New features of version 3 of the HYDRUS (2D/3D) computer software package

Agricultural soil landscapes of hummocky ground moraines are characterized by 3D spatial patterns of soil types that result from profile modifications due to the combined effect of water and tillage erosion. We hypothesize that crops reflect such soil landscape patterns by increased or reduced plant and root growth. Root development may depend on the thickness and vertical sequence of soil horizons as well as on the structural development state of these horizons at different landscape positions. The hypotheses were tested using field data of the root density (RD) and the root lengths (RL) of winter wheat using the minirhizotron technique. We compared data from plots at the CarboZALF‐D site (NE Germany) that are representing a non‐eroded reference soil profile (Albic Luvisol) at a plateau position, a strongly eroded profile at steep slope (Calcaric Regosol), and a depositional profile at the footslope (Anocolluvic Regosol). At each of these plots, three Plexiglas access tubes were installed down to approx. 1.5 m soil depth. Root measurements were carried out during the growing season of winter wheat (September 2014–August 2015) on six dates. The root length density (RLD) and the root biomass density were derived from RD values assuming a mean specific root length of 100 m g−1. Values of RD and RLD were highest for the Anocolluvic Regosol and lowest for the Calcaric Regosol. The maximum root penetration depth was lower in the Anocolluvic Regosol because of a relatively high and fluctuating water table at this landscape position. Results revealed positive relations between below‐ground (root) and above‐ground crop parameters (i.e., leaf area index, plant height, biomass, and yield) for the three soil types. Observed root densities and root lengths in soils at the three landscape positions corroborated the hypothesis that the root system was reflecting erosion‐induced soil profile modifications. Soil landscape position dependent root growth should be considered when attempting to quantify landscape scale water and element balances as well as agricultural productivity.

Section: Introductionmentioning

confidence: 99%

Root development of winter wheat in erosion‐affected soils depending on the position in a hummocky ground moraine soil landscape

Herbrich

Gerke

Sommer

2017

“…The soil hydrology on this hillslope was simulated by HYDRUS‐3D model ( Šimůnek et al, ). In the simulation, two soil sub‐layers (0 to 20 cm and 20 cm to bedrock) were considered.…”

Section: Methodsmentioning

confidence: 99%

Investigating the spatio‐temporal variations of nitrate leaching on a tea garden hillslope by combining HYDRUS‐3D and DNDC models

Lai

Liu

Zhou

et al. 2019

Spatio-temporal variations of nitrate-nitrogen (NO À 3 -N) leaching is driven by both soil hydrology and biogeochemistry. However, the widely used soil hydrology and biogeochemistry models have their weaknesses in simulating soil N cycling and soil water movement processes, respectively. In this study, we proposed an alternative approach by simply combining the HYDRUS-3D and DNDC models to investigate the spatio-temporal variations of NO À 3 -N leaching on a representative tea garden hillslope in Taihu Lake Basin, China. Results showed that the soil hydrology and N cycle were well simulated by HYDRUS-3D and DNDC models, respectively. Based on the leaching equation, the soil water flux simulated by HYDRUS-3D and soil NO À 3 -N content simulated by DNDC were combined to calculate the leachate NO À 3 -N concentrations with good accuracy. The accumulative NO À 3 -N leaching flux during the simulation year was 71.7 kg N ha -1 , with remarkable spatio-temporal variations on this hillslope. Hot spots of NO À 3 -N leaching were observed in blocks 24, 27, 31, 34, 37, and 40 with accumulative leaching fluxes > 82.0 kg N ha -1 y -1 . The spatial variation of NO À 3 -N leaching was mainly controlled by soil texture and soil hydraulic properties. Hot moments of NO À 3 -N leaching were observed after the applications of spring fertilizer (16 March) and basal fertilizer (30 October). The temporal variation of NO À 3 -N leaching was mainly controlled by precipitation and the spring fertilization. Methods and findings of this study will be benefit for the risk assessment of non-point source N loss and the precise agricultural management.

“…For the water balance simulations at the forest plots, we applied a model chain of the models BROOK90 ( Federer , 1995) and HYDRUS‐1D ( Šimůnek et al, 2013). Each model describes detailed hydrological processes in an important compartment of the forest ecosystem.…”

Section: Methodsmentioning

confidence: 99%

“…Equation (4) is solved numerically using Galerkin type linear finite element schemes. Integration in time is achieved using an implicit (backwards) finite difference scheme ( Šimůnek et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

Impact of stoniness correction of soil hydraulic parameters on water balance simulations of forest plots

Wegehenkel

Wagner

Amoriello

et al. 2016

Long‐term estimates of the water balance of forests are essential for forest ecosystem analysis. Such estimates can be achieved by using numerical modelling approaches for water balance calculations. These modelling approaches require data, e.g., for the description of the hydraulic properties of forest soils using soil water retention or hydraulic conductivity functions. However, these functions are usually valid only for fine‐earth conditions. However, many forest soils, especially those located in mountainous regions, contain larger fractions of gravel and rock fragments in the soil profiles. In our study, the impact of stoniness correction of soil hydraulic functions on the performance of a numerical water balance modelling approach was evaluated by using continuously measured time series of daily throughfall, soil water contents and pressure heads provided from three different ICP Forests Level II plots with high stoniness in the soil profiles. These measured data were compared with the corresponding model outputs. The application of stoniness correction of the soil hydraulic functions improved the model performance in terms of the Nash‐Sutcliffe Index NS and the coefficient of determination R2. This indicated the need for such a correction.