A Case Study: Heavy Rainfall Event Comparison Between Daily Satellite Rainfall Estimation Products with IMD Gridded Rainfall Over Peninsular India During 2015 Winter Monsoon

India Meteorological department (IMD) used INSAT-3D Metrological Satellite Imager data to drive two type rainfall estimation products viz-Hydro Estimate (HE) and INSAT Multi-Spectral Rainfall Algorithm (IMSRA) on half hourly rainfall rate and daily accumulated rainfall in millimeter (mm). Integrated Multi-Satellite Retrieval for GPM (IMERG) product is being derived by NASA and JAXA by using Global Precipitation Mission (GPM) satellites data. IMSRA and GPM (IMERG) are gridded data at 10 km spatial resolution and HE is available at pixel level (4 km at Nadir). IMD provides gridded rainfall data at 0.25° × 0.25° resolution which is based on wide coverage of 6955 actual observation. In present study, validation of INSAT-3D based Hydro Estimator (HE), INSAT Multi-Spectral Rainfall Algorithm (IMSRA) and Integrated Multi-Satellite Retrieval for GPM (IMERG) of Global Precipitation Mission (GPM) satellites are carried out with IMD gridded data set for heavy rainfall event during winter monsoon, over peninsular India (November–December 2015). In validation, Nash–Sutcliffe efficiencies (NSE), RMSE, Correlation, Skilled scores are calculated at grid level for heavy and very heavy rain categories and the values of NSE of HE (? 32.36, ? 3.12), GPM (? 68.67, ? 2.39) and IMSRA (? 0.02, 0.28) on 16th November 2015 and HE (? 13.65, ? 1.69), GPM (? 43.79, ? 2.94) and IMSRA (? 1.08, ? 1.60) on 2nd Dec 2015, for heavy and very heavy rainfall. On both days, HE is showing better rainfall estimate compare to GPM for Heavy rainfall and GPM showing better estimation for very heavy rainfall events. In all the cases IMSRA is underestimating, if daily rain fall exceeded 75 mm.     

Rainfall Estimation from Combined Observations Using KALPANA-IR and TRMM- Precipitation Radar Measurements over Indian Region

In the present study an attempt has been made to improve the rainfall estimation technique developed recently by Mishra et al. (2009a, 2009b) based on KALPANA and Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR) data over the Indian land and oceanic region. The algorithm for rainfall estimation was basically based on synergistically analyzing the thermal infra-red radiances from Kalpana/INSAT data along with the high resolution, horizontal and vertical rainfall estimates from PR. Presently the augmentation is based on the data base of precipitable water and relative humidity from National Centre for Environmental Prediction-Global forecast System (NCEP-GFS) data as a background field to correct for the biases in earlier algorithm. The algorithm is tested for many case studies of monsoon rainfall over India and adjoining oceanic regions. The rainfall from the present scheme is compared with the standard TRMM-3B42 rain product. The validation with the Automatic Weather Station (AWS) rain gauge and the Global Precipitation and Climatology Project (GPCP) version 2 rain products shows that the present scheme is able to retrieve the rainfall with a very good accuracy. These studies are aimed at the rainfall retrievals in near future from both INSAT-3D and Megha-Tropiques, IR and MW imagers respectively.     

Forecasting of onset of southwest monsoon over Kerala coast using satellite data

The Global Precipitation Climatology Project and in situ gauge data have been used in the present study of the Indian monsoon for the region bounded by 8/spl deg/ to 13/spl deg/N; 70/spl deg/ to 95/spl deg/E, from March 1 to May 31 for the years 1979 to 2001. The monsoon onset dates over Kerala, as declared by India Meteorological Department has been used in the present study as an indicator of the onset of this event. For each year, the midday of the pentad with the rainfall peak was located in the period from 1st April to 10th May and identified as the pre-monsoon rainfall peak (PMRP). The analysis showed that the PMRP exists about six pentads prior to the onset of the monsoon over the Kerala coast. A regression equation developed using the first 20 years of data (1979-1998) with a standard error estimate of four days was used for predicting the onset dates for the years 1999, 2000 and 2001, with encouraging results. Thus, we feel that the pre-monsoon rainfall estimate from the satellite data can be used for predicting the monsoon onset over Kerala coast.     

