Inverse Modeling Approach Research Articles

An inverse modeling method to assess the source term of the Fukushima Nuclear Power Plant accident using gamma dose rate observations

Abstract. The Chernobyl nuclear accident, and more recently the Fukushima accident, highlighted that the largest source of error on consequences assessment is the source term, including the time evolution of the release rate and its distribution between radioisotopes. Inverse modeling methods, which combine environmental measurements and atmospheric dispersion models, have proven efficient in assessing source term due to an accidental situation (Gudiksen, 1989; Krysta and Bocquet, 2007; Stohl et al., 2012a; Winiarek et al., 2012). Most existing approaches are designed to use air sampling measurements (Winiarek et al., 2012) and some of them also use deposition measurements (Stohl et al., 2012a; Winiarek et al., 2014). Some studies have been performed to use dose rate measurements (Duranova et al., 1999; Astrup et al., 2004; Drews et al., 2004; Tsiouri et al., 2012) but none of the developed methods were carried out to assess the complex source term of a real accident situation like the Fukushima accident. However, dose rate measurements are generated by the most widespread measurement system, and in the event of a nuclear accident, these data constitute the main source of measurements of the plume and radioactive fallout during releases. This paper proposes a method to use dose rate measurements as part of an inverse modeling approach to assess source terms. The method is proven efficient and reliable when applied to the accident at the Fukushima Daiichi Nuclear Power Plant (FD-NPP). The emissions for the eight main isotopes 133Xe, 134Cs, 136Cs, 137Cs, 137mBa, 131I, 132I and 132Te have been assessed. Accordingly, 105.9 PBq of 131I, 35.8 PBq of 132I, 15.5 PBq of 137Cs and 12 134 PBq of noble gases were released. The events at FD-NPP (such as venting, explosions, etc.) known to have caused atmospheric releases are well identified in the retrieved source term. The estimated source term is validated by comparing simulations of atmospheric dispersion and deposition with environmental observations. In total, it was found that for 80% of the measurements, simulated and observed dose rates agreed within a factor of 2. Changes in dose rates over time have been overall properly reconstructed, especially in the most contaminated areas to the northwest and south of the FD-NPP. A comparison with observed atmospheric activity concentration and surface deposition shows that the emissions of caesiums and 131I are realistic but that 132I and 132Te are probably underestimated and noble gases are likely overestimated. Finally, an important outcome of this study is that the method proved to be perfectly suited to emergency management and could contribute to improve emergency response in the event of a nuclear accident.

Adaptation of a resistive model to pesticide volatilization from plants at the field scale: Comparison with a dataset

Volatilization from plants is known to greatly contribute to pesticide emission into the atmosphere. Modeling would allow estimating this contribution, but few models are actually available because of our poor understanding of processes occurring at the leaf surface, competing with volatilization, and also because available datasets for validating models are lacking. The SURFATM-Pesticides model was developed to predict pesticide volatilization from plants. It is based on the concept of resistances and takes into account two processes competing with volatilization (leaf penetration and photodegradation). Model is here presented and simulated results are compared with the experimental dataset obtained at the field scale for two fungicides applied on wheat, fenpropidin and chlorothalonil. These fungicides were chosen because they are largely used, as well as because of their differentiated vapor pressures. The model simulates the energy balance and surface temperature which are in good agreement with the experimental data, using the climatic variables as inputs. The model also satisfactorily simulates the volatilization fluxes of chlorothalonil. In fact, by integrating estimated rate coefficients of leaf penetration and photodegradation for chlorothalonil giving in the literature, the volatilization fluxes were estimated to be 24.8 ng m−2 s−1 compared to 23.6 ng m−2 s−1 measured by the aerodynamic profile method during the first hours after application. At six days, the cumulated volatilization fluxes were estimated by the model to be 19 g ha−1 compared to 17.5 g ha−1 measured by the inverse modeling approach. However, due to the lack of data to estimate processes competing with volatilization for fenpropidin, the volatilization of this compound is still not well modeled yet. Thus the model confirms that processes competing with volatilization represent an important factor affecting pesticide volatilization from plants.

