Clustering CAMELS using hydrological signatures with high spatial predictability

Abstract

Abstract. The behavior of every catchment is unique. Still, we need ways to classify them as this helps to improve hydrological theories. Usually catchments are classified along either their attributes classes (e.g. climate, topography) or their discharge characteristics, which is often captured in hydrological signatures. However, recent studies have shown that many hydrological signatures have a low predictability in space and therefore only dubious hydrological meaning. Therefore, this study uses hydrological signatures with the highest predictability in space to cluster 643 catchments from the continental United States (CAMELS (Catchment Attributes and MEteorology for Large-Sample Studies) dataset) into ten groups. We then evaluated the connection between catchment attributes with the hydrological signatures with quadratic regression, both in the overall CAMELS dataset and the ten clusters. In the overall dataset, aridity had the strongest connection to the hydrological signatures, especially in the eastern United States. However, the clusters in the western United States showed a more heterogeneous pattern with a larger influence of forest fraction, the mean elevation or the snow fraction. From this, we conclude that catchment behavior can be mainly attributed to climate in regions with homogenous topography. In regions with a heterogeneous topography, there is no clear pattern of the catchment behavior, as catchments show high spatial variability in their attributes. The classification of the CAMELS dataset with the hydrological signatures allows testing hydrological models in contrasting environments.

Future streamflow regime changes in the United States: assessment using functional classification

“…Our approach circumvents the computation and selection of (a large number of) streamflow characteristics by applying the clustering procedure on a functional representation of the mean annual hydrographs directly. The five regime clusters identified also show spatial similarities with the ten catchment clusters formed byJehn et al (2019) for the same set of catchments using a small set of hydrological streamflow characteristics. However, our clustering scheme avoids the formation of very small clusters seen inJehn et al (2019).…”

“…The five regime clusters identified also show spatial similarities with the ten catchment clusters formed byJehn et al (2019) for the same set of catchments using a small set of hydrological streamflow characteristics. However, our clustering scheme avoids the formation of very small clusters seen inJehn et al (2019). Similarly toJehn et al (2019)…”

“…However, our clustering scheme avoids the formation of very small clusters seen inJehn et al (2019). Similarly toJehn et al (2019)…”

Advances in Quantifying Streamflow Variability Across Continental Scales: 1. Identifying Natural and Anthropogenic Controlling Factors in the USA Using a Spatially Explicit Modeling Method

“…In contrast, many continental-scale applications have employed parsimonious mechanistic model structures with fewer controlling parameters (Archfield et al, 2015), but these simplified models have had mixed success at improving prediction accuracy (e.g., Bock et al, 2016;Arheimer et al, 2019). The methods for upscaling and spatial interpolation have included the following: the regionalization of catchment model parameters (e.g., Bock et al, 2016;Beck et al, 2016;Livneh & Lettenmaier, 2013) and measures of hydrological variability (e.g., Jehn et al, 2019;Addor et al, 2017) based on geographic proximity and similarities in hydrological and climatic conditions; the simultaneous calibration of models across representative catchments having similar watershed attributes (e.g., Arheimer et al, 2019); the use of spatial transfer functions based on the regression of catchment model parameters on watershed characteristics (e.g., Hundecha et al, 2016;Rakovec et al, 2016); and the aggregate use of model outcomes across large scales from independent calibrations in individual watersheds (e.g., Newman et al, 2015;Weiskel et al, 2014;Wolock & McCabe, 1999). Despite advances that have contributed to improved spatial sharing of hydrological information across continental scales (e.g., Bock et al, 2016;Hundecha et al, 2016;Rakovec et al, 2016), a persistent challenge over large scales is developing statistical methods for estimating parameters that are spatially and structurally consistent.…”

“…Finally, many of the continental-scale applications have emphasized natural hydrological processes based on monitoring in undeveloped headwater catchments (Archfield et al, 2015), without explicit components to separately account for cultural influences (e.g., Wolock & McCabe, 1999;Weiskel et al, 2014;Bock et al, 2016;Beck et al, 2016;Newman et al, 2015;Jehn et al, 2019;Livneh & Lettenmaier, 2013). Whereas the focus on undeveloped catchments is necessary to accurately describe natural water cycling processes, an explicit accounting of the effects of anthropogenic activities on streamflow is needed to advance process understanding across large scales and to improve the management utility of hydrologic models.…”