Rainfall-runoff modelling by using SM2RAIN-derived and state-of-the-art satellite rainfall products over Italy

Satellite rainfall products (SRPs) are becoming more accurate with ever increasing spatial and temporal resolution. This evolution can be beneficial for hydrological applications, providing new sources of information and allowing to drive models in ungauged areas. Despite the large availability of rainfall satellite data, their use in rainfall-runoff modelling is still very scarce, most likely due to measurement issues (bias, accuracy) and the hydrological community acceptability of satellite products.In this study, the real-time version (3B42-RT) of Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, and a new SRP based on the application of SM2RAIN algorithm (Brocca et al., 2014) to the ASCAT (Advanced SCATterometer) soil moisture product, SM2R_ASC, are used to drive a lumped hydrologic model over four basins in Italy during the 4-year period 2010–2013.The need of the recalibration of model parameter values for each SRP is highlighted, being an important precondition for their suitable use in flood modelling. Results shows that SRPs provided, in most of the cases, performance scores only slightly lower than those obtained by using observed data with a reduction of Nash–Sutcliffe efficiency (NS) less than 30% when using SM2R_ASC product while TMPA is characterized by a significant deterioration during the validation period 2012–2013. Moreover, the integration between observed and satellite rainfall data is investigated as well. Interestingly, the simple integration procedure here applied allows obtaining more accurate rainfall input datasets with respect to the use of ground observations only, for 3 out 4 basins. Indeed, discharge simulations improve when ground rainfall observations and SM2R_ASC product are integrated, with an increase of NS between 2 and 42% for the 3 basins in Central and Northern Italy. Overall, the study highlights the feasibility of using SRPs in hydrological applications over the Mediterranean region with benefits in discharge simulations also in well gauged areas.     

Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data

The hard-rock hilly Aravalli terrain of Rajasthan province of India suffers with frequent drought due to poor and delayed monsoon, abnormally high summer-temperature and insufficient water resources. In the present study, detailed analysis of meteorological and hydrological data of the Aravalli region has been carried out for the years 1984–2003. Standardised Precipitation Index (SPI) has been used to quantify the precipitation deficit. Standardised Water-Level Index (SWI) has been developed to assess ground-water recharge-deficit. Vegetative drought indices like Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) and Vegetation Health Index (VHI) have been computed using NDVI values obtained from Global Vegetation Index (GVI) and thermal channel data of NOAA AVHRR satellite. Detailed analyses of spatial and temporal drought dynamics during monsoon and non-monsoon seasons have been carried out through drought index maps generated in Geographic Information Systems (GIS) environment. Analysis and interpretation of these maps reveal that negative SPI anomalies not always correspond to drought. In the Aravalli region, aquifer-stress shifts its position time to time, and in certain pockets it is more frequent. In comparison to hydrological stress, vegetative stress in the Aravalli region is found to be slower to begin but quicker to withdraw.     

Albedo-rainfall Feedback Over Indian Monsoon Region Using Long Term Observations Between 1981 to 2000

Albedo determines radiation balance of land (soil-canopy complex) surface and influence boundary layer structure of the atmosphere. Accurate surface albedo determination is important for weather forecasting, climate projection and ecosystem modelling. Albedo-rainfall feedback relationship has not been studied so far using observations on spatial scale over Indian monsoon region due to lack of consistent, systematic and simultaneous long-term measurements of both. The present study used dekadal (ten-day) composite of satellite (e.g. NOAA) based Pathfinder AVHRR Land (PAL) datasets between 1981 and 2000 over India (68–100°E, 5–40°N) at 8 km spatial resolution. Land surface albedo was computed using linear transformation of red and near infrared (NIR) surface reflectances. The cloud effects were removed using a smoothening filter with harmonic analysis applied to time series data in each year. The monthly, annual and long term means were computed from dekadal reconstructed albedo. The mean per year and coefficient of variation (CV) of surface albedo over seventeen years, averaged over Indian land region, were found to show a significantly decreasing (0.15 to 0.14 and 60 to 40%, respectively) trend between 1981 and 2000. Among all the land use patterns, the inter-annual variation of albedo of Himalayan snow cover showed a significant and the steepest reducing trend (0.42 – 0.35) followed by open shurbland, grassland and cropland. No significant change was noticed over different forest types.. This could be due to increase in snow melting period and snow melt area. A strong inverse exponential relation (correlation coefficient r = 0.95, n = 100) was found between annual rainfall and annual albedo over seven rainfall zones. The decreasing trend in snow-albedo of accumulation period (September to March) follows the declining trend in measured south-west monsoon rainfall between 1988 (980 mm) to 1998 (880 mm) over India. This finding perhaps suggests the possible reversal of reported coupling of increased snowfall followed by lower monsoon rainfall.     

