Testing the use of single- and multi-mission satellite altimetry for the calibration of hydraulic models

Domeneghetti, Alessio; Molari, Giada; Tourian, Mohammad J.; Tarpanelli, Angelica; Behnia, Sajedeh; Moramarco, Tommaso; Sneeuw, Nico; Brath, Armando

doi:10.1016/j.advwatres.2021.103887

Cited by 18 publications

(12 citation statements)

References 38 publications

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“…Kittel et al (2021b) present RMSE between 0.61 and 0.89 m using CryoSat-2 observations only on the Zambezi catchment. Pujol et al (2020) use Envisat observations and synthetic SWOT to infer channel geometries, resulting in WSE RM-SEs around 0.94 m. When using a multi-mission approach, Domeneghetti et al (2021) report RMSEs between 0.68 and 0.89 m in the Po River. The parameters most sensitive to the calibration were the roughness coefficient and the low-flow depth, while the cross section form exponent and correction factor had a lower impact.…”

Section: Model Performancementioning

confidence: 99%

River hydraulic modeling with ICESat-2 land and water surface elevation

Frías

Liu

et al. 2023

Hydrol. Earth Syst. Sci.

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Abstract. Advances in geodetic altimetry instruments are providing more accurate measurements, thus enabling satellite missions to produce useful data for narrow rivers and streams. Altimetry missions produce spatially dense land and water surface elevation (WSE) measurements in remote areas where in situ data are scarce that can be combined with hydraulic and/or hydrodynamic models to simulate WSE and estimate discharge. In this study, we combine ICESat-2 (Ice, Cloud and land Elevation Satellite) land and water surface elevation measurements with a low-parameterized hydraulic calibration to simulate WSE and discharge without the need for surveyed cross-sectional geometry and a rainfall–runoff model. ICESat-2 provides an opportunity to map river cross-sectional geometry very accurately, with an along-track resolution of 0.7 m, using the ATL03 product. These measurements are combined with the inland water product ATL13 to calibrate a steady-state hydraulic model to retrieve unobserved hydraulic parameters such as river depth or the roughness coefficient. The low-parameterized model, together with the assumption of steady-state hydraulics, enables the application of a global search algorithm for a spatially uniform parameter calibration at a manageable computational cost. The model performance is similar to that reported for highly parameterized models, with a root mean square error (RMSE) of around 0.41 m. With the calibrated model, we can calculate the WSE time series at any chainage point at any time for an available satellite pass within the river reach and estimate discharge from WSE. The discharge estimates are validated with in situ measurements at two available gauging stations. In addition, we use the calibrated parameters in a full hydrodynamic model simulation, resulting in a RMSE of 0.59 m for the entire observation period.

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Section: Model Performancementioning

confidence: 99%

River hydraulic modeling with ICESat-2 land and water surface elevation

Frías

Liu

et al. 2023

Hydrol. Earth Syst. Sci.

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“…However, recent studies overcome the inadequate temporal sampling through the combination of multiple satellite altimetry missions thanks to the development of multi-mission merging approaches (Tourian et al 2016(Tourian et al , 2017Zakharova et al 2020;Boergens et al 2017Boergens et al , 2019Schwatke et al 2015). The improved frequency, along with the high accuracy of the altimetry spatial missions (i.e., Sentinel-3 and CryoSat -2), and the recent re-elaboration of past mission focusing on inland water (FDR4ALT, Cryo-TEMPO, HYDROCOASTAL projects funded by ESA) suggest the use of satellite altimetry data for hydrological and hydraulic applications (Birkinshaw et al 2010;Getirana, 2009;Michailovsky et al 2013;Domeneghetti et al 2021;Tarpanelli et al 2013): altimetry-derived water levels, as traditional in situ river stage measurements, have several uses in the estimation of flooded areas (Sanyal and Lu 2005), and river discharge (Bjerklie et al 2003;Sichangi et al 2016;Tarpanelli et al 2017). As in the traditional procedure, by fitting the satellite measurements of water stage (difference between the altimetry-derived water level and the bottom of the section) with the simultaneous ground measurements of river discharge is possible to establish the functional law, called rating curve.…”

