Enhanced hyporheic exchange flow around woody debris does not increase nitrate reduction in a sandy streambed

Anthropogenic nitrogen pollution is a critical problem in freshwaters. Although riverbeds are known to attenuate nitrate, it is not known if large woody debris (LWD) can increase this ecosystem service through enhanced hyporheic exchange and streambed residence time. Over a year, we monitored the surface water and pore water chemistry at 200 points along a ~50m reach of a lowland sandy stream with three natural LWD structures. We directly injected 15N-nitrate at 108 locations within the top 1.5m of the streambed to quantify in situ denitrification, anammox and dissimilatory nitrate reduction to ammonia, which, on average, contributed 85%, 10% and 5% of total nitrate reduction, respectively. Total nitrate reducing activity ranged from 0-16µM h-1 and was highest in the top 30cm of the stream bed. Depth, ambient nitrate and water residence time explained 44% of the observed variation in nitrate reduction; fastest rates were associated with slow flow and shallow depths. In autumn, when the river was in spate, nitrate reduction (in situ and laboratory measures) was enhanced around the LWD compared with non-woody areas, but this was not seen in the spring and summer. Overall, there was no significant effect of LWD on nitrate reduction rates in surrounding streambed sediments, but higher pore water nitrate concentrations and shorter residence times, close to LWD, indicated enhanced delivery of surface water into the streambed under high flow. When hyporheic exchange is too strong, overall nitrate reduction is inhibited due to short flow-paths and associated high oxygen concentrations

Equilibrio, stabilità e profitto: Una ricostruzione austriaca

In the theoretical framework of General Equilibrium Theory, agents's cognitive abilities do not go beyond mere calculation. In the Austrian perspective, on the contrary, agents are endowed with evaluation skills which make them capable of making real entrepreneurial choices. In this paper we propose a modification of the assumptions on rational behavior to account for evaluation processes: this allows us to provide a solution to the problem of general stability of General Equilibrium Theory

Consciously Uncertain: A Bayesian Analysis of Preferences Formation

We investigate experimentally whether players deliberately use irrelevant market cues to shape their evaluations of a traded item. We implement a repeated Vickrey median price selling auction of an unusual bad where players are informed on the market price and on the three lowest or highest asks. We elicited players’ consideration of market signals through a questionnaire at the end of the auction. We find that extreme information has a stronger influence on players’ evaluations than the market price. However, players’ consideration of the market signals explains their behavioral reactivity to the market price but not to the extremes. Hence, players deliberately use an unbiased estimator of the central tendency of the appraisals distribution, while extreme asks’ influence is consistent with a priming effect

Data from: Suburban watershed nitrogen retention: estimating the effectiveness of stormwater management structures

Excess nitrogen (N) is a primary driver of freshwater and coastal eutrophication globally, and urban stormwater is a rapidly growing source of N pollution. Stormwater best management practices (BMPs) are used widely to remove excess N from runoff in urban and suburban areas, and are expected to perform under a wide variety of environmental conditions. Yet the capacity of BMPs to retain excess N varies; and both the variation and the drivers thereof are largely unknown, hindering the ability of water resource managers to meet water quality targets in a cost-effective way. Here, we use structured expert judgment (SEJ), a performance-weighted method of expert elicitation, to quantify the uncertainty in BMP performance under a range of site-specific environmental conditions and to estimate the extent to which key environmental factors influence variation in BMP performance. We hypothesized that rain event frequency and magnitude, BMP type and size, and physiographic province would significantly influence the experts’ estimates of N retention by BMPs common to suburban Piedmont and Coastal Plain watersheds of the Chesapeake Bay region. Expert knowledge indicated wide uncertainty in BMP performance, with N removal efficiencies ranging from 40%. Experts believed that the amount of rain was the primary identifiable source of variability in BMP efficiency, which is relevant given climate projections of more frequent heavy rain events in the mid-Atlantic. To assess the extent to which those projected changes might alter N export from suburban BMPs and watersheds, we combined downscaled estimates of rainfall with distributions of N loads for different-sized rain events derived from our elicitation. The model predicted higher and more variable N loads under a projected future climate regime, suggesting that current BMP regulations for reducing nutrients may be inadequate in the future

Expert uncertainty estimates for all 60 questions in the SEJ protocol document

This tab-delimited text file contains raw values provided by all 10 experts for all 60 questions in the structured expert judgment (SEJ) protocol document (Appendix S3). The six columns are defined as follows: “Expert” refers to the expert identification codes used in the article; “Qnum” refers to the number of each question in the SEJ protocol document (Appendix S3); “Qid” is a brief coded description of the key variables in each question (codes are arranged as watershed location – storm event number – site description – influent/effluent load – pre/post restoration condition; see Appendix S3 for the full details of each question); “Percentile.05” is the 5th percentile of the probability distribution envisioned by each expert to describe the total nitrogen load for each question (units are kg total nitrogen); “Percentile.50” and “Percentile.95” refer to the 50th and 95th percentiles of the distribution, respectively (units are kg total nitrogen). Note that expert names are not associated with their estimates; the ordering of expert names in Appendix S2 is different than the ordering of their estimates in the data