A Web GIS Application for Dams and Drought in India

Dams and diversion are built in India for meeting needs of water and energy. Due to variability of monsoon in space and time, precipitation falls short or exceeds causing in extreme cases drought and floods respectively. Water resource planners and engineers need information on dams and diversion. Drought information is needed in disaster management. For dissemination of these spatial data, Web GIS technology can be utilized, which is amalgamation of several information technologies. For Web GIS application, a high end, powerful and open source software, namely Mapserver is available. The software is CGI technology based. An application on dams and drought information for India is conceptualized using Mapserver. It is planned to write the application by modifying available tutorial. This will require writing DHTML pages, writing logic, using available libraries etc. Separate DHTML pages will be written for dam and drought applications. For dam application pages will be written for storage, hydropower and all dams. The drought application will provide maps of rainfall over districts for different SPI and time scales.     

Flood forecasting in Niger-Benue basin using satellite and quantitative precipitation forecast data

Availability of reliable, timely and accurate rainfall data is constraining the establishment of flood forecasting and early warning systems in many parts of Africa. We evaluated the potential of satellite and weather forecast data as input to a parsimonious flood forecasting model to provide information for flood early warning in the central part of Nigeria. We calibrated the HEC-HMS rainfall-runoff model using rainfall data from post real time Tropical Rainfall Measuring Mission (TRMM) Multi satellite Precipitation Analysis product (TMPA). Real time TMPA satellite rainfall estimates and European Centre for Medium-Range Weather Forecasts (ECMWF) rainfall products were tested for flood forecasting. The implication of removing the systematic errors of the satellite rainfall estimates (SREs) was explored. Performance of the rainfall-runoff model was assessed using visual inspection of simulated and observed hydrographs and a set of performance indicators. The forecast skill was assessed for 1–6 days lead time using categorical verification statistics such as Probability Of Detection (POD), Frequency Of Hit (FOH) and Frequency Of Miss (FOM). The model performance satisfactorily reproduced the pattern and volume of the observed stream flow hydrograph of Benue River. Overall, our results show that SREs and rainfall forecasts from weather models have great potential to serve as model inputs for real-time flood forecasting in data scarce areas. For these data to receive application in African transboundary basins, we suggest (i) removing their systematic error to further improve flood forecast skill; (ii) improving rainfall forecasts; and (iii) improving data sharing between riparian countries.     

Inter-comparison of satellite rainfall products for representing rainfall diurnal cycle over the Nile basin

In this study, the authors inter-compared the performance of three satellite-rainfall products in representing the diurnal cycle of rain occurrence and rain rate over the Nile basin in eastern Africa. These products are the real time (RT) and post-real-time (PRT) (bias adjusted) versions of Tropical Rainfall Measuring Mission (TRMM) and other sources product known as TRMM-3B42 and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) product which is based on the CPC morphing technique (CMORPH). The rainfall diurnal cycles are re-produced using these products with specific focus on assessing effects of geographic location and topographic features. The performance of the satellite products in representing rainfall diurnal cycle shows large variation over the Nile basin. The products overestimate rain occurrence over the lakes, islands, and shores and underestimate occurrence over mountain tops. Overall, CMORPH performs better than TRMM-3B42 RT and TRMM-3B42 PRT in capturing the diurnal cycle of rain rate in Lake Tana basin. However, the difference between the two products is very small for Lake Victoria basin, where both products perform more favorably. Over most of the Nile basin areas, the use of fine versus coarse temporal and spatial resolution of the CMORPH product showed large differences for diurnal cycle of rain occurrence than that of rain rate. Results also show that the bias adjustment of TRMM-3B42 product does not necessarily bring improvements probably since the adjustments are not performed based on local rain gauge data.     

Large area soil moisture estimation and mapping using space-borne multi-frequency passive microwave data

The paper reports the estimation of surface soil moisture (SM) using surface wetness Index (SWI) retrieved from multi-frequency passive microwave radiometer. A change detection algorithm was followed which transforms SWI variations in to SM variations using per pixel soil property of field capacity and air-dry status. Estimated soil moisture was compared with the point measurements made at the Monmouth and De Kalb sites of Illinois (USA) for the validation. Sensitivity of the SWI to the variations of rainfall at various vegetation fractions is analyzed. RMS error of volumetric soil moisture is found to be in the range of 6.35 to 8.85 %. The method works well up to the vegetation fraction of 40 %. Applications of the technique are demonstrated by the spatio-temporal analysis of estimated soil moisture maps for India. Characteristic increase in soil moisture was observed with the progress of monsoon from 25 to 32 week in northern India and 46 to 52 week in the costal parts of Tamil Nadu in south.     

