Perfluoroalkyl acids (PFAAs), a group of synthetic compounds associated with adverse human health impacts, are commonly found in effluent discharged from wastewater treatment facilities. When that effluent is used for irrigation, the fate of PFAAs depends strongly on vadose zone solute retention properties and loading history. The relative importance of PFAA retention factors under natural conditions remains uncertain, and the historical record of effluent PFAA concentrations is limited. Using soil cores collected from the Penn State Living Filter (irrigated with treated wastewater effluent for nearly 60 years), we evaluated PFAA transport under near-natural conditions, and estimated historical PFAA concentrations in the irrigated effluent. Total perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) masses stored in soils in 2014 were more than 450 times greater than the masses applied during the 2020 effluent irrigation. Equilibrium piston-flow transport models reproduced the observed PFOS and PFOA profiles, allowing us to estimate historical effluent PFOS and PFOA concentrations: 70-170 ng L-1 and 1000-1300 ng L-1, respectively. Estimated concentrations were comparable to concentrations measured in other wastewater effluents in the 1990s and 2000s, indicating that when interpreted with transport modeling, wastewater-irrigated soils function as integrated records of historical PFAA loading. Simulated PFOS breakthrough to groundwater occurred 50 years after the start of wastewater irrigation, while simulated PFOA breakthrough occurred after only 10 years of irrigation. Thus, while wastewater irrigation of soils facilitates retention and reduces effluent PFAA loading to surface waters, the resulting increased PFAA storage in soils potentially creates long-term sources of PFAAs to groundwater.

The wetting properties of pore walls have a strong effect on multiphase flow through porous media. However, the fluid flow behaviour in porous materials with both complex pore structures and non-uniform wettability are still unclear. Here, we performed unsteady-state quasi-static oil- and waterflooding experiments to study multiphase flow in two sister heterogeneous sandstones with variable wettability conditions (i.e. one natively water-wet and one chemically treated to be mixed-wet). The pore-scale fluid distributions during this process were imaged by laboratory-based X-ray micro-computed tomography (micro-CT). In the mixed-wet case, we observed pore filling events where the fluid interface appeared to be at quasi-equilibrium at every position along the pore body (13% by volume), in contrast to capillary instabilities typically associated with slow drainage or imbibition. These events corresponded to slow displacements previously observed in unsteady-state experiments, explaining the wide range of displacement time scales in mixed-wet samples. Our new data allowed us to quantify the fluid saturations below the image resolution, indicating that slow events were caused by the presence of microporosity and the wetting heterogeneity. Finally, we investigated the sensitivity of the multi-phase flow properties to the slow filling events using a state-of-the-art multi-scale pore network model. This indicated that pores where such events took place contributed up to 19% of the sample’s total absolute permeability, but that the impact on the relative permeability may be smaller. Our study sheds new light on poorly understood multiphase fluid dynamics in complex rocks, of interest to e.g. groundwater remediation and subsurface CO2 storage.

The present study focuses on quantifying the impact of the choice of spatio-temporal resolution and hydrology models on the projection of extreme flow and their link to the catchment size. We use two process-based distributed hydrology models forced with a large-ensemble regional climate model (50-member ClimEx dataset) over the 1990-2100 period at different spatio-temporal scales. The extreme summer-fall flow corresponding with each spatio-temporal resolution was extracted by pooling the members together and computing the empirical cumulative distribution function. The results show that by refining the time-step from daily to sub-daily, the summer-fall extreme flow projected over the future period exceeds that of the reference period for the small but not large catchments. By increasing the catchment size, the hydrology model’s contribution to the variability of extreme flow increases. Moreover, the choice of spatial resolution affects the extreme flow’s trend in terms of magnitude, significance, and direction. But no pattern regarding the catchment size and spatial discretization variations exists.

