ALBERT

Description: Abstract 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 three to nine years and analyzed 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.

Description: Low water levels in the Great Lakes have recently had significant financial impacts on the region's commercial shipping, which transports hundreds of millions of dollars' worth of bulk goods each year. Cargo capacity is a function of a ship's draft, the distance between water level and the ship's bottom, and lower water levels force ships to reduce cargo loads to prevent running aground in shallow harbors and locks. Financial risk transfer instruments, such as index-based insurance contracts, may provide an adaptable method for managing these financial risks. In this work, a relationship between water levels and shipping revenues is developed and used in an actuarial analysis of the frequency and magnitude of revenue losses. This analysis is used to develop a standardized suite of binary financial contracts, which are indexed to water levels and priced according to predefined thresholds. These contracts are then combined to form hedging portfolios with different objectives for the shippers. Results suggest that binary contracts could substantially reduce the risk of financial losses during low lake level periods and at a relatively low cost of only one to three percent of total revenues, depending on coverage level. This article is protected by copyright. All rights reserved.

Description: Abstract The paper presents oxygen and hydrogen isotopes of 284 precipitation event samples systematically collected in Irkutsk, in the Baikal region (southeast Siberia), between June 2011 and April 2017. This is the first high‐resolution dataset of stable isotopes of precipitation from this poorly studied region of continental Asia, which has a high potential for isotope‐based paleoclimate research. The dataset revealed distinct seasonal variations: relatively high δ18O (up to –4‰) and δD (up to –40‰) values characterise summer air masses, while lighter isotope composition (–41‰ for δ18O and –322‰ for δD) is characteristic of winter precipitation. Our results show that air temperature mainly affects the isotope composition of precipitation, while no significant correlations were obtained for precipitation amount and relative humidity. A new temperature dependence was established for weighted mean monthly precipitation: +0.50‰/°C (r2 = 0.83; p 〈 0.01; n = 55) for δ18O and +3.8‰/°C (r2 = 0.83, p 〈 0.01; n = 55) for δD. Secondary fractionation processes (e.g. contribution of recycled moisture) were identified mainly in summer from low d excess. Backward trajectories assessed with the HYSPLIT model indicate that precipitation with the lowest mean δ18O and δD values reaches Irkutsk in winter related to moisture transport from the Arctic. Precipitation originating from the west/southwest with the heaviest mean isotope composition reaches Irkutsk in summer, thus representing moisture transport across Eurasia. Generally, moisture transport from the west i.e. the Atlantic Ocean predominates throughout the year. A comparison of our new isotope dataset with simulation results using the ECHAM5‐wiso climate model reveals a good agreement of variations in δ18O (r2 = 0.87; p 〈 0.01; n = 55) and air temperature (r2 = 0.99; p 〈 0.01; n = 71). However, the ECHAM5‐wiso model fails to capture observed variations in d excess (r2 = 0.14; p 〈 0.01; n = 55). This disagreement can be partly explained by a model deficit of capturing regional hydrological processes associated with secondary moisture supply in summer.

Description: The investigation focuses on the analysis of dissolved sulfonamides, tetracyclines, analgesics, anticonvulsants and hormones in surface water. Runoff event and baseflow samples were analyzed in two small river catchments of different landuse in Luxembourg. For most of the flood events similar pollutant loads to those transported during one day with average baseflow discharge were observed. The maximum contents during flood events and the Event Mean Concentrations are controlled by pre-event hydro-climatological conditions. For all substances under investigation maximum concentrations and Event Mean Concentrations show a decrease with raising antecedent rainfall. In addition, the inter-storm and intra-storm variability of the pollutant transport were determined. Runoff generation and corresponding transport of xenobiotic compounds show a complex pattern with many interrelated processes, taking place within bedrock, soil, anthropogenic facilities, the channel, and in different parts of the basins under investigation. Different sources of pollutants can be identified and related to particular locations in the basin. The influence of the sewer systems is obvious. In the agricultural Mess basin higher rainfall amounts lead to greater quantities of laterally inflowing soil water with higher concentrations of dissolved oxytetracycline. This originates from veterinary medicines administered to livestock and enters the environment through the application of organic fertilizers, especially by slurry that is applied to the fields. Copyright © 2012 John Wiley & Sons, Ltd.

