Vadose Zone Model Research Articles

The value of soil temperature data versus soil moisture data for state, parameter, and flux estimation in unsaturated flow model

AbstractThis synthetic study explores the value of near‐surface soil moisture and soil temperature measurements for the estimation of soil moisture and soil temperature profiles, soil hydraulic and thermal parameters, and latent heat and sensible heat fluxes using data assimilation (ensemble Kalman filter) in combination with unsaturated zone flow modeling (HYDRUS‐1D), for 12 United States Department of Agriculture soil textures in a homogeneous and bare soil scenario. The soil moisture profile is estimated with a root mean square error (RMSE) of 0.04 cm3/cm3 for univariate soil temperature assimilation and 0.01 cm3/cm3 for univariate soil moisture assimilation. Soil temperature assimilation performs better for soils with higher clay content compared to soils with higher sand content. The latent and sensible heat fluxes are estimated with smaller RMSE for univariate soil temperature assimilation compared to univariate soil moisture assimilation for 8 out of 12 soil types. As the climate condition changes from hot semi‐arid to sub‐humid climate, the soil moisture assimilation performs better for high permeable soil but worse for low permeable soil. In summary, the findings suggest that for most soil texture classes, assimilating soil temperature in vadose zone models is skillful to improve latent heat flux, soil moisture profile, and soil hydraulic parameters. Joint assimilation with soil moisture can further enhance the accuracy of the model outputs for all range of soil texture and climate conditions.

Nitrate contamination of urban groundwater and heavy rainfall: Observations from Dakar, Senegal

AbstractIn low‐income urban areas of major cities in Africa, sanitation provision derives primarily from onsite systems often comprising septic tanks and pit latrines. Such systems rely upon the ability of the surrounding soil and substratum to attenuate contaminants like nitrate and pathogenic microorganisms in wastewater. Here, we assess soil–water and solute dynamics in Quaternary aeolian sands underlying a densely populated suburb (Keur Massar) of Dakar (Senegal) using high‐frequency monitoring and vadose zone modeling (Hydrus‐1D). Observations of rainfall intensity, soil moisture content, and shallow groundwater‐level fluctuations and nitrate concentrations were carried out at an experimental site adjacent to a septic tank supplied by toilets used by a primary school. Rapid rises in soil moisture content and episodic recharge contributions observed in groundwater levels caused by heavy (>10 mm h−1) and extreme (>20 mm h−1) rainfall are well modeled (R2 = .79–.83; RMSE = 0.012–0.019) by pore‐matrix flow in the unsaturated zone by the Darcy–Richards equation. Spot sampling around the most intense rainfall of 2020 (45 mm h−1) reveals a fivefold rise and fall in the concentration of nitrate in soil moisture (∼500 to ∼2,500 mg L−1). These measurements provide new insight into the hydrological dynamics by which shallow groundwater is grossly contaminated (>500 mg L−1) by nitrate through episodic flushing by heavy rainfall of wastewater from a vast estimated network of over 250,000 septic tanks underlying this suburb of Dakar.

Two-Layer numerical model of soil moisture dynamics: Model assessment and Bayesian uncertainty estimation.

