Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability.

Despite efforts to collect genomics and phenomics ('omics') and environmental data, spatiotemporal availability and access to digital resources still limit our ability to predict plants' response to changes in climate. Our goal is to quantify the improvement in the predictability of maize yields by enhancing climate data. Large-scale experiments such as the Genomes to Fields (G2F) are an opportunity to provide access to 'omics' and climate data.

Developing a novel parameter-free optimization framework for flood routing.

The Muskingum model is a popular hydrologic flood routing technique; however, the accurate estimation of model parameters challenges the effective, precise, and rapid-response operation of flood routing. Evolutionary and metaheuristic optimization algorithms (EMOAs) are well suited for parameter estimation task associated with a wide range of complex models including the nonlinear Muskingum model. However, more proficient frameworks requiring less computational effort are substantially advantageous.

Environmental water demand assessment under climate change conditions.

Measures taken to cope with the possible effects of climate change on water resources management are key for the successful adaptation to such change. This work assesses the environmental water demand of the Karkheh river in the reach comprising Karkheh dam to the Hoor-al-Azim wetland, Iran, under climate change during the period 2010-2059. The assessment of the environmental demand applies (1) representative concentration pathways (RCPs) and (2) downscaling methods.