The impacts of climate change and increased water use for irrigation make it difficult to manage sustainable water use and food production. Sufficient research has not been conducted on how humans adapt to water risks due to climate change. One of the difficulties in considering adaptation measures is that adaptation actions in one sector conflict with the interests of other stakeholders in the basin and trade-off relationships emerge among various sectors. Here, we examined how an effective adaptation in one sector (agriculture) influences the other (water resources) by calculating the “benefits of agricultural production” and “drought risk” under current and future climate scenarios. We built a framework consisting of two process-based models of hydrology and crop science and evaluated shifting of the transplantation date as a promising measure to avoid the degradation of rice quality in Japan. Shifting the transplantation date had opposing effects on the total yield and quality of rice, with an earlier date increasing the total yield and a later date increasing the quality. Furthermore, an earlier transplantation date reduced the drought risk. Thus, in terms of the preferred adaptation options, total yield and drought were synergistic, whereas rice quality and drought were trade-offs. Our results imply that the current transplantation date has resulted from the farmers’ motivation to maximize total yield, but this motivation may change to other factors, possibly rice quality, due to climate change. Overall, this study contributes to the understanding of how interconnected systems evolve when climate or socio-economic conditions change.

Under the dual influence of socioeconomic development and climate change, water resources in China are under increasing stress. It is of great significance to comprehensively explore the changing trend of China’s water footprint (WF) in the future, clarify the water resource challenges that China will face, and alleviate water shortage and water pollution problems. This paper uses System Dynamics (SD) to build a simulation model of China’s WF, calculate China’s WF from 2000 to 2019, and for the first time, simulate and optimize China’s WF from 2020 to 2050 under the SSP-RCP scenario matrix composed of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The results are as follows. (1) From 2000 to 2019, China’s WF increased to 2009 and began to decline. The main contributors are gray WF and agricultural WF. (2) From 2020–2050, different socioeconomic development and climate change conditions under the five SSP-RCP scenarios will lead to different trends in China’s WF and its composition. (3) Based on the changes in WF and water resources supply/demand ratio under different scenarios, the SSP1-2.6 scenario is the best scenario to mitigate a future water shortage and water pollution in China. The research results can help decision-makers formulate relevant management policies and socioeconomic development models for water resource utilization and provide decision support for alleviating water shortages and water pollution in China.

Discharge of groundwater-derived pollutants to inland and marine coastal waters is influenced by the transport and reactive processes occurring in nearshore aquifers. The effect of shoreline change on these processes and subsequent discharge of pollutants to coastal waters is unclear. The objective of this study was to evaluate the impact of shoreline recession (landward movement of the mean shoreline) on the transport of nitrogen [N] and phosphorus [P] in a nearshore aquifer and their discharge to coastal waters. Field investigations were conducted on a permeable unconfined nearshore aquifer on Lake Huron, Canada, in years coinciding with historically low and high lake water levels. At the site, a septic system-derived nutrient-rich (N and P) groundwater plume is moving towards the lake and the mean shoreline position moved ~30 m landward between sampling years due coastal erosion and mean lake water level increase. Data indicate PO4-P fluxes to the lake were higher following shoreline recession due to shortened travel pathways. In contrast, NO3-N fluxes were governed by the specific geochemical conditions near the sediment-water interface, which are not only a function of the shoreline position. Further, findings show shoreline recession may modify mineral phases that tend to sequester pollutants (e.g., iron oxides) near the sediment-water interface and this may possibly mediate release of sediment-bound pollutants. The findings provide new insights into potential impacts of shoreline change on chemical discharge to coastal waters as needed to inform long-term water quality predictions and management.

Physical properties of soils are ubiquitously heterogeneous. This spatial variability has a profound, yet still partially understood, impact on conservative transport. Moreover, molecular diffusion is often a disregarded process that can have an important counter-intuitive effect on transport: diffusion can prevent non-Fickian tailing by mobilizing mass otherwise trapped in low velocity zones. Here, we focus on macroscopically homogeneous soils presenting small scale heterogeneity, as described by the Miller-Miller method. We then analyze the dynamic control of soil heterogeneity, advection and diffusion on conservative transport. We focus especially on the importance of diffusion and of its tortuosity-dependent spatial variability on the overall transport. Our results indicate that high Peclet number systems are highly sensitive to the degree of heterogeneity, which promotes non-Fickian transport. Also, diffusion appears to have a profound impact on transport, depending on both the degree of heterogeneity and the Peclet number. For a high Peclet number and a very heterogeneous system, diffusion leads to the counter-intuitive decrease of non-Fickian macrodispersion described previously. This is not observed for a low Peclet number due to the non-trivial impact of the spatial variability in the diffusion coefficient, which appears to be a significant controlling factor of transport by promoting or preventing the accumulation of mass in low velocity zones. Globally, this work (1) highlights the complex, synergistic effect of soil heterogeneity, advective fluxes and diffusion on transport and (2), alerts on potential upscaling challenges when the spatial variability of such key processes cannot be properly described.

