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Institute: search.filters.UBSInstitut.Institut für Wasser- und UmweltsystemmodellierungSubject: search.filters.subject.550Publication Type: search.filters.UBSTyp.Zeitschriftenartikel

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    ItemOpen Access
    Modeling of evaporation-driven multiple salt precipitation in porous media with a real field application
    (2020) Mejri, Emna; Helmig, Rainer; Bouhlila, Rachida
    Soil and groundwater salinization are very important environmental issues of global concern. They threaten mainly the arid and semiarid regions characterized by dry climate conditions and an increase of irrigation practices. Among these regions, the south of Tunisia is considered, on the one hand, to be a salt-affected zone facing a twofold problem: The scarcity of water resources and the degradation of their quality due to the overexploitation of the aquifers for irrigation needs. On the other hand, this Tunisian landform is the only adequate area for planting date palm trees which provide the country with the first and most important exportation product. In order to maintain the existence of these oases and develop the date production, a good understanding of the salinization problem threatening this region, and the ability to predict its distribution and evolution, should not be underestimated. The work presented in this paper deals with the Oasis of Segdoud in southern Tunisia, with the objective of modeling the evaporation-driven salt precipitation processes at the soil profile scale and under real climatic conditions. The model used is based on the one developed and presented in a previous work. In order to fulfil the real field conditions, a further extension of the geochemical system of the existing model was required. The precipitated salts considered in this work were halite (NaCl), gypsum (CaSO4) and thenardite (Na2SO4). The extended model reproduces very well the same tendencies of the physico-chemical processes of the natural system in terms of the spatio-temporal distribution and evolution of the evaporation and multiple-salt precipitation. It sheds new lights on the simulation of sequences of salt precipitation in arid regions. The simulation results provide an analysis of the influence of salt precipitation on hydrodynamic properties of the porous medium (porosity and permeability). Moreover, the sensitivity analysis done here reveals the influence of the water table level on the evaporation rate.
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    ItemOpen Access
    Seasonal dynamics of gaseous CO2 concentrations in a karst cave correspond with aqueous concentrations in a stagnant water column
    (2023) Class, Holger; Keim, Leon; Schirmer, Larissa; Strauch, Bettina; Wendel, Kai; Zimmer, Martin
    Dissolved CO2 in karst water is the key driving force of karstification. Replenishment of CO2 concentrations in karst water occurs by meteoric water that percolates through the vadose zone, where CO2 produced from microbial activity is dissolved. CO2 can thus be transported with the percolating water or in the gas phase due to ventilation in karst systems. We measured seasonally fluctuating CO2 concentrations in the air of a karst cave and their influence on aqueous CO2 concentrations in different depths of a stagnant water column. The observed data were compared to numerical simulations. The data give evidence that density-driven enhanced dissolution of gaseous CO2 at the karst water table is the driving force for a fast increase of aqueous CO2 during periods of high gaseous concentrations in the cave, whereas during periods of lower gaseous concentrations, the decline of aqueous CO2 is limited to shallow water depths in the order of 1 m. This is significant because density-driven CO2 dissolution has not been previously considered relevant for karst hydrology in the literature. Attempts at reproducing the measured aqueous CO2 concentrations with numerical modeling revealed challenges related to computational demands, discretization, and the high sensitivity of the processes to tiny density gradients.
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    ItemOpen Access
    Experimental and simulation study on validating a numerical model for CO2 density-driven dissolution in water
    (2020) Class, Holger; Weishaupt, Kilian; Trötschler, Oliver
    Carbon dioxide density-driven dissolution in a water-filled laboratory flume of the dimensions 60~cm length, 40~cm height, 1~cm thickness was visualized using a pH-sensitive color indicator. We focus on atmospheric pressure conditions, like in caves where CO2 concentrations are typically higher. Varying concentrations of carbon dioxide were applied as boundary conditions at the top of the experimental setup, leading to the onset of convective fingering at differing times. The data were used to validate a numerical model implemented in the numerical simulator Dumux. The model solves the Navier-Stokes equations for density-induced water flow with concentration-dependent fluid density and a transport equation including advective and diffusive processes for the carbon dioxide dissolved in water. The model was run in 2D, 3D, and pseudo-3D on two different grids. Without any calibration or fitting of parameters, the results of the comparison between experiment and simulation show satisfactory agreement with respect to the onset time of convective fingering as well as the number and the dynamics of the fingers. Grid refinement matters in particular in the uppermost part where fingers develop. The 2D simulations consistently overestimated the fingering dynamics. This successful validation of the model is the prequisite for employing it in situations with background flow and for a future study of karstification mechanisms related to CO2-induced fingering in caves.
