1 CHAPTER 1 INTRODUCTION 1.1 GENERAL Precipitation is the primary input to a watershed system. Hydrologic analysis cannot be performed with confidence until the precipitation input is adequately measured in both the spatial and temporal dimensions. Currently, rain gauges are used to measure point rainfall in the field, but spatial variation is lacking when these point measurements are extrapolated to provide aerial distributions. Precipitation usually varies significantly in both space and time; therefore, a limited number of rain gauge stations in the watershed would have a major impact on the accuracy of runoff and flood estimations (Bevan and Hornberger 1982). Assuming that rainfall is uniform over an area, many hydrological models were developed earlier, and many a time the results were at variance with the observed data. For efficient rainfall-runoff estimation of a watershed, a dense rain gauge network is desired, which involves high installation and operational costs (Mapiam et al 2009). Suresh (2010) stated that a dense network of 2 km x 2 km might be required for planning purposes in India. However, owing to the practical difficulties, an acceptable network of rain gauges has been proposed as 4 km x 4 km. But, this network density is also functionally impractical in view of topographical and terrain constraints, cost restrictions, and maintenance-related problems. At present, India has on an average of one rain gauge station for every 3402 sq. km area (Guhathakurta and Rajeevan 2008).
2 Meteorological long range radars can be extremely useful for the observation of rainfall events over a wide area. Radar s ability to provide detailed information, in both time and space with precipitation patterns is unsurpassed by rain gauges. Many researchers conclude that meteorological radars have several advantages over the conventional rain gauges, as a single site can obtain coverage over a wide area with high temporal and spatial resolution (Wyss et al 1990; Borga 2002; Meischner 2004; Mikayla and Paul 2005; Eloise and Peter 2010). Precipitation estimation by the radar can be useful to work out water influx in catchments and flash flood forecasting in almost real-time basis in the absence of conventional rain gauge network. Radar is an active remote-sensing system operating at microwave wavelength. Shorter wavelengths are useful for smaller particles, but the signal gets attenuated quickly. Thus, 10 cm wavelength S-band radar is preferred, but is more expensive than a 5 cm C-band radar or 3 cm X-band radar. Radar works under the principle of Rayleigh scattering; it is the elastic scattering of light or other Electro Magnetic Radiation (EMR) by particles much smaller than the wavelength of the EMR, which may be individual atoms, molecules or particles. The sensor transmits a microwave (radio) signals towards a target and detects the backscattered radiation. Rain drop scatters some pulses back to the receiver by the process of reflection, refraction and scattering. Intensity of pulse returned, and the return time is recorded. The strength of the backscattered signal is measured to discriminate between different targets; and the time delay between the transmitted and reflected signals determines the distance (or range) to the target. The reflectivity (Z) is then converted to rainfall intensity (R) using Marshall-Palmer s (1948) Equation (1.1). Z= 200 R 1.6 (1.1)
3 India Meteorological Department (IMD) has installed a number of Doppler Weather Radars (DWR) along coastal India. These DWR based rainfall estimates could provide significant complementary rainfall information where rain gauge data is either inadequate or absent. The S-band Doppler weather radar operating at Cyclone Detection Radar centre (CDR), Chennai utilizes a constant Z-R relationship. The rainfall data obtained from radar and rain gauge was used as an input to Hydrologic Engineering Center Hydrological Modeling System (HEC-HMS) model to simulate rainfallrunoff processes for two watersheds in Chennai basin. 1.2 THE PROBLEM STATEMENT India has been traditionally vulnerable to various hazards such as floods, droughts and cyclones. About 8 % of the total landmass is prone to cyclones. The East coast of the Indian subcontinent experiences more cyclones than the West coast. Post-monsoon storms are more violent than the storms of the monsoon season. Out of the cyclones that develop in the Bay of Bengal, over 58 per cent approach and cross the East coast in October and November months. The cyclones which hit Tamil Nadu state cause extensive damages to agriculture and normal human life. In the recent past, cyclone Nilam made landfall on Tamil Nadu coast between Mahabalipuram and Kalpakkam on 31-Oct-2012 with strong winds, and people remained indoors throughout the day with intermittent spells of rain and strong wind. Cyclone Thane resulted in heavy rainfall on 30-Dec-2011, on the North Tamil Nadu coast between Cuddalore and Pondicherry. The storm rendered the city inaccessible by damaging road network and left at least 46 dead in Tamil Nadu and Pondicherry. Severe cyclonic storm Jal near Chennai, had a peak intensity on 07-Nov-2010 and affected normal life. In the year 1985, the ungauged watershed, Nemam was breached by a storm surge produced by the post-monsoon cyclone and caused significant flooding in Adyar River.
