Her, Young Gu

Hydrologist / Agricultural Engineer

Dr. Her is an assistant professor of hydrology in the Agricultural and Biological Engineering (ABE) Department and the Tropical Research and Education Center (TREC) at the University of Florida (UF). He specializes in hydrologic and water quality modeling, best management practices including agricultural conservation and urban low impact development practices, uncertainty analysis, and geospatial data analysis. His research focuses on quantifying the effects of climate variability and sea level rise on agriculture and water resources and developing mitigation and adaptation strategies for the global and regional scale changes.


“Modeling is an extrapolation from incomplete data”



"Essentially, all models are incomplete, but many are useful"

Modified from George E. P. Box

  • A MS student, Redjino joined us in 2017 August.

    • Redjino joined TREC in Homestead in 2018 August

    • He started streamflow and sediment monitoring in Haiti from 2018 August.

    • He has been preparing a watershed model and crop model to simulate the impacts of deforestation on agricultural productivity.

  • A PhD student, Satbyeol joined us in 2018 January.

    • Satbyeol submitted her first research paper (at UF) about integrated sediment modeling to a peer-reviewed journal (JAWRA) in 2018 September.

    • She began developing an R package for hydrological frequency analyses.

    • She has been trying to quantify the amount of water and nutrient getting in Lake Okeechobee using two watershed models, SWAT and WAM.

  • A post-doc, Junghun joined us in 2018 April.

    • He submitted a research paper about parsimonious rainfall-runoff modeling to a peer-reviewed journal (Journal of Hydrology) in 2018 August.

    • He published a research paper about the meteorological impacts of a large dam in a peer-reviewed journal (Weather).

    • Junghun has been trying to quantify the hydrological ecosystem service using a two-dimensional hydrologic model.

Comparison of uncertainty in parameters and GCMs

The quantification of uncertainty in the ensemble-based predictions of climate change and the corresponding hydrological impact is necessary for the development of robust climate change adaptation plans. Although the equifinality of hydrological modeling has been discussed for a long time, its influence on the hydrological analysis of climate change has not been studied enough to provide clear ideas about the relative contributions of uncertainty contained in both multi-GCM and multi-parameter ensembles to the projections of hydrological components. We demonstrated that uncertainty in multi-GCM ensembles could be an order of magnitude larger than that of multi-parameter ensembles for direct runoff projections, suggesting that the selection of appropriate GCMs should be much more emphasized than that of a parameter set among behavioral ones. When projecting soil moisture and groundwater, on the other hand, the hydrological modeling equifinality was more influential than the multi-GCM ensemble uncertainty. In addition, uncertainty in a hydrological simulation of climate change impact was much more closely associated with uncertainty in the ensemble projections of precipitation than temperature, indicating a need to pay closer attention to the precipitation data for improved modeling reliability. Uncertainty in the ensemble projections of the individual hydrological components showed unique responses to uncertainty in the precipitation and temperature ensembles. This study also proposed a framework to quantify the contributions of individual GCMs incorporated towards the overall uncertainty of hydrological climate change assessment.

Time series of precipitation projected weather gage stations in Florida

Recently, the Intergovernmental Panel on Climate Change (IPCC) launched the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the fifth Assessment Report (AR5), whereby a multiple general circulation model (multi-GCM) ensembles analysis was facilitated through the provision of climate model outputs that comply with community standards. The multi-GCM ensembles have served as a framework for accommodating probabilistic approaches in interpretation of climate change predictions and decision-making processes, and ensemble averaging can improve the accuracy of a climate projection by allowing GCM errors cancel each other out. The latest project output, CMIP5 has known as the most advanced generation of climate models, and thus it is desired to use them in new climate change impact studies. However, it has not been extensively utilized in climate change impact analysis yet due to the relatively short exposure to the public. Many climate models and their projections are currently available, but climate change studies have employed only a few of them depending on the preference of a modeler and the ease of use, which leads to the neglect or underestimation of uncertainty and bias in the ensemble predictions of climate change. In addition, there is no study identifying GCMs that is the most appropriate to Florida in terms of the reliability and accuracy as Florida has extreme regional and localized weather events such as hurricanes and heavy rains. This study is developing an ensemble of comprehensive climate projections for Florida using the up-to-date climate models and scenarios.