The spatiotemporal character of rainfall is particularly important for hydrologic modeling, as we... more The spatiotemporal character of rainfall is particularly important for hydrologic modeling, as well as hydroclimatic risk estimation and impact assessment. Existing atmospheric reanalysis datasets offer extensive record lengths and global coverage, but usually their spatial resolution is coarse for distributed hydrologic simulations at small spatial scales. On the other hand, the temporal coverage of high‐resolution radar‐based rainfall estimates can be rather short for risk applications. To address these shortcomings, we simultaneously bias‐correct and downscale a state‐of‐the‐art atmospheric reanalysis (ERA5) rainfall dataset, using the radar‐based Stage IV precipitation product as fine resolution reference, to develop an hourly CONUS‐wide precipitation product over a 4‐km grid, which extends back to 1979. In this regard, we refine an existing parametric quantile mapping framework based on a two‐component theoretical distribution model, where we impose continuity of the parametric forms via optimal threshold selection to transition between higher and lower rain rates. An evaluation over the probability frequency and time domains, using NOAA’s raingauge measurements as benchmark, reveals that the developed product benefits from the strengths of the calibration datasets, demonstrating good performance and robust behavior over all studied time periods and Köppen climate classification zones, including snow‐prone regions or areas where mesoscale convective systems become dominant. The accuracy of the yielded high spatial‐resolution rain rates, especially in low probability events, shows that the developed product can be effectively used for hydroclimatic risk applications and frequency analysis, while its high temporal and spatial resolution makes it particularly useful for distributed hydrologic modeling.
Flash floods develop over small spatiotemporal scales, an attribute that makes their predictabili... more Flash floods develop over small spatiotemporal scales, an attribute that makes their predictability a particularly challenging task. The serious threat they pose for human lives, along with damage estimates that can exceed one billion U.S. dollars in some cases, urge toward more accurate forecasting. Recent advances in computational science combined with state-of-the-art atmospheric models allow atmospheric simulations at very fine (i.e., subkilometer) grid scales, an element that is deemed important for capturing the initiation and evolution of flash flood–triggering storms. This work provides some evidence on the relative gain that can be expected from the adoption of such subkilometer model grids. A necessary insight into the complex processes of these severe incidents is provided through the simulation of three flood-inducing heavy precipitation events in the Alps for a range of model grid scales (0.25, 1, and 4 km) with the Regional Atmospheric Modeling System–Integrated Commun...
Flash floods develop over small spatiotemporal scales, an attribute that makes their predictabili... more Flash floods develop over small spatiotemporal scales, an attribute that makes their predictability a particularly challenging task. The serious threat they pose for human lives, along with damage estimates that can exceed one billion U.S. dollars in some cases, urge toward more accurate forecasting. Recent advances in computational science combined with state-of-the-art atmospheric models allow atmospheric simulations at very fine (i.e., subkilometer) grid scales, an element that is deemed important for capturing the initiation and evolution of flash flood–triggering storms. This work provides some evidence on the relative gain that can be expected from the adoption of such subkilometer model grids. A necessary insight into the complex processes of these severe incidents is provided through the simulation of three flood-inducing heavy precipitation events in the Alps for a range of model grid scales (0.25, 1, and 4 km) with the Regional Atmospheric Modeling System–Integrated Commun...
This study investigates the error characteristics of six quasi-global satellite precipitation pro... more This study investigates the error characteristics of six quasi-global satellite precipitation products and their error propagation in flow simulations for a range of mountainous basin scales (255–6967 km2) and two different periods (May–August and September–November) in northeast Italy. Statistics describing the systematic and random error, the temporal similarity, and error ratios between precipitation and runoff are presented. Overall, strong over-/underestimation associated with the near-real-time 3B42/Climate Prediction Center morphing technique (CMORPH) products is shown. Results suggest positive correlation between the systematic error and basin elevation. Performance evaluation of flow simulations yields a higher degree of consistency for the moderate to large basin scales and the May–August period. Gauge adjustment for the different satellite products is shown to moderate their error magnitude and increase their correlation with reference precipitation and streamflow simulat...
