The Solver of Hydrological Unstructured Domain (SHUD) is an integrated multi-process, multi-scale, multi-timestep hydrologic model where the dominant hydrological processes are fully coupled using the semi-discrete Finite Volume Method. The hydrologic processes in land surface, aquifer and river are fully coupled and solved together. The high spatial and temporal resolution in SHUD provides detailed and reliable hydrologic metrics in a watershed. The SHUD System consists of SHUD (the hydrologic model) and SHUDtoolbox (a data processing tool kit). The new SHUD system provides capabilities for public data downloads, pre-processing, hydrologic modeling, automatic calibration, post-processing and spatial visualization, and is fully open-source and ready for hydrological modelers to use. Here we introduce the philosophy of SHUD, from perceptual to computational structures of watershed hydrology, and select two watersheds (one in Africa and the other in California) as examples to demonstrate the workflow and capabilities of SHUD System. In modeling these examples, we exploit national/global public datasets and exemplify the data management, hydrologic analysis, model calibration and visualization capabilities. This modeling system not only supports quick deployment of hydrologic modeling functionality, but also benefits community modeling in other hydrology-related research fields, such as limnology, agriculture, climate change and Coupled-Natural-Human systems.

Migration is a complex and interdisciplinary problem involving multiple factors such as social interactions, resource scarcity, and geographical features. These factors must be incorporated in migration models, but how? We feel that the issue how different factors should be incorporated is not carefully addressed in existing models. Configuring factors in ways that are theoretically unsound can lead to false migration patterns and undermine the usefulness of models; indeed, factor configurations may be more critical than the factors themselves or other inputs. Therefore, we ask: i) How important is factor configuration to output results comparing with other inputs?; ii) How do different factor configurations produce different migration patterns?; and iii) How can multimodality of certain output distributions be controlled in a management perspective? To address the questions, we develop a “toy” migration agent-based model (ABM) and explore three possible configurations between two factors: i) two factors are perfectly substitutable (ADD), ii) both factors are indispensable (AND), and iii) either is enough (OR). ABM results are analyzed by global sensitivity analysis (GSA) and Monte-Carlo Filtering (MCF). The relative importance of factor configurations quantified by GSA emphasizes why we need to consider how the factors are incorporated. Depending on factor configurations, we also observe unimodal or multimodal output distributions. MCF is then applied to the ABM-GSA results to address how policymakers should control certain inputs to sustain systems with desirable outputs. Altogether, we have integrated ABM, GSA, and MCF to disentangle complexity of migration models and better understand underlying mechanisms and patterns of migration.

Over 270 major dams have been constructed in the Southeastern United States (SEUS) during the past century, changing natural flow patterns and affecting stream temperatures. Projected increases in air temperature combined with changes in precipitation may result in water scarcity and affect maximum stream temperatures during the summer for some regions in the SEUS. Currently existing reservoirs mitigate water shortages during drought by releasing more water but reducing residence time, the ratio of reservoir volume to inflow. Regulating stream temperature in the summer can be done by either increasing residence time or releasing more water. In this study, we investigate the extent to which the current reservoir infrastructure can be used to mitigate the impacts of climate change under current reservoir regulations as well as the range of operating rules that could minimize climate change impacts on both streamflow and river temperature. We use the Variable Infiltration Capacity (VIC) hydrological model to simulate runoff, which is then used as input to a large-scale river routing-reservoir model (MOSART-WM) to simulate reservoir operations and produce regulated streamflow. VIC and MOSART-WM outputs are then used as input to a stream temperature model that accounts for thermal stratification in reservoirs (RBM-res). Climate change projections are based on two representative concentration pathways (RCPs) and multiple global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We compare modeled changes with those from a model implementation that does not include any reservoirs and which therefore lacks any flow regulation (VIC->MOSART-RBM) to evaluate the resilience of current reservoir infrastructures. We also evaluate different reservoir operating rules (residence time versus low flow mitigation) to investigate the extent to which the current reservoir system can be used to mitigate the impacts of climate changes on both streamflow and stream temperature.