Variability of aerosol optical depth over indian subcontinent using modis data

High AOD is observed over the Ganga basin throughout the year unlike southern India, is alarming as this basin is one of the most productive basins of Indian subcontinent having population of more than 460 million. AOD is found to be increasing rapidly since 2000 in summer season that may cause adverse effect to the agricultural crops and also to the human health. Increased aerosol loading may likely affect the rainfall which is responsible for the observed drought conditions over the Indian subcontinent. Detailed analysis of AOD, crop yields and rainfall data are required to understand the impact of increasing aerosol loading over the Indian subcontinent.     

Hydrological modelling of a small watershed using satellite data and gis technique

A distributed parameter model Soil and Water Assessment Tool (SWAT) has been tested on daily and monthly basis for estimating surface runoff and sediment yield from a small watershed “Chhokeranala” in eastern India using satellite data and Geographical Information System (GIS). Several maps like watershed and sub-watershed boundaries, drainage network, landuse/cover and soil texture have been generated. The SWAT model has been verified for the initial phase of monsoon season in the year 2002 using daily rainfall and air temperature. Performance of the model has been also evaluated to simulate the surface runoff and sediment yield on sub-watershed basis for two months (July-August 2002). The results show a good agreement between observed and simulated runoff and sediment yield during the study period. Capability of the model for generating rainfall has been evaluated for 10 years (1992 - 2001) period. The model simulated daily rainfall shows close agreement with the observed rainfall. The present results show that the SWAT model can be used for satisfactory simulation of daily and monthly rainfall, runoff and sediment yield.     

A combined deficit index for regional agricultural drought assessment over semi-arid tract of India using geostationary meteorological satellite data

The untimely onset and uneven distribution of south-west monsoon rainfall lead to agricultural drought causing reduction in food-grain production with high vulnerability over semi-arid tract (SAT) of India. A combined deficit index (CDI) has been developed from tri-monthly sum of deficit in antecedent rainfall and deficit in monthly vegetation vigor with a lag period of one month between the two. The formulation of CDI used a core biophysical (e.g., NDVI) and a hydro-meteorological (e.g., rainfall) variables derived using observation from Indian geostationary satellites. The CDI was tested and evaluated in two drought years (2009 and 2012) within a span of five years (2009–2013) over SAT. The index was found to have good correlation (0.49–0.68) with standardized precipitation index (SPI) computed from rain-gauge measurements but showed lower correlation with anomaly in monthly land surface temperature (LST). Significant correlations were found between CDI and reduction in agricultural carbon productivity (0.67–0.83), evapotranspiration (0.64–0.73), agricultural grain yield (0.70–0.85). Inconsistent correlation between CDI and ET reduction was noticed in 2012 in contrast to consistent correlation between CDI and reduction in carbon productivity both in 2009 and 2012. The comparison of CDI-based drought-affected area with those from existing operational approach showed 75% overlapping regions though class-to-class matching was only 40–45%. The results demonstrated that CDI is a potential indicator for assessment of late-season regional agricultural drought based on lag-response between water supply and crop vigor.     

Variability of Altimetric Range Correction Parameters over Indian Tropical Region using JASON-1 & JASON-2 Radar Altimeters

Region-specific atmospheric range correction maps are generated over the Indian tropical region from Jason-1 & Jason-2 radar altimeters data. Seasonal and spatial variability of wet tropospheric correction (WTC), ionospheric correction (IC), dry tropospheric correction (DTC), and sea state bias (SSB) correction are analyzed over the Bay of Bengal and the Arabian Sea. Two year atmospheric range correction data from JASON-1 (2008) & JASON-2 altimeters (2009) has been analyzed where each Jason cycle is exactly 9.9156?days repeat. The monthly and yearly mean variation of the range correction parameters has been studied over the Indian continent. For precise study, four different regions were selected as the Region of Interest in the North & South of the Arabian Sea and Bay of Bengal. WTC, Significant Wave Height (SWH), Wind Speed (WS) and SSB show the higher values during monsoon months. The yearly mean WTC over Indian Tropical region was 26.22?cm in 2008 and 26.20?cm in 2009. SSB Correction values mainly depend on the SWH and wind speed. The yearly mean SSB correction over Indian Tropical region was 6.87?cm in 2008 and 7.02?cm in 2009. DTC values are less during monsoon season and it shows a high value in the month of January. The yearly mean DTC over Indian Tropical region was 230.42?cm in 2008 and 230.43?cm in 2009.The IC values mainly depend on frequency and total electron content (TEC) in the ionosphere which further depends on the solar activity. The yearly mean IC over Indian Tropical region was higher in 2008 (2.98?cm) in comparison to mean IC in 2009 (2.29?cm). This study is useful to understand the variability of atmospheric correction parameters especially over Indian continent.     