DESCRIPTION A study comparing XPOL and two C-band polarimetric estimates in a NE Italian Alps bas... more DESCRIPTION A study comparing XPOL and two C-band polarimetric estimates in a NE Italian Alps basin. In situ rainfall observations reported by a dense raingauge network and two disdrometers. XPOL estimates show high correlations (0.70-0.99) and low MRE (21%) against in situ data. The two C-band radar estimates gave higher MREs (50-70%) and lower correlations (0.48-0.81). Runoff simulations based on XPOL estimates are very close to the gaugebased simulations. The ones obtained by the C-band estimates resulted in underestimate runoff response. (UNDER REVISION)
The work examines the seasonality and large-scale atmospheric circulation patterns associated wit... more The work examines the seasonality and large-scale atmospheric circulation patterns associated with debris-flow occurrence in the Trentino–Alto Adige region (eastern Italian Alps). Analysis is based on classification algorithms applied to a uniquely dense archive of debris flows and hourly rain gauge precipitation series covering the period 2000–2009. Results highlight the seasonal and synoptic forcing patterns linked to debris flows in the study area. Summer and fall season account for 92% of the debris flows in the record, while atmospheric circulation characterized by zonal west, mixed and meridional south and southeast (SE–S) patterns account for 80%. Both seasonal and circulation patterns exhibit geographical preference. In the case of seasonality, there is a strong north–south separation of summer–fall dominance, while spatial distribution of dominant circulation patterns exhibits clustering, with both zonal west and mixed patterns prevailing in the northwest and central east p...
Accurate quantitative precipitation estimation over mountainous basins is of great importance bec... more Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near?real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige Riv...
Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Environment, 2019
With the advent of remote sensing, monitoring of the temporal and spatial variability of precipit... more With the advent of remote sensing, monitoring of the temporal and spatial variability of precipitation, and frequency of extremes, is now possible at global scale. Since satellite-based precipitation products (SPP) represent indirect measurements, their reliability for assessing hydroclimatic hazards has to be verified. In this chapter, we evaluate six global-scale high-resolution SPP in terms of extreme values (>90th quantile) using more than 6 years (within the period 2000–15) of reference precipitation data from rain gauge networks in nine mountainous regions: Eastern Italian Alps, Swiss Alps, Western Black Sea of Turkey, French Cevennes, Peruvian Andes, Colombian Andes, Himalayas over Nepal, Blue Nile in East Africa, Taiwan, and the US Rocky mountains. The following products are evaluated at daily accumulations and on a 0.25° regular grid: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis, Integrated MultisatellitE Retrievals for Global Precipitation M...
Floods are among the most devastating natural hazards in terms of both the number of people affec... more Floods are among the most devastating natural hazards in terms of both the number of people affected globally and the proportion of individual fatalities. The prediction of flood hazard requires accurate precipitation estimation produced at fine space–time scales. The high space–time variability, the scarcity of ground-based sensors, and the observational limitations associated with their operation in mountainous terrain make monitoring of flood-producing storms and their hydrologic response a particularly challenging task. Precipitation measurements from space-borne sensors offer unique advantages relative to ground-based sensors, since they are uninhibited by terrain or spatial inconsistencies and can provide quantitative precipitation estimates at quasi-global scale. The significance of these advantages in quantitative precipitation estimation has been recognized by the hydrologic community, where numerous studies in the past decades have demonstrated the use of satellite precipitation data for the prediction of floods over the globe. This chapter provides an overview of the satellite precipitation applications in flood modeling to highlight current challenges and opportunities in satellite-precipitation-driven flood prediction.
