Catchment-scale Richards equation-based modeling of evapotranspiration via boundary condition switching and root water uptake schemes

In arid and semiarid climate catchments, where annual evapotranspiration (ET) and rainfall are typically comparable, modeling ET is important for proper assessment of water availability and sustainable land use management. The aim of the present study is to assess different parsimonious schemes for representing ET in a process-based model of coupled surface and subsurface flow. A simplified method for computing ET based on a switching procedure for the boundary conditions of the Richards equation at the soil surface is compared to a sink term approach that includes root water uptake, root distribution, root water compensation, and water and oxygen stress. The study site for the analysis is a small pasture catchment in southeastern Australia. A comprehensive sensitivity analysis carried out on the parameters of the sink term shows that the maximum root depth is the dominant control on catchment-scale ET and streamflow. Comparison with the boundary condition switching method demonstrates that this simpler scheme (only one parameter) can successfully reproduce ET when the vegetation root depth is shallow (not exceeding approximately 50 cm). For deeper rooting systems, the switching scheme fails to match the ET fluxes and is affected by numerical artifacts, generating physically unrealistic soil moisture dynamics. It is further shown that when transpiration is the dominant contribution to ET, the inclusion of oxygen stress and root water compensation in the model can have a considerable effect on the estimation of both ET and streamflow; this is mostly due to the water fluxes associated with the riparian zone. Key Points: Simple, parsimonious ET schemes for integrated hydrological models are assessed Boundary condition switching is suitable only for shallow root depths Oxygen stress and root water compensation influence riparian zone ET dynamics

Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope

Data assimilation has recently been the focus of much attention for integrated surface–subsurface hydrological models, whereby joint assimilation of water table, soil moisture, and river discharge measurements with the ensemble Kalman filter (EnKF) has been extensively applied. Although the EnKF has been specifically developed to deal with nonlinear models, integrated hydrological models based on the Richards equation still represent a challenge, due to strong nonlinearities that may significantly affect the filter performance. Thus, more studies are needed to investigate the capabilities of the EnKF to correct the system state and identify parameters in cases where the unsaturated zone dynamics are dominant, as well as to quantify possible tradeoffs associated with assimilation of multi-source data. Here, the CATHY (CATchment HYdrology) model is applied to reproduce the hydrological dynamics observed in an experimental two-layered hillslope, equipped with tensiometers, water content reflectometer probes, and tipping bucket flow gages to monitor the hillslope response to a series of artificial rainfall events. Pressure head, soil moisture, and subsurface outflow are assimilated with the EnKF in a number of scenarios and the challenges and issues arising from the assimilation of multi-source data in this real-world test case are discussed. Our results demonstrate that the EnKF is able to effectively correct states and parameters even in a real application characterized by strong nonlinearities. However, multi-source data assimilation may lead to significant tradeoffs: the assimilation of additional variables can lead to degradation of model predictions for other variables that are otherwise well reproduced. Furthermore, we show that integrated observations such as outflow discharge cannot compensate for the lack of well-distributed data in heterogeneous hillslopes.</p

Ensemble Kalman Filter Assimilation of ERT Data for Numerical Modeling of Seawater Intrusion in a Laboratory Experiment