Low-current tributary-estuaries and embayments along the margin of the Hudson River are uniquely at risk for harmful algal blooms of cyanobacteria (cyanoHABs) due to rising temperatures as a result of climate change. An increased prevalence of cyanoHABs in near-shore, low-current sections of the Hudson River could be extremely harmful to nearby communities, aquatic organisms and wildlife. To address this increased risk, it is imperative to understand the current in-stream and upstream abiotic environmental controls (nutrients, water temperature, etc.) on the current background levels of cyanobacteria within the Hudson River. It is also important to understand how these controls and cyanobacterial populations vary spatially with relation to the higher risk, lower-flow sections along the margins of the Hudson River. Locations of tributary-estuaries of special concern within the Hudson Valley include Esopus Creek in Saugerties, Rondout Creek in Kingston, and Wappingers Creek in Wappingers, NY. Other locations of concern are embayments along the Hudson River such as Long Dock Park in Beacon, Port Ewen in Kingston and Norrie Point in Staatsburg, NY. Given the lower-flow nature of these sites, elevated surface water temperatures are likely a result of settled, striated layers from decreased current. These locations are also susceptible to growth of the invasive species Trapa natans or commonly known as the European water chestnut. High concentrations of nutrients like nitrogen and phosphorous within the water chestnut bloom and the captured sunlight from metabolic processes like photosynthesis can create an ideal microhabitat for harmful algae like cyanobacteria. The background levels of cyanobacteria in outflows of tributaries, and their lower-flow estuary extensions were observed alongside the water quality within the water chestnut blooms of these sites at varying depths. By studying the weekly changes in background abundance of cyanobacteria and their drivers occurring at contrasting locations along the Hudson River, it was found that the strongest controls included turbidity, temperature and levels of phosphorous. In locations of low turbidity and high surface water temperatures, the background levels of cyanobacteria were higher in these lower-flow areas than in areas with increased turbidity. Cyanobacteria was found in greater number within water chestnut blooms than in whole water samples outside the area of the bloom. High surface temperature and riverbed temperature also related to higher levels of cyanobacteria. Given the concluded information, it is apparent that invasive water chestnuts within lower-flow extensions of the Hudson River hold a greater threat than originally understood; creating an ideal habitat for potential cyanoHABs in the wake of climate change.

The Southwest U.S. comprising of the four states-Arizona, New Mexico, Colorado, and Utah-is the hottest and driest region of the United States. Most of the precipitation arrives during the winter season, but the summer precipitation makes a significant contribution to the reliability of water resources and the health of ecology. However, summer precipitation and its extremes, over this region exhibit high degree of spatial and temporal variability. In this study we developed a novel spatial Bayesian hierarchical model to capture the space-time variability of –summer season 3-day maximum precipitation over the southwest U.S. In modeling framework, the data layer the extremes at each station are assumed to be distributed as Generalized Extreme Value (GEV) distribution with non-stationary parameters. In addition, the extremes across space is assumed to be related via a Gaussian Copula. In the process layer, the parameters are modeled as a linear function of large scale climate variables and regional mean precipitation covariates. This is akin to a Generalized Linear Model (GLM). The parameters of the covariates at each station are spatially modeled using spatial Gaussian processes to capture the spatial dependency and enable generating the spatial field of the hydroclimate extremes. The likelihood estimates of the GLM at each station form the initial priors. The posterior distribution of the model parameters and consequently the predictive posterior GEV distribution of the hydroclimate extremes at any arbitrary location, or grid and for any year are obtained. The model is demonstrated by application to extreme summer precipitation at 73 stations from this region. The model validation indicates that return levels and their associated uncertainty have a well-defined spatial structure and furthermore, they capture the historical variability very well. The posterior distribution of the GEV parameters were generated on a 1/8th degree grid, providing maps of various return levels for all the years. Maps of return levels provide information about the spatial and temporal variations of the risk of extreme precipitation in the Southwest U.S. that will be of immense help in management and planning of natural resources and infrastructure.