<p>Satellite based flood detection can enhance understanding of risk to humans and infrastr... more <p>Satellite based flood detection can enhance understanding of risk to humans and infrastructures, geomorphic processes, and ecological effects.&#160; Such application of optical satellite imagery has been mostly limited to the detection of water exposed to sky, as plant canopies tend to obstruct water visibility in short electromagnetic wavelengths.&#160; This case study evaluates the utility in multi-temporal thermal infrared observations from Landsat 8 as a basis for detecting sub-canopy fluvial inundation resulting in ambient temperature change.</p><p>We selected three flood events of 2016 and 2019 along sections of the Mississippi, Cedar, and Wapsipinicon Rivers located in Iowa, Minnesota, and Wisconsin, United States.&#160; Classification of sub-canopy water involved logical, threshold-exceedance criteria to capture thermal decline within channel-adjacent vegetated zones.&#160; Open water extent in the floods was mapped based on short-wave infrared thresholds determined parametrically from baseline (non-flooded) observations.&#160; Map accuracy was evaluated using higher-resolution (0.5&#8211;5.0 m) synchronic optical imagery.</p><p>Results demonstrate improved ability to detect sub-canopy inundation when thermal infrared change is incorporated: sub-canopy flood class accuracy was comparable to that of open water in previous studies.&#160; The multi-temporal open-water mapping technique yielded high accuracy as compared to similar studies.&#160; This research highlights the utility of Landsat thermal infrared data for monitoring riparian inundation and for validating other remotely sensed and simulated flood maps.</p>
12 Flood prediction across scales and more specifically in ungauged areas remains still a great 1... more 12 Flood prediction across scales and more specifically in ungauged areas remains still a great 13 challenge that limits the efficiency of flood risk mitigation strategies and disaster preparedness. 14 Building upon the recent success of Machine Learning (ML) models on streamflow prediction, 15 this work presents a prototype ML-based framework for flood warning and flood peak 16 prediction. The fundamental elements of the proposed system consist of a) a LSTM model for 17 classifying storm events to threat/no-threat given a threshold based on the 90th flow percentile and 18 b) the flood peak prediction models. The selected ML-models for flood peak prediction are the 19 Histogram based Gradient Boosting Regressor and the Random Forest. One of the strengths, and 20 reason for selection, of these decision-tree models is their degree of interpretability. This is 21 exploited in the study to help us spatially disentangle the role of both the static and dynamic drivers 22 of flood peak res...
Effective flash flood warning procedures are usually hampered by observational limitations of pre... more Effective flash flood warning procedures are usually hampered by observational limitations of precipitation over mountainous basins where flash floods occur. Satellite rainfall estimates are available over complex terrain regions offering a potentially viable solution to the observational coverage problem. However, satellite estimates of heavy rainfall rates are associated with significant biases and random errors that non-linearly propagate in hydrologic modeling imposing severe limitations on the use of these products in flood forecasting. In this study we investigate the use of three quasi-global and near-real-time high-resolution satellite-rainfall products (3B42, PERSIANN, CMORPH) for simulating flash floods over complex terrain basins. The study uses major flash flood events on medium size mountainous basins (600-1500 km2) in Northern Italian Alps. Comparison of satelliterainfall with rainfall derived from gauge-calibrated weather radar estimates showed that although satellite...
The spatial and temporal scale of flash flood occurrence provides limited opportunities for measu... more The spatial and temporal scale of flash flood occurrence provides limited opportunities for measurements and observations using of conventional monitoring networks. These observational difficulties, often accompanied by a lack of instrumental data have turned the focus to event-based, post-disaster studies. Post-flood surveys are particularly useful, as they provide the opportunity to observe aspects of hydrological behaviour of catchments under rare runoff conditions and extreme meteorological forcing, by capitalizing on field evidence.
The spatial and temporal scale of flash flood occurrence provides limited opportunities for obser... more The spatial and temporal scale of flash flood occurrence provides limited opportunities for observations and measurements using conventional monitoring networks, turning the focus to event-based, post-disaster studies. Post-flood surveys exploit field evidence to make indirect discharge estimations, aiming to improve our understanding of hydrological response dynamics under extreme meteorological forcing. However, discharge estimations are associated with demanding fieldwork aiming to record in small timeframes delicate data and data prone-to-be-lost and achieve the desired accuracy in measurements to minimize various uncertainties of the process. In this work, we explore the potential of unmanned aerial systems (UAS) technology, in combination with the Structure for Motion (SfM) and optical granulometry techniques in peak discharge estimations. We compare the results of the UAS-aided discharge estimations to estimates derived from differential Global Navigation Satellite System (d-...
