60 results about "SWAT model" patented technology

The invention discloses an SSP scene reservoir group flood control scheduling method considering hydrological forecast uncertainty, and the method comprises the steps: obtaining SSP climate scene meteorological data to drive an SWAT model to generate daily runoff, and deducing and designing a flood hydrograph; a flow error sequence is calculated based on historical simulation and measured data, optimal distribution is optimized through fitting of a multi-probability distribution model, and a 95% confidence interval is generated through Monte Carlo simulation to correct design flood; and constructing an optimization model with the goal of minimizing the highest water level of flood regulation, bringing in various constraints, solving an optimal scheduling process by adopting a discrete differential dynamic programming algorithm, and finally refining an adaptive scheduling rule. The method solves the problems that prediction uncertainty is not considered and solving efficiency is low in the prior art, flood control safety and dispatching adaptability of the reservoir group are improved, and the method is suitable for flood control dispatching of the reservoir group under climate change.

The invention discloses a drainage basin water quality pollution source flux identification method based on an SWAT model, computer equipment and a storage medium, and the method comprises the steps: obtaining topographic data, land utilization data, soil type data and meteorological data, and enabling the topographic data, the land utilization data, the soil type data and the meteorological data to form a drainage basin spatial database; based on the preprocessed digital elevation model, determining a water flow direction and setting a reasonable water collection area threshold value, and dividing sub-basins to construct a basin calculation unit network topology structure; obtaining pollution source data, and constructing an SWAT-CUP model according to the pollution source data; performing system calibration and verification on the watershed hydrological process by adopting an SUFI-2 algorithm in the SWAT-CUP model; carrying out system calibration and verification on the total nitrogen load of the drainage basin by adopting an SWAT-CUP model; and based on the calibrated SWAT hydrological model, the input condition of the pollution source is changed. The method can cover the whole watershed and quantify the contribution rate of different pollution sources; cost is high, operation is simple, and long-term change can be dynamically simulated.

The invention relates to the technical field of water and soil loss dynamic monitoring and early warning methods in water conservancy projects, and discloses a water and soil loss dynamic monitoring and early warning method, which comprises the following steps: synchronously acquiring multi-dimensional data of terrain, rainfall, soil humidity and vegetation coverage through unmanned aerial vehicle remote sensing and a ground sensor network; registering and correcting the multi-source heterogeneous data based on a space-time fusion algorithm; the improved SWAT model is combined with a machine learning algorithm to dynamically simulate a water and soil loss process; and triggering graded early warning according to a preset risk threshold and generating prevention and control suggestions. The system comprises a data acquisition module, a data fusion processing module, a water and soil loss simulation prediction module and an early warning response module. According to the scheme, the real-time performance, the spatial resolution and the early warning accuracy of water and soil loss monitoring are remarkably improved, and efficient decision support is provided for ecological protection of water conservancy projects.

The invention provides a large-scale agricultural irrigation area grid scale underground water level spatial and temporal distribution efficient prediction method, and belongs to the technical field of underground water prediction.The method comprises the steps that firstly, an SWAT model of a drainage basin where an irrigation area is located and an MODFLOW model covering the irrigation area are constructed respectively, the SWAT model and the MODFLOW model are coupled to obtain an SWAT-MODFLOW model, and parameter correction is conducted; secondly, an irrigation area underground water level data set is constructed through scene combination and model simulation and used for training and screening the LSTM model with the highest precision, the optimal control range of each LSTM model is determined through space compression and reconstruction, and a multi-LSTM model is obtained; and finally, carrying out parallel calculation on the multiple LSTM models to predict the spatial and temporal distribution of the grid scale underground water level of the irrigation area. According to the method, the optimal control range of each LSTM model is determined through space compression-reconstruction, the prediction of the underground water level spatial and temporal distribution of the grid scale of the large-scale agricultural irrigation area is finally realized through multi-LSTM model parallel calculation, and the prediction efficiency is improved on the premise of ensuring the prediction precision of the underground water level of the irrigation area.