Planning for hydropower, water resources management, and climate change adaptation requires statistically unbiased hydrologic predictions. However, all hydrologic models contain systematic errors, e.g., incorrect mathematical representations of physical processes and effects of uncertainties in data sources. Statistical post-processing, or bias correction, is often used to reduce the effects of these systematic errors in model outputs. A large number of techniques for performing bias correction has been developed, primarily in the context of correcting statistical properties of independent locations. However, when bias correcting streamflow predictions within the same stream network, this assumption of spatial independence breaks down. Independently bias correcting locations from the headwaters to the mouth of a river system destroys the spatial consistency of the streamflow across a river network. We describe work toward maintaining spatial consistency in streamflow bias correction using a number of locations in the western United States. We simulate the hydrology of the Columbia River in the Pacific Northwestern United States, a river system that spans a number of hydroclimatic and flow regimes that contains a large number of flow gages. We develop a mapping from the modeled output at the gages with flow observations, which we use as the basis for training a machine learning (ML) model to perform the site-specific bias correction. We then apply the ML model to local streamflow contributions for each river segment, including river segments without flow observations. Finally, we combine the local bias corrections across the stream network, to create accumulated bias-corrected streamflow time series that are spatially-consistent across the stream network. We compare our method against several commonly used bias correction techniques to evaluate both model performance and spatial consistency.

Logjams in a stream create backwater conditions and locally force water to flow through the streambed, creating zones of transient storage within the surface and subsurface of a stream. We investigate the relative importance of logjam distribution density, logjam permeability, and discharge on transient storage in a simplified experimental channel. We use physical flume experiments in which we inject a salt tracer, monitor fluid conductivity breakthrough curves in surface water, and use breakthrough-curve skew to characterize transient storage. We then develop numerical models in HydroGeoSphere to reveal flow paths through the subsurface (or hyporheic zone) that contribute to some of the longest transient-storage timescales. In both the flume and numerical model, we observe an increase in backwater and hyporheic exchange at logjams. Observed complexities in transient storage behavior may depend largely on surface water flow in the backwater zone. As expected, multiple successive logjams provide more pervasive hyporheic exchange by distributing the head drop at each jam, leading to distributed but shallow flow paths. Decreasing the permeability of a logjam or increasing the discharge both facilitate more surface water storage and elevate the surface water level upstream of a logjam, thus increasing hyporheic exchange. Multiple logjams with low permeability result in the greatest magnitude of transient storage, suggesting that this configuration maximizes solute retention in backwater zones, while hyporheic exchange rates also increase. Understanding how logjam characteristics affect solute transport through both the channel and hyporheic zone has important management implications for rivers in forested, or historically forested, environments.

A coupled hydrologic and snowpack stable water isotope model assesses controls on isotopic inputs across a large, mountainous basin. The most depleted isotope conditions occur in the upper subalpine where snow accumulation is high and rainfall is low. Snowmelt evolution over time indicates isotopic enrichment is not dictated by melt fractionation but is determined by elevation which controls the amount, phase and isotopic mass of spring precipitation coincident with the ablation period. With respect to snowpack kinetic fractionation, its effect on snowmelt is a balance between energy and snow-availability. It is highest above treeline and in the shrub-dominated upper montane where vegetation shading is low, while deep snowpack and conifer forests limit the influence of kinetic fractionation in the subalpine. Wet years reduce the effects of snowpack fraction on snowmelt across the basin, except in the lower montane where added snowfall bolsters water-limited conditions.

