Information on water table depth (WTD) in polder areas and wetlands is important in, for example, estimating emissions of greenhouse gases, assessing the agricultural and ecological potential, and flood risk management. The seasonal variation of WTDs is summarized with averages of the yearly highest (shallowest) and lowest (deepest) water tables (MHW and MLW). These characteristics show short-distance variations within the fields in polder areas, which cannot be mapped using geostatistical interpolation techniques or physical modelling against reasonable costs or with acceptable accuracy. The within-field variations depend on soil type and water management. MHW and MLW were determined from auger hole measurements of WTDs at locations being selected following stratified simple random sampling in subareas classified by soil type and water management. Within these subareas, a further classification was made on the basis of distance to ditches. For each subarea spatial distribution functions of MHW and MLW were made, taking censored observations into account. Uncertainty was quantified by 10,000 bootstrap realisations of the spatial distribution functions. From these realisations maps depicting summary statistics for the spatial distribution of WTD-characteristics within the subareas were derived, as well as a map with probabilities of exceedance of a critical level that can serve as input for risk analysis.

圩区和湿地的地下水位深度 (WTD) 信息在估算温室气体排放、评估农业和生态潜力以及洪水风险管理等方面非常重要。WTD 的季节性变化用年度最高（最浅）和最低（最深）地下水位（MHW 和 MLW）的平均值进行总结。这些特征显示了圩区田地内的短距离变化，这些变化无法使用地统计插值技术或物理建模以合理的成本或可接受的精度进行映射。田间变化取决于土壤类型和水管理。MHW 和 MLW 是根据在按土壤类型和水管理分类的分区中进行分层简单随机抽样后选定位置的 WTD 的螺旋孔测量值确定的。在这些子区域内，根据到沟渠的距离进行了进一步的分类。对于MHW和MLW的每个分区空间分布函数，考虑到删失观测。不确定性通过空间分布函数的 10,000 个自举实现来量化。从这些实现中，导出了描述子区域内 WTD 特征空间分布的汇总统计数据的地图，以及具有超过临界水平的概率的地图，可以作为风险分析的输入。000 空间分布函数的 bootstrap 实现。从这些实现中，导出了描述子区域内 WTD 特征空间分布的汇总统计数据的地图，以及具有超过临界水平的概率的地图，可以作为风险分析的输入。000 空间分布函数的 bootstrap 实现。从这些实现中，导出了描述子区域内 WTD 特征空间分布的汇总统计数据的地图，以及具有超过临界水平的概率的地图，可以作为风险分析的输入。