A general process-oriented model for two competing plant species

International audienc

A fast and automated hydrologic calibration tool for SWAT

In order to apply hydrological models in the water resources investigation successfully, careful calibration and uncertainty analysis are required. Although many automatic calibration methods were developed, the time consumed for running the hydrologic model is still a problem for hydrologic modelers. To reduce the computational complexity and increase performance of the calibration procedure, a software package (Fast Automated Calibration Tool, FACT) that works on Soil and Water Assessment Tool (SWAT) was developed. Sequential Uncertainty Fitting Algorithm (SUFI-2) was chosen to build the software package on, because SUFI-2 combines optimization with uncertainty analysis and can handle a large number of parameters. SUFI-2 implemented in SWAT-CUP (a software program that was developed for SWAT) is very useful in an interactive manner, however; it has some drawbacks which are the time consumed, user interaction requirement and update problems of SWAT model files. In this study, the calibration procedure was implemented in a MATLAB script, which completes the full calibration in one single run. The developed tool was applied on Sarisu-Eylikler Basin SWAT model that had r(2) = 0.41, NSE = 0.11 between 1992 and 2010 without calibration. When the model was calibrated using SUFI-2 in SWAT-CUP, the model was improved to r(2) = 0.57, NSE = 0.44, P-factor = 0.69 and R-factor = 1.00 at the end of 42 iterations using 100 simulation counts in each iteration. However; when the developed calibration tool was applied with 250 simulation counts without any user interaction for each iteration, the model was improved to r(2) = 0.59, NSE = 0.57, P-factor = 0.72, and R-factor = 1.32 at the end of three iterations. Thus, the developed calibration procedure took a shorter time (unlike SUFI2 in SWAT-CUP, FACT takes several hours) compared to SWAT-CUP with minimum user involvement

EPIC Modeling of Soil Organic Carbon Sequestration in Croplands of Iowa

Depending on management, soil organic carbon (SOC) is a potential source or sink for atmospheric CO2. We used the EPIC model to study impacts of soil and crop management on SOC in corn (Zea mays L.) and soybean (Glycine max L. Merr.) croplands of Iowa. The National Agricultural Statistics Service crops classification maps were used to identify corn–soybean areas. Soil properties were obtained from a combination of SSURGO and STATSGO databases. Daily weather variables were obtained from first order meteorological stations in Iowa and neighboring states. Data on crop management, fertilizer application and tillage were obtained from publicly available databases maintained by the NRCS, USDA-Economic Research Service (ERS), and Conservation Technology Information Center. The EPIC model accurately simulated state averages of crop yields during 1970–2005 (R2 = 0.87). Simulated SOC explained 75% of the variation in measured SOC. With current trends in conservation tillage adoption, total stock of SOC (0–20 cm) is predicted to reach 506 Tg by 2019, representing an increase of 28 Tg with respect to 1980. In contrast, when the whole soil profile was considered, EPIC estimated a decrease of SOC stocks with time, from 1835 Tg in 1980 to 1771 Tg in 2019. Hence, soil depth considered for calculations is an important factor that needs further investigation. Soil organic C sequestration rates (0–20 cm) were estimated at 0.50 to 0.63 Mg ha−1 yr−1 depending on climate and soil conditions. Overall, combining land use maps with EPIC proved valid for predicting impacts of management practices on SOC. However, more data on spatial and temporal variation in SOC are needed to improve model calibration and validation

Corn stover harvest N and energy budgets in central Iowa.

Harvesting corn stover removes N from the fields, but its effect on subsurface drainage and other N losses is uncertain. We used the Root Zone Water Quality Model (RZWQM) to examine N losses with 0 (NRR) or 50% (RR) corn residue removal within a corn and soybean rotation over a 10-yr period. In general, all simulations used the same pre-plant or post-emergence N fertilizer rate (200 kg ha−1 yr−1). Simulated annual corn yields averaged 10.7 Mg ha−1 for the post emergence applications (NRRpost and RRpost), and 9.5 and 9.4 Mg ha−1 yr−1 for NRRpre and RRpre. Average total N input during corn years was 19.3 kg N ha−1 greater for NRRpre compared to RRpre due to additional N in surface residues, but drainage N loss was only 1.1 kg N ha−1 yr−1 greater for NRRpre. Post-emergence N application with no residue removal (NRRpost) reduced average drainage N loss by 16.5 kg ha−1 yr−1 compared to pre-plant N fertilization (NRRpre). The farm-gate net energy ratio was greatest for RRpost and lowest for NRRpre (14.1 and 10.4 MJ output per MJ input) while greenhouse gas intensity was lowest for RRpost and highest for NRRpre (11.7 and 17.3 g CO2-eq. MJ−1 output). Similar to published studies, the simulations showed little difference in N2O emissions between scenarios, decreased microbial immobilization for RR compared to NRR, and small soil carbon changes over the 10-yr simulation. In contrast to several previous modeling studies, the crop yield and N lost to drain flow were nearly the same between NRR and RR without supplemental N applied to replace N removed with corn stover. These results are important to optimizing the energy and nitrogen budgets associated with corn stover harvest and for developing a sustainable bioenergy industry.This article is published as Malone, R. W., S. Herbstritt, L. Ma, T. L. Richard, R. Cibin, P. W. Gassman, H. H. Zhang et al. "Corn stover harvest N and energy budgets in central Iowa." Science of the Total Environment 663 (2019): 776-792. DOI: 0.1016/j.scitotenv.2019.01.328. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted