山区丘陵地区农业非点源污染负荷估算方法比较研究

为比较农业非点源污染负荷估算方法在山区丘陵地区的适用性和精度等,对比分析了机理模型SWAT模型法和经验模型流失系数法分别估算典型山区丘陵地区铁岭市的农业非点源污染负荷的差异。结果表明,SWAT模型法计算出的铁岭市农田地表径流总氮负荷量为850.2 t,总磷负荷量为70.5 t;流失系数法计算出的铁岭市农田地表径流总氮负荷量为937.0 t,总磷负荷量为71.4 t。流失系数法相对于SWAT模型法的计算结果,氮、磷负荷量相对误差分别为10.2%、1.3%。研究表明,SWAT模型法在山区丘陵地区的适用性较好,计算结果精度较高;流失系数法操作简便,但由于田间试验的尺度效应及未考虑入河过程,其计算结果精度较低,结果一般偏大。

Energy Balance-Based SWAT Model to Simulate the Mountain Snowmelt and Runoff——Taking the Application in Juntanghu Watershed(China) as an Example

In order to predict long-term flooding under extreme weather conditions in central Asia, an energy balance-based distributed snowmelt runoff model was developed and coupled with the Soil and Water Assessment Tool(SWAT) model. The model was tested at the Juntanghu watershed on the northern slope of the Tian Shan Mountains, Xinjiang,China. We compared the performances of temperature-index method and energy balanced method in SWAT model by taking Juntanghu river basin as an application example(as the simulation experiment was conducted in Juntanghu River, we call the energy balanced method as SWAT-JTH). The results suggest that the SWAT snowmelt model had overall Nash-Sutcliffe efficiency(NSE) coefficients ranging from 0.61 to 0.85 while the physical based approach had NSE coefficients ranging from 0.58 to0.69. Overall, on monthly scale, the SWAT model provides better results than that from the SWAT-JTH model. However, results generated from both methods seem to be fairly close at a daily scale. Thestructure of the temperature-index method is simple and produces reasonable simulation results if the parameters are well within empirical ranges. Although the data requirement for the energy balance method in current observation is difficult to meet and the existence of uncertainty is associated with the experimental approaches of physical processes, the SWAT-JTH model still produced a reasonably high NSE. We conclude that using temperature-index methods to simulate the snowmelt process is sufficient, but the energy balance-based model is still a good choice to simulate extreme weather conditions especially when the required data input for the model is acquired.

Uncertainty analysis of hydrological model parameters based on the bootstrap method:A case study of the SWAT model applied to the Dongliao River Watershed,Jilin Province,Northeastern China

As an important tool for the description and analysis of hydrological processes,the watershed hydrological model has been increasingly applied to watershed hydrological simulations and water resource management.However,in most cases,model parameters are only determined in a calibration scheme which fits the modeled data to observations,thus significant uncertainties exist in the model parameters.How to quantitatively evaluate the uncertainties in model parameters and the resulting uncertainty impacts on model simulations has always been a question which has attracted much attention.In this study,two methods based on the bootstrap method(specifically,the model-based bootstrap and block bootstrap)are used to analyze the parameter uncertainties in the case of the SWAT(Soil and Water Assessment Tool)model applied to a hydrological simulation of the Dongliao River Watershed.Then,the uncertainty ranges of five sensitivity parameters are obtained.The calculated variation coefficients and the variable parameter contributions show that,among the five parameters,ESCO and CN2 have relatively high uncertainties:the variation coefficients and contribution rates are 23.98 and 70%,14.43 and 18%,respectively.The three remaining parameters have relatively low uncertainties.We compare the two uncertainty ranges of parameters acquired by the two bootstrap methods,and find that the uncertainty ranges of parameters acquired by the block bootstrap are narrower than those acquired by the model-based bootstrap.Further analysis of the effects of parameter uncertainties on the model simulation reveals that the parameter uncertainties have great impacts on results of the model simulation,and in the model calibration stage 60%70%of runoff observations were within the corresponding 95%confidence interval.The uncertainty in the model simulation during the flood season(i.e.the wet period)is relatively higher than that during the dry season.