A novel time-series station in the Wadden Sea (NW Germany): First results on continuous nutrient and methane measurements

In autumn 2002 a time-series station was installed in the tidal inlet between the Islands of Langeoog and Spiekeroog (Southern North Sea, NW Germany) to continuously measure physical, chemical, and meteorological parameters, even during extreme weather conditions (gale-force storms, drifting ice). Inside the pole of the station sensor tubes are installed in direction of the prevailing tidal currents. The tubes are equipped with hydrographic sensors (pressure, temperature, conductivity) and allow retrieval of water for nutrient analysis by automated instruments located inside the pole. Dissolved methane and the nutrients ammonia, nitrite, nitrate, phosphate, and silicate are measured at the station.     

A comparison of cytochrome P4501A (CYP1A) mRNA inducibility in four species of Atlantic coast anadromous fishes

Quantification of levels of cytochrome P4501A (CYP1A) gene expression in sentinel species of fishes has been proposed as a management tool to evaluate contamination of aquatic systems. Based on preliminary studies, we hypothesized that differences in CYP1A mRNA inducibility among individuals, populations, or species might lead to spurious conclusions when using this approach in environmental monitoring programs. To address this possibility, we quantitated and compared CYP1A mRNA induction levels in four species of common Atlantic Coast estuarine fish: smooth flounder, hogchoker, striped bass, and Atlantic tomcod, which were treated with model chemicals (beta naphthoflavone (β-NF), or benzo[a]pyrene at 10 ppm) known to induce CYP1A mRNA, or were exposed to contaminated environments. Species-specific CYP1A DNA probes were generated from PCR (polymerase chain reaction) amplification of genomic DNA using conserved oligonucleotide primers, and, along with cloned rainbow trout and Atlantic tomcod CYP1A cDNA probes were used to quantify CYP1A mRNA levels in northern blot analyses. Successful PCR amplification of CYP1A hybridizable DNA fragments was observed for all four species. Results from northern blot analyses showed large differences in CYP1A mRNA induction among species; only Atlantic tomcod exhibited significant induction of CYP1A mRNA for both chemically treated (97-fold) and environmentally exposed fish (34-fold). Significant, although lower, levels of induction were observed in β-NF treated (14-fold) smooth flounder, but not in environmentally exposed smooth flounder. Only low levels (not significant) of CYP1A gene induction were detected in hogchokers and striped bass. We conclude that CYP1A mRNA inducibility differed significantly among fish taxa perhaps due to differences in regulation of gene expression, suggesting that careful selection of sentinel species should be exercised prior to the use of CYP1A mRNA induction in environmental monitoring programs. However, the significance of differences in CYP1A mRNA inducibility in relation to higher level biological endpoints has yet to be determined.     

Stock Origins of Subadult and Adult Atlantic Sturgeon, Acipenser oxyrinchus, in a Non-natal Estuary, Long Island Sound

The anadromous acipenserid Atlantic sturgeon Acipenser oxyrinchus was listed in 2012 under the U.S. Endangered Species Act as having four endangered and one threatened distinct population segment (DPS) in American waters. Anthropogenic activities outside of natal estuaries, particularly bycatch, may hinder the abilities of some populations to rebuild. Most Atlantic sturgeon are residential for their first 2–6 years within their natal estuaries, whereas older subadults and adults may migrate to non-natal estuaries and coastal locations. Previous studies demonstrated that subadults and adults aggregate during summer at locations in Long Island Sound (LIS) and its tributary, the Connecticut River; however, the population origin of these fish is unknown. Because of its geographic proximity and relatively robust population, we hypothesized that the LIS and Connecticut River aggregations were almost solely derived from the Hudson River. We used microsatellite nuclear DNA analysis at 11 loci and mitochondrial DNA control region sequence analyses to estimate the relative contributions of nine Atlantic sturgeon populations and the five DPS to these aggregations using individual-based assignment tests and mixed-stock analysis. From 64 to 73 % of specimens from LIS were estimated to be of Hudson origin. Similarly, 66–76 % of specimens from the Connecticut River were of Hudson origin. However, moderate numbers of specimens were detected from distant spawning populations in the southeastern DPS and from two populations once thought to be extirpated or nearly so, the James River (6–7.3 %), and the Delaware River (7.6–12 %). Additionally, specimens were detected from all five DPS in both the LIS and Connecticut River collections. These results highlight the difficulty of evaluating the status of individual Atlantic sturgeon populations because of the propensity of subadults and adults to migrate for extended duration to distant sites where they may be vulnerable to anthropogenic disturbances.     

Verification of short-term runoff forecasts for a small Philippine basin (Marikina)

Storm runoff from the Marikina River Basin frequently causes flood events in the Philippine capital region Metro Manila. This paper presents and evaluates a system to predict short-term runoff from the upper part of that basin (380 km²). It was designed as a possible component of an operational warning system yet to be installed. For the purpose of forecast verification, hindcasts of streamflow were generated for a period of 15 months with a time-continuous, conceptual hydrological model. The latter was fed with real-time observations of rainfall. Both ground observations and weather radar data were tested as rainfall forcings. The radar-based precipitation estimates clearly outperformed the raingauge-based estimates in the hydrological verification. Nevertheless, the quality of the deterministic short-term runoff forecasts was found to be limited. For the radar-based predictions, the reduction of variance for lead times of 1, 2 and 3 hours was 0.61, 0.62 and 0.54, respectively, with reference to a “no-forecast” scenario, i.e. persistence. The probability of detection for major increases in streamflow was typically less than 0.5. Given the significance of flood events in the Marikina Basin, more effort needs to be put into the reduction of forecast errors and the quantification of remaining uncertainties.     

