Extreme Peak Research Articles

Disentangling aggregated uncertainty sources in peak flow projections under different climate scenarios

Evaluation of peak flood magnitude and frequency in the future at a catchment scale under global warming is crucial for water resource management and flood risk management. Climate model outputs provide a leading source of information to quantify the effect of the foreseen natural and anthropogenic climate change on the environment and natural systems. However, modelling climate change impact on peak flow is subject to considerable uncertainties from the climate model discrepancies, bias correction methods, and hydrological model parameters. This study develops a framework to examine changes and disentangle uncertainties in peak flow, which is tested at five Awash catchments in Ethiopia, a region exposed to extreme flood risk. The results showed that projected extreme precipitation and peak flow magnitude could increase substantially in the coming decades by 30% to 55%. The uncertainty analysis confirms that the dominant factor in peak flood projection is climate models in catchments like Akaki (55%) and Awash H (51%), but the bias correction methods in Awash B (58%) and Kela (50%), respectively. The least important factor is the hydrological parameter set for flood projections. Moreover, the findings reveal that peak flood risks would noticeably increase in the near and far future in all catchments, in Awash located in the Tropical dry region. Therefore, various state water agencies from local to national scales must take certain non-structural/structural measures to mitigate flood risks in the future, and to adapt to future climate.

A hybrid approach to study large conformational transitions of biomolecules from single particle XFEL diffraction data.

X-ray free-electron laser (XFEL) is the latest generation of the X-ray source that could become an invaluable technique in structural biology. XFEL has ultrashort pulse duration, extreme peak brilliance, and high spatial coherence, which could enable the observation of the biological molecules in near nature state at room temperature without crystallization. However, for biological systems, due to their low diffraction power and complexity of sample delivery, experiments and data analysis are not straightforward, making it extremely challenging to reconstruct three-dimensional (3D) structures from single particle XFEL data. Given the current limitations to the amount and resolution of the data from such XFEL experiments, we propose a new hybrid approach for characterizing biomolecular conformational transitions by using a single 2D low-resolution XFEL diffraction pattern in combination with another known conformation. In our method, we represent the molecular structure with a coarse-grained model, the Gaussian mixture model, to describe large conformational transitions from low-resolution XFEL data. We obtain plausible 3D structural models that are consistent with the XFEL diffraction pattern by deforming an initial structural model to maximize the similarity between the target pattern and the simulated diffraction patterns from the candidate models. We tested the proposed algorithm on two biomolecules of different sizes with different complexities of conformational transitions, adenylate kinase, and elongation factor 2, using synthetic XFEL data. The results show that, with the proposed algorithm, we can successfully describe the conformational transitions by flexibly fitting the coarse-grained model of one conformation to become consistent with an XFEL diffraction pattern simulated from another conformation. In addition, we showed that the incident beam orientation has some effect on the accuracy of the 3D structure modeling and discussed the reasons for the inaccuracies for certain orientations. The proposed method could serve as an alternative approach for retrieving information on 3D conformational transitions from the XFEL diffraction patterns to interpret experimental data. Since the molecules are represented by Gaussian kernels and no atomic structure is needed in principle, such a method could also be used as a tool to seek initial models for 3D reconstruction algorithms.

Economic Dispatch of Electrical Power in South Africa: An Application to the Northern Cape Province

Power utility companies rely on forecasting for the operation of electricity demand. This presents an applicationof linear quantile regression, non-linear quantile regression, and additive quantile regression models for forecasting extreme electricity demand at peak hours such as 18:00, 19:00, 20:00 and 21:00 using Northern Cape data for the period 01 January 2000 to 31 March 2014. The selection of variables was done using the least absolute shrinkage and selection operator. Additive quantile regression models were found to be the best fitting models for hours 18:00, and 19:00, whereas linear quantile regression models were found to be the best fitting models for hours 20:00, and 21:00. Out of sample forecasts for seven days (01 to 07 April 2014) were used to solve the unit commitment problem using mixed-integer programming. The unit commitment problem results showed that it is less costly to use all the generating units such as hydroelectric, wind power, concentrated solar power and solar photovoltaic. The main contribution of this study is in the development of models for forecasting hourly extreme peak electricity demand. These results could be useful to system operators in the energy sector who have to maintain the minimum cost by scheduling and dispatching electricity during peak hours when the grid is constrained due to peak load demand.