4 Currently, IMD operates 14 Doppler weather radars across India. Chennai DWR is the first radar in the country facing the sea at one side and the city's landscape on the other sides. DWR s wind speed and wind directions data is transmitted almost every 10 minutes, providing information that is vital for decisions on aircraft navigation. Using the potential of the radar to track rainfall in various parts of the city and suburbs, the meteorological department is planning for nowcasting', a short-term forecasting. DWR tracked cyclones and depressions formed over the Bay of Bengal, enabling meteorologists to issue timely severe cyclonic storm warnings. Nisha in the year 2008, Laila and Jal in the year 2010 and Thane in the year 2011 were some of the recent cyclones tracked by the radar. The availability of high-resolution radar rainfall data provides an opportunity to utilize it as an input to hydrological model for investigating the severe storm and the subsequent flood events. 1.3 WEATHER RADAR IN HYDROLOGICAL APPLICATIONS Radar hydrology uses radar derived rainfall data for water resources applications. The reliability and accuracy of runoff estimation depend not only on the quality and resolution of the hydrologic models, but also to a high degree on the density and quality of input data from observations. Radar derived rainfall estimates complement the existing ground observations, and it is recommended to utilize radar data at smaller scales and satellite observations at a larger scale (Meischner 2004). There is a wealth of literature showing the utilization of radar data to describe the start and end of rainfall events, and hydrologists have explained the ability and limitation of utilizing radar data as input to the isolated events and lumped model. It has been proven that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are
5 inaccurate. Radar s ability to see larger areas almost concurrently is one of the major advantages of radar technology and the problem of inadequate rain gauges can be addressed by radar rainfall data (Suresh 2012). The key benefits in using the weather radars are listed below: The weather radar data are available at fine spatial and temporal resolutions, and are suitable for areas where rain gauges are inadequate or absent. Radars assist the meteorologist to predict the forthcoming storms before they strike the catchment of interest. Forecasting the flash flood and severe thunderstorm could be possible using radar data. Existing rain gauges can be supplemented with the radar rainfall data. The study on rainfall variability on a micro scale is possible with radar data. Precipitation type and intensity could be determined. The watershed management could be planned in a better way based on the rainfall estimation over the catchments. However, the weather radar does not measure rainfall directly; algorithms are used to estimate the rainfall from radar observations. The radar data requires quality control check and calibration, before being converted
6 into precipitation products as input to hydrologic models. The common errors in the radar estimates of rainfall are ground clutter, attenuation, bright band and vertical profile. The antenna feed of S-band DWR at CDR is located at an elevation of 53 m above mean sea level, so the ground clutters close to the radar site have been eliminated. The bright band occurs at a height well above 5200 m over Chennai; hence for correcting the melting layer enhanced reflectivity (bright band correction), algorithms proposed by Smith (1986) and Andrieu and Creutin (1995) have been deployed. The above-mentioned errors are considered to be practically non-existent as the reflectivity (Z) at 1 km above ground level has been considered for rain rate (R) estimation upto 100 km radius from DWR location (Suresh et al 2005). 1.4 RADAR RAINFALL ADJUSTMENT PROCEDURES The weather radar acquires instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted to rainfall using algorithms. So the radar data requires adjustment prior to using it as the input to any model (Lopez et al 2005). Radar calibration is a crucial step in producing high-quality data for the quantitative use of weather radar. Adjustment is a modification of the radar quantity to match an external quantity. The modification is application dependent (time and space issues abound here) is the general definition for 'adjustment' proposed in 2001- American Meteorological Society Radar Calibration Workshop. However, there is no universally-approved methodology for adjustment and quality control of radar derived precipitation values. One of the most common methods is the adjustment of radar data to gauge measurements of precipitation (Joe and Smith 2001).