Study on Regional Variations of Aerosol Loading Using Long Term Satellite Data Over Indian Region

Satellite-based measurements of aerosols are one of the most effective ways to understand the role of aerosols in climate in terms of spatial and temporal variability. In the present study, we attempted to analyse spatial and temporal variations of satellite derived aerosol optical depth (AOD) over Indian region using moderate resolution imaging spectrometer over a period of 2001–2011. Due to its vast spatial extent, Indian region and adjacent oceanic regions are divided into different zones for analysis. The land mass is sub divided into five different zones such as Indo Gangetic Plain (IGP), Indian mainland, North Eastern India (NE), South India-1 (SI-1), South India-2 (SI-2). Oceanic areas are divided into Arabian Sea and Bay of Bengal. Arabian Sea is further divided as three zones viz. Northern AS (NAS), Central AS (CAS) and Eastern AS (EAS) zones. Bay of Bengal is divided as North BoB (NBoB), West BoB (WBoB), Central BoB (CBoB), and East BoB (EBoB). The study revealed that among all the land regions, IGP showed the highest peak AOD value (0.52 ± 0.17) while SI-2 showed the lower values of AOD in all the months compared to all India average. The maximum AOD is observed during premonsoon season for all regions. During the winter, average AOD levels were substantially lower than the summer averages. Peak of aerosol loading (0.35 ± 0.159) is observed in March over NE region, whereas in all other regions, peak is observed during May. Frequency distribution of long term AOD (<0.2, 0.3–0.5, >0.5) shows a shift of frequency distribution of AOD from <0.3 to 0.3–0.5 during the study period in all regions except IGP. In IGP shift of frequency of AOD values occurs from 0.3–0.5 to >0.5. Oceanic areas also shows seasonal variation of AOD. Over Arabian Sea, high AOD values with greater variations were observed in summer monsoon season while in Bay of Bengal it is observed during winter monsoon. This is due to the high wind speed prevailing in Arabian Sea during monsoon season which results in production of more sea salt aerosol. Highest AOD values are observed over NAS during monsoon season and over NBOB during winter season. Lowest AOD values with its lower variations observed in both the central region of Arabian Sea and Bay of Bengal.     

IRS-LISS-III and PAN data analysis for landslide susceptibility mapping using heuristic approach in active tectonic region of Himalaya

Structurally disturbed zones of Himalaya are among the worst landslide affected regions in the world. Although landslides are induced/triggered either by torrential rain during monsoon or by seismic activity in the region, the inherent terrain conditions characterize the prevailing basic conditions susceptible to landslides. Using remotely sensed data and Geographic Information System (GIS), geological and terrain factors can be integrated for preparation of factor maps and demarcation of areas susceptible to landslides. Moderate to high resolution data products available from Indian Remote Sensing satellites have been utilized for deriving geological and terrain factor maps, which were integrated using knowledge driven heuristic approach in Integrated Land and Water Information System (ILWIS) GIS. The resultant map shows division of the area into landslide susceptibility classes ranked in terms of hazard potential in one of the structurally disturbed zones in western Himalaya around Rishikesh.     

Investigating flash floods potential areas using ASCAT and TRMM satellites in the Western Cape Province,South Africa

The aim of the study was to evaluate flash flood potential areas in the Western Cape Province of South Africa, by integrating remote sensing products of high rainfall intensity, antecedent soil moisture and topographic wetness index (TWI). Rainfall has high spatial and temporal variability, thus needs to be quantified at an area in real time from remote sensing techniques unlike from sparsely distributed, point gauge network measurements. Western Cape Province has high spatial variation in topography which results in major differences in received rainfall within areas not far from each other. Although high rainfall was considered as the major cause of flash flood, also other contributing factors such as topography and antecedent soil moisture were considered. Areas of high flash flood potential were found to be associated with high rainfall, antecedent precipitation and TWI. Although TRMM 3B42 was found to have better rainfall intensity accuracy, the product is not available in near real time but rather at a rolling archive of three months; therefore, Multi- sensor precipitation estimate rainfall estimates available in near real time are opted for flash flood events. Advanced Scatterometer (ASCAT) soil moisture observations were found to have a reasonable r value of 0.58 and relatively low MAE of 3.8 when validated with in situ soil moisture measurements. The results of this study underscore the importance of ASCAT and TRMM satellite datasets in mapping areas at risk of flooding.     