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    ItemOpen Access
    Assessment of uncertainties in a complex modeling chain for predicting reservoir sedimentation under changing climate
    (2023) Pesci, María Herminia; Mouris, Kilian; Haun, Stefan; Förster, Kristian
    Long-term predictions of reservoir sedimentation require an objective consideration of the preceding catchment processes. In this study, we apply a complex modeling chain to predict sedimentation processes in the Banja reservoir (Albania). The modeling chain consists of the water balance model WaSiM, the soil erosion and sediment transport model combination RUSLE-SEDD, and the 3d hydro-morphodynamic reservoir model SSIIM2 to accurately represent all relevant physical processes. Furthermore, an ensemble of climate models is used to analyze future scenarios. Although the capabilities of each model enable us to obtain satisfying results, the propagation of uncertainties in the modeling chain cannot be neglected. Hence, approximate model parameter uncertainties are quantified with the First-Order Second-Moment (FOSM) method. Another source of uncertainty for long-term predictions is the spread of climate projections. Thus, we compared both sources of uncertainties and found that the uncertainties generated by climate projections are 408% (for runoff), 539% (for sediment yield), and 272% (for bed elevation in the reservoir) larger than the model parameter uncertainties. We conclude that (i) FOSM is a suitable method for quantifying approximate parameter uncertainties in a complex modeling chain, (ii) the model parameter uncertainties are smaller than the spread of climate projections, and (iii) these uncertainties are of the same order of magnitude as the change signal for the investigated low-emission scenario. Thus, the proposed method might support modelers to communicate different sources of uncertainty in complex modeling chains, including climate impact models.
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    Pressure management via brine extraction in geological CO2 storage : adaptive optimization strategies under poorly characterized reservoir conditions
    (2019) González-Nicolás, Ana; Cihan, Abdullah; Petrusak, Robin; Zhou, Quanlin; Trautz, Robert; Godec, Michael; Birkholzer, Jens T.
    Industrial-scale injection of CO2 into the subsurface increases the fluid pressure in the reservoir, which if not properly controlled can potentially lead to geomechanical damage (i.e., fracturing of the caprock or reactivation of faults) and subsequent CO2 leakage. Brine extraction is one approach for managing formation pressure, effective stress, and plume movement in response to CO2 injection. The management of the extracted brine can be expensive (i.e., due to transportation, treatment, disposal, or re-injection), with added cost to the carbon capture and sequestration (CCS); thus, minimizing the volume of extraction brine is of great importance to ensure that the economics of CCS are favorable. The main objective of this study is to demonstrate the use of adaptive optimization methods in the planning of brine extraction and to investigate how the quality of initial site characterization data and the use of newly acquired monitoring data (e.g. pressure at observation wells) impact the optimization performance. We apply an adaptive management approach that integrates monitoring, calibration, and optimization of brine extraction rates to achieve pre-defined pressure constraints. Our results show that reservoir pressure management can be extremely benefited by early and high frequency pressure monitoring during early injection times, especially for poor initial reservoir characterization. Low frequencies of model calibration and optimization with monitoring data may lead to optimization problems, because either pressure buildup constraints are violated or excessively high extraction rates are proposed. The adaptive pressure management approach may constitute an effective tool to manage pressure buildup under uncertain reservoir conditions by minimizing the volumes of extracted brine while controlling pressure buildup.