This study develops a new AI-based Self-Adaptive DPC (SADPC) system based on stepwise inference combing with genetic algorithm optimization technologies, including a filtered-clustering inference prediction model (FCI simulator), a stepwise inference controller (SI emulator), a model predictive control controller (MPC controller), a 1st-stage optimizer, and a 2nd-stage optimizer. This system effectively reflects the dynamics and complexity of the biodegradation process and realizes the control for the remediation system based on the feedback information. To achieve this goal, a statistical model for simulating the bioremediation process through the FCI simulator is proposed, which can predict the resulting contamination situation based on the previous contamination situation and control action. Then a bridge between control actions and contamination situations is established through the SI emulator, which can generate a control action based on a given contamination situation. Through running the SADPC system, the desired control action can be identified. Results show that The SADPC system increases the removal rate of benzene and arrives at the remediation goal earlier than other systems. This suggested decision makers that guidelines and policies on remediation-oriented SADPC systems could be tentatively investigated, developed, and applied in the future effort.

Sediment transport load monitoring is important in civil and environmental engineering fields. Monitoring the total load is difficult, especially because of the cost of the bed load transport measurement. This study proposes estimation models for the suspended load to total load ratio (Fsus) using dimensionless hydro-morphological variables. Two prominent variable combinations were identified using the recursive feature elimination procedure of support vector regression (SVR): (1) W/h, d*, Reh, Frd, and Rew and (2) Reh, Fr, and Frd. The explicit interactions between Fsus and the two combinations were revealed by two modern symbolic regression methods: multi-gene genetic programming and Operon. The five-variable SVR model showed the best performance (R2=0.7722). The target dataset was clustered by applying a self-organizing map and Gaussian mixture model. Through these steps, Reh and Frd are determined as the two most influential variables. Subsequently, the one-at-a-time sensitivity of the input variables of the empirical models was investigated. By referring to the clustering and sensitivity analyses, this study provides physical insights into Fsus controlling relationships. For example, Fsus is proportional to Reh and is inversely related to Frd. The empirical models developed in this study are applicable in practice and easy to implement in other real-time surrogate suspended-sediment monitoring methods, because they only require basic measurable hydro-morphological variables, such as velocity, depth, width, and mean bed material grain size.

Reducing flood risk through improved disaster planning and risk management requires accurate and reliable estimates of flood damages. Models can provide such information by calculating the costs of flooding to exposed assets, such as buildings within a community. Computational or data constraints often lead to the construction of such models from coarse aggregated data, the effect of which is poorly understood. Through the application of a novel spatial segregation framework, we are able to show mathematically that aggregating flood grids through averaging will always introduce a systematic error in a particular direction in partially inundated regions. By applying this framework to a case study we spatially attribute these errors and demonstrate how the exposure of buildings can be an order of magnitude more sensitive to these errors than uninhabited regions. This work provides insight into, and recommendations for, upscaling grids used by flood risk models. Further, we demonstrate a positive dependence of systematic error magnitude on scale coarseness, suggesting coarse models be used with caution and greater attention be paid to issues of scale.