We propose a machine learning-based approach to estimate the flood defense standard (FDS) for ungauged sites. We adopted random forest regression (RFR) to characterize the relationship between the observed FDS and ten explanatory factors contained in publicly available datasets. We compared RFR with multiple linear regression (MLR) and demonstrated the proposed approach in the conterminous United States (CONUS) and England, respectively. The results showed the following: (1) RFR performed better than MLR, with a Nash–Sutcliffe efficiency (NSE) of 0.82 in the CONUS and 0.73 in England. A negative NSE when using MLR indicated that the relationship between the FDS and each explanatory factor did not obey an explicit linear function. (2) River flood factors had higher importance than physical and socio-economic factors in the FDS estimation. The proposed approach achieved the highest performance using all factors for prediction and could not provide satisfactory predictions (NSE < 0.6) using physical or socio-economic factors individually. (3) We estimated the FDS for all ungauged sites in the CONUS and England. Approximately 80% and 29% of sites were identified as high or highest standard (> 100-year return period) in the CONUS and England, respectively. (4) We incorporated the estimated FDS in large-scale flood modeling and compared the model results with official flood hazard maps in three case studies. We identified obvious overestimations in protected areas when flood defenses were not taken into account; and flood defenses were successfully represented using the proposed approach.

Lakes and reservoirs are an important part of the terrestrial water cycle. However, relatively little attention has been given to lake and reservoir water balance modelling, their impacts, and interaction with complex terrestrial system processes. In this work, we present the implementation of lakes and reservoirs into mizuRoute, a vector-based routing model (termed mizuRoute-Lakes) that is agnostic to the choice of hydrologic or land model. In this work, we demonstrate capabilities of mizuRoute-Lake in modeling the water balance of lakes and reservoirs namely (1) data-driven lake/reservoir models; (2) multi-model lake models; and (3) abstraction from lakes, reservoirs, and river segments. Applications presented in this work are at global, regional, and local scales. The data-driven and parametric capabilities that are provided in mizuRoute enable incorporating past or future altimetry data (e.g. from the Surface Water and Ocean Topography, SWOT, mission for estimation of lakes and reservoirs storage) or information from water management model simulations regarding water demand and reservoir operation under climate change scenarios. We believe the capabilities presented in mizuRoute-Lake will enable the modellers to diagnose and compare water balance models in a more rigorous manner.

A catchment in southern England, UK, included a substantial area of bare ground within the surrounding heathland and woodland. Runoff from this area has, in the past, contributed large volumes of sediment to a large lake; although this input is now significantly reduced as a result of previous and on-going management works that are reported on in this paper. Historic realignment and re-sectioning of the main watercourse, has also resulted in the overdeepening, vertical and lateral erosion of the stream channel resulting in downstream transport of sediment to the lake. In addition to sediment erosion, the associated limited connectivity with the floodplain and focus of sediment transport in the fluvial channel has been a key factor in the shallowing and deterioration in the condition of the lake. Over the last 15 years a wide range of investigative, monitoring and management work has been undertaken within the catchment by a partnership between UK Government organisations, a local authority and a charity, with continuous involment by the author throughout this period. This work has evaluated the causes and effects associated with this erosion and transportation, tested and defined viable practical solutions (the delivery of natural sediment and flood management solutions and habitat restoration) and delivered a series of sustainable management interventions to reduce erosion, promote sediment deposition and to reconnect the stage zero and larger fluvial pathways to the floodplain – supporting the restoration of the lake. These works have resulted in the reduction in erosion at source and increased deposition through the catchment system, ultimately contributing to the improvement in condition of the lake and associated wetland habitats. Works in the headwaters of the catchment focused on defining the existing distribution, status and significance of areas of sediment generation, transport and deposition to the stream and lake, facilitating sustainable sediment management within this area. Works in the lower reaches focused on slowing flow velocities and diverting higher velocity sediment rich flows into new channels to reconnect with the floodplain and promote deposition. Management measures included the use of small diversion channels through woodland with the creation of glades to increase understory recovery and sediment deposition; use of geotextile cells filled with sand, gravel or stone to increase the flow path, reduce velocity and promote out of channel flooding and deposition of sediment; use of scrub and woody material to form leaky dams and increase channel roughness promoting out of bank flooding and deposition; use of online ponds, backwaters and embayments; blanking off channels to promote overland flow through woodland to reduce flow depth and velocity and promote deposition; use of leaky dams to promote higher flows transporting sediment into new sinuous channels and allowing out of bank flooding to promote sediment deposition.