7 For this study, the adjustment of radar data was conducted by matching the mean accumulations of rainfall measured using rain gauge and of radar rainfall estimations, at the locations of the respective rain gauges. Typically automatic rain gauges are used for radar derived rainfall comparison and calibration (Waleed et al 2009). Since the Chennai basin has inadequate automatic rain gauges, both recording as well as non-recording rain gauges were considered for radar rainfall data calibration. The estimated radar derived rainfall data was adjusted by multiplying the primary value by a calibration factor. It was observed that the original radar rainfall data is underestimating the rainfall for three cyclonic storm events; hence radar derived rainfall data was adjusted before using it as an input to the hydrological model. Software tools had been developed for reformatting the radar data into the format compatible with HEC-HMS model. 1.5 OVERALL FRAMEWORK The outline of the research methodology is illustrated. Collection of Cartosat-1 30 m DEM from National Remote Sensing Centre (NRSC), Hyderabad. Basin model preparation Watershed delineation and basin model preparation for HEC-HMS using HEC-GeoHMS tool, which is a GIS preprocessor. Initial parameter estimation using soil type and land use maps.
8 Rain gauge data collection from State Ground and Surface Water Resources Data Centre and IMD, Chennai. Radar rainfall data collection from CDR, Chennai. Reformatting the radar data to standard ASCII format using a newly developed VB script. Meteorologic model preparation Geo-referencing the radar data using ArcGIS tool. Converting ASCII grid format to gridded HEC-DSS format using newly written script. Creating grid set from single grids using HEC-GridUtil tool. Extracting the radar rainfall data at rain gauge locations. Identification and application of radar rainfall calibration factor to the original radar data using PERL script (for batch processing). Three cyclonic storm events were taken for analysis. Control specifications 25-Nov-2008 to 01-Dec-2008 - Nisha cyclone 02-Nov-2009 to 10-Nov-2009 - Phyan cyclone 04-Nov-2010 to 09-Nov-2010 - Jal cyclone Basin model, meteorologic model and control specifications creation in HEC-HMS. HEC-HMS model Model calibration using curve number parameter. HEC-HMS model validation for gauged watershed. Simulation of rainfall-runoff processes using rain gauge and radar derived rainfall inputs. Analyzing the results and provide suggestions.
9 1.6 OBJECTIVES OF THE STUDY The objectives of the research are to: 1. Compare the daily rainfall data derived from radar and rain gauges at the locations of the rain gauges. 2. Study the feasibility of using the DWR rainfall data for rainfall-runoff estimation of watersheds in Chennai basin. 3. Simulate the rainfall-runoff processes with HEC-HMS using conventional rain gauge data for gauged and ungauged watersheds using daily and hourly rainfall inputs. 4. Simulate the rainfall-runoff processes with HEC-HMS using radar derived rainfall data for gauged and ungauged watersheds using daily and hourly radar inputs. 5. Investigate the implication of DWR based rainfall-runoff estimation for gauged and ungauged watersheds in Chennai basin. 1.7 ORGANIZATION OF THESIS The presentation of the work is arranged into six chapters. The current chapter deals with brief introduction about the weather radar in hydrological applications, problem description and the study objectives. Previous investigations on various topics related to Doppler weather radar applications in hydrology are described in the second chapter, i.e. review of literature. The third chapter deals with the study area description which includes the location constraints with respect to DWR site, climate and rainfall data, soil type and land use details. Methodology adopted for the
10 research is presented in the fourth chapter, which focuses on the framework of research methodology, radar data processing approach, radar rainfall data adjustment procedures and HEC-HMS model with calibration procedures. The fifth chapter presents the results and discussion, which highlights the importance of utilizing CDR's radar rainfall data for hydrological purposes. Finally the overall summary, conclusions, limitations of the study and suggestions for future work are presented in the sixth chapter.