Identifying non-independent anthropogenic risks using a behavioral individual-based model

Anthropogenic disturbances contribute to an animal's perception of and responses to the predation risk of its environment. Because an animal rarely encounters threatening stimuli in isolation, multiple disturbances can act in non-independent ways to shape an animal's landscape of fear, making it challenging to isolate their effects for effective and targeted management. We present extensions to an existing behavioral agent-based model (ABM) to use as an inverse modeling approach to test, in a scenario-sensitivity analysis, whether threatened Alberta boreal caribou (Rangifer tarandus caribou) differentially respond to industrial features (linear features, forest cutblocks, wellsites) and their attributes: presence, density, harvest age, and wellsite activity status. The spatially explicit ABM encapsulates predation risk, heterogeneous resource distribution, and species-specific energetic requirements, and successfully recreates the general behavioral mechanisms driving habitat selection. To create various industry-driven, predation-risk landscape scenarios for the sensitivity analysis, we allowed caribou agents to differentially perceive and respond to industrial features and their attributes. To identify which industry had the greatest relative influence on caribou habitat use and spatial distribution, simulated caribou movement patterns from each of the scenarios were compared with those of actual caribou from the study area, using a pattern-oriented, multi-response optimization approach. Results revealed caribou have incorporated forestry- and oil and gas features into their landscape of fear that distinctly affect their spatial and energetic responses. The presence of roads, pipelines and seismic lines, and, to a minor extent, high-density cutblocks and active wellsites, all contributed to explaining caribou behavioral responses. Our findings also indicated that both industries produced interaction effects, jointly impacting caribou spatial and energetic patterns, as no one feature could adequately explain anti-predator movement responses. We demonstrate that behavior-based ABMs can be applied to understanding, assessing, and isolating non-consumptive anthropogenic impacts, in support of wildlife management.

An inverse modeling approach for stress estimation in mitral valve anterior leaflet valvuloplasty for in-vivo valvular biomaterial assessment

Estimation of regional tissue stresses in the functioning heart valve remains an important goal in our understanding of normal valve function and in developing novel engineered tissue strategies for valvular repair and replacement. Methods to accurately estimate regional tissue stresses are thus needed for this purpose, and in particular to develop accurate, statistically informed means to validate computational models of valve function. Moreover, there exists no currently accepted method to evaluate engineered heart valve tissues and replacement heart valve biomaterials undergoing valvular stresses in blood contact. While we have utilized mitral valve anterior leaflet valvuloplasty as an experimental approach to address this limitation, robust computational techniques to estimate implant stresses are required. In the present study, we developed a novel numerical analysis approach for estimation of the in-vivo stresses of the central region of the mitral valve anterior leaflet (MVAL) delimited by a sonocrystal transducer array. The in-vivo material properties of the MVAL were simulated using an inverse FE modeling approach based on three pseudo-hyperelastic constitutive models: the neo-Hookean, exponential-type isotropic, and full collagen–fiber mapped transversely isotropic models. A series of numerical replications with varying structural configurations were developed by incorporating measured statistical variations in MVAL local preferred fiber directions and fiber splay. These model replications were then used to investigate how known variations in the valve tissue microstructure influence the estimated ROI stresses and its variation at each time point during a cardiac cycle. Simulations were also able to include estimates of the variation in tissue stresses for an individual specimen dataset over the cardiac cycle. Of the three material models, the transversely anisotropic model produced the most accurate results, with ROI averaged stresses at the fully-loaded state of 432.6±46.5 kPa and 241.4±40.5 kPa in the radial and circumferential directions, respectively. We conclude that the present approach can provide robust instantaneous mean and variation estimates of tissue stresses of the central regions of the MVAL.

