Thresholds of hydrologic flow regime of a river and investigation of climate change impact—the case of the Lower Brahmaputra river Basin

The sustainability of social-ecological systems depends on river flows being maintained within a range to which those systems are adapted. In order to determine the extent of this natural range of variation, we assess ecological flow thresholds and the occurrence of potentially damaging flood events to society in the context of the Lower Brahmaputra river basin. The ecological flow threshold was calculated using twenty-two ‘Range of Variability (RVA)’ parameters, considering the range between?±?1 standard deviation from the mean of the natural flow. Damaging flood events were calculated using flood frequency analysis of Annual Maxima series and using the flood classification of Bangladesh. The climate change impacts on future river flow were calculated by using a weighted ensemble analysis of twelve global circulation models (GCMs) outputs driving a large-scale hydrologic model. The simulated climate change induced altered flow regime of the Lower Brahmaputra River Basin was then investigated and compared with the calculated threshold flows. The results demonstrate that various parameters including the monthly mean of low flow (January, February and March) and high flow (June, July and August) periods, the 7-day average minimum flow, and the yearly maximum flow will exceed the threshold conditions by 1956–1995 under the business-as-usual A1B and A2 future scenarios. The results have a number of policy level implications for government agencies of the Lower Brahmaputra River Basin, specifically for Bangladesh. The calculated thresholds may be used as a good basis for negotiations with other riparian countries of the basin. The methodological approach presented in this study can be applied to other river basins and provide a useful basis for transboundary water resources management.     

Identification of groundwater mean transit times of precipitation and riverbank infiltration by two‐component lumped parameter models

Groundwater transit time is an essential hydrologic metric for groundwater resources management. However, especially in tropical environments, studies on the transit time distribution (TTD) of groundwater infiltration and its corresponding mean transit time (mTT) have been extremely limited due to data sparsity. In this study, we primarily use stable isotopes to examine the TTDs and their mTTs of both vertical and horizontal infiltration at a riverbank infiltration area in the Vietnamese Mekong Delta (VMD), representative of the tropical climate in Asian monsoon regions. Precipitation, river water, groundwater, and local ponding surface water were sampled for 3 to 9 years and analysed for stable isotopes (δ¹⁸O and δ²H), providing a unique data set of stable isotope records for a tropical region. We quantified the contribution that the two sources contributed to the local shallow groundwater by a novel concept of two‐component lumped parameter models (LPMs) that are solved using δ¹⁸O records. The study illustrates that two‐component LPMs, in conjunction with hydrological and isotopic measurements, are able to identify subsurface flow conditions and water mixing at riverbank infiltration systems. However, the predictive skill and the reliability of the models decrease for locations farther from the river, where recharge by precipitation dominates, and a low‐permeable aquitard layer above the highly permeable aquifer is present. This specific setting impairs the identifiability of model parameters. For river infiltration, short mTTs (<40 weeks) were determined for sites closer to the river (<200 m), whereas for the precipitation infiltration, the mTTs were longer (>80 weeks) and independent of the distance to the river. The results not only enhance the understanding of the groundwater recharge dynamics in the VMD but also suggest that the highly complex mechanisms of surface–groundwater interaction can be conceptualized by exploiting two‐component LPMs in general. The model concept could thus be a powerful tool for better understanding both the hydrological functioning of mixing processes and the movement of different water components in riverbank infiltration systems.     

Vodyanitskii mud volcano,Sorokin trough,Black Sea: Geological characterization and quantification of gas bubble streams

Vodyanitskii mud volcano is located at a depth of about 2070 m in the Sorokin Trough, Black sea. It is a 500-m wide and 20-m high cone surrounded by a depression, which is typical of many mud volcanoes in the Black Sea. 75 kHz sidescan sonar show different generations of mud flows that include mud breccia, authigenic carbonates, and gas hydrates that were sampled by gravity coring. The fluids that flow through or erupt with the mud are enriched in chloride (up to ∼650 mmol L⁻¹ at ∼150-cm sediment depth) suggesting a deep source, which is similar to the fluids of the close-by Dvurechenskii mud volcano. Direct observation with the remotely operated vehicle Quest revealed gas bubbles emanating at two distinct sites at the crest of the mud volcano, which confirms earlier observations of bubble-induced hydroacoustic anomalies in echosounder records. The sediments at the main bubble emission site show a thermal anomaly with temperatures at ∼60 cm sediment depth that were 0.9 °C warmer than the bottom water. Chemical and isotopic analyses of the emanated gas revealed that it consisted primarily of methane (99.8%) and was of microbial origin (δD-CH₄ = −170.8‰ (SMOW), δ¹³C-CH₄ = −61.0‰ (V-PDB), δ¹³C-C₂H₆ = −44.0‰ (V-PDB)). The gas flux was estimated using the video observations of the ROV. Assuming that the flux is constant with time, about 0.9 ± 0.5 × 10⁶ mol of methane is released every year. This value is of the same order-of-magnitude as reported fluxes of dissolved methane released with pore water at other mud volcanoes. This suggests that bubble emanation is a significant pathway transporting methane from the sediments into the water column.     

