m4r4– A Collaborative Platform using GIS and Remote Sensing to

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					m4r4– A Collaborative Platform using GIS and Remote Sensing to Manage Extreme Events

                                        Chris S. Renschler

               National Center for Geographic Information and Analysis (NCGIA)
                     MCEER – Earthquake Engineering to Extreme Events
                           Dept. of Geography, University at Buffalo
                                      106 Wilkeson Quad
                                Buffalo, New York 14261, USA.

                                   Email: rensch@buffalo.edu

Extended Abstract

      Practical decision-making by disaster managers for assessing the impact of extreme events
on human activities often involves using environmental process models linked with Geographic
Information Systems (GIS) and latest remote sensing imagery. Optimum use of these data
gathering, GIScience and environmental modeling techniques for such decision-support requires
careful and coordinated consideration of how natural and man-made hazard processes, the
gathered observations, the modeling algorithms and related uncertainties are represented in data
and how the simulation models are used. To avoid wasting resources and time on inappropriate
data collection, improper model use, and the resulting poor decision-making, there is a pressing
need for implementing a scientific and functional framework within which to examine design,
development, implementation, and use geo-spatial assessment tools for disaster managers. To be
useful for researchers, and decision-makers, integrated environmental system simulation
approaches in natural and built environments must consider the spatial and temporal variability of
natural processes of events and their extremes.

      Collaborating researchers and decision-makers, if possible, want to utilize all and the latest
data sources that are available at variable scales. The concept and tools of this interdisciplinary,
multi-hazard research allow collaborative researchers and students to investigate and describe
representations of environmental system properties and processes of the natural and built
environments at various scales using process-based environmental models, GIS, and remote
sensing. Consequently, a holistic, integrated uncertainty framework was developed that supports
the development, evaluation and utilization of models for effective environmental decision-
support. Renschler (2006) proposed such an integrated framework combining a scaling theory
(Renschler, 2003), a geospatial data management tool (Renschler and Namikawa, 2005), and a
GIS-based environmental modeling interface (Renschler, 2003), allowing interdisciplinary
collaborators to efficiently handle and communicate the transformation of geospatial information
of properties and processes across scales.

      The framework integrates our fundamental understanding and ability to communicate how
we (a) represent the spatiotemporal variability, extremes, and uncertainty of environmental
properties and processes in the digital domain, how we (b) transform their spatiotemporal
representation across scales during data processing and modeling in the digital domain, and how
we design and develop tools for (c) geo-spatial data management and (d) geo-spatial process
modeling and implement them to effectively (e) support decision- and policy-making in natural
resources and hazard management at various spatial and temporal scales of interest. The
framework is applied to MCEER’s overall goal to enhance the resiliency of communities against
      Enhancing the resiliency of communities against multiple-hazards can be achieved through
improving engineering and management tools for critical infrastructure systems (lifelines such as
water supply, electric power, and hospitals) and emergency management functions. Resilience is
characterized by reduced probability of system failure, reduced consequences due to failure, and
reduced time to system restoration. The resiliency framework of Bruneau et al. (2003) allows a
community to define each measure of resilience in the context of four specific domains: technical,
organizational, social and economic.

      Besides, for the built environment or physical lifelines, MCEER’s resilience approach is
also valid for the interdependent exposure and recovery of ecological, economic, and socio-
cultural lifelines. For example, a healthy and ecologically functional coastal wetland or an
integrated planning of business and lifeline locations in communities along the coastal regions of
the US portion of the Gulf of Mexico enhance the communities’ resilience against multiple
coastal hazards, such as rapidly approaching hurricanes, earthquakes or tsunamis, and slow-onset
hazards such as sea level rise or coastal erosion.

       MCEER’s r4-approach - robustness, rapidity, resourcefulness, redundancy – plays a key
role in the described research effort by focusing and enhancing the collaborative research and
teaching activities in four core Extreme Event research areas: measuring, monitoring, modeling,
and managing (m4) natural properties and processes of Extreme events. The methodology and
implementation of the m4r4–Collaborative Platform within the Geospatial Project Management
Tool (GeoProMT) (Renschler and Namikawa, 2006) is currently under development. The m4r4–
Collaborative Platform will be fully integrated in the GeoProMT platform and enhance the
interdisciplinary understanding of Extreme Events, reduce their risk, and enhance the resilience
against multiple hazards. The project focuses on the analysis of scenarios to help reduce the
failure of key critical facilities and lifelines and determine the best paths for prompt recovery and
maximum resilience.


Bruneau, M., S.E. Chang, R.T. Eguchi, G.C. Lee, T.D. O'Rourke, A.M. Reinhorn, M. Shinozuka,
   K. Tierney, W.A. Wallace, and D. von Winterfeldt. 2003. A Framework to Quantitatively
   Assess and Enhance the Seismic Resilience of Communities. Earthquake Spectra 19 (4): 733-
Renschler, C.S., 2003. Designing geo-spatial interfaces to scale process models: The GeoWEPP
   approach. Hydrological Processes 17: 1005-1017.
Renschler, C.S., 2006. Spatial and Temporal Model Validation: Representing Landscape
   Properties and Processes across Scales. In: Voinov, A., Jakeman, A., Rizzoli, A., Eds.,
   Proceedings of the iEMSs Third Biennial Meeting: Summit on Environmental Modelling and
   Software. iEMSs, Burlington, VT, USA, July 2006.
Renschler, C.S., and Namikawa, L.M., 2005. The Need and Development for Dynamic Integrated
   GIS Enhancement and Support Tools (DIGEST) -- The Geospatial Project Management Tool
   (GeoProMT). Third International Workshop on Remote Sensing for Post-Disaster Response,
   Chiba University, Chiba, Japan, September 12-13, 2005

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