Ten members of the Hydrosystems Research Group attended the 2017 American Geophysical Union (AGU) Fall Meeting held December 11-15 in New Orleans.
Chinedum Eluwa presented a poster on Exploring Evidence of Land Surface Dynamics of River Basin Development in East Africa which explores the correlation of seasonal soil moisture and latent heat flux over currently dammed/irrigated areas on downwind precipitation in the East Africa region.
Sungwook Wi presented a poster showing a Framework for Human-Hydrologic System Model Development Integrating Hydrology and Water Management: Application to the Cutzamala Water System in Mexico. This poster presents a general approach to developing computational models of human-hydrologic systems where human modification of hydrologic surface processes are significant or dominant. The integrated modeling framework enables evaluation and simulation of model errors throughout the river basin, including errors in representation of the human component processes and represents an initial step towards fuller understanding and prediction of the many and varied processes that determine the hydrologic fluxes and state variables in real river basins.
Katherine Schlef gave a talk on the Projections of Flood Risk using Credible Climate Signals in the Ohio River Basin where she shows future projections of flood risk created by forcing a statistical model with projection of the teleconnections from general circulation models. She also compared the results using this method to the results of the traditional model chain that of using the historic trend.
Baptiste Francois gave a talk on Using Deep Learning to Assess Future Flood Magnitude and Frequency in the Semi-arid and Snowmelt-dominated Missouri River Headwater Catchments. In this talk he explored the utility of Deep Learning (DL) for assessing flood magnitude change under climate change through using multiple hidden layers within artificial neural networks (ANNs). He also compared ANN’s performance with outputs from two hydrological models of differing complexity (i.e. VIC, SAC-SMA) and evaluated the modeling capability of ANNs for three snow-dominated catchments that represent different flood regimes.
Hassan Khan presented a poster on The Effect of Climate Change and Transaction Costs on Performance of a Groundwater Market. In the poster he showed a developed multi-agent system model where individual benefits of each self-interested agent are maximized subject to bounds on irrigation requirements and water use permits. This economic model is coupled with a calibrated physically based groundwater model for the study region. He also showed that permitting farmers to trade results in increased economic benefits and reduced environmental violations but of course the benefits of trading are dependent on the total allocations and the resulting level of water demand. He also showed that high transaction costs can reduce the efficiency of the cap-and-trade system even below that of water quotas.
Umit Taner gave a presentation on the Holistic Uncertainty Analysis in River Basin Modeling for Climate Vulnerability Assessment. In his presentation he showed a holistic framework that allows analysis of climate, hydrologic and water management uncertainty in water resources systems analysis with the aid of a water system model designed to integrate component models for hydrology processes and water management activities. He also demonstrated this in a case study for the St. Croix Basin located at border of United States and Canada.
Sarah Freeman presented a poster on Designing Freshwater Resilience for the Mexico City Metropolitan Area. In this poster the Mexico City Metropolitan Area (MCMA) and the Cutzamala Water supply system were used to demonstrate a quantitative framework to evaluate investment strategies which seek resilience for the water supply system of MCMA. She also evaluated the best performing investment portfolios across different resilience performance metrics which encompass social equity, environmental and economic objectives using a multi-objective optimization and decisions under deep uncertainty approaches. Finally, she used novel data visualizations to translate complexities of the study results into actionable information for decision makers.