A two-layer model based on the integrated form of Richards' equation (RE) was recently developed to simulate the soil water movement in the roots layer and the vadose zone with a relatively shallow and dynamic water table. The model simulates thickness-averaged volumetric water content and matric suction as opposed to point values and was numerically verified for three soil textures using HYDRUS as a benchmark. However, the strengths and limitations of the two-layer model and its performance in stratified soils and under actual field conditions have not been tested. This study further examined the two-layer model using two numerical verification experiments and, most importantly, tested its performance at site-level under actual, highly variable hydroclimate conditions. Moreover, model parameters were estimated and uncertainty and sources of errors were quantified using a Bayesian framework. First, the two-layer model was evaluated for 231 soil textures under varying soil layer thicknesses with a uniform soil profile. Second, the two-layer model was assessed for stratified conditions where the top and bottom soil layers have contrasting hydraulic conductivities. The model was evaluated by comparing soil moisture and flux estimates to those from the HYDRUS model. Last, a case study of model application using data from a Soil Climate Analysis Network (SCAN) site was presented. Bayesian Monte Carlo (BMC) method was implemented for model calibration and quantifying sources of uncertainty under real hydroclimate and soil conditions. For a homogeneous soil profile, the two-layer model generally had excellent performance in estimating volumetric water content and fluxes, while the model performance slightly declined with increasing layer thickness and coarser textured soils. The model configurations regarding layer thicknesses and soil textures that generate accurate soil moisture and flux estimations were further suggested. With the two layers of contrasting permeability, model-simulated soil moisture contents and fluxes agreed well with those computed by HYDRUS, indicating that the two-layer model accurately handles the water flow dynamics around the layer interface. In the field application, given the highly variable hydroclimate conditions, the two-layer model combined with the BMC method showed good agreement with the observed average soil moisture of the root zone and the vadose zone below (RMSE <0.021 during calibration and <0.023 during validation periods). The contribution of parametric uncertainty to the total model uncertainty was too small compared to other sources. The numerical tests and the site level application showed that the two-layer model can reliably simulate thickness-averaged soil moisture and estimate fluxes in the vadose zone under various soil and hydroclimate conditions. Results also indicated that the BMC method could be a robust framework for vadose zone hydraulic parameters identification and model uncertainty estimation.

Potential effects on groundwater quality associated with infiltrating stormwater through dry wells for aquifer recharge

Dry wells (gravity-fed infiltration wells) have frequently been used to recharge aquifers with stormwater, especially in urban areas, as well as manage flood risk and reduce surface water body contamination from stormwater pollutants. However, only limited assessment of their potential adverse impacts on groundwater quality exists. Dry well recharge can bypass significant portions of the filtering-capacity of the vadose zone. Stormwater and groundwater monitoring data and analysis of transport of a wide range of historic and current-use stormwater chemicals of concern is lacking. To address these gaps, two dry wells were constructed with vegetated and structural pretreatment features to assess the likelihood of stormwater contaminants reaching the aquifer. We monitored, assessed, and compared the presence of contaminants in stormwater to water quality in the vadose zone and shallow groundwater after it passed through the dry well. The dry wells were installed at a suburban residential and at a suburban commercial site. The selected sites were overlying a regional, unconsolidated, and highly heterogeneous alluvial aquifer system. Stormwater, vadose zone, and groundwater samples were collected during five storms and analyzed for over 200 contaminants of concern. Relatively few contaminants were detected in stormwater, generally at low concentrations. Prior to stormwater entering the dry well, 50–65% of contaminants were removed by vegetated pretreatment. In groundwater, metals such as aluminum and iron were detected at similar concentrations in both upgradient and downgradient wells, suggesting the source of these metals was not dry well effluent. Naturally occurring metals such as chromium and arsenic were not detected in stormwater but were found at elevated concentrations in groundwater. A modeling assessment suggests that the travel time of metals and hydrophobic organic contaminants to the water table at these sites ranges from years to centuries, whereas water soluble pesticides would likely reach the water table within days to months. The modeling assessment also showed that more vulnerable sites with higher fraction of alluvial sands would have much shorter contaminant travel times. However, none of the contaminants assessed reached concentrations that pose a risk to human health across the scenarios considered. No evidence was found, either through direct measurements or vadose zone modeling, that contaminants present in suburban stormwater degraded or would degrade groundwater quality at the studied sites and site conditions. Future work is needed to address emerging contaminants of concern.

Vadose zone modeling to identify controls on groundwater recharge in an unconfined granular aquifer in a cold and humid environment with different meteorological data sources

Groundwater recharge (GR) is a complex process that is difficult to quantify. Increasing attention has been given to unsaturated zone modeling to estimate GR and better understand the processes controlling it. Continuous soil-moisture time series have been shown to provide valuable information in this regard. The objectives of this study were to (i) analyze the processes and factors controlling GR in an unconfined granular aquifer in a cold and humid environment and (ii) assess the uncertainties associated with the use of data from different sources. Soil moisture data monitored over three years at three experimental sites in southern Quebec (Canada) were used to calibrate the HYDRUS-1D model and to estimate ranges of possible GR in a region where groundwater is increasingly used as a source of fresh water. The simulations identified and quantified important factors responsible for the near-surface water balance that leads to GR. The resulting GR estimates from 2016 to 2018 showed marked differences between the three sites, with values ranging from 347 to 735 mm/y. Mean GR for the three sites was 517 mm/y for 2016–2018 and 455 mm/y for the previous 12-year period. GR was shown to depend on monthly variations in precipitation and on soil textural parameters in the root zone, both controlling soil-water retention and evapotranspiration. Monthly recharge patterns showed distinct preferential GR periods during the spring snowmelt (38–45% of precipitation) and in the fall (29% of precipitation). The use of different meteorological datasets was shown to influence the GR estimates.

