Impacts of climate change on the seasonality of low flows in 134 catchments in the river Rhine basin using an ensemble of bias-corrected regional climate simulations. Discussion paper

The impacts of climate change on the seasonality of low flows are analysed for 134 sub-catchments covering the River Rhine basin upstream of the Dutch–German border. Three seasonality indices for low flows are estimated, namely seasonality ratio (SR), weighted mean occurrence day (WMOD) and weighted persistence (WP). These indices are related to the discharge regime, timing and variability in timing of low flow events respectively. The three indices are estimated from: (1) observed low flows; (2) simulated low flows by the semi distributed HBV model using observed climate; (3) simulated low flows using simulated inputs from seven climate scenarios for the current climate (1964–2007); (4) simulated low flows using simulated inputs from seven climate scenarios for the future climate (2063–2098) including different emission scenarios. These four cases are compared to assess the effects of the hydrological model, forcing by different climate models and different emission scenarios on the three indices. The seven climate scenarios are based on different combinations of four General Circulation Models (GCMs), four Regional Climate Models (RCMs) and three greenhouse gas emission scenarios.\ud \ud Significant differences are found between cases 1 and 2. For instance, the HBV model is prone to overestimate SR and to underestimate WP and simulates very late WMODs compared to the estimated WMODs using observed discharges. Comparing the results of cases 2 and 3, the smallest difference is found in the SR index, whereas large differences are found in the WMOD and WP indices for the current climate. Finally, comparing the results of cases 3 and 4, we found that SR has decreased substantially by 2063–2098 in all seven subbasins of the River Rhine. The lower values of SR for the future climate indicate a shift from winter low flows (SR > 1) to summer low flows (SR < 1) in the two Alpine subbasins. The WMODs of low flows tend to be earlier than for the current climate in all subbasins except for the Middle Rhine and Lower Rhine subbasins. The WP values are slightly larger, showing that the predictability of low flow events increases as the variability in timing decreases for the future climate. From comparison of the uncertainty sources evaluated in this study, it is obvious that the RCM/GCM uncertainty has the largest influence on the variability in timing of low flows for future climate

The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models

This paper investigates the skill of 90-day low-flow forecasts using two conceptual hydrological models and one data-driven model based on Artificial Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV, GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low-flow forecasts for five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the models. The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the models are compared based on their skill of low-flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90-day-ahead low flows in the very dry year 2003 without precipitation data. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions

The skill of seasonal ensemble low flow forecasts for four different hydrological models

This paper investigates the skill of 90 day low flow forecasts using two conceptual hydrological models and two data-driven models based on Artificial Neural Networks (ANNs) for the Moselle River. One data-driven model, ANN-Indicator (ANN-I), requires historical inputs on precipitation (P), potential evapotranspiration (PET), groundwater (G) and observed discharge (Q), whereas the other data-driven model, ANN-Ensemble (ANN-E), and the two conceptual models, HBV and GR4J, use forecasted meteorological inputs (P and PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low flow forecasts without any meteorological forecasts as input (ANN-I) and five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the other three models (GR4J, HBV and ANN-E). The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the four models are compared based on their skill of low flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict low flows using ensemble seasonal meteorological forcing. The largest range for 90 day low flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90 day ahead low flows in the very dry year 2003 without precipitation data, whereas ANN-I predicted the magnitude of the low flows better than the other three models. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Furthermore, the hit rate of ANN-E is higher than the two conceptual models for most lead times. However, ANN-I is not successful in distinguishing between low flow events and non-low flow events. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions

Seasonal and long-term prediction of low flows in the Rhine Basin

The aim of this paper is to give information about a new project on the Rhine River. In this project we intent to identify appropriate low flow prediction models for the seasonal and long term by comparing uncertainties in low flows predicted by different pre-selected models

Doppler colour flow imaging of fetal intracerebral arteries relative to fetal behavioural states in normal pregnancy

In 14 normally developing term fetuses, the relationship between the blood flow velocity waveforms at cerebral arterial level (internal carotid artery, anterior, middle and posterior cerebral artery) and fetal behavioural states was studied using Doppler colour flow imaging. Behavioural state dependent changes in absolute flow velocities occurred in all vessels, except for the middle cerebral artery. These changes suggest preferential blood flow to the left heart resulting in increased flow to the cerebrum during fetal behavioural state 2F (active sleep) when compared with fetal behavioural state 1F (quiet sleep). The middle cerebral artery supplies the neocerebrum. This developing part of the cerebrum does not seem to take part in the regulation of fetal behaviour. In the internal carotid artery, an inverse relationship between peak systolic velocity and fetal heart rate could be established, which can be explained by a shorter rapid filling phase at raised fetal heart rate according to the Frank-Starling Law

Вплив техногенного чинника на формування скупчень метану в пісковиках

Исследовано влияние горных работ в процессе добычи угля на физические свойства песчаников по результатам опробования керна геологоразведочных скважин и горных выработок. Установлено, что коэффициент открытой пористости песчаников, в зоне влияния горных работ существенно отличается от соответствующих показателей в нетронутом массиве. Показано, что такое разуплотнение, за счёт трещинообразвания, способствует увеличению открытой пористости песчаников в 1,2-1,4 раза и формированию проницаемости, соответствующей коллекторам III-IV класса.Influence of mining operations has been investigated in the coal mining process on physical properties of sandstones, on results of core assay of geological prospecting holes and mining workings. It was set that open porosity coefficient of sandstones, in the affected zone of mining operations substantially differs from the proper indexes in natural array. It is shown, that such volume expension due to cracks formation, promotes increasing of sandstones open porosity in 1,2-1,4 time and forming of permeability corresponding of the III-IV class collectors

Development of a dermal matrix from glycerol preserved allogeneic skin

Dermal substitutes can be used to improve the wound healing of deep burns when placed underneath expanded, thin autologous skin grafts. Such dermal matrix material can be derived from xenogeneic or human tissue. Antigenic structures, such as cells and hairs must be removed to avoid adverse inflammatory response after implantation. In this study, a cost-effective method using low concentrations of NaOH for the de-cellularization of human donor skin preserved in 85% glycerol is described. The donor skin was incubated into NaOH for different time periods; 2, 4, 6 or 8 weeks. These dermal matrix prototypes were analyzed using standard histology techniques. Functional tests were performed in a rat subcutaneous implant model and in a porcine transplantation model; the prototypes were placed in full thickness excision wounds covered with autologous skin grafts. An incubation period of 6 weeks was most optimal, longer periods caused damage to the collagen fibers. Elastin fibers were well preserved. All prototypes showed intact biocompatibility in the rat model by the presence of ingrowing blood vessels and fibroblasts at 4 weeks after implantation. An inflammatory response was observed in the prototypes that were treated for only 2 or 4 weeks with NaOH. The prototypes treated with 6 or 8 weeks NaOH were capable to reduce wound contraction in the porcine model. In neo-dermis of these wounds, elastin fibers derived from the prototype could be observed at 8 weeks after operation, surrounded by more random orientated collagen fibers. Thus, using this effective low cost method, a dermal matrix can be obtained from human donor skin. Further clinical studies will be performed to test this material for dermal substitution in deep (burn) wounds