Collective effects and synchronization of demand in real-time demand response

Future energy systems will be dominated by variable renewable power generation and interconnected sectors, leading to rapidly growing complexity. Flexible elements are required to balance the variability of renewable power sources, including backup generators and storage devices, but also flexible consumers. Demand response (DR) aims to adapt the demand to the variable generation, in particular by shifting the load in time. In this article, we provide a detailed statistic analysis of the collective operation of many DR units. We establish and simulate a model for load shifting in response to real-time electricity pricing using local storage systems. We show that DR drives load shifting as desired but also induces strong collective effects that may threaten system stability. The load of individual households synchronizes, leading to extreme demand peaks. We provide a detailed statistical analysis of the grid load and quantify both the likelihood and extent of extreme demand peaks.

The Impact of Thermal Inertia on the Indoor Thermal Environment of Light Steel Framing Constructions

Typically, reinforced concrete and brick masonry construction is the most common construction system of the majority of the southern European residential building stock. However, the lightweight steel framing (LSF) construction system has been progressively assuming a relevant position in the residential sector. Since LSF is not the traditional construction system, the indoor thermal environment of these buildings has not been widely studied and discussed considering the southern European climate context. The low thermal inertia of this construction system is commonly pointed to as a possible weakness in warmer climates. The present work aims to address this research gap by evaluating and comparing the LSF and masonry construction systems in terms of the indoor thermal environment focusing on the level of thermal inertia. The considered methodology lies in a long-term experimental campaign based on the construction and monitoring of two identical experimental test cells, differing only in the construction system. The test cells are in the central region of Portugal. The monitoring period elapsed over an entire year. Dynamic simulations are also carried out with a model experimentally validated to consider a wider range of climatic conditions. It is shown that internally insulating the ground floor has an impact on the indoor thermal environment of the LSF test cell by accentuating the indoor air temperature fluctuations and magnitude of the extreme peak values. However, the results also reveal that the faster and closer response to the outdoor conditions may be beneficial for LSF buildings during the heating season.

Complexity and Persistence of Price Time Series of the European Electricity Spot Market

The large variability of renewable power sources is a central challenge in the transition to a sustainable energy system. Electricity markets are central for the coordination of electric power generation. These markets rely evermore on short-term trading to facilitate the balancing of power generation and demand and to enable systems integration of small producers. Electricity prices in these spot markets show pronounced fluctuations, featuring extreme peaks as well as occasional negative prices. In this article, we analyze electricity price time series from the European Power Exchange market, in particular the hourly day-ahead, hourly intraday, and 15-min intraday market prices. We quantify the fluctuations, correlations, and extreme events and reveal different time scales in the dynamics of the market. The short-term fluctuations show remarkably different characteristics for time scales below and above 12 h. Fluctuations are strongly correlated and persistent below 12 h, which contributes to extreme price events and a strong multifractal behavior. On longer time scales, they get anticorrelated and price time series revert to their mean, witnessed by a stark decrease of the Hurst coefficient after 12 h. The long-term behavior is strongly influenced by the evolution of a large-scale weather pattern with a typical time scale of four days. We elucidate this dependence in detail using a classification into circulation weather types. The separation in time scales enables a superstatistical treatment, which confirms the characteristic time scale of four days, and motivates the use of q-Gaussian distributions as the best fit to the empiric distribution of electricity prices.3 MoreReceived 6 December 2021Revised 21 February 2022Accepted 28 February 2022DOI:https://doi.org/10.1103/PRXEnergy.1.013002Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasComplex systemsEconophysicsEnergy sourcesFractional Brownian motionPatterns in complex systemsSolar energyStochastic processesWind energyPhysical SystemsFractalsMultiple time scale dynamicsTechniquesNon-Markovian processesTime series analysisStatistical PhysicsInterdisciplinary PhysicsGeneral Physics

Chaotic and Clumpy Galaxy Formation in an Extremely Massive Reionization-era Halo