Carbon fluxes and pelagic ecosystem dynamics near two western Antarctic Peninsula Adélie penguin colonies: an inverse model approach

An inverse food-web model for the western Antarctic Peninsula (WAP) pelagic food web was constrained with data from Palmer Long Term Ecological Research (PAL-LTER) project annual austral summer sampling cruises. Model solutions were generated for 2 regions with Adelie penguin Pygoscelis adeliae colonies presenting different population trends (a northern and a southern colony) for a 12 yr period (1995�2006). Counter to the standard paradigm, comparisons of carbon flow through bacteria, microzooplankton, and krill showed that the diatomkrilltop predator food chain is not the dominant pathway for organic carbon exchanges. The food web is more complex, including significant contributions by microzooplankton and the microbial loop. Using both inverse model results and network indices, it appears that in the northern WAP the food web is dominated by the microbial food web, with a temporal trend toward its increasing importance. The dominant pathway for the southern WAP food web varies from year to year, with no detectable temporal trend toward dominance of microzooplankton versus krill. In addition, sensitivity analyses indicated that the northern colony of Adelie penguins, whose population size has been declining over the past 35 yr, appears to have sufficient krill during summer to sustain its basic metabolic needs and rear chicks, suggesting the importance of other processes in regulating the Adelie population decline.

Detection, fate and transport of estrogen family hormones in soil

Estrone (E1), 17β-estradiol (E2), and estrone-sulfate (E1-3S) are released into the environment in significant amounts. They are known to adversely affect the endocrine systems of aquatic organisms. Although previous studies clearly demonstrate that free hormones sorb strongly to soil and degrade quickly, significant amounts of free and the more persistent conjugated estrogens can be still detected in various environmental media. To date, E1-3S has been considered as a metabolite that forms either during the animal hormone cycle or as a degradation product of precursor hormones like E2-3S. We performed small-scale laboratory column tests to investigate two major features: transport and degradation of E2, and formation of E1-3S and E1. To evaluate the influence of soil microbial activity, one portion of soil was autoclaved and the background solution treated with sodium azide. The results demonstrate that (i) E2 is degraded to E1 and E1-3S in non-autoclaved soil, and to E1 in autoclaved soil, (ii) the formation of E1-3S is biologically driven, and (iii) the transformation of E2 to E1 does not require biological interaction. An inverse modeling approach was used to quantify the transport parameters and degradation rate coefficients.

Geostatistical inverse modeling for super-resolution mapping of continuous spatial processes

The increasing availability of satellite images derived from multiple sensors creates opportunities for broader spatial and temporal coverage but also methodological challenges. We present a geostatistical inverse modeling (GIM) approach for merging coarse-resolution images with variable resolutions and for super-resolution (i.e., predictions at the sub-pixel level) mapping of continuous spatial processes. GIM can explicitly account for the differences in spatial supports of multiple datasets. The restricted maximum likelihood method was used for parameter estimations associated with the change-of-support problem. We used GIM to produce both spatial predictions of a target image and prediction uncertainties, while preserving the values of original measurements. GIM is totally data driven, and covariance parameters for a target resolution can be directly derived from measurements. We also developed a moving-window GIM approach to accommodate spatial nonstationarity and reduce computational burden associated with large image data. First, we demonstrated GIM and moving-window GIM on synthetic images. Aggregated synthetic images with variable resolutions were merged to produce a single resolution image. The results show that the two approaches can produce accurate spatial predictions and generate prediction uncertainties. Second, we applied moving-window GIM for merging aerosol optical depth (AOD) data with variable resolutions, which were derived from two satellite sensors. The modeling results show that moving-window GIM can be applied for merging complementary AOD data from two sensors and for super-resolution mapping of global AOD distributions. Therefore, we can conclude that GIM is a practical solution for merging complementary coarse-resolution images and for super-resolution mapping of continuous spatial processes.