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    Evaluation and error decomposition of IMERG product based on multiple satellite sensors
    (2023) Li, Yunping; Zhang, Ke; Bardossy, Andras; Shen, Xiaoji; Cheng, Yujia
    The Integrated Multisatellite Retrievals for GPM (IMERG) is designed to derive precipitation by merging data from all the passive microwave (PMW) and infrared (IR) sensors. While the input source errors originating from the PMW and IR sensors are important, their structure, characteristics, and algorithm improvement remain unclear. Our study utilized a four-component error decomposition (4CED) method and a systematic and random error decomposition method to evaluate the detectability of IMERG dataset and identify the precipitation errors based on the multi-sensors. The 30 min data from 30 precipitation stations in the Tunxi Watershed were used to evaluate the IMERG data from 2018 to 2020. The input source includes five types of PMW sensors and IR instruments. The results show that the sample ratio for IR (Morph, IR + Morph, and IR only) is much higher than that for PMW (AMSR2, SSMIS, GMI, MHS, and ATMS), with a ratio of 72.8% for IR sources and a ratio of 27.2% for PMW sources. The high false ratio of the IR sensor leads to poor detectability performance of the false alarm ratio (FAR, 0.5854), critical success index (CSI, 0.3014), and Brier score (BS, 0.1126). As for the 4CED, Morph and Morph + IR have a large magnitude of high total bias (TB), hit overestimate bias (HOB), hit underestimate bias (HUB), false bias (FB), and miss bias (MB), which is related to the prediction ability and sample size. In addition, systematic error is the prominent component for AMSR2, SSMIS, GMI, and Morph + IR, indicating some inherent error (retrieval algorithm) that needs to be removed. These findings can support improving the retrieval algorithm and reducing errors in the IMERG dataset.
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    Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS
    (2022) Gyawali, Dhiraj Raj; Bárdossy, András
    Given the importance of snow on different land and atmospheric processes, accurate representation of seasonal snow evolution, including distribution and melt volume, is highly imperative to any water resources development trajectories. The limitation of reliable snowmelt estimation in mountainous regions is, however, further exacerbated by data scarcity. This study attempts to develop relatively simple extended degree-day snow models driven by freely available snow-cover images. This approach offers relative simplicity and a plausible alternative to data-intensive models, as well as in situ measurements, and has a wide range of applicability, allowing for immediate verification with point measurements. The methodology employs readily available MODIS composite images to calibrate the snowmelt models on spatial snow distribution in contrast to the traditional snow-water-equivalent-based calibration. The spatial distribution of snow-cover is simulated using different extended degree-day models with parameters calibrated against individual MODIS snow-cover images for cloud-free days or a set of images representing a period within the snow season. The study was carried out in Baden-Württemberg (Germany) and in Switzerland. The simulated snow-cover data show very good agreement with MODIS snow-cover distribution, and the calibrated parameters exhibit relative stability across the time domain. Furthermore, different thresholds that demarcate snow and no-snow pixels for both observed and simulated snow cover were analyzed to evaluate these thresholds' influence on the model performance and identified for the study regions. The melt data from these calibrated snow models were used as standalone inputs to a modified Hydrologiska Byråns Vattenbalansavdelning (HBV) without the snow component in all the study catchments to assess the performance of the melt outputs in comparison to a calibrated standard HBV model. The results show an overall increase in Nash–Sutcliffe efficiency (NSE) performance and a reduction in uncertainty in terms of model performance. This can be attributed to the reduction in the number of parameters available for calibration in the modified HBV and an added reliability of the snow accumulation and melt processes inherent in the MODIS calibrated snow model output. This paper highlights that the calibration using readily available images used in this method allows for a flexible regional calibration of snow-cover distribution in mountainous areas with reasonably accurate precipitation and temperature data and globally available inputs. Likewise, the study concludes that simpler specific alterations to processes contributing to snowmelt can contribute to reliably identify the snow distribution and bring about improvements in hydrological simulations, owing to better representation of the snow processes in snow-dominated regimes.
