Spatial Decision Making and Analysis for Flood Forecasting
The application of flood forecasting models requires the efficient management of large spatial and temporal datasets, involving data acquisition, storage, processing, analysis and display of model results. Difficulty in linking data, analysis tools, and m
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Spatial Decision Making and Analysis for Flood Forecasting Lei Wang and Xin Zhang
Abstract The application of flood forecasting models requires the efficient management of large spatial and temporal datasets, involving data acquisition, storage, processing, analysis and display of model results. Difficulty in linking data, analysis tools, and models is one of the barriers to be overcome in developing an integrated flood forecasting system. The current revolution in technology and the online availability of spatial data facilitate Canadians’ need for information sharing in support of decision making. This need has resulted in studies demonstrating the suitability of the web as a medium for implementation of flood forecasting. Web-based Spatial Decision Support Services (WSDSS) provides comprehensive support for information retrieval, model analysis and extensive visualization functions for decision-making support and information services. This chapter develops a prototype WSDSS that integrates models, analytical tools, databases, graphical user interfaces, and spatial decision support services to help the public and decision makers to easily access flood and flood-threatened information. Flood WSDSS helps to mitigate flood disasters through river runoff prediction, flood forecasting, and flood information (flood discharge, water level and flood frequency) dissemination. The ultimate aim of this system is to improve access to flood model results by the public and decision makers. Keywords Flood forecasting • GIS • Web • Spatial decision support system
L. Wang (*) Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China Hainan Province Key Laboratory of Earth Observation, Sanya Institute of Remote Sensing, Sanya 572029, Hainan Province, China e-mail: [email protected] X. Zhang State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China e-mail: [email protected] © Springer International Publishing Switzerland 2017 X. Zhang et al. (eds.), Modeling with Digital Ocean and Digital Coast, Coastal Research Library 18, DOI 10.1007/978-3-319-42710-2_6
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L. Wang and X. Zhang
Introduction
Chapters 4 and 5 have applied SCS CN model for runoff prediction, LP3, GEV, PL and GP models for flood frequency analysis and prediction, and the CA and singularity fractal model for flood threshold selection and characteristics description. However, these flood forecasting models are not accessible to the public and decision-makers. Due to the background difference, it is very difficult for the public and decision-makers to use these models directly for flood forecasting and management as they require familiarity with flood forecasting models and analysis tools. Difficulty in linking data and analysis tools and models is one of the barriers to be overcome in developing an integrated flood forecasting system. The main reason for developing a new system and tool is that the public and decision mak
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