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Flow Duration Curves from Surface Reflectance in the Near Infrared Band

Angelica Tarpanelli, Alessio Domeneghetti
2021 Applied Sciences  
For a detailed description of the procedure, the reader is invited to read Tarpanelli et al. [25] .  ...  Recently, Tarpanelli et al. [25] demonstrated that the surface reflectance ratio calculated between a dry and a wet pixel is a good estimator of the flow related variables.  ... 
doi:10.3390/app11083458 fatcat:coiyf6lckzetbiwjqpgerusfhy

From Surface Flow Velocity Measurements to Discharge Assessment by the Entropy Theory

Tommaso Moramarco, Silvia Barbetta, Angelica Tarpanelli
2017 Water  
Silvia Barbetta and Angelica Tarpanelli contributed equally to the discussion of methodology, results analysis and the manuscript writing.  ... 
doi:10.3390/w9020120 fatcat:vfc6mh2zkjcdxo4gx5462znxe4

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

Luca Brocca, Luca Ciabatta, Christian Massari, Stefania Camici, Angelica Tarpanelli
2017 Water  
Luca Ciabatta, Christian Massari, Stefania Camici and Angelica Tarpanelli contributed to the acquisition and processing of ground and satellite-based datasets, to the analysis and the elaboration of the  ... 
doi:10.3390/w9020140 fatcat:xbdhsy2lkvd5rnvlp3ohobufwm

Flow Duration Curve from Satellite: Potential of a Lifetime SWOT Mission

Alessio Domeneghetti, Angelica Tarpanelli, Luca Grimaldi, Armando Brath, Guy Schumann
2018 Remote Sensing  
General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: Abstract: A flow duration curve (FDC) provides a comprehensive description of the hydrological regime of a catchment and its knowledge is fundamental for many water-related applications (e.g., water management and supply, human and irrigation purposes, etc.). However, relying on historical streamflow records,
more » ... are constrained to gauged stations and, thus, typically available for a small portion of the world's rivers. The upcoming Surface Water and Ocean Topography satellite (SWOT; in orbit from 2021) will monitor, worldwide, all rivers larger than 100 m in width (with a goal to observe rivers as small as 50 m) for a period of at least three years, representing a potential groundbreaking source of hydrological data, especially in remote areas. This study refers to the 130 km stretch of the Po River (Northern Italy) to investigate SWOT potential in providing discharge estimation for the construction of FDCs. In particular, this work considers the mission lifetime (three years) and the three satellite orbits (i.e., 211, 489, 560) that will monitor the Po River. The aim is to test the ability to observe the river hydrological regime, which is, for this test case, synthetically reproduced by means of a quasi-2D hydraulic model. We consider different river segmentation lengths for discharge estimation and we build the FDCs at four gauging stations placed along the study area referring to available satellite overpasses (nearly 52 revisits within the mission lifetime). Discharge assessment is performed using the Manning equation, under the assumption of a trapezoidal section, known bathymetry, and roughness coefficient. SWOT observables (i.e., water level, water extent, etc.) are estimated by corrupting the values simulated with the quasi-2D model according to the mission requirements. Remotely-sensed FDCs are compared with those obtained with extended (e.g., 20-70 years) gauge datasets. Results highlight the potential of the mission to provide a realistic reconstruction of the flow regimes at different locations. Higher errors are obtained at the FDC tails, where very low or high flows have lower likelihood of being observed, or might not occur during the mission lifetime period. Among the tested discretizations, 20 km stretches provided the best performances, with root mean absolute errors, on average, lower than 13.3%.
doi:10.3390/rs10071107 fatcat:vvx6ouwx45efrgiion34g2kxwi

Preface: Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics

Alessio Domeneghetti, Guy J.-P. Schumann, Angelica Tarpanelli
2019 Remote Sensing  
This Special Issue is a collection of papers that focus on the use of remote sensing data and describe methods for flood monitoring and mapping. These articles span a wide range of topics; present novel processing techniques and review methods; and discuss limitations and challenges. This preface provides a brief overview of the content.
doi:10.3390/rs11080943 fatcat:sciy72pjb5cdzl4hkjqkv3kb3e

Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe?

