SWAT模型在斯里兰卡河流径流预测中的运用

本文运用SWAT模型和新安江模型对斯里兰卡卡鲁河流域上游地区日径流进行了预测.卡鲁河是斯里兰卡的第二大河,由于流域的降雨量很大,上游地区河流沿峡谷流下,中下游平原地区河床平坦.卡鲁河流域的洪水变的很正常.应用SWAT模型来对卡鲁河的日径流量进行预测,并同应用新安江模型所得到的结果做对比.研究表明,新安江模型要比SWAT (分布式水文模型)模型在卡鲁河日径流量预测上稍微好一些.实际上,或许数据质量不高或不恰当是部分原因,因为SWAT的输出成果严格取决于其输入的数据质量.此外,在斯里兰卡,许多人的日常用水是靠井水.当把流域看作一个整体,通常都是一个很大的范围,那样的话就不可能详尽的记录所有各个小规模的水利用,例如:小灌溉、小规模的家畜管理和工业水利用.这些水利用累积起来或许就很可观.这些数据的缺失对分布式水文模型在水平衡的应用有着独特的影响.但是概念水文模型(如新安江模型)可以根据实际情况在校正中调节它的参数,因为这些参数并没有实质的物理含义.因此,在流域特征和模型输入数据有限或不完整的情况下,概念水文模型比分布式水文模型更具优势.

Application of the SWAT Model for River Flow Forecasting in Sri Lanka

Abstract In the present study, the SWAT model and the Xinanjiang model have been used for daily flow forecasting of the Kalu River upper catchment in Sri Lanka. Kalu River is the second largest river in Sri Lanka and due to heavy rainfalls over the catchment, steep river slopes with narrow valleys in the upper catchment and mild riverbed slopes with wide and flat plains in the middle and lower catchments, the floods in Kalu River basin have become regular. The SWAT model has been used for daily river flow predictions in the Kalu River, and compared with the results obtained using the Xinanjiang model. In this study, the Xinanjiang model has performed slightly better than the SWAT model for forecasting the daily flow of Kalu River. In fact it might be partly attributable due to the poor quality and inadequate data, since the output of the SWAT (distributed model) strictly depends on the quality of input data. In addition, many people in Sri Lanka use well water for their domestic purposes. When considering a catchment as a whole, normally it is a very large area, and therefore it is not possible to record or count all the individual minor scale water utilizations in detail such as small irrigation, animal husbandry in minor scale and industrial water utilizations in minor scale. The cumulative value of such water utilizations might be large. The absence of these data may specially affect the distributed models in water balancing. But the conceptual watershed models (e.g. Xinanjiang model) are capable of adjusting their parameters while calibrating, according to the situation since most of their parameters have no physical background. As a result conceptual watershed models show better performance than distributed models where the catchment characteristics and model inputs are limited or incomplete.