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Geoscientific models manage myriad and increasingly complex data structures as trans -disciplinary models are integrated. They often incur significant redundancy with cross-cutting tasks. Reflection, the ability of a program to inspect and modify its structure and behavior at runtime, is known as a powerful tool to improve code reusability, abstraction, and separation of concerns. Reflection is rarely adopted in high-performance Geoscientific models, especially with Fortran, where it was previously deemed implausible. Practical constraints of language and legacy often limit us to feather-weight, native-language solutions. We demonstrate the usefulness of a structural-reflection-emulating, dynamically-linked metaObjects, gd . We show real-world examples including data structure self-assembly, effortless input/output (IO) and upgrade to parallel I/O, recursive actions and batch operations. We share gd and a derived module that reproduces MATLAB-like structure in Fortran and C++. We suggest that both a gd representation and a Fortran-native representation are maintained to access the data, each for separate purposes. Embracing emulated reflection allows generically-written codes that are highly re-usable across projects. [ABSTRACT FROM AUTHOR]

Soil moisture statistical fractal is an important tool for downscaling remotely-sensed observations and has the potential to play a key role in multi-scale hydrologic modeling. The fractal was first introduced two decades ago, but relatively little is known regarding how its scaling exponents evolve in time in response to climatic forcings. Previous studies have neglected the process of moisture re-distribution due to regional groundwater flow. In this study we used a physically-based surface-subsurface processes model and numerical experiments to elucidate the patterns and controls of fractal temporal evolution in two U.S. Midwest basins. Groundwater flow was found to introduce large-scale spatial structure, thereby reducing the scaling exponents ( τ ), which has implications for the transferability of calibrated parameters to predict τ . However, the groundwater effects depend on complex interactions with other physical controls such as soil texture and land use. The fractal scaling exponents, while in general showing a seasonal mode that correlates with mean moisture content, display hysteresis after storm events that can be divided into three phases, consistent with literature findings: (a) wetting, (b) re-organizing, and (c) dry-down. Modeling experiments clearly show that the hysteresis is attributed to soil texture, whose “patchiness” is the primary contributing factor. We generalized phenomenological rules for the impacts of rainfall, soil texture, groundwater flow, and land use on τ evolution. Grid resolution has a mild influence on the results and there is a strong correlation between predictions of τ from different resolutions. Overall, our results suggest that groundwater flow should be given more consideration in studies of the soil moisture statistical fractal, especially in regions with a shallow water table. [ABSTRACT FROM AUTHOR]

Trans-disciplinary hydrologic models oriented toward practical questions must be accompanied by accurate parameterization techniques. This paper describes the effects of different choices in the integration of various data sources on outcomes of the model Process-based Adaptive Watershed Simulator coupled with the Community Land Model (PAWS + CLM). Using our Hierarchical Stochastic Selection method, the represented land use percentages are much closer to the raw dataset, and lead to a 26% difference in carbon flux from that of the traditional dominant classes method. River bed elevations extracted using a novel algorithm agree well with the groundwater table and significantly increase baseflow contribution to streams relative to a coarse-DEM-based model. The inclusion of additional information in the soil pedotransfer functions drastically shifts ET, Net Primary Production and recharge. These results indicate that judicious treatment of input data has strong impacts on hydrologic and ecosystem fluxes. We emphasize the need to report details of data integration procedures. [ABSTRACT FROM AUTHOR]

