Extending the historical in situ gauge network over Indian river reaches utilizing the Surface Water and Ocean Topography (SWOT) mission

References

• Alsdorf, D.E., et al., 2007. Measuring surface water from space. Reviews of Geophysics, 45 (2), 2002. doi:10.1029/2006RG000197.
   Web of Science ®Google Scholar
• Alsdorf, D.E. and Lettenmaier, D.P., 2003. Tracking fresh water from space. Science, 301 (80), 301. doi:10.1126/science.1089802.
   PubMedGoogle Scholar
• Alsdorf, D., Lettenmaier, D., and Vorosmarty, C., 2003. The need for global, satellite-based observations of terrestrial surface waters. Eos, Transactions American Geophysical Union, 84 (29), 269–276. doi:10.1029/2003EO290001.
   Google Scholar
• Altenau, E.H., et al., 2021. The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): a global river network for satellite data products. Water Resources Research, 57 (7), e2021WR030054. doi:10.1029/2021WR030054.
   Web of Science ®Google Scholar
• Andreadis, K.M., et al., 2007. Prospects for river discharge and depth estimation through assimilation of swath-altimetry into a raster-based hydrodynamics model. Geophysical Research Letters, 34 (10). doi:10.1029/2007GL029721.
   Web of Science ®Google Scholar
• Andreadis, K.M., Brinkerhoff, C.B., and Gleason, C.J., 2020. Constraining the assimilation of SWOT observations with hydraulic geometry relations. Water Resources Research, 56 (5), e2019WR026611. doi:10.1029/2019WR026611.
   Web of Science ®Google Scholar
• Biancamaria, S., et al., 2010. Preliminary characterization of SWOT hydrology error budget and global capabilities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3 (1), 6–19. doi:10.1109/JSTARS.2009.2034614.
   Web of Science ®Google Scholar
• Biancamaria, S., et al., 2011a. Assimilation of virtual wide swath altimetry to improve Arctic river modeling. Remote Sensing of Environment, 115, 373–381. doi:10.1016/J.RSE.2010.09.008.
   Web of Science ®Google Scholar
• Biancamaria, S., et al., 2017. Satellite radar altimetry water elevations performance over a 200 m wide river: evaluation over the Garonne River. Journal of Advanced Research, 59 (1), 128–146. doi:10.1016/j.asr.2016.10.008.
   Google Scholar
• Biancamaria, S., Hossain, F., and Lettenmaier, D.P., 2011b. Forecasting transboundary river water elevations from space. Geophysical Research Letters, 38 (11), 1–5. doi:10.1029/2011GL047290.
   Google Scholar
• Biancamaria, S., Lettenmaier, D.P., and Pavelsky, T.M., 2016. The SWOT mission and its capabilities for land hydrology. Survey Geophysical, 37, 307–337. doi:10.1007/s10712-015-9346-y.
   Web of Science ®Google Scholar
• Birkett, C.M., 1995. The contribution of TOPEX/POSEIDON to the global monitoring of climatically sensitive lakes. Journal of Geophysical Research: Oceans, 100 (C12), 25179–25204. doi:10.1029/95JC02125.
   Web of Science ®Google Scholar
• Birkett, C.M., 1998. Contribution of the TOPEX NASA radar altimeter to the global monitoring of large rivers and wetlands. Water Resources Research, 34 (5), 1223–1239. doi:10.1029/98WR00124.
   Web of Science ®Google Scholar
• Brisset, P., et al. 2018. On the assimilation of altimetric data in 1D Saint–Venant river flow models. Advances in Water Resources, 119, 41–59. doi:10.1016/J.ADVWATRES.2018.06.004.
   Web of Science ®Google Scholar
• Bullock, A. and Acreman, M., 2003. The role of wetlands in the hydrological cycle. Hydrology and Earth System Sciences, 7 (3), 358–389. doi:10.5194/hess-7-358-2003.
   Web of Science ®Google Scholar
• Calmant, S. and Seyler, F., 2006. Continental surface waters from satellite altimetry. Comptes Rendus Geoscience, 338 (14–15), 1113–1122. doi:10.1016/j.crte.2006.05.012.
   Web of Science ®Google Scholar
• Calmant, S., Seyler, F., and Cretaux, J.F., 2008. Monitoring continental surface waters by satellite altimetry. Survey Geophysical, 29 (4–5), 247–269. doi:10.1007/s10712-008-9051-1.
   Web of Science ®Google Scholar
• Crétaux, J.-F. and Birkett, C., 2006. Lake studies from satellite radar altimetry. Comptes Rendus Geoscience, 338, 1098–1112. doi:10.1016/j.crte.2006.08.002.
