A regional similarity-based approach for sub-daily rainfall nonparametric generation

ABSTRACT Rainfall time series with high temporal resolution are required for estimating storm events for the design of urban drainage systems, for performing rainfall-runoff simulation in small catchments and for modeling flash-floods. Nonetheless, large and continuous sub-daily rainfall samples are often unavailable. For dealing with the limited availability of high-resolution rainfall records, in both time and space, this paper explored an alternative version of the k-nearest neighbors algorithm, coupled with the method of fragments (KNN-MOF model), which utilizes a state-based logic for simulating consecutive wet days and a regionalized similarity-based approach for sampling fragments from hydrologically similar nearby stations. The proposed disaggregation method was applied to 40 rainfall gauging stations located in the São Francisco and Doce river catchments. Disaggregation of daily rainfall was performed for the durations of 60, 180 and 360 minutes. Results indicated the model presented an appropriate performance to disaggregate daily rainfall, reasonably reproducing sub-daily summary statistics. In addition, the annual block-maxima behavior, even for low exceedance probabilities, was relatively well described, although not all expected variability in the quantiles was properly summarized by the model. Overall, the proposed approach proved a sound and easy to implement alternative for simulating continuous sub-daily rainfall amounts from coarse-resolution records.

摘要：高时间分辨率降雨时间序列，是开展城市排水系统设计的暴雨事件估算、小型集水区降雨径流模拟以及山洪灾害建模的必要数据。然而，大尺度且连续的亚日降雨样本往往难以获取。针对时空维度下高分辨率降雨记录可用性不足的问题，本文提出一种改进的k近邻算法（k-nearest neighbors algorithm），结合片段法（fragments method）构建KNN-MOF模型。该模型采用基于状态的逻辑模拟连续降雨日，并通过区域化相似性方法从水文相似的邻近雨量站中采样降雨片段。所提出的降雨分解方法被应用于圣弗朗西斯科（São Francisco）与多西河（Doce）流域的40个雨量站，针对60、180、360分钟三种时长开展日降雨分解实验。结果表明，该模型具备良好的日降雨分解性能，能够合理复现亚日降雨的汇总统计特征。此外，即便针对低超越概率场景，年块极大值分布特征也得到了较好的刻画，尽管模型未能完整复现分位数中的全部预期变异性。总体而言，该方法为从粗分辨率降雨记录模拟连续亚日降雨总量提供了一种可靠且易于实现的替代方案。