Abstract
Many cities face tremendous water-related challenges in this Century of the City. Urban areas are particularly susceptible not only to excesses and shortages of water but also to impaired water quality. Even moderate rainfall can quickly fill and overflow urban water courses. To addresses these challenges, we will over the coming 4 years synergistically integrate advances in computing and cyber-infrastructure, environmental modeling, geoscience, and information science to develop integrative solutions for urban water challenges that will change the way municipalities and stakeholders plan and manage their actions, resources and civil infrastructure for sustainable cities. We will develop a system empowered by distributed computing and cyber-infrastructure for integrative sensing, high-resolution modeling and uncertainty-assessed prediction of water quantity and quality for a large urban area. The resulting system will enable multi-scale and multi-dimensional risk-based decision making related to threats and risks associated with urban water to a wide spectrum of users and stakeholders, and advance general understanding of urban sustainability and associated challenges through environmental, social and economic response of a large city as an uncertain dynamic system to extreme precipitation, urbanization and climate change. This paper details this vision by providing a blueprint for the development of iSPUW: Integrated Sensing and Prediction of Urban Water for sustainable cities.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Peirce, N.R., Johnson, C.W.: Century of the City: No Time to Loose. Rockefeller Foundation, New York (2009)
Brown, P.: The changing face of urban water management. Water 21, 28–30 (2009)
Daigger, G.T.: A vision for urban water and wastewater management in 2050. In: Grayman, W.M., Loucks, D.P., Saito, L. (eds.) Toward A Sustainable Water Future: Visions For 2050, pp. 113–121. American Society of Civil Engineers, Washington (2012)
Grigg, N.: Water Finance: Public Responsibilities and Private Opportunities. Wiley, Hoboken (2011)
Tucci, C.E.M.: Integrated urban water management in large cities: a practical tool for assessing key water management issues in the large cities of the developing world. World Bank (2009)
Stocker, T.F., et al.: Technical summary. In: Stocker, T.F., et al. (eds.) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York (2013)
National Research Council: Weather Radar Technology Beyond NEXRAD. The National Academies Press, Washington (2002)
Chandrasekar, V., Lim, S.: Retrieval reflectivity in a networked radar environment. J Atmos. Oceanic Technol. 25(10), 17555–17567 (2008). doi:10.1175/2008JTECHA1008.1
Wang, Y.T., Chandrasekar, V.: Algorithm for estimation of the specific differential phase. J Atmos Oceanic Technol. 26(12), 2565–2578 (2009). doi:10.1175/2009JTECHA1358.1
Cifelli, R., Chandrasekar, V., Lim, S., Kennedy, P.C., Wang, Y., Rutledge, S.A.: A new dual-polarization radar rainfall algorithm: application in Colorado precipitation events. J Atmos Oceanic Technol. 28(3), 352–364 (2011). doi:10.1175/2010JTECHA1488.1
Kerkez, B., Zhao, Y.: A machine-to-machine architecture for the real-time study of urban watersheds. In: AGU Fall meeting 2013, San Francisco, CA (2013)
Robinson, D.A., Campbell, C.S., Hopmans, J.W., Hornbuckle, B.K., Jones, S.B., Knight, R., Ogden, F., Selker, J., Wendroth, O.: Soil moisture measurement for ecological and hydrological watershed-scale observatories: a review. Vadose Zone J. 7(1), 358 (2008)
Seo, D.-J., Herr, H., Schaake, J.: A statistical post-processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction. Hydrol. Earth Syst. Sci. Discuss. 3, 1987–2035 (2006)
Seo, D.-J., Seed, A., Delrieu, G.: Radar-based rainfall estimation. In: Testik, F., Gebremichael, M. (eds.) AGU Book Volume on Rainfall: State of the Science. Wiley, Hoboken (2010)
Seo, D.-J., Siddique, R., Zhang, Y., Kim, D.: Improving real-time estimation of heavy-to-extreme precipitation using rain gauge data via conditional bias-penalized optimal estimation. J. Hydrol. 519, 1824–1835 (2014)
Koren, V., Reed, S., Smith, M., Zhang, Z., Seo, D.-J.: Hydrology laboratory research modeling system (HL-RMS) of the US national weather service. J. Hydrol. 291(3–4), 297–318 (2004)
Rossman, L.A.: Storm Water Management Model User’s Manual, EPA/600/R-05/040. U.S Environmental Protection Agency, Cincinnati, OH (2007)
Evensen, G.: Sequential data assimilation with nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99(C5), 143–162 (1994)
Zupanski, M.: Maximum likelihood ensemble filter: theoretical aspects. Mon. Weather Rev. 133, 1710–1720 (2005)
Pokhrel, P., Yilmaz, K.K., Gupta, H.V.: Multiple-criteria calibration of a distributed watershed model using spatial regularization and response signatures. J. Hydrol. 418–419, 49–60 (2012)
Weerts, A.H., El Serafy, G.Y.H.: Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models. Water Resour. Res. 42, W09403 (2006)
Granger, C.W.J.: Testing for causality: a personal viewpoint. J. Econ. Dyn. Control 2, 329–352 (1980)
Liu, Y., Niculescu-Mizil, A., Lozano, A.C., Lu, Y.: Learning temporal causal graphs for relational time-series analysis. In: Proceedings of the 27th International Conference on Machine Learning, pp. 687–694 (2010)
Pearl, J.: Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge (2009)
Claassen, T., Heskes, T.: A bayesian approach to constraint based causal inference. In: Proceedings of the 28th Annual Conference on Uncertainty in Artificial Intelligence, pp. 207–216 (2012)
Acknowledgments
This material is based upon work supported by the National Science Foundation under Grant No. IIP-1237767 and CyberSEES-1442735.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Seo, DJ., Kerkez, B., Zink, M., Fang, N., Gao, J., Yu, X. (2015). iSPUW: A Vision for Integrated Sensing and Prediction of Urban Water for Sustainable Cities. In: Ravela, S., Sandu, A. (eds) Dynamic Data-Driven Environmental Systems Science. DyDESS 2014. Lecture Notes in Computer Science(), vol 8964. Springer, Cham. https://doi.org/10.1007/978-3-319-25138-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-25138-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25137-0
Online ISBN: 978-3-319-25138-7
eBook Packages: Computer ScienceComputer Science (R0)