Estimation of groundwater recharge in a shallow sandy aquifer using unsaturated zone modeling and water table fluctuation method

Quantification of groundwater recharge is one of the most important issues in hydrogeology, especially in view of the ongoing changes in climate and land use. In this study, we use numerical models of 1D vertical flow in the vadose zone and the water table fluctuation (WTF) analysis to investigate local-scale recharge of a shallow sandy aquifer in the Brda outwash plain in northern Poland. We show that these two methods can be jointly used to improve confidence in recharge estimation. A set of preliminary numerical simulations based on soil water content measurements from 4 grassland and pine forest profiles provided a wide range of recharge estimates (263 mm to 839 mm for a 3-year period). Additional simulations were performed with the lower boundary condition specified as a functional relationship between the groundwater table elevation and the rate of groundwater outflow from the vertical profile (horizontal drains boundary condition). In this way, we could reproduce the water table fluctuations resulting from recharge and lateral discharge to nearby lakes. The agreement between simulated and observed groundwater levels differed depending on the specific set of parameters characterizing vadose zone flow, which allowed us to find the most representative parameter sets and refine the range of plausible recharge estimates (501 mm to 573 mm per 3 years). The recharge rates from WTF (410 mm to 606 mm per 3 years) were in good agreement with numerical simulations, providing that the effect of the natural recession of groundwater table due to lateral outflow was considered (master recession curve method). Our results show that: (i) the proposed approach combining 1D vadose zone modeling and WTF improves recharge estimation, (ii) multiple types of observations, including groundwater table positions, are needed to calibrate and validate vadose zone flow models, and (iii) extended periods of observations and simulations are necessary to capture year-to-year variability in the recharge rates.

Modeling shallow ground temperatures around hot buried pipelines in cold regions

Transmitting fluids at high operating temperatures through buried pipelines is common in the oil and gas industry. Pipelines are insulated to maintain the fluid temperature within an operating threshold and reduce heat loss to the subsurface. In cold regions, damaged insulation around pipelines can raise ground temperatures by tens of degrees and keep ground frost free during the winter. This can lead to environmental and geotechnical issues, operational inefficiencies and, in extreme cases, pipeline failure. When supported by field data, modeling of coupled heat and water transfer in the subsurface can be used to understand baseline and disturbed ground temperatures and to design thermal remediation plans. However, simulating ground temperatures under the influence of snow cover, freeze-thaw conditions as well as a shallow heat source is challenging. This case study presents a simple and effective approach using a thermal boundary layer and a time-varying Dirichlet boundary condition to simulate these conditions with coupled vadose zone and groundwater models. The approach was validated by comparing the model results to a temperature survey along a boiler feed water pipeline corridor in northern Alberta, Canada. Model results were used to better understand the influence of high-temperature pipelines on subsurface thermal regimes, to help design a ground temperature monitoring program, and successfully identify and remediate pipeline insulation damage.

