Recent studies highlight linkages among the architecture of ecological networks, their persistence facing environmental disturbance, and the related patterns of biodiversity. A hitherto unresolved question is whether the structure of the landscape inhabited by organisms leaves an imprint on their ecological networks. We analyzed, based on pyrosequencing profiling of the biofilm communities in 114 streams, how features inherent to fluvial networks affect the co-occurrence networks that the microorganisms form in these biofilms. Our findings suggest that hydrology and metacommunity dynamics, both changing predictably across fluvial networks, affect the fragmentation of the microbial co-occurrence networks throughout the fluvial network. The loss of taxa from co-occurrence networks demonstrates that the removal of gatekeepers disproportionately contributed to network fragmentation, which has potential implications for the functions biofilms fulfill in stream ecosystems. Our findings are critical because of increased anthropogenic pressures deteriorating stream ecosystem integrity and biodiversity.stream networks | hydrological regime S treams and rivers sculpt the continental surface, forming fluvial networks (1), within which the biodiversity ranks among the highest on Earth (2). The dendritic nature of fluvial networks has been shown to affect the spatial and temporal patterns of microbial, invertebrate, and fish biodiversity (3-8). Ecological theory and observations posit that the local environment governs the dynamics and diversity of ecological communities in headwaters, the smallest and most abundant streams in fluvial networks. In contrast, dispersal ensures that communities further downstream are shaped not only by their immediate environment but also by upstream processes (3-9). Thus, the dynamics of the metacommunity, which comprises all interconnected communities in a landscape (10), are inextricably linked to the organization and hydrology of the fluvial network (5-8). This perception is essential to understand, predict, and manage streams and rivers and their resistance and resilience to human alterations across scales (that is, from patches to the catchment) (11).Ecological interactions are often usefully represented as networks (12). For example, analyses of food webs and mutualistic (e.g., pollination) networks have demonstrated that network organization can be linked to network persistence, to disturbance (12-16), or to species coexistence and richness (17). Microbial communities are so diverse and poorly studied that mapping out the interactions on the basis of biological knowledge is currently impossible for all but the simplest of habitats. Therefore, cooccurrence networks are increasingly used to infer microbial interactions (18,19) in soils (20), oceans (21), lakes (22), and even in global genomic surveys (23).A key question is whether the organization of microbial cooccurrence networks and their response to disturbance reflect physical characteristics inherent in fluvial networks such as geo...

River floods claim thousands of lives every year, but effective and high‐resolution methods to map human exposure to floods at the global scale are still lacking. We use satellite nightlight data to prove that nocturnal lights close to rivers are consistently related to flood damages. We correlate global data of economic losses caused by flooding events with nighttime lights and find that increasing nightlights are associated to flood damage intensification. Then, we analyze the temporal evolution of nightlights along the river network all over the world from 1992 to 2012 and obtain a global map of nightlight trends, which we associate with increasing human exposure to floods, at 1 km2 resolution. An enhancement of exposure to floods worldwide, particularly in Africa and Asia, is revealed, which may exacerbate the projected effects of climate change on flood‐related losses and therefore argues for the development of valuable flood preparedness and mitigation strategies.

In this Commentary, we argue that it is possible to improve the physical realism of hydrologic models by making better use of existing hydrologic theory. We address the following questions: (1) what are some key elements of current hydrologic theory; (2) how can those elements best be incorporated where they may be missing in current models; and (3) how can we evaluate competing hydrologic theories across scales and locations? We propose that hydrologic science would benefit from a model-based community synthesis effort to reframe, integrate, and evaluate different explanations of hydrologic behavior, and provide a controlled avenue to find where understanding falls short. MotivationThe discipline of hydrology continues to be an exciting field, with ongoing advances in field observational techniques, availability of global data products, and increasing computational power. Now, perhaps more than ever before, we are rising to the challenge of building models of everywhere [Beven, 2007]. Key efforts include building continental-domain hydrologic models for water security assessments [Schewe et al., 2014;Mizukami et al., 2015] and improving the representation of hydrologic processes in Earth System Models [Clark et al., 2015a]. These efforts require moving beyond the traditional tactics used in hydrology, such as detailed analysis and modeling of individual catchments, which makes it difficult to generalize results to large domains and other hydrologic regimes. Instead, hydrologic synthesis across space and across many elements of hydrologic theory is needed, in order to improve the physical realism and general applicability of hydrologic models, i.e., to improve hydrologic process representations across a large range of catchments . To this end, some have argued (somewhat optimistically) that advances in modern hydrologic modeling efforts are possible through progress on the following fundamental research challenges: identifying consistently observed behaviors across research watersheds, formulating the laws that govern macroscale hydrologic behavior, and unifying process explanations across watersheds in order to develop theory of hydrology at the catchment scale [e.g., Dooge, 1986;Sivapalan, 2005;McDonnell et al., 2007].The needs of the hydrologic modeling community as articulated in this way are admittedly sizeable and potentially insurmountable. This has led others to adopt a rather pessimistic view, doubting if it is even possible to generalize hydrologic behaviors given the unique character of individual basins [Beven, 2000]. This raises the question, do we now, and/or will we always, lack the necessary information on climate, topography, vegetation, soils, and subsurface structure required to develop powerful and exceptionless explanations? Put differently, are the problems of underdetermination so pronounced that we cannot move Key Points: We seek to increase the physical realism of hydrologic models through better way existing theory We seek to improve the way models are used to integrate and eva...

This paper addresses the signatures of catchment geomorphology on base flow recession curves. Its relevance relates to the implied predictability of base flow features, which are central to catchment‐scale transport processes and to ecohydrological function. Moving from the classical recession curve analysis method, originally applied in the Finger Lakes Region of New York, a large set of recession curves has been analyzed from Swiss streamflow data. For these catchments, digital elevation models have been precisely analyzed and a method aimed at the geomorphic origins of recession curves has been applied to the Swiss data set. The method links river network morphology, epitomized by time‐varying distribution of contributing channel sites, with the classic parameterization of recession events. This is done by assimilating two scaling exponents, β and bG, with |dQ/dt| ∝ Qβ where Q is at‐a‐station gauged flow rate and N(l) ∝ N(l)∝G(l)bG where l is the downstream distance from the channel heads receding in time, N(l) is the number of draining channel reaches located at distance l from their heads, and G(l) is the total drainage network length at a distance greater or equal to l, the active drainage network. We find that the method provides good results in catchments where drainage density can be regarded as spatially constant. A correction to the method is proposed which accounts for arbitrary local drainage densities affecting the local drainage inflow per unit channel length. Such corrections properly vanish when the drainage density become spatially constant. Overall, definite geomorphic signatures are recognizable for recession curves, with notable theoretical and practical implications.