Inverting for volcanic SO<sub>2</sub> flux at high temporal resolution using spaceborne plume imagery and chemistry-transport modelling: the 2010 Eyjafjallajökull eruption case study

Abstract. Depending on the magnitude of their eruptions, volcanoes impact the atmosphere at various temporal and spatial scales. The volcanic source remains a major unknown to rigorously assess these impacts. At the scale of an eruption, the limited knowledge of source parameters, including time variations of erupted mass flux and emission profile, currently represents the greatest issue that limits the reliability of volcanic cloud forecasts. Today, a growing number of satellite and remote sensing observations of distant plumes are becoming available, bringing indirect information on these source terms. Here, we develop an inverse modelling approach combining satellite observations of the volcanic plume with an Eulerian regional chemistry-transport model (CHIMERE) to characterise the volcanic SO2 emissions during an eruptive crisis. The May 2010 eruption of Eyjafjallajökull is a perfect case study to apply this method as the volcano emitted substantial amounts of SO2 during more than a month. We take advantage of the SO2 column amounts provided by a vast set of IASI (Infrared Atmospheric Sounding Interferometer) satellite images to reconstruct retrospectively the time series of the mid-tropospheric SO2 flux emitted by the volcano with a temporal resolution of ~2 h, spanning the period from 1 to 12 May 2010. We show that no a priori knowledge on the SO2 flux is required for this reconstruction. The initialisation of chemistry-transport modelling with this reconstructed source allows for reliable simulation of the evolution of the long-lived tropospheric SO2 cloud over thousands of kilometres. Heterogeneities within the plume, which mainly result from the temporal variability of the emissions, are correctly tracked over a timescale of a week. The robustness of our approach is also demonstrated by the broad similarities between the SO2 flux history determined by this study and the ash discharge behaviour estimated by other means during the phases of high explosive activity at Eyjafjallajökull in May 2010. Finally, we show how a sequential IASI data assimilation allows for a substantial improvement in the forecasts of the location and concentration of the plume compared to an approach assuming constant flux at the source. As the SO2 flux is an important indicator of the volcanic activity, this approach is also of interest to monitor poorly instrumented volcanoes from space.

Evaluation of Anaerobic Biofilm Reactor Kinetic Parameters Using Ant Colony Optimization

Fixed bed reactors with naturally attached biofilms are increasingly used for anaerobic treatment of industry wastewaters due their effective treatment performance. The complex nature of biological reactions in biofilm processes often poses difficulty in analyzing them experimentally, and mathematical models could be very useful for their design and analysis. However, effective application of biofilm reactor models to practical problems suffers due to the lack of knowledge of accurate kinetic models and uncertainty in model parameters. In this work, an inverse modeling approach based on ant colony optimization is proposed and applied to estimate the kinetic and film thickness model parameters of wastewater treatment process in an anaerobic fixed bed biofilm reactor. Experimental data of pharmaceutical industry wastewater treatment process are used to determine the model parameters as a consequence of the solution of the rigorous mathematical models of the process. Results were evaluated for different modeling configurations derived from the combination of mathematical models, kinetic expressions, and optimization algorithms. Analysis of results showed that the two-dimensional mathematical model with Haldane kinetics better represents the pharmaceutical wastewater treatment in the biofilm reactor. The mathematical and kinetic modeling of this work forms a useful basis for the design and optimization of industry wastewater treating biofilm reactors.

OP 11. Optimized tDCS electrode configurations for five targets determined via an inverse FE modeling approach