Shallow glacial aquifers systems are the primary source of drinking water for millions of residents in the upper Midwest and Great Lakes regions of the United States. Studies show that a significant number of municipal and private groundwater wells in these regions are impacted by high nitrate concentrations, which can have negative health impacts for humans. Reducing nitrate contamination through good land management practices will reduce the need for costly nitrate treatment systems and help mitigate other ecological concerns related to nutrient pollution of groundwater. This study presents a Python-based modelling tool that uses a local groundwater flow model and historical land use data (USDA CropScape) to estimate nitrate concentrations at a high-capacity pumping well. Nitrate concentrations predicted by this model are within 5% of median annual values observed at a study site in Waupaca, WI. The model is user-friendly and can easily be adapted to other locations, where it has the potential to help local and state agencies, landowners, and growers make cost-effective decisions about land-use and agricultural practices.

Freshwater ecosystems are globally significant sources of greenhouse gases (GHG) to the atmosphere. Generally, we assume that in-situ production of GHG in streams is limited by turbulent reaeration and high dissolved oxygen concentrations, so stream GHG flux is highest in headwater streams that are connected to their watersheds and serve as conduits for the release of terrestrially derived GHG. Low-gradient streams contain pool structures with longer residence times conducive to the in-situ production of GHG, but these streams, and the longitudinal heterogeneity therein, are seldom studied. We measured continuous ecosystem metabolism alongside concentrations and fluxes of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) from autumn to the following spring along an eight kilometer segment of a low-gradient third order stream in the North Carolina Piedmont. We characterized spatial and temporal patterns of GHG in the context of channel geomorphology, hydrology, and ecosystem metabolic rates using linear mixed effects models. We found that stream metabolic cycling was responsible for most of the CO2 flux over this period, and that in-channel aerobic metabolism was a primary driver of both CH4 and N2O fluxes as well. Long water residence times, limited reaeration, and substantial organic matter from terrestrial inputs foster conditions conducive to the in-stream accumulation of CO2 and CH4 from microbial respiration. Streams like this one are common in landscapes with low topographic relief, making it likely that the high contribution of instream metabolism to GHG fluxes that we observed is a widespread yet understudied behavior of many small streams.

Urbanization tends to increase runoff volumes, which might cause flooding and reduce groundwater recharge. Since the design of impermeable urban elements is based on the water flow volume before their construction, once they are erected the induced change to the local drainage pattern might generate flooding of the newly developed and previously developed areas. As such, precise modeling is essential to allow municipal watershed-sensitive hydrological design, which may prevent impervious urban surface expansion negative impacts. The digital elevation model that represents the watershed relief at any given location is the hydrological modeling base layer, which is necessary for describing urban landscapes and watersheds. The common notion is that the finer the elevation model resolution is, the more precise the hydrological model will be. Nevertheless, it is suggested that over-accuracy might be redundant. In the same manner, the land use classification resolution should be aligned with the modeling requirements. Such careful evaluation of the modeling resolution will reduce the computing resources needed for the modeling procedure and may be utilized as a sensitivity filter for insignificant tributaries of the hydrological network. This paper demonstrates a nominal procedure for urban watershed sub-basin analysis, which is the initial stage for detailed urban runoff modeling. It was found that the scale-optimized model performed very well and was found suitable for the prediction of runoff volume and discharge from a mainly urban, mountainous karstic watershed.