Abstract The SPT 0311–58 system at z = 6.900 is an extremely massive structure within the reionization epoch and offers a chance to understand the formation of galaxies at an extreme peak in the primordial density field. We present 70 mas Atacama Large Millimeter/submillimeter Array observations of the dust continuum and [C ii] 158 μm emission in the central pair of galaxies and reach physical resolutions of ∼100–350 pc, among the most detailed views of any reionization-era system to date. The observations resolve the source into at least a dozen kiloparsec-size clumps. The global kinematics and high turbulent velocity dispersion within the galaxies present a striking contrast to recent claims of dynamically cold thin-disk kinematics in some dusty galaxies just 800 Myr later at z ∼ 4. We speculate that both gravitational interactions and fragmentation from massive parent disks have likely played a role in the overall dynamics and formation of clumps in the system. Each clump individually is comparable in mass to other 6 < z < 8 galaxies identified in rest-UV/optical deep field surveys, but with star formation rates elevated by a factor of ~3-5. Internally, the clumps themselves bear close resemblance to greatly scaled-up versions of virialized cloud-scale structures identified in low-redshift galaxies. Our observations are qualitatively similar to the chaotic and clumpy assembly within massive halos seen in simulations of high-redshift galaxies.

The Impact of Neglecting Climate Change and Variability on ERCOT’s Forecasts of Electricity Demand in Texas

Abstract The Electric Reliability Council of Texas (ERCOT) manages the electric power across most of Texas. They make short-term assessments of electricity demand on the basis of historical weather over the last two decades, thereby ignoring the effects of climate change and the possibility of weather variability outside the recent historical range. In this paper, we develop an empirical method to predict the impact of weather on energy demand. We use that with a large ensemble of climate model runs to construct a probability distribution of power demand on the ERCOT grid for summer and winter 2021. We find that the most severe weather events will use 100% of available power—if anything goes wrong, as it did during the 2021 winter, there will not be sufficient available power. More quantitatively, we estimate a 5% chance that maximum power demand would be within 4.3 and 7.9 GW of ERCOT’s estimate of best-case available resources during summer and winter 2021, respectively, and a 20% chance it would be within 7.1 and 17 GW. The shortage of power on the ERCOT grid is partially hidden by the fact that ERCOTs seasonal assessments, which are based entirely on historical weather, are too low. Prior to the 2021 winter blackout, ERCOT forecast an extreme peak load of 67 GW. In reality, we estimate hourly peak demand was 82 GW, 22% above ERCOT’s most extreme forecast and about equal to the best-case available power. Given the high stakes, ERCOT should develop probabilistic estimates using modern scientific tools to predict the range of power demand more accurately.

Drought Characteristics Encompassing Climate Indices in the Yangtze River Basin Using Nonstationary and Copula-Based Methods

Abstract In this study, a nonstationary standardized precipitation index (NSPI) is calculated by fitting precipitation data to the nonstationary gamma model with climate indices as covariates and compared with stationary standardized precipitation index (SSPI) in fitting observed precipitation and identifying meteorological droughts during 1964–2016 in the Yangtze River basin (YRB). Results show that changing trends of NSPI are roughly consistent with those of SSPI, but the NSPI performs better than SSPI for fitting precipitation. Using the NSPI, spatiotemporal variations and joint return period of drought characteristics are investigated by using modified Mann–Kendall and copula function. It is found that the west YRB experiences mostly severe droughts but shows a mitigating tendency, while in the north-central region, moderate droughts are dominant but have an aggravating tendency. Drought peak shares a spatial distribution pattern similar to intensity, with higher averages in the west and south-central basin and lower averages in the north-central part, but they exhibit a higher occurrence frequency of droughts. The joint return periods of drought characteristics reveal that slight and moderate droughts with duration D of no more than 3 months are more likely to occur in the YRB with return periods of 2–25 years. The severe- and extreme-intensity droughts with more than 3-month duration occur rarely in the YRB, but extreme peak droughts with slight or moderate intensity could hit most of the basin in 100 years. In addition, for most drought scenarios, droughts exhibit longer return periods in the north-central and southeast parts relative to other regions of the YRB. Significance Statement Computation of a widely used standardized precipitation index depends heavily on the assumption of stationarity in precipitation, but this assumption is questionable under a changing climate. This study is the first time that a nonstationary standardized precipitation index is calculated to comprehensively assess drought in the Yangtze River basin (YRB). Results show that the basin is prone to no more than 3-month-duration slight and moderate droughts with return periods of 2–25 years, while extreme-intensity droughts longer than 3 months occur rarely. In addition, for most drought scenarios, the droughts exhibit longer return periods in the north-central and southeast parts relative to other regions of the YRB. These results are important for drought monitoring and early warning in the YRB.

