Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins.

Information on water availability in basins can be crucial for making decisions for effective water resource management in basins. As the operation of hydrometric stations in Korea is mainly focused on flood season and large rivers, most basins have lack or no observed data. Consequently, this complicates water resource planning and management. Remote sensing data is emerging as a powerful alternative to hydrological information in ungauged basins. This study investigated the applicability of Satellite-Remote Sensed Data (SRSD) as a source for model calibration in Prediction in Ungauged Basins (PUB) through modeling. Remote sensed leaf area index (LAI), actual evapotranspiration, and soil moisture data were used. Each SRSD was used alone to calibrate a hydrologic model to predict the daily streamflow for 28 basins in Korea. A vegetation module was added to the existing hydrologic model to use LAI. Among the SRSDs tested, the model calibrated with LAI had the most robust performance, predicting streamflow with acceptable accuracy compared to the traditional calibration based on streamflow. In particular, since the model account for vegetation actively interacting with evapotranspiration and soil moisture in the season of low flow, the LAI-calibrated model showed an advantage in improving the flow prediction performance. Although further research is required to utilize evapotranspiration and soil moisture data, the overall results of the LAI-based calibration were promising for predicting streamflow in ungauged basins where observations are scarce or absent, given that the satellite-derived LAI data were used alone without any preprocessing such as a bias correction. However, the prediction performance of the LAI-calibrated model was found to have a statistically significant relationship with local conditions. Therefore, by evaluating and improving the potential of SRSD in different region and climatic conditions, it is expected that the application of the SRSD-only calibration method can be extended to various ungauged basins.

A Data Mining Approach to Predict In Situ Detoxification Potential of Chlorinated Ethenes.

Despite advances in physicochemical remediation technologies, in situ bioremediation treatment based on Dehalococcoides mccartyi (Dhc) reductive dechlorination activity remains a cornerstone approach to remedy sites impacted with chlorinated ethenes. Selecting the best remedial strategy is challenging due to uncertainties and complexity associated with biological and geochemical factors influencing Dhc activity. Guidelines based on measurable biogeochemical parameters have been proposed, but contemporary efforts fall short of meaningfully integrating the available information. Extensive groundwater monitoring data sets have been collected for decades, but have not been systematically analyzed and used for developing tools to guide decision-making. In the present study, geochemical and microbial data sets collected from 35 wells at five contaminated sites were used to demonstrate that a data mining prediction model using the classification and regression tree (CART) algorithm can provide improved predictive understanding of a site's reductive dechlorination potential. The CART model successfully predicted the 3-month-ahead reductive dechlorination potential with 75.8% and 69.5% true positive rate (i.e., sensitivity) for the training set and the test set, respectively. The machine learning algorithm ranked parameters by relative importance for assessing in situ reductive dechlorination potential. The abundance of Dhc 16S rRNA genes, CH4, Fe(2+), NO3(-), NO2(-), and SO4(2-) concentrations, total organic carbon (TOC) amounts, and oxidation-reduction potential (ORP) displayed significant correlations (p < 0.01) with dechlorination potential, with NO3(-), NO2(-), and Fe(2+) concentrations exhibiting precedence over other parameters. Contrary to prior efforts, the power of data mining approaches lies in the ability to discern synergetic effects between multiple parameters that affect reductive dechlorination activity. Overall, these findings demonstrate that data mining techniques (e.g., machine learning algorithms) effectively utilize groundwater monitoring data to derive predictive understanding of contaminant degradation, and thus have great potential for improving decision-making tools. A major need for realizing the predictive capabilities of data mining approaches is a curated, open-access, up-to-date and comprehensive collection of biogeochemical groundwater monitoring data.

An upscaled approach for transport in media with extended tailing due to back-diffusion using analytical and numerical solutions of the advection dispersion equation.

The mono-continuum advection-dispersion equation (mADE) is commonly regarded as unsuitable for application to media that exhibit rapid breakthrough and extended tailing associated with diffusion between high and low permeability regions. This paper demonstrates that the mADE can be successfully used to model such conditions if certain issues are addressed. First, since hydrodynamic dispersion, unlike molecular diffusion, cannot occur upstream of the contaminant source, models must be formulated to prevent "back-dispersion." Second, large variations in aquifer permeability will result in differences between volume-weighted average concentration (resident concentration) and flow-weighted average concentration (flux concentration). Water samples taken from wells may be regarded as flux concentrations, while soil samples may be analyzed to determine resident concentrations. While the mADE is usually derived in terms of resident concentration, it is known that a mADE of the same mathematical form may be written in terms of flux concentration. However, when solving the latter, the mathematical transformation of a flux boundary condition applied to the resident mADE becomes a concentration type boundary condition for the flux mADE. Initial conditions must also be consistent with the form of the mADE that is to be solved. Thus, careful attention must be given to the type of concentration data that is available, whether resident or flux concentrations are to be simulated, and to boundary and initial conditions. We present 3-D analytical solutions for resident and flux concentrations, discuss methods of solving numerical models to obtain resident and flux concentrations, and compare results for hypothetical problems. We also present an upscaling method for computing "effective" dispersivities and other mADE model parameters in terms of physically meaningful parameters in a diffusion-limited mobile-immobile model. Application of the latter to previously published studies of systems that exhibit early breakthrough and extended tailing shows that the upscaled mADE model is able to describe the observed behavior with reasonable accuracy given only known physical parameters for the systems without any model calibration.

Cost optimization of DNAPL source and plume remediation under uncertainty using a semi-analytic model.

Dense non-aqueous phase liquid (DNAPL) spills represent a potential long-term source of aquifer contamination, and successful low-cost remediation may require a combination of both plume management and source treatment. In addition, substantial uncertainty exists in many of the parameters that control field-scale behavior of DNAPL sources and plumes. For these reasons, cost optimization of DNAPL cleanup needs to consider multiple treatment options and their associated costs while also gauging the influence of prediction uncertainty on expected costs. In this paper, we present a management methodology for field-scale DNAPL source and plume management under uncertainty. Using probabilistic methods, historical data and prior information are combined to produce a set of equally likely realizations of true field conditions (i.e., parameter sets). These parameter sets are then used in a simulation-optimization framework to produce DNAPL cleanup solutions that have the lowest possible expected net present value (ENPV) cost and that are suitably cautious in the presence of high uncertainty. For simulation, we utilize a fast-running semi-analytic field-scale model of DNAPL source and plume evolution that also approximates the effects of remedial actions. The degree of model prediction uncertainty is gauged using a restricted maximum likelihood method, which helps to produce suitably cautious remediation strategies. We test our methodology on a synthetic field-scale problem with multiple source architectures, for which source zone thermal treatment and electron donor injection are considered as remedial actions. The lowest cost solution found utilizes a combination of source and plume remediation methods, and is able to successfully meet remediation constraints for a majority of possible scenarios. Comparisons with deterministic optimization results show that not taking into account uncertainty can result in optimization strategies that are not aggressive enough and result in greater overall total cost.