANUSPLINE, considering that precipitation increases by 5% per 100m elevation increase, dividing the basin into 16 precipitation zones;

[0253] MODIS LAI data: daily LAI is generated by fitting a double logistic function, the formula is:

[0254]

[0255] where L max =4.0, L min =0.5, k=0.2, t0=150, t1=300 are fitting parameters;

[0256] DEM data: resampled to 1km x 1km, projected to WGS_1984_UTM_Zone_43N, extracted basin slope, flow direction and flow accumulation.

[0257] 3. Coupling model construction

[0258] MIKE SHE model construction:

[0259] Grid setting: 145x110 grid cells, 1km x 1km, using irregular triangular network (TIN) to represent terrain;

[0260] Module parameters: evapotranspiration module input LAI and RD time series, Manning coefficient of surface runoff module initially set to 5m 1 / 3 / s, saturated flow module hydraulic conductivity coefficient set according to 5 zones (as shown in Table 1);

[0261] Boundary conditions: zero flux boundary around the basin, outlet as variable head boundary, based on Yuzimen Lake station observation water level setting.

[0262] MIKE 11 model construction:

[0263] River network: main river about 970km long, divided into 1200 calculation units, average length 800m;

[0264] Cross-section data: Based on DEM and field measurement data, the shape of the river cross-section is constructed, and the Manning coefficient is initially set to 20 m 1 / 3 / s;

[0265] Boundary conditions: The upstream input flow process is output by MIKE SHE, and the downstream is set as a water level boundary (historical minimum water level + 0.5 m).

[0266] Coupling settings:

[0267] River link: 120 river links are generated in MIKE SHE, with a spacing of about 8 km, matching the MIKE 11 river node coordinates;

[0268] Time step: MIKE SHE calculation step 1 hour, MIKE 11 calculation step 30 minutes, time synchronization is realized by linear interpolation.

[0269] 4. Model parameter calibration

[0270] Calibration period: January 1, 2002 to December 31, 2004, daily runoff data is used;

[0271] Sensitive parameters: river Manning coefficient, surface Manning coefficient, river bed permeability coefficient, 5-zone hydraulic conductivity coefficient;

[0272] Optimization algorithm: genetic algorithm, population size 100, iteration 200 generations, fitness function Fitness = 0.7 x EF2 + 0.3 x R;

[0273] Calibration results: After calibration, the river Manning coefficient is 25 m 1 / 3 / s, the surface Manning coefficient is 8 m 1 / 3 / s, the river bed permeability coefficient is 5.3 x 10 -7 m / s, and the 5-zone hydraulic conductivity coefficient is shown in Table 1;

[0274] Verification period: January 1, 2006 to December 31, 2007, verification results EF2 = 0.59-0.62, R = 0.73-0.81 (as shown in Table 2).

[0275] 5. Climate change scenario simulation

[0276] Scenario settings: 5 scenarios as described in section 4.1, climate sequence generation based on RCP8.5 scenario of HadGEM3-GC3.1;

[0277] Simulation period: 2021-2050, a total of 30 years, running once a year;

[0278] Result analysis:

[0279] Annual average runoff: Scenario 3 (precipitation + 25%, temperature + 1℃) has the most runoff increase (2.05%), and scenario 4 (precipitation - 25%, temperature + 1℃) has the most runoff decrease (1.81%);

[0280] Intra-annual distribution of runoff: Temperature rise causes the spring runoff peak to advance 5-7 days, and the summer runoff proportion to decrease by 8%-10%;

[0281] Extreme events: The frequency of extreme drought events under scenario 4 increases by 2-3 times, and the river cutoff days increase from 5 days to 22 days.

[0282] 6. Application effect

[0283] This embodiment shows that the method realizes high-precision runoff simulation in the Ti Zha Fu River Basin, providing a scientific basis for watershed water resources planning. Based on the simulation results, the local water conservancy department adjusts the reservoir scheduling scheme, advancing the irrigation period from the traditional 6-8 months to 5-7 months, and improving the water resource utilization rate from 45% to 58%. At the same time, the method provides support for climate change adaptation strategies, and suggests implementing artificial snow increasing engineering in the upper reaches of the basin to cope with the risk of reduced precipitation, which is expected to increase winter water storage by 12%, and alleviate the spring water shortage problem.

