1,720,967 research outputs found
Stochastic Scenarios for 21st Century Rainfall Seasonality, Daily Frequency, and Intensity in South Florida
No full textWe demonstrate that a nonhomogeneous hidden Markov model (NHMM) can be useful for simulating future daily rainfall at 19 stations in South Florida. Using upper atmosphere circulation variables that are typically better represented than precipitation in general circulation models (GCMs), a NHMM conditioned on GCM circulation variables is shown to provide credible stochastic simulations of daily precipitation for future conditions. Seasonality changes as well as changes in seasonal extreme precipitation quantiles, total seasonal rainfall, and number of wet days are assessed. The Coupled Model Intercomparison Project phase 5 simulation of the coupled ocean-atmosphere Euro-Mediterranean Center on Climate Change Climate Model CMCC-CMS for 1948–2100 is used for the demonstration. Seasonality changes emerge naturally from the driving variables, and each season is not modeled separately. The future projections for CMCC-CMS indicate that South Florida may have drier conditions for most of the year. The number of wet days reduces, while extreme rainfall frequency increases. These findings are consistent with recent rainfall trends. A modest reduction in total rainfall in the February–May period and a slight increase in the September–October projected rainfall is noted. Changes in the expression of the North Atlantic subtropical high in the CMCC-CMS simulations appear to influence the new seasonality and patterns of rainfall
Projecting changes in Tanzania rainfall for the 21st century
No full textA non-homogeneous hidden Markov model (NHMM) is developed using a 40-year record (1950–1990) 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 inMAMand 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
A Combined Kohonen Networks and Complex Networks approach for the analysis of Large Scale Atmospheric Features and River Floods in England and Germany
No full textFloods and other hydroclimatic extremes may represent specific states of organization of the atmospheric circula-
tion. Given this hypothesis an open question is how best to identify such states, and their space-time persistence.
Such a mapping would facilitate a physically meaningful identification of the potential severity, frequency and
duration of such events in future climates. With this in view, the link between large scale atmospheric circulation
and the extreme floods in Germany and England is investigated by a combined Kohonen Networks and Complex
Networks approach. Historical data from 57 streamflow gages in England and 68 in Germany and the Reanalysis
Historical Data of the Atmospheric Circulation Fields, bounded from 90W to 70E and from 20N to 80N, are used
for the purpose. The common period of record is from 1960 to 2012.
A finite number of typical atmospheric configurations of the considered region are identified by using the Kohonen
Networks approach. This approach is preceded by the application of the Principal Component Analysis of the
selected atmospheric variable; a number of PCs is retained to explain more than the 99% of the variance. Then
the historical sequence of the atmospheric fields, by using k-nearest neighbor methods, is transformed into a
binary matrix which identifies, at each time step, the atmospheric configuration most similar to one of the typical
ones identified by the Kohonen Network. A further binary matrix is constructed by using as a threshold the 99th
percentile of the discharge rates. Finally the Event Synchronization method is applied determining synchroniza-
tion, causality and delay between the extreme floods in each streamflow gage and the associated atmospheric
circulation feature. We find that the proposed approach can be useful and effective to identify the most critical at-
mospheric circulation patterns responsible of the extreme floods and thus to be used as part of a prediction strategy.Floods and other hydroclimatic extremes may represent specific states of organization of the atmospheric circula-
tion. Given this hypothesis an open question is how best to identify such states, and their space-time persistence.
Such a mapping would facilitate a physically meaningful identification of the potential severity, frequency and
duration of such events in future climates. With this in view, the link between large scale atmospheric circulation
and the extreme floods in Germany and England is investigated by a combined Kohonen Networks and Complex
Networks approach. Historical data from 57 streamflow gages in England and 68 in Germany and the Reanalysis
Historical Data of the Atmospheric Circulation Fields, bounded from 90W to 70E and from 20N to 80N, are used
for the purpose. The common period of record is from 1960 to 2012.