Streamflow losses beneath non-perennial streams are potentially a major contribution to recharge, though measurements are often challenging due to the transient nature of these non-continuous (both spatially and temporally) streamflow. Significant investigative efforts for ephemeral streams have been described in literature, yet streams with intermittent streamflows lack this level of effort, particularly over an entire hydrological cycle. In this study, streambed water content and temperature were continuously logged over a year for an intermittent stream under semi-arid conditions in a wadi (arroyo) in Central Morocco. The results show that streambed water content and temperature are complementary data for identifying and classifying infiltration events, with respect to determining their duration, depth of water content increase and flow velocity within the sediments. Water content measurements easily allow distinguish between downward surface water percolation as well as upward groundwater wetting front. Over the entire year, the calculated total potential recharge based on temperature modeling was 425 mm. During winter and spring when the alluvium has a higher water moisture, this recharge is predominantly generated by floods. Normal streamflow generally generates low infiltration but contributes to wetting the sediment. During the summer, brief flashfloods over dry sediment result in shallower and slow wetting from infiltration, despite of their higher peak streamflows. Thus, for this wadi, there is clear seasonality (seasonal variation) in relations between amounts of streamflow, streamflow loss and depth of wetting into the streambed, as well as upward advance of wetting through deeper streambed sediments from groundwater receiving lateral mountain-front recharge.

We propose an approach to upscale solute transport in spatially periodic porous media. Our methodology relies on pore scale information to predict large scale transport features, including explicit reconstruction of the solute plume, breakthrough curves at fixed distances, and spatial spreading transverse to the main flow direction. The proposed approach is grounded on the recently proposed trajectory-based Spatial Markov model (tSMM), which upscales transport based on information collected from advective-diffusive particle trajectories across one periodic element. In previous works, this model has been applied solely to one-dimensional transport in a single periodic pore geometry. In this work we extend the tSMM to the prediction of multi-dimensional solute plumes. This is obtained by analyzing the joint space-time probability distribution associated with discrete particles, as yielded by the tSMM. By comparing numerical results from fully resolved simulations and predictions obtained with the tSMM over a wide range of Péclet numbers, we demonstrate that the proposed approach is suitable for modeling transport of conservative and linearly decaying solute species in a realistic pore space and showcase the applicability of the model to predict steady state solute plumes. Additionally, we evaluate the model performance as a function of numerical parameters employed in the tSMM parameterization.

The Beijing-Tianjin-Hebei Metropolitan Region is the biggest urbanized megalopolis in North China. The region has one the lowest water resources availability per capita in China and around the world. Rapid economic development in the past 40 years has resulted in substantially increased water consumption in the region which led to various water security problems including huge burden on water supply, groundwater overdraft, serious damage of river and lake ecosystems etc. Water scarcity has now become one of the largest limiting factors that hinders the further development of the region. The objective of water resources management in this region is thus to ensure healthy balances between the natural and the societal water circulation. This means we need to protect the natural hydrological flow to maintain water's service functions, and in the meantime fulfill the increasing water demand for economic growth. In the present study, we inspected past evolution and future trend of the region's water budget, and subsequently proposed methods for restricting water use and promoting multi-source water supply optimization. This research also reviewed some new concepts and technologies which can potentially help us overcome the water security challenges in the Beijing-Tianjin-Hebei region.

The increasing frequency of devastating floods from heavy rainfall associated with climate change has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep learning models may improve prediction of river stages from rainfall. Here we use the framework of multilayer perceptron (MLP) neural networks to develop such a river stage model. The MLP is constructed for the Shimanto river, which lies in southwestern Japan under a mild, rain-heavy climate. Our input for stage estimation, as well as prediction, is long-term rainfall time series. With a one-year time series of rainfall, the model estimates the stage with 50 cm RMSE for about 10 m of stage peaks as well as accurately simulate stage-time fluctuations. Furthermore, the forecast model can predict the stage without rainfall forecasts up to three hours ahead. To estimate the base flow stages as well as flood peaks with high precision we find the rainfall time series should be at least one year. This indicates that the use of a long rainfall time series enables one to model the contributions of ground water and evaporation. Given that the delay between the arrival time of rainfall at a rain-gauge to the outlet change is well simulated, the physical concepts of runoff appear to be soundly embedded in the MLP.