[0284] Industrial applicability

[0285] The method of the present application can be widely applied to runoff simulation and water resources management in cold and arid mountainous basins, and has significant industrial applicability:

[0286] Water conservancy project planning: Provide runoff prediction support for reservoir design and irrigation area planning to avoid mismatch between project scale and actual water resources;

[0287] Flood warning system: Improve the accuracy of flood evolution simulation, extend the warning time, and reduce disaster losses;

[0288] Ecological protection management: Quantify the impact of climate change on ecological flow, and provide scientific basis for wetland protection and river ecological restoration;

[0289] New energy development: Provide runoff stability evaluation for small hydropower development to reduce development risk;

[0290] Cross-border water resources management: Provide technical support for runoff prediction and water resources allocation of international rivers.

[0291] The technology has been developed into a software system (name: Runoff Simulation and Climate Change Assessment System in Cold and Arid Mountainous Areas V1.0) which can run under the Windows operating system, support GIS / RS data import, model construction, parameter calibration and scenario simulation, and has a friendly operation interface, facilitating popularization and application. According to the test, it only takes 4.5 hours to simulate the runoff process of the Tizinafu River Basin for 30 years on a computer configured with Intel i7-11700K CPU and 16GB memory, meeting the business operation requirements.

[0292] The runoff simulation method in cold and arid mountainous areas based on the MIKE SHE / MIKE 11 coupling model combined with GIS / RS data provided by the application effectively solves the problems of low simulation accuracy, insufficient description of surface-groundwater flow interaction and poor adaptability to climate change of traditional models in data scarce areas through technical innovations such as multi-source data fusion, model collaborative coupling, parameter partition calibration and climate change scenario simulation. The application of the method in the Tizinafu River Basin shows that its simulation accuracy reaches EF2>0.68, which can accurately quantify the impact of ±25% precipitation and ±1℃ temperature on annual runoff, providing strong technical support for water resources management in cold and arid regions.

[0293] In the future, the remote sensing data fusion algorithm can be further optimized to improve the simulation capability of extreme climate events, and artificial intelligence technology can be combined to realize automatic calibration and updating of parameters, promoting the application of the technology in larger areas of ungauged mountainous basins and contributing to water resources security under the background of global change.

[0294] I. The specific application field of the application or related products.

[0295] 1. Application field

[0296] Water conservancy planning: suitable for reservoir design and irrigation area planning in cold and arid mountainous areas, such as optimization of reservoir group dispatching in the mountainous area of the Tarim River Basin.

[0297] Flood warning system: used for flood routing simulation in high-altitude arid areas, such as flood warning in the Tizinafu River Basin.

[0298] Ecological flow management: supporting wetland protection and river ecological restoration in cold and arid areas, such as ecological water transfer dispatching in the Ebinur Lake Basin.

[0299] New energy development: providing runoff stability evaluation for small hydropower projects in mountainous areas, such as small hydropower planning in the northern foothills of the Kunlun Mountains in Xinjiang.

[0300] Cross-border water resources management: serving runoff prediction of international rivers, such as water resources allocation negotiation in the upper reaches of the Amu River.

[0301] 2. Related products

[0302] Software product: develop "cold and arid mountainous runoff simulation and climate change assessment system V1.0", including:

[0303] Multi-source data fusion module (supporting 12 types of remote sensing data such as TRMM, MODIS access);

[0304] MIKE SHE / MIKE 11 coupling calculation engine (integrated three-dimensional groundwater-channel interaction algorithm);

[0305] Intelligent parameter calibration module (built-in genetic algorithm and five-zone optimization strategy);

[0306] Climate change scenario analysis platform (supporting 16 climate model output downscaling).

[0307] Hardware integrated system: distributed runoff simulation server linked with water conservancy monitoring terminal.

[0308] Second, the related evidence of the technical effects obtained by the embodiments of the present application.

[0309] Tarim River upstream flood warning system

[0310] Application background: in 2019, the flood warning in this area lagged 12 hours, resulting in 300 hectares of farmland being damaged, and it is urgent to improve the simulation accuracy.

[0311] Technical improvement:

[0312] Kalman filter is adopted to fuse TRMM and VIC precipitation data, and the accuracy is improved from 65% to 85%;

[0313] The MIKE SHE / MIKE 11 coupling step is optimized to 1 hour, and the flood evolution simulation error is reduced from 25% to 8%;

[0314] Integrate real-time radar data assimilation module to dynamically update soil moisture parameters.

[0315] Warning effect:

[0316] The simulation error of flood peak flow is reduced from 40% to 12%.

[0317] Technical effect evidence

[0318] Table 3 Comparison of simulation accuracy data

[0319]

[0320] Data source: comparison test of daily runoff data of Tizinafu River Basin from 2002 to 2007

[0321] 2. Climate change response sensitivity analysis

[0322] · Precipitation change test: Set precipitation ±25% scenario, annual average runoff change rate is +1.82% / -1.88%, sensitivity coefficient S_P=0.073 (i.e. precipitation changes by 1%, runoff changes by 0.073%).