Introduction Transcranial direct current stimulation (tDCS) has shown potential in improving brain function in both healthy subjects and patients suffering from a wide range of neuropathologies. Unfortunately, the effects are too small and short-lived for tDCS to be used as a clinical therapy. Increasing the effect size of tDCS could possibly be achieved by better targeting the current, both in direction and amplitude. Volume conduction modeling has shown that the areas with the highest electric fields strengths do not, as is often assumed, lie beneath the electrodes (Datta et al., 2009). Attempts at optimization have been published for point and ring electrodes (Im et al., 2008; Dmochowski et al., 2011), but not for the square patches used in most labs. In order to find for these electrodes, configurations that do result in maximum stimulation at the target area, we propose an inverse modeling approach. Objectives We simulate tDCS for ∼7000 configurations and determine which of these lead to optimal electric fields, both in strength and direction, at the five most target locations in tDCS research: motor cortex (M1), dorsolateral prefrontal cortex, inferior frontal gyrus, occipital cortex and cerebellum. Methods A detailed finite element (FE) head model was made by automatic segmentation of MR images aided by manual corrections. The model contains over 4 million elements and 11 tissue types. Special attention was given to the skull, the main barrier for the tDCS current, by including the spongiosa layer and skull holes. Brain anisotropy was derived from DTI measurements. On the skin surface, a grid of 89 points was placed consisting of the standard 10–10 EEG system and extra points on the cheeks and neck. For each combination of 2 points, we placed 5×5cm electrode patches onto the model, centred on the two points, and simulated 1mA tDCS. At each target, we placed a cylindrical volume in the brain and selected the configurations leading to highest mean field strength or optimal direction in the target volume. Results For all targets, the optimized configurations did not include the commonly used configurations. Often, highest field strengths were found in configurations that are near-perpendicular to the standard configurations. Optimization based on either strength or direction of the field lead to completely different configurations. Conclusion The optimized configurations found in this study suggest that improved results of tDCS can be expected. We found different optimized configurations by looking at either strength or direction of the field. Comparing these optimized configurations experimentally will not only verify our modeling approach, but also provide valuable information on the mechanisms behind tDCS. This approach can be used to optimize stimulation for any target location.

Reactivity of sandstone and siltstone samples from the Ketzin pilot CO2 storage site-Laboratory experiments and reactive geochemical modeling

To evaluate mineralogical-geochemical changes within the reservoir of the Ketzin pilot CO2 storage site in Brandenburg, Germany, two sets of laboratory experiments on sandstone and siltstone samples from the Stuttgart Formation have been performed. Samples were exposed to synthetic brine and pure CO2 at experimental conditions and run durations of 5.5 MPa/40 °C/40 months for sandstone and 7.5 MPa/40 °C/6 months for siltstone samples, respectively. Mineralogical changes in both sets of experiments are generally minor making it difficult to differentiate natural variability of the whole rock samples from CO2-induced alterations. Results of sandstone experiments suggest dissolution of the anorthite component of plagioclase, anhydrite, K-feldspar, analcime, hematite and chlorite + biotite. Dissolution of the anorthite component of plagioclase, anhydrite and K-feldspars is also observed in siltstone experiments. In an inverse modeling approach, an extensive set of equilibrium simulations was set up in order to reproduce the experimental observations of the sandstone experiments. Simulations generally show fairly good matches with the experimental observations. Best matches with measured brine data are obtained from mineral combinations of albite, analcime, anhydrite, dolomite, hematite, illite, and kaolinite. The major discrepancies during equilibrium modeling, however, are reactions involving Fe2+ and Al3+. The best matching subsets of the equilibrium models were finally run including kinetic rate laws. These simulations reveal that experimentally determined brine data was well matched, but reactions involving K+ and Fe2+ are not fully covered. The modeling results identified key primary minerals as well as key chemical processes, but also showed that the models are not capable of covering all possible contingencies.

Estimation of Soil Carbon Input in France: An Inverse Modelling Approach

Development of a quantitative understanding of soil organic carbon (SOC) dynamics is vital for management of soil to sequester carbon (C) and maintain fertility, thereby contributing to food security and climate change mitigation. There are well-established process-based models that can be used to simulate SOC stock evolution; however, there are few plant residue C input values and those that exist represent a limited range of environments. This limitation in a fundamental model component (i.e., C input) constrains the reliability of current SOC stock simulations. This study aimed to estimate crop-specific and environment-specific plant-derived soil C input values for agricultural sites in France based on data from 700 sites selected from a recently established French soil monitoring network (the RMQS database). Measured SOC stock values from this large scale soil database were used to constrain an inverse RothC modelling approach to derive estimated C input values consistent with the stocks. This approach allowed us to estimate significant crop-specific C input values (P < 0.05) for 14 out of 17 crop types in the range from 1.84 ± 0.69 t C ha−1 year−1 (silage corn) to 5.15 ± 0.12 t C ha−1 year−1 (grassland/pasture). Furthermore, the incorporation of climate variables improved the predictions. C input of 4 crop types could be predicted as a function of temperature and 8 as a function of precipitation. This study offered an approach to meet the urgent need for crop-specific and environment-specific C input values in order to improve the reliability of SOC stock prediction.