The spatiotemporal character of rainfall is particularly important for hydrologic modeling, as we... more The spatiotemporal character of rainfall is particularly important for hydrologic modeling, as well as hydroclimatic risk estimation and impact assessment. Existing atmospheric reanalysis datasets offer extensive record lengths and global coverage, but usually their spatial resolution is coarse for distributed hydrologic simulations at small spatial scales. On the other hand, the temporal coverage of high‐resolution radar‐based rainfall estimates can be rather short for risk applications. To address these shortcomings, we simultaneously bias‐correct and downscale a state‐of‐the‐art atmospheric reanalysis (ERA5) rainfall dataset, using the radar‐based Stage IV precipitation product as fine resolution reference, to develop an hourly CONUS‐wide precipitation product over a 4‐km grid, which extends back to 1979. In this regard, we refine an existing parametric quantile mapping framework based on a two‐component theoretical distribution model, where we impose continuity of the parametric forms via optimal threshold selection to transition between higher and lower rain rates. An evaluation over the probability frequency and time domains, using NOAA’s raingauge measurements as benchmark, reveals that the developed product benefits from the strengths of the calibration datasets, demonstrating good performance and robust behavior over all studied time periods and Köppen climate classification zones, including snow‐prone regions or areas where mesoscale convective systems become dominant. The accuracy of the yielded high spatial‐resolution rain rates, especially in low probability events, shows that the developed product can be effectively used for hydroclimatic risk applications and frequency analysis, while its high temporal and spatial resolution makes it particularly useful for distributed hydrologic modeling.
Flash floods develop over small spatiotemporal scales, an attribute that makes their predictabili... more Flash floods develop over small spatiotemporal scales, an attribute that makes their predictability a particularly challenging task. The serious threat they pose for human lives, along with damage estimates that can exceed one billion U.S. dollars in some cases, urge toward more accurate forecasting. Recent advances in computational science combined with state-of-the-art atmospheric models allow atmospheric simulations at very fine (i.e., subkilometer) grid scales, an element that is deemed important for capturing the initiation and evolution of flash flood–triggering storms. This work provides some evidence on the relative gain that can be expected from the adoption of such subkilometer model grids. A necessary insight into the complex processes of these severe incidents is provided through the simulation of three flood-inducing heavy precipitation events in the Alps for a range of model grid scales (0.25, 1, and 4 km) with the Regional Atmospheric Modeling System–Integrated Commun...
Flash floods develop over small spatiotemporal scales, an attribute that makes their predictabili... more Flash floods develop over small spatiotemporal scales, an attribute that makes their predictability a particularly challenging task. The serious threat they pose for human lives, along with damage estimates that can exceed one billion U.S. dollars in some cases, urge toward more accurate forecasting. Recent advances in computational science combined with state-of-the-art atmospheric models allow atmospheric simulations at very fine (i.e., subkilometer) grid scales, an element that is deemed important for capturing the initiation and evolution of flash flood–triggering storms. This work provides some evidence on the relative gain that can be expected from the adoption of such subkilometer model grids. A necessary insight into the complex processes of these severe incidents is provided through the simulation of three flood-inducing heavy precipitation events in the Alps for a range of model grid scales (0.25, 1, and 4 km) with the Regional Atmospheric Modeling System–Integrated Commun...
This study investigates the error characteristics of six quasi-global satellite precipitation pro... more This study investigates the error characteristics of six quasi-global satellite precipitation products and their error propagation in flow simulations for a range of mountainous basin scales (255–6967 km2) and two different periods (May–August and September–November) in northeast Italy. Statistics describing the systematic and random error, the temporal similarity, and error ratios between precipitation and runoff are presented. Overall, strong over-/underestimation associated with the near-real-time 3B42/Climate Prediction Center morphing technique (CMORPH) products is shown. Results suggest positive correlation between the systematic error and basin elevation. Performance evaluation of flow simulations yields a higher degree of consistency for the moderate to large basin scales and the May–August period. Gauge adjustment for the different satellite products is shown to moderate their error magnitude and increase their correlation with reference precipitation and streamflow simulat...