Crystalline basement aquifers are characterized by complex flow pathways controlled by varying overburden stratigraphy and thickness as well as fracture network and connectivity within the crystalline rocks. Understanding the hydraulic connection within the fracture network and the overburden regolith is critical to predicting recharge/discharge and contaminant transport pathways. In this study, we combined geophysical imaging with multiple hydraulic testing to quantify hydraulic connectivity within the crystalline basement aquifers at the Ibadan Hydrogeophysical Research Site (IHRS) in Ibadan, Nigeria. The 50 m × 50 m field experimental site is first of its kind established in 2019 to investigate hydrological dynamics within these complex crystalline basement aquifers in sub–Saharan Africa. We acquired multiple parallel 2D electrical resistivity profiles which were also jointly inverted to obtain multiple 2D and 3D electrical resistivity tomograms of the subsurface. The resistivity tomograms were later constrained with lithological profiles from 4 test wells installed down to depths of 30 m at the site to create a conceptual model elucidating potential flow pathways. We also performed a series of 12 hours pumping tests and a NaCl tracer test to estimate flow and transport parameters including hydraulic conductivity, aquifer storage, yield, and groundwater travel time and to assess connection between the four test wells. The resistivity tomograms show 3 major resistivity zones interpreted as a clay-rich topsoil, a saturated weathered overburden, and a fractured basement rock. The delineated fractured bedrock shows an undulating topography with several primary fracture successions at 9, 14, 16 and 22 m. Hydraulic conductivities from pumping tests range from 2.6 x 10-7 to 1.2 x 10-5 m/s for the fractures and 1.7 x 10-10 to 6.4 x 10-6 m/s for the matrix while specific storage range from 3.5 x 10-8 to 1.8 x 10-3. Preferential flow is also observed with stronger connection between wells A and C. Results of this study provide a basis for detailed numerical study which will be focused on predicting recharge and solute transport under different flow and climate regime. This work will provide a scalable framework for a sustainable management of groundwater resources within the crystalline basements of Nigeria.

Field kits for testing the level of a toxicant in the environment are inherently less accurate than a laboratory instrument. Using a specific example, we argue here that kit measurements still have a key role to play when the spatial distribution of a toxicant is very heterogeneous. The context is provided by the groundwater arsenic problem in Bangladesh. We combine here two data sets, a blanket survey of 6595 wells over a 25 km2 based on laboratory measurements and 900 paired kit and laboratory measurements from the same area. We explore different hypothetical mitigation scenarios based on actual data that rely on households with a high-arsenic well switching to a nearby low-arsenic well. We show that the decline in average exposure to arsenic from relying on kit rather than laboratory data is modest in relation to the logistical and financial challenge of delivering exclusively laboratory data. Our analysis indicates that the 50 ug/L threshold used in Bangladesh to distinguish safe and unsafe wells, rather than the WHO guideline of 10 ug/L, is close to optimal in terms of average exposure reduction. We also show, however, that providing kit data at the maximum possible resolution rather than merely classifying wells as unsafe or safe would be even better. These findings are relevant as the government of Bangladesh is about to launch a new blanket testing campaign of millions of wells using field kits.

This evaluates the potential for a newly proposed non-linear subsurface flux equation to improve the performance of the hydrological Hillslope Link Model (HLM). The equation contains parameters that are functionally related to the hillslope steepness and the presence of tile drainage. As a result, the equation allows a better representation of hydrograph recession curves, hydrograph timing, and total runoff volume. The authors explore the new parameterization’s potential by comparing a set of diagnostic and prognostic setups in HLM. In the diagnostic approach, they configure 12 different scenarios with spatially uniform parameters over the state of Iowa. In the prognostic case, they use information from topographical maps and known locations of tile drainage to distribute parameter values. To assess performance improvements, they compare simulation results to streamflow observations during a 17-year period (2002–2018) at 140 U.S. Geological Survey (USGS) gauging stations. The operational setup of the HLM model used at the Iowa Flood Center (IFC) serves as a benchmark to quantify overall model improvement. In particular, the new equation provides better representation of recession curves and the total streamflow volumes. However, when comparing the diagnostic and prognostic setups, the authors find discrepancies in the spatial distribution of hillslope scale parameters. The results suggest that more work is required when using maps of physical attributes to parameterize hydrological models. The findings also demonstrate that the diagnostic approach is a useful strategy to evaluate models and assess changes in their formulations.

