Most of the river basins in the developing world, even today, are either undeveloped or very unsuitable. Similarly for meteorological observations, it is found that most of the rain gauges remain non-functional due to various logistical and technical issues.
Rainfall measurements from space based platform
RAINFALL PREDICTION TECHNIQUES
Qualitative rainfall prediction by synoptic weather monitoring
The data from both the channels should be studied in synchronization for better estimation of cloud properties over the area. This information helps to decide whether a particular system (of cloud) will arrive or depart from the area of concern within a certain time interval.
Quantitative rainfall forecasting by Numerical Weather Prediction (NWP) Recent advances in technology have enabled rainfall forecast and
It has been subjected to real-time weather prediction tests over several regions since the mid-90s by researchers such as Droegemeier et al. This group of agencies consists of the National Center for Atmospheric Research's (NCAR) Mesoscale and Micro scale Meteorology (MMM) Division, the National Oceanic and Atmospheric Administration's (NOAA), the National Center for Environmental Prediction (NCEP), and the Earth System Research Laboratory (ESRL). The Department of Defense's Air Force Weather Agency (AFWA) and the Naval Research Laboratory (NRL), the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma and the Federal Aviation Administration (FAA) in the United States.
HYDROLOGICAL RAINFALL RUNOFF MODELS FOR FLOOD PREDICTION
Lumped models for run-off computations
As studied by Cavallini in 1993, the unit hydrograph with hod has the advantage of providing a complete storm hydrograph to describe the rainfall runoff relationship. Several other methods based on the concept of Unit Hydrograph are also being developed by different investigators to study rainfall-runoff behavior.
Distributed models for run-off computations
To compensate for the coarseness of the discretization, VIC is unique in its incorporation of sub-grid variability to describe variations in land parameters as well as precipitation distribution. 1997, the calibrated values of parameters are also related to the grid size used in the digital terrain analysis.
USE OF TOPOGRAPHIC INFORMATION FOR RIVER CHANNEL AND FLOOD PLAIN STUDIES
Sources of topographic Information
Contour survey data are the most primitive form of topographic information for various types of flood-related studies. It is also one of the most accurate topographical information in terms of vertical accuracy.
Globally available coarse Digital Elevation Models
In recent years, there has been an extraordinary development of new technologies for obtaining topographical information about altitude. As the same needs to be studied for rivers laden with friable alluvial sediments like that of the Brahmaputra valley, ASTER was taken as the global course DEM for its comparison with local finer DEMs to extract river cross-sections as part of this research work.
Locally generated moderate and fine resolution Digital Elevation models
Regarding the applicability of the ASTER DEM for the extraction of geomorphic features from floodplains, Kamp et al. However, whether the same view applies to lower elevation floodplains, such as that of the Brahmaputra Valley, needs to be investigated and is in the present research work attempted.
FLOOD HAZARD ZONATION TECHNIQUES
Flood hazard zonation by occurred inundation records
The simplest form of flood hazard categorization is time series analysis of historical flood maps in a GIS platform. The time series of these annual inundation maps were overlaid in a GIS platform to produce flood hazard maps.
Flood hazard zonation by flood inundation modelling based on flood frequency
In this approach, the degree of hazard zoning depends on the degrees of the input flood maps. This model has been widely used for floodplain inundation modeling for hazard zoning based on flood frequency.
Flood hazard zonation by multiple criteria
2008 attempted a multiparametric approach using an analytical hierarchy to generate a flood risk index (FRI) in the Kosi River basin, where proximity to an active channel was used as one of the parameters.
IN CONCLUSION OF LITERATURE REVIEW
Delineation of flood hazard areas or scientific identification of flood risk areas is another relatively new area of research for long-term flood risk reduction management planning. It was noted that a very limited number of researchers have attempted this third approach of multi-criteria zonal flood risk determination. It was noted that many combinations of these parameters have not yet been tested, so there is a huge amount of work to be done on this third approach to flood hazard zoning.
THE STUDY AREA
INTRODUCTION
The length of embankments constructed in this region to provide temporary protection against floods is 28% of the length in all of India (Sarma, 2004). The water yield in the Brahmaputra basin is among the highest in the world (Goswami, 1985). Second, the problem of drainage congestion, especially near the mouths of the tributaries during high floods of the river.