   Web of Science ®Google Scholar
• Desai, S., 2018. Surface Water and Ocean Topography Mission (SWOT) project, science requirements document, JPL document D-61923 [WWW Document]. Available from: https://swot.jpl.nasa.gov/system/documents/files/2176_2176_D-61923_SRD_Rev_B_20181113.pdf.
   Google Scholar
• Döll, P., et al., 2008. Advances and visions in large-scale hydrological modelling: findings from the 11th workshop on large-scale hydrological modelling. Advances in Geosciences, 18, 51–61. doi:10.5194/adgeo-18-51-2008.
   Google Scholar
• Durand, M., et al., 2008. Estimation of bathymetric depth and slope from data assimilation of swath altimetry into a hydrodynamic model. Geophysical Research Letters, 35 (20), 20401. doi:10.1029/2008GL034150.
   Web of Science ®Google Scholar
• Durand, M., et al., 2010. The surface water and ocean topography mission: observing terrestrial surface water and oceanic submesoscale eddies. Procedding of IEEE, 98 (5), 766–779. doi:10.1109/JPROC.2010.2043031.
   Web of Science ®Google Scholar
• Durand, M., et al., 2016. An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope. Water Resources Research, 52 (6), 4527–4549. doi:10.1002/2015WR018434.
   Web of Science ®Google Scholar
• Durand, M., 2019. Interface between rivers and lakes: philosophies and challenges [WWW Document]. Available from: https://www.aviso.altimetry.fr/fileadmin/documents/user_corner/SWOTST/SWOTST2019/oral/Hydrology_Data_Products_Workshop/10_RiverLakeInterface-v4.pdf.
   Google Scholar
• Elmer, N.J., et al., 2020. Generating proxy SWOT water surface elevations using WRF-hydro and the CNES SWOT hydrology simulator. Earth and Space Science Open Archive, 31. doi:10.1002/essoar.10502399.1.
   Google Scholar
• Emery, C.M., et al., 2018. Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product. Hydrology and Earth System Sciences, 22 (4), 2135–2162. doi:10.5194/hess-22-2135-2018.
   Web of Science ®Google Scholar
• Engman, E.T., 1995. Recent advances in remote sensing in hydrology. Reviews of Geophysics, 33, 967–975. doi:10.1029/95RG00403.
   Web of Science ®Google Scholar
• Frappart, F., et al., 2006. Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin. Remote Sensing Environment, 100 (2), 252–264. doi:10.1016/j.rse.2005.10.027.
   Web of Science ®Google Scholar
• Frasson, R.P.D.M., et al., 2017. Automated river reach definition strategies: applications for the surface water and ocean topography mission. Water Resources Research, 53 (10), 8164–8186. doi:10.1002/2017WR020887.
   Web of Science ®Google Scholar
• Garambois, P.A., et al., 2020. Variational estimation of effective channel and ungauged anabranching river discharge from multi-satellite water heights of different spatial sparsity. Journal of Hydrology, 581, 124409. doi:10.1016/J.JHYDROL.2019.124409.
   Web of Science ®Google Scholar
• Gleason, C.J. and Hamdan, A.N., 2017. Crossing the (watershed) divide: satellite data and the changing politics of international river basins. Geographical Journal, 183 (1), 2–15. doi:10.1111/GEOJ.12155.
   Web of Science ®Google Scholar
• Gleason, C.J., Wada, Y., and Wang, J., 2018. A hybrid of optical remote sensing and hydrological modeling improves water balance estimation. Journal of Advances in Modeling Earth Systems, 10 (1), 2–17. doi:10.1002/2017MS000986.
   Web of Science ®Google Scholar
• Gupta, H.V., Sorooshian, S., and Yapo, P.O., 1998. Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information. Water Resources Research, 34 (4), 751–763. doi:10.1029/97WR03495.
   Web of Science ®Google Scholar
• Hossain, F., et al., 2014. Proof of concept of an altimeter-based river forecasting system for transboundary flow inside Bangladesh. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (2), 587–601. doi:10.1109/JSTARS.2013.2283402.
   Web of Science ®Google Scholar
• Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82 (1), 35–45. doi:10.1115/1.3662552.
   Google Scholar
• Kauffeldt, A., et al., 2016. Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes on continental level. Environmental Modelling and Software, 75, 68–76. doi:10.1016/j.envsoft.2015.09.009.
