A global algorithm for identifying changing streamflow regimes: application to Canadian natural streams (1966–2010)

Climate change affects natural streamflow regimes globally. To assess alterations in streamflow regimes, typically temporal variations in one or a few streamflow characteristics are taken into account. This approach, however, cannot see simultaneous changes in multiple streamflow characteristics, does not utilize all the available information contained in a streamflow hydrograph, and cannot describe how and to what extent streamflow regimes evolve from one to another. To address these gaps, we conceptualize streamflow regimes as intersecting spectrums that are formed by multiple streamflow characteristics. Accordingly, the changes in a streamflow regime should be diagnosed through gradual, yet continuous changes in an ensemble of streamflow characteristics. To incorporate these key considerations, we propose a generic algorithm to first classify streams into a finite set of intersecting fuzzy clusters. Accordingly, by analyzing how the degrees of membership to each cluster change in a given stream, we quantify shifts from one regime to another. We apply this approach to the data, obtained from 105 natural Canadian streams, during the period of 1966 to 2010. We show that natural streamflow in Canada can be categorized into six regime types, with clear hydrological and geographical distinctions. Analyses of trends in membership values show that alterations in natural streamflow regimes vary among different regions. Having said that, we show that in more than 80 % of considered streams, there is a dominant regime shift that can be attributed to simultaneous changes in streamflow characteristics, some of which have remained previously unknown. Our study not only introduces a new globally relevant algorithm for identifying changing streamflow regimes but also provides a fresh look at streamflow alterations in Canada, highlighting complex and multifaceted impacts of climate change on streamflow regimes in cold regions.

The spatial extent of hydrological and landscape changes across the mountains and prairies of Canada in the Mackenzie and Nelson River basins based on data from a warm-season time window

East of the Continental Divide in the cold interior of Western Canada, the Mackenzie and Nelson River basins have some of the world's most extreme and variable climates, and the warming climate is changing the landscape, vegetation, cryosphere, and hydrology. Available data consist of streamflow records from a large number (395) of natural (unmanaged) gauged basins, where flow may be perennial or temporary, collected either year-round or during only the warm season, for a different series of years between 1910 and 2012. An annual warm-season time window where observations were available across all stations was used to classify (1) streamflow regime and (2) seasonal trend patterns. Streamflow trends were compared to changes in satellite Normalized Difference Indices. Clustering using dynamic time warping, which overcomes differences in streamflow timing due to latitude or elevation, identified 12 regime types. Streamflow regime types exhibit a strong connection to location; there is a strong distinction between mountains and plains and associated with ecozones. Clustering of seasonal trends resulted in six trend patterns that also follow a distinct spatial organization. The trend patterns include one with decreasing streamflow, four with different patterns of increasing streamflow, and one without structure. The spatial patterns of trends in mean, minimum, and maximum of Normalized Difference Indices of water and snow (NDWI and NDSI) were similar to each other but different from Normalized Difference Index of vegetation (NDVI) trends. Regime types, trend patterns, and satellite indices trends each showed spatially coherent patterns separating the Canadian Rockies and other mountain ranges in the west from the poorly defined drainage basins in the east and north. Three specific areas of change were identified: (i) in the mountains and cold taiga-covered subarctic, streamflow and greenness were increasing while wetness and snowcover were decreasing, (ii) in the forested Boreal Plains, particularly in the mountainous west, streamflows and greenness were decreasing but wetness and snowcover were not changing, and (iii) in the semi-arid to sub-humid agricultural Prairies, three patterns of increasing streamflow and an increase in the wetness index were observed. The largest changes in streamflow occurred in the eastern Canadian Prairies.

Nonstationary Analyses of the Maximum and Minimum Streamflow in Tamsui River Basin, Taiwan

This study aims to detect non-stationarity of the maximum and minimum streamflow regime in Tamsui River basin, northern Taiwan. Seven streamflow gauge stations, with at least 27-year daily records, are used to characterize annual maximum 1- and 2-day flows and annual minimum 1-, 7-, and 30-day flows. The generalized additive models for location, scale, and shape (GAMLSS) are used to dynamically detect evolution of probability distributions of the maximum and minimum flow indices with time. Results of time-covariate models indicate that stationarity is only noted in the 4 maximum flow indices out of 35 indices. This phenomenon indicates that the minimum flow indices are vulnerable to changing environments. A 16-category distributional-change scheme is employed to classify distributional changes of flow indices. A probabilistic distribution with complex variations of mean and variance is prevalent in the Tamsui River basin since approximate one third of flow indices (34.3%) belong to this category. To evaluate impacts of dams on streamflow regime, a dimensionless index called the reservoir index (RI) serves as an alternative covariate to model nonstationary probability distribution. Results of RI-covariate models indicate that 7 out of 15 flow indices are independent of RI and 80% of the best-fitted RI-covariate models are generally worse than the time-covariate models. This fact reveals that the dam is not the only factor in altering the streamflow regime in the Tamsui River, which is a significant alteration, especially the minimum flow indices. The obtained distributional changes of flow indices clearly indicate changes in probability distributions with time. Non-stationarity in the Tamsui River is induced by climate change and complex anthropogenic interferences.

