Publications

Moradkhani H., Nearing G., Abbaszadeh P., Pathiraja S. (2018), Fundamentals of Data Assimilation and Theoretical Advances. In: Duan et al. (eds), Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg, doi: 10.1007/978-3-642-40457-3_30-1.

Pathiraja, S., D. Anghileri, P. Burlando, A. Sharma, L. Marshall, and H. Moradkhani (2018), Insights on the impact of systematic model errors on data assimilation performance in changing catchments, Advances in Water Resources, 113,202-222.

Walls, M., Magliocca, N.R., and McConnell, V. (2018). Modeling coastal land and housing markets: Understanding the competing influences of amenities and storm risks. Ocean and Coastal Management, 157: 95-110. DOI: https://doi.org/10.1016/j.ocecoaman.2018.01.021.

Sarge, M. A., Daggett, S., & VanDyke, M. S.(2018). Using theory to inform water conservation in business communities: Formative  research from a chamber initiative. Applied Environmental Education & Communication, 17, 198-214. doi: 10.1080

Munasinghe, D., S. Cohen, Y.F. Huang, Y.P. Tsang, J. Zhang, and Z. Fang, (2018), Intercomparison of Satellite Remote Sensing-Based Flood Inundation Mapping Techniques, Journal of the American Water Resources Association, 54 (4): 834–846. https://doi.org/10.1111/1752-1688.12626

*Domangue, R., Mortazavi, B. (2018). Nitrate reduction pathways in the presence of excess nitrogen in a shallow eutrophic estuary. Environmental Pollution, 238, 599-606.

Johansen, R., Beck, R., Nowosad, J., Nietch, C., Xu, M., Shu, S., Yang, B., Liu, H., Emery, E., Reif, M. and Harwood, J., 2018. Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations. Harmful Algae, 76, pp.35-46.

Berhane, T.M., Lane, C.R., Wu, Q., Autrey, B.C., Anenkhonov, O.A., Chepinoga, V.V. and Liu, H., 2018. Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory. Remote Sensing, 10(4), p.580.

Pathiraja, S., H. Moradkhani, L. Marshall, A. Sharma, G, Geenens (2018), Data Driven Model Uncertainty Estimation in Data Assimilation, Water Resources Research, doi: 10.1002/2018WR022627.

Zhang, J., Y. Huang, D. Munasinghe, Z. Fang, Y. Tsang, and S. Cohen (2018), Comparative Analysis of Inundation Mapping Approaches for the 2016 Flood in the Brazos River, Texas, Journal of the American Water Resources Association, 54 (4), 820–833. https://doi.org/10.1111/1752-1688.12623 

Hameed, M., A. Ahmadalipour, H. Moradkhani (2018), Apprehensive Drought Characteristics over Iraq: Results of a Multidecadal Spatiotemporal Assessment, Geosciences, Special Issue Drought Monitoring and Prediction, 8, 8, 58.

Magliocca, N.R., McConnell, V., and Walls, M. (2018). Integrating global sensitivity approaches to deconstruct spatial and temporal sensitivities of complex spatial agent-based models. Journal of Artificial Societies and Social Simulation, 21(1). DOI: 10.18564/jasss.3625.

Huang, Y., Liu, H., Yu, B., Wu, J., Kang, E.L., Xu, M., Wang, S., Klein, A. and Chen, Y., 2018. Improving MODIS snow products with a HMRF-based spatio-temporal modeling technique in the Upper Rio Grande Basin. Remote Sensing of Environment, Volume 204, January 2018, Pages 568-582.

Ellis, E.C., Magliocca, N.R., Stevens. C.J., and Fuller, D.Q. (2018). Evolving the Anthropocene: Linking multi-level selection with long-term social-ecological change. Sustainability Science. DOI: 10.1007/s11625-017-0513-6

Abbaszadeh, P., H. Moradkhani, and H. Yan (2018), Enhancing Hydrologic Data Assimilation by Evolutionary Particle Filter and Markov Chain Monte Carlo , Advances in Water Resources, 111, 192-204, doi: 10.1016/j.advwatres.2017.11.011.