A finite number of typical atmospheric configurations of the considered region are identified by using the Kohonen
Networks approach. This approach is preceded by the application of the Principal Component Analysis of the
selected atmospheric variable; a number of PCs is retained to explain more than the 99% of the variance. Then
the historical sequence of the atmospheric fields, by using k-nearest neighbor methods, is transformed into a
binary matrix which identifies, at each time step, the atmospheric configuration most similar to one of the typical
ones identified by the Kohonen Network. A further binary matrix is constructed by using as a threshold the 99th
percentile of the discharge rates. Finally the Event Synchronization method is applied determining synchroniza-
tion, causality and delay between the extreme floods in each streamflow gage and the associated atmospheric
circulation feature. We find that the proposed approach can be useful and effective to identify the most critical at-
mospheric circulation patterns responsible of the extreme floods and thus to be used as part of a prediction strategy
21st Century Projections of High Streamflow Events in the UK and Germany
Full text linkRadiative effects of anthropogenic changes in atmospheric composition are expected to enhance the hydrological
cycle leading to more frequent and intense floods. To explore if there will be an increased risk of river flooding
in the future, 21st century projections under global warming scenarios of High Streamflow Events (HSEs) for UK
and German rivers are carried out, using a model that statistically relates large-scale atmospheric predictors - 850
hPa Geopotential Height (GPH850) and Integrated Water Vapor Transport (IVT) - to the occurrence of HSEs in
one or simultaneously in several streamflow gauges. Here, HSE is defined as the streamflow exceeding the 99th
percentile of daily flowrate time series measured at streamflow gauges.
For the common period 1960-2012, historical data from 57 streamflow gauges in UK and 61 streamflow gauges in
Germany, as well as, reanalysis data of GPH850 and IVT fields, bounded from 90W to 70E and from 20N to 80N
are used.
The link between GPH850 configurations and HSEs, and more precisely, identification of the GPH850 states
potentially able to generate HSEs, is performed by a combined Kohonen Networks (Self Organized Map, SOM)
and Event Syncronization approach. Complex network and modularity methods are used to cluster streamflow
gauges that share common GPH850 configurations. Then a model based on a conditional Poisson distribution, in
which the parameter of the Poisson distribution is assumed to be a nonlinear function of GPH850 and IVT, allows
for the identification of GPH850 state and threshold of IVT beyond which there is the HSE highest probability.
Using that model, projections of 21st century changes in frequency of HSEs occurrence in UK and Germany are
estimated using the simulated fields of GPH850 and IVT from selected GCMs belonging to the Coupled Model
Inter-comparison Project Phase 5 (CMIP5). Among the different GCMs, those are selected whose retrospective
predictor fields have consistent statistics with the corresponding reanalysis data
A Combined Atmospheric Rivers and Geopotential Height Analysis for the Detection of High Streamflow Event Probability Occurrence in UK and Germany
Full text linkThe role of atmospheric rivers (ARs) in inducing High Streamflow Events (HSEs) in Europe has been confirmed
by numerous studies. Here, we assume as HSEs the streamflows exceeding the 99th percentile of daily flowrate
time series measured at streamflow gauges.
Among the indicators of ARs are: the Integrated Water Vapor (IWV) and Integrated Water Vapor Transport (IVT).
For both indicators the literature suggests thresholds in order to identify ARs. Furthermore, local thresholds of
such indices are used to assess the occurrence of HSEs in a given region.
Recent research on ARs still leaves room for open issues: 1) The literature is not unanimous in defining which of the
two indicators is better. 2) The selection of the thresholds is based on subjective assessments. 3) The predictability
of HSEs at the local scale associated with these indices seems to be weak and to exist only in the winter months.
In order to address these issues, we propose an original methodology: (i) to choose between the two indicators
which one is the most suitable for HSEs predictions; (ii) to select IWT and/or IVT (IVT/IWV) local thresholds
in a more objective way; (iii) to implement an algorithm able to determine whether a IVT/IWV configuration is
inducing HSEs, regardless of the season. In pursuing this goal, besides IWV and IVT fields, we introduce as further
predictor the geopotential height at 850 hPa (GPH850) field, that implicitly contains information about the pattern
of temperature, direction and intensity of the winds. In fact, the introduction of the GPH850 would help to improve
the assessment of the occurrence of HSEs throughout the year. It is also plausible to hypothesize, that IVT/IWV
local thresholds could vary in dependence of the GPH850 configuration.
In this study, we propose a model to statistically relate these predictors, IVT/IWV and GPH850, to the simultaneous
occurrence of HSEs in one or more streamflow gauges in UK and Germany. Historical data from 57 streamflow
gauges in UK and 61 streamflow gauges in Germany, as well as reanalysis data of the 850 hPa geopotential fields
bounded from 90W to 70E and from 20N to 80N are used. The common period is 1960 to 2012. The link between
GPH850 and HSEs, and more precisely, the identification of the GPH850 states potentially able to generate HSEs
is performed by a combined Kohonen Networks (Self Organized Map, SOM) and Event Syncronization approach.
