Optimization Of HSPF Runoff Simulation And Its Response To Rain Gauge Density

In recent years,the intensity and frequency of extreme events such as flood and drought have changed greatly due to the influence of climate change and human activities.This poses a serious threat to the socio-economic development and the daily life of the residents.Water cycle and water resource change have also become more complicated.Hydrological model is an approximate simulation of complex hydrological phenomena,which can realize the quantitative study of runoff.However,the hydrological model can't completely simulate the environment,so the uncertainty of hydrological model can universally exist.The error of input data is one of the main sources of uncertainty.The accuracy of precipitation data which is important input data of hydrological model influences the simulation accuracy of runoff.The rain gauge network is one of the main ways to obtain precipitation data.At present,researchers can acquire abundant precipitation data through advanced technology,but it costs a lot of manpower and financial resources and has redundancy and deficiency in data.Therefore,we should research the influence of rain gauge network density on runoff simulation of hydrological model,and discuss the optimal quantity and optimal distribution of rain gauge network.By these means,we can achieve the purpose of setting as few rain gauges as possible without making the simulation effect of hydrological models worse.It is of great significance to improve the utilization efficiency of the rain gauge network and the cost of data acquisition.The Jinjiang basin was selected as the study area.Based on the sensitivity analysis of parameter in The Hydrological Simulation Program in Fortran model(HSPF).The Parameter Estimation automatic calibration model(PEST)with optimization multi-objective function were applied in HSPF calibration of Shanmei reservoir basin.In addition,it was extended to the Jinjiang basin and the daily time step HSPF model was constructed.On this basis.we set up six kinds of density scenarios for rain gauge network.By combining the multi criteria optimization model based on information entropy,we obtained the rain gauge combination scheme with minimum data deviation and minimum overlap information,and regarded it as good and best rain gauge networks in different density scenarios.Through comparing and analyzing spatial distribution characteristics of the best rain gauge network and the daily runoff simulation results based on different rain gauge network scenarios,we discussed the impact of rain gauge density on runoff simulation.The results show as follow:(1)Multi-objective calibration performed better with respect to the trend of runoff,total runoff,flow duration curves,and goodness-of-fit measures(NSE,R2,RMSE and PBLAS)than single objective calibration.When the squared error of daily flow objective function was improved by separating wet and dry seasons,the problem of dry flow was neglected in traditional multi-objective calibration was solved and the simulation results of daily and monthly runoff were significantly improved during the dry season.(2)HSPF model can simulate well for daily and monthly time scale runoff process in the Jinjiang basin.In the calibration and validation period of daily runoff simulation,the value of the NSE were above 0.75,the value of the R2 were above 0.76,and the value of the PBIAS were within 10%.In the calibration and validation period of monthly runoff simulation,the value of the NSE were above 0.85,the value of the R2 were above 0.87,and the value of the PBIAS were within 10%.(3)In the best rain gauge network,there are more rain gauges in the mountain areas in the upper reaches of Jinjiang river,which have higher values of information entropy,and less rain gauges located in the downriver valley area where values of information entropy are smaller.(4)The density of rain gauge network has certain influence on the daily runoff simulation.When the number of rain gauges was above 11,the value of the NSE were above 0.70,the value of the R2 were above 0.75,the value of the PBIAS were between-4%and 3%,and the value of the RMSE were below 100 m3/s during the calibration period,the value of the NSE were above 0.82,the value of the R2 were above 0.84,the value of the PBIAS were between-7%and 1%,and the value of the RMSE were below 99 m3/s during the validation period.