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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-GNSS) surveys and hydrologic modelling. The application in the catchment of the Soures torrent in Greece, after a catastrophic flood, shows that the UAS-aided method determined peak discharge with accuracy, providing very similar values compared to the ones estimated by the established traditional approach. The technique proved to be particularly effective, providing flexibility in terms of resources and timing, although there are certain limitations to its applicability, related mostly to the optical granulometry as well as the condition of the channel. The application highlighted important advantages and certain weaknesses of these emerging tools in indirect discharge estimations, which we discuss in detail. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Rainfall Monitoring, Modelling and Forecasting in Urban Environment. UrbanRain18: 11th International Workshop on Precipitation in Urban Areas. Conference Proceedings

On November 15, 2017, a high intensity convective storm, reaching 300 mm in 13 h in the core zone of the event, hit the western part of the region of Attica in Greece, causing a catastrophic flash flood in the town of Mandra and a tragic loss of 24 people, making it the most deadly flood in the country, in a period of 40 years. The research team surveyed the area during and after the flood using a combination of systematic ground and aerial observations with the aid of an unmanned aerial vehicle (UAV), aiming to reconstruct the basic physical and hydrological characteristics of the flood and its impacts. The analysis produced detailed flood extent and depth maps that provided a comprehensive description of the physical characteristics of floodwaters across the inundated area. Peak discharge was estimated, using a UAV-derived digital surface model, at two locations, corresponding to the two main tributaries and indicated an impressive hydrological response, between 9 and 10 m3/s/km2. Impact analysis on the basis of these observations showed an extensive diversity, including effects in geomorphology, vegetation, buildings, infrastructure and human population. Analysis of meteorological, botanical and geomorphological evidence lead to the conclusion that this flash flood was a very rare event. Results demonstrate that the combination of aerial and ground observations allow an enhanced and holistic reconstruction of a flash flood and its impacts with high accuracy, leading to the conclusion that the approach used has a significant potential in many aspects of flood disaster investigations. © 2018 Elsevier Ltd

Of the boundary conditions that affect the simulation of convective precipitation, soil moisture is one of the most important. In this study, we explore the impact of the soil moisture on convective precipitation, and factors affecting it, through an extensive numerical experiment based on four convective precipitation events that caused moderate to severe flooding in the Gard region of southern France. High-spatial-resolution (1 km) weather simulations were performed using the integrated atmospheric model Regional Atmospheric Modeling System/Integrated Community Limited Area Modeling System (RAMS/ICLAMS). The experimental framework included comparative analysis of five simulation scenarios for each event, in which we varied the magnitude and spatial distribution of the initial volumetric water content using realistic soil moisture fields with different spatial resolution. We used precipitation and surface soil moisture from radar and satellite sensors as references for the comparison of the sensitivity tests. Our results elucidate the complexity of the relationship between soil moisture and convective precipitation, showing that the control of soil water content on partitioning land surface heat fluxes has significant impacts on convective precipitation. Additionally, it is shown how different soil moisture conditions affect the modeled microphysical structure of the clouds, which translates into further changes in the magnitude and distribution of precipitation. © 2019 American Meteorological Society.

The estimation of heavy precipitation events is a particularly difficult task, especially over high mountainous terrain typically associated with scant availability of in situ observations. Therefore, quantification of precipitation variability in such data-limited regions relies on remote sensing estimates, due to their global coverage and near real-time availability. However, strong underestimation of precipitation associated with low-level orographic enhancement often limits the quantitative use of these data in applications. This study utilizes state-of-the-art numerical weather prediction simulations, toward the reduction of quantitative errors in satellite precipitation estimates and an insight on the nature of detection limitations. Satellite precipitation products based on different retrieval algorithms (Climate Prediction Center morphing method, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System, and Global Satellite Mapping of Precipitation) are evaluated for their performance in a number of storm events over mountainous areas with distinct storm characteristics: Upper Blue Nile in Ethiopia and Alto Adige in NE Italy. High-resolution (1 and 2 km) simulations from the Regional Atmospheric Modeling System/Integrated Community Limited Area Modeling System are used to derive adjustments to the magnitude of satellite estimates. Finally, a microphysical investigation is presented for occurrences of erroneous precipitation detection from the satellite instruments. Statistical indexes showcase improvement in numerical weather prediction-adjusted satellite products and microphysical commodities among cases of no detection are discussed. ©2018. American Geophysical Union. All Rights Reserved.

Even though rare, mega-fires raging during very dry and windy conditions, record catastrophic impacts on infrastructure, the environment and human life, as well as extremely high suppression and rehabilitation costs. Apart from the direct consequences, mega-fires induce long-term effects in the geomorphological and hydrological processes, influencing environmental factors that in turn can affect the occurrence of other natural hazards, such as floods and mass movement phenomena. This work focuses on the forest fire of 2007 in Peloponnese, Greece that to date corresponds to the largest fire in the country's record that burnt 1773 km2, causing 78 fatalities and very significant damages in property and infrastructure. Specifically, this work examines the occurrence of flood and mass movement phenomena, before and after this mega-fire and analyses different influencing factors to investigate the degree to which the 2007 fire and/or other parameters have affected their frequency. Observational evidence based on several data sources collected during the period 1989–2016 show that the 2007 fire has contributed to an increase of average flood and mass movement events frequency by approximately 3.3 and 5.6 times respectively. Fire affected areas record a substantial increase in the occurrence of both phenomena, presenting a noticeably stronger increase compared to neighbouring areas that have not been affected. Examination of the monthly occurrence of events showed an increase even in months of the year were rainfall intensity presented decreasing trends. Although no major land use changes has been identified and chlorophyll is shown to recover 2 years after the fire incident, differences on the type of vegetation as tall forest has been substituted with lower vegetation are considered significant drivers for the observed increase in flood and mass movement frequency in the fire affected areas. © 2017 Elsevier B.V.