Hydrologic modeling of reach scale fluxes from flood irrigated fields

Agricultural water is of considerable interest to water managers and policymakers as irrigation—particularly flood irrigation—accounts for the largest portion of freshwater use. However, characterization of how and when flood applied water contributes to storage and adjacent surface water bodies via return flow remains limited, particularly relative to the volume of studies addressing surface processes. This study focuses on improving our understanding of return flow paths and explicitly quantifying subsurface return flow contributions using hydro-geophysical observations coupled with a vadose zone transport flow model. Our approach, which simulates return flow in agricultural fields in a small, heavily instrumented field site in northern Wyoming, leverages daily measured values of evapotranspiration obtained from a large aperture scintillometer and atmospheric conditions, and uses seismic data to characterize heterogeneous structure of the soil profiles at field scale. We use these high-resolution, site specific datasets to parameterize a process-based vadose zone flow model that quantifies hydrologic response to flood irrigation along the stream reach, allowing us to identify return flow fluxes out of the fields. Using our approach, we found that the majority of applied irrigation water (60%) left the fields through subsurface flow paths, while only 13% was consumed by vegetation. Subsurface runoff over less-permeable soil layers, governed by the subsurface structural characteristics, was identified as the dominant subsurface flow process contributing to fluxes out of the fields. Importantly, our findings about the importance of subsurface return flow paths nuance previous research which has emphasized surface flow paths. By improving our understanding of return flow processes through a high- resolution vadose zone model that leverages climatological, geophysical, and hydrological observations, this study advances our understanding of return flow processes. Understanding how subsurface characteristics influence return flow generation in semi-arid agriculturally productive regions will help managers and policy makers balance desires for improved irrigation efficiency against concerns about irrigation-dependent ecosystem services.

Discovery of a large subsoil nitrate reservoir in an arroyo floodplain and associated aquifer contamination

AbstractIn an area of elevated nitrate (NO3) groundwater concentrations in the northern Chihuahuan Desert in central New Mexico (United States), a large reservoir of nitrate was found in the subsoil of an arroyo floodplain. Nitrate inventories in the floodplain subsoils ranged from 10,000 to 38,000 kg NO3-N/ha—over twice as high as any previously measured arid region. The floodplain subsoil NO3 reservoir was over 100 times higher than the adjacent desert (59–95 kg NO3-N/ha). Chloride mass balance calculations of subsoils indicate arroyo floodplain subsoils have undergone negative recharge since 2600–8600 yr ago, while the surrounding desert has had negative recharge since 13,000–17,000 yr ago. Compared to the adjacent desert, plant communities are larger and more abundant in the floodplain, though subsoil NO3 is apparently not utilized. We demonstrate that NO3 accumulates in the subsoil of the floodplain through evaporation of monsoon season precipitation funneled into the arroyo. Through a one-dimensional vadose zone model, we show that the NO3 inventories in the arroyo floodplain could be acquired 8 to 75 times faster than through atmospheric deposition through the lateral movement of water from the arroyo channel to the adjacent unsaturated zone. As aquifer recharge occurs through the arroyo, channel migration across the floodplain likely flushes subsoil NO3 to the aquifer. High NO3 concentrations and molar ratios of NO3 to Cl in monitoring wells beneath the arroyo floodplain indicate a subsoil NO3 source. These results have major implications for land-use planning in arroyo and ephemeral stream floodplains as well as arid region soil biogeochemistry.

Inverse modeling towards parameter estimation of the nonlinear soil hydraulic functions using developed multistep outflow procedure

Modeling and monitoring vadose zone processes have a key role to effectively identify and solve important emerging issues in hydrology and related disciplines. However, major gaps and challenges remain concerning the further conceptualization of the zone complexities and parameterization of relevant models. Consequently, the main focus of this investigation was to establish a modern developed version of the multistep outflow experiments for the vadose zone characterization and modeling. Besides, soil hydraulic properties (K-θ-h) were determined by the Mualem/Van Genuchten model based on the inverse solution of the Richards equation for the four loamy textured soils. The adjustable parameters were estimated with the least-square approach through the implementation of the generalized reduced gradient (GRG) algorithm. Then, the inverse manner was evaluated by comparing optimized hydraulic functions with those measured independently using an equilibrium condition. The results revealed the optimized attributes complied well with those determined using equilibrium analysis and steady-state situations. The reliable estimation of the parameters to inversely optimize the K-θ-h functions was possible only by measuring the cumulative outflow as a function of time. The simultaneous optimization of the unsaturated hydraulic functions carried out from a single transient experiment. This protocol appears well suited for vadose zone simulation. Furthermore, along with the tensiometer elimination and no independent measurements of Ks and θs, the developed version is generally more cost-effective, faster and more convenient than the conventional method. Finally, we conclude the combination of proposed procedure with GRG algorithm can provide a reasonable procedure to inversely estimate the hydraulic parameters.