THE PAGLADIA RIVER AND ITS WATERSHED
- The basin description
- The Pagladia river system
- Meteorology of Pagladia river basin
- Hydrology and flood frequency of Pagladia river basin
- The river channel and floodplain morphometry
The floodplain of the Pagladia River starts at a place known as Thalkuchi where the main channel of the river is joined by two more rivers namely Mutanga and Nona. Among them the upstream part of the floodplain, ie. the upper middle reach and the lower middle reach of the river, the occurrence of floods is found mainly due to excess runoff and insufficient channel capacity downstream of the confluences. But the lower part of the floodplain towards the river outlet with the river Brahmaputra.
THE JIADHAL RIVER AND ITS WATERSHED
- The basin description
- The Jiadhal river system
- Meteorology of the Jiadhal river basin
- Hydrology and flood frequency of Jiadhal river basin
- The river channel and flood plain morphometry
The lower part of the sub-basin falls within the floodplain alluvial formation on the northern bank of Brahmaputra. Although many tributaries join the river in this range, the two tributaries namely Sika and Sido meet the Jiadhal in Arunachal Pradesh at a place called Tinmukh. In this catchment, hypsometry analysis shows that approximately 65% of the total catchment area belongs to the flat (90 m to 250 m) alluvial floodplains.
RAINFALL FORECAST BY WRF NUMERICAL WEATHER PREDICTION MODEL
INTRODUCTION
- The model description
Accurate rainfall forecasting has been the key to successfully predicting flood discharge, as researchers around the world believe. The WRF effort was a partnership between the National Center for Atmospheric Research (NCAR) Division of Mesoscale and Microscale Meteorology (MMM), the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA), and the Earth Systems Research Laboratory. (ESRL), the Air Force Weather Agency (AFWA) and the Department of Defense Naval Research Laboratory (NRL), the University of Oklahoma's Center for Analysis and Prediction of Storms (CAPS), and the Federal Aviation Administration (FAA ), with the participation of university scientists. The Advanced Research WRF (ARW) modeling system has been under development in recent years.
FLOOD DISCHARGE COMPUTATION BY LUMPED AND DISTRIBUTED
The results presented in this chapter mainly suggest that the utility of WRF-derived precipitation and TRMM measured precipitation remains similar until local data are assimilated into the WRF model. The WRF precipitation forecast is significantly improved after the local-scale weather parameter is assimilated. The next chapter discusses the usability of this WRF-derived precipitation forecast as the primary input for discharge forecasting leading to river basin-level flood forecasting.
HYDROLOGICAL MODELS USING WRF DERIVED RAINFALL INPUTS
THE LUMPED RATIONAL MODEL
- Model description and parameters
Another important factor associated with this rational model is the time of concentration (TOC) of the watershed or basin, which is the time for water to move from the most distant point of the watershed to its outlet. Rainfall intensity I for the duration equal to the time of concentration was calculated by the average of the. 9km * 9km WRF-derived gridded 3 hourly values in hourly rate of rain spread over the basins.
THE DISTRIBUTED HEC-HMS MODEL
- Model description and parameters
- Geospatial approach followed in building the distributed model
Ia = Initial withdrawal (m) i.e. the amount of water before runoff such as infiltration, rainfall interception by vegetation etc. K is the travel time between two channel sections and X is a dimensionless factor between 0.0 to 0.5 that weighs the influence of the inflow and outflow hydrographs on the storage within reach. The important steps followed while setting up the distributed HEC-HMS model are Curve number grid generation by integrating land cover and soil for assignment of soil groups, Stream and DEM processing, Generation of CN grid grid, calculation of basin delay time, routing parameters K and x assignment etc.
VALIDATION OF FLOOD DISCHARGE COMPUTED BY BOTH LUMPED AND DISTRIBUTED HYDROLOGICAL
- Discharge validation in Jiadhal river basin in upper Assam
As part of the primary validation in the month of July, the discharge calculated using the lumped rational method has been shown to perform moderately in relation to observed river stage during 2011, but has performed satisfactorily during 2012 as shown in Figure 5.13 (a ). Regarding discharge calculated by the distributed approach, some contradictions have been observed during July 2011, but also the case with the lumped rational method, the distributed HEC-HMS. Again as part of the secondary validation, the general trend of overestimation by the lumped model also continued in the month of July, although the flood discharge calculated by the distributed HEC-HMS model was also found to be closer in most cases as depicted in Figure 5.14 (a).
