Articles | Volume 23, issue 10
https://doi.org/10.5194/nhess-23-3305-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-3305-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Oct 2023
Research article |
| 23 Oct 2023
| 23 Oct 2023
Sentinel-1-based analysis of the severe flood over Pakistan 2022
Florian Roth
CORRESPONDING AUTHOR
Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Bernhard Bauer-Marschallinger
Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Mark Edwin Tupas
Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City, Philippines
Christoph Reimer
EODC Earth Observation Data Centre for Water Resources Monitoring GmbH, Vienna, Austria
Peter Salamon
Joint Research Centre, European Commission, Via E. Fermi 2749, 21027 Ispra, Italy
Wolfgang Wagner
Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Related authors
J. Zhao, F. Roth, B. Bauer-Marschallinger, W. Wagner, M. Chini, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 911–918, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-911-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-911-2023, 2023
M. Tupas, C. Navacchi, F. Roth, B. Bauer-Marschallinger, F. Reuß, and W. Wagner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W1-2022, 495–502, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-495-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-495-2022, 2022
Serigne Bassirou Diop, Job Ekolu, Yves Tramblay, Bastien Dieppois, Stefania Grimaldi, Ansoumana Bodian, Juliette Blanchet, Ponnambalam Rameshwaran, Peter Salamon, and Benjamin Sultan
Nat. Hazards Earth Syst. Sci., 25, 3161–3184, https://doi.org/10.5194/nhess-25-3161-2025, https://doi.org/10.5194/nhess-25-3161-2025, 2025
Short summary
Short summary
West Africa is very vulnerable to river floods. Current flood hazards are poorly understood due to limited data. This study is filling this knowledge gap using recent databases and two regional hydrological models to analyze changes in flood risk under two climate scenarios. Results show that most areas will see more frequent and severe floods, with some increasing by over 45 %. These findings stress the urgent need for climate-resilient strategies to protect communities and infrastructure.
Andrea Betterle and Peter Salamon
Nat. Hazards Earth Syst. Sci., 24, 2817–2836, https://doi.org/10.5194/nhess-24-2817-2024, https://doi.org/10.5194/nhess-24-2817-2024, 2024
Short summary
Short summary
The study proposes a new framework, named FLEXTH, to estimate flood water depth and improve satellite-based flood monitoring using topographical data. FLEXTH is readily available as a computer code, offering a practical and scalable solution for estimating flood depth quickly and systematically over large areas. The methodology can reduce the impacts of floods and enhance emergency response efforts, particularly where resources are limited.
Margarita Choulga, Francesca Moschini, Cinzia Mazzetti, Stefania Grimaldi, Juliana Disperati, Hylke Beck, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 28, 2991–3036, https://doi.org/10.5194/hess-28-2991-2024, https://doi.org/10.5194/hess-28-2991-2024, 2024
Short summary
Short summary
CEMS_SurfaceFields_2022 dataset is a new set of high-resolution maps for land type (e.g. lake, forest), soil properties and population water needs at approximately 2 and 6 km at the Equator, covering Europe and the globe (excluding Antarctica). We describe what and how new high-resolution information can be used to create the dataset. The paper suggests that the dataset can be used as input for river, weather or other models, as well as for statistical descriptions of the region of interest.
J. Zhao, F. Roth, B. Bauer-Marschallinger, W. Wagner, M. Chini, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 911–918, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-911-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-911-2023, 2023
Shaun Harrigan, Ervin Zsoter, Hannah Cloke, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 27, 1–19, https://doi.org/10.5194/hess-27-1-2023, https://doi.org/10.5194/hess-27-1-2023, 2023
Short summary
Short summary
Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
M. Tupas, C. Navacchi, F. Roth, B. Bauer-Marschallinger, F. Reuß, and W. Wagner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W1-2022, 495–502, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-495-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-495-2022, 2022
Vera Thiemig, Goncalo N. Gomes, Jon O. Skøien, Markus Ziese, Armin Rauthe-Schöch, Elke Rustemeier, Kira Rehfeldt, Jakub P. Walawender, Christine Kolbe, Damien Pichon, Christoph Schweim, and Peter Salamon
Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, https://doi.org/10.5194/essd-14-3249-2022, 2022
Short summary
Short summary
EMO-5 is a free and open European high-resolution (5 km), sub-daily, multi-variable (precipitation, temperatures, wind speed, solar radiation, vapour pressure), multi-decadal meteorological dataset based on quality-controlled observations coming from almost 30 000 stations across Europe, and is produced in near real-time. EMO-5 (v1) covers the time period from 1990 to 2019. In this paper, we have provided insight into the source data, the applied methods, and the quality assessment of EMO-5.