The invention discloses a basin hydrological simulation method and system embedded with a canopy interception mechanism, and the method comprises the steps: embedding a revised Gash canopy interception model module in an SWAT model, dividing a rainfall event into a wetting stage, a saturation stage and a drying stage according to the parameters of rainfall, a leaf area index and a tree height, dynamically calculating the canopy interception amount and the interception evaporation amount, and carrying out the calculation of the canopy interception amount and the interception evaporation amount. The effective rainfall capacity is used for replacing original rainfall input in the SWAT model, the adjusting effect of the forest canopy on the rainfall process is reflected more truly, and the basin hydrological simulation precision is improved. According to the method, the response and simulation capability of the SWAT model to the hydrological process of the underlying surface of the forest is effectively improved, the dynamic response to effective rainfall and evaporation terms is enhanced, the method is suitable for watershed hydrological process modeling of various forest types, and the reliability of flood forecasting and water conservation evaluation is improved.

The invention discloses a watershed ditch and pond-road low-cost ecological sand control method based on hydrological connectivity, which belongs to the field of ecological environment protection and mainly comprises the steps of data acquisition, DEM data correction, confluence network construction, hydrological structure connectivity identification, SWAT model construction, key source area identification, scene analysis and cost benefit analysis. Under the influence of a ditch pond-road system, the key source area identification technology based on hydrological structure connectivity and the drainage basin key source area ditch pond road low-cost water regulation and soil conservation method achieve the dual purposes of reducing the water and soil regulation and control cost and continuously improving the ecological benefit by identifying the key source area.

The invention discloses a hydrological analysis method and system based on SWAT model runoff reconstruction and a storage medium. The hydrological analysis method comprises the following steps: 1, obtaining research basin runoff data, meteorological data, digital elevation model data, land utilization data and soil data; 2, based on the data in the step 1, analyzing the evolution trend and potential mutation points in the runoff time sequence period by adopting a mutability test method; 3, dividing a natural period and a variation period based on the mutation points, and obtaining a natural runoff sequence by using an SWAT model; and 4, based on a SWAT model simulation result, extracting multiple hydrological indexes representing hydrological situation changes by adopting an IHA method, extracting ecologically-related hydrological indexes ERHIs from the hydrological indexes by adopting a PCA method, and calculating contribution rates of climate changes and human activities to the ecologically-related hydrological indexes by adopting a quantitative attribution method. The runoff driving mechanism of climate factors and human activities is disclosed, and the physical interpretability and decision support capability of the SWAT model are enhanced.

The invention discloses an artificial wetland space optimization arrangement method and system, a non-point source pollution model is constructed based on a hydrological response unit, target meteorological data, target pollution data and an SWAT model, and the simulation precision of the non-point source pollution process can be improved. Climate scene data is constructed through future climate data, a preset path rule and a simple quantile algorithm, and the influence of climate change on pollution load can be reflected. Furthermore, a wetland optimization model is constructed based on a non-point source pollution model and a target function, the scientificity of an optimization strategy is enhanced, candidate wetland areas are identified by a spatial analysis algorithm based on a complex terrain index CTI, and the maximum potential area of each candidate wetland area is counted, so that efficient identification of potential arrangement areas can be realized. The constructed wetland optimization regulation and control scheme is generated based on the preset genetic algorithm, and the constructed wetland space optimization arrangement is performed according to the scheme, so that the optimization and practicability of the constructed wetland layout can be realized.

The invention discloses a runoff prediction method based on a physical information gating network. The runoff prediction method comprises the following steps: S1, constructing an SWAT model; s2, constructing a data set; s3, global feature extraction; s4, extracting local features; s5, gate control weight calculation is carried out; and S6, performing adaptive fusion and prediction. According to the runoff prediction method, whether the model focuses on surface runoff or underground runoff can be dynamically determined according to the current moment state and the historical time sequence memory of the drainage basin, and the adaptability of the model to different hydrological scenes is remarkably improved. A double-branch architecture combining Autoformer and TCN is adopted, so that the problem that a single model is difficult to consider both periodic trend and mutation characteristics is effectively solved. And an introduced feature attention and time attention mechanism is combined with the change of a physical gating value, so that multi-level physical interpretability is provided for a prediction result of the deep learning model. The end-to-end joint optimization strategy avoids the problem of separation of traditional physical model parameter calibration and deep learning model training, the convergence speed is high, and the simulation precision is high.

