中文摘要：

城市需水过程和城市土地利用单元的类型密切相关,不同建设用地类型对水资源的需求强度具有显著差异.本研究基于8类城市建设用地与3类主要城市需水过程的空间关系建立了分布式城市需水预测模型.以厦门市为对象进行模型的应用研究,通过PEST软件（parameter estimation）率定了建设用地单元上的需水参数,分析了参数的合理性,预测了厦门市2020年城市的需水总量及其空间分布.结果表明厦门市2020年的城市需水总量将达到36657万吨,比2014年增长24.17%;居民用地和工业用地的需水强度大于其他建设用地类型,厦门岛内居民用地的需水强度远大于厦门市其他行政区居民用地的需水强度.厦门市城市建成区需水量的空间分布与人口密度具有很好的相关性,不同用地类型上需水强度差异明显.

英文摘要：

The prediction of urban water demand is likely to play a significant role in the planning of urban water supply and drainage pipe network. The traditional water demand forecasting methods, including the water index method and the fuzzy water demand prediction model, are widely used with some deficiencies. The water demand prediction results varied among different water quantity indexes with subjectivity. The prediction results by fuzzy water demand prediction model, with fuzzy model structure, are lack of spatial distribution. Meanwhile, the process of urban water demand is closely related to the land use of urban unit. The difference of intensity and characteristic of water demand is obvious among different land use types. In this paper, a distributed urban water demand prediction model was established based on the relationship of water demand process, considering of the eight types of urban construction land use data and three types of major water demand data. In this model, we assumed the linear relationship between water demand and land use unit, ignored the fire water demand because with more randomness than other water demand process. The distributed model for urban water demand prediction was applied in Xiamen. We obtained the land use data and water demand data in 2004-2014 of Xiamen from official statistics. The monolayer construction area and the number of building stories in the residential units obtained by the high definition satellite image and the actual measurement were used to calculated the area of residential land unit, while the area of other land unit was obtained from official land planning in 2020 of Xiamen. The water demand parameters of every urban construction land unit were calibrated by PEST, and the rationality of parameters was analyzed. We observed the stability of the water demand parameters in the model while the results showed great similarity with different calibration period in the same validation period. The water demand of Xiamen and its spatial distribution in 2020 were pred