Using hydrological and climatic catchment clusters to explore drivers of catchment behavior

Abstract

Abstract. The behavior of every catchment is unique. Still, we seek for ways to classify them as this helps to improve hydrological theories. In this study, we use hydrological signatures that were recently identified as those with the highest spatial predictability to cluster 643 catchments from the CAMELS dataset. We describe the resulting clusters concerning their behavior, location and attributes. We then analyze the connections between the resulting clusters and the catchment attributes and relate this to the co-variability of the catchment attributes in the eastern and western US. To explore whether the observed differences result from clustering catchments by either climate or hydrological behavior, we compare the hydrological clusters to climatic ones. We find that for the overall dataset climate is the most important factor for the hydrological behavior. However, depending on the location, either aridity, snow or seasonality has the largest influence. The clusters derived from the hydrological signatures partly follow ecoregions in the US and can be grouped into four main behavior trends. In addition, the clusters show consistent low flow behavior, even though the hydrological signatures used describe high and mean flows only. We can also show that most of the catchments in the CAMELS dataset have a low range of hydrological behaviors, while some more extreme catchments deviate from that trend. In the comparison of climatic and hydrological clusters, we see that the widely used Köppen–Geiger climate classification is not suitable to find hydrologically similar catchments. However, in comparison with novel, hydrologically based continuous climate classifications, some clusters follow the climate classification very directly, while others do not. From those results, we conclude that the signal of the climatic forcing can be found more explicitly in the behavior of some catchments than in others. It remains unclear if this is caused by a higher intra-catchment variability of the climate or a higher influence of other catchment attributes, overlaying the climate signal. Our findings suggest that very different sets of catchment attributes and climate can cause very similar hydrological behavior of catchments – a sort of equifinality of the catchment response.

How Do Climate and Catchment Attributes Influence Flood Generating Processes? A Large‐Sample Study for 671 Catchments Across the Contiguous USA

“…This has been noted for other flow behavior as well. Jehn et al (2020) notice that hydrologic catchment cluster are most strongly shaped by climate but that vegetation and soil information play a role as well. Similar conclusions have been reached by Berghuijs, Sivapalan, et al (2014) for similarity in a seasonal water balance.…”

A review of hydrologic signatures and their applications

“…An alternative to signature-based clusters is to use climate or watershed descriptors to derive clusters, and look for similarities in signature values in each cluster. Climate-based clusters such as the Köppen-Geiger classes produce different patterns to signature-based clusters (Jehn, Bestian, Breuer, Kraft, & Houska, 2020). However, climate descriptors can be targeted towards creating hydrology-relevant clusters, by using descriptors such as aridity that is related to the water balance (Berghuijs, Sivapalan, Woods, & Savenije, 2014).…”

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

“…Such regimes are undergoing changes and expected to further change under future climate conditions (Addor et al, 2014;Arnell, 1999;Brunner et al, 2019b;Horton et al, 2006;Laghari et al, 2012;Leng et al, 2016;Milano et al, 2015). Regime changes are caused by changes in precipitation seasonality and intensity (Brönnimann et al, 2018) and seasonal shifts and decreases in melt contributions (Stewart et al, 2005;Farinotti et al, 2016;Jenicek et al, 2018) related to reduced snow and glacier storage (Beniston et al, 2018;Mote et al, 2005Mote et al, , 2018. Predicted regime changes are relatively robust (Addor et al, 2014) compared to changes in high and low flows, which are highly uncertain (Brunner et al, 2019c;Madsen et al, 2014) because of diverse uncertainty sources introduced in various steps along the modeling chain (Clark et al, 2016).…”