A comprehensive understanding of the behaviours of the various geophysical processes and an effective evaluation of time series (else referred to as "stochastic") simulation models require, among others, detailed investigations across temporal scales. In this work, we propose a novel and detailed methodological framework for advancing and enriching such investigations in a hydroclimatic context. This specific framework is primarily based on a new feature compilation for multi-scale hydroclimatic analyses, and can facilitate largely interpretable feature investigations and comparisons in terms of temporal dependence, temporal variation, "forecastability", lumpiness, stability, nonlinearity (and linearity), trends, spikiness, curvature and seasonality. Multifaceted characterizations are herein obtained by computing the values of the proposed feature compilation across nine temporal resolutions (i.e., the 1-day, 2-day, 3-day, 7-day, 0.5-month, 1-month, 2-month, 3-month and 6-month ones) and three hydroclimatic time series types (i.e., temperature, precipitation and streamflow) for 34-year-long time series records originating from 511 geographical locations across the contiguous United States. Based on the acquired information and knowledge, similarities and differences between the examined time series types with respect to the evolution patterns characterizing their feature values with increasing (or decreasing) temporal resolution are identified. Moreover, the computed features are used as inputs to unsupervised random forests for detecting any meaningful clusters between the examined hydroclimatic time series. This clustering plays an illustrative role within this research, as it facilitates the identification of spatial patterns (with them consisting an important scientific target in hydroclimatic research) and their cross-scale comparison...

翻译：全面理解各种地球物理过程的行为并有效评估时间序列（亦称"随机"）模拟模型，需要开展跨时间尺度的详细调查。本文提出一种新颖且详尽的方法框架，旨在推进并丰富水文气候领域的此类研究。该框架主要基于针对多尺度水文气候分析的新特征体系，能够促进时间依赖性、时间变异性、"可预测性"、聚块性、稳定性、非线性（及线性）、趋势、尖峰性、曲率及季节性方面的高可解释性特征调查与比较。通过计算该特征体系在九种时间分辨率（即1日、2日、3日、7日、0.5月、1月、2月、3月和6月）及三种水文气候时间序列类型（即温度、降水和径流）下的数值，针对源自美国本土511个地理位置的34年长序列记录，获得了多维度的表征。基于获取的信息与知识，识别了所研究时间序列类型在特征值随时间分辨率增减的演化模式上的异同。此外，将计算所得特征作为无监督随机森林的输入，用于检测所研究水文气候时间序列间的有意义聚类。该聚类在本研究中发挥说明性作用，有助于识别空间格局（其构成水文气候研究的重要科学目标）并进行跨尺度比较……