Agricultural drought assessment at disaggregated level using AWiFS/WiFS data of Indian Remote Sensing satellites

Spatial differences in drought proneness and intensity of drought caused by differences in cropping patterns and crop growing environments within a district indicate the need for agricultural drought assessment at disaggregated level. The objective of this study is to use moderate resolution satellite images for detailed assessment of the agricultural drought situation at different administrative units (blocks) within a district. Monthly time composite NDVI images derived from moderate resolution AWiFS (60 m) and WiFS (180 m) images from Indian Remote Sensing satellites were analysed along with ground data on rainfall and crop sown areas for the kharif seasons (June – November) of 2002 (drought year), 2004 (early season drought) and 2005 (good monsoon year). The impact of the 2002 meteorological drought on crop area in different blocks of the district was assessed. The amplitude of crop condition variability in a severe drought year (2002) and a good year (2005) was used to map the degree of vulnerability of different blocks in the district to agricultural drought. The impact of early season deficit rainfall in 2004 on the agricultural situation and subsequent recovery of the agricultural situation was clearly shown. Agricultural drought assessment at disaggregated level using moderate resolution images is useful for prioritizing the problem areas within a district to undertake, in season drought management plans, such as alternate cropping strategies, as well as for end of the season drought relief management actions. The availability of ground data on rainfall, cropping pattern, crop calendar, irrigation, soil type etc., is very crucial in order to interpret the seasonal NDVI patterns at disaggregated level for drought assessment. The SWIR band of AWiFS sensor is a potential data source for assessing surface drought at the beginning of the season.     

Use of high-resolution satellite data,GIS and NRCS-CN technique for the estimation of rainfall-induced run-off in small catchment of Jharkhand India

The present study demonstrates the use of NRCS-CN technique for rainfall-induced run-off estimation using high-resolution satellite data for small watershed of Palamu district, Jharkhand. The CN model was applied to the daily rainfall data of 15 years (1986–2000) along with use of large-scale thematic maps (1:10,000) pertaining to land use/land cover using IRS-P6 LISS-IV satellite data. The LU/LC map was spatially intersected with the hydrological soil group map to calculate the watershed area under different hydrological similar units for assigning CN values to compute discharge. The study showed that Daltonganj watershed exhibits an average run-off volume of 7,881,019 m³ from an average cumulative monsoon rainfall of 821 mm and the average actual direct run-off generated during the southwest monsoon season was 203 mm. The strong correlation between rainfall and run-off as well as between observed run-off and estimated run-off indicated high accuracy of run-off estimation by NRCS-CN technique.     

Validation of a TRMM-based global Flood Detection System in Bangladesh

Although the TRMM-based Flood Detection System (FDS) has been in operation in near real-time since 2006, the flood ‘detection’ capability has been validated mostly against qualitative reports in news papers and other types of media. In this study, a more quantitative validation of the FDS over Bangladesh against in situ measurements is presented. Using measured stream flow and rainfall data, the study analyzed the flood detection capability from space for three very distinct river systems in Bangladesh: (1) Ganges– a snowmelt-fed river regulated by upstream India, (2) Brahmaputra – a snow-fed river that is braided, and (3) Meghna – a rain-fed and relatively flashier river. The quantitative assessment showed that the effectiveness of the TRMM-based FDS can vary as a function of season and drainage basin characteristics. Overall, the study showed that the TRMM-based FDS has great potential for flood prone countries like Bangladesh that are faced with tremendous hurdles in transboundary flood management. The system had a high probability of detection overall, but produced increased false alarms during the monsoon period and in regulated basins (Ganges), undermining the credibility of the FDS flood warnings for these situations. For this reason, FDS users are cautioned to verify FDS estimates during the monsoon period and for regulated rivers before implementing flood management practices. Planned improvements by FDS developers involving physically-based hydrologic modeling should transform the system into a more accurate tool for near real-time decision making on flood management for ungauged river basins of the world.