Atmospheric temperature changes over the 20th century at very high elevations in the European Alps from englacial temperatures

AbstractGiven the paucity of observations, a great deal of uncertainty remains concerning temperature changes at very high altitudes over the last century. Englacial temperature measurements performed in boreholes provide a very good indicator of atmospheric temperatures for very high elevations although they are not directly related to air temperatures. Temperature profiles from seven deep boreholes drilled at three different sites between 4240 and 4300 m above sea level in the Mont Blanc area (French Alps) have been analyzed using a heat flow model and a Bayesian inverse modeling approach. Atmospheric temperature changes over the last century were estimated by simultaneous inversion of these temperature profiles. A mean warming rate of 0.14°C/decade between 1900 and 2004 was found. This is similar to the observed regional low altitude trend in the northwestern Alps, suggesting that air temperature trends are not altitude dependent.

A FEM model for simulating temperature field in coaxial laser cladding of TI6AL4V alloy using an inverse modeling approach

In this paper, a 3D thermal finite element (FE) model is built for the simulation of temperature field in the laser cladding of Ti6Al4V (TC4) alloy. Instead of directly acquiring the geometric parameters of cladding layer and heat source for a concrete FE simulation through the measured data in experiments, this model is able to achieve temperature distributions for laser cladding with varying combinations of process parameters by constructing an adaptive cladding layer and moving heat source model using an inverse modeling approach. First, correlation experiments of single-track laser cladding TC4 powder on TC4 substrate were carried out based on central composite design (CCD) with the cladding parameters, laser power (400–600W), scanning speed (500–700mm/min) and powder feed rate (30–60rev/min). Then the relationships between process parameters and the geometries of cladding layer as well as the radius of spherical heat source are derived via regression analysis as an inverse manner. In addition, the spherical heat source is originally employed in the FE thermal simulation of coaxial laser cladding process. Detailed 3D transient thermal analyses have been performed with temperature-dependent material properties and the calibration of heat source is also provided. The proposed model is validated by the numerical and experimental results. It is found that it has potential to be applied in the thermal simulation of laser cladding with varying process parameters, considering the variation of the characteristic dimensions of deposition bead and the heat source.

Is the marine osmium isotope record a probe for CO2 release from sedimentary rocks?

The marine osmium (Os) isotope composition shows some significant variability during the past 250,000yr, which also correlates with climate sensitive proxies, e.g. oxygen isotopes. This correlation led to the assumption that the marine Os may in part contain information about past changes of continental weathering as a direct response to large-scale events, such as Northern hemisphere glaciation–deglaciation cycles. Here we test this hypothesis by analyzing marine Os isotope records applying an inverse modeling approach, with a particular focus on variable weathering rates of Os-rich lithologies, such as organic-rich and sulfide-rich sedimentary rocks. Assuming loess represents the Os isotope composition of average continental silicate crust, weathering thereof would produce an Os isotope composition that is close to the modern day seawater value. Therefore, silicate weathering is unlikely to exert any significant control on the marine Os isotope composition. Instead, the isotopic variability is thus more likely to be controlled by weathering of rock types enriched in Os and with an isotope composition distinct from silicates and seawater. Weathering of these Os-rich lithologies is closely linked to high rates of physical erosion and is thus expected to show a strong response to large-scale rock-grinding glaciation events. By combining a model of variable weathering rates to an Os-cycle model we present a new mechanistic model that is able to explain the Late Pleistocene marine Os isotope variability and we propose that this variability is best explained by a changing continental Os fluxes derived from the dissolution of aforementioned Os-rich lithologies. The weathering of such rocks promotes a set of weathering reactions that releases CO2 into the atmosphere, and we contend that the marine Os isotope record may be suited to gauge relative changes in the magnitude of these processes. The modeling results presented here suggest that variable weathering rates of organic-rich sedimentary deposits could play an important role in the long-term atmospheric CO2 budget and the interplay with CO2 consumption by silicate weathering.