Efecto del entrenamiento de resistencia en centros o domiciliario en la fuerza muscular y pico de VO2 en pacientes con insuficiencia cardíaca: revisión sistemática y metaanálisis

ObjectiveTo analyze the components of resistance training (RT) exercises and evaluate the effects of RT on improving muscle strength and oxygen consumption (VO2) peak based on either center-based rehabilitation or home-based rehabilitation in patients with heart failure with reduced ejection fraction (HFrEF). MethodsAccording to the PRISMA guidelines, articles were searched through five databases, including Embase, MEDLINE, CINAHL, PEDro and Cochrane. RevMan 5.3 software was used to perform the meta-analysis. ResultsNine randomized controlled trial studies met the study criteria, including a total of 299 respondents. In the center-based respondents (n = 81 for intervention group vs. n = 81 for control group), RT resulted in significant effects on both muscle strength of lower extremity (SDM = 1.46, 95% CI = 0.41–2.50, n = 151) and upper extremity (SDM = 0.46, 95% CI = 0.05–0.87, n = 97) and VO2 peak (MD = 1.45 ml/kg/min, 95% CI = 0.01–2.89, n = 114). In the home-based respondents (n = 71 for intervention group vs. n = 66 for control group), RT resulted in significant effects on muscle strength of both lower extremity (SDM = 0.58, 95% CI: 0.20–0.97, n = 113) and upper extremity (SDM = 0.84, 95% CI: 0.24–1.44, n = 47) and VO2 peak (MD = 5.43 ml/kg/min, 95% CI: 0.23–10.62, n = 89). ConclusionThe RT exercise could increase muscle strength and VO2 peak at either center-based or home-based rehabilitation and should be considered as a part of the care of patients with HFrEF.

Climate change impact on extreme precipitation and peak flood magnitude and frequency: observations from CMIP6 and hydrological models

Changes in climate intensity and frequency, including extreme events, heavy and intense rainfall, have the greatest impact on water resource management and flood risk management. Significant changes in air temperature, precipitation, and humidity are expected in future due to climate change. The influence of climate change on flood hazards is subject to considerable uncertainty that comes from the climate model discrepancies, climate bias correction methods, flood frequency distribution, and hydrological model parameters. These factors play a crucial role in flood risk planning and extreme event management. With the advent of the Coupled Model Inter-comparison Project Phase 6, flood managers and water resource planners are interested to know how changes in catchment flood risk are expected to alter relative to previous assessments. We examine catchment-based projected changes in flood quantiles and extreme high flow events for Awash catchments. Conceptual hydrological models (HBV, SMART, NAM and HYMOD), three downscaling techniques (EQM, DQM, and SQF), and an ensemble of hydrological parameter sets were used to examine changes in peak flood magnitude and frequency under climate change in the mid and end of the century. The result shows that projected annual extreme precipitation and flood quantiles could increase substantially in the next several decades in the selected catchments. The associated uncertainty in future flood hazards was quantified using aggregated variance decomposition and confirms that climate change is the dominant factor in Akaki (C2) and Awash Hombole (C5) catchments, whereas in Awash Bello (C4) and Kela (C3) catchments bias correction types is dominate, and Awash Kuntura (C1) both climate models and bias correction methods are essential factors. For the peak flow quantiles, climate models and hydrologic models are two main sources of uncertainty (31% and 18%, respectively). In contrast, the role of hydrological parameters to the aggregated uncertainty of changes in peak flow hazard variable is relatively small (5%), whereas the flood frequency contribution is much higher than the hydrologic model parameters. These results provide useful knowledge for policy-relevant flood indices, water resources and flood risk control and for studies related to uncertainty associated with peak flood magnitude and frequency.

Extremely high but localized pulses of coral recruitment in the southwestern lagoon of New Caledonia and implications for conservation

Recruitment processes largely drive spatial distributions, dynamics, and recovery potential of marine communities. Determining scales of variation in recruitment rates and composition can help in understanding population replenishment mechanisms, while identifying recruitment hotspots is crucial for improving conservation strategies, particularly for threatened marine ecosystems such as coral reefs. We examined the spatial and interannual variability (2012-2014) of coral recruitment at multiple scales within and among reef habitats (14 stations) in the southwestern lagoon of New Caledonia. Recruit assemblages were characterized by high recruitment rates compared to other regions (overall mean of 34.9 recruits per 11 × 11 × 1 cm settlement tile, corresponding to 1220.9 recruits m-2) and strong dominance of Acroporidae. We found a marked spatial heterogeneity among habitats but also exceptionally high interannual variation (100-fold), with extreme recruitment peaks (up to 13572.8 recruits m-2, with a maximum of 811 recruits on a single tile) recorded in 2014 at some fringing and mid-shelf reefs, the highest records ever reported to date. These encouraging results contrast with other reefs where recent declines in coral recruitment rates have been documented with major concerns for their resilience capacities. However, the marked spatio-temporal variability of coral recruitment complicates conservation strategies, as it makes it difficult to identify ‘recruitment hotspots’ as priority sites to protect for their potential capacity to boost the replenishment of local populations.