Ponding at the soil surface exerts profound impacts on infiltration. However, the effects of ponding depth on infiltration, especially the development of a saturated zone below the soil surface, have not been considered in present infiltration models. A new general Green-Ampt model solution (GAMS) was derived for a one-dimensional vertical infiltration into soils under a uniform initial moisture distribution with ponding on its surface. An expression was included in the new solution for simulating the saturated layer developed below the soil surface as long as the pressure head at the surface is greater than the water-entry suction. The GAMS simulates the infiltration processes closer to the numerical solution by HYDRUS-1D than the traditional and a recently improved Green-Ampt model. Moreover, an inversion method to improve the estimates of soil hydraulic parameters from one-dimensional vertical infiltration experiments that is based on the GAMS was suggested. The effect of ponding depth (hp), initial soil moisture content, soil texture, and hydraulic soil properties (Ks, hd and n) in the saturated zone was also evaluated. The results indicate that the saturated zone developed at a much faster rate than the unsaturated zone during infiltration. Generally, a larger saturated zone was found for soils with higher initial soil moisture content, coarser texture, higher Ks values and lower hd and n. Our findings reveal that including the saturated zone in the infiltration model yields a better estimate for the soil hydraulic parameters. The proposed GAMS model can improve irrigation design and rainfall-runoff simulations.

Sustainability of China’s numerous cities are threatened by both quantity- and quality-induced water scarcity, which can be measured by the water footprint from a consumption (WFcons) or production (WFprod) perspective. Although WFcons was widely assessed, the changes in WFprod of China’s cities were still unclear. Taking 31 major cities as examples, this study revealed the dynamics of urban WFprod in China from 2011 to 2016. First, the spatiotemporal patterns of WFprod and water deficit were evaluated and then the main reasons for the WFprod dynamics and its implications for urban sustainability were explored. A large-scale decrease in urban WFprod in China was found, with the average WFprod decreasing from 13.8 billion m³ to 10.3 billion m³ and the per capita WFprod decreasing from 1614.8 m³/person to 1184.0 m³/person (i.e., falling by more than a quarter in just six years). Such shrinkage was particularly evident in drylands, eliminating the water deficit in Xi’an and Xining. The reduction in grey WFprod caused by implementing water pollution prevention policies and other relevant measures played the most important role in the savings. In the future, the implementation of updated pollution discharge standards is projected to allow more cities to escape water deficits; however, the rapid growth of the domestic and ecological blue WFprod caused by urbanization and urban greening would destabilize this prospect. Thus, attention should be given to both water pollution prevention and domestic and ecological blue WFprod restriction to further alleviate urban water scarcity in China.

Estimating dissipation timeframes and contaminant mass discharge rates of dense non-aqueous phase liquids (DNAPLs) source zones is of key interest for environmental-management support. Upscaled mathematical modeling of DNAPL dissolution provides a practical approach for assimilating site characterization and downgradient monitoring data to constrain future system behavior. Yet significant uncertainties on predictions of source zone dissipation rates may arise from inadequate or inaccurate conceptual assumptions in parameterization designs. These implications were investigated through upscaled modeling, sensitivity, and uncertainty analyses of high-resolution flow-cell experiments. Sensitivity results emphasized the role of local groundwater velocity and source dimensions in mass transfer scaling by strongly influencing error with respect to DNAPL persistence and dissolution rates. Linear uncertainty analyses, facilitated by PEST ancillary software, demonstrated the worth of monitoring profiles for constraining DNAPL saturations and dispersive mass transfer rates, responsible for source zone longevity. Nonlinear analyses performed with the iterative ensemble smoother PESTPP-iES, facilitated the quantification of unbiased source dissipation uncertainties from DNAPL delineation data. Conversely, monitoring data assimilation without consideration of flow-field heterogeneity and saturation distribution along the flow path biased model predictions. Our analyses provided practical recommendations on upscaled model design to assimilate available site data and support remedial-decision making.

Hyperspectral remote sensing is thought to be a useful technology for assessing the condition of inland waters. However, non-optically active water quality parameters are rarely explored in hyperspectral remote sensing applications, despite they are highly valued in the aquatic environment condition. This study intends to evaluate the performance of non-optically active water quality parameters using Zhuhai-1 hyperspectral imagery. Focusing on total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3-N) and nitrate-nitrogen (NO3-N) in Taipu River, we constructed empirical models to evaluate the precision of water quality inversion from OHS by comparing with Sentinel-2, and determined the sensitive bands of different water quality parameters. The final results showed that the polynomial model based on OHS had the greatest potential in retrieving TN, TP and NH3-N concentration, and the R2 was 0.9678, 0.7924, 0.7682 respectively. The combination of R(510)/R(820) and R(700)/R(806), R(940)/R(820) and R(806)/R(926), R(709)/R(806) and R(746)/R(620) were most sensitive to TN, TP and NH3-N respectively. The OHS and Sentinel-2 both had potential in retrieving NO3-N. The R2 was 0.9791 from OHS and was 0.9513 from Sentinel-2. The sensitive bands of NO3-N were R(596)/R(665) and R(466)/R(580) from OHS, and Red Eage3/Blue and SWIR1/Blue from Sentinel-2. We also analyzed the drivers of the spatial distribution of water quality in Taipu River, the results showed negative impacts of farmland and urban land on water quality, and beneficial impacts of forest land on water quality. This study represented a promising step in hyperspectral remote sensing for retrieving inland non-optically active water quality parameters utilizing Zhuhai-1.

