Hydro-Climatic Trends, Variability, and Regime Shifts

Unraveling trends and variability in hydro-climatic parameters (for example, precipitation and temperature) is a fundamental research problem that is of great importance to environmental resource management, especially under the urgent circumstances of climate change [1]. Apart from the trends and variability, hydro-climatic systems are subject to regime shifts, which are manifested by an abrupt change from one system state to another [2,3]. The regime shift could be irreversible or reversible, and it could occur as a result of other state variables or physical processes.

This Special Issue (SI) of Atmosphere was proposed to invite diverse contributions related to a better understanding of such nonstationary hydro-climatic phenomena as trends, variability, and regime shifts at varied spatiotemporal scales. Studies of this kind should be rooted in the diagnostics of observed data with high-quality and long-term records. Using modeling approaches (statistical and/or numerical) to study the underlying mechanisms of the observed phenomena would be encouraged. Topics of interest included (1) linking the regional phenomena with large-scale, remote climate oscillations (e.g., El Niño–Southern Oscillation); (2) developing new detection techniques for trends, variability, and regime shifts; (3) examining the recent extreme events or reviewing the historical “black swan” events that break hydro-climatic stationarity; (4) assessing the implications/impact of the nonstationary phenomena on weather and climate services (e.g., weather and climate forecasting) and environmental resource management; (5) discussing the relationship between the nonstationary phenomena and anthropogenic activities; and (6) projections of future changes in any of the aforementioned topics. This SI has solicited five valuable articles covering the above topics, and the important findings of each article are summarized as follows.

The first article, written by West et al. [4], indicates that a broad set of large-scale atmospheric–oceanic circulation (teleconnection) patterns can exert influence over monthly precipitation in Great Britain. Five atmospheric circulation indices, namely the North Atlantic Oscillation (NAO), East Atlantic Pattern (EA), Scandinavian Pattern, East Atlantic–West Russia Pattern, and Polar/Eurasia Pattern, are adopted as explanatory variables in univariate and multivariate regression models to examine the strength of the relationship between each teleconnection index and the standard precipitation index time series for the Integrated Hydrological Unit Groups of Great Britain from January 1950 to December 2015. Their findings not only confirm the well-known dominance of the NAO in the north-west during winter, but also reinforce the EA’s role as the second leading mode of climate variability across Great Britain.

The second article, written by Huang et al. [5], presents two novel trend detection methods based on regularized minimal-energy tensor-product B-splines, a smoothing technique, and an application of these methods to drought analysis in Taiwan. Their findings highlight the importance of inter-method comparison, linearity and normality in data series, and time interval selection when interpreting the results of a trend analysis.

The third article, written by Chen et al. [6], showcases a trend analysis and entropy-based analysis of regime shifts for climate data within the longleaf pine (Pinus palustris Mill.) range, which is an important ecosystem in the southeastern United States. Their results disclose no dramatic changes in most climate statistics, except for an increasing trend in annual air temperature that is consistent with global warming. Their findings also suggest that while more evidence is needed, the interactions between climate and longleaf pine forests may have played a role in stabilizing the climate within the range.

The fourth article, written by Long et al. [7], focuses on the western Himalayan region, in which a changing climate is evident and has had a great impact on the global-scale hydroclimate because of the retreat of glaciers. A trend analysis based on Sen’s slope estimator and linear regression reveals significant increasing trends in the average, maximum, and minimum temperatures in this region from 1980 to 2020. Their findings imply that socio-economic issues (such as food insecurity) and ecological imbalances in this region have been escalating.

The fifth article, written by Achite et al. [8], presents a novel framework that coupled a conceptual hydrological model with a deep-learning (DL) model for streamflow simulation in a snow-covered basin in northern Sweden. While the deep-conceptual learning-based framework is region- and climate-specific at the current stage, the enhanced performance in a rainfall-runoff simulation has demonstrated the capability and usefulness of such a framework.

To sum up, the above five articles in this SI exhibit comprehensive coverage of the proposed topics (e.g., new trend detection methods and DL model), aspects (e.g., trends and variability in hydro-climatic data series as well as ecological indicators), and study regions (e.g., Europe, Asia, and America). We expect that these articles can supplement the understanding of various hydro-climatic phenomena and become useful references in the field of hydroclimatology.