Impact of COVID-19 pandemic on the quality of test output in haemostasis laboratories.

IntroductionThe high incidence of thrombotic events in patients with COVID‐19 affects health care worldwide and results in an increased workload in haemostasis laboratories due to more frequent testing of D‐dimer, haemostatic parameters and anti‐Xa tests. However, the impact of this increase in assay requests on the quality of performance in haemostasis laboratories remains unclear. In this study, the impact of the COVID‐19 pandemic on the quality of performance and management of haemostasis laboratories was evaluated.MethodsThe impact on the quality of performance was studied using external quality assessment data from 2019 to 2020 derived from ECAT surveys. A questionnaire was sent to Dutch haemostasis laboratories to identify challenges and management strategies. Furthermore, the number of assays performed in 2019 and 2020 was supplied by four Dutch hospitals, located in regions with different disease incidence.ResultsNo differences in response rate nor the quality of the measurements were observed between the EQA surveys in 2019 and 2020. The questionnaire results showed a large increase of >25% in the number of test requests for anti‐Xa, D‐dimer and fibrinogen assays in 2020 compared to 2019. Extreme peaks in test requests were also observed in the four evaluated hospitals. Additionally, 84% of the respondents indicated that they had experienced increased work pressure, and increased sick leave was observed in 71% of the participating laboratories.ConclusionsThe enormous increase in test requests, especially for D‐dimer assays and anti‐Xa activity, did not affect the quality of performance within haemostatic laboratories during the COVID‐19 pandemic.

Anomalous two-photon Compton scattering

X-ray free-electron lasers can generate radiation pulses with extreme peak intensities at short wavelengths. This enables the investigation of laser–matter interactions in a regime of high fields, yet at a non-relativistic ponderomotive potential, where ordinary rules of light–matter interaction may no longer apply and nonlinear processes are starting to become observable. Despite small cross-sections, first nonlinear effects in the hard x-ray regime have recently been observed in solid targets, including x-ray-optical sum-frequency generation (XSFG), x-ray second harmonic generation (XSHG) and two-photon Compton scattering (2PCS). Nonlinear interactions of bound electrons in the x-ray range are fundamentally different from those dominating at optical frequencies. Whereas in the optical regime nonlinearities are predominantly caused by anharmonicities of the atomic potential in the chemical bonds, x-ray nonlinearities far above atomic resonances are expected to be due to nonlinear oscillations of quasi-free electrons, including inner-shell atomic electrons. While the quasi-free-electron model agrees reasonably well with the experimental data for XSFG and XSHG, 2PCS measurements have led to unexpected results: the energy of the nonlinearly scattered photons from non-relativistic electrons shows a substantial unexpected red shift in addition to the Compton shift that is well beyond that predicted by a nonlinear quantum electrodynamics model for free electrons.A potential explanation for the spectral broadening is based on a previously unexplored scattering process that involves the whole atom rather than just quasi-free electrons. A first simulation that includes the atomic binding potential was successful in describing a broadening of the spectrum of the nonlinearly scattered photons to longer wavelengths for soft x-rays. However, the same model does not show any broadening at hard x-ray wavelengths, which is in agreement with other simulation approaches. To this point no calculation has been able to reproduce the experimentally observed broadening.Here we present further experimental data of 2PCS for an extended parameter range using additional diagnostics. In particular, we present measurements of the electron momentum distribution during the interaction that strongly suggest that the spectral broadening is not caused by an increased plasma temperature. We extend our measurement of the magnitude of the red shift in beryllium to in addition to the Compton shift expected for free electrons and expand the measurement of the angular distribution to include forward scattering angles. We also present first measurements of 2PCS from diamond.