   Web of Science ®Google Scholar
• Kouraev, A.V., et al., 2004. Ob’ river discharge from TOPEX/Poseidon satellite altimetry (1992–2002). Remote Sensing Environment, 93 (1–2), 238–245. doi:10.1016/J.RSE.2004.07.007.
   Web of Science ®Google Scholar
• Legresy, B., et al., 2005. ENVISAT radar altimeter measurements over continental surfaces and ice caps using the ICE-2 retracking algorithm. Remote Sensing Environment, 95, 150–163. doi:10.1016/J.RSE.2004.11.018.
   Web of Science ®Google Scholar
• Loukas, A. and Vasiliades, L., 2014. Streamflow simulation methods for ungauged and poorly gauged watersheds. Natural Hazards and Earth System Sciences, 14 (7), 1641–1661. doi:10.5194/NHESS-14-1641-2014.
   Web of Science ®Google Scholar
• Madsen, H., 2000. Automatic calibration of a conceptual rainfall–runoff model using multiple objectives. Journal of Hydrology, 235 (3–4), 276–288. doi:10.1016/S0022-1694(00)00279-1.
   Web of Science ®Google Scholar
• Medina, C.E., et al., 2008. Water level fluctuations derived from ENVISAT Radar Altimeter (RA-2) and in situ measurements in a subtropical waterbody: lake Izabal (Guatemala). Remote Sensing Environment, 112, 3604–3617. doi:10.1016/J.RSE.2008.05.001.
   Web of Science ®Google Scholar
• Michailovsky, C.I., Milzow, C., and Bauer-Gottwein, P., 2013. Assimilation of radar altimetry to a routing model of the Brahmaputra River. Water Resources Research, 49 (8), 4807–4816. doi:10.1002/wrcr.20345.
   Web of Science ®Google Scholar
• Milzow, C., Krogh, P.E., and Bauer-Gottwein, P., 2011. Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment. Hydrology and Earth System Sciences, 15 (6), 1729–1743. doi:10.5194/hess-15-1729-2011.
   Web of Science ®Google Scholar
• Munier, S., et al., 2015. SWOT data assimilation for operational reservoir management on the upper Niger River Basin. Water Resources Research, 51 (1), 554–575. doi:10.1002/2014WR016157.
   Web of Science ®Google Scholar
• Nair, A.S., et al., 2021. Monitoring lake levels from space: preliminary analysis with SWOT. Frontiers in Water, 3, 161. doi:10.3389/FRWA.2021.717852.
   Google Scholar
• Nair, A.S., et al., 2022a. Evaluating SWOT water level information using a large scale hydrology simulator: a case study over India. Journal of Advanced Research, 70 (5), 1362–1374. doi:10.1016/J.ASR.2022.05.001.
   Google Scholar
• Nair, A.S., et al., 2022b. Exploring the potential of SWOT mission for reservoir monitoring in Mahanadi basin. Journal of Advanced Research, 69 (3), 1481–1493. doi:10.1016/J.ASR.2021.11.019.
   Google Scholar
• Normandin, C., et al., 2018. Evolution of the performances of radar altimetry missions from ERS-2 to Sentinel-3A over the Inner Niger Delta. Remote Sensing, 10 (6), 833. doi:10.3390/rs10060833.
   Web of Science ®Google Scholar
• Oubanas, H., et al., 2018a. Discharge estimation in ungauged basins through variational data assimilation: the potential of the SWOT mission. Water Resources Research, 54 (3), 2405–2423. doi:10.1002/2017WR021735.
   Web of Science ®Google Scholar
• Oubanas, H., et al., 2018b. River discharge estimation from synthetic SWOT-type observations using variational data assimilation and the full Saint-Venant hydraulic model. Journal of Hydrology, 559, 638–647. doi:10.1016/J.JHYDROL.2018.02.004.
   Web of Science ®Google Scholar
• Paiva, R.C.D., et al., 2013. Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon. Hydrology and Earth System Sciences, 17 (7), 2929–2946. doi:10.5194/hess-17-2929-2013.
   Web of Science ®Google Scholar
• Papa, F., et al., 2006. Wetland dynamics using a suite of satellite observations: a case study of application and evaluation for the Indian Subcontinent. Geophysical Research Letters, 33. doi:10.1029/2006GL025767.
   Web of Science ®Google Scholar
• Papa, F., Legrésy, B., and Rémy, F., 2003. Use of the topex–poseidon dual-frequency radar altimeter over land surfaces. Remote Sensing Environment, 87, 136–147. doi:10.1016/S0034-4257(03)00136-6.
   Web of Science ®Google Scholar
• Pavelsky, T.M., et al., 2014. Assessing the potential global extent of SWOT river discharge observations. Journal of Hydrology, 519, 1516–1525. doi:10.1016/j.jhydrol.2014.08.044.
