The duration of inundation or saturation (i.e., hydroperiod) controls many wetland functions. In particular, it is a key determinant of whether a wetland will provide suitable breeding habitat for amphibians and other taxa that often have specific hydrologic requirements. Yet, scientists and land managers often are challenged by a lack of sufficient monitoring data to enable the understanding of the wetting and drying dynamics of small depressional wetlands. In this study, we present and evaluate an approach to predict daily inundation dynamics using a large wetland water-level dataset and a random forest algorithm. We relied on predictor variables that described characteristics of basin morphology of each wetland and atmospheric water budget estimates over various antecedent periods. These predictor variables were derived from datasets available over the conterminous United States making this approach potentially extendable to other locations. Model performance was evaluated using two metrics, median hydroperiod and the proportion of correctly classified days. We found that models performed well overall with a median balanced accuracy of 83% on validation data. Median hydroperiod was predicted most accurately for wetlands that were infrequently inundated and least accurate for permanent wetlands. The proportion of inundated days was predicted most accurately in permanent wetlands (99%) followed by frequently inundated wetlands (98%) and infrequently inundated wetlands (93%). This modeling approach provided accurate estimates of inundation and could be useful in other depressional wetlands where the primary water flux occurs with the atmosphere and basin morphology is a critical control on wetland inundation and hydroperiods.

淹没或饱和的持续时间（即水期）控制着许多湿地功能。特别是，它是湿地是否能为两栖动物和其他通常具有特定水文要求的类群提供合适繁殖栖息地的关键决定因素。然而，科学家和土地管理者经常面临缺乏足够监测数据的挑战，无法了解小型洼地湿地的干湿动态。在这项研究中，我们提出并评估了一种使用大型湿地水位数据集和随机森林算法来预测每日洪水动态的方法。我们依赖于描述每个湿地盆地形态特征的预测变量以及之前各个时期的大气水预算估计。这些预测变量源自美国本土可用的数据集，使得这种方法有可能扩展到其他地点。使用两个指标评估模型性能：中位水周期和正确分类天数的比例。我们发现模型总体表现良好，验证数据的中位平衡准确度为 83%。对于很少被淹没的湿地，中位水周期的预测最准确，而对于永久湿地的预测最不准确。永久湿地的淹没天数比例预测最准确（99%），其次是经常淹没的湿地（98%）和不经常淹没的湿地（93%）。