Modeling groundwater redox conditions at national scale through integration of sediment color and water chemistry in a machine learning framework.

Redox conditions play a crucial role in determining the fate of many contaminants in groundwater, impacting ecosystem services vital for both the aquatic environment and human water supply. Geospatial machine learning has previously successfully modelled large-scale redox conditions. This study is the first to consolidate the complementary information provided by sediment color and water chemistry to enhance our understanding of redox conditions in Denmark. In the first step, the depth to the first redox interface is modelled using sediment color from 27,042 boreholes. In the second step, the depth of the first redox interface is compared against water chemistry data at 22,198 wells to classify redox complexity. The absence of nitrate containing water below the first redox interface is referred to as continuous redox conditions. In contrast, discontinuous redox conditions are identified by the presence of nitrate below the first redox interface. Both models are built using 20 covariate maps, encompassing diverse hydrologically relevant information. The first redox interface is modelled with a mean error of 0.0 m and a root-mean-squared error of 8.0 m. The redox complexity model attains an accuracy of 69.8 %. Results indicate a mean depth to the first redox interface of 8.6 m and a standard deviation of 6.5 m. 60 % of Denmark is classified as discontinuous, indicating complex redox conditions, predominantly collocated in clay rich glacial landscapes. Both maps, i.e., first redox interface and redox complexity are largely driven by the water table and hydrogeology. The developed maps contribute to our understanding of subsurface redox processes, supporting national-scale land-use and water management.

Multi-Constrained Catchment Scale Optimization of Groundwater Abstraction Using Linear Programming.

Due to increasing water demands globally, freshwater ecosystems are under constant pressure. Groundwater resources, as the main source of accessible freshwater, are crucially important for irrigation worldwide. Over-abstraction of groundwater leads to declines in groundwater levels; consequently, the groundwater inflow to streams decreases. The reduction in baseflow and alteration of the streamflow regime can potentially have an adverse effect on groundwater-dependent ecosystems. A spatially distributed, coupled groundwater-surface water model can simulate the impacts of groundwater abstraction on aquatic ecosystems. A constrained optimization algorithm and a simulation model in combination can provide an objective tool for the water practitioner to evaluate the interplay between economic benefits of groundwater abstractions and requirements to environmental flow. In this study, a holistic catchment-scale groundwater abstraction optimization framework has been developed that allows for a spatially explicit optimization of groundwater abstraction, while fulfilling a predefined maximum allowed reduction of streamflow (baseflow [Q95] or median flow [Q50]) as constraint criteria for 1484 stream locations across the catchment. A balanced K-Means clustering method was implemented to reduce the computational burden of the optimization. The model parameters and observation uncertainties calculated based on Bayesian linear theory allow for a risk assessment on the optimized groundwater abstraction values. The results from different optimization scenarios indicated that using the linear programming optimization algorithm in conjunction with integrated models provides valuable information for guiding the water practitioners in designing an effective groundwater abstraction plan with the consideration of environmental flow criteria important for the ecological status of the entire system.

Drivers and barriers for taking account of geological uncertainty in decision making for groundwater protection.

The geological conceptual model is considered a major source of uncertainty in groundwater modelling and well capture zone delineation. However, how to account for it in groundwater policy and management remains largely unresolved. We explore the drivers and barriers to account for geological conceptual uncertainty in groundwater protection amongst decision makers and stakeholders in an agricultural groundwater catchment in Denmark. Using a groundwater model, we analyze the impact of alternative geological conceptual models on capture zone delineation. A focus area, which covers multiple modelled capture zones, is defined and considered for groundwater protection. Model uncertainty and focus area are discussed at two workshops, one with local and national stakeholders and another with local farmers. The drivers to account for model uncertainty include: i) safer drinking water protection by considering a larger area for protection than identified from a single geological model; and ii) stability over time of management plans. The main barrier is the additional cost to the stakeholders for the protection of a larger area. We conclude that integration of geological uncertainty in groundwater protection plans may be improved through: i) better communication between the research community and the national water authority; ii) more constraining guidelines regarding the estimation of geological uncertainty; and iii) the development of a framework ensuring knowledge transfer to the local water authorities and detailing how to integrate uncertainty in management plans.

Groundwater modeling in integrated water resources management--visions for 2020.

Groundwater modeling is undergoing a change from traditional stand-alone studies toward being an integrated part of holistic water resources management procedures. This is illustrated by the development in Denmark, where comprehensive national databases for geologic borehole data, groundwater-related geophysical data, geologic models, as well as a national groundwater-surface water model have been established and integrated to support water management. This has enhanced the benefits of using groundwater models. Based on insight gained from this Danish experience, a scientifically realistic scenario for the use of groundwater modeling in 2020 has been developed, in which groundwater models will be a part of sophisticated databases and modeling systems. The databases and numerical models will be seamlessly integrated, and the tasks of monitoring and modeling will be merged. Numerical models for atmospheric, surface water, and groundwater processes will be coupled in one integrated modeling system that can operate at a wide range of spatial scales. Furthermore, the management systems will be constructed with a focus on building credibility of model and data use among all stakeholders and on facilitating a learning process whereby data and models, as well as stakeholders' understanding of the system, are updated to currently available information. The key scientific challenges for achieving this are (1) developing new methodologies for integration of statistical and qualitative uncertainty; (2) mapping geological heterogeneity and developing scaling methodologies; (3) developing coupled model codes; and (4) developing integrated information systems, including quality assurance and uncertainty information that facilitate active stakeholder involvement and learning.

The inadequacy of monitoring without modelling support.

There is much to gain in joining monitoring and modelling efforts, especially in the present process of implementing the European Water Framework Directive and in the coming implementation of the Groundwater Directive. Nevertheless, present practises in the water management world suggest that most often models are not considered an option when monitoring obligations in the WFD are solved. The present paper analyses the constraints, such as perceived insufficiency of data for modelling, lack of explicit requirement for modelling in the WFD and its associated technical guidance documents, lack of awareness about what models can do and lack of confidence in models by water managers and policy makers. The findings have mainly emerged from a series of Harmoni-CA workshops aiming at bringing the monitoring and modelling communities together for a discussion of benefits and constraints in the joint use of monitoring and modelling. The workshops were attended by scientists, water managers, policy makers, stakeholders and consultants. The overall conclusion is that modelling can significantly improve the benefits of monitoring data; by quality assurance of data, interpolation and extrapolation in space and time, development of process understanding (conceptual models), and the assessment of impacts of pressures and effects of programmes of measures.

Pesticide transport in an aerobic aquifer with variable pH--modeling of a field scale injection experiment.

Three-dimensional reactive transport simulations were undertaken to study the sorption and degradation dynamics of three herbicides in a shallow aerobic aquifer with spatially variable pH during a 216 days injection experiment. Sorption of two phenoxy acids [(+/-)-2-(4-chloro-2-methylphenoxy) propanoic acid] (MCPP) and [(+/-)-2-(2,4-dichlorophenoxy)propanoic acid] (dichlorprop) was found to be negligible. Degradation of the phenoxy acids was rapid after an initial lag phase. Degradation of the phenoxy acids could only be reproduced satisfactorily by growth-linked microbial degradation. The model fit to the field data was slightly improved if degradation was assumed to be influenced by the local pH that was observed to increase with depth ( approximately 4.5--5.7). In the observed pH-range the nitroaromatic herbicide [2-Methyl-4,6-dinitrophenol] (DNOC) was partly dissociated (pK(a)=4.31) and present in both the neutral and ionized form. The model simulations demonstrated that most of the observed spatial variation in sorption of DNOC could be explained by assuming that only the neutral form of DNOC was subject to sorption. A varying flow field was observed during the injection experiment and the model simulations documented that this most likely resulted in different migration paths for DNOC and the non-sorbing solutes. The model simulations indicated that degradation of DNOC was an important process. The degradation rate of DNOC remained constant over time and was simulated adequately by first-order kinetics. Again, the model fit to field observation was slightly improved if local pH was assumed to influence the degradation rate. Only the maximum utilization rate was estimated from the field data, while the remaining degradation parameters where successfully transferred from the laboratory study.