Compound Flooding: Dependence at Sub-daily Scales Between Extreme Storm Surge and Fluvial Flow

Estuaries are potentially exposed to compound flooding where weather-driven extreme sea levels can occur synchronously with extreme fluvial discharge to amplify the hazard. The likelihood of compound flooding is difficult to determine due to multiple interacting physical processes operating at sub-daily scales, and poor observation records within estuaries with which to determine potential future probabilistic scenarios. We hypothesize that fluvial extremes can occur within the peak of the surge in small/steep catchments because of rapid runoff times, whilst the length-scale in larger/flatter catchments will result in fluvial and marine extremes being out-of-phase. Data (15 min river flow and hourly sea level) spanning 40 years were analyzed to assesses the behaviour and timings of fluvial and sea level extremes in two contrasting estuaries: Humber and Dyfi (United Kingdom). Compound events were common in the Dyfi, a small/steep catchment on Britain’s west coast with fast fluvial response times. Almost half of the 937 skew-surge events (95th-percentile) occurred within a few hours of an extreme fluvial peak, suggesting that flood risk is sensitive to the storm timing relative to high tide—especially since flows persisted above the 95th-percentile typically for less than 12 h. Compound events were more frequent during autumn/winter than spring/summer. For the Humber, a larger/flatter catchment on the east coast with slower fluvial response times, extreme fluvial and skew-surge peaks were less frequent (half as many as the Dyfi) and compound events were less common (15% of events co-occurred). Although flows in the Humber persisted above the 95th-percentile for typically between one and 4 days, hence overlapping several high tides and possibly other surges. Analysis of 56 flooding events across both estuaries revealed: 1) flooding is more common in the Dyfi than Humber; 2) Dyfi flooding is driven by 99th-percentile flows lasting hours and co-occurring with a 95th percentile skew-surge; 3) Humber flooding was driven by 95th-percentile flows lasting days, or surge-driven—but rarely co-occurring. Our results suggest that compound flooding studies require at least hourly data (previous analyses have often used daily-means), especially for smaller systems and considering the potential intensification of rainfall patterns into the future.

Future projections of flooding characteristics in the Lancang-Mekong River Basin under climate change

Climate change intensifies hydrological cycle, bringing critical challenges to the global and regional socio-economic development. Here we used four global climate models to project future precipitation that drives hydrological modeling of future 30-year daily runoff in a large international river basin, the Lancang-Mekong River Basin (LMRB). We applied four probability distribution functions to fit the flood peak and maximum 3-day flood volume series at four major hydrologic stations. The copula function was used to establish the joint distribution between extreme precipitation and flood peak to estimate the impact of precipitation change on floods. Results show that annual maximum 30-day precipitation (AM30D) and flood peak at Pakse station are highly correlated. Future basin-averaged AM30D is projected to increase under most climate scenarios, although AM30D varies widely in spatial distribution. The extreme values of AM30D (e.g., AM1D and AM3D) in the future (2021–2050) are larger than those in historical period (1981–2004). The temporal distribution of future AM30D is more uneven, showing the “bimodal” distribution. For the Yun Jinghong station, large uncertainty is estimated in change of direction of annual maximum flood peak and maximum 3-day flood volume. As for the other three stations, they are all projected to have larger flood risks in spite of different magnitudes. The larger the return period, the larger the increase, the greater the impact of climate change. The change of basin-averaged AM30D precipitation in the future will lead to the increase of flood peak at Pakse station via the bivariate frequency analysis. From the perspective of water conservancy project safety, considering the “adverse principle”, flood control design according to the design results derived from the joint distribution of two variables between AM30D and flood peak can reduce the risk of flood disaster. This study can provide scientific reference and basis for flood control and disaster reduction and water resources cooperation policy development in LMRB.

Multivariate Flood Frequency Analysis in Large River Basins Considering Tributary Impacts and Flood Types

AbstractIn contrast to the basic assumption of a homogeneous population underlying common approaches to flood frequency analysis, flood events often arise from different runoff‐generating processes. In many large river basins, the diversity of these processes within tributary basins and the superposition of their flood waves increase the complexity of statistical flood modeling. Under these circumstances, the allocation of the most effective flood protection measures requires a spatially explicit analysis of flood‐generating processes and the determination of the probability of downstream flood scenarios. For large basins, flood scenarios are often derived using individual historical floods along with model‐based simulations. We, instead, performed hydrograph‐based flood‐type classification and volume‐based runoff analyses for the Upper Danube River to estimate the contributions of subbasins to floods at downstream locations. Using this information, we generated long synthetic samples of peak‐volume‐pairs to apply a multivariate statistical flood‐frequency model that yields a conditional probability of a flood peak given the peaks in tributary stations. The results show that only certain combinations of flood types may result in extreme peaks downstream of confluences. They also highlight the need to distinguish runoff‐generation mechanisms for the larger floods from ones that drive smaller, more frequent events. Through an example with the Rhine River, we demonstrate how the statistical model can be generalized for complex river networks featuring several tributary confluences. Finally, design floods for different scenarios of flood‐type combinations and assigned probabilities are derived, an approach that can be used to possible climate impacts to flood frequency.

