During the last century, we have observed a warming climate with more intense precipitation extremes in some regions, likely due to increases in the atmosphere's water holding capacity. ...Traditionally, infrastructure design and rainfall‐triggered landslide models rely on the notion of stationarity, which assumes that the statistics of extremes do not change significantly over time. However, in a warming climate, infrastructures and natural slopes will likely face more severe climatic conditions, with potential human and socioeconomical consequences. Here we outline a framework for quantifying climate change impacts based on the magnitude and frequency of extreme rainfall events using bias corrected historical and multimodel projected precipitation extremes. The approach evaluates changes in rainfall Intensity‐Duration‐Frequency (IDF) curves and their uncertainty bounds using a nonstationary model based on Bayesian inference. We show that highly populated areas across the United States may experience extreme precipitation events up to 20% more intense and twice as frequent, relative to historical records, despite the expectation of unchanged annual mean precipitation. Since IDF curves are widely used for infrastructure design and risk assessment, the proposed framework offers an avenue for assessing resilience of infrastructure and landslide hazard in a warming climate.
Key Points
A methodology for deriving nonstationarity precipitation Intensity‐Duration‐Frequency curves in a warming climate
Urban areas across the United States may experience extreme rainfall up to 20% more intense and twice as frequently relative to historical records
Infrastructure design guidelines should be revised to include the expected changes in extreme events in a warming climate
Several studies have shown that statistics of streamflow time series, in particular empirical moments, scale with physical properties of the drainage basin, such as the catchment area. Those scaling ...laws have been extensively used to estimate statistics of streamflow series at ungauged sites. The role of climate variability and change has not been considered in such models. Further, most studies are based on classical statistics, where parameter uncertainties are usually neglected or not formally considered. In this paper we develop and apply hierarchical Bayesian models, to both assess regional and at-site trends in time in a spatial scaling framework, and simultaneously provide a rigorous framework for assessing and reducing parameter and model uncertainties. The models are tested with reconstructed natural inflow series from over 40 hydropower sites in Brazil with catchments areas varying from 2588 to 823,555
km
2. Both annual maximum flood series and monthly streamflow are considered. Cross-validated results show that the Hierarchical Bayesian models are able to skillfully estimate monthly and flood flow probability distribution parameters for sites that were not used in model fitting. The models developed can be used to provide record augmentation at sites that have short records, or to estimate flow at ungauged sites, even in the absence of an assumption of time stationarity. Since model uncertainties are accounted for, the precision of the estimates can be quantified and hypotheses tests for regional and at-site trends can be formally made. A formal inclusion of climate predictors to facilitate seasonal forecasting or climate change scenario development is also feasible. This is indicated, but not developed here.
Abstract
Most Amazonia drought studies have focused on rainfall deficits and their impact on river discharges, while the analysis of other important driver variables, such as temperature and soil ...moisture, has attracted less attention. Here we try to better understand the spatiotemporal dynamics of Amazonia droughts and associated climate teleconnections as characterized by the Palmer Drought Severity Index (PDSI), which integrates information from rainfall deficit, temperature anomalies, and soil moisture capacity. The results reveal that Amazonia droughts are most related to one dominant pattern across the entire region, followed by two seesaw kind of patterns: north‐south and east‐west. The main two modes are correlated with sea surface temperature (SST) anomalies in the tropical Pacific and Atlantic oceans. The teleconnections associated with global SST are then used to build a seasonal forecast model for PDSI over Amazonia based on predictors obtained from a sparse canonical correlation analysis approach. A unique feature of the presented drought prediction method is using only a few number of predictors to avoid excessive noise in the predictor space. Cross‐validated results show correlations between observed and predicted spatial average PDSI up to 0.60 and 0.45 for lead times of 5 and 9 months, respectively. To the best of our knowledge, this is the first study in the region that, based on cross‐validation results, leads to appreciable forecast skills for lead times beyond 4 months. This is a step forward in better understanding the dynamics of Amazonia droughts and improving risk assessment and management, through improved drought forecasting.
Key Points
Spatiotemporal dynamics and teleconnections associated with Amazonia droughts are investigated based on the PDSI indices
A drought forecast model for Amazonia is developed and tested based on the global SST field and sparse canonical correlation analysis
This is the first study in the region that, based on cross‐validation, leads to appreciable forecast skills for lead times beyond 4 months
•Estimation of IDF curves for rainfall data comprises a classical task in hydrology.•Stationary assumption can be inadequate and lead to poor quantile estimates.•We model annual maximum series ...conditioned on the daily rainfall.•The Bayesian beta model is used to produce nonstationary IDF curves for Korea.•Model provides future climate IDF curves based on climate change scenarios.