Challenges in determining soil moisture and evaporation fluxes using distributed temperature sensing methods

To protect fragile groundwater-dependent environments of arid zones, it is important to monitor soil moisture and groundwater evaporation. Hence, it is important to assess new methods to quantify these environmental variables. In this work, we propose a new method to determine groundwater evaporation rates by combining the actively heated fiber-optic (AHFO) method with vadose zone modeling, assuming that the evaporation front remains at the soil surface. In our study, the AHFO method yielded estimates of the soil moisture (θ) profile with a spatial resolution of ~6.5 mm and with an error of 0.026 m3 m−3. The numerical model resulted in a slightly different θ profile than that measured, where the largest differences occurred at the soil surface. Sensitivity and uncertainty analyses highlighted that a better precision is required when determining the soil hydraulic parameters. To improve the proposed method, the soil heat-vapor-water dynamics should be included and the assumption that the evaporation front remains at the soil surface must be relaxed. Additionally, if the AHFO calibration curve is enhanced, the errors of the estimated θ profile can be reduced and thus, successful estimation of the evaporation rates for a wider range of soil textures can be achieved. The spatial scales measured are an important advantage of the proposed method that should be further explored to improve the analysis presented here.

Unsaturated Soil Hydraulic Properties Identification using Numerical Inversion and In-Situ Experiments from Mnasra Area, Morocco

In soil physics, accurate estimation of hydrodynamic properties is essential for effective groundwater management. Experimentally, determination of these properties in the laboratory or in-situ conditions is difficult step, long and may involve uncertainties significant for the vast majority practice use. Frequently, inverse methods were established to determine accurate parameters in the field scale based on measured soil moisture and hydraulic conductivity over time and depth. These inversions methods involve hydraulic soil-models using adapted optimization algorithms that ensure a best combination of parameters minimizing a cost function. In this work, direct and inverse optimization approaches has been proposed to estimate the effective hydraulic parameters, based on the temporal description of the soil physical and hydraulic parameters using field water content measurements from located in Mnasra area (northwest of Morocco). The Levenberg Marquardt optimization algorithm coupling with the vadose zone model (VZM), numerical code in Fortran, has been used to inverse estimation of hydraulic parameters based on transients soil water content measurements. These uses have been conducted using temporal measurements of soil moisture in depths: 20, 40, 60 and 80 cm. The estimated properties are in good agreement with those measured in-situ. Overall, it was concluded that this technique can greatly widen the range of hydrological problems admissible for inversion and can be used for many applications in hydrologic engineering, either alone or in conjunction with traditional techniques.

A unique vadose zone model for shallow aquifers: the Hetao irrigation district, China

Abstract. Rapid population growth is increasing pressure on the world water resources. Agriculture will require crops to be grown with less water. This is especially the case for the closed Yellow River basin, necessitating a better understanding of the fate of irrigation water in the soil. In this paper, we report on a field experiment and develop a physically based model for the shallow groundwater in the Hetao irrigation district in Inner Mongolia, in the arid middle reaches of the Yellow River. Unlike other approaches, this model recognizes that field capacity is reached when the matric potential is equal to the height above the groundwater table and not by a limiting soil conductivity. The field experiment was carried out in 2016 and 2017. Daily moisture contents at five depths in the top 90 cm and groundwater table depths were measured in two fields with a corn crop. The data collected were used for model calibration and validation. The calibration and validation results show that the model-simulated soil moisture and groundwater depth fitted well. The model can be used in areas with shallow groundwater to optimize irrigation water use and minimize tailwater losses.

Interplays between State and Flux Hydrological Variables across Vadose Zones: A Numerical Investigation

Knowledge of both state (e.g., soil moisture) and flux (e.g., actual evapotranspiration (ETa) and groundwater recharge (GR)) hydrological variables across vadose zones is critical for understanding ecohydrological and land-surface processes. In this study, a one-dimensional process-based vadose zone model with generated soil hydraulic parameters was utilized to simulate soil moisture, ETa, and GR. Daily hydrometeorological data were obtained from different climate zones to drive the vadose zone model. On the basis of the field phenomenon of soil moisture temporal stability, reasonable soil moisture spatiotemporal structures were reproduced from the model. The modeling results further showed that the dependence of ETa and GR on soil hydraulic properties varied considerably with climatic conditions. In particular, the controls of soil hydraulic properties on ETa and GR greatly weakened at the site with an arid climate. In contrast, the distribution of mean relative difference (MRD) of soil moisture was still significantly correlated with soil hydraulic properties (most notably residual soil moisture content) under arid climatic conditions. As such, the correlations of MRD with ETa and GR differed across different climate regimes. In addition, the simulation results revealed that samples with average moisture conditions did not necessarily produce average values of ETa and GR (and vice versa), especially under wet climatic conditions. The loose connection between average state and flux hydrological variables across vadose zones is partly because of the high non-linearity of subsurface processes, which leads to the complex interactions of soil moisture, ETa, and GR with soil hydraulic properties. This study underscores the importance of using soil moisture information from multiple sites for inferring areal average values of ETa and GR, even with the knowledge of representative sites that can be used to monitor areal average moisture conditions.