This work presents a first evaluation of the performance of the Integrated Multisatellite Retrievals for GPM (IMERG) precipitation product over the upper Blue Nile basin of Ethiopia. One of the unique features of this study is the availability of hourly rainfall measurements from an experimental rain gauge network in the area. Both the uncalibrated and calibrated versions of IMERG are evaluated, and their performance is contrasted against another high-resolution satellite product, which is the Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH). The analysis is performed for hourly and daily time scales and at spatial scales that correspond to the nominal resolution of satellite products, which is 0.1° spatial resolution. The period analyzed is focused on a single wet season (May-October 2014). Evaluation is performed using several statistical and categorical error metrics, as well as spatial correlation analysis to assess the ability of satellite products to represent spatial variability of precipitation in the area. Results show that both IMERG products have a better bias ratio and correlation coefficient on both time scales as compared to CMORPH. Comparison statistics show a slight improvement in the skill of detecting rainfall events in IMERG products compared to CMORPH. Results also show a decreasing trend in the detection ability of satellite products for increasing threshold values, highlighting the need to further improve detection during heavy precipitation. © 2016 American Meteorological Society.

The error in satellite precipitation-driven complex terrain flood simulations is characterized in this study for eight different global satellite products and 128 flood events over the Eastern Italian Alps. The flood events are grouped according to two flood types: Rain floods and flash floods. The satellite precipitation products and runoffsimulations are evaluated based on systematic and random error metrics applied on the matched event pairs and basin-scale event properties (i.e., rainfall and runoffcumulative depth and time series shape). Overall, error characteristics exhibit dependency on the flood type. Generally, timing of the event precipitation mass center and dispersion of the time series derived from satellite precipitation exhibits good agreement with the reference; the cumulative depth is mostly underestimated. The study shows a dampening effect in both systematic and random error components of the satellite-driven hydrograph relative to the satellite-retrieved hyetograph. The systematic error in shape of the time series shows a significant dampening effect. The random error dampening effect is less pronounced for the flash flood events and the rain flood events with a high runoffcoefficient. This event-based analysis of the satellite precipitation error propagation in flood modeling sheds light on the application of satellite precipitation in mountain flood hydrology. © 2016 by the authors.

In response to the vital role of geomorphological analysis in natural hazards study, geomorphology, distributed hydrology and other related disciplines, we present the first global basin morphometric product of 30 characteristics, 9 archived elementary including stream order, stream number, stream length, basin relief, basin length, basin perimeter, maximal flow length, down valley length and overland flow length, and 21 derivable from these elementary morphometric characteristics. Characteristics of basins discharging to every grid-cell of the global earth surface are generated at 30 arcsec resolution from the remotely sensed HydroSHEDS dataset. This product does not exist in previous literature because tree-structural grid-cells bring large computational redundancy to basin delineation and thus computing basin characteristics. To generate this product within a reasonable timeframe, we introduce in this paper an efficient framework to reduce the algorithm complexity to linear, O(. N). We have presented a few geomorphologic findings using the generated product. © 2016 Elsevier Ltd.

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 runoffare 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 simulations. Moreover, ratios of precipitation to streamflow simulation error metrics show dependencies in terms of magnitude and variability. Random error and temporal dissimilarity are shown to reduce from basin-average rainfall to the streamflow simulations, while the systematic error exhibits no clear pattern in the rainfall-runofftransformation. © 2016 American Meteorological Society.

The September 2013 flash flood-triggering rainfall event in Colorado highlighted the strong underestimation of remote sensing techniques over mountainous terrain. In this work, the use of highresolution rainfall forecasts for adjusting weather radar- [Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation (Q3)] and satellite-based [CPC morphing technique (CMORPH) and TRMM 3B42RT] rainfall estimates is examined. Evaluation of the adjustment procedures is based on theNCEP Stage IV product. Results show that 1-km-grid-resolution rainfall forecasts provided by a numerical weather prediction model [Regional Atmospheric Modeling System and Integrated Community Limited Area Modeling System (RAMS-ICLAMS)] adequately captured total rainfall amounts during the event and could therefore be used to adjust biases in radar and satellite rainfall estimates. Two commonly used adjustment procedures according to 1) mean field bias and 2) probability density functionmatching are examined. Findings indicate that both procedures are successful in improving the original radar and satellite rainfall estimates, with the first method consistently providing the highest bias reduction while the second exhibits higher improvement in RMSE and correlation. © 2015 American Meteorological Society.