Christian Massari, Luca Brocca, Angelica Tarpanelli, Tommaso Moramarco
2015 Remote Sensing  
Tarpanelli and Luca Brocca) and (iii) the analysis of the results (Christian Massari, Luca Brocca, Angelica Tarpanelli and Tommaso Moramarco).  ...  and Christian Massari), (ii) the implementation of the filtering, rescaling and processing of the observations along with the run of the simulations and the manuscript preparation (Christian Massari, Angelica  ... 
doi:10.3390/rs70911403 fatcat:ib6l6ps73jeo7josipjlj5ediu

River Discharge Estimation by Using Altimetry Data and Simplified Flood Routing Modeling

Angelica Tarpanelli, Silvia Barbetta, Luca Brocca, Tommaso Moramarco
2013 Remote Sensing  
A methodology to estimate the discharge along rivers, even poorly gauged ones, taking advantage of water level measurements derived from satellite altimetry is proposed. The procedure is based on the application of the Rating Curve Model (RCM), a simple method allowing for the estimation of the flow conditions in a river section using only water levels recorded at that site and the discharges observed at another upstream section. The European Remote-Sensing Satellite 2, ERS-2, and the
more » ... tal Satellite, ENVISAT, altimetry data are used to provide time series of water levels needed for the application of RCM. In order to evaluate the usefulness of the approach, the results are compared with the ones obtained by applying an empirical formula that allows discharge estimation from remotely sensed hydraulic information. To test the proposed procedure, the 236 km-reach of the Po River is investigated, for which five in situ stations and four satellite tracks are available. Results show that RCM is able to appropriately represent the discharge, and its performance is better than the empirical formula, although this latter does not require upstream hydrometric data. Given its simple formal structure, the proposed approach can be conveniently utilized in ungauged sites where only the survey of the cross-section is needed.
doi:10.3390/rs5094145 fatcat:fuprdu3nnrfmzni57jowm3ilzq

River Flow Monitoring by Sentinel-3 OLCI and MODIS: Comparison and Combination

Angelica Tarpanelli, Filippo Iodice, Luca Brocca, Marco Restano, Jérôme Benveniste
2020 Remote Sensing  
Tarpanelli et al.  ...  [15] and successively Tarpanelli et al.  ... 
doi:10.3390/rs12233867 fatcat:c6idhz4c3bdulh4ipcc25yezti

The use of remote sensing-derived water surface data for hydraulic model calibration

Alessio Domeneghetti, Angelica Tarpanelli, Luca Brocca, Silvia Barbetta, Tommaso Moramarco, Attilio Castellarin, Armando Brath
2014 Remote Sensing of Environment  
Tarpanelli, Brocca, Melone, & Moramarco, 2013 in Italy, Matgen, Schumann, Henry, Hoiffmann, & Pfister, 2007 in Luxemburg, Schumann, Neal, Mason, & Bates, 2011 in England) , as well as in urban areas  ...  Fig. 1, Table 1 , Tarpanelli, Barbetta, et al., 2013; Bercher & Kosuth, 2012) , the results of the calibration exercises reported in Section 4 clearly highlight that remotely-sensed data can be particularly  ... 
doi:10.1016/j.rse.2014.04.007 fatcat:fpim5atqpncxll3mbdez44nsva

Water Level measurement using COSMO-SkyMed Synthetic Aperture Radar

Filippo Biondi, Angelica Tarpanelli, Pia Addabbo, Carmine Clemente, Danilo Orlando
2020 2020 IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace)  
In this work, temporal series of Synthetic Aperture Radar (SAR) data are used to estimate water elevations. The proposed method is based on a Sub-Pixel Offset Tracking (technique) to retrieve the displacement of the double-bounce scattering effect of man made structures located in the proximity of the water surface. The experimental setup is focused on the cases of the Mosul dam in Iraq and the Missouri river in Kansas city. The proposed approach is applied to real data from the COSMO-SkyMed
more » ... gram. Results validated with in-situ and satellite radar altimeter measurements prove the effectiveness of the proposed method in measuring the water levels.
doi:10.1109/metroaerospace48742.2020.9160246 fatcat:okrarvuxbfewjgv72niwgty6o4

Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

Sara Modanesi, Christian Massari, Alexander Gruber, Hans Lievens, Angelica Tarpanelli, Renato Morbidelli, Gabrielle J. M. De Lannoy
2021 Hydrology and Earth System Sciences  
Abstract. Worldwide, the amount of water used for agricultural purposes is rising, and the quantification of irrigation is becoming a crucial topic. Because of the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale land surface models (LSMs) is improving, but it is still hampered by the lack of information about dynamic
more » ... op rotations, or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. This work represents the first and necessary step towards building a reliable LSM data assimilation system which, in future analysis, will investigate the potential of high-resolution radar backscatter observations from Sentinel-1 to improve irrigation quantification. Specifically, the aim of this study is to couple the Noah-MP LSM running within the NASA Land Information System (LIS), with a backscatter observation operator for simulating unbiased backscatter predictions over irrigated lands. In this context, we first tested how well modelled surface soil moisture (SSM) and vegetation estimates, with or without irrigation simulation, are able to capture the signal of aggregated 1 km Sentinel-1 backscatter observations over the Po Valley, an important agricultural area in northern Italy. Next, Sentinel-1 backscatter observations, together with simulated SSM and leaf area index (LAI), were used to optimize a Water Cloud Model (WCM), which will represent the observation operator in future data assimilation experiments. The WCM was calibrated with and without an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that using an irrigation scheme provides a better calibration of the WCM, even if the simulated irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chances of having error cross-correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture, and vegetation estimates via future data assimilation.
doi:10.5194/hess-25-6283-2021 fatcat:pi35llmvaffmhgphinuwhgkiue

