Simulation And Prediction Of Eco-hydrological System Response In Ecological Protection And Restoration Of Dasha River Watershed

Since the 18th CPC National Congress,General Secretary Xi Jinping has put forward the systematic and holistic ecological protection and restoration concept of"mountain,water,forest,field,lake and grass are a life community"from the strategic height of ecological civilization construction.This concept has become an action guide for the country to promote the construction of ecological civilization,improve the ecological environment governance capacity and enhance the ecosystem service function.Ecological protection and restoration at the watershed scale can fully consider the integrity of natural geographical units and the relevance of the ecosystem.Dasha River Watershed is an important ecological barrier in the central region and a key node of the national ecological security strategic pattern,and the implementation of ecological protection and restoration is very important to enhance the ecological service function.In this thesis,the Dasha River Watershed is taken as the research object,and the eco-hydrology SWAT model is established through data collection.The point source and non-point source pollution in the basin are mainly analyzed.The ammonia nitrogen（NH₄-N）and Total phosphorus（TP）in surface water quality are selected as remediation evaluation factors,and the effects of different ecological protection and remediation measures are quantitatively evaluated.The vulnerability of groundwater in the phreatic aquifer under the influence of human activities is studied,and the influence of hydrology and water quality under the synergistic effect of climate change and land use change is analyzed.The main conclusions are as follows:（1）By constructing SWAT model in the study area,the runoff and nutrient load in the basin are simulated.According to the calibration and verification results of the model,the simulation of the model is satisfactory.By analyzing the monthly changes of NH₄-N and TP load,it is found that the NH₄-N load is larger in the month with less rainfall,while the changes of TP load are positively correlated with the monthly rainfall.The seasonal and annual changes of NH₄-N and TP load are similar to the monthly changes.By analyzing the spatial distribution of NH₄-N and TP,it is found that the load increases gradually along the main channel of Dasha River.Analysis of the proportion of different sources of industrial point source,fertilizer application,livestock breeding and rural living source shows that the NH₄-N load is 94%,5.12%,0.71%and 0.17%respectively.In the TP load emissions,they are 22.99%,35.48%,35.95%and 5.58%respectively.（2）By identifying the critical source areas（CSAs）of NH₄-N and TP,it is found that the CSAs of NH₄-N are mainly concentrated in eight sub-basins in the south of the study area,with a unit area load of 16.66kg/ha to 286.06kg/ha,covering 0.83%of the study area.There are 12 critical sub-basins of non-point source NH₄-N,with the unit area load of 1.73 kg/ha to 29.48 kg/ha,covering 1.08%of the study area.There are 11CSAs of TP,covering 1.03%of the study area,and the load per unit area is 9.76 kg/ha to 292.83 kg/ha.There are 13 CSAs of non-point source TP,with a unit area load of5.76 kg/ha to 232.39 kg/ha,covering 1.16%of the study area.By superimposing the source factors and driving factors,29 critical multi-factor source areas in the basin were identified,mainly distributed in the south of the basin,covering 12.00%of the total area of the study area.There are 28 critical non-point source areas,covering 11.00%of the total area of the study area.By analyzing the influencing factors,it is found that the multi-factor identification method of CSAs is more reasonable.（3）The NH₄-N and TP in surface water quality were selected as evaluation indexes for ecological protection and restoration of river basin.By implementing single and combined ecological restoration measures to evaluate the optimization efficiency of restoration indexes,it was found that the optimized amount of NH₄-N and TP in the scenario of 50%reduction of point source was 2.47 times and 2.15 times that of 20%reduction of point source.The optimal amount of NH₄-N and TP in the scenario of 50%reduction of chemical fertilizer application is 3.41 times and 2.30 times that of 20%reduction of chemical fertilizer application.After returning farmland to forest,the NH₄-N load decreased by 314.82kg.TP load decreased by 2151.88kg.By setting the vegetation filter belt,the NH₄-N load decreased by 1712.07kg and the TP load decreased by39940.96kg.Set the optimization of NH₄-N and TP in the grass-planting waterway to be2257.36kg and 35043.42kg respectively.With the increase of the permeability coefficient of hard coverage area,the load optimization rate of NH₄-N and TP is improved in most sub-basins,and the overall optimization efficiency is the worst.After setting up the combined measures,the effect is the most remarkable.（4）According to the groundwater vulnerability assessment of the phreatic aquifer in the basin,on the basis of DRASTIC model,the DRASTIC,AHP-DRASTIC-OR,AHP-DRASTIC-NR,AHP-DRASHCL-OR,AHP-DRASHCL-NR and VWM-DRASHCL models are established considering the modification of parameters and weight optimization based on variable weight theory.The results show that the areas with extremely low,low,medium,high and extremely high vulnerability levels in the original model account for 7.28%,31.97%,36.21%,18.90%and 5.64%respectively.Compared with the method of AHP-DRASTIC-OR and DRASTIC,the areas with extremely high vulnerability are changed,more concentrated and less covered.The results of AHP-DRASTIC-NR show obvious changes,and the distribution of different levels of vulnerability areas is more uniform,with the largest proportion of vulnerability areas in the middle area.In AHP-DRASHCL-OR,high and extremely high vulnerability areas are concentrated in the west,accounting for 29.98%of the area.AHP-DRASHCL-NR optimizes the parameter score,and the area with extremely low vulnerability decreases,while the area with extremely high vulnerability increases.The results of VWM-DRASHCL method show that the variable weight model can improve the degree of dispersion by optimizing the parameter weights.The correlation results show that the vulnerability index of the above six models and the AUC values of the four hydrochemical parameters increase in turn.The AUC value of is the lowest,while that of VWM-DRASHCL is the highest.Combined with the variable weight model,the parameters related to aquifer and human activities are improved,and the accuracy and rationality of evaluation are improved.（5）By analyzing the present situation of land use in the river basin and predicting the future evolution trend,it is found that under the natural evolution trend scenario,the areas increased by water bodies,urban land,rural land,industrial land and unused land,while the areas decreased by cultivated land,woodland and grassland.Under the scenario of human intervention protection,the area of cultivated land,urban land and unused land decreased,while the area of cultivated land,woodland,grassland and water area increased.Climate models of different data sets are analyzed,and RCP4.5 and RCP8.5 emission scenarios of BCC-CSM1-1 climate model are selected as future meteorological data,and future hydrological system changes are evaluated by coupling future land use changes.According to the forecast results,the predicted runoff is positively correlated with the annual rainfall predicted by each other.Under the same climate model,the cross-section flow of natural evolution trend scenario of land use is larger than that of human intervention protection scenario.The prediction of water quality in the future section shows that the influence of land use change on pollutant concentration is greater than that of future climate change.The prediction results of groundwater vulnerability evolution trend in phreatic aquifer show that,compared with the natural evolution trend scenario,the spatial distribution of vulnerability areas in the human intervention protection scenario is similar,with the lowest vulnerability area becoming larger and the highest vulnerability area becoming smaller.