DESCRIPTION A study comparing XPOL and two C-band polarimetric estimates in a NE Italian Alps bas... more DESCRIPTION A study comparing XPOL and two C-band polarimetric estimates in a NE Italian Alps basin. In situ rainfall observations reported by a dense raingauge network and two disdrometers. XPOL estimates show high correlations (0.70-0.99) and low MRE (21%) against in situ data. The two C-band radar estimates gave higher MREs (50-70%) and lower correlations (0.48-0.81). Runoff simulations based on XPOL estimates are very close to the gaugebased simulations. The ones obtained by the C-band estimates resulted in underestimate runoff response. (UNDER REVISION)
The work examines the seasonality and large-scale atmospheric circulation patterns associated wit... more The work examines the seasonality and large-scale atmospheric circulation patterns associated with debris-flow occurrence in the Trentino–Alto Adige region (eastern Italian Alps). Analysis is based on classification algorithms applied to a uniquely dense archive of debris flows and hourly rain gauge precipitation series covering the period 2000–2009. Results highlight the seasonal and synoptic forcing patterns linked to debris flows in the study area. Summer and fall season account for 92% of the debris flows in the record, while atmospheric circulation characterized by zonal west, mixed and meridional south and southeast (SE–S) patterns account for 80%. Both seasonal and circulation patterns exhibit geographical preference. In the case of seasonality, there is a strong north–south separation of summer–fall dominance, while spatial distribution of dominant circulation patterns exhibits clustering, with both zonal west and mixed patterns prevailing in the northwest and central east p...
Accurate quantitative precipitation estimation over mountainous basins is of great importance bec... more Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near?real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige Riv...
Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Environment, 2019
With the advent of remote sensing, monitoring of the temporal and spatial variability of precipit... more With the advent of remote sensing, monitoring of the temporal and spatial variability of precipitation, and frequency of extremes, is now possible at global scale. Since satellite-based precipitation products (SPP) represent indirect measurements, their reliability for assessing hydroclimatic hazards has to be verified. In this chapter, we evaluate six global-scale high-resolution SPP in terms of extreme values (>90th quantile) using more than 6 years (within the period 2000–15) of reference precipitation data from rain gauge networks in nine mountainous regions: Eastern Italian Alps, Swiss Alps, Western Black Sea of Turkey, French Cevennes, Peruvian Andes, Colombian Andes, Himalayas over Nepal, Blue Nile in East Africa, Taiwan, and the US Rocky mountains. The following products are evaluated at daily accumulations and on a 0.25° regular grid: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis, Integrated MultisatellitE Retrievals for Global Precipitation M...
Floods are among the most devastating natural hazards in terms of both the number of people affec... more Floods are among the most devastating natural hazards in terms of both the number of people affected globally and the proportion of individual fatalities. The prediction of flood hazard requires accurate precipitation estimation produced at fine space–time scales. The high space–time variability, the scarcity of ground-based sensors, and the observational limitations associated with their operation in mountainous terrain make monitoring of flood-producing storms and their hydrologic response a particularly challenging task. Precipitation measurements from space-borne sensors offer unique advantages relative to ground-based sensors, since they are uninhibited by terrain or spatial inconsistencies and can provide quantitative precipitation estimates at quasi-global scale. The significance of these advantages in quantitative precipitation estimation has been recognized by the hydrologic community, where numerous studies in the past decades have demonstrated the use of satellite precipitation data for the prediction of floods over the globe. This chapter provides an overview of the satellite precipitation applications in flood modeling to highlight current challenges and opportunities in satellite-precipitation-driven flood prediction.