Complex network and modularity methods are used to cluster streamflow gauges that share common GPH850
configurations. Then a model based on a conditional Poisson distribution is carried out, in which the parameter of
the Poisson distribution is assumed to be a nonlinear function of GPH850 state and IVT/ IWV. This model allows
for the identification of the threshold of IVT/IWV beyond which there is the HSE highest probabilit
A stacked ensemble learning and non‐homogeneous hidden Markov model for daily precipitation downscaling and projection
Full text linkGlobal circulation models (GCMs) are routinely used to project future climate conditions worldwide, such as temperature and precipitation. However, inputs with a finer resolution are required to drive impact-related models at local scales. The non-homogeneous hidden Markov model (NHMM) is a widely used algorithm for the precipitation statistical downscaling for GCMs. To improve the accuracy of the traditional NHMM in reproducing spatiotemporal precipitation features of specific geographic sites, especially extreme precipitation, we developed a new precipitation downscaling framework. This hierarchical model includes two levels: (1) establishing an ensemble learning model to predict the occurrence probabilities for different levels of daily precipitation aggregated at multiple sites and (2) constructing a NHMM downscaling scheme of daily amount at the scale of a single rain gauge using the outputs of ensemble learning model as predictors. As the results obtained for the case study in the central-eastern China (CEC), show that our downscaling model is highly efficient and performs better than the NHMM in simulating precipitation variability and extreme precipitation. Finally, our projections indicate that CEC may experience increased precipitation in the future. Compared with ~26 years (1990–2015), the extreme precipitation frequency and amount would significantly increase by 21.9%–48.1% and 12.3%–38.3%, respectively, by the late century (2075–2100) under the Shared Socioeconomic Pathway 585 climate scenario
Large scale climate and rainfall seasonality in a Mediterranean Area: Insights from a non-homogeneous Markov model applied to the Agro-Pontino plain
No full textIn 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
Novel stacking models for improved extreme rainfall predictions under climate change scenarios.
Full text linkFuture projections under global warming scenarios of local extreme precipitations by downscaling models is still open challenge. A number of downscaling statistical models have been proposed to link large scale atmospheric circulation features, as simulated by Global Circulation Models (GCMs) and/or Regional Circulation Models (RCMs), to the temporal and spatial distribution of local rainfalls. Despite the efforts, comparisons between simulations and observations show that statistical downscaling methods, although able to realistically reproduce most of the mean rainfall attributes as seasonal or monthly rainfall amount, fail to simulate extreme precipitation with acceptable accuracy. This is due to the difficulties to: (i) select the optimal set of atmospheric variables used as predictors; (ii) solve the non-linear dependencies that link the rains to the atmospheric variables; (iii) assess the temporal dependencies between wet and dry states. To overcome such criticalities, in order to improve extreme precipitation forecasting, in this study we introduce in rainfall downscaling a paradigm already known in other disciplines of data science: the "stacking models". Stacking models combine different simulations from multiple predictive models. According to this approach we used Random Forest, extreme gradient boosting and Non-homogeneous Hidden Markov Model (NHMM). The validation was performed first on the individual models, calibrating the parameters individually and evaluating them globally with a cross validation approach. The performance of the proposed stacking model is assessed by comparing the daily rainfall amount simulations with those obtained by a state-of-the-art NHMM model, in which the probability of the rainfall occurrence is just modeled using a logistic regression with parameters depending upon climatology variables. We show that the stacking model outperforms the latter model, especially in simulating the extreme precipitations. Furthermore, such performance improvement is obtained by using a minor number of atmospheric predictors. Once the downscaling model has been calibrated and validated, we evaluated changes of precipitation extremes under climate change scenarios. The simulations were performed using the variables obtained from a GCM, Community Climate System Model v4 - NCAR, whose scenario is defined by CMIP5 - RCP 8.5. To evaluate the confidence bands of the simulated rainfall it was used an ensemble of simulations obtained by running the latter GCM with different initial conditions. The Lazio region was chosen as a study case. The Lazio Region is located in Central Italy, whose hydrogeological features make it particularly vulnerable to eventual future changes of hydrological cycle such as those induced by climate change. The Mediterranean is made up of many of these vulnerable areas, which makes the application of the method to this case study of general interest
Efficiency assessment of existing pumping/hydraulic network systems to mitigate flooding in low-lying coastal regions under different scenarios of sea level rise. The Mazzocchio area study case
Full text linkRising 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
An event synchronization method to link heavy rainfall events and large-scale atmospheric circulation features
No full textHeavy 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
- …