   Web of Science ®Google Scholar
• Pedinotti, V., et al., 2014. Assimilation of satellite data to optimize large-scale hydrological model parameters: a case study for the SWOT mission. Hydrology and Earth System Sciences, 18 (11), 4485–4507. doi:10.5194/hess-18-4485-2014.
   Web of Science ®Google Scholar
• Reichle, R.H., 2008. Data assimilation methods in the Earth sciences. Advances in Water Resources, 31 (11), 1411–1418. doi:10.1016/j.advwatres.2008.01.001.
   Web of Science ®Google Scholar
• Rodriguez, E., 2015. RiverObs documentation [WWW Document]. Jet Propuls. Lab. Available from: https://github.com/SWOTAlgorithms/RiverObs.
   Google Scholar
• Santos da Silva, J., et al., 2010. Water levels in the Amazon basin derived from the ERS 2 and ENVISAT radar altimetry missions. Remote Sensing Environment, 114 (10), 2160–2181. doi:10.1016/j.rse.2010.04.020.
   Web of Science ®Google Scholar
• Siqueira, V.A., et al., 2018. Toward continental hydrologic-hydrodynamic modeling in South America. Hydrology and Earth System Sciences, 22 (9), 4815–4842. doi:10.5194/HESS-22-4815-2018.
   Web of Science ®Google Scholar
• Sivapalan, M., 2003. Prediction in ungauged basins: a grand challenge for theoretical hydrology. Hydrological Processes, 17 (15), 3163–3170. doi:10.1002/HYP.5155.
   Web of Science ®Google Scholar
• Sneddon, C. and Fox, C., 2006. Rethinking transboundary waters: a critical hydropolitics of the Mekong basin. Political Geography, 25 (2), 181–202. doi:10.1016/J.POLGEO.2005.11.002.
   Web of Science ®Google Scholar
• Sneddon, C. and Fox, C., 2012. Water, geopolitics, and economic development in the conceptualization of a region. Eurasian Geographical Economics, 53 (1), 143–160. doi:10.2747/1539-7216.53.1.143.
   Web of Science ®Google Scholar
• Soman, M.K. and Indu, J., 2022. Sentinel-1 based Inland water dynamics Mapping System (SIMS). Environmental Modelling and Software, 149, 105305. doi:10.1016/j.envsoft.2022.105305.
   Web of Science ®Google Scholar
• Sood, A. and Smakhtin, V., 2015. Global hydrological models: a review. Hydrological Sciences Journal, 60 (4), 549–565. doi:10.1080/02626667.2014.950580.
   Web of Science ®Google Scholar
• Tourian, M.J., Schwatke, C., and Sneeuw, N., 2017. River discharge estimation at daily resolution from satellite altimetry over an entire river basin. Journal of Hydrology, 546, 230–247. doi:10.1016/j.jhydrol.2017.01.009.
   Web of Science ®Google Scholar
• Verma, K., et al., 2021. Satellite altimetry for Indian reservoirs. Water Science and Engineering, 14 (4), 277–285. doi:10.1016/J.WSE.2021.09.001.
   Web of Science ®Google Scholar
• Verma, K. and Indu, J., 2021. Effect of satellite altimetry sampling error in estimating reservoir storage and outflow. Geocarto International, 37 (25), 7580–7590. doi:10.1080/10106049.2021.1980615.
   Web of Science ®Google Scholar
• Verma, K. and Indu, J., 2023. Applicability of SWOT data in calibrating WRF-Hydro hydrological model over the Tawa River basin. Geocarto International, 38 (1). doi:10.1080/10106049.2023.2185292.
   Google Scholar
• Wanders, N., et al., 2014. The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models. Water Resources Research, 50 (8), 6874–6891. doi:10.1002/2013WR014639.
   Web of Science ®Google Scholar
• Wolf, A.T., et al., 1999. International river basins of the world. International Journal of Water Resources Development, 15 (4), 387–427. doi:10.1080/07900629948682.
   Google Scholar
• Yamazaki, D., et al., 2017. A high-accuracy map of global terrain elevations. Geophysical Research Letters, 44 (11), 5844–5853. doi:10.1002/2017GL072874.
   Web of Science ®Google Scholar
• Yoon, Y., et al., 2012. Estimating river bathymetry from data assimilation of synthetic SWOT measurements. Journal of Hydrology, 464–465, 363–375. doi:10.1016/J.JHYDROL.2012.07.028.
   Web of Science ®Google Scholar