Analysis Of Rainfall Characteristics And Their Influence On Runoff And Water Quality In Maozhou River

In the context of climate change and rapid urbanization,the health of urban rivers in China is under great threat.The urban water cycle process is complicated and greatly affected by human activities.Meanwhile,the lack of measured hydrological data in a long time series leads to the difficulty in runoff prediction and water quality analysis of urban watercourses.At present,hydrological models based on physical mechanism and neural network models are important tools for urban rainfall runoff simulation and prediction.However,research on the analysis and summary of the simulation characteristics of these models are still relatively lacking.In addition,understanding the impact of rainfall on runoff pollution is an important part of urban river water quality management.The Maozhou River is the longest river in Shenzhen,but due to the impact of industrialization and urbanization in recent years,frequent flooding and waterlogging incidents have occurred in this basin,and the water quality of rivers has been seriously polluted.At present,researches on the applicability of rainfall runoff model and runoff pollution analysis are still insufficient.In response to these problems,this study takes the Maozhou River Basin as the research area.Firstly,it analyzes and summarizes the spatial and temporal distribution of rainfall and the typical rainfall characteristics of the Maozhou River Basin.Then comprehensively considering factors such as data acquisition and watershed characteristics,and using the physical mechanism-driven hydrological model(SOBEK)and Artificial Neural Network(ANN),Adaptive Network-based Fuzzy Inference System(ANFIS)network with Long Short-Term Memory(LSTM)in the Maozhou River Basin,analysis the performance of different models in complex urban river basin water environment,and the influence of rainfall characteristics on model performance.Finally,the influence of rainfall characteristics on runoff pollution is analyzed by means of mathematical statistics and machine learning,and the prediction model of runoff pollution is established on this basis.The main contents and conclusions of this study are as follows:1.Analysis of the temporal and spatial distribution of rainfall and analysis of typical rainfall characteristics: The proportion of light rain and rain in the Maozhou River Basin is relatively high,the heavy rain contributes the most to the total rainfall,and the rainfall shows a certain upward trend in recent years.The uneven spatial distribution of rainfall in this basin is affected by terrain and other factors,showing a decreasing trend from southeast to northwest.Through statistical analysis of the 3707 rainfall events in the watershed,the distribution and typical values of different rain-type rainfall characteristics(such as rainfall,drought period,rainfall intensity and etc.)in the watershed are summarized.The front peak rainfall in the Maozhou River Basin is more typical,with the increase of rainfall depth,the uniform and post peak rainfall gradually increase;Second,the comparison between the SOBEK model and the neural network model: The SOBEK model shows a strong adaptability in the Maozhou River Basin,The overall NSE is 0.891,and the NSE values of the rainfall runoff simulations are concentrated at 0.7-0.8,but SOBEK modeling requires a lot of basic data,and it also takes a long time to determine.Neural network models require far less input data and model debugging than hydrological models based on physical mechanisms.The simulation results are comparable to traditional hydrological models,and even show certain advantages.ANN modeling is relatively simple,the overall simulation accuracy NSE is 0.755,but due to the limitations of the model itself,the prediction stability of the ANN model is poor.The accuracy of the ANFIS model is better.The NSE is 0.864,but it has higher requirements for the processing of input data,and it is more cumbersome when processing large amounts of data.The simulated response stability and flood peak prediction performance of LSTM have shown great advantages.The overall NSE can reach 0.945,demonstrating the great potential of the LSTM model in urban rainfall and runoff analysis.In addition,this study used the principal component analysis method to explore the relationship between the rainfall characteristics of the rainfall runoff events and the performance of the model,and found that these models have certain advantages and complementary spaces;Thirdly,the analysis of the impact of rainfall characteristics on water pollution: This study uses average concentration COD of rainfall events(EMC)as a characterization factor of water pollution,analysis of the Shiyan River,a first-level tributary of the Maozhou River,found that the above peak rainfall and light rain have a greater risk of pollution.The drought period and rainfall intensity are the most important rainfall characteristics that affect EMC.Through the construction of the rainfall characteristic EMC prediction model,it is found that under typical rainfall characteristic scenarios,the EMC value of small rain runoff pollution is about 2 times that of medium rain and 5 times that of heavy rain.In addition,according to the EMC prediction results under typical rainfall characteristics,the method of estimating the annual load of rainfall runoff pollution in the watershed has been improved.Through this study,it provides a research idea for the analysis of urban rainfall runoff and water quality pollution,and at the same time provides a certain reference for urban rainwater management,rainwater utilization and river water quality management.