The invention discloses an SWAT model correction and application method based on remote sensing data, and relates to the technical field of hydrology and water resource model optimization. Comprising the following steps: S1, acquiring various remote sensing data in a hydrological process; s2, preprocessing various remote sensing data; s3, a corresponding SWAT model is established, and parameter correction is realized through the established SWAT model; and S4, applying various remote sensing data to the established SWAT model, and analyzing and monitoring landform and river channel information in the hydrological process. According to the method, parameter correction is realized through the established SWAT model, various remote sensing data are applied to the established SWAT model, and landform and river channel information in the hydrological process is analyzed and monitored.

The invention discloses a water conservancy regulation and control information fused plain river network area pollutant flux simulation correction method, which realizes deep fusion of a physical mechanism and deep learning, utilizes a physical reference of an SWAT model as a basic constraint, utilizes deep learning to excavate a nonlinear residual error, and cooperates with ReLU physical constraint and a graph topology transmission mechanism to realize simulation correction of the flux of pollutants in a plain river network area. The method has the interpretability of a physical model and the high precision of a data-driven model, and is particularly suitable for monitoring a complex watershed with scarce data. High-precision inversion and correction are carried out on the day-by-day runoff, the total nitrogen flux and the total phosphorus flux of the whole watershed through a small amount of key node monitoring data, and powerful support is provided for space-time analysis of plain river network area non-point source pollution and water environment fine management. The problem of false runoff production in a plain river network area is solved, cascade correction logic with runoff as quality is established, and the problems of runoff simulation distortion and large pollution flux estimation error of a traditional hydrological model are avoided.

The invention discloses a pixel scale water environment safety assessment method based on a non-point source water body pollution risk, and belongs to the technical field of environmental protection. According to the method, through an agricultural non-point source pollution direct evaluation method, the pollution risk of terrestrial agricultural non-point source output to surface water is accurately quantified, a DPeRS model is introduced, quantitative evaluation of the remote sensing pixel scale agricultural non-point source river entering amount is achieved, and the pixel scale agricultural non-point source river entering concentration is quantified as a non-point source pollution risk evaluation index in combination with an SWAT model. With surface water environment quality improvement as a target, an agricultural non-point source pollution in-water risk grade threshold value is determined based on a nitrogen and phosphorus concentration limit value in a surface water environment quality standard and a national secondary pollution general survey agricultural non-point source proportion, and a pixel scale agricultural non-point source in-water pollution risk prominent area is rapidly screened. High-precision target positioning of remote sensing pixel scale agricultural non-point source pollution is realized, and source-targeted agricultural non-point source pollution precise prevention and control and agricultural non-point source pollution treatment effect evaluation are facilitated.

The application discloses a method for reducing nitrogen and phosphorus loss of slope farmland, which comprises the following steps: step 1, determining a slope farmland catchment area and estimating nitrogen and phosphorus loss load of a SWAT model; step 2, designing a drainage ditch, a rain and sewage diversion device and a diversion pipe according to the estimation result of the model; and step 3, designing a vegetation retention pool according to the runoff of the slope farmland; the vegetation retention pool effectively reduces the loss of nitrogen and phosphorus of the slope farmland through the absorption of plants, the filtration of sediments and the retention effect of water flow. The method can realize diversion treatment of small rain and high-concentration nitrogen and phosphorus runoff in the early stage of heavy rain, and the nitrogen reduction rate of the slope farmland drainage reaches 70-80%, and the phosphorus reduction rate reaches 80-90%.