Description: Abstract Worldwide, aquifers in low‐lying coastal areas are threatened by saltwater occurrence, as a result of small head gradients, high groundwater abstraction rates and drain‐management of the landscape, which is likely to intensify with climate change. Numerical models can serve as tools to identify the sources of the salt and thus to increase understanding of the driving mechanisms and important parameters controlling the extent of saltwater intrusions. This way, areas vulnerable to sea level rise can be identified and managed. Challenges include unknown initial salt concentrations, heterogeneous geology, and anthropogenic alterations. In this study, hydrogeological, geophysical and geochemical data are used to develop a numerical density‐dependent groundwater flow and transport model with the objective to understand the history of a saltwater‐affected groundwater system and its likely response to historic and future changes. The extent of the simulated saltwater intrusion compares well with Airborne Electromagnetic data that show salt water up to 20km inland. The results reveal that the salt water originates from a combination of laterally intruding seawater and vertically infiltrating transgression water. Main features controlling the progression of the modern seawater into the coastal aquifers are high‐permeable, deep Miocene sand aquifers, buried valleys that provide preferential flow paths in combination with extensive Miocene clay layers that delay saltwater intrusion. Anthropogenic activity enhances the saltwater inflow from the ocean and induces transient conditions. Future scenarios show that saltwater progression due to non‐stationarity leads to enhanced contamination of the deeper aquifers. Climate change affects primarily the shallow aquifer systems.

Description: In subsurface aquifers, dispersion of contaminant, or tracer, is mainly driven by spatial fluctuations in the flow field caused by heterogeneity of the hydraulic conductivity. Measurements of conductivity, however, are usually sparse. To assess the resulting uncertainty in the transport of tracers, Monte Carlo (MC) methods are usually applied, where the transport statistics are sampled over a large number of probable hydraulic conductivity realizations. In this paper, an alternative method is described that provides accurate transport statistics at a computational expense that is three orders of magnitude lower than conventional MC. The new method is applicable for conductivity fields with multi-variate Gaussian characterization involving conductivity measurements for both small and high log-conductivity variances. The new method is validated against MC for different dispersion scenarios, where the region of interest spans tens of log-conductivity correlation lengths.

Description: Over the past decade, British Columbia has experienced the largest mountain pine beetle (MPB) outbreak on record. This study used the eddy-covariance (EC) technique to examine the impact of the MPB attack on evapotranspiration ( E ) and associated canopy characteristics of two lodgepole pine stands with secondary structure (trees, saplings and seedlings surviving the attack) located in central BC. MPB-06, an 85-year-old almost pure stand of pine trees, was first attacked in 2006 and by 2010 ~80% of the trees had been killed. MPB-03, a 110-year-old stand with an overstory consisting of over 90% pine and a developed sub-canopy, was first attacked in 2003 and by 2007 had 〉 95% pine canopy mortality. EC measurements began in August 2006 at MPB-06 and in March 2007 at MPB-03, and continued for four years. Annual total E ranged from 226 mm to 237 mm at MPB-06, and from 280 to 297 mm at MPB-03, showing relatively little year-to-year change at both sites over the four years. Increased E from the accelerated growth of the surviving vegetation (secondary structure, shrubs and herbs) compensated for reduction in E due to the death of the overstory. Monthly average daytime canopy conductance, the Priestley-Taylor ( α ), and the canopy-atmosphere decoupling coefficient ( Ω ) steadily increased during the growing season reaching approximate maximum values of 5 mm s -1, 0.75 and 0.12, respectively. Potential evapotranspiration was approximated using a vapor pressure deficit-dependent α obtained at high soil water content. Calculated water deficits indicated some water-supply limitation to the surviving trees and understory at both sites. Rates of root zone drainage during the growing season were low relative to precipitation. This article is protected by copyright. All rights reserved.

Description: The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least-squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as that the formal generalized likelihood function developed by Schoups and Vrugt [2010]. For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least-squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least-squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least-squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM ( zs ) ) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and the DREAM ( ZS) -based global sensitivity analysis yield the same ranking of model importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

Description: ABSTRACT When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC , AICc , BIC , and KIC . However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, C ε , of measurement errors for estimating the negative log-likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, , of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum-likelihood inverse modeling to iteratively infer the unknown from the residuals during model calibration. The inferred was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using resolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using obtained from the iterative two-stage method also improved predictive performance of the individual models and model averaging in both synthetic and experimental studies.