The estimation of intensity-duration-frequency (IDF) curves for rainfall data comprises a classical task in hydrology studies to support a variety of water resources projects, including urban drainage and the design of flood control structures. In a changing climate, however, traditional approaches based on historical records of rainfall and on the stationary assumption can be inadequate and lead to poor estimates of rainfall intensity quantiles. Climate change scenarios built on General Circulation Models offer a way to access and estimate future changes in spatial and temporal rainfall patterns at the daily scale at the utmost, which is not as fine temporal resolution as required (e.g. hours) to directly estimate IDF curves. In this paper we propose a novel methodology based on a four-parameter beta distribution to estimate IDF curves conditioned on the observed (or simulated) daily rainfall, which becomes the time-varying upper bound of the updated nonstationary beta distribution. The inference is conducted in a Bayesian framework that provides a better way to take into account the uncertainty in the model parameters when building the IDF curves. The proposed model is tested using rainfall data from four stations located in South Korea and projected climate change Representative Concentration Pathways (RCPs) scenarios 6 and 8.5 from the Met Office Hadley Centre HadGEM3-RA model. The results show that the developed model fits the historical data as good as the traditional Generalized Extreme Value (GEV) distribution but is able to produce future IDF curves that significantly differ from the historically based IDF curves. The proposed model predicts for the stations and RCPs scenarios analysed in this work an increase in the intensity of extreme rainfalls of short duration with long return periods.
•We model peak river stage using readily available tools and climate indicators.•NINO3 index and river stage at the beginning of the year are used as predictors.•Time indexing of GEV parameters ...reveals a changing flood hazard for Manaus.•Model provides an early flood alert system for Manaus.•The model is informative for dynamic flood risk management.
Historically, flood risk management and flood frequency modeling have been based on assumption of stationarity, i.e., flood probabilities are invariant across years. However, it is now recognized that in many places, extreme floods are associated with specific climate states which may recur with non-uniform probability across years. Conditional on knowledge of the operating climate regime, the probability of a flood of a certain magnitude can be higher or lower in a given year. Here we explore nonstationary flood risk for the streamflow series of the Negro River at the city of Manaus in Brazil by investigating climate teleconnections associated with the interannual variability of the peak flows. We evaluate attributes and the fit of a generalized extreme value (GEV) distribution with nonstationary parameters to the annual peak series of the Negro River stages. The annual peak flood occurs between May and July and its magnitude depends on the Negro River stage at the beginning of the year and on the previous December sea surface temperature (SST) of a region in the tropical Pacific Ocean. A statistically significant monotonic trend is also observed in the peak level series. The indexing of the parameters of a GEV distribution to the NINO3 index and to the observed river stage at the beginning of the year reveals a changing flood hazard for the city, with the joint occurrence of high values associated with La Niña conditions in the previous December and high river stages in January preceding the flood season. The proposed model is shown to be useful for quantifying the changing flood hazard several months in advance for Manaus, thus providing an early flood alert system for the city and may be an important tool for the dynamic flood risk management for the region.
Streamflow simulation and forecasts have been widely used in water resources management, particularly for flood and drought analysis and for the determination of optimal operational rules for ...reservoir systems used for water supply and energy production. Here we include climate information in a periodic-auto-regressive model in order to provide monthly streamflow forecasts for 54 hydropower sites in Brazil. Large scale climate information is included in the model through the use of climate indices obtained from the sea surface temperature field of the tropical Pacific and sub-tropical Atlantic oceans and the low-level zonal wind field over southeast Brazil. Correlation analysis of climate predictors and streamflow data show that the dependence of the latter on climate variability is seasonal and also a function of the lead time of the forecasts. A ridge regression framework is adopted in order to shrink parameter estimates and improve model outputs. The proposed model is compared with an ordinary linear regression based model with predictors selected by the BIC criterion and with the classical linear periodic-auto-regressive model (PAR), where no climate information is used. Cross-validated results show that the inclusion of climate indexes is able to improve forecast skills up to 3 months lead time. Higher skills are observed for reservoirs with large catchment areas.
ABSTRACT This study demonstrates the potential for enhancing monthly streamflow forecasting in Brazil through the incorporation of climatic indices. It extends the conventional periodic ...autoregressive model (PAR) for streamflow forecasts by integrating climate information, represented by three key climate indices reflecting sea surface temperatures in the Pacific and Atlantic Oceans, as well as zonal wind patterns in southeastern Brazil. Using the Kling-Gupta Efficiency (KGE) skill metric, our findings reveal that the inclusion of climate data consistently outperforms existing PAR models in numerous scenarios. Notably, during May, the proposed model enhances forecasts for 79% of the reservoirs (124 out of 157), while in January, it reduces forecast variance for up to 90% of the reservoirs (141 out of 157).