[0323] 1.82% / -1.88%, sensitivity coefficient S_P=0.073 (i.e. precipitation changes by 1%, runoff changes by 0.073%).

[0324] · Temperature change test: Under the temperature +1℃ scenario, annual runoff increases by 0.07%, and the spring runoff peak is advanced by 5-7 days, which is consistent with the warming trend in Central Asia in the IPCC AR6 report.

[0325] 3. Software simulation effect

[0326] · Visualization interface: The runoff hydrograph of the Tizinafu River Basin in 2023 generated by the system is compared with the measured value (Fig. 4), showing that EF2=0.93 in the non-flood season and EF2=0.67 in the flood season. Figure 1

[0327] · Error distribution cloud map: The precipitation error is reduced from 20-30% to 5-10% before and after TRMM data fusion, and the accuracy in high-altitude mountainous areas is improved significantly.

[0328] 4. Theoretical analysis and analogy evidence

[0329] · Comparison with international similar technologies: The HEC-HMS model developed by the USGS has an average EF2 of 0.45 in data-scarce areas, which is improved by 38% by the method of the application; the standard version of MIKE SHE of the DHI company in Europe has an average EF2 of 0.58, which is improved by 24% by coupling optimization.

[0330] · Physical mechanism advantage: The three-dimensional saturated flow module reduces the simulation deviation of groundwater-surface water exchange from 30% to 15% compared with the traditional linear reservoir model, which is consistent with the isotope tracing results of Andermann et al. (2012) in the Himalayas.

[0331] Figure 3 The figure shows the saturated area division results of the Tizinafu River Basin in the embodiment of the application. The figure is based on terrain humidity index and underlying surface parameters for zoning optimization, forming five representative hydrological response units. This spatial division structure provides a scientific basis for subsequent parameter calibration, significantly reduces the simulation error caused by regional heterogeneity of parameters, and lays a spatial data foundation for parameter optimization of distributed models in complex mountainous areas.

[0332] Figure 4 ​A coupling calculation flowchart of the MIKE SHE / MIKE 11 model used in the application. The structure breaks through the limitations of traditional one-way driving models and realizes two-way feedback between evapotranspiration, surface runoff, river water dynamics, the vadose zone and the saturated zone. In particular, the three-dimensional-one-dimensional linkage mechanism built between saturated flow and river flow enables the dynamic process of groundwater-surface water to be simulated synchronously, effectively improving the response capability of the model to the whole process of watershed water cycle.

[0333] Figure 5 and Figure 6 are the runoff simulation results of the calibration period from 2002 to 2004 and the verification period from 2006 to 2007, respectively. The figure compares the degree of agreement between the TRMM satellite data, the VIC model simulation data and the actual observed runoff. The results show that the data fusion method proposed in the application has high accuracy in the calibration period and the verification period, and the EF2 efficiency coefficient is better than that of the traditional single-source data model, especially in the response accuracy of the runoff process during the flood peak period.

[0334] Figure 7 The figure shows the simulated annual average runoff change trend under the climate change scenario, which reflects that the climate scenario library constructed by the application can effectively quantify the influence of factors such as precipitation variability and temperature extremes on the runoff process of the watershed. Under extreme climate conditions, the model can simulate the interannual fluctuation range and trend change, support the water resources management department to carry out medium and long term scheduling planning, and provide decision basis for coping with climate variation.

[0335] Figure 8 The figure shows the sensitivity analysis conclusion of each main parameter on the simulation result. The results show that the river Manning coefficient, the river bed permeability coefficient and the slope flow velocity coefficient have high sensitivity to the simulation accuracy, while most of the saturated zone and evaporation parameters are non-sensitive variables. This analysis supports the strategy of "key parameter calibration + secondary parameter fixation" proposed in the application, which reduces the difficulty of model parameter adjustment, and at the same time improves the stability and practicality of the complex mountainous area model under the driving of multiple source data.

[0336] It should be noted that embodiments of the present application can be realized by hardware, software, or a combination of software and hardware. The hardware portion can be realized by a special logic; the software portion can be stored in a memory and executed by a proper instruction execution system, such as a microprocessor or a specially designed hardware. A person of ordinary skill in the art can understand that the above-mentioned apparatus and method can be realized by computer executable instructions and / or included in processor control codes, such as a carrier medium, such as a magnetic disk, CD or DVD-ROM, a programmable memory, such as a read-only memory (firmware), or a data carrier, such as an optical or electronic signal carrier. The apparatus of the present application and its modules can be realized by a hardware circuit, such as a very large scale integrated circuit or a gate array, a semiconductor, such as a logic chip, a transistor, or a programmable hardware device, such as a field programmable gate array, a programmable logic device, or the like, by software executed by various types of processors, or by a combination of the above-mentioned hardware circuit and software, such as firmware.

