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Downscaling stream flow time series from monthly to daily scales using an auto-regressive stochastic algorithm: StreamFARM
No full textSummaryDownscaling methods are used to derive stream flow at a high temporal resolution from a data series that has a coarser time resolution. These algorithms are useful for many applications, such as water management and statistical analysis, because in many cases stream flow time series are available with coarse temporal steps (monthly), especially when considering historical data; however, in many cases, data that have a finer temporal resolution are needed (daily).In this study, we considered a simple but efficient stochastic auto-regressive model that is able to downscale the available stream flow data from monthly to daily time resolution and applied it to a large dataset that covered the entire North and Central American continent. Basins with different drainage areas and different hydro-climatic characteristics were considered, and the results show the general good ability of the analysed model to downscale monthly stream flows to daily stream flows, especially regarding the reproduction of the annual maxima. If the performance in terms of the reproduction of hydrographs and duration curves is considered, better results are obtained for those cases in which the hydrologic regime is such that the annual maxima stream flow show low or medium variability, which means that they have a low or medium coefficient of variation; however, when the variability increases, the performance of the model decreases
Multi-Risk Assessment in the Veneto Region: An Approach to Rank Seismic and Flood Risk
No full textEffective disaster risk management in a given area relies on the analysis of all relevant risks potentially affecting it. A proper multi-risk evaluation requires the ranking of analyzed risks and the estimation of overall expected impacts, considering possible hazards (and vulnerabilities) interactions as well. Due to their complex and challenging modelling, such interactions are usually neglected, and the analysis of risks derived from different sources are commonly performed through independent analysis. However, often the assessment procedures adopted for the analysis as well as the metrics used to express various risks are different, making results of single risk analyses hardly comparable. To overcome this issue, an approach that allows for comparing and ranking risks is presented in this study. The approach is demonstrated through an application for an Italian region. Earthquakes and floods are the investigated hazards. First, in order to select the case study area, the municipalities within the Veneto region where both risks could be highest are identified by adopting an index-based approach. Then, the harmonization of seismic and flood risk assessment procedure is performed. Sub-municipal areas are selected as scale of analysis and direct economic losses are chosen as common impact metrics. The results of the single risk analyses are compared using risk curves as standardization tool. The EAL (expected annual losses) are estimated through risk curves and the ratios between EAL due to floods and earthquakes are mapped, showing in which area risk is significantly higher than the other
Rain FARM: a Phase-Conserving, Nonlinearly-Transformed Autoregressive Model for Downscaling LAMs Rainfall Predictions.
No full textThe flash flood of the Bisagno Creek on 9th October 2014: An "unfortunate" combination of spatial and temporal scales
No full textOn the 9th October, 2014 a strong event hit the central part of Liguria Region producing disastrous consequences to the city of Genoa where the Bisagno Creek flooded causing one death and lots of damage. The precipitation pattern responsible for the event had peculiar spatial and temporal characteristics that led to an unexpected flash flood. The temporal sequence of rainfall intensities and the particular severity of rainfall showers at small temporal scale, together with the size of the sub-basin hit by the most intense part of the rainfall were the unfortunate concurrent ingredients that led to an “almost perfect” flash flood. The peak flow was estimated to be a 100–200 years order return period.
The effects of the spatial and temporal scales of the precipitation pattern were investigated by coupling a rainfall downscaling model with a hydrological model setting up an experiment that follows a probabilistic approach.
Supposing that the correct volume of precipitation at different spatial and temporal scales is known, the experiment provided the probability of generating events with similar effects in terms of streamflow.
Furthermore, the study gives indications regarding the goodness and reliability of the forecasted rainfall field needed, not only in terms of total rainfall volume, but even in spatial and temporal pattern, to produce the observed ground effects in terms of streamflow
Rainfall Downscaling by a Phase-Conserving, Nonlinearly-Transformed Autoregressive Model: Validation on Radar Precipitation Estimates
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