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Reliability of RZWQM model predictions: Model calibration for performance and robustness.

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2018 ASABE Annual International Meeting  1800369.(doi:10.13031/aim.201800369)
Authors:   Robert P Anex, Lei Gu, Matthew Helmers
Keywords:   Climate change, Inverse modeling, Palmer Drought Severity Index (PDSI), Prediction robustness, RZWQM, PEST..

Abstract. Models like the RZWQM play an important role in synthesizing observations under conditions for which no experimental data are available. The RZWQM has been used to make predictions of the impact of management practices on the movement of nitrate and pesticides to runoff and through tile drainage systems. Typically, if the calibrated model replicates measured field data reasonably well it is assumed that predictions under other conditions will be reasonably accurate. Unfortunately, this assumption is not always correct, as we have shown for prediction of nitrate loss from a tile-drained, corn-soybean experiment in Northern Iowa. Predictive uncertainty is only reduced by model calibration if the information content of the calibration dataset is able to constrain the model parameters relevant to the processes controlling the desired prediction.

Using experimental data over 12 years, we investigated the robustness of RZWQM predictions of crop yield, subsurface drainage flow, and nitrate-N loss for multiple model calibrations using the PEST parameter estimation software. We tested the use of the Palmer Drought Severity Index (PDSI) as an indicator of the soil moisture related information content of calibration data. Using the PDSI, we identified a single year‘s observations that when added to a five-year calibration improved the Nash-Sutcliffe model efficiency coefficient (NSE) from -0.22 to 0.7. We suggest using the range of PDSI as a metric in evaluating the suitability of RZWQM calibrations for making predictions about impacts under unobserved climate conditions.

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