Your search found 9 records
1 Ayana, E. K. 2007. Validation of radar altimetry lake level data and it's application in water resource management. MSc thesis. Enschede, Netherlands: International Institute for Geo-information Science and Earth Observation (ITC). 76p.
Water management ; International waters ; Radar satellite ; Measuring instruments ; Remote sensing ; Lakes ; Data analysis ; Statistical methods ; Cartography ; Satellite imagery / Ethiopia / Lake Tana / Nile River Basin
(Location: IWMI HQ Call no: 333.91 G000 AYA Record No: H043878)
(2.20 MB) (2.198MB)
2 Sene, K. 2010. Hydrometeorology: forecasting and applications. London, UK: Springer. 355p.
Hydrometeorology ; Hydrology ; Weather forecasting ; Radar satellite ; Meteorological stations ; Climate change ; Rain ; Runoff ; Water levels ; Water quality ; Water supply ; Water power ; Water demand ; Rivers ; Flow discharge ; Catchment areas ; Monitoring ; Models ; Energy generation ; Decision support systems ; Flooding ; Drought ; Snowmelt ; Reservoirs ; Dams ; Drainage systems ; Environmental impact ; Lakes ; Risk management ; Early warning systems
(Location: IWMI HQ Call no: 551.57 G000 SEN Record No: H046312)
(0.35 MB)
3 Amarnath, Giriraj; Inada, Yoshiaki; Ghosh, Surajit; Yakob, Umer; Alahacoon, Niranga; Kota, Harada; Inoue, Ryosuke; Schlaffer, S. 2014. Earth observation technologies for flood-risk mapping, modeling and management. Training manual prepared for Capacity Building Workshop on Earth Observation Technologies for Flood-risk mapping, Modeling and Management, Peradeniya, Sri Lanka, 18-21 November 2014. Peradeniya, Sri Lanka: University of Peradeniya. Postgraduate Institute of Science. 170p.
Earth observation satellites ; Satellite imagery ; Radar satellite ; Early warning systems ; Flood control ; Risk management ; Models ; Capacity building ; Rain ; Runoff ; Climate change ; Impact assessment ; Hydraulics ; Case studies ; Training materials / Sri Lanka / Thailand / Mundeni Aru Basin
(Location: IWMI HQ Call no: IWMI Record No: H046777)
(11.97 MB)
4 Fenta, A. A.; Rientjes, T.; Haile, Alemseged Tamiru; Reggiani, P. 2014. Satellite rainfall products and their reliability in the Blue Nile Basin. In Melesse, A. M.; Abtew, W.; Setegn, S. G. (Eds.). Nile river basin: ecohydrological challenges, climate change and hydropolitics. Dordrecht, Netherlands: Springer. pp.51-67.
Satellite observation ; Radar satellite ; Satellite imagery ; Meteorological stations ; River basins ; Rain ; Measurement ; Remote sensing ; GIS ; Precipitation ; Case studies / Ethiopia / Blue Nile Basin
(Location: IWMI HQ Call no: e-copy only Record No: H046898)
(0.37 MB)
In the Upper Blue Nile (UBN) basin, there is very sparse and uneven distribution of ground-based meteorological stations which constrain assessments on rainfall distributions and representation. To assess the diurnal cycle of rainfall across the UBN basin, satellite observations from Tropical Rainfall Measuring Mission (TRMM) were used in this study. Data of 7 years (2002–2008) of Precipitation Radar (PR) and TRMM Microwave Imager (TMI) were processed, with analyses based on geographic information system (GIS) operations, statistical techniques, and harmonic analysis. Diurnal cycle patterns of rainfall occurrence and rain rate from three in-situ weather stations are well represented by the satellite observations. Harmonic analysis depicts large differences in the mean of the diurnal cycle, amplitude, and time of the amplitude across the study area. Diurnal cycle of rainfall occurrence has a single peak in Lake Tana, Gilgel Abbay, and Jemma subbasins and double peaks in Belles, Dabus, and Muger subbasins. Maximum rain rate occurs in the morning (Gilgel Abbay, Dabus, and Jemma), afternoon (Belles, Beshilo, and Muger), and evening (Lake Tana and along the river gorges). Results of this study indicate that satellite observations provide an alternative source of data to characterize diurnal cycle of rainfall in data-scarce regions. We noticed, however, that there are a number of constraints to the use of satellite observations. For more accurate assessments, satellite products require validation by a network of well-distributed ground stations. Also, we advocate bias correction.
5 Amarnath, Giriraj; Umer, Yakob Mohammed; Alahacoon, Niranga; Inada, Yoshiaki. 2015. Modelling the flood-risk extent using LISFLOOD-FP in a complex watershed: case study of Mundeni Aru River Basin, Sri Lanka. Proceedings of the International Association of Hydrological Sciences, 370:131-138. [doi: https://doi.org/10.5194/piahs-370-131-2015]
Flood control ; Watersheds ; River basins ; Models ; Satellite observation ; Radar satellite ; Natural disasters ; Risk management ; Hydraulics ; Rain ; Case studies / Sri Lanka / Mundeni Aru River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H047060)
http://www.proc-iahs.net/370/131/2015/piahs-370-131-2015.pdf
https://vlibrary.iwmi.org/pdf/H047060.pdf
https://vlibrary.iwmi.org/pdf/H047060.pdf
(1.04 MB) (1.04 MB)
Flood management is adopting a more risk-based approach, whereby flood risk is the product of the probability and consequences of flooding. Two-dimensional flood inundation modeling is a widely used tool to aid flood-risk management. The aim of this study is to develop a flood inundation model that uses historical flow data to produce flood-risk maps, which will help to identify flood protection measures in the rural areas of Sri Lanka. The LISFLOOD-FP model was developed at the basin scale using available historical data, and also through coupling with a hydrological modelling system, to map the inundation extent and depth. Results from the flood inundation model were evaluated using Synthetic Aperture Radar (SAR) images to assess product accuracy. The impacts of flooding on agriculture and livelihoods were analyzed to assess the flood risks. It was identified that most of the areas under paddy cultivation that were located near the middle and downstream part of the river basin are more susceptible to flood risks. This paper also proposes potential countermeasures for future natural disasters to prevent and mitigate possible damages.