Abstract: Process-based watershed models are useful tools for understanding the impacts of natural and anthropogenic influences on water resources and for predicting water and solute fluxes exported from watersheds to receiving water bodies. The applicability of process-based hydrologic models has been previously limited to small catchments and short time frames. Computational demands, especially the solution to the three-dimensional subsurface flow domain, continue to pose significant constraints. This paper documents the mathematical development, numerical testing and the initial application of a new distributed hydrologic model PAWS (Process-based Adaptive Watershed Simulator). The model solves the governing equations for the major hydrologic processes efficiently so that large scale applications become relevant. PAWS evaluates the integrated hydrologic response of the surface–subsurface system using a novel non-iterative method that couples runoff and groundwater flow to vadose zone processes approximating the 3D Richards equation. The method is computationally efficient and produces physically consistent solutions. All flow components have been independently verified using analytical solutions and experimental data where applicable. The model is applied to a medium-sized watershed in Michigan (1169km2) achieving high performance metrics in terms of streamflow prediction at two gages during the calibration and verification periods. PAWS uses public databases as input and possesses full capability to interact with GIS datasets. Future papers will describe applications to other watersheds and the development and application of fate and transport modules. [ABSTRACT FROM AUTHOR]

Abstract: The longitudinal dispersion coefficient (D) is an important parameter needed to describe the transport of solutes in rivers and streams. The dispersion coefficient is generally estimated from tracer studies but the method can be expensive and time consuming, especially for large rivers. A number of empirical relations are available to estimate the dispersion coefficient; however, these relations are known to produce estimates within an order of magnitude of the tracer value. The focus of this paper is on using the shear-flow dispersion theory to directly estimate the dispersion coefficient from velocity measurements obtained using an Acoustic Doppler Current Profiler (ADCP). Using tracer and hydrodynamic data collected within the same river reaches, we examined conditions under which the ADCP and tracer methods produced similar results. Since dead zones / transient storage (TS) are known to influence the dispersion coefficient, we assessed the relative importance of dead zones in different stream reaches using two tracer-based approaches: (1) TS modeling which explicitly accounts for dead zones and (2) the advection–dispersion equation (ADE) which does not have separate terms for dead zones. Dispersion coefficients based on the ADE tend to be relatively high as they describe some of the effects of dead zones as well. Results based on the ADCP method were found to be in good agreement with the ADE estimates indicating that storage zones play an important role in the estimated dispersion coefficients, especially at high flows. For the river sites examined in this paper, the tracer estimates of dispersion were close to the median values of the ADCP estimates obtained from multiple datasets within a reach. The ADCP method appears to be an excellent alternative to the traditional tracer-based method if care is taken to avoid spurious data and multiple datasets are used to compute a distance-weighted average or other appropriate measure that represents reach-averaged conditions. [Copyright &y& Elsevier]

Abstract: The success of transient storage (TS) modeling for natural streams depends, in part, on the ability to describe the dispersion process accurately. Evidence based on stream tracer data shows that solute transport processes often do not follow the classical second-order dispersion model (e.g., early breakthrough and faster than Fickian travel times were observed). While models based on space-fractional dispersion are a promising alternative, different definitions of fractional derivatives exist in the literature. Unlike integer-order derivatives, fractional derivatives represent convolutions of concentration with long-range spatial correlation and numerical approximations can produce dense matrices. Therefore issues of both accuracy and computational efficiency need to be examined to successfully identify model parameters for natural streams. In this paper, we first compare the performance of several numerical approaches for solving the space-fractional dispersion equation. We examine three different numerical approaches to approximate the space-fractional derivatives including: (a) a fully-implicit scheme based on the shifted Grünwald–Letnikov (GL) approximation (b) a three-point implicit representation based on the GL formula and (c) a three-point implicit scheme based on mass conservation and the Caputo definition of the fractional derivative. We then use an operator-splitting technique to evaluate a TS model based on space-fractional dispersion (the FSTS model) and test the model against analytical solutions and stream tracer data. A sequence acceleration method (Richardson extrapolation) significantly improves the performance of all schemes examined. Results indicate that the fully-implicit GL method with Richardson extrapolation produces the most accurate solutions while the three-point implicit GL scheme has a stringent time-step restriction to produce acceptable solutions. The three-point implicit scheme based on the Caputo derivative produces accurate solutions in a fraction of the time taken by the fully-implicit GL method and represents the best trade-off between accuracy and computational efficiency for practical applications. The scheme is suitable for parameter estimation and is used to successfully describe tracer data in a natural stream. [Copyright &y& Elsevier]