Section: Altimetrymentioning

confidence: 99%

Flood Modeling and Prediction Using Earth Observation Data

Schumann

Giustarini²,

Tarpanelli

et al. 2022

Surv Geophys

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The ability to map floods from satellites has been known for over 40 years. Early images of floods were rather difficult to obtain, and flood mapping from satellites was thus rather opportunistic and limited to only a few case studies. However, over the last decade, with a proliferation of open-access EO data, there has been much progress in the development of Earth Observation products and services tailored to various end-user needs, as well as its integration with flood modeling and prediction efforts. This article provides an overview of the use of satellite remote sensing of floods and outlines recent advances in its application for flood mapping, monitoring and its integration with flood models. Strengths and limitations are discussed throughput, and the article concludes by looking at new developments.

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“…Past studies show how spatially distributed vegetation roughness values are responsible for increases in mean flow depth and reductions in mean velocity relative to an unvegetated roughness scenario (Abu-Aly et al, 2014). Using the same reach of the Po River, Domeneghetti et al (2021) evaluated the performance of several satellite altimetry products to calibrate a two-dimensional hydraulic model, but the roughness coefficient was assumed to be constant through time. Remote sensing data from multitemporal satellite imagery could provide spatially distributed roughness parameterizations that are dynamic (i.e., representing intra-annual and annual changes in vegetation coverage).…”

Section: Future Recommendationsmentioning

confidence: 99%

Deriving Planform Morphology and Vegetation Coverage From Remote Sensing to Support River Management Applications

Boothroyd

Nones

Guerrero

2021

Front. Environ. Sci.

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With the increasing availability of big geospatial data (e.g., multi-spectral satellite imagery) and access to platforms that support multi-temporal analyses (e.g., cloud-based computing, Geographical Information Systems, GIS), the use of remotely sensed information for monitoring riverine hydro-morpho-biodynamics is growing. Opportunities to map, quantify and detect changes in the wider riverscape (i.e., water, sediment and vegetation) at an unprecedented spatiotemporal resolution can support flood risk and river management applications. Focusing on a reach of the Po River (Italy), satellite imagery from Landsat 5, 7, and 8 for the period 1988–2018 were analyzed in Google Earth Engine (GEE) to investigate changes in river planform morphology and vegetation dynamics associated with transient hydrology. An improved understanding of these correlations can help in managing sediment transport and riparian vegetation to reduce flood risk, where biogeomorphic processes are commonly overlooked in flood risk mapping. In the study, two established indices were analyzed: the Modified Normalized Difference Water Index (MNDWI) for monitoring changes in the wetted river planform morphology, inferring information about sediment dynamics, and the Normalized Difference Vegetation Index (NDVI) for evaluating changes in vegetation coverage. Results suggest that planform changes are highly localized with most parts of the reach remaining stable. Using the wetted channel occurrence as a measure of planform stability, almost two-thirds of the wetted channel extent (total area = 86.4 km2) had an occurrence frequency >90% (indicating stability). A loss of planform complexity coincided with the position of former secondary channels, or zones where the active river channel had narrowed. Time series analysis of vegetation dynamics showed that NDVI maxima were recorded in May/June and coincided with the first peak in the hydrological regime (occurring in late spring and associated with snowmelt). Seasonal variation in vegetation coverage is potentially important for local hydrodynamics, influencing flood risk. We suggest that remotely sensed information can provide river scientists with new insights to support the management of highly anthropized watercourses.

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Testing the use of single- and multi-mission satellite altimetry for the calibration of hydraulic models

Cited by 18 publications

References 38 publications

River hydraulic modeling with ICESat-2 land and water surface elevation

River hydraulic modeling with ICESat-2 land and water surface elevation

Flood Modeling and Prediction Using Earth Observation Data

Deriving Planform Morphology and Vegetation Coverage From Remote Sensing to Support River Management Applications

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