Induced polarization (IP) is increasingly applied for hydrological, environmental and agricultural purposes. Interpretation of IP data is based on understanding the relationship between the IP signature and the porous media property of interest. Mechanistic models on the IP phenomenon rely on the Poisson-Nernst-Plank equations, where diffusion and electromigration fluxes are the driving forces of charge transport and are directly related to IP. However, to our knowledge, the impact of advection flux on IP was not investigated experimentally and was not considered in any IP model. In this work, we measured the spectral IP (SIP) signature of porous media under varying flow conditions, in addition to developing and solving a model for SIP signature of porous media, which takes flow into consideration. The experimental and the model results demonstrate that as bulk velocity increases, polarization and relaxation time decrease. Using a numerical model, we established that fluid flow near the particle deforms the electrical double layer (EDL) structure, accounting for the observed reduction in polarization. We found a qualitative agreement between the model and the measurements. Still, the model overestimates the impact of flow rate on SIP signature, which we explain in terms of the flow boundary conditions. Overall, our results demonstrate the sensitivity of the SIP signature to fluid flow, highlighting the need to consider fluid velocity in the interpretation of the SIP signature of porous media, and opening an exciting new direction for noninvasive measurements of fluid flow at the EDL scale.

Does river topography have stage thresholds for maintaining fluvial landforms, and if so how can they be quantified? Geomorphic covariance structure analysis offers a novel, systematic framework for evaluating nested topographic patterns in river corridors. In this study, a threshold in mountain river stage was hypothesized to exist; above this stage landform structure is organized to be freely self-maintaining via flow convergence routing morphodynamics. A 13.2 km segment of the canyon-confined Yuba River, California, was studied using 2944 cross-sections. Geomorphic covariance structure analysis was carried out on a meter-resolution topographic model to test the hypothesis. A critical stage threshold governing flow convergence routing morphodynamics was evident in several metrics. Below this threshold, narrow/high “nozzle” and wide/low “oversized” landforms that are out-of-phase with flow convergence routing morphodynamics dominated (excluding “normal channel”), while above it wide/high “wide bar” and narrow/low “constricted pool” landforms consistent with the flow convergence mechanism were dominant. Three-level nesting of co-located base-bankfull-flood stage landforms was dictated by canyon confinement, with nozzle-nozzle-nozzle nesting as the top permutation, excluding normal channel. In conclusion, this study demonstrates a significantly different and highly effective approach to finding process-based fluvial thresholds that can complement pre-existing methods, such as estimating incipient sediment motion, to get at more powerful dynamics controlling fluvial landforms structure.

Due to its substantial role on the Earth’s biogeochemical cycles and human health, nitrogen is recognized as one of the major water quality indicators of Sustainable Development Goal 6.3.2. Quantifying these potential impacts in large spatial scales still appears to be a grand challenge because of the high computational demand required by the distributed physically based global models and their intensive data requirements for calibration and validation. The former prevents a comprehensive analysis of the full spectrum of the model behavior under different conditions, and the latter impinges on the reliability of model-based inference. To tackle this problem, we developed a data-driven model using a spatio-temporal Random Forest algorithm to predict levels of nitrogen at 0.5-degree spatial resolution from 1992 to 2010 across the world. Several variables representing livestock, climate, hydrology, topography, etc. have been selected as predictors. The response variable of interest was nitrate–nitrite, which is responsible for the high risk of infant methemoglobinemia. Our results indicate that changes in the nitrogen concentration is mainly driven by cattle and sheep population, fertilizer application, precipitation, and temperature variability, implying livestock population, climate change, and anthropogenic forces can be important risk factors for global water quality deterioration. Furthermore, using the predicted levels of nitrogen, we characterized large-scale water quality patterns, and thus identified a few major ‘hot spots’ of water quality. The proposed model can also help assess potential impacts of future scenarios (e.g., livestock production or land use change) on global water quality conditions for better development of effective policy strategies.