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    Diagnosing similarities in probabilistic multi-model ensembles : an application to soil-plant-growth-modeling
    (2022) Schäfer Rodrigues Silva, Aline; Weber, Tobias K. D.; Gayler, Sebastian; Guthke, Anneli; Höge, Marvin; Nowak, Wolfgang; Streck, Thilo
    There has been an increasing interest in using multi-model ensembles over the past decade. While it has been shown that ensembles often outperform individual models, there is still a lack of methods that guide the choice of the ensemble members. Previous studies found that model similarity is crucial for this choice. Therefore, we introduce a method that quantifies similarities between models based on so-called energy statistics. This method can also be used to assess the goodness-of-fit to noisy or deterministic measurements. To guide the interpretation of the results, we combine different visualization techniques, which reveal different insights and thereby support the model development. We demonstrate the proposed workflow on a case study of soil–plant-growth modeling, comparing three models from the Expert-N library. Results show that model similarity and goodness-of-fit vary depending on the quantity of interest. This confirms previous studies that found that “there is no single best model” and hence, combining several models into an ensemble can yield more robust results.
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    Is precipitation responsible for the most hydrological model uncertainty?
    (2022) Bárdossy, András; Kilsby, Chris; Birkinshaw, Stephen; Wang, Ning; Anwar, Faizan
    Rainfall-runoff modeling is highly uncertain for a number of different reasons. Hydrological processes are quite complex, and their simplifications in the models lead to inaccuracies. Model parameters themselves are uncertain-physical parameters because of their observations and conceptual parameters due to their limited identifiability. Furthermore, the main model input-precipitation is uncertain due to the limited number of available observations and the high spatio-temporal variability. The quantification of model output uncertainty is essential for their use. Most approaches used for the quantification of uncertainty in rainfall-runoff modeling assign the uncertainty to the model parameters. In this contribution, the role of precipitation uncertainty is investigated. Instead of a standard sensitivity analysis of the model output with respect to the input variations, it is investigated to what extent realistic precipitation fields could improve model performance. Realistic precipitation fields are defined as gridded realizations of precipitation which reproduce the observed values at the observation locations, with values which reproduce the distribution of the observed values and with spatial variability the same as the spatial variability of the observations. The above conditions apply to each observation time step. Through an inverse modeling approach based on Random Mixing precipitation fields fulfilling the above conditions and reproducing the discharge output better than using traditional interpolated observations can be obtained. These realizations show how much rainfall runoff models may profit from better precipitation input and how much remains for the parameter and model concept uncertainty. The methodology is applied using two hydrological models with a contrasting basis, SHETRAN and HBV, for three different mesoscale sub-catchments of the Neckar basin in Germany. Results show that up to 50% of the model error can be attributed to precipitation uncertainty. Further, inverting precipitation using hydrological models can improve model performance even in neighboring catchments which are not considered explicitly.
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    Strategies for simplifying reactive transport models : a Bayesian model comparison
    (2020) Schäfer Rodrigues Silva, Aline; Guthke, Anneli; Höge, Marvin; Cirpka, Olaf A.; Nowak, Wolfgang
    For simulating reactive transport on aquifer scale, various modeling approaches have been proposed. They vary considerably in their computational demands and in the amount of data needed for their calibration. Typically, the more complex a model is, the more data are required to sufficiently constrain its parameters. In this study, we assess a set of five models that simulate aerobic respiration and denitrification in a heterogeneous aquifer at quasi steady state. In a probabilistic framework, we test whether simplified approaches can be used as alternatives to the most detailed model. The simplifications are achieved by neglecting processes such as dispersion or biomass dynamics, or by replacing spatial discretization with travel‐time‐based coordinates. We use the model justifiability analysis proposed by Schöniger, Illman, et al. (2015, https://doi.org/10.1016/j.jhydrol.2015.07.047) to determine how similar the simplified models are to the reference model. This analysis rests on the principles of Bayesian model selection and performs a tradeoff between goodness‐of‐fit to reference data and model complexity, which is important for the reliability of predictions. Results show that, in principle, the simplified models are able to reproduce the predictions of the reference model in the considered scenario. Yet, it became evident that it can be challenging to define appropriate ranges for effective parameters of simplified models. This issue can lead to overly wide predictive distributions, which counteract the apparent simplicity of the models. We found that performing the justifiability analysis on the case of model simplification is an objective and comprehensive approach to assess the suitability of candidate models with different levels of detail.