Prediction of nitrate accumulation and leaching beneath groundwater irrigated corn fields in the Upper Platte basin under a future climate scenario

Understanding the impacts of future climate change on soil hydrological processes and solute transport is crucial to develop appropriate strategies to minimize the adverse impacts of agricultural activities on groundwater quality. To evaluate the direct effects of climate change on the transport and accumulation of nitrate-N, we developed an integrated modeling framework combining climatic change, nitrate-N infiltration in the unsaturated zone, and groundwater level fluctuations. The study was based on a center-pivot irrigated corn field at the Nebraska Management Systems Evaluation Area (MSEA) site. Future groundwater recharge (GR) and actual evapotranspiration (ETa) rates were predicted via an inverse vadose zone modeling approach by using climatic data generated by the Weather Research and Forecasting (WRF) climate model under the RCP 8.5 scenario, which was downscaled from the global CCSM4 model to a resolution of 24 km by 24 km. A groundwater flow model was first calibrated on the basis of historical groundwater table measurements and then applied to predict the future groundwater table in 2057–2060. Finally, the predicted future GR rate, ETa rate, and groundwater level, together with future precipitation data from the WRF climate model, were used in a three-dimensional (3D) model to predict nitrate-N concentrations in the subsurface (saturated and unsaturated parts) from 2057 to 2060. The future GR was predicted to decrease in the study area, as compared with the average GR data from the literature. Correspondingly, the groundwater level was predicted to decrease (30 to 60 cm) over the 5 years of simulation in the future. The nitrate-N mass in the simulation domain was predicted to increase but at a slower rate than in the past. Sensitivity analysis indicated that the accumulation of nitrate-N is sensitive to groundwater table elevation changes and irrigation rates.

On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties

Core Ideas Cosmic‐ray neutron data are used to inversely estimate soil hydraulic properties. The forward neutron operator COSMIC is coupled with the vadose zone model HYDRUS‐1D. Bayesian analyses confirm the information content of cosmic‐ray neutron data. Observations of soil moisture content from remote sensing platforms can be used in conjunction with hydrological models to inversely estimate soil hydraulic properties (SHPs). In recent years, cosmic‐ray neutron sensing (CRNS) has proven to be a reliable method for the estimation of area‐average soil moisture at field scales. However, its use in the inverse estimation of the effective SHPs is largely unexplored. Thus, the main objective of this study was to assess the information content of aboveground fast‐neutron counts to estimate SHPs using both a synthetic modeling study and actual experimental data from the Rollesbroich catchment in Germany. For this, the forward neutron operator COSMIC was externally coupled with the hydrological model HYDRUS‐1D. The coupled model was combined with the Affine Invariant Ensemble Sampler to calculate the posterior distributions of effective soil hydraulic parameters as well as the model‐predictive uncertainty for different synthetic and experimental scenarios. Measured water contents at different depths were used to assess estimated SHPs. The analysis of both synthetic and actual CRNS data from homogenous and heterogeneous soil profiles, respectively, led to confident estimations of the shape parameters α and n, while higher uncertainty was observed for the saturated hydraulic conductivity. Furthermore, results demonstrated that neutron data are less influenced by local sources of uncertainty compared with near‐surface point measurements. The simultaneous use of CRNS and water content data further reduced the overall uncertainty, opening up new perspectives for the combination of CRNS with other remote sensing techniques for the inverse estimation of the effective SHPs.