The invention provides a comprehensive monitoring method for nutrient loss of a freeze-thaw basin, which comprises the following four steps of: 1, analyzing hydrometeorological characteristics of the basin based on remote sensing data, and determining a key period of nutrient loss; 2, dividing a sub-basin and a river network structure by using an SWAT model, and scientifically arranging representative sampling points; thirdly, surface water collection is carried out in combination with the determined sampling scheme, and laboratory analysis is carried out on key indexes such as total nitrogen and total phosphorus; and 4, identifying a key period and a source region of pollutant loss based on the river network convergence relationship and the monitoring data. The method integrates three technologies of remote sensing identification, model simulation and field observation, has the advantages of low cost, high efficiency, high representativeness and strong adaptability, and is especially suitable for data scarce areas such as medium and high latitude freeze thawing areas. The method has a good application prospect in the fields of water environment monitoring and pollution identification.

The application provides a large-scale agricultural irrigation area grid scale groundwater level space-time distribution efficient prediction method, belongs to the technical field of groundwater prediction, and steps include: firstly, a SWAT model of a basin where an irrigation area is located and a MODFLOW model covering the irrigation area are respectively constructed, the two are coupled to obtain a SWAT-MODFLOW model, and parameter correction is carried out; secondly, an irrigation area groundwater level data set is constructed through scenario combination and model simulation, is used for training and screening the highest-precision LSTM model, and the optimal control range of each LSTM model is determined through space compression and reconstruction, and multiple LSTM models are obtained; finally, the multiple LSTM models are parallelly calculated to predict the space-time distribution of the grid scale groundwater level of the irrigation area. The optimal control range of each LSTM model is determined through space compression-reconstruction, finally, the prediction of the space-time distribution of the grid scale groundwater level of the large-scale agricultural irrigation area is realized through the parallel calculation of the multiple LSTM models, and the prediction efficiency is improved on the premise of ensuring the prediction accuracy of the irrigation area groundwater level.

The invention relates to the technical field of pollution tracing, and discloses a drainage basin pollution tracing method, device and equipment and a storage medium. The method comprises the following steps: inputting obtained target pollution source emission data and basic data into a target SWAT model, then operating the target SWAT model, and simulating and outputting sub-basin pollution load of each sub-basin in a target basin; determining contribution proportions of different pollution source categories to the total pollution load of the target drainage basin based on an isotopic tracing method; determining a load vector of each pollution source category of the target drainage basin based on the contribution proportion and the drainage basin pollution simulation total load; constructing a sub-basin load distribution matrix; determining a unit load matrix based on the sub-basin load distribution matrix; and analyzing the unit load matrix, and tracing to obtain the dominant pollution category of each sub-basin. According to the scheme, by constructing the unit load matrix, the main source sub-drainage basins of various pollution sources are defined, the space traceability of the pollution sources is realized, and the traceability accuracy is improved.

This invention provides a method and system for identifying agricultural non-point source pollution risks in lake basins. The method includes: acquiring meteorological and crop data and inputting them into a constructed basin SWAT model; running the model monthly to obtain the runoff of each river and the concentration and output of non-point source pollutants in each river; constructing a river-lake estuary MIKE21 model to simulate the changes in lake non-point source pollutants for the current month and predict the next month, obtaining lake water quality indicators, and determining whether the lake currently meets the requirements of the lake's water function zone. If so, the risk area under the minimum reduction inflow load scenario is determined, and the basin SWAT model is called again for basin-wide agricultural non-point source pollution control. If not, the input load of each inflow river sub-basin is reduced one by one until the water quality meets the standards. Based on the basin's agricultural characteristics and meteorological and hydrological forecasts, this invention identifies and predicts key areas and time periods for agricultural non-point source pollution and proposes small-basin and segmented area management, demonstrating significant environmental and ecological benefits as well as socio-economic benefits.

