| 引用本文: | 臧帅宏,李致家,黄迎春,李巧玲.基于自相似河网结构的河网消退系数Cs计算方法研究.湖泊科学,2019,31(3):788-800. DOI:10.18307/2019.0317 |
| ZANG Shuaihong,LI Zhijia,HUANG Yingchun,LI Qiaoling.Calculation method and application of river network regression coefficient Cs based on self-similarity of river network structure. J. Lake Sci.2019,31(3):788-800. DOI:10.18307/2019.0317 |
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| 摘要: |
| 新安江模型河网汇流参数Cs对洪峰模拟影响较大，目前Cs的确定需依赖于大量的历史数据，因此Cs的确定成为无资料地区和资料匮乏区水文模型应用中亟需解决的棘手问题.本文基于参数的物理意义，通过自相似河网结构的假定，构建Cs与河网形态、流域下垫面特征的相关联系，提出基于河链蓄量方程的Cs估算方法，对半干旱、半湿润和湿润地区等不同水文气象分区的11个流域的Cs值进行推算并代入新安江模型中进行模拟，经比较发现，11个流域子流域Cs计算均值与新安江模型率定结果相近，说明该Cs计算方法是合理的.选取陈河、屯溪两个典型流域研究单元流域属性对Cs的影响，由结果可以看出Cs与流域面积、河链数、河宽呈正相关，与单元流域距离出口的远近呈负相关，这表明流域分块后各单元流域Cs值不一致，而新安江模型中采用相同Cs值对不同单元进行调节必然会造成汇流计算的误差.为进一步提高该方法在无资料地区的应用效果，将新安江模型汇流模块修改为每个单元使用对应的Cs计算值进行滞后演算，以陈河和屯溪流域为例采用新安江模型Cs率定值、Cs计算均值以及修改后新安江模型3种不同方案进行模拟比较，从模拟结果可以得出，修改后的模型具有明显优势，将模型参数与下垫面条件建立了联系，模型物理机制提高且参数的独立性增强，对于新安江模型在无资料地区的应用具有重要的指导意义. |
| 关键词: 新安江模型 河网消退系数 参数规律 汇流计算 |
| DOI：10.18307/2019.0317 |
| 分类号: |
| 基金项目:国家自然科学基金项目（51679061）和国家重点研发计划项目（2016YFC0402705）联合资助. |
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| Calculation method and application of river network regression coefficient Cs based on self-similarity of river network structure |
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ZANG Shuaihong1, LI Zhijia1,2, HUANG Yingchun1, LI Qiaoling1
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1.College of Hydrology and Water Resources, Hohai University, Nanjing 210098, P.R.China;2.National Cooperative Innovation Center for Water Safety & Hydro-Science of Hohai University, Nanjing 210098, P.R.China
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| Abstract: |
| The river network parameter Cs of Xin'anjiang model has significant influence on the simulation of flood peak, but it is difficult to estimate and transfer directly for the data-limited regions. Therefore, the determination of Cs is a difficult problem and has to be solved urgently for the application of hydrological models in ungauged basins. Based on the flow calculation process of the self-similar river network structure, this study presents an estimation method of Cs based on the river chain storage equation. For 11 selected catchments, including humid, semi-humid and semi-arid areas, the Cs values were calculated and statistically analyzed. Compared with the model based optimized method, the difference of the Cs values are extremely small and the model performances are similar. Which indicates that the Cs estimation method has certain applicability. Chenhe and Tunxi catchments are used to investigate the effect of the sub-catchment properties on the estimation of Cs. Results show that with the increasing of catchment size and the number of river chains, the Cs values increase. Meanwhile, the closer the sub-catchment is to the outlet, the higher the Cs value becomes, which indicates that the Cs value of each sub-catchment changes after the watershed blocked. Hence it must cause the error of the confluence calculation with the same Cs value. In addition, the Cs value for the sub-catchment is normally smaller than the that for the whole catchment. Because the whole catchment Cs value represents all storage function of the whole catchment, and when catchment is divided into blocks, each sub-catchment uses its own Cs for storage, and the river below the outlet of the sub-catchment is calculated by the Muskingum algorithm, and Cs value for the whole catchment is larger than that for the sub-catchment. In order to further improve the application effect of this method in the data-limited regions, the confluence calculation process of the Xin'anjiang model has been improved in this study. The confluence calculation module of the Xin'anjiang model is modified so that the confluence is calculated in each sub-catchment separately with different Cs values. The simulation results of the Xin'anjiang model and the modified model in study catchments show that both of two models could obtain reasonable forecasting results. In contrast, the modified model shows more advantages, as it considers the spatial variability of catchment and establishes relationship between model parameters and surface conditions to strengthen the physical mechanism and parameter independence of the model, which can obviously enhance the flood forecasting accuracy in ungauged basins. |
| Key words: Xin'anjiang model river network regression coefficient parameter regionalization confluence calculation |
