Importance of Crop Yield in Calibrating Watershed Water Quality Simulation Tools1

Abstract

Surendran Nair, Sujithkumar, Kevin W. King, Jonathan D. Witter, Brent L. Sohngen, and Norman R. Fausey, 2011. Importance of Crop Yield in Calibrating Watershed Water Quality Simulation Tools. Journal of the American Water Resources Association (JAWRA) 47(6):1285–1297. DOI: 10.1111/j.1752‐1688.2011.00570.x Abstract: Watershed‐scale water‐quality simulation tools provide a convenient and economical means to evaluate the environmental impacts of conservation practices. However, confidence in the simulation tool’s ability to accurately represent and capture the inherent variability of a watershed is dependent upon high quality input data and subsequent calibration. A four‐stage iterative and rigorous calibration procedure is outlined and demonstrated for Soil Water Analysis Tool (SWAT) using data from Upper Big Walnut Creek (UBWC) watershed in central Ohio, USA. The four stages and the sequence of their application were: (1) parameter selection, (2) hydrology calibration, (3) crop yield calibration, and (4) nutrient loading calibration. Following the calibration, validation was completed on a 10 year period. Nash‐Sutcliffe efficiencies for streamflow over the validation period were 0.5 for daily, 0.86 for monthly, and 0.87 for annual. Prediction efficiencies for crop yields during the validation period were 0.69 for corn, 0.54 for soybeans, and 0.61 for wheat. Nitrogen loading prediction efficiency was 0.66. Compared to traditional calibration approaches (no crop yield calibration), the four‐stage approach (with crop yield calibration) produced improved prediction efficiencies, especially for nutrient balances.

Hydrological responses of land use change from cotton (Gossypium hirsutum L.) to cellulosic bioenergy crops in the Southern High Plains of Texas, USA

“…After achieving a satisfactory streamflow calibration, the model was further calibrated for prediction of cotton lint yield under both irrigated and dryland systems to obtain a good match between the simulated and reported cotton lint yields. Previous SWAT modeling studies that performed crop yield calibration (Hu et al, 2007;Nair et al, 2011;Sarkar et al, 2011;Avila-Carrasco et al, 2012) suggested adjusting the biomass/ energy ratio (BIO_E) and maximum leaf area index (BLAI) to calibrate the SWAT model for crop yield prediction. Among these studies, Sarkar et al Harvest and Kill parameters (Kill on July 1) Default *Auto-irrigation was simulated in appropriate proportion of cotton area based on county cotton irrigation acreage summary reports.…”

Development and Application of Algorithms for Simulating Terraces within SWAT

“…The normal fraction N in sorghum grain was reduced by 30% to compensate for the nitrogen stress for grain sorghum. This adjustment was close to the calibrated percentage change (-26%) of winter wheat from Nair et al (2011). The fraction of porosity for anion exclusion was also decreased from 0.5 to 0.3 to keep nitrate from leaking out of soil profile.…”

“…The minimum and optimal temperatures for plant growth (table 6) were adjusted to reproduce the crop yield variations between years. Following Nair et al (2011), BIO_E was reduced from 25 to 20 for soybean and from 33.5 to 30 for grain sorghum. HI was adjusted to 0.22, which was lower than the value of 0.27 obtained by Nair et al (2011), who also reduced the maximum potential LAI in decreasing the soybean yield and somehow obtained the higher HI.…”

“…Following Nair et al (2011), BIO_E was reduced from 25 to 20 for soybean and from 33.5 to 30 for grain sorghum. HI was adjusted to 0.22, which was lower than the value of 0.27 obtained by Nair et al (2011), who also reduced the maximum potential LAI in decreasing the soybean yield and somehow obtained the higher HI. The normal fraction N in sorghum grain was reduced by 30% to compensate for the nitrogen stress for grain sorghum.…”

“…Therefore, parameters affecting temperature and nitrogen stress were selected for calibrating both crops separately (table 6). The adjustment ranges of these parameters were selected based on published literature (Nair et al, 2011;Peacock, 1982;Yu et al, 2004). It should be noted that there were very limited crop yield data (nine for grain sorghum and six for soybean), and the main purpose of crop yield calibration was to better represent the plant growth processes of the plots.…”