The invention belongs to the technical field of groundwater pollution control and treatment, and discloses a method for effectively reducing migration of groundwater pollutants in a karst basin, and the method comprises the following specific use steps: S1, data preparation and preprocessing; s2, constructing and improving an SWAT model; s3, performing SWAT model simulation and result analysis; s4, an MODFLOW model is constructed; s5, carrying out MODFLOW model simulation and reaction migration process description; and S6, coupling the SWAT model and the MODFLOW model. According to the method, an SWAT model is improved, distribution of hydrological response units is optimized, a surface karst zone module is added, description of the model on the underground migration process of nitrogen and phosphorus along with karst water is completed, a verification code is obtained by constructing a needed space and attribute database and utilizing actually measured data verification and model adaptability, and meanwhile, the model can be used for analyzing the nitrogen and phosphorus content of the karst water. In the gurge drainage basin, the simulation precision of the model meets the requirement, and the certainty coefficient R2 and the Nash efficiency coefficient are both larger than 0.66.

The invention discloses a drainage basin base flow change attribution analysis method and system, and relates to the field of drainage basin water resource management, and the method comprises the steps: obtaining multi-source basic data of a to-be-researched region; based on daily flow data in the multi-source basic data, a heuristic segmentation algorithm is adopted to determine an annual runoff abrupt change point set; dividing the data according to the first mutation point to obtain reference period data and influence period data; based on the reference period multi-source data and an improved SWAT model of an embedded base flow extraction module, obtaining a base flow simulation sequence; and obtaining the contribution rate of the climate change and the human activity to the basic flow change of the drainage basin through a scene reconstruction and difference decomposition calculation method in combination with the data of the reference period and the influence period. According to the method, by accurately depicting the basin base flow process and the base flow-runoff synergistic effect, multi-time-scale refined quantitative analysis of the contribution rate of human activities and sub-items thereof and climate changes to the base flow change is realized, and the scientificity of basin water resource management decision is remarkably improved.

The invention relates to the technical field of water and soil loss dynamic monitoring and early warning methods in water conservancy projects, and discloses a water and soil loss dynamic monitoring and early warning method, which comprises the following steps: synchronously acquiring multi-dimensional data of terrain, rainfall, soil humidity and vegetation coverage through unmanned aerial vehicle remote sensing and a ground sensor network; registering and correcting the multi-source heterogeneous data based on a space-time fusion algorithm; the improved SWAT model is combined with a machine learning algorithm to dynamically simulate a water and soil loss process; and triggering graded early warning according to a preset risk threshold and generating prevention and control suggestions. The system comprises a data acquisition module, a data fusion processing module, a water and soil loss simulation prediction module and an early warning response module. According to the scheme, the real-time performance, the spatial resolution and the early warning accuracy of water and soil loss monitoring are remarkably improved, and efficient decision support is provided for ecological protection of water conservancy projects.

The invention belongs to the related technical field of hydrology and water resource engineering, and discloses a drainage basin runoff prediction method and system fusing a graph attention network and physical constraints. The method comprises the steps of inputting preprocessed hydrological and geographic parameters of a to-be-processed watershed into an SWAT model, and obtaining attributes of each sub-watershed in the to-be-processed watershed, a topological relation of the watershed and simulated runoff volume of each sub-watershed in a specified time sequence; encoding the output of the SWAT model by using an encoder to obtain static features and dynamic features of each sub-basin; taking the static characteristics and the dynamic characteristics as input of a multi-head attention network to obtain a runoff corrected value of each sub-basin; and correcting the simulated runoff volume by using the runoff volume correction value to obtain a final predicted value of the runoff volume in a specified time sequence, so as to predict the runoff volume of the drainage basin. According to the invention, the problems of high parameter sensitivity, lack of physical constraints and simple attention mechanism design in hydrological prediction are solved.

The application relates to the field of karst basin runoff simulation calculation, and particularly discloses a karst SWAT model runoff simulation method coupled with an early-stage precipitation index, which comprises the following steps: step one, establishing a SWAT model with the characteristics of a karst area, generating a land use map with the characteristics of the karst area and hydrological response units (HRUs) with the distribution characteristics of the karst and non-karst; step two, modifying the source code of the SWAT model to construct a K-SWAT model with the function of karst runoff generation and concentration; step three, embedding a normalized early-stage precipitation index (MIWAPI) in the K-SWAT model to construct a K-W-D-SWAT model with the function of identifying the effect of dry and wet events on the karst runoff generation and concentration process; and step four, optimizing the parameters of the SWAT model, the K-SWAT model and the K-W-D-SWAT model by using the SUFI-2 algorithm in the SWAT-CUP software, and evaluating the runoff simulation level of each model in the karst area. The karst SWAT model runoff simulation method coupled with the early-stage precipitation index is suitable for the simulation of the runoff process in the karst area with complex climate change, and can improve the simulation accuracy and rationality of the hydrological process in the karst area.