[0337] The above description is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any modification, equivalent replacement, and improvement within the technical range disclosed by the present application, and within the spirit and principle of the present application, should be covered within the protection scope of the present application.

Claims

1. A runoff simulation method for cold and arid mountainous basins based on a MIKE SHE and MIKE 11 coupled model combined with GIS / RS data, characterized in that, The method comprises the following steps: 1) Obtain remote sensing precipitation data, land surface temperature, vegetation index, snow cover index, digital elevation model, land use data and soil property data, and perform coordinate unification, resolution resampling and error correction processing on the data to form a hydrological model input data set; 2) Build a coupled hydrological model, simulate surface runoff, unsaturated zone seepage and saturated zone groundwater processes using MIKE SHE, simulate river flow processes using MIKE 11, and establish a two-way data exchange mechanism between groundwater and river flow; 3) According to the terrain humidity index, divide the parameter space into different zones, set the surface flow resistance coefficient, permeability parameter and evaporation parameter of each zone respectively, and calibrate the parameters using historical observed runoff data; 4) Based on precipitation, air temperature, snowmelt and other climate variables, build a climate scenario data set, input the model for scenario-driven simulation, and obtain runoff output results under different scenarios; 5) Perturb the model input parameters, statistically evaluate the simulation results using uncertainty analysis methods, and obtain the change interval and probability distribution of the simulation output.

2. The method of claim 1, wherein, Multi-source heterogeneous data acquisition and processing includes dividing the basin into multiple precipitation zones using TRMM satellite precipitation data, downscaling to a one-kilometer grid using the inverse distance weighting method, and correcting the elevation, resampling the NASA Aster digital elevation data to a one-kilometer grid, and projecting it to the Universal Transverse Mercator coordinate system, and using MODIS data to retrieve leaf area index and root depth time series and fuse with limited site observation data.

3. The method of claim 1, wherein, Coupled model construction generates river links at the grid boundaries of the distributed hydrological model and matches them with the node coordinates of the one-dimensional river hydraulic model to achieve two-way exchange of flow and water level data.

4. The method of claim 1, wherein, Model parameter zoning calibration divides the calibration zones based on the hydrogeological characteristics of the basin, and uses genetic algorithm to optimize the river Manning coefficient, surface Manning coefficient and river bed permeability coefficient, with the optimization target being the weighted Nash efficiency coefficient and correlation coefficient, and the weight ratio being 7:

3.

5. The method of claim 1, wherein, Climate change scenario simulation includes presetting precipitation increase by 25%, precipitation decrease by 25%, temperature increase by one degree Celsius, and their combination scenarios, generating climate sequences through bootstrap sampling in a thirty-year simulation period, and quantifying the annual average runoff change rate, runoff distribution coefficient and extreme flow event frequency.

6. The method of claim 1, wherein, It also includes extreme climate event simulation, by presetting three consecutive years of precipitation less than 40% of the historical average and temperature higher than the historical average by two degrees Celsius, and evaluating the duration of reservoir storage below the dead water level, the duration of river cutoff and the satisfaction rate of ecological water demand.

7. The method of claim 1, wherein, It also includes model uncertainty analysis, generates one hundred parameter combinations through Latin hypercube sampling, calculates the runoff simulation range under the 90% position confidence interval, and quantifies the contribution rate of input parameters, of which the precipitation data uncertainty contribution rate is more than 50%.

8. A cold and arid mountainous basin runoff simulation system based on a MIKE SHE and MIKE 11 coupled model combined with GIS / RS data, the system comprising a preprocessing module, a coupled model construction module, a calibration module and a simulation module, respectively used to implement the method steps of any one of claims 1 to 7.

9. The system of claim 8, wherein, The preprocessing module is used for acquiring remote sensing precipitation data, land surface parameter data, terrain data and site observation data, and completing data downscaling, resampling, projection and fusion processing.

10. The system of claim 8, wherein, The calibration module comprises a genetic algorithm engine, which is used for optimizing the Manning coefficient of the river channel, the Manning coefficient of the ground surface and the riverbed permeability coefficient, and adopting a weighted Nash efficiency coefficient and a correlation coefficient as a fitness function.

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