Groundwater depletion is a concern around the world with implications for food security, ecological resilience, and human conflict. Long-term perspectives provided by tree ring-based reconstructions can improve understanding of factors driving variability in groundwater elevations, but such reconstructions are rare to date. Here, we report a set of new 546-year tree-ring chronologies developed from living and remnant longleaf pine (Pinus palustris) trees that, when combined with existing bald cypress (Taxodium distichum) tree-ring chronologies, were used to create a set of nested reconstructions of mean annual groundwater elevation for North Central Florida that together explain 63% of the variance in instrumental measurements and span 1498–2015. Split calibration confirms the skill of the reconstructions, but coefficient of efficiency metrics and significant autocorrelation in the regression residuals indicate a weakening relationship between tree growth and groundwater elevation over recent decades. Comparison to data from a nearby groundwater well suggests extraction of groundwater is likely contributing to this weakening signal. Periodicity within the reconstruction and comparison with global sea surface temperatures highlight the role of El Niño-Southern Oscillation (ENSO) in driving groundwater elevations, but the strength of this role varies substantially over time. Atlantic and Pacific sea surface temperatures modulate ENSO influences, and comparisons to multiple proxy-based reconstructions indicate an inconsistent and weaker influence of ENSO prior to the 1800s. Our results highlight the dynamic influence of ocean-atmospheric phenomena on groundwater resources in North Central Florida and build on instrumental records to better depict the long-term range of groundwater elevations.

The pore structure of marine sediments varies with the distribution of gas-hydrate, hence affecting the gas-water permeability. CT image is a conventional approach to view the internal structure, while for hydrate-bearing sediment investigation, rather poor resolution of obtained image has limited the accuracy of the analysis. Recently, super-resolution (SR) reconstruction techniques have been used to enhance the spatial resolution of CT images with varying degrees of improvement. Typical Image Pairs-Based SR (PSR) methods require higher resolution matching images for training, which is challenging for hydrate samples in dynamic temperature and pressure conditions. Here, we introduced a self-supervised learning (SLSR) method that only relies on a single input image to complete the process of training and reconstruction. We conducted a complete training to establish an end-to-end network consisting of two sub-networks, an SR network and a downscaling network. Self-built datasets from three hydrate samples with different sediment grains were trained and tested. Compared with the typical method, the SR results show that our method provides higher resolution while improving clarity. Moreover, in the subsequent calculation of porosity parameters, it has the highest consistency with the liquid saturation method. This study contributes to investigating the water seepage and energy transfer in the gas hydrate bearing sediments, which is particularly important for the exploration and development of marine natural gas hydrate resources. The image super-resolution method established by us has also a broad application prospect in the field of CT imaging.

Optimization models for minimizing pollutant exposure from groundwater resources require time and resources that many communities might not have ready access to due to their economic conditions. In such cases, it might be useful to develop a “rule of thumb” approach for suggestions in case of uncertainties and inadequate means to address these uncertainties. Monte Carlo analysis was performed for a simplified groundwater system and the effects of extraction patterns, distance to pollution source, dispersivity, pollutant pulse period, pore water velocity and decay were examined for minimizing the high pollutant exposure risk from the extracted groundwater. Results indicate that, in a high uncertainty scenario, the best bet for minimizing the risk of high pollutant exposure would be to adopt a frequent extraction pattern and supply the water as a mixture of extractions from multiple wells spread over an area. These findings can be used as a “rule of thumb” wherever time and resources might be the limiting factors.