We apply a hierarchical clustering algorithm to the Parameter-elevation Relationships on Independent Slopes Model (PRISM) database. The method employs linkage clustering while forcing spatial contiguity. We apply it to the lower-48 United States, deriving regions that are based on temperature and precipitation averages and anomalies, as well as statistical parameters underlying several drought and intense precipitation measures. Resulting regions make intuitive sense from the perspective of driving influences on temperature and precipitation averages and anomalies, and are compatible with results from another empirically derived clustering scheme. Regions selected for individual variables show high similarity across different time frames. There is slightly less similarity when comparing regions created for different monthly or daily hydroclimate variables, and relatively low similarity between monthly vs. daily measures. It is unlikely that any one regionalization solution could summarize hydroclimate extremes given the wide range of variables used to describe them, but geographically sensitive datasets like PRISM and flexible algorithms provide useful methods for regionalization that can aid in drought monitoring and forecasting, and with impacts and planning associated with heavy precipitation.

我们将层次聚类算法应用于独立坡度模型（PRISM）数据库的参数高程关系。该方法采用链接聚类，同时强制空间连续性。我们将其应用于美国 48 个州以下地区，根据温度和降水平均值和异常情况以及几种干旱和强降水措施背后的统计参数得出区域。从对温度和降水平均值和异常的驱动影响的角度来看，所得区域具有直观意义，并且与另一个凭经验得出的聚类方案的结果兼容。为各个变量选择的区域在不同时间范围内表现出高度相似性。比较针对不同的每月或每日水文气候变量创建的区域时，相似性稍低，并且每月与每日测量值之间的相似性相对较低。鉴于用于描述极端水文气候的变量范围广泛，任何一种区域化解决方案都不可能概括极端水文气候，但 PRISM 等地理敏感数据集和灵活的算法为区域化提供了有用的方法，有助于干旱监测和预测，并具有影响和影响。与强降水相关的规划。