Third-order depositional sequences reflecting Milankovitch cyclicity

The origin of third-order depositional sequences remains debatable, and in many cases it is not clear whether they were controlled by tectonic activity and/or by eustatic sea-level changes. In Oxfordian and Berriasian–Valanginian carbonate-dominated sections of Switzerland, France, Germany and Spain, high-resolution sequence-stratigraphic and cyclostratigraphic analyses show that the sedimentary record reflects Milankovitch cyclicity. Orbitally induced insolation changes translated into sea-level fluctuations, which in turn controlled accommodation changes. Beds and bedsets formed in rhythm with the precession and 100-kyr eccentricity cycles, whereas the 400-kyr eccentricity cycle contributed to the creation of major depositional sequences. Biostratigraphical data allow the correlation of many of the 400-kyr sequence boundaries with third-order sequence boundaries recognized in European basins. This implies that climatically controlled sea-level changes contributed to the formation of third-order sequences. Furthermore, this cyclostratigraphical approach improves the relative dating of stratigraphic intervals.     

Ordering and composition of scapolite: Field observations and structural interpretations

Field observations, experimental and crystallographic data and thermodynamic considerations all suggest that Al-Si order-disorder is a crucial factor in explaining composition and stability of the mineral scapolite. Over the whole compositional range, scapolites have Al^IV-O-Al^IV bonds with the exception of one intermediate member with an Al/Si ratio of 1/2. Scapolites of this composition are the lowest temperature form and appear in areas with argillaceous carbonates and evaporites which have been subjected to progressive metamorphism. In similar areas without evaporites, the onset of the CO₃-scapolite stability field is approx. 150° C higher with an Al/Si ratio in the scapolite of about 5/7. This particular CO₃-scapolite is a compromise between the number of Al-O-Al bonds and the volume of the anion site occupied by CO₃. Based on field- and experimental data, temperature-composition diagrams for scapolite, plagioclase and calcite have been constructed. These diagrams may be explained in the light of contrasting Al-Si order-disorder in plagioclase and scapolite, i.e. at low temperature, plagioclase endmembers and intermediate scapolite members are stable, towards higher temperatures the ∩-shaped temperature-composition field of plagioclase and the V-shaped one of scapolite interfere in a complicated way. Electron microscopy of Al-rich scapolite, 632/n extinction rules. But these scapolites (with or without Cl-anion) show domain boundaries. We interpret them as APB's in the Al/Si ordering pattern on T₂-T₃ sites which reverses when a displacement R=1/2 [111] is applied.     

Response of the Greenland and Antarctic Ice Sheets to Multi-Millennial Greenhouse Warming in the Earth System Model of Intermediate Complexity LOVECLIM

Calculations were performed with the Earth system model of intermediate complexity LOVECLIM to study the response of the Greenland and Antarctic ice sheets to sustained multi-millennial greenhouse warming. Use was made of fully dynamic 3D thermomechanical ice-sheet models bidirectionally coupled to an atmosphere and an ocean model. Two 3,000-year experiments were evaluated following forcing scenarios with atmospheric CO₂ concentration increased to two and four times the pre-industrial value, and held constant thereafter. In the high concentration scenario the model shows a sustained mean annual warming of up to 10°C in both polar regions. This leads to an almost complete disintegration of the Greenland ice sheet after 3,000 years, almost entirely caused by increased surface melting. Significant volume loss of the Antarctic ice sheet takes many centuries to initiate due to the thermal inertia of the Southern Ocean but is equivalent to more than 4 m of global sea-level rise by the end of simulation period. By that time, surface conditions along the East Antarctic ice sheet margin take on characteristics of the present-day Greenland ice sheet. West Antarctic ice shelves have thinned considerably from subshelf melting and grounding lines have retreated over distances of several 100 km, especially for the Ross ice shelf. In the low concentration scenario, corresponding to a local warming of 3?C4°C, polar ice-sheet melting proceeds at a much lower rate. For the first 1,200 years, the Antarctic ice sheet is even slightly larger than today on account of increased accumulation rates but contributes positively to sea-level rise after that. The Greenland ice sheet loses mass at a rate equivalent to 35 cm of global sea level rise during the first 1,000 years increasing to 150 cm during the last 1,000 years. For both scenarios, ice loss from the Antarctic ice sheet is still accelerating after 3,000 years despite a constant greenhouse gas forcing after the first 70?C140 years of the simulation.     

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.