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.

In humid areas, biocrusts cover topsoils of inland dunes and influence soil characteristics, which, in turn, may affect the hydrophobicity of soils. The hydrophobicity of topsoils typically increases with increasing organic matter content. In addition, the soil organic matter quality, for example, described by the ratio of its hydrophilic and hydrophobic functional groups, also influences hydrophobicity. Because biocrust development goes along with an increase in the organic matter content and a shift in microbial community composition, the chemical character of soil organic matter likely changes over time, which, in turn, affects the hydrophobicity of the crusts. We hypothesize that the hydrophobicity of biocrusts increases during succession because of increasing amounts and aliphatic character of organic matter. We compared organic matter contents and Fourier‐transform infrared spectra of cyanobacterial biocrusts and moss‐dominated biocrusts at two European inland dunes. The organic carbon content as well as the hydrophobicity increased during crust development at both sites. Older moss‐dominated biocrusts showed the highest hydrophobicity and the highest organic carbon content. Moreover, at one study site, the hydrophobicity of the biocrusts did increase with decreasing ratio between hydrophilic and hydrophobic (i.e., aliphatic) moieties of soil organic matter. At the second study site, this effect was only visible for the moss‐dominated biocrust. We conclude that biocrust development and organic matter accumulation go ahead with changes in the organic matter composition and induce increased hydrophobicity with a strong impact on water redistribution in inland dune ecosystems. This knowledge will help to improve nature protection strategies in rare ecosystems.

Tightly constraint parameter ranges are seen as an important goal in constructing hydrological models, a difficult task in complex models. However, many studies show that complex models are often good at capturing the behaviour of a river. Therefore, this study explores the trade-offs between tightly constrained parameters and the ability to predict hydrological signatures, that capture the behaviour of a river. To accomplish this we built five models of differing complexity, ranging from a simple lumped model to a semi-lumped model with eight spatial subdivisions. All models are built within the same modelling framework, use the same data, and are calibrated with the same algorithm. We also consider two different methods for the potential evapotranspiration. We found that that there is a clear trade-off along the axis of complexity. While the more simple models can constrain their parameters quite well, they fail to get the hydrological signatures right. It is the other way around for the more complex models. The method of evapotranspiration only influences the parameters directly related to it. This study highlights that it is important to focus not only on parametric uncertainty. Tightly constrained parameters can be misguiding as they give credibility to oversimplified model structures.

The ambiguous representation of hydrological processes has led to the formulation of the multiple hypotheses approach in hydrological modeling, which requires new ways of model construction. However, most recent studies focus only on the comparison of predefined model structures or building a model step by step. This study tackles the problem the other way around: we start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash-Sutcliffe; percentage bias, PBIAS; and the ratio between the root mean square error and the standard deviation of the measured data). Through this incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 and the final Model 15 with HBV Light. The final model is not able to outperform HBV Light, but we find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and fewer model parameters.

<p>Abrupt sunlight reduction scenarios such as a nuclear winter, an asteroid impact or an eruption of a supervolcano would decimate agriculture as it is practised today. We therefore need resilient food sources for such an event. One promising candidate is seaweed, as it can grow quickly in a wide range of environmental conditions. To explore the feasibility of seaweed in a nuclear winter, we simulate the growth of seaweed on a global scale using an empirical model based on <em>Gracilaria tikvahiae</em> forced by nuclear winter climate simulations. We assess how quickly global seaweed production could be scaled to provide a significant fraction of global food demand. We find seaweed can be grown in tropical oceans, even in nuclear winter. The simulated growth is high enough to allow a scale up to an equivalent of 70 % of the global human caloric demand, while only using a small fraction of the global ocean area. The results also show that the growth of seaweed increases with the severity of the nuclear war, as more nutrients become available due to upwelling. This means that seaweed has the potential to be a viable resilient food source for abrupt sunlight reduction scenarios. </p>

Abstract. The ambiguous representation of hydrological processes has led to the formulation of the multiple hypotheses approach in hydrological modeling, which requires new ways of model construction. However, most recent studies focus only on the comparison of predefined model structures or building a model step by step. This study tackles the problem the other way around: we start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash–Sutcliffe; percentage bias, PBIAS; and the ratio between the root mean square error and the standard deviation of the measured data). Through this incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 and the final Model 15 with HBV Light. The final model is not able to outperform HBV Light, but we find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and fewer model parameters.

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.

Hydrological theory often assumes an exponential relationship between storage and discharge, but how often do we find such a behavior in real catchments? We analyze catchment attributes, discharge and weather conditions from 1992 to 2018 for 88 catchments in Hesse, Germany. All catchments have similar weather conditions, but a wide range of catchment characteristics. We find that only a fraction of catchments follow a roughly exponential behavior in their storage-discharge relationship. Far more catchments are complex and depict irregular patterns with even extreme variation from 1 year to another. This large set of catchments with similar weather conditions reveals that physical attributes that drive catchment complexity are diverse and include soil permeability, hydraulic conductivity, geology, and soil type. We link the examined simple and complex behavior to the fill and spill hypotheses and the interconnectedness of the catchments. Simpler catchments have more preferential flow and more connected hillslopes and thus lower runoff generation thresholds. This creates a more straightforward relationship between storage and discharge.