{'fraction_non_alphanumeric': 0.019867549668874173, 'fraction_numerical': 0.0011037527593818985, 'mean_word_length': 5.592727272727273, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Autoregressive Noise, Deserialization, and Trend Detection and Quantification in Annual River Discharge Time Series

The evaluation of long-term trends in yearly discharge records, such as annual peak daily flow or total annual runoff, is important to a variety of issues including water resource planning, flood hazard studies, and the assessment of historical data for evidence of anthropogenic climate change effects. Prewhitening or deserialization procedures have recently been developed and applied to adjust statistical tests of monotonic trend, and the nonparametric Mann-Kendall test in particular, for sensitivity to serial dependence. Deserialization attributes much or all of the observed serial correlation in a time series to an autoregressive process; however, deterministic processes can also lead to a large lag-1 serial correlation coefficient, and the physical basis for autoregressive noise may be weaker for annual rather than more finely-discretized (e.g., daily) streamflow records. In this paper, the potential consequences of using such procedures are investigated through a suite of Monte Carlo simulations. We find that prewhitening can substantially and inappropriately reduce the power of trend significance tests and increase slope estimate errors. The choice of whether deserialization is applied is to some degree left to the judgement and conservatism of the individual practitioner. We suggest that such procedures not be applied to a given annual hydrologic time series unless there is a strong site-specific physical basis for the assumption of AR(1) noise and that if deserialization is performed, very recently-developed multi-stage techniques appear preferable. We also present a number of useful ancillary results regarding trend identification in streamflow-derived data.