RESUMO Esse estudo demonstra o potencial de aperfeiçoamento das previsões mensais de vazão ao sistema hidroelétrico brasileiro por meio da incorporação de índices climáticos. A modelagem proposta amplia o modelo periódico autoregressivo (PAR) convencional para previsões de vazão a partir da inclusão de informações climáticas, representadas por três índices climáticos-chave que refletem as temperaturas da superfície do mar nos Oceanos Pacífico e Atlântico, bem como padrões de vento zonal no sudeste do Brasil. Usando a métrica de Kling-Gupta Efficiency (KGE), os resultados obtidos revelam que a inclusão de informação climática supera consistentemente os modelos PAR existentes em inúmeras situações. Em particular, durante o mês de maio, o modelo proposto melhora as previsões para 79% dos reservatórios (124 de 157), enquanto em janeiro, reduz a variância das previsões em até 90% dos reservatórios (141 de 157).
Numerous statistical and dynamical models have been developed in recent years to forecast ENSO events. However, for most of these models predictability for lead times over 10 months is limited. It ...has been hypothesized that the tropical Pacific thermocline structure may have critical information to permit longer lead ENSO forecasts. Models that use subsurface sea temperature information have already been known to produce better long lead forecasts. Here, a two-stage statistical ENSO forecasting model is developed and demonstrated using the spatially distributed depth of the 20°C isotherm (D
20) as a proxy for the thermocline. In the first stage, a nonlinear dimension reduction method maximum variance unfolding (MVU) is used to decompose theD
20data into canonical modes. The leading spatial patterns as well as lagged values of Niño-3 are then used as predictors in a set of linear regression models to predict the Niño-3 index at lead times of up to 24 months. Cross-validated forecasts using this methodology are shown to have higher skill than those that use a dimension reduction of the same thermocline data using principal component analysis (PCA). The first three modes of theD
20data as revealed by MVU account for 89% of the variance of the data, as compared to only 48% of the variance if PCA is used. The spatial patterns of the MVU modes partition the data field in a different way than the PC modes, even though some similarities exist as to the main regions that are active. These patterns and their temporal structure are discussed here, with a view to understanding the possible source of the longer-range predictability of ENSO using the MVU modes. The skill of the PCA- and the MVU-based forecasts of Niño-3 varies depending on the starting month of the forecast for short lead times (5–10 months). However, for the lead times longer than 1 yr, the MVU-based forecast skill is not seasonally variable, while the PCA-based models do not provide significant skill at these lead times irrespective of the starting month of the forecast. Similar conclusions are obtained for forecast models for the Niño-3.4 and Niño-1.2 indices. The differences between the MVU- and PCA-based models are most marked for the Niño-1.2 long lead forecasts.
Endothelial dysfunction (ED) is a hallmark in type 2 diabetes mellitus (T2DM) that favor both atherogenesis and ischemia and reperfusion injury (IRI). Sodium-glucose-2 co-transporter inhibitors ...(SGLT2i) may hypothetically improve microvascular and macrovascular functions via a broad spectrum of mechanisms, being superior to traditional antidiabetic therapy such as sulfonylurea, even in subjects under equivalent glycemic control. Hence, the present clinical trial was designed to compare the effect of these two treatments on markers of arterial wall function and inflammation in T2DM patients as well as on the potential mediating parameters.
ADDENDA-BHS2 is a prospective, single-center, active-controlled, open, randomized trial. Ninety-eight participants (40-70 years old) with HbA1c 7-9% were randomized (1:1, stratified by gender, BMI and HbA1c levels) to either dapagliflozin 10 mg/day or glibenclamide 5 mg/day on top of metformin. The primary endpoint was the change of flow-mediated dilation (FMD) after a 12-week period of treatment evaluated at rest and after IRI between dapagliflozin and glibenclamide arms. Secondary outcomes were defined as the difference between treatments regarding: plasma nitric oxide (NO) change after FMD, plasma isoprostane, plasma levels of vascular inflammatory markers and systemic inflammatory markers, plasma levels of adipokines, anthropometric measures, glucose control parameters, office and ambulatory BP control. Safety endpoints were defined as systolic and diastolic function assessed by echocardiography and retinopathy change. Serious adverse events were recorded. The study protocol was approved by the Independent Scientific Advisory Committee.
The ADDENDA-BHS2 trial is an investigator-initiated clinical trial comparing the effect of dapagliflozin versus glibenclamide on several aspects of vascular function in high cardiovascular risk T2DM patients. Besides, a large clinical and biochemical phenotype assessment will be obtained for exploring potential mediations and associations.
Clinical trial registration: NCT02919345 (September, 2016).
Efficient management of water and energy is an important goal of sustainable development for any nation. Streamflow forecasts, have been used in complex optimization models to maximize water use ...efficiency and electrical energy production. In this paper we develop a statistical model for the long term forecasts of hydroenergy inflow into the Brazilian hydropower system, which consists of more than 70 hydropower reservoirs. At present, the planning of reservoir operation and energy production in Brazil is made with no reliable long term (one season or longer lead times) streamflow forecasts. Here we use the NINO3 index and the main modes of the tropical Pacific thermocline structure as climate predictors in order to achieve skillfull forecasts at long leads. Cross-validated results show that about 50% of the total hydroenergy inflow can be predicted with moderate accuracy up to 20 month lead time.
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