不同尺度和数据基础的水文模型适用性评估研究——淮河流域为例

在淮河流域蚌埠站以上区域构建人工神经网络(ANN)、HBV-D 模型和SWIM模型等水文模型,评估不同时间、空间尺度和数据基础下水文模型的适宜性。得出:①时间尺度上三个模型对数据要求不同, ANN模型需要月尺度数据即可建立降水-径流关系且能取得较好的模拟效果,HBV-D 和SWIM模型为日尺度水文模型,需逐日降水、温度和径流量等数据,SWIM模型还需作物管理、营养盐和土壤侵蚀等数据;②空间尺度上,ANN模型适应于大尺度,HBV-D 模型适用于1×10⁴ km²及以上流域,SWIM模型更适合于1×10⁴ km²以下小流域降水-径流关系建立;③模拟效果分析,月尺度统计上ANN模型对水文模拟的整体效果较好,但不适合用于气候变化背景下水文水资源等研究,而有物理基础的HBV-D和SWIM模型虽模拟的纳希效率系数不及ANN模型,但在气候变化背景下仍是较好的工具。

The Applicability Assessment of Hydrological Models with Different Resolution and Database in the Huaihe River Basin,China

In this paper,the applicability of three hydrological models, including artificial neural network (ANN) model, Hydrologiska Byråns Vattenbalansavdelning-D (HBV-D) model and Soil and Water Integrated Model (SWIM), are examined at different temporalspatial scales and databases in the Huaihe River basin which is above the Bengbu hydrological gauging station. It is found that ANN model only needs monthly data to build rainfall-runoff relationship and can obtain well simulation results, but HBV-D and SWIM models require data on daily scale such as daily precipitation, daily temperature and daily runoff. SWIM model even requires crop management data, nutrient data, soil erosion data etc. In addition, on spatial scale, the applicability of ANN model is adequate to large-scale basin, SWIM model may only be suitable for small-scale basin with an area of less than 10000 km², and HBV-D model can apply to a basin of about 10000 km². Furthermore, according to simulation results, ANN model can get better result for overall hydrological simulation, but it is not suitable for the hydrological and water resources research under climate change. Although their Nash-Sutcliffe coefficients are less than ANN model, the physically based distributed hydrological model, HBV-D model and the SWIM model are good tools to study impacts of climate change, which is significantly controlled by model structure.