Calibration of a crop model to irrigated water use using a genetic algorithm

Bulatewicz, Tom; Jin, Wu; Staggenborg, Scott A.; Lauwo, S. Y.; Miller, Michelle; Das, Sanjoy; Andresen, Daniel; Peterson, Jeffrey M.; Steward, David R.; Welch, Stephen M.

doi:10.5194/hess-13-1467-2009

Cited by 20 publications

(8 citation statements)

References 78 publications

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“…These methods have been increasingly applied to parameter optimizations in various hydrological models (Bastani et al, 2010;Bulatewicz et al, 2009;Uddameri and Kuchanur, 2007) and to those in numerical weather predictions (Fang et al, 2009;Krishnakumar, 1989;Lee et al, 2006;Yu et al, 2013). Micro-GA applied to this study is an improved version of GA with smaller generation sizes and simplified genetic modifications, hence efficiently reducing the computational resources (Krishnakumar, 1989;Reeves, 1993;Wang et al, 2010).…”

Section: Micro-gamentioning

confidence: 99%

Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm

Hong

Park

et al. 2014

Geosci. Model Dev.

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Abstract. Optimization of land surface models has been challenging due to the model complexity and uncertainty. In this study, we performed scheme-based model optimizations by designing a framework for coupling "the micro-genetic algorithm" (micro-GA) and "the Noah land surface model with multiple physics options" (Noah-MP). Micro-GA controls the scheme selections among eight different land surface parameterization categories, each containing 2-4 schemes, in Noah-MP in order to extract the optimal scheme combination that achieves the best skill score. This coupling framework was successfully applied to the optimizations of evapotranspiration and runoff simulations in terms of surface water balance over the Han River basin in Korea, showing outstanding speeds in searching for the optimal scheme combination. Taking advantage of the natural selection mechanism in micro-GA, we explored the model sensitivity to scheme selections and the scheme interrelationship during the micro-GA evolution process. This information is helpful for better understanding physical parameterizations and hence it is expected to be effectively used for further optimizations with uncertain parameters in a specific set of schemes.

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Section: Micro-gamentioning

confidence: 99%

Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm

Hong

Park

et al. 2014

Geosci. Model Dev.

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“…We previously calibrated the model for use in western Kansas (Bulatewicz et al, 2009) and further refined the parameters to support nonirrigated cropping for this study. The model component, developed in an earlier effort (Bulatewicz et al, 2014) and implemented using the simple model wrapper (Castronova and Goodall, 2010), has an embedded set of simulated output data from EPIC collected by executing the model for all combinations of the relevant inputs (soil, crop, management, weather).…”

Section: Crop Production Modelmentioning

confidence: 99%

Conserving the Ogallala Aquifer in southwestern Kansas: from the wells to people, a holistic coupled natural–human model

Aistrup¹,

Bulatewicz²,

Kulcsár³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The impact of water policy on conserving the Ogallala Aquifer in Groundwater Management District 3 (GMD3) in southwestern Kansas is analyzed using a system-level theoretical approach integrating agricultural water and land use patterns, changing climate, economic trends, and population dynamics. In so doing, we (1) model the current hyper-extractive coupled natural-human (CNH) system, (2) forecast outcomes of policy scenarios transitioning the current groundwater-based economic system toward more sustainable paths for the social, economic, and natural components of the integrated system, and (3) develop public policy options for enhanced conservation while minimizing the economic costs for the region's communities. The findings corroborate previous studies showing that conservation often leads initially to an expansion of irrigation activities. However, we also find that the expanded presence of irrigated acreage reduces the impact of an increasingly drier climate on the region's economy and creates greater long-term stability in the farming sector along with increased employment and population in the region. On the negative side, conservation lowers the net present value of farmers' current investments and there is not a policy scenario that achieves a truly sustainable solution as defined by Peter H. Gleick. This study reinforces the salience of interdisciplinary linked CNH models to provide policy prescriptions to untangle and address significant environmental policy issues.

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“…(2) dry matter production and partitioning to plant tissues resulting in growth; and 3 We previously calibrated the model for use in western Kansas (Bulatewicz et al, 2009) and further refined the parameters to support non-irrigated cropping for this study. The model component, developed in an earlier effort (Bulatewicz et al, 2014) and implemented using the Simple Model Wrapper (Castronova and Goodall, 2010), has an embedded set of simulated output data from EPIC collected by executing the model for all combinations of the relevant inputs (soil, crop, management, weather).…”

Section: Crop Production Modelmentioning

confidence: 99%

Sustaining the Ogallala Aquifer: From the Wells to People, A Holistic CNH Model

Aistrup

Bulatewicz

Kulcsár

et al. 2017

Preprint

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Abstract. The impact of water policy on sustaining the Ogallala Aquifer is analyzed using a system-level theoretical approach integrating agricultural water and land use tendencies, changing climate, economic trends, and population dynamics. In so doing, we 1) model the current hyper-extractive coupled natural-human system (CNH), 2) forecast future outcomes of policy scenarios transitioning the current groundwater-based economic system toward more sustainable paths for the social, economic and natural components of the integrated system, and 3) communicate model projections to inform public policies for enhanced sustainability while minimizing the economic pain for the region's communities. The findings corroborate previous studies showing that conservation often leads initially to an expansion of irrigation activities. However, we also find that the expanded presence of irrigated acreage will reduce the impact of an increasingly dryer climate on the region's economy and create greater long-term stability in the farming sector along with increased employment and population in the region. The primary negative aspect of more extensive conservation policies are on the net present value of farmers' current investments in their operations. This study reinforces the salience of interdisciplinary linked CNH models to provide policy prescriptions to untangle and address significant environmental policy issues.

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Calibration of a crop model to irrigated water use using a genetic algorithm

Cited by 20 publications

References 78 publications

Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm

Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm

Conserving the Ogallala Aquifer in southwestern Kansas: from the wells to people, a holistic coupled natural–human model

Sustaining the Ogallala Aquifer: From the Wells to People, A Holistic CNH Model

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