The application discloses a hilly mountainous basin water quality pollution prediction method based on a SWAT hydrological model and a PLUS land use prediction model, and comprises the following steps: data collection and preprocessing: collecting relevant data of a basin to be predicted and preprocessing; SWAT model construction and rating: constructing a SWAT model of the basin, and rating and verifying model parameters by using historical hydrological and water quality data; PLUS model construction and land use prediction: constructing a PLUS model of the basin based on historical land use data and driving factor data, and simulating land use changes under different future scenarios; SWAT-PLUS model coupling: inputting future land use data predicted by the PLUS model into the SWAT model, simulating hydrological processes and water quality changes under different future scenarios; water quality pollution prediction and analysis. The application can comprehensively consider the influence of hydrological processes and land use changes on water quality pollution, thereby accurately predicting the water quality pollution condition of the hilly mountainous basin, and providing a scientific basis for basin water environment management.

The present application relates to a kind of water quantity and water quality automatic regulation and control method, device, storage medium and electronic equipment at watershed scale, the method comprises: constructing watershed SWAT model, the model parameter of watershed SWAT model is verified, obtain the watershed SWAT model after verification, obtain the meteorological data of target region in target period predicted by atmospheric circulation model;The target water quantity and water quality of the target region in the target period are obtained, meteorological data is input into the watershed SWAT model after verification, obtain the water quantity and water quality of target region in target period, the water quantity and water quality regulation and control measure parameter of best management measure in the watershed SWAT model after verification is adjusted using non-dominated sorting genetic method with elite strategy, until the deviation between water quantity and water quality of target period and target water quantity and water quality is within the threshold range for stopping, obtain the water quantity and water quality regulation and control measure of the target region, reduce the artificial cost, improve the efficiency and precision of water quantity and water quality regulation and control.

The invention provides a runoff simulation method and system coupled with SWAT and a machine learning algorithm. The runoff simulation method comprises the steps that drainage basin spatial data, meteorological data and actually measured runoff data are collected and preprocessed; taking an intermediate physical variable output by the SWAT model and meteorological data as feature variables of ML; the method comprises the following steps that: a SWAT model is established, a graph attention network (GAT) learns a directed convergence topological relation among sub-basins output by the SWAT model, a long short term memory (LSTM) network captures hydrological time sequence dependence, a grid search method is adopted to optimize hyper-parameters, and an SL (SWAT + GAT + LSTM) and SGL (SWAT + GAT + LSTM) coupling model is established; and the system evaluates the value of the coupling model for improving the runoff simulation precision. According to the method, the advantages of a physical mechanism and data processing are fused, the problems that a traditional model is low in simulation precision and space correlation description of an existing coupling model does not conform to reality are solved, accurate simulation of extreme flood events is achieved, and scientific support can be provided for optimal configuration of water resources and flood disaster prevention and control.

The invention belongs to the technical field of water conservancy projects, and particularly relates to a data-free area runoff sediment transport simulation method and system based on an SWAT model. The method comprises the steps that a data-free small watershed is selected as a target watershed, and a system collects comprehensive geographic information data of the target watershed; based on a hydrological similarity principle, selecting data basins with similar hydrological attributes as reference basins; constructing, calibrating and verifying a reference basin SWAT hydrological model; and transplanting the verified model parameters to the target drainage basin to realize long-term sequence simulation of annual runoff volume and sediment transport volume of the target drainage basin. Compared with the prior art, the parameter transplantation method based on the SWAT model realizes runoff-sediment transport synchronous simulation under the condition of lack of actual measurement data, data requirements and calculation cost are reduced, and simulation results can provide scientific basis for regional water resource total amount analysis, drainage basin treatment measure evaluation and hydraulic engineering design; the method is of great significance in improving the scientific management capability and strategic planning level of water resources in areas without data.

The invention discloses a southwest karst small watershed nitrogen and phosphorus simulation method based on an improved SWAT model. The southwest karst small watershed nitrogen and phosphorus simulation method mainly comprises the steps of improving hydrological response unit division and improving a hydrological process and nitrogen and phosphorus loads of the hydrological process. According to the improved model, the particularity of karst landforms is considered, the simulation accuracy of the rainfall infiltration amount and the underground runoff amount is improved, the peak value of the monthly average flow simulation result in the rainy season is increased, the base flow in the dry season is reduced, the actual situation is better met, and therefore the hydrological process of the southwest karst small watershed can be more accurately simulated. And 2, a nitrogen and phosphorus load calculation mode is improved, the migration process of nitrogen and phosphorus in a surface karst zone is fully considered, a simulation result shows that the surface karst zone has an increment effect on the nitrogen and phosphorus load of a drainage basin water outlet, the change rule of the nitrogen and phosphorus load along with rainfall can be reflected more accurately, and simulation of nitrogen and phosphorus migration is more accurate.

The application discloses a watershed water and sediment nitrogen and phosphorus driving mechanism quantitative analysis method, relates to the field of quantitative analysis, acquires target watershed basic data and constructs a SWAT model, calibrates and verifies the model; climate change and agricultural management scenarios are set, the temporal and spatial loss processes of the water and sediment nitrogen and phosphorus of the watershed under different scenarios are simulated through the SWAT model; a prediction model is constructed based on the simulation results of the SWAT, and the influence degree of each driving factor on the loss of the water and sediment nitrogen and phosphorus is quantified in combination with the SHAP method; based on the spatial distribution characteristics and the SHAP quantification results, high loss areas and key driving factors are identified, and a partitioned management strategy is proposed. Through the comprehensive physical process simulation capability of the SWAT model, the nonlinear relationship capturing advantage of the XGBoost model and the driving mechanism analysis function of the SHAP method, systematic quantitative analysis of the driving mechanism of the loss of the water and sediment nitrogen and phosphorus of the watershed is realized, and scientific basis is provided for precise control and adaptive strategy making of the watershed.

The invention discloses an improved snow melting calculation method for improving the simulation precision of snow melting runoff in spring. Aiming at the problems of single starting condition, fixed snow melting factor, rough time step size and the like of an existing snow melting module of the SWAT model during simulation of spring snow melting runoff, the invention provides an improved snow melting calculation method which comprises the following steps: adding a daily accumulated temperature calculation function in the snow melting module to realize dual snow melting starting judgment based on daily accumulated temperature and a snow melting base temperature threshold value; a regional snow melting factor parameterization scheme is adopted, and snow melting factors are optimized according to the phenological characteristics of accumulated snow; and establishing an hour-scale snow melting process calculation module to realize refined simulation of snow melting runoff production. The simulation precision of the spring snow melting runoff is remarkably improved, and reliable technical support is provided for spring flood forecasting and water resource management of the cold region drainage basin.

The invention provides a method for obtaining the contribution rate of a driving factor of agricultural irrigation water demand in an arid region, and belongs to the field of agricultural water resource management, and the method comprises the steps: constructing a research region SWAT model database; utilizing an improved SWAT model to simulate hydrological cycle components to calculate effective precipitation and crop evapotranspiration; calculating a net irrigation water demand by using effective rainfall and crop evapotranspiration, and calculating a gross irrigation water demand by combining an irrigation water utilization coefficient; and quantitatively solving the driving contribution of each factor based on an LMDI factor decomposition method. Through integration of precipitation temperature correction, distributed hydrological process simulation and an LMDI factor decomposition method of an SWAT model sub-basin according to an elevation zone, high-precision estimation of regional irrigation water demand and quantitative characterization of a driving factor contribution rate are realized, and a scientific regulation and control basis is provided for agricultural water resource management in an arid region.