Ashwini Petchiappan, Susan C. Steele-Dunne, Mariette Vreugdenhil, Sebastian Hahn, Wolfgang Wagner, and Rafael Oliveira
Hydrol. Earth Syst. Sci., 26, 2997–3019, https://doi.org/10.5194/hess-26-2997-2022, https://doi.org/10.5194/hess-26-2997-2022, 2022
Short summary
Short summary
This study investigates spatial and temporal patterns in the incidence angle dependence of backscatter from the ASCAT C-band scatterometer and relates those to precipitation, humidity, and radiation data and GRACE equivalent water thickness in ecoregions in the Amazon. The results show that the ASCAT data record offers a unique perspective on vegetation water dynamics exhibiting sensitivity to moisture availability and demand and phenological change at interannual, seasonal, and diurnal scales.
Paolo Filippucci, Luca Brocca, Raphael Quast, Luca Ciabatta, Carla Saltalippi, Wolfgang Wagner, and Angelica Tarpanelli
Hydrol. Earth Syst. Sci., 26, 2481–2497, https://doi.org/10.5194/hess-26-2481-2022, https://doi.org/10.5194/hess-26-2481-2022, 2022
Short summary
Short summary
A high-resolution (1 km) rainfall product with 10–30 d temporal resolution was obtained starting from SM data from Sentinel-1. Good performances are achieved using observed data (gauge and radar) over the Po River Valley, Italy, as a benchmark. The comparison with a product characterized by lower spatial resolution (25 km) highlights areas where the high spatial resolution of Sentinel-1 has great benefits. Possible applications include water management, agriculture and index-based insurances.
Francesco Dottori, Lorenzo Alfieri, Alessandra Bianchi, Jon Skoien, and Peter Salamon
Earth Syst. Sci. Data, 14, 1549–1569, https://doi.org/10.5194/essd-14-1549-2022, https://doi.org/10.5194/essd-14-1549-2022, 2022
Short summary
Short summary
We present a set of hazard maps for river flooding for Europe and the Mediterranean basin. The maps depict inundation extent and depth for flood probabilities for up to 1-in-500-year flood hazards and are based on hydrological and hydrodynamic models driven by observed climatology. The maps can identify two-thirds of the flood extent reported by official flood maps, with increasing skill for higher-magnitude floods. The maps are used for evaluating present and future impacts of river floods.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
Short summary
Short summary
The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
A. Iglseder, M. Bruggisser, A. Dostálová, N. Pfeifer, S. Schlaffer, W. Wagner, and M. Hollaus
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 567–574, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-567-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-567-2021, 2021
Rui Tong, Juraj Parajka, Andreas Salentinig, Isabella Pfeil, Jürgen Komma, Borbála Széles, Martin Kubáň, Peter Valent, Mariette Vreugdenhil, Wolfgang Wagner, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 1389–1410, https://doi.org/10.5194/hess-25-1389-2021, https://doi.org/10.5194/hess-25-1389-2021, 2021
Short summary
Short summary
We used a new and experimental version of the Advanced Scatterometer (ASCAT) soil water index data set and Moderate Resolution Imaging Spectroradiometer (MODIS) C6 snow cover products for multiple objective calibrations of the TUWmodel in 213 catchments of Austria. Combined calibration to runoff, satellite soil moisture, and snow cover improves runoff (40 % catchments), soil moisture (80 % catchments), and snow (~ 100 % catchments) simulation compared to traditional calibration to runoff only.
Cited articles
Bauer-Marschallinger, B., Cao, S., Tupas, M. E., Roth, F., Navacchi, C., Melzer, T., Freeman, V., and Wagner, W.: Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube, Remote Sens., 14, 3673, https://doi.org/10.3390/rs14153673, 2022. a, b, c
Dasgupta, A., Hostache, R., Ramsankaran, R., Grimaldi, S., Matgen, P., Chini, M., Pauwels, V. R., and Walker, J. P.: Earth observation and hydraulic data assimilation for improved flood inundation forecasting, in: Earth observation for flood applications, Elsevier, 255–294, https://doi.org/10.1016/B978-0-12-819412-6.00012-2, 2021. a
Global Flood Monitoring: GFM Product Definition Document, https://extwiki.eodc.eu/GFM/PDD/GFMoutputLayers#output-layer-exclusion-mask (last access: 23 September 2022), 2022. a
Global Flood Monitoring Service: Global Flood Awareness System, https://www.globalfloods.eu/ (last access: 20 October 2023), 2023. a
Hoque, R., Nakayama, D., Matsuyama, H., and Matsumoto, J.: Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh, Nat. Hazards, 57, 525–548, 2011. a