On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation

Javier Loizu, Christian Massari, Jesús Álvarez-Mozos, Angelica Tarpanelli, Luca Brocca, Javier Casalí
2018 Advances in Water Resources  
Highlights  ASCAT soil moisture data were assimilated into a conceptual and a physically-based model.  Optimal EnKF assimilation set-ups improved streamflow simulation in Mediterranean catchments.  Improvements varied from 6 to 45% from the validation run.  Linear re-scaling method outperformed variance matching and cumulative distribution function.  Largest improvements were achieved assuming observation errors within 1-6%.
doi:10.1016/j.advwatres.2017.10.034 fatcat:ofs6mzle7zctfnz2taqsrcfage

Editorial for the Special Issue "Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge"

Carl J. Legleiter, Tamlin Pavelsky, Michael Durand, George H. Allen, Angelica Tarpanelli, Renato Frasson, Inci Guneralp, Amy Woodget
2020 Remote Sensing  
River discharge is a fundamental hydrologic quantity that summarizes how a watershed transforms the input of precipitation into output as channelized streamflow[...]
doi:10.3390/rs12142304 fatcat:lxuo6cznvbefnk4jhehha2zmva

Estimating irrigation water use over the contiguous United States by combining satellite and reanalysis soil moisture data

Felix Zaussinger, Wouter Dorigo, Alexander Gruber, Angelica Tarpanelli, Paolo Filippucci, Luca Brocca
2019 Hydrology and Earth System Sciences  
<p><strong>Abstract.</strong> Effective agricultural water management requires accurate and timely information on the availability and use of irrigation water. However, most existing information on irrigation water use (<span class="inline-formula">IWU</span>) lacks the objectivity and spatiotemporal representativeness needed for operational water management and meaningful characterization of land–climate interactions. Although optical remote sensing has been used to map the area affected by
more » ... igation, it does not physically allow for the estimation of the actual amount of irrigation water applied. On the other hand, microwave observations of the moisture content in the top soil layer are directly influenced by agricultural irrigation practices and thus potentially allow for the quantitative estimation of IWU. In this study, we combine surface soil moisture (SM) retrievals from the spaceborne SMAP, AMSR2 and ASCAT microwave sensors with modeled soil moisture from MERRA-2 reanalysis to derive monthly IWU dynamics over the contiguous United States (CONUS) for the period 2013–2016. The methodology is driven by the assumption that the hydrology formulation of the MERRA-2 model does not account for irrigation, while the remotely sensed soil moisture retrievals do contain an irrigation signal. For many CONUS irrigation hot spots, the estimated spatial irrigation patterns show good agreement with a reference data set on irrigated areas. Moreover, in intensively irrigated areas, the temporal dynamics of observed IWU is meaningful with respect to ancillary data on local irrigation practices. State-aggregated mean IWU volumes derived from the combination of SMAP and MERRA-2 soil moisture show a good correlation with statistically reported state-level irrigation water withdrawals (IWW) but systematically underestimate them. We argue that this discrepancy can be mainly attributed to the coarse spatial resolution of the employed satellite soil moisture retrievals, which fails to resolve local irrigation practices. Consequently, higher-resolution soil moisture data are needed to further enhance the accuracy of IWU mapping.</p>
doi:10.5194/hess-23-897-2019 fatcat:g4foaxeudfcjvjfi6yj4xda4nu

High-resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River basin as a case study

Paolo Filippucci, Luca Brocca, Raphael Quast, Luca Ciabatta, Carla Saltalippi, Wolfgang Wagner, Angelica Tarpanelli
2022 Hydrology and Earth System Sciences  
It has already been applied worldwide to both regional (Tarpanelli et al., 2017) and global (Ciabatta et al., 2018; Brocca et al., 2019) satellite SM products, obtaining satisfactory results, in particular  ... 
doi:10.5194/hess-26-2481-2022 fatcat:egqrpglfcrentitpb5iclg7bvu
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