Irrigation variability and climate change affect derived distributions of simulated water recharge and nitrate leaching

ABSTRACT Irrigation (‘blue’) water has high value as municipalities seek water security under growing populations and projected climates, but spatial variability makes estimating return flows to groundwater challenging. We demonstrate a framework for simulating spatially variable infiltration and derived distributions of return flows using an agricultural and vadose zone model to simulate recharge and nitrate leaching under irrigated corn in semi-arid northeastern Colorado, USA. Derived distributions indicated increased historical recharge (2–42%) as the spatial variability of applied irrigation increased. Projected climate in 2050 increased recharge above historical rates by up to 58%, but climatic effects decreased with increasing irrigation variability.

Vadose Zone Modeling in a Small Forested Catchment: Impact of Water Pressure Head Sampling Frequency on 1D-Model Calibration

The characterization of vadose zone processes is a primary goal for understanding, predicting, and managing water resources. In this study, the issue of soil water monitoring on a vertical profile in the small forested Strengbach catchment (France) is investigated using numerical modeling with the long-term sequences 1D-Richards’ equation and parameter estimation through an inverse technique. Three matric potential sensors produce the observation data, and the meteorological data is monitored using an automatic weather station. The scientific questions address the selection of the calibration sequence, the initial starting point for inverse optimization and monitoring frequency used in the inverse procedure. As expected, our results show that the highly variable data period used for the calibration provides better estimations when simulating the long-term sequence. For the starting point of the initial parameters, handmade iterative initial parameters estimation leads to better results than a laboratory analysis or set of ROSETTA parameters. Concerning the frequency of monitoring, weekly and daily datasets provide efficient results compared to hourly data. As reported in other articles, the accuracy of the boundary conditions is important for estimating soil hydraulic parameters and accessing water stored in the layered profile.

Effect of hysteresis on water flow in the vadose zone under natural boundary conditions, Siloam Village case study, South Africa

Abstract A one-dimensional vadose zone model was used to simulate flow under natural boundary conditions. The effects of hysteresis and temporal variability of meteorological conditions were evaluated. Simulations were performed in HYDRUS-1D code for the period April 2013–January 2014 (6601 hours) at three different locations in a delineated portion of the sub-quaternary catchment A80A of Nzhelele with different soil textures. Soil hydraulic characteristics were estimated in a Rosetta library dynamically linked to the HYDRUS-1D model which is based on the numerical solution of a one-dimensional Richard's equation. Analysis of the simulation results suggests that ignoring hysteresis for soils of similar textural class does not lead to any significant deviation of the model predicted soil moisture, unlike for soils with different textural classes.

Modeling Subsurface Fate of S‐Metolachlor and Metolachlor Ethane Sulfonic Acid in the Westliches Leibnitzer Feld Aquifer

Core Ideas Lysimeter experiments allow site‐specific knowledge about the fate of pesticides. Lysimeter‐based model calibration provides integrated parameter sets. Lysimeter scale‐based models were compiled to represent aquifer scale. Lysimeter scale‐based models were coupled with a groundwater transport model. The groundwater model reproduced observed metabolite groundwater concentrations. Pesticides and their metabolites have been increasingly detected in groundwater bodies in southeastern Austria in recent years. The main objective of this study was to model the fate of the herbicide S‐metolachlor (2‐chloro‐N‐(2‐ethyl‐6‐methylphenyl)‐N‐[(1S)‐2‐methoxy‐1‐methylethyl]acetamide; SMET) and the main metabolite metolachlor ethane sulfonic acid (MESA) at the Westliches Leibnitzer Feld (WLF) aquifer. For this purpose, a modeling approach based on coupling the one‐dimensional vadose zone model PEARL and the two‐dimensional groundwater flow and solute transport model FEFLOW was developed. To calibrate the one‐dimensional pesticide fate model, we used leachate concentrations of SMET and MESA from lysimeter experiments. Additionally, samples of representative soil types in the WLF aquifer were analyzed to infer SMET‐ and MESA‐specific fate parameters (e.g., half‐life DT50, Freundlich sorption coefficient Kfoc), which were used for the PEARL model. The results show that using SMET fate parameters derived from the lysimeter data considerably improved the fit of the simulation results with the field observations compared with the application of standard laboratory‐derived fate parameters accounting for soil type differences. Although locally an overestimation of the monitoring data prevailed, the description of the subsurface fate of pesticides will improve the interpretation of concentration data and the design of mitigation measures.