Non-flow periods in fluvial ecosystems are a global phenomenon. Streambed drying and rewetting by sporadic rainfalls could drive considerable changes in the microbial communities that govern stream nitrogen (N) availability at different temporal and spatial scales. We performed a microcosm-based experiment to investigate how dry period duration (DPD) (0, 3, 6, and 9 weeks) and magnitude of sporadic rewetting by rainfall (0, 4, and 21 mm applied at end of dry period) affected stocks of N in riverbed sediments, ammonia-oxidizing bacteria (AOB) and archaea (AOA) and rates of ammonia oxidation (AO), and emissions of nitrous oxide (N2O) to the atmosphere. While ammonium (NH4+) pool size decreased, nitrate (NO3−) pool size increased in sediments with progressive drying. Concomitantly, the relative and absolute abundance of AOB and, especially, AOA (assessed by 16S rRNA gene sequencing and quantitative PCR of ammonia monooxygenase genes) increased, despite an apparent decrease of AO rates with drying. An increase of N2O emissions occurred at early drying before substantially dropping until the end of the experiment. Strong rainfall of 21 mm increased AO rates and NH4+ in sediments, whereas modest rainfall of 4 mm triggered a notable increase of N2O fluxes. Interestingly, such responses were detected only after 6 and 9 weeks of drying. Our results demonstrate that progressive drying drives considerable changes in in-stream N cycling and the associated nitrifying microbial communities, and that sporadic rainfall can modulate these effects. Our findings are particularly relevant for N processing and transport in rivers with alternating dry and wet phases – a hydrological scenario expected to become more important in the future.

River water quality monitoring at limited temporal resolution can lead to imprecise and inaccurate classification of physicochemical status due to sampling error. Bayesian inference allows for the quantification of this uncertainty, which can assist decision-making. However, implicit assumptions of Bayesian methods can cause further uncertainty in the uncertainty quantification, so-called second-order uncertainty. In this study, and for the first time, we rigorously assessed this second-order uncertainty for inference of common water quality statistics (mean and 95th percentile) based on sub-sampling high-frequency (hourly) total reactive phosphorus (TRP) concentration data from three watersheds. The statistics were inferred with the low-resolution sub-samples using the Bayesian lognormal distribution and bootstrap, frequentist t test, and face-value approach and were compared with those of the high-frequency data as benchmarks. The t test exhibited a high risk of bias in estimating the water quality statistics of interest and corresponding physicochemical status (up to 99% of sub-samples). The Bayesian lognormal model provided a good fit to the high-frequency TRP concentration data and the least biased classification of physicochemical status (< 5% of sub-samples). Our results suggest wide applicability of Bayesian inference for water quality status classification, a new approach for regulatory practice that provides uncertainty information about water quality monitoring and regulatory classification with reduced bias compared to frequentist approaches. Furthermore, the study elucidates sizeable second-order uncertainty due to the choice of statistical model, which could be quantified based on the high-frequency data.

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&lt;p&gt;We desire to know out of different motivations. According to Aristotle, scientists can feel happy or &lt;em&gt;eudaimon&lt;/em&gt; when they fulfill the final cause of humans, reasoning, by providing knowledge. Freud argued that infants start to learn in order to distinguish between conditions that cause them pain or pleasure. We want to increase chances of achieving desired outcomes and avoiding undesired outcomes of our decisions by understanding causalities between events and predicting future events. In Geoscientific contexts, we may want to understand nature in order to satisfy different desires such as physical and psychological comforts, ethical dignity and continuation of existence, which are inseparable from but also conflict often against each other. We seek optimal decisions by means of the Geoscientific knowledge amidst the conflicting desires and natural conditions that hamper the desires.&lt;/p&gt; &lt;p&gt;All formations in the universe and all our perceptions are impermanent. Buddhism views that the course of life in which one is born, ages, gets ill and dies is suffering, if one clings to satisfactions, existence or non-existence as they are impermanent. A human being is seen in Buddhism as an ever-changing flux comprised of body (&lt;em&gt;rupa&lt;/em&gt; in Pali language), senses (&lt;em&gt;vedana&lt;/em&gt;), perceptions (&lt;em&gt;sanna&lt;/em&gt;), volitions (&lt;em&gt;sankhara&lt;/em&gt;) and consciousness (&lt;em&gt;vinnana&lt;/em&gt;), or the five aggregates (&lt;em&gt;khandha&lt;/em&gt;). Lasting peacefulness can be experienced when one understands the impermanence of its five aggregates, or selflessness (&lt;em&gt;sunnata&lt;/em&gt;), which is a goal of Buddhist practices.&lt;/p&gt; &lt;p&gt;From this Buddhist perspective, satisfactions of material needs provided by Geoscience do not last permanently. Geoscience may help humans satisfy their basic needs, but the standards of basic needs seem to be ever-growing, influenced often by materialism which overlooks spiritual sources of happiness and technocentric hopes for sustainability in the future. According to Buddhism, our experiences and actions (&lt;em&gt;kamma&lt;/em&gt;) condition our perceptions, volitions and habits, and reifying them as constant or substantial leads us to assume that certain desires &amp;#8216;ought&amp;#8217; to be met as basic living standards. However, such standards are subjective judgements that cannot be justified by factual propositions in &amp;#8216;is&amp;#8217; forms.&lt;/p&gt; &lt;p&gt;It can be satisfying for scientists to perform their professional tasks of providing knowledge required for fulfilling the human needs. However, epistemic and aleatory uncertainties in Geoscience can frustrate their desire to know. Geoscientists may suffer from the frustration, if they cling to their tasks and desires, failing to see satisfactions as impermanent and uncertainties as natural processes.&lt;/p&gt; &lt;p&gt;It is important to note that Buddhism does not compel dogmatically ascetic life styles or nihilistic worldviews but suggests ways to cease suffering. The Threefold Training (ethics, mindfulness and wisdom), the practice methods of Buddhism, can be applied in pursuing Geoscience as opportunities to experience lasting peacefulness. Scientists can create peaceful conditions by helping others with their knowledge, and let go of their reification and desires through mindfulness and the Buddhist ontology. Studying human desires and providing honest information about uncertainties and physical boundaries of satisfying the desires would be also parts of the practice.&lt;/p&gt;

The ecosystem function of vegetation to attenuate export of nutrients is of substantial importance for securing water quality. This ecosystem function is at risk of deterioration due to an increasing risk of large‐scale forest dieback under climate change. The present study explores the response of the nitrogen (N) cycle of a forest catchment in the Bavarian Forest National Park, Germany, in the face of a severe bark beetle (Ips typographus Linnaeus) outbreak and resulting large‐scale forest dieback using top‐down statistical‐mechanistic modeling. Outbreaks of bark beetle killed the dominant tree species Norway spruce (Picea abies (L.) H.Karst.) in stands accounting for 55% of the catchment area. A Bayesian hierarchical model that predicts daily stream NO3 concentration (C) over three decades with discharge (Q) and temperature (T) (C‐Q‐T relationship) outperformed alternative statistical models. A catchment model was subsequently developed to explain the C‐Q‐T relationship in top‐down fashion. Annually varying parameter estimates provide mechanistic interpretations of the catchment processes. Release of NO3 from decaying litter after the dieback was tracked by an increase of the nutrient input parameter cs0. The slope of C‐T relation was near zero during this period, suggesting that the nutrient release was beyond the regulating capacity of the vegetation and soils. Within a decade after the dieback, the released N was flushed out and nutrient retention capacity was restored with the regrowth of the vegetation.