Modeling biopore effects on root growth and biomass production on soils with pronounced sub-soil clay accumulation

Soils with subsoil clay accumulation account for more than 20% of the global land surface. These soils are characterized by vertical differences with respect to soil texture and increasing bulk density below the topsoil, which in turn affects root penetration into the subsoil. Biopores are preferential pathways for roots and assist in overcoming physical barriers like high density soil layers. An integration of these relationships into cropping systems models at the field scale is on-going. This paper presents a new approach to model the effect of biopores on root development in soils with clay accumulation at the plot scale. In this approach, the effect of biopores on root elongation rate depends on bulk density and on a biopore-root growth threshold (MPRT), which is the biopore volume at which the resistance of soil strength to root penetration is completely offset by the density of the biopores. The approach was integrated into a model solution of the model framework SIMPLACE (Scientific Impact assessment and Modeling PLatform for Advanced Crop and Ecosystem management). MPRT was parameterized for spring wheat using the inverse modeling approach based on root observations from a multi-factorial field experiment on a Haplic Luvisol. The observed biopore densities (>2mm diameter) were between 300 and 660poresm−2 (equivalent to a volumetric proportion of 0.38–0.83%) depending on the preceding crop. Observed soil bulk densities ranged between 1.31 and 1.62gcm−3. For spring wheat, the best fit between simulated and observed root densities in different layers was obtained with a MPRT of 0.023m3m−3 (equivalent to 2.3% of soil volume). The mean simulated total above ground biomass was sensitive to MPRT and had the best agreement with observed values when a MPRT between 0.023 and 0.032 m3m−3 was used in the simulations. Scenario simulations with the parameterized model at the same site demonstrate the importance of biopores for biomass production of short-cycle spring wheat when prolonged dry spells occur. The simulations allow a rough quantification of the biopore effects with respect to root elongation rate and biomass production at the plot scale with the potential to be extended to the field scale.

The application of an innovative inverse model for understanding and predicting landslide movements (Salazie cirque landslides, Reunion Island)

The prediction of landslide movement acceleration is a complex problem, among others identified for deep-seated landslides, and represents a crucial step for risk assessment. Within the scope of this problem, the objective of this paper is to explore a modelling method that enables the study of landslide function and facilitates displacement predictions based on a limited data set. An inverse modelling approach is proposed for predicting the temporal evolution of landslide movement based on rainfall and displacement velocities. Initially, the hydrogeology of the studied landslides was conceptualised based on correlative analyses. Subsequently, we applied an inverse model with a Gaussian-exponential transfer function to reproduce the displacements. This method was tested on the Grand Ilet (GI) and Mare-a-Poule-d’Eau (HB) landslides on Reunion Island in the Indian Ocean. We show that the behaviour of landslides can be modelled by inverse models with a bimodal transfer function using a Gaussian-exponential impulse response. The cumulative displacements over 7 years of modelling (2 years of calibration period for GI, and 4 years for HB) were reproduced with an RMSE above 0.9. The characteristics of the bimodal transfer function are directly related to the hydrogeological functioning demonstrated by the correlative analyses: the rapid reaction of a landslide can be associated with the effect of a preferential flow path on groundwater level variations. Thus, this study shows that the inverse model using a Gaussian-exponential transfer function is a powerful tool for predicting deep-seated landslide movements and for studying how they function. Beyond modelling displacements, our approach effectively demonstrates its ability to contribute relevant data for conceptualising the sliding mechanisms and hydrogeology of landslides.

