Trends in AMAX timeseries

The methodology we adopt is based on the standard NRFA trend testing approach outlined by Hannaford et al. (2021). In brief, monotonic trends were assessed using the Mann-Kendall (MK) test (Mann 1945; Kendall 1975), a non-parametric rank-based approach that is widely supported for use in streamflow analysis (e.g. Hannaford & Marsh 2008; Murphy et al. 2013). The MK Z statistic (MKZ) follows the standard normal distribution with a mean of zero and a variance of one. A positive (or negative) value of MKZ indicates an increasing (or decreasing) trend. The probability of Type 1 errors set at the 5% significance level allowed the evaluation of statistical significance. A two-tailed MK test was chosen; hence, the null hypothesis of ‘no trend present’ (increasing or decreasing) is rejected when MKZ is outside ±1.96 using traditional statistical testing.

The analysis was undertaken on the full AMAX record available at each station (with the exclusion of rejected and unrepresentative periods). As is widely noted in the literature (e.g. MacDonald & Sangster 2017), decadal-scale variability results in flood-rich and flood-poor periods that can affect the robustness of trends based on the period of data that is selected, users should be aware of this and note that trends have been carried out on the available data and may not reflect longer-term trends (outside of the period of observed data).

The trend analysis is repeated with every release of the NRFA Peak Flow Dataset and run on stations that are ‘Suitable for Pooling’ or ‘Suitable for QMED’ .

The stations were selected based on the following missing data criteria to mitigate the impacts of inevitable gaps in data:

• No more than 10% of missing data.
• 27 or more years of data (≥27 AMAX).

In catchments largely free from the influence of anthropogenic factors, most lengthy river flow and groundwater records in the UK are characterised by substantial variations about a relatively stable mean. This long-term stability underpins water management strategies and engineering design procedures. However, the modest size of UK rivers makes them particularly vulnerable to flow regime changes - be they driven by climatic variations, water management practices or land use change. Trend analysis can allow the differentiation of climate-driven trends from the ‘noisy’ background hydrological variability that characterises much of the UK.

Anthropogenic global climate change is expected to have a profound impact on the management of water resources across the globe. In the UK, the latest report from the UK Climate Projections project (UKCP18; UKCP18 key results - Met Office) envisages a substantially modified future climate: all areas are projected to be warmer, more so in the summer than in winter, with hotter summers more common. Despite overall drying trends in summer, UKCP18 predicts increases in intensity of heavy summer rainfall events, and future climate change is projected to bring about a change in seasonality of extremes. Such scenarios would present major challenges for sustainable water resources management. Any associated future trends, such as increases in the frequency or severity of floods or droughts, would have far-reaching economic and environmental implications.

In light of a perceived increase in hydrological extremes in the UK over the last decade and an intensification of the hydrological cycle, trend analysis is an important technique for contributing to the debate, and to put short-term variability into a long-term context.

Our understanding of the nature and magnitude of future climate change impacts is hindered, however, by the hydrological responses of catchments to climatic changes, as the relationships between temperature, rainfall, evaporation and soil moisture are complex. Observational evidence plays a vital role in addressing these uncertainties and achieving a fuller reconciliation between model-based scenarios and what we see on the ground.

Hydrological monitoring programmes and reference networks have an essential role to play in acquiring the hydrological data necessary to characterise variability and discern any emerging trends. The identification and interpretation of trends in observed time series are therefore a necessary foundation for the development of appropriate water policy and management responses to climate-driven change.