Hostache, R., Chini, M., Giustarini, L., Neal, J., Kavetski, D., Wood, M., Corato, G., Pelich, R.-M., and Matgen, P.: Near-real-time assimilation of SAR-derived flood maps for improving flood forecasts, Water Resour. Res., 54, 5516–5535, 2018. a
Khan, A. N.: Analysis of 2010-flood causes, nature and magnitude in the Khyber Pakhtunkhwa, Pakistan, Nat. Hazards, 66, 887–904, 2013. a
NASA Earth Observatory: Devastating Floods in Pakistan, https://earthobservatory.nasa.gov/images/150279/devastating-floods-in-pakistan (last access: 23 September 2022), 2022. a
Otto, F. E., Zachariah, M., Saeed, F., Siddiqi, A., Kamil, S., Mushtaq, H., Arulalan, T., AchutaRao, K., Chaithra, S., Barnes, C., Philip, S., Kew, S., Vautard, R., Koren, G., Pinto, I., Wolski, P., Vahlberg, M., Singh, R., Arrighi, J., van Aalst, M., Thalheimer, L., Raju, E., Li, S., Yang, W., Harrington, L. J., and Clarke, B.: Climate change increased extreme monsoon rainfall, flooding highly vulnerable communities in Pakistan, Environ. Res.: Climate, 2, 025001, https://doi.org/10.1088/2752-5295/acbfd5, 2023. a
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution mapping of global surface water and its long-term changes, Nature, 540, 418–422, 2016. a
Qasim, S., Khan, A. N., Shrestha, R. P., and Qasim, M.: Risk perception of the people in the flood prone Khyber Pukhthunkhwa province of Pakistan, Int. J. Disast. Risk Reduct., 14, 373–378, 2015. a
Roth, F., Bauer-Marschallinger, B., Tupas, M. E., Reimer, C., Salamon, P., and Wagner, W.: Sentinel-1 based analysis of the Pakistan Flood in 2022, TU Wien [data set], https://doi.org/10.48436/zvvmh-nan78, 2022. a
Salamon, P., Mctlormick, N., Reimer, C., Clarke, T., Bauer-Marschallinger, B., Wagner, W., Martinis, S., Chow, C., Böhnke, C., Matgen, P., Chini, M., Hostache, R., Molini, L., Fiori, E., and Walli, A.: The new, systematic global flood monitoring product of the copernicus emergency management service, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 11–16 July 2021, Brussels, Belgium, 1053–1056, 2021. a
Schumann, G., Giustarini, L., Tarpanelli, A., Jarihani, B., and Martinis, S.: Flood Modeling and Prediction Using Earth Observation Data, Surv. Geophys., 44, 1553–1578, 2023. a
Schumann, G. J., Brakenridge, G. R., Kettner, A. J., Kashif, R., and Niebuhr, E.: Assisting flood disaster response with earth observation data and products: A critical assessment, Remote Sens., 10, 1230, https://doi.org/10.3390/rs10081230, 2018. a
The Editors of Encyclopaedia: Sindh, https://www.britannica.com/place/Sindh-province-Pakistan (last access: 10 March 2023), 2023a. a
The Editors of Encyclopaedia: Pakistan, https://www.britannica.com/place/Pakistan/Economy (last access: 5 June 2023), 2023b. a
Thomas, M., Tellman, E., Osgood, D. E., DeVries, B., Islam, A. S., Steckler, M. S., Goodman, M., and Billah, M.: A framework to assess remote sensing algorithms for satellite-based flood index insurance, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 16, 2589–2604, 2023. a
Wagner, W., Bauer-Marschallinger, B., Navacchi, C., Reuß, F., Cao, S., Reimer, C., Schramm, M., and Briese, C.: A Sentinel-1 backscatter datacube for global land monitoring applications, Remote Sens., 13, 4622, https://doi.org/10.3390/rs13224622, 2021. a
Wang, Y.: Using Landsat 7 TM data acquired days after a flood event to delineate the maximum flood extent on a coastal floodplain, Int. J. Remote Sens., 25, 959–974, 2004. a
Wania, A., Joubert-Boitat, I., Dottori, F., Kalas, M., and Salamon, P.: Increasing timeliness of satellite-based flood mapping using early warning systems in the Copernicus Emergency Management Service, Remote Sens., 13, 2114, https://doi.org/10.3390/rs13112114, 2021. a
Westerhoff, R. S., Kleuskens, M. P. H., Winsemius, H. C., Huizinga, H. J., Brakenridge, G. R., and Bishop, C.: Automated global water mapping based on wide-swath orbital synthetic-aperture radar, Hydrol. Earth Syst. Sci., 17, 651–663, https://doi.org/10.5194/hess-17-651-2013, 2013. a
Short summary
In August and September 2022, millions of people were impacted by a severe flood event in Pakistan. Since many roads and other infrastructure were destroyed, satellite data were the only way of providing large-scale information on the flood's impact. Based on the flood mapping algorithm developed at Technische Universität Wien (TU Wien), we mapped an area of 30 492 km2 that was flooded at least once during the study's time period. This affected area matches about the total area of Belgium.
In August and September 2022, millions of people were impacted by a severe flood event in...
Altmetrics
Final-revised paper
Preprint