Calibrating the Root Zone Water Quality Model

Abstract

Techniques to reliably calibrate computer models are needed before the models can be applied to help solve natural resource problems. The USDA‐ARS Root Zone Water Quality Model (RZWQM) is a comprehensive simulation model designed to predict hydrologic and chemical response, including potential for ground‐water contamination, of agricultural management systems. RZWQM Version 3.2 was calibrated and evaluated at sites in Iowa, Minnesota, Missouri, Nebraska, and Ohio as part of the Management Systems Evaluation Areas (MSEA) project and at a site near Sterling in northeastern Colorado. Soil horizon description and a description of the physical and hydraulic properties of the soil were required to initialize the model. Calibration for nutrient cycling involved adjusting the model coefficients for mineralization, infiltration, and denitrification. Initial N pool sizes were estimated using medium to long‐term computer simulations. Maximum N uptake rate, plant respiration, specific leaf area, and the effect of age at the time of propagule development and senescence were used to calibrate the plant production and yield component. To match the observed results for soil water, N, and plant growth, an iterative approach for calibrating the model was followed. When done methodically, total biomass estimates were within 5%, yield estimates were within l0%, and N uptake was within 20% of field measurements. Calibration of the C and N dynamics module produced results that were generally within 20 to 50 kg ha−1 of measured values for soil profile NO−3‐N. Independent evaluations of the calibrated model focused on four indicator output variables related to plant growth—total biomass, yield, N uptake, and N in the soil profile. Predictions matched the observed data in most cases. The crop model is very sensitive to plant N content. Even small errors in simulating N uptake levels can result in substantial errors in estimates of yield and total aboveground biomass. The model predicted biomass and yield well on irrigated and most dryland management systems and adequately simulated crop variables at various positions along the landscape.

Optimizing Irrigation Rates for Cotton Production in an Extremely Arid Area Using RZWQM2-Simulated Water Stress

“…Model performance is considered acceptable if -15% < PBIAS < 15%, R 2 > 0.70, ME > 0.5, and rRMSE < 30% (Hanson et al, 1999;Ahuja et al, 2000;Moriasi et al, 2007). For soil water content, the above statistics were compared with values from other similar studies.…”

Modification, Calibration and Validation of APSIM to Suit Maize (Zeamays L.) Production System: A Case of Nkango Irrigation Scheme in Malawi

“…The N movement in soil is predominantly by three basic mechanisms of solute transport namely Advection, diffusion, and dispersion [9,10,11]. Based on these three basic mechanisms several models have been developed to infer behaviour of N in the soil and some of these models are as follows: LEACHMN [12], RZWQM [13,HYDRUS program [16], and APSIM [17].…”

Effects of Subsurface Drainage Systems on Water and Nitrogen Footprints Simulated with RZWQM2

“…During initialization, the cropping and tillage system in place during the study period was executed in order to set up the microbial and residue pools and to establish the hydraulic and nutrient cycles within the soil profile. Measured soil carbon content was allocated between the fast, medium, and slow organic matter pools and initialized as 5%, 35%, and 60%, respectively, based on similar methods described by Kumar et al (1999) and Hanson et al (1999). Percent SOM was measured at depth increments of 0-10 cm, 10-20 cm, 20-40 cm, and 40-60 cm in 2011, 2013, and 2015 at the field site.…”

“…Based on modeling methods demonstrated by Landa et al (1999) and Hanson et al (1999), multiple iterations of 26 years of typical management and climate data were continuously run until the soil humus and biota pools reached a steady state and the sum of the humus carbon pools were close to measured values, or around 2% to 3% (table 1). Minor iterative adjustments to microbial death rates, humus decay rates, and organic matter inter-pool transfer coefficients were required until the slow humus pool size was stable and the microbial and organic matter pool sizes reached equilibrium Kumar et al, 1999).…”