• A novel approach combining an integrated surface/subsurface model and a groundwater parameter-estimation model, was able to better constrain the model. • The groundwater system may be nearing, if not exceeding, its sustainable groundwater production capacity and the speed of recovery is not indicative of sustainability. • Results from using conventionally-assumed uniform recharge distort calibrated K fields and impacts assessment The quantification of recharge and trans-valley underflow is needed in arid regions to estimate the impacts of new water withdrawals on the water table. However, for mountainous desert areas, such estimates are highly challenging, due to data scarcity, heterogeneous soils, and long residence times. Conventional assessment employs isolated groundwater models configured with simplified uniform estimates of recharge. Here, we employed a data-constrained surface-subsurface process model to provide an ensemble of spatially distributed recharge and underflow estimates using perturbed parameters. Then, the Model-Independent Parameter Estimation and Uncertainty Quantification (PEST) package was used to calibrate the aquifer hydraulic conductivity field in MODFLOW for this ensemble and reject implausible recharge values. This novel dual-model approach, broadly applicable to mountainous arid regions, was designed to maximally exploit available data sources. It can assimilate groundwater head observations, reject unrealistic parameters, and narrow the range of estimated drawdowns due to pumping. We applied this approach to the Chuckwalla basin in California, USA to determine natural recharge. Simulated recharge concentrates along alluvial fans at the mountain fronts and ephemeral washes where run-off water infiltrates. If an evenly distributed recharge was employed as in conventional studies, it would result in regional biases in estimated drawdown and larger uncertainty bounds. We also note that the speed of groundwater recovery does not guarantee sustainability: heavy pumping induces large hydraulic gradients that initially recover quickly when pumping is halted, but the system may not ultimately recover to pre-pumping levels. [ABSTRACT FROM AUTHOR]

• Machine learning models were trained for predicting mean NO3 in 2061 US rivers. • Five watershed indexes can explain 69% of spatial variability in NO3 concentrations. • Nitrogen input rates and urban areas are top drivers, followed by climate (MAP, MAT). • The models can predict mean NO3 concentrations in chemically-ungauged rivers. Nitrate is one of the most widespread and persistent pollutants in our time. Our understanding of nitrate dynamics has advanced substantially in the past decades, although its predominant drivers across gradients of climate, land use, and geology have remained elusive. Here we collated nitrate data from 2061 rivers along with 32 watershed characteristic indexes and developed machine learning models to reconstruct long-term mean (multi-year average) nitrate concentrations in the contiguous United States (CONUS). The trained models show similarly satisfactory model performance and can predict nitrate concentrations in chemically-ungauged places with about 70% accuracy. Further analysis revealed that five (out of 32) indexes (drivers) can explain about 70% of spatial variations in mean nitrate concentrations. The five influential drivers are nitrogen application rates N rate and urban area A urban % (human drivers), mean annual precipitation and temperature (climate drivers), and sand percent Sand% (soil property driver). Nitrate concentrations in undeveloped sites are primarily modulated by climate and soil property; they decrease with increasing mean discharge and Sand%. Nitrate concentrations in agriculture and urban sites increase with N rate and A urban % until reaching their apparent maxima around 10,000 kg/km2/yr and around 25%, respectively. Results indicate that nitrate concentrations may remain similar or increase with growing human population. In addition, nitrate concentrations can increase even without human input, as warming escalates water demand and reduces mean discharge in many places. These results allude to a conceptual model that highlights the impacts of distinct drivers: while human drivers predominate nitrogen input to land and rivers, climate drivers and soil properties modulate its transport and transformation, the balance of which determine long-term mean concentrations. Such mechanism-based insights and forecasting capabilities are essential for water management as we expect changing climate and growing agriculture and urbanization. [Display omitted] [ABSTRACT FROM AUTHOR]