<p>Satellite based flood detection can enhance understanding of risk to humans and infrastr... more <p>Satellite based flood detection can enhance understanding of risk to humans and infrastructures, geomorphic processes, and ecological effects.&#160; Such application of optical satellite imagery has been mostly limited to the detection of water exposed to sky, as plant canopies tend to obstruct water visibility in short electromagnetic wavelengths.&#160; This case study evaluates the utility in multi-temporal thermal infrared observations from Landsat 8 as a basis for detecting sub-canopy fluvial inundation resulting in ambient temperature change.</p><p>We selected three flood events of 2016 and 2019 along sections of the Mississippi, Cedar, and Wapsipinicon Rivers located in Iowa, Minnesota, and Wisconsin, United States.&#160; Classification of sub-canopy water involved logical, threshold-exceedance criteria to capture thermal decline within channel-adjacent vegetated zones.&#160; Open water extent in the floods was mapped based on short-wave infrared thresholds determined parametrically from baseline (non-flooded) observations.&#160; Map accuracy was evaluated using higher-resolution (0.5&#8211;5.0 m) synchronic optical imagery.</p><p>Results demonstrate improved ability to detect sub-canopy inundation when thermal infrared change is incorporated: sub-canopy flood class accuracy was comparable to that of open water in previous studies.&#160; The multi-temporal open-water mapping technique yielded high accuracy as compared to similar studies.&#160; This research highlights the utility of Landsat thermal infrared data for monitoring riparian inundation and for validating other remotely sensed and simulated flood maps.</p>
12 Flood prediction across scales and more specifically in ungauged areas remains still a great 1... more 12 Flood prediction across scales and more specifically in ungauged areas remains still a great 13 challenge that limits the efficiency of flood risk mitigation strategies and disaster preparedness. 14 Building upon the recent success of Machine Learning (ML) models on streamflow prediction, 15 this work presents a prototype ML-based framework for flood warning and flood peak 16 prediction. The fundamental elements of the proposed system consist of a) a LSTM model for 17 classifying storm events to threat/no-threat given a threshold based on the 90th flow percentile and 18 b) the flood peak prediction models. The selected ML-models for flood peak prediction are the 19 Histogram based Gradient Boosting Regressor and the Random Forest. One of the strengths, and 20 reason for selection, of these decision-tree models is their degree of interpretability. This is 21 exploited in the study to help us spatially disentangle the role of both the static and dynamic drivers 22 of flood peak res...
Effective flash flood warning procedures are usually hampered by observational limitations of pre... more Effective flash flood warning procedures are usually hampered by observational limitations of precipitation over mountainous basins where flash floods occur. Satellite rainfall estimates are available over complex terrain regions offering a potentially viable solution to the observational coverage problem. However, satellite estimates of heavy rainfall rates are associated with significant biases and random errors that non-linearly propagate in hydrologic modeling imposing severe limitations on the use of these products in flood forecasting. In this study we investigate the use of three quasi-global and near-real-time high-resolution satellite-rainfall products (3B42, PERSIANN, CMORPH) for simulating flash floods over complex terrain basins. The study uses major flash flood events on medium size mountainous basins (600-1500 km2) in Northern Italian Alps. Comparison of satelliterainfall with rainfall derived from gauge-calibrated weather radar estimates showed that although satellite...
The spatial and temporal scale of flash flood occurrence provides limited opportunities for measu... more The spatial and temporal scale of flash flood occurrence provides limited opportunities for measurements and observations using of conventional monitoring networks. These observational difficulties, often accompanied by a lack of instrumental data have turned the focus to event-based, post-disaster studies. Post-flood surveys are particularly useful, as they provide the opportunity to observe aspects of hydrological behaviour of catchments under rare runoff conditions and extreme meteorological forcing, by capitalizing on field evidence.
The spatial and temporal scale of flash flood occurrence provides limited opportunities for obser... more The spatial and temporal scale of flash flood occurrence provides limited opportunities for observations and measurements using conventional monitoring networks, turning the focus to event-based, post-disaster studies. Post-flood surveys exploit field evidence to make indirect discharge estimations, aiming to improve our understanding of hydrological response dynamics under extreme meteorological forcing. However, discharge estimations are associated with demanding fieldwork aiming to record in small timeframes delicate data and data prone-to-be-lost and achieve the desired accuracy in measurements to minimize various uncertainties of the process. In this work, we explore the potential of unmanned aerial systems (UAS) technology, in combination with the Structure for Motion (SfM) and optical granulometry techniques in peak discharge estimations. We compare the results of the UAS-aided discharge estimations to estimates derived from differential Global Navigation Satellite System (d-...
Uploads
Papers