Functional data clustering as a powerful tool to group streamflow regimes and flood hydrographs

<p>Grouping catchments according to their seasonal streamflow or flood behavior can be essential in regionalization studies, climate impact assessments, or model choice and evaluation. Classical clustering approaches often rely on a selection of indices derived from streamflow/flood hydrographs to identify groups of similar hydrographs and ignore valuable information provided through the temporal (auto-)correlation pattern. To exploit this temporal information, we propose a functional clustering approach to identify catchments with similar streamflow regimes or flood hydrographs. Functional data clustering expresses hydrograph shapes as continuous functions by projecting them onto a set of basis functions (here B-splines) and clusters the resulting basis coefficients using classical clustering algorithms such as hierarchical or k-means clustering. <br>We apply this functional clustering approach to (1) a large set of catchments in the United States in order to identify regions with similar streamflow regimes and (2) a large set of catchments in Switzerland in order to identify regions with similar flood reactivity. We show that both the streamflow regime and flood reactivity regions are not only similar in terms of their streamflow/hydrograph behavior but also in terms of physiography and climate. We use the streamflow regime clusters derived using functional data clustering to assess future streamflow regime changes in the United States and demonstrate that they are beneficial in climate impact assessments, e.g. to indicate which types of catchments are particularly prone to future change. Further, we use the flood reactivity regions in a regionalization study to derive design hydrographs in ungauged catchments. We conclude that functional clustering approaches are beneficial in climate impact assessments and regionalization studies and might potentially also be valuable to cluster other types of hydrological phenomena such as drought events or long-term streamflow behavior.</p>

The Spatial Extent of Hydrological and Landscape Changes across the Mountains and Prairies of Canada in the Mackenzie and Nelson River Basins Based on Data from a Warm Season Time Window

The cold interior of Western Canada, east of the Continental Divide, has one of the world's most extreme and variable climates and is experiencing rapid environmental change. In the large Mackenzie and Nelson River basins, the warming climate is changing the landscape, vegetation, cryosphere, and hydrology. This is a study of a large number (395) of natural (unmanaged) gauged basins where streamflow may be continuous or temporary, and observed streamflow records had been collected either year-round or during only the warm season. Each station may have records for a different series of years between 1910 and 2012. Instead of a common period of years and a small number of stations, as in many trend studies, an annual warm season time window where observations were available across all stations is used to classify [1] streamflow regime using dynamic time-warping, and [2] seasonal trend patterns with k-means clustering. The trends in seasonal streamflow patterns were compared to changes in satellite Normalized Difference Vegetation, Water, and Snow Indices (NDVI, NDWI, and NDSI) for each gauged basin using Landsat 5 TM imagery between 1985 and 2010. Twelve streamflow regime types were identified using dynamic time-warping which overcomes timing differences in streamflow generation due to latitude or elevation. These streamflow regime types exhibit a strong connection to location; the spatial distribution follows ecozones and shows a strong distinction between mountains and plains in the study area. Clustering of seasonal trends using the annual common time window resulted in six trend patterns that also have a strong and distinct spatial organization. The trend patterns include one with decreasing streamflow, four with different patterns of increasing streamflow, and one with stations without trend structure. Trends in the mean, minimum, and maximum of three satellite indices were determined; the spatial patterns of trends in NDWI and NDSI were similar to each other, but different from NDVI trends. Streamflow regime types, the trend patterns, and satellite indices trends each showed spatially coherent patterns reflecting the influence of sources in the Canadian Rockies and other range in the west and poorly defined drainage basins due to post-glacial topography in the east and north. The overlap between hydrological and satellite index trends were not consistent across the study area. Three particular areas of change were identified: [i] in the mountains or lake-dominated, cold taiga-covered subarctic, north of 60° N, streamflow and greenness were increasing while wetness and snowcover were decreasing, [ii] in the forested Boreal Plains, particularly in the mountain west, streamflows and greenness were decreasing but wetness and snowcover were not changing, and [iii] in the semi-arid to sub-humid agricultural Prairies three patterns of increasing streamflow and an increase in the wetness index were observed. The largest changes in streamflow occurred in the eastern Canadian Prairies, where there were only a few increases in greenness and snow indices.