Transport properties of low-sanidine single-crystals, glasses and melts at high temperature

Thermal diffusivity (D) was measured using laser-flash analysis from oriented single-crystal low-sanidine (K_0.92Na_0.08Al_0.99Fe³⁺ _0.005Si_2.95O₈), and three glasses near KAlSi₃O₈. Viscosity measurements of the three supercooled liquids, in the range 10^6.8 to 10^12.3 Pa s, confirm near-Arrhenian behavior, varying subtly with composition. For crystal and glass, D decreases with T, approaching a constant near 1,000 K: D _sat ∼ 0.65 ± 0.3 mm² s⁻¹ for bulk crystal and ∼0.53 ± 0.03 mm² s⁻¹ for the glass. A rapid decrease near 1,400 K is consistent with crossing the glass transition. Melt behavior is approximated by D = 0.475 ± 0.01 mm² s⁻¹. Thermal conductivity (k _lat) of glass, calculated using previous heat capacity (C _P) and new density data, increases with T because C _P strongly increases with T. For melt, k _lat reaches a plateau near 1.45 W m⁻¹ K⁻¹, and is always below k _lat of the crystal. Melting of potassium feldspars impedes heat transport, providing positive thermal feedback that may promote further melting in continental crust.     

Methane in the southern North Sea: Sources, spatial distribution and budgets

Measurements of methane (CH₄) so far have always shown supersaturation in the entire North Sea relative to the atmospheric partial pressure and the distribution of surface CH₄ reveals a distinct increase towards the shore. Since North Sea sediments presumably are an insignificant source for CH₄ the coastal contribution via rivers and tidal flats gains in importance.In this work, CH₄ data from the River Weser, the back barrier tidal flats of Spiekeroog Island (NW Germany), and the German Bight are presented. Results from the River Weser are compared to other rivers draining into the German Bight. Measurements in the tidal flat area of Spiekeroog Island highlight this ecosystem as an additional contributor to the overall CH₄ budget of the southern North Sea. A tidally driven CH₄ pattern is observed for the water column with maximum values during low tide. Tidal flat sediments turn out to be the dominating source because pore waters discharged during low tide are highly enriched in CH₄. In contrast, the freshwater contribution to the tidal flats by small coastal tributaries has almost no impact on water column CH₄ concentrations. The CH₄ level seems to be disturbed irregularly by wind forcing due to elevated degassing and prevention of advective flow when tidal flats remain covered by water.Based on our data, two model calculations were used to estimate the impact of tidal flats on the CH₄ budget in the German Bight. Our results demonstrate that the back barrier tidal flats of the east Frisian Wadden Sea contribute CH₄ in an order of magnitude between the Wash estuary and River Elbe and thus have to be considered in budget calculations.     

How dynamical models can learn from the data��an example with a simplified ENSO model

Most dynamical models of the natural system contain a number of empirical parameters which reflect our limited understanding of the simulated system or describe unresolved subgrid-scale processes. While the parameterizations basically introduce some uncertainty to the model results, they also hold the prospect of tuning the model. In general, a deterministic tuning is related to an inversion of the model which is often impossible or requires considerable computing effort for most climate models. Another way to adjust the model parameters to a specific observed process is stochastic fitting where a set of parameters and model output are taken as random variables. Here, we present a dynamical?Cstatistical approach with a simplified model of the El Ni?o?CSouthern Oscillation (ENSO) cycle whose parameters are adjusted to simulated and observed data by means of Bayesian statistics. As ENSO model, we employ the Schop?CSuarez delay oscillator model. Monte Carlo experiments highlight the large sensitivity of the model results to varied model parameters and initial values. The statistical adjustment is done by Bayesian model averaging of the Monte Carlo experiments. Applying the method to simulated data, the posterior ensemble mean is much closer to the reference data than the prior ensemble mean. The learning effect of the model is evident in the leading empirical orthogonal functions and statistically significant in the mean state. When the method is applied to the observed ENSO time series, the ENSO model in its classical setup is not able to account for the temporally varying periodicity of the observed ENSO phenomenon. An improved setup with continuous adjustment periods and extended parameter range is developed in order to allow the model to learn from the data gradually. The improved setup leads to promising results during the twentieth century and even a weak forecast skill over 6?months. Thus, the described method offers a promising tool for data assimilation in dynamical weather and climate models. However, the simplified ENSO model is barely appropriate for operational ENSO forecasts owing to its limited physical complexity.     

Hydrological model parameter (in)stability – “crash testing” the HBV model under contrasting flood seasonality conditions

This paper investigates the transferability of calibrated HBV model parameters under stable and contrasting conditions in terms of flood seasonality and flood generating processes (FGP) in five Norwegian catchments with mixed snowmelt/rainfall regimes. We apply a series of generalized (differential) split-sample tests using a 6-year moving window over (i) the entire runoff observation periods, and (ii) two subsets of runoff observations distinguished by the seasonal occurrence of annual maximum floods during either spring or autumn. The results indicate a general model performance loss due to the transfer of calibrated parameters to independent validation periods of ?5 to ?17%, on average. However, there is no indication that contrasting flood seasonality exacerbates performance losses, which contradicts the assumption that optimized parameter sets for snowmelt-dominated floods (during spring) perform particularly poorly on validation periods with rainfall-dominated floods (during autumn) and vice versa.