Your search found 3 records
1 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)
2 Thakur, P. K.; Garg, V.; Kalura, P.; Agrawal, B.; Sharma, V.; Mohapatra, M.; Kalia, M.; Aggarwal, S. P.; Calmant, S.; Ghosh, Surajit; Dhote, P. R.; Sharma, R.; Chauhan, P. 2021. Water level status of Indian reservoirs: a synoptic view from altimeter observations. Advances in Space Research, 68(2):619-640. [doi: https://doi.org/10.1016/j.asr.2020.06.015]
Water levels ; Estimation ; Reservoirs ; Lakes ; Inland waters ; Water resources ; Satellite observation ; Altimeters ; Time series analysis / India
(Location: IWMI HQ Call no: e-copy only Record No: H050798)
(7.37 MB)
Most of the part of India is already under water-stressed condition. In this regard, the continuous monitoring of the water levels (WL) and storage capacity of reservoirs, lakes, and rivers is very important for the estimation and utilization of water resources effectively. The long term ground observed WL of many of the water bodies is not easily available, which may be very critical for proper water resources management. Satellite radar altimetry is the remote sensing technique, which is being used to study sea surface height for the last three decades. The advancement in radar technology with time has provided the opportunity to exploit the technique to retrieve the WL of inland water bodies. In the current study, an attempt has been made to generate long term time series on WL of around 29 geometrically complicated inland water bodies in India. These water bodies are mainly large reservoirs namely Ban Sagar, Balimela, Bargi, Bhakra, Gandhi Sagar, Hasdeo, Indravati, Jalaput, Kadana, Kolab, Mahi Bajaj, Maithon, Massanjore, Pong, Ramganga, Ranapratap Sagar, Rihand, Sardar Sarovar, Shivaji Sagar, Tilaiya, Ujjani, and Ukai. The WL of these water bodies was retrieved for around two decades using the European Remote-Sensing Satellite – 2 (ERS-2), ENVISAT Radar Altimeter – 2 (ENVISAT RA-2), and Saral-AltiKa altimeters data through Ice-1 retracking algorithm. Further, an attempt has also been made to estimate the WL of gauged/ungauged lakes namely Mansarovar, Pangong, Chilika, Bhopal, and Rann of Kutch over which Saral-AltiKa pass was there. As after July 2016, the SARAL-AltiKa is operating in the drifting orbit, systematic repeated observation of WL data of all reservoirs was not possible. The data of drifted tracks of Saral-AltiKa were tested for WL estimation of Ban Sagar reservoir. As the ERS-2, ENVISAT RA-2 and Saral-AltiKa all were having almost the same passing tracks, a long term WL series of these lakes could be generated from 1997 to 2016. However, at present only Sentinel – 3 is in orbit, the continuous altimeter based WL monitoring of some of these reservoirs (Gandhi Sagar, Nathsagar, Ranapratap, Ujjani, and Ukai) was attempted through Sentinel-3A satellite data from 2016 to 2018. The accuracy of the retrieved WL was than validated against the observed WL. In most of the reservoirs, a systematic bias was found due to the different characteristics and geoid height of each reservoir. The coefficient of determination, R2 , value for a majority of reser voirs was as good as 0.9. In the case of ERS-2, the values of R2 varied for 0.44–0.97 with root mean square error (RMSE) in the range of 0.63–2.72 m. These statistics improved with the ENVISAT RA-2 data analysis, the R2 value reached more than 0.90 for around 11 reservoirs. The highest, 0.99, for Hasdeo and Shivaji Sagar Reservoirs with RMSE of 0.44 and 0.56, respectively. Further, the accuracy improved with the analysis of Saral-AltiKa data. The R2 was always more than 0.9 for each reservoir and the lowest RMSE reduced to 0.03. Therefore, it can be said that the accuracy and consistency of WL retrieval through satellite altimetry has improved with time. Furthermore, the altimeter based retrieved WL may be used in hydrological studies and can contribute to better water resources management.
3 Magotra, B.; Prakash, V.; Saharia, M.; Getirana, A.; Kumar, S.; Pradhan, R.; Dhanya, C. T.; Rajagopalan, B.; Singh, R. P.; Pandey, A.; Mohapatra, M.. 2024. Towards an Indian land data assimilation system (ILDAS): a coupled hydrologic-hydraulic system for water balance assessments. Journal of Hydrology, 629:130604. [doi: https://doi.org/10.1016/j.jhydrol.2023.130604]
Water balance ; Assessment ; Stream flow ; Water resources ; Models ; Precipitation ; Evapotranspiration ; Soil moisture ; Water storage ; Uncertainty ; Moderate resolution imaging spectroradiometer / South Asia / Indian / Rajasthan
(Location: IWMI HQ Call no: e-copy only Record No: H052546)
(18.30 MB)
Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF’s ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.
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