Cities and towns along the tidal Hudson River are highly vulnerable to flooding through the combination of storm tides and high streamflows, compounded by sea level rise. Here a three-dimensional hydrodynamic model, validated by comparing peak water levels for 76 historical storms, is applied in a probabilistic flood hazard assessment. In simulations, the model merges streamflows and storm tides from tropical cyclones (TCs), offshore extratropical cyclones (ETCs) and inland ''wet extratropical'' cyclones (WETCs). The climatology of possible ETC and WETC storm events is represented by historical events (1931-2013), and simulations include gauged streamflows and inferred ungauged streamflows (based on watershed area) for the Hudson River and its tributaries. The TC climatology is created using a stochastic statistical model to represent a wider range of storms than is contained in the historical record. TC streamflow hydrographs are simulated for tributaries spaced along the Hudson, modeled as a function of TC attributes (storm track, sea surface temperature, maximum wind speed) using a statistical Bayesian approach. Results show WETCs are important to flood risk in the upper tidal river (e.g., Albany, New York), ETCs are important in the estuary (e.g., New York City) and lower tidal river, and TCs are important at all locations due to their potential for both high surge and extreme rainfall. The raising of floods by sea level rise is shown to be reduced by * 30-60% at Albany due to the dominance of streamflow for flood risk. This can be explained with simple channel flow dynamics, in which increased depth throughout the
Heavy rainfall, floods and other hydroclimatic extremes may be related to specific states of organization of the atmospheric circulation. The identification of these states and their linkage to local extremes could facilitate a physically meaningful quantification of local extremes in future climates and could allow forecasting extremes conditioned on the large‐scale atmospheric state. A novel methodology is presented that combines non‐linear, non‐parametric methods to link heavy precipitation events (HPEs) to atmospheric circulation states. Using daily rainfall data for the period 1951–2015 from 37 gauges in the Lazio region in Italy, HPEs are defined. For the same period, two atmospheric variables, namely, the 850 hPa geopotential height field and the integrated vapour transport (IVT), are derived from reanalysis data. The geopotential configurations driving heavy precipitation in the region are identified by combing self‐organized maps and event synchronization. First, a finite number of representative geopotential configurations is identified. Rainfall gauges are pooled into clusters, which show synchronized occurrence of heavy precipitation. Furthermore, geopotential configurations are identified, which tend to drive HPEs. For these geopotential states, the probability of HPE occurrence as a function of IVT is calculated through a local logistic regression model. Finally, it is explored whether the identified patterns are related to the occurrence of atmospheric rivers, which govern the atmospheric humidity transport from the tropics and subtropics to Europe. The relation found demonstrates the reliability of the proposed methodology.
In the context of climate change and variability, there is considerable interest in how large scale climate indicators influence regional precipitation occurrence and its seasonality. Seasonal and longer climate projections from coupled ocean-atmosphere models need to be downscaled to regional levels for hydrologic applications, and the identification of appropriate state variables from such models that can best inform this process is also of direct interest. Here, a Non-Homogeneous Hidden Markov Model (NHMM) for downscaling daily rainfall is developed for the Agro-Pontino Plain, a coastal reclamation region very vulnerable to changes of hydrological cycle. The NHMM, through a set of atmospheric predictors, provides the link between large scale meteorological features and local rainfall patterns. Atmospheric data from the NCEP/NCAR archive and 56-years record (1951-2004) of daily rainfall measurements from 7 stations in Agro-Pontino Plain are analyzed. A number of validation tests are carried out, in order to: 1) identify the best set of atmospheric predictors to model local rainfall; 2) evaluate the model performance to capture realistically relevant rainfall attributes as the inter-annual and seasonal variability, as well as average and extreme rainfall patterns. Validation tests show that the best set of atmospheric predictors are the following: mean sea level pressure, temperature at 1000 hPa, meridional and zonal wind at 850 hPa and precipitable water, from 20°N to 80°N of latitude and from 80°W to 60°E of longitude. Furthermore, the validation tests show that the rainfall attributes are simulated realistically and accurately. The capability of the NHMM to be used as a forecasting tool to quantify changes of rainfall patterns forced by alteration of atmospheric circulation under climate change and variability scenarios is discussed
A non-homogeneous hidden Markov model (NHMM) is developed using a 40-year record of daily rainfall at 11 stations in Tanzania and National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) re-analysis atmospheric fields of a number of meteorological variables. The following atmospheric fields, temperature at 1000 hPa, geo-potential height at 1000 hPa, meridional winds and zonal winds at 850 hPa, and zonal winds at the equator from 10 to 1000 hPa, in a region defined by 25 ∘ S-25 ∘ N and 25 ∘ -75 ∘ E are identified as appropriate predictors for the downscaling of the seasonal regime of daily rainfall in Tanzania. The NHMM is used to predict future rainfall patterns under a comparatively high greenhouse gas emissions scenario [Representative Concentration Pathway 8.5 (RCP8.5)], using predictors from the CMCC-CMS (Centro Mediterraneo sui Cambiamenti Climatici) simulations from 1950 to 2100. Instead of pre-specifying a fixed rainy season, the model considers seasonality of precipitation to be determined by the 21st century simulations of the atmospheric variables used as predictors. The future downscaled precipitation simulations for the RCP8.5 scenario indicate that in the 21st century Tanzania may experience: (1) a slight decrease in the number of wet days and seasonal rainfall in MAM and JJAS, but not in OND; (2) a reduction of annual total rainfall; and (3) an intensification of the frequency and intensity of extreme rainfall, as identified by 90th, 95th, and 99th percentiles.
River floods cause extensive losses to economy, ecology, and society throughout the world.They are driven by the space-time structure of catchment rainfall, which is determined by large-scale, or even global-scale, atmospheric processes. The identification of coherent, large-scale atmospheric circulation structures that determine the moisture transport and convergence associated with rainfall-induced flooding can help improve its predictability and phenomenology. In this paper, we extend a methodology, used for the analysis of extreme rainfall events, to high streamflow events (HSEs). The approach combines multiple machine learning methods to link HSEs to atmospheric circulation patterns.
Abstract:Rising of the sea level and/or heavy rainfall intensification significantly enhance the risk of flooding in low-lying coastal reclamation areas. Therefore, there is a necessity to assess whether channel hydraulic networks and pumping systems are still efficient and reliable in managing risks of flooding in such areas in the future. This study addresses these issues for the pumping system of the Mazzocchio area, which is the most depressed area within the Pontina plain, a large reclamation region in the south of Lazio (Italy). For this area, in order to assess climate change impact, a novel methodological approach is proposed, based on the development of a simulation-optimization model, which combines a multiobjective evolutionary algorithm and a hydraulic model. For assigned extreme rainfall events and sea levels, the model calculates sets of Pareto optimal solutions which are obtained by defining two optimality criteria: (a) to minimize the flooding surface in the considered area; (b) to minimize the pumping power necessary to mitigate the flooding. The application shows that the carrying capacity of the hydraulic network downstream of the pumping system is insufficient to cope with future sea level rise and intensification of rainfall.
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