Discovery of possible mega-thrust earthquake along the Seram Trough from records of 1629 tsunami in eastern Indonesian region

Arthur Wichmann’s “Earthquakes of the Indian Archipelago” documents several large earthquakes and tsunami throughout the Banda Arc region that can be interpreted as mega-thrust events. However, the source regions of these events are not known. One of the largest and well-documented events in the catalog is the great earthquake and tsunami affecting the Banda Islands on August 1, 1629. It caused severe damage from a 15-m tsunami that arrived at the Banda Islands about a half hour after violent shaking stopped. The earthquake was also recorded 230 km away in Ambon, but no tsunami is mentioned. This event was followed by at least 9 years of uncommonly frequent seismic activity in the region that tapered off with time, which can be interpreted as aftershocks. The combination of these observations indicates that the earthquake was most likely a mega-thrust event. We use an inverse modeling approach to numerically reconstruct the tsunami, which constrains the likely location and magnitude of the 1629 earthquake. Only, linear numerical models are applied due to the low resolution of bathymetry in the Banda Islands and Ambon. Therefore, we apply various wave amplification factors (1.5–4) derived from simulations of recent, well-constrained tsunami to bracket the upper and lower limits of earthquake moment magnitudes for the event. The closest major earthquake sources to the Banda Islands are the Tanimbar and Seram Troughs of the Banda subduction/collision zone. Other source regions are too far away for such a short arrival time of the tsunami after shaking. Moment magnitudes predicted by the models in order to produce a 15-m tsunami are Mw of 9.8–9.2 on the Tanimbar Trough and Mw 8.8–8.2 on the Seram Trough. The arrival times of these waves are 58 min for Tanimbar Trough and 30 min for Seram Trough. The model also predicts 5-m run-up for Ambon from a Tanimbar Trough source, which is inconsistent with the historical records. Ambon is mostly shielded from a wave generated by a Seram Trough source. We conclude that the most likely source of the 1629 mega-thrust earthquake is the Seram Trough. Only one earthquake >Mw 8.0 is recorded instrumentally from the eastern Indonesia region although high rates of strain (50–80 mm/a) are measured across the Seram section of the Banda subduction zone. Enough strain has already accumulated since the last major historical event to produce an earthquake of similar size to the 1629 event. Due to the rapid population growth in coastal areas in this region, it is imperative that the most vulnerable coastal areas prepare accordingly.

The role of oxide interlayers in back reflector configurations for amorphous silicon solar cells

Thin oxide interlayers are commonly added to the back reflector of thin-film silicon solar cells to increase their current. To gain more insight in the enhancement mechanism, we tested different back reflector designs consisting of aluminium-doped zinc oxide (ZnO:Al) and/or hydrogenated silicon oxide (SiOx:H) interlayers with different metals (silver, aluminium, and chromium) in standard p-i-n a-Si:H solar cells. We use a unique inverse modeling approach to show that in most back reflectors the internal metal reflectance is lower than expected theoretically. However, the metal reflectance is increased by the addition of an oxide interlayer. Our experiments demonstrate that SiOx:H forms an interesting alternative interlayer because unlike the more commonly used ZnO:Al it can be deposited by plasma-enhanced chemical vapour deposition and it does not reduce the fill factor. The largest efficiency enhancement is obtained with a double interlayer of SiOx:H and ZnO:Al.

Natural vs. artificial groundwater recharge, quantification through inverse modeling

Abstract. Estimating the change in groundwater recharge from an introduced artificial recharge system is important in order to evaluate future water availability. This paper presents an inverse modeling approach to quantify the recharge contribution from both an ephemeral river channel and an introduced artificial recharge system based on floodwater spreading in arid Iran. The study used the MODFLOW-2000 to estimate recharge for both steady- and unsteady-state conditions. The model was calibrated and verified based on the observed hydraulic head in observation wells and model precision, uncertainty, and model sensitivity were analyzed in all modeling steps. The results showed that in a normal year without extreme events, the floodwater spreading system is the main contributor to recharge with 80% and the ephemeral river channel with 20% of total recharge in the studied area. Uncertainty analysis revealed that the river channel recharge estimation represents relatively more uncertainty in comparison to the artificial recharge zones. The model is also less sensitive to the river channel. The results show that by expanding the artificial recharge system, the recharge volume can be increased even for small flood events, while the recharge through the river channel increases only for major flood events.