Simulation of Long-Term Soil Hydrological Conditions at Three Agricultural Experimental Field Plots Compared with Measurements

Abstract

Soil hydrological conditions influence crop growth and groundwater recharge and, thus, precise knowledge of such conditions at field scale is important for the investigation of agricultural systems. In our study, we analyzed soil hydrological conditions at three agricultural experimental field plots with sandy soils and different crop rotations using a 22-year period from 1993 to 2014 with daily volumetric soil water contents measured by the Time Domain Reflectometry with Intelligent MicroElements (TRIME)-method and pressure heads determined by automatic recording tensiometers. These measured data were compared with soil water contents and pressure heads simulated by a process-based agroecosystem model. Within this 22-year period, time spans with a better model performance and periods with a lower goodness of fit between simulations and observations were observed. The lower goodness of fit in the summer periods was attributed to inadequate calculations of root water uptake. Measurement errors of the TRIME-probes and differences between soil water contents measured by TRIME and pressure heads observed by tensiometers due to different measurement volumes, precision and measuring principles were identified as further reasons for mismatches between simulated and measured model outputs.

The Relationship between the Physicochemical Properties and Permeability of the Fluvisols and Eutric Cambisols in the Zagreb Aquifer, Croatia

“…This is due to the soil texture and close contact with gravels and sands. Similar results were reported by Wegehenkel et al [51].…”

Crop growth and soil water fluxes at erosion‐affected arable sites: Using weighing lysimeter data for model intercomparison

“…This intercomparison study considered 12 simulation models, which have been described in detail in separate publications: AgroC (AGC; Klosterhalfen et al., 2017), DailyDayCent (DDC; Del Grosso et al., 2001, 2006; Parton et al., 2001; Yeluripati et al., 2009); Daisy (DY; Abrahamsen & Hansen, 2000; Hansen, Abrahamsen, Petersen, & Styczen, 2012); Expert‐N (EN; Biernath et al., 2011; Engel & Priesack, 1993; Priesack, Gayler, & Hartmann, 2006) coupled to CERES (ENCE), GECROS (ENGE), SPASS (ENSP), and SUCROS (ENSU); HERMES (HER; Kersebaum, 1995, 2007); MONICA (MON; Nendel et al., 2011; Nendel, Kersebaum, Mirschel, & Wenkel, 2014); THESEUS (TH; Wegehenkel, Luzi, Sowa, Barkusky, & Mirschel, 2019) and two hydrological models, namely, Hydrus‐1D (THD; Šimůnek, van Genuchten, & Šejna, 2016) and HydroGeoSphere (THGS; Aquanty, 2013; Brunner & Simmons, 2012). Model simulations were performed on a daily time step, except for the DY and THGS model, which simulated crop growth (DY) and/or water flow (THGS) on an hourly basis.…”

Same soil, different climate: Crop model intercomparison on translocated lysimeters

“…Eleven models participated in the study to predict environmental and agronomic variables; AgroC (AC) (Klosterhalfen et al., 2017), DailyDayCent (DC) (Del Grosso et al., 2001), Daisy (DY) (Hansen et al., 2012), HERMES (HE) (Kersebaum, 2007), MONICA (MO) (Nendel et al., 2011), THESEUS (TH) (Wegehenkel et al., 2019), Expert‐N (Priesack et al., 2006) coupled to CERES (CE), GECROS (GE), SPASS (SP), and SUCROS (SU), and one hydrological model HydroGeoSphere (HG) (AQUANTY, 2013). A multi‐model mean (MM) was calculated considering the corresponding simulation outputs of the 10 crop models, excluding HG, which does not simulate crop growth.…”

“…(2021). Changes in climatic conditions (e.g., induced by future climate change) will affect not only crop growth but also the soils (e.g., Robinson et al., 2016, 2019). Thus, the ability of individual crop models to predict effects of changing climatic conditions on soil ecosystems remains untested for the range beyond the measured site‐specific variability of environmental conditions.…”