6 Amarnath, Giriraj; Pandey, Rajesh; Alahacoon, Niranga. 2015. Earth observation technologies for flood-risk mapping and forecast rating curve for flood recession agriculture in Nigeria. Training manual prepared for Capacity building workshop on Earth Observation Technologies for Flood-risk mapping and Forecast rating curve for Flood recession Agriculture in Nigeria, Abuja, Nigeria, 5-7 May 2015. Colombo, Sri Lanka: International Water Management Institute (IWMI). 84p.
Earth observation satellites ; Satellite imagery ; Satellite surveys ; Radar satellite ; Flood control ; Mapping ; Risk management ; Weather forecasting ; Hydrology ; Models ; Computer applications ; Remote sensing ; Water levels ; Flow discharge ; Capacity building ; Training materials ; Case studies / Nigeria / Niger River / Benue River
(Location: IWMI HQ Call no: IWMI Record No: H047076)
(0.33 MB)
7 Ray, K.; Mohapatra, M.; Bandyopadhyay, B. K.; Rathore, L. S. (Eds.) 2015. High-impact weather events over the SAARC Region. Cham, Switzerland: Springer International Publishing; New Delhi, India: Capital Publishing Company. 414p. [Selected papers presented at the SAARC Seminar on High Impact Weather Events over SAARC Region, New Delhi, India, 2-4 December, 2013] [doi: https://doi.org/10.1007/978-3-319-10217-7]
Weather forecasting ; Simulation models ; Remote sensing ; Radar satellite ; Satellite observation ; Assimilation ; Monsoon climate ; Rainfall patterns ; Hail ; Natural disasters ; Thunderstorms ; Cyclones ; Drought ; Temperature ; Clouds ; Early warning systems ; Diagnostic techniques ; Performance evaluation ; Statistical methods ; Agriculture ; Monitoring ; Assessment ; Coastal area ; Case studies / South Asia / India / Bangladesh / Pakistan / Arabian Sea / Bay of Bengal / Uttar Pradesh / Gujarat / Bihar / Delhi / Uttarakhand / Cherrapunji
(Location: IWMI HQ Call no: 551.6 G570 RAY Record No: H047218)
(0.37 MB)
8 Alahacoon, Niranga; Pani, Peejush; Matheswaran, Karthikeyan; Samansiri, S.; Amarnath, Giriraj. 2016. Rapid emergency response mapping for the 2016 floods in Kelani river basin, Sri Lanka. Paper presented at the 37th Asian Conference on Remote Sensing (ACRS): Promoting Spatial Data Infrastructure for Sustainable Economic Development, Colombo, Sri Lanka, 17-21 October 2016. 9p.
Natural disasters ; Disaster preparedness ; Flooding ; Emergency relief ; River basins ; Radar satellite ; Satellite imagery ; Landslides ; Remote sensing / Sri Lanka / Kelani River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H047943)
Beginning on 14 May 2016, a low pressure area over the Bay of Bengal caused torrential rain to fall across Sri Lanka. Some locations saw over 350 mm (13.77 inches) of rain fall in 24 hours. Floods and landslides have caused havoc in as many as 19 districts of the country, including around Colombo, causing floods and landslides which affected half a million people with causality reported over 100 and estimated economic losses closer to $2billion. In recent years, due to an increasing number in the frequency and intensity of extreme meteorological events potentially related to climate change, a growing attention has been paid to the operational use of satellite remote sensing applied to emergency response and relief measures. This is mainly due to the large and timely availability of different types of remotely sensed data as well as geospatial information acquired in the field which may be potentially exploited in the different phases of the disaster management cycle. IWMI jointly with Disaster Management Centre (DMC), Sri Lanka activated disaster charter with Sentinel Asia and escalated International Disaster Charter to access satellite images during the crisis response phase to support government agencies in relief and rescue measures. A total of 13 satellite images both microwave and optical datasets (ALOS-2, Sentinel-1, RISAT-1, RADARSAT-2, TerraSAR-X, FORMOSAT, Landsat-8) were provided by various space agencies to generate flood situation maps on a daily basis. The emergency flood situation maps were regularly shared to national and international organizations within 3-4 hours after the post-event image is acquired by the space agencies to support in relief measures. The derived flood maps were overlaid with local administrative division to give specific information on the priority area to the DMC and Air Force authorities to focus relief measures. These rapid response maps can further be used for postdisaster relief policy and damage assessment.
9 Alahacoon, Niranga; Matheswaran, Karthikeyan; Pani, Peejush; Amarnath, Giriraj. 2018. A decadal historical satellite data and rainfall trend analysis (2001–2016) for flood hazard mapping in Sri Lanka. Remote Sensing, 10(3):1-18. [doi: https://doi.org/10.3390/rs10030448]
Satellite imagery ; Satellite observation ; Radar satellite ; Rain ; Mapping ; Flooding ; Flood control ; Natural disasters ; Economic situation ; River basins ; Monsoon climate ; Risk management ; Catchment areas / Sri Lanka
(Location: IWMI HQ Call no: e-copy only Record No: H048581)
(10.8 MB)
Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR) and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016) for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km2 for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km2. The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC) and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures.
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