EXTRACTION OF RIVER CHANNEL AND FLOOD PLAIN TOPOGRAPHY BY DIGITAL
Since regular hydrographic survey for river cross-sections is a tedious and cost-intensive process and rarely available to researchers, the next chapter is dedicated to the extraction of river cross-sections from digital elevation model data from different sources and with different horizontal and vertical resolution.
ELEVATION MODELS
INTRODUCTION
With an average vertical error of 16 m (Sanders et al. 2007), the SRTM DEM has not proven suitable for accurate extraction of small undulations in flat alluvial floodplains. On the other hand, ASTER is claimed to be relatively useful for river geometry extraction (Gichamo et al. 2011) on a stable river in Hungary. In the present research work, we attempted to make a comparison of terrain extraction capabilities between the ASTER global DEM and photogrammetrically generated CARTOSAT-I stereo DEM in two flood-prone alluvial catchments of the Brahmaputra Valley, with special focus on the extraction of river cross-sections and longitudinal profile.
THE ASTER DEM
- Data acquisition by ASTER
- CARTOSAT-I platform, sensor and orbit characteristics
- CARTOSAT-I stereo data processing for DEM generation
- Longitudinal river bed profile extraction
Here, the cross sections at critical locations of the river channels are extracted using TIN developed from both the ASTER global DEM as well as the locally generated CARTOSAT-I stereo DEM. The following figures show the extracted cross sections in HEC-Geo RAS environment from ASTER and CARTOSAT-I stereo DEM. Some of the cross sections where ground survey bathymetric data were not available are.
COMPARISON OF RIVER BASIN HYPSOMETRY AS COMPUTED FROM ASTER GDEM AND CARTOSAT-I STEREO
- Hypsometry of the Pagladia river basin
- Hypsometry of the Jiadhal river basin
According to the developed hypsometric curve from ASTER, about 44% of the total area is occupied by the lower elevation ranges between 32 m to 250 m followed by about 21% of the highest elevation range between 2250 m and 2500 m. Another significant observation was that bathymetry -the comparison with soil survey data sometimes also reveals horizontal displacement of the channel. In this chapter basically the floodplain topography of the two river systems has been studied.
FLOOD HAZARD ZONATION USING AN INNOVATIVE COMBINATION OF
The significant presence of cloud was found to be the source of the sudden spikes in the DEM-derived cross-section. This was observed and conclusively confirmed at the section at Thalkuchi in the Pagladia river. In order to determine the degree of flood risk associated with different parts of the floodplain, the topography under study was used as one of the parameters in the multi-criteria flood risk evaluation carried out in the next chapter of this research work.
GEOSPATIAL AND MORPHOLOGICAL FIELD PARAMETERS
INTRODUCTION
- Validation of flood hazard zonation in Jiadhal river basin
Finally, when multi-criteria zoning was performed by combining both RS indices and field parameters for the entire Pagladia Basin flood zone and validated against the same calculated from actual flood inundation data, it was obtained an encouraging correlation of 0.76 (Figure 7.12). . The Jiadhal River, as a set of RS indices and field parameters performed similarly to that of the middle reach with correlations of 0.44 and 0.84, respectively. The combined effect of both RS indices and field parameters also gave satisfactory results when the middle and lower reaches were considered together.
SUMMARY AND CONCLUSION
INTRODUCTION
RS indices have given satisfactory results mainly in countries with very frequent and severe floods. But the terrain parameters have identified possible flood zones even in the infrequently flooded areas of the middle extent of both flood plains. Thus, this FHZ exercise has proven to be useful for flood plains with insufficient accurate elevation information.
SUMMARY
- On rainfall prediction by NWP techniques
- On usability of WRF rainfall prediction as input in stream flow prediction
- On extraction of river hydraulic geometry from DEM
- On multi-criteria flood hazard zonation (FHZ) based on Remote sensing indices and morphologic field parameters
International Journal of Remote Sensing, 17, p. Jiadhal Sub-Basin Master Plan, Report Volume 2000, Brahmaputra Board, Ministry of Water Resources, Govt. Journal of Remote Sensing of Environment, 109, p. 2007), “Evaluation of on-line DEMs for flood inundation modeling”. Journal of Atmospheric Physics, 76, p. 2002), "Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation."
