||Technology behind FMIS
“GIS is a system of hardware, software and procedures designed to support the capture, management, manipulation, analysis, modeling and display of spatially referenced data for solving complex planning and management problems.”
- Rhind (1989)
“GIS is a computer system capable of assembling, storing, manipulating and displaying geographically referenced information, i.e. data identified according to their locations.”
- USGS (United States Geological Survey) (1997)
Remote Sensing usually refers to the gathering and processing of information about earth’s environment, particularly its natural and cultural resources, through the use of photographs and related data acquired from an aircraft or a satellite.
-Simonett, D.S. (1983)
Remote Sensing Systems offer four basic components to measure and record data about an area from a distance. These components include the energy source, the transmission path, the target and the satellite sensor.
Remote sensing provides important coverage, mapping and classification of land cover features, such as vegetation, soil, water ,forests etc.
This provides important information for land classification and land-use management.
Remote sensing is an interesting and exploratory science, as it provides images of areas in a fast and cost-efficient manner, and attempts to demonstrate the "what is happening right now" in a study area. Satellite and digital imagery acquired recently, provide more overall detail to assist the researcher in the classification process.
MM5 Rainfall forecast Model
The Indian region experiences a wide variety of weather systems such as the monsoons, tropical cyclones, western disturbances and heavy rainfall episodes involving severe thunderstorms. In order to study these systems, the MM5 model is run at a nested 90, 30 and 10 km resolutions on realtime basis over the Indian region on a CRAY-SV1 machine.
Using high-speed computer to tackle the computational demand, numerical weather prediction (NWP) is the technique used to forecast weather by solving a set of equations within a numerical model that describes the evolution of meteorological variables representing the atmospheric state. These variables include temperature, wind, pressure and moisture content.
In the model, the overall atmospheric state at any given instant is represented by the values of the variables at systematically arranged points set up within a three-dimensional grid. The larger the set of grid points, the higher the computational demand, the finer the model resolution and the more details in the future state of the atmosphere can be described.
Approximations and assumptions are made in the governing equations and representation of the physical processes. To solve the set of governing equations, initial conditions have to be properly represented and set up using the latest information. In practice, the initial state of the atmosphere is analyzed by taking a previous short-range model forecast, ingesting the latest meteorological observations to update the situation, and then starting a new forecast cycle.
Methods for Formulating the Real Time Flood Forecast
The methods for formulating the real time flood forecast may be categorized under two groups: (i) statistical and (ii) deterministic methods
(A) Statistical methods
Methods base on statistical approach makes use of the statistical techniques, can be presented either in the form of graphical relations or mathematical equations. A large number of data, covering a wide range of conditions are analyzed to derive the relationships which inter-alia include gauge to gauge relationship with or without additional parameter and rainfall peak stage relationships. These methods are more commonly used by Central Water Commission to issue real time flood forecast in India.
Multi tributary Difference in Gauge model
A discrete, linear, time-invariant model has been developed by Chander, S. and et. al. for operational flood forecast of river Brahmaputra at Dibrugarh. This model is based on the difference of the gauge reading at the forecasting station and the upstream base station in the tributary. The use of differences of gauge readings as input in the model takes care of the aggradation or degradation of the river bed of the tributary and the main river.
(B) Deterministic methods
SSARR (Stream flow Synthesis and Reservoir Regulation) model, Sacramento model, NAM-System 11 FF model and SWAT are some of the conceptual watershed models for formulating the real time flood forecast. However, these conceptual models are not being utilized on operational basis because of inadequacy of data and problems associated with the technical capability of the organizations.
Unit Hydrograph based models: In India, the applications of unit hydrograph technique are restricted to the catchments of sizes less than 5000 Sq. Km. However, for the catchments of sizes more than 5000 Sq. Km., a network model is developed. In this model the catchment is divided into sub-catchments and the main river is divided into sub-reaches, considering the two consecutive nodes. The nodes are the points where the tributary of the sub-catchments join the main river. The principle of unit hydrograph is applied for converting the excess rainfall to direct surface runoff for each sub-catchment considered as lateral flow to the river and the flood routing technique is used for routing the direct surface runoff at the upstream node through the river sub-reaches up to the downstream node. The computations are performed for the network model structure to estimate the direct surface runoff at the outlet of the catchment. HEC-1 model has an option of the network model simulation using these concepts. This model uses the unit hydrograph technique with constant loss rate to forecast the runoff. Forecasting is accomplished by re-estimating the unit hydrograph parameters and the loss rate parameters as additional rainfall runoff data are reported and using these updated parameters the future flows are estimated for forecasting. Gosain (1984) has used a Unit Hydrograph based real-time flood forecast model on River Yamuna. The same will be used on Bagmati system.
SWAT is the acronym for Soil and Water Assessment Tool, a river basin, or watershed, scale model developed by Dr. Jeff Arnold for the USDA Agricultural Research Service (ARS). SWAT was developed to predict the water and sediment yield in large complex watersheds with varying soils, land use and management conditions. SWAT is a continuous time model. The model has a real-time flood forecasting component also. The present version of the model has a interface on ArcGIS. The model is a public domain model
HEC-RAS is an integrated package of hydraulic analysis programs, in which the user interacts with the system through the use of a Graphical User Interface (GUI). The system is capable of performing Steady and Unsteady Flow water surface profile calculations, and will include Sediment Transport and several hydraulic design computations in the future.
HEC-RAS has the ability to import three-dimensional (3D) river schematic and cross section data created in a GIS or CADD system. While the HEC-RAS software only utilizes two-dimensional data during the computations, the three-dimensional information is used in the program for display purposes. After the user has completed a hydraulic analysis, the computed water surface profiles can be exported back to the GIS or CADD system for development and display of a flood inundation map. The HEC has developed an Arc View GIS extension called GeoRAS that was specifically designed to process geospatial data for use with HEC-RAS. The Geo-RAS software allows a user to write geometric data to a file in the required format for HEC-RAS. HEC-RAS contains five optional methods for specifying floodplain encroachments. This is again a public domain model and shall be used for assessing flood inundation areas.
MIKE 11 cross-section data option allows the user to import data from the MIKE11/Flood Program after converting to Raw Text file format. MIKE 11 is a one–dimensional river hydraulics model developed by Danish Hydraulic Institute but is a licensed software. This model can also be used but needs to be procured.
ANN Model Another class of Black Box models in the form of Artificial Neural Network (ANN) has been introduced in modeling real time problems wherein the nonlinear relationship between the rainfall and runoff process is modeled. The use of ANN in real time flood forecasting is of very recent origin and shall be used on the Bagmati basin.