Natural Resources, School of


Date of this Version

Fall 12-2014


Okalebo, J.A. 2014. Decision Support Tools to Address Climate Change: Climate Model - Land Surface Models, Zea mays L. (Corn) Phenology and Evapotranspiration-Yield Sensitivity Models For Nebraska, USA. (Doctoral dissertation). University of Nebraska-Lincoln.


A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Natural Resource Sciences (Climate Assessment and Impacts), Under the Supervision of Professors Kenneth Hubbard and Ayse Kilic. Lincoln, Nebraska: December, 2014

Copyright (c) 2014 Jane Asiyo Okalebo


Nebraska's climate is highly variable and is expected to change in the future with anthropogenic global warming (AGW), resulting in warmer spring and summer temperatures coupled with more erratic rainfall events. This has strong implications for agriculture in the region, yet it is not clear that current modeling and decision-support tools are adequate to address these looming changes and provide planning, mitigation and adaptation strategies. To address climate change and its implications to agriculture in Nebraska, a set of robust decision support tools are very crucial. This study herein are divided into three chapters, with each chapter addressing a specific tool/s and its usefulness as a support decision tool. The first chapter, examines climate models and land surface models that provide weather forecasts. The usefulness of climate models and land surface models (LSM) hinges on their accuracy. Two candidate LSMs were evaluated: the Noah and the Community Land Surface Model (Version 3.5). The findings are helpful in selecting useful models that can be applied to make weather predictions in the near future for yield predictions and decision making. The second chapter examines the current modeling of phenological sensitivity and development of corn to temperature using thermal units also known as, Growing Degree Days (GDDs) based on an upper and lower temperature threshold of 30°C and 10°C respectively. Additionally, the accuracy of closest weather station data in modelling corn phenology for rainfed and irrigated sites was evaluated. In the third chapter the sensitivity of corn to water stress during different growth periods/stages is examined with the intention of supporting irrigation scheduling decisions with limited water resources. Since crops are not equally sensitive to growth in all stages of their development, multiplicative empirical models are developed using two approaches. The new sensitivity coefficients are also compared to those derived for the USA cornbelt by Meyer et al. (1993). The models developed will facilitate analysis of deficit irrigation strategies and their impacts on crop yields thereby offering a means of sustaining high corn yields in the future in lieu of imminent climate changes.

Co-Advisers: Kenneth Hubbard and Ayse Kilic