Can commercial buildings cope with Australian bushfires? An IAQ analysis

The Australian 2019/20 summer witnessed an extraordinary bushfire season characterised by unprecedented duration, geographical reach and impact. The aftermath of the bushfires includes increased health-related implications on people due to short and long exposure to poor air quality. The current advice from the Australian authorities in such events is to remain indoors, as it was assumed indoor air quality (IAQ) is healthy. This paper examined that assumption and presents the case study of an office building in Canberra subjected to the 2019/20 bushfires, responding to the need of understanding the ability of air-conditioning buildings to cope with such unprecedented and extreme weather events. Measured data for indoor concentration of CO₂, PM₁₀ and PM_2.5 recorded a prolonged period of concerning levels, as well as extreme concentration peaks. This poses a significant risk to the occupants’ health. The values showed peaks up to 12 times higher for PM₁₀ and 24 times higher for PM_2.5 than the recommended critical thresholds. The infiltration factor and protection performance analysis suggest that old filtering systems and low airtightness levels are not optimal in protecting the indoor environment from outdoor air pollutants. <em><strong>Practice relevance</strong></em> Results show that the concentration of the outdoor pollutant significantly exceeded thresholds for a prolonged time, posing a health risk to the population. The case study presented has been partially able to protect its occupants thanks to the heating, ventilation and air-conditioning (HVAC) response, but this brings up the concern about all those spaces where mechanical ventilation may not be installed or have the same filtering system. Evidence provided here calls for an urgent update of the protection agenda to account for extreme weather events with regard to the diverse indoor built environments, because relying on the mechanical ventilation system is no more sufficient to provide healthy and safe environments.

Correlational linkage analysis (Frequently Applied Designs)

Correlational linkage analysis (Frequently Applied Designs)

The role of input and hydrological parameters uncertainties in extreme hydrological simulations

AbstractQuantifying possible sources of uncertainty in simulations of hydrological extreme events is very important for better risk management in extreme situations and water resource planning. The main objective of this research work is to identify and address the role of input data quality and hydrological parameter sets, and uncertainty propagation in hydrological extremes estimation. This includes identifying and estimating their contribution to flood and low flow magnitude using two objective functions (NSE for flood and LogNSE for low flow), 20,000 Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological parameter sets, and three frequency distribution models (Log‐Normal, Pearson‐III, and Generalized Extreme Value). The influence of uncertainty on the simulated flow is not uniform across all the selected three catchments due to different flow regimes and runoff generation mechanisms. The result shows that the uncertainty in high flow frequency modeling mainly comes from the input data quality. In the modeling of low flow frequency, the main contributor to the total uncertainty is model parameterization. The total uncertainty of QT90 (extreme peak flow quantile at 90‐year return period) quantile shows that the interaction of input data and hydrological parameter sets have a significant role in the total uncertainty. In contrast, in the QT10 (extreme low flow quantile at 10‐year return period) estimation, the input data quality and hydrological parameters significantly impact the total uncertainty. This implies that the primary factors and their interactions may cause considerable risk in water resources management and flood and drought risk management. Therefore, neglecting these factors and their interaction in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to considerable risk.Recommendations for Water Resource Managers The role of hydrological parameters and climate input data is significant in flood and low flow estimations and significantly impacts water resources and extremes management. Input data dominantly controlled flood magnitude and frequency, whilst the low flow magnitude and frequency were dominantly affected by both input data quality and hydrological parameters. It is crucial to consider the main features that cause considerable risk in water resource management and extreme risk management. Neglecting the primary factors and their interaction in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to considerable risk. Improved extreme water management through a complex modeling approach to better prepare for the impact of extreme high and low flow changes are the best long‐ and short‐term plans and strategies to combat and minimize the risk in water‐related sectors of the local economy.