Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations

Abstract. The paper presents a methodology for the estimation of uncertainty of inundation extent, which takes account of the uncertainty in the observed spatially distributed information and implements a fuzzy evaluation methodology. The Generalised Likelihood Uncertainty Estimation (GLUE) technique and the 2-D LISFLOOD-FP model were applied to derive the set of uncertain inundation realisations and resulting flood inundation maps. Conditioning of the inundation maps on fuzzified Synthetic Aperture Radar (SAR) images results in much more realistic inundation risk maps which can better depict the variable pattern of inundation extent than previously used methods. It has been shown that the evaluation methodology compares well to traditional approaches and can produce flood hazard maps that reflect the uncertainties in model evaluation.

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Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-3-2243-2006 ◽

2006 ◽

Vol 3 (4) ◽

pp. 2243-2277 ◽

Cited By ~ 6

Author(s):

F. Pappenberger ◽

K. Frodsham ◽

K. Beven ◽

R. Romanowicz ◽

P. Matgen

Keyword(s):

Flood Hazard ◽

Evaluation Methodology ◽

Flood Inundation ◽

Fuzzy Evaluation ◽

Sar Images ◽

Distributed Information ◽

Inundation Maps ◽

Spatially Distributed ◽

Inundation Extent ◽

Traditional Approaches

Abstract. The paper presents a methodology for the estimation of uncertainty of inundation extent, which takes account of the uncertainty in the observed spatially distributed information and implements a fuzzy evaluation methodology. The Generalised Likelihood Uncertainty Estimation (GLUE) technique and the 2-D LISFLOOD-FP model were applied to derive the set of uncertain inundation realisations and resulting flood inundation maps. Conditioning of the inundation maps on fuzzified Synthetic Aperture Radar (SAR) images results in much more realistic inundation risk maps which can better depict the variable pattern of inundation extent than previously used methods. It has been shown that the methodology compares well to traditional approaches and can produce flood hazard maps that reflect the uncertainties in model evaluation.

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Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

The Cryosphere ◽

10.5194/tc-14-935-2020 ◽

2020 ◽

Vol 14 (3) ◽

pp. 935-956 ◽

Cited By ~ 3

Author(s):

Carlo Marin ◽

Giacomo Bertoldi ◽

Valentina Premier ◽

Mattia Callegari ◽

Christian Brida ◽

...

Keyword(s):

Snow Water Equivalent ◽

Melting Process ◽

Liquid Water Content ◽

Sar Images ◽

Distributed Information ◽

Snow Modeling ◽

Alpine Regions ◽

Snow Properties ◽

Wet Snow ◽

Spatially Distributed

Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

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Development of algorithms for evaluating performance of flood simulation models with satellite-derived flood

H2Open Journal ◽

10.2166/h2oj.2020.117 ◽

2020 ◽

Author(s):

Tushar Surwase ◽

P. Manjusree ◽

Sachin Prakash ◽

Saikiran Kuntla

Keyword(s):

Flood Hazard ◽

Simulation Models ◽

Synthetic Aperture ◽

Flood Inundation ◽

Hazard Maps ◽

Sar Images ◽

Flood Simulation ◽

Severe Event ◽

Technique Evaluation ◽

Best Fit

Abstract Flood inundation simulation models are widely used for simulating severe events of flood, generating hazard maps, risk assessment, and to identify flood vulnerable locations. It is important to assess the degree of accuracy of flood model results as these results may be one of the triggering parameters considered in developing flood hazard maps, flood mitigation policies, and land using planning where multi-criteria analysis is approached. In the present study, an algorithm is developed in order to know the performance of flood models by validating it with flood footprints extracted from synthetic aperture radar (SAR) images using multi-segmentation and Otsu's thresholding technique. Evaluation of the performance of the model is based on two best fit criteria called F1 and F2. For this, HEC-RAS model is used for simulating the severe event of flood witnessed in Mahanadi River in Odisha stretching between Tikarpara and Mundali during September 2008.Three simulations were made by considering three different Manning's roughness for river and floodplain. The model gives appreciable results and best fit F1 = 0.85 and F2 = 0.74 was found for Manning's roughness 0.020.

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Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

10.5194/tc-2019-175 ◽

2019 ◽

Author(s):

Carlo Marin ◽

Giacomo Bertoldi ◽

Valentina Premier ◽

Mattia Callegari ◽

Christian Brida ◽

...

Keyword(s):

Snow Water Equivalent ◽

Melting Process ◽

Liquid Water Content ◽

Sar Images ◽

Distributed Information ◽

Snow Modeling ◽

Alpine Regions ◽

Wet Snow ◽

Spatially Distributed ◽

Multi Temporal

Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of: i) the snow melt onset; ii) the snow gliding and wet-snow avalanches; iii) the release of snow contaminants and iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to be obtained. On the other hand, active microwave sensors such as the Synthetic Aperture Radar (SAR) mounted on board of satellites, are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 days over several places in the world. In this paper we analyze the correlation between the multi-temporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering two hydrological years. We found that the multi-temporal SAR measurements allow the identification of the three melting phases that characterize the melting process i.e., moistening, ripening and runoff. In detail, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water, and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease, but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially-distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

Download Full-text