Using Inverse Modeling by HYDRUS-1D to Predict Some Soil Hydraulic Parameters from Soil Water Evaporation

The objective of this research was to assess the use of unsaturated water flow in terms of soil water evaporation, which was determined by evaluating some soil hydraulic parameters in different soil textures. The results show that the predicted values of these parameters, which were obtained through inverse modeling with the HYDRUS-1D software and depend on the change of the volumetric water content, exhibited a significant agreement with the measured values from laboratory or field simulation data for soil water evaporation at 5. 10. 20. and 45 days of measurement. At the same time, inverse simulation was conducted on soil hydraulic parameters obtained from a 5-day laboratory soil evaporation period to predict field infiltration values and water retention curve, which showed a significant agreement with measured values for all soil textures.

Determination of Soil Hydraulic Parameters and Evaluation of Water Dynamics and Nitrate Leaching in the Unsaturated Layered Zone: A Modeling Case Study in Central Croatia

Nitrate leaching through soil layers to groundwater may cause significant degradation of natural resources. The aims of this study were: (i) to estimate soil hydraulic properties (SHPs) of the similar soil type with same management on various locations; (ii) to determine annual water dynamics; and (iii) to estimate the impact of subsoil horizon properties on nitrate leaching. The final goal was to compare the influence of different SHPs and layering on water dynamics and nitrate leaching. The study was conducted in central Croatia (Zagreb), at four locations on Calcaric Phaeozem, Calcaric Regosol, and Calcaric Fluvic Phaeozem soil types. Soil hydraulic parameters were estimated using the HYPROP system and HYPROP-FIT software. Water dynamics and nitrate leaching were evaluated using HYDRUS 2D/3D during a period of 365 days. The amount of water in the soil under saturated conditions varied from 0.422 to 0.535 cm3 cm–3 while the hydraulic conductivity varied from 3 cm day−1 to 990.9 cm day−1. Even though all locations have the same land use and climatic conditions with similar physical properties, hydraulic parameters varied substantially. The amount and velocity of transported nitrate (HYDRUS 2D/3D) were affected by reduced hydraulic conductivity of the subsoil as nitrates are primarily transported via advective flux. Despite the large differences in SHPs of the topsoil layers, the deeper soil layers, having similar SHPs, imposed a buffering effect preventing faster nitrate downward transport. This contributed to a very similar distribution of nitrates through the soil profile at the end of simulation period. This case study indicated the importance of carefully selecting relevant parameters in multilayered soil systems when evaluating groundwater pollution risk.

Uncertainty in soil hydraulic parameters: A review of basics and methods

The paper is a review article on the basics of uncertainty, necessity of its quantification and a comparative study of various methods of uncertainty estimation. The paper primarily focusses on uncertainty estimation of soil hydraulic parameters as of their pivotal importance in groundwater flow and transport simulations, soil moisture modelling techniques etc. The deterministic and probabilistic approaches of uncertainty quantification are studied and an understanding of uncertainty based on field scale measurements, empirical methods and pedotransfer functions is established. A comparative analysis of the basic methods of uncertainty analysis Monte Carlo, Bayesian, FORM/SORM and GLUE is done and the preferential use based on the importance is suggested. Bayesian approach was most suitable for evaluating parametric uncertainty, Monte Carlo was one of the most powerful tools but computationally expensive, FORM was applicable to both numerical and analytical solutions but didn’t guarantee a global convergence and GLUE was conceptually simple but gave only a statistical measure.

Coupling the Community Land Model 5.0 with the Parallel Data Assimilation Framework

<p>Land surface models are important tools to improve our understanding of interacting ecosystem processes, but their predictions are associated with uncertainties related to model forcings, parameters and process simplifications. As high-quality observations become more and more available, they can be used to constrain the uncertainty of land surface model predictions. In this study, we use data assimilation for the fusion of data into the Community Land Model 5.0 (CLM5). CLM5 simulates a broad variety of important land surface processes including moisture and energy partitioning, surface runoff, subsurface runoff, photosynthesis and carbon and nitrogen storage in vegetation and soil. Here, we focus on water movement in soils and related soil hydraulic parameters and assimilate in-situ soil moisture data into CLM5 to improve the estimate of model states and soil hydraulic parameters. To do this, we have coupled the Parallel Data Assimilation Framework (PDAF) with CLM5. This coupling is based on the online variant of PDAF, i.e., data assimilation occurs during simulation runtime in the main memory and not via input/output files. Online coupling requires modification of the model source code, but we aim to keep the modifications to the CLM5 code minimal so that maintenance of the ongoing CLM5 developments remains straightforward. To this end, our approach reuses the existing CLM5 ensemble mode with only necessary adjustments to connect the PDAF parallel communicators. Furthermore, we developed the coupling in the framework of the Terrestrial System Modeling Platform (TSMP). TSMP is a highly modular modeling system for the fully integrated soil-vegetation-atmosphere system. To illustrate the potential of this coupling, we use the ensemble Kalman Filter to perform simultaneous state and parameter updates in a forest headwater catchment.</p>

Updating the soil hydraulic parameters in a 3D subsurface model

<p>Soil moisture is an important variable for land surface processes. To make good model predictions of soil moisture, the flow processes in the subsurface need to be captured well. Flow in the subsurface strongly depends on the soil hydraulic parameters. Information about model parameters is often not available, at least not for the entire domain of interest. The resulting parameter uncertainty needs to be accounted for in the applied model. Data assimilation can account for parameter and model errors as well as for all other possible sources of uncertainty if observations are available that can be used to condition the model states. Thus, the parameter uncertainty might be reduced and model predictions improved. However, including the parameters increases the size of the state vector and thus the computational burden. Especially for large models, this can be a problem. Furthermore, the updates can produce unphysical parameter combinations which in unsaturated zone models often lead to numerical problems.</p><p>In this work, we test the effect of updating the soil hydraulic parameters along with soil moisture in a 3D subsurface hillslope model. We use the ensemble Kalman filter for data assimilation and synthetic observations of soil moisture. In a similar study using a 1D unsaturated flow model, parameter updates were found to be the best way to handle parameter uncertainty. Updating parameters resulted in improved predictions of soil moisture, although not necessarily in more realistic model parameters. The parameter updates should rather be considered a method of treating parameter uncertainty than a method for parameter identification. In the 1D settings, updating all uncertain parameters led to the best results. Whether this still holds and is feasible for a more complex 3D model is the question addressed in this presentation.</p>

Post-fire infiltration modeling – some soil physical considerations

<p>Wildfires can change watershed hydrologic processes and increase the risks for soil erosion, flooding and debris flow after a fire. While fire-induced changes to the soil have significant effects on infiltration and runoff, the physical mechanisms remain unclear. A growing body of research suggests these mechanisms include soil water repellency (SWR) and the alteration of soil structure. The objective of this study was to relate SWR, soil structure, soil moisture to infiltration using a process-based, soil physics approach to better model infiltration into fire-affected soil, The ultimate goal is to improve the prediction of post-fire runoff with process-based hydrology models. Our research shows the effects of SWR and soil structure on infiltration can be captured by the soil hydraulic parameters of sorptivity and hydraulic conductivity, respectively. SWR reduces sorptivity and controls the early stage of infiltration during a storm. Changes in soil structure affect hydraulic conductivity and later stages of infiltration. Additionally, results show SWR can have an effect on unsaturated hydraulic conductivity but does not significantly affect saturated hydraulic conductivity. The study also highlights the important role soil water content plays for post-fire infiltration since both sorptivity and unsaturated hydraulic conductivity are functions of soil water content.</p>