Seawater intrusion in coastal aquifers is a worldwide problem exacerbated by aquifer overexploitation and climate changes. To limit the deterioration of water quality caused by saline intrusion, research studies are needed to identify and assess the performance of possible countermeasures, e.g., underground barriers. Within this context, numerical models are fundamental to fully understand the process and for evaluating the effectiveness of the proposed solutions to contain the saltwater wedge; on the other hand, they are typically affected by uncertainty on hydrogeological parameters, as well as initial and boundary conditions. Data assimilation methods such as the ensemble Kalman filter (EnKF) represent promising tools that can reduce such uncertainties. Here, we present an application of the EnKF to the numerical modeling of a laboratory experiment where seawater intrusion was reproduced in a specifically designed sandbox and continuously monitored with electrical resistivity tomography (ERT). Combining EnKF and the SUTRA model for the simulation of density-dependent flow and transport in porous media, we assimilated the collected ERT data by means of joint and sequential assimilation approaches. In the joint approach, raw ERT data (electrical resistances) are assimilated to update both salt concentration and soil parameters, without the need for an electrical inversion. In the sequential approach, we assimilated electrical conductivities computed from a previously performed electrical inversion. Within both approaches, we suggest dual-step update strategies to minimize the effects of spurious correlations in parameter estimation. The results show that, in both cases, ERT data assimilation can reduce the uncertainty not only on the system state in terms of salt concentration, but also on the most relevant soil parameters, i.e., saturated hydraulic conductivity and longitudinal dispersivity. However, the sequential approach is more prone to filter inbreeding due to the large number of observations assimilated compared to the ensemble size

Identification of high-permeability subsurface structures with multiple point geostatistics and normal score ensemble Kalman filter

Alluvial aquifers are often characterized by the presence of braided high-permeable paleo-riverbeds, which constitute an interconnected preferential flow network whose localization is of fundamental importance to predict flow and transport dynamics. Classic geostatistical approaches based on two-point correlation (i.e., the variogram) cannot describe such particular shapes. In contrast, multiple point geostatistics can describe almost any kind of shape using the empirical probability distribution derived from a training image. However, even with a correct training image the exact positions of the channels are uncertain. State information like groundwater levels can constrain the channel positions using inverse modeling or data assimilation, but the method should be able to handle non-Gaussianity of the parameter distribution. Here the normal score ensemble Kalman filter (NS-EnKF) was chosen as the inverse conditioning algorithm to tackle this issue. Multiple point geostatistics and NS-EnKF have already been tested in synthetic examples, but in this study they are used for the first time in a real-world casestudy. The test site is an alluvial unconfined aquifer in northeastern Italy with an extension of approximately 3 km2. A satellite training image showing the braid shapes of the nearby river and electrical resistivity tomography (ERT) images were used as conditioning data to provide information on channel shape, size, and position. Measured groundwater levels were assimilated with the NS-EnKF to update the spatially distributed groundwater parameters (hydraulic conductivity and storage coefficients). Results from the study show that the inversion based on multiple point geostatistics does not outperform the one with a multiGaussian model and that the information from the ERT images did not improve site characterization. These results were further evaluated with a synthetic study that mimics the experimental site. The synthetic results showed that only for a much larger number of conditioning piezometric heads, multiple point geostatistics and ERT could improve aquifer characterization. This shows that state of the art stochastic methods need to be supported by abundant and high-quality subsurface data

Water balance complexities in ephemeral catchments with different land uses: Insights from monitoring and distributed hydrologic modeling

Although ephemeral catchments are widespread in arid and semiarid climates, the relationship of their water balance with climate, geology, topography, and land cover is poorly known. Here we use 4 years (2011–2014) of rainfall, streamflow, and groundwater level measurements to estimate the water balance components in two adjacent ephemeral catchments in south-eastern Australia, with one catchment planted with young eucalypts and the other dedicated to grazing pasture. To corroborate the interpretation of the observations, the physically based hydrological model CATHY was calibrated and validated against the data in the two catchments. The estimated water balances showed that despite a significant decline in groundwater level and greater evapotranspiration in the eucalypt catchment (104–119% of rainfall) compared with the pasture catchment (95–104% of rainfall), streamflow consistently accounted for 1–4% of rainfall in both catchments for the entire study period. Streamflow in the two catchments was mostly driven by the rainfall regime, particularly rainfall frequency (i.e., the number of rain days per year), while the downslope orientation of the plantation furrows also promoted runoff. With minimum calibration, the model was able to adequately reproduce the periods of flow in both catchments in all years. Although streamflow and groundwater levels were better reproduced in the pasture than in the plantation, model-computed water balance terms confirmed the estimates from the observations in both catchments. Overall, the interplay of climate, topography, and geology seems to overshadow the effect of land use in the study catchments, indicating that the management of ephemeral catchments remains highly challenging

