空间尺度转换是近年来区域生态水文研究领域的一个基本研究问题。其需要主要是源于模型的输入数据与所能提供的数据空间尺度不一致以及模型所代表的地表过程空间尺度与所观测的地表过程空间尺度不吻合。综述了目前区域生态水文模拟研究中常用的空间尺度转换研究方法,包括向上尺度转换和向下尺度转换。详细论述了2种向下尺度转换方法:统计学经验模型和动态模型。前者是通过将GCM大尺度数据与长期的历史观测数据比较从而建立统计学相关模型,然后利用这个统计学经验模型进行向下的空间尺度转换.然而动态模型并不直接对GCM数据进行向下尺度的转换,而是对与GCM进行动态耦合的区域气候模型(RCM)的输出数据进行空间尺度转换.通常后者所获得的数据精度要比前者高,但是一个主要缺点就是并不是全球所有的研究区域都有对应的RCM。还详细论述了2种向上尺度转换方法:统计学经验模型和斑块模型。前者是建立一个能代表小尺度信息在大尺度上分布的密度分布概率函数,然后利用这个函数在所需的大尺度上进行积分而求得大尺度所需的信息。而后者是根据相似性最大化原则将大尺度划分为若干个可操作的小尺度斑块,然后将计算的每个小尺度斑块的信息平均化得到大尺度所需的信息。通常在计算这种斑块化的小尺度信息的时候,对每个小尺度也会采用统计学经验模型来计算代表整个斑块小尺度的信息。建议用斑块模型与统计学经验模型相集合的方法来实现向上的空间尺度转换 Spatial scale conversion is a basic research issue in the field of regional eco-hydrology in recent years. The main reason is that the input data originating from the model is inconsistent with the spatial scale of the data that can be provided and the spatial scale of the surface process represented by the model does not coincide with the observed spatial scale of the surface process. The methods of spatial scale transformation commonly used in the research of regional hydrological and hydrological simulations are summarized, including upward scale conversion and downward scale conversion. Two kinds of downscaling methods are discussed in detail: statistical experience model and dynamic model. The former establishes a statistical correlation model by comparing GCM large-scale data with long-term historical observation data, and then uses this statistical empirical model to conduct downward spatial scale conversion. However, the dynamic model does not directly perform downward scaling on the GCM data Instead of spatially scaling the output data of a regional climate model (RCM) that is dynamically coupled with the GCM, the latter usually obtains more accurate data than the former, but one of the major drawbacks is not all of the studies worldwide The area has a corresponding RCM. Also discussed in detail two kinds of upward scale conversion methods: statistical experience model and plaque model. The former is to establish a density distribution probability function that can represent large-scale information on the basis of small-scale information, and then use this function to integrate the required large-scale information to obtain the information needed for large-scale. The latter divides the large scale into several small scale patches according to the principle of maximizing the similarity, and then averages the calculated information of each small scale patch to get the information needed for large scale. Often when calculating this plaque-like, small-scale information, statistical empirical models are also used for each small scale to calculate the small-scale information that represents the entire patch. It is suggested that the upward spatial scale conversion be achieved by a combination of patch models and statistical empirical models