Stream network dynamics of non-perennial rivers: numerical simulations data

Input and output files of the numerical simulations run with the integrated surface-subsurface hydrological model CATH

Long-term Impacts of Partial Afforestation on Water and Salt Dynamics of an Intermittent Catchment under Climate Change

Soil salinization is a major environmental issue in arid and semi-arid regions, and has been accelerated in some areas by removal of native vegetation cover. Partial afforestation can be a practical mitigation strategy if efficiently integrated with farms and pastures. Using an integrated surface-subsurface hydrological model, this study evaluates the water and salt dynamics and soil salinization conditions of a rural intermittent catchment in the semi-arid climate of southeast Australia subjected to four different partial afforestation configurations under different climate change scenarios, as predicted by several general circulation models. The results show that the locations of afforested areas can induce a retarding effect in the outflow of groundwater salt, with tree planting at lower elevations showing the steadier salt depletion rates. Moreover, except for the configuration with trees planted near the outlet of the catchment, the streamflow is maintained under all other configurations. It appears that under both Representative Concentration Pathways considered (RCP 4.5 and RCP 8.5), the Hadley Centre Global Environmental Model represents the fastest salt export scheme, whereas the Canadian Earth System Model and the Model for Interdisciplinary Research on Climate represent the slowest salt export scheme. Overall, it is found that the location of partial afforestation generally plays a more significant role than the climate change scenarios

Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

Real-time monitoring of soil matric potential has now become a common practice for precision irrigation management. Some crops, such as cranberries, are susceptible to both water and anoxic stresses. Excessive variations in soil matric potential in the root zone may reduce plant transpiration, due to either saturated or dry soil conditions, thereby reducing productivity. A timely supply of the right amount of water is, therefore, fundamental for efficient irrigation management. In this paper, we compare the capabilities of a machine learning-based model and a physics-based model to predict soil matric potential in the root zone. The machine learning model is a random forest algorithm, while the physics-based model is a two-dimensional solver of Richards equation (HYDRUS 2D). After training and calibration on a dataset collected in a cranberry field located in Québec (Canada), the performance of the two models is evaluated for 30 different time frames of 72-h soil matric potential forecasts. The results highlight that both models can accurately forecast the soil matric potential in the root zone. The machine learning-based model can achieve better performance when compared to the physics-based model, but forecasting accuracy decreases rapidly toward the end of the 72-h lead time, while the error for the Richards equation-based model does not increase with time and remain small compared to the typical measurement error

Editorial: Hydro-informatics for sustainable water management in agrosystems, volume II.

This second volume of the Research Topic entitled “Hydro-informatics for Sustainable Water Management in Agrosystems” marks the induction of the theme as a periodical topic in the Frontiers in Water journal. This is an important step in a journey aiming at exploring appropriate or opportune application of Hydroinformatics in the agricultural domain. As part of the first volume, the field of Agricultural Hydroinformatics (Celicourt et al., 2020) was proposed as a holistic approach leveraging information and communication technologies to sustainably transform the socio-natural assemblage, i.e., farmers, lands, crops, pipes, pumps, livestock, rivers (Bakker, 2012; Barnes, 2012), that shapes, and is shaped by water. Initially, this vision was materialized through a diverse range of: (a) technical studies focusing on the performance of subsurface drainage and irrigation systems, and (b) phenomenological ones aiming at designing software tools for farmers empowerment in a context of raising water saving awareness. The papers that make up this volume highlight a new set of topics (water data gaps filling, drought impacts management, soil compaction and hydrodynamic properties, irrigation systems efficiency, and sediment concentrations and loads modeling) that further extend the scope and horizon of the application of Hydroinformatics in agriculture