Dependence of flood peaks and volumes in modeled discharge time series: effect of different uncertainty sources

Flood estimates needed for designing efficient and cost-effective flood protection structures are usually derived using observed peak discharges. This approach neglects, firstly, that floods are characterized not only by peak discharge but also by flood volume, and, secondly, that these characteristics are subject to modifications under climate and land use changes. Bivariate flood frequency analysis based on simulated discharge time series makes it possible to consider both flood peak and flood volume in design flood estimation. Further, this approach considers changes in discharge characteristics by using discharge series generated from climate time series used as an input for a hydrological model. Such series are usually not available at an hourly resolution but at a certain aggregation level (e.g. 24 h) and might not perfectly represent observed precipitation distributions. In this study, we therefore investigate how the aggregation and distribution of precipitation series and discharge distribution affect flood peaks and volumes and their dependence. We propose a framework for assessing the uncertainty in bivariate design flood estimates that is caused by different factors in the modeling chain, which consists of precipitation-discharge modeling, flood event sampling, and bivariate flood frequency analysis. The uncertainty sources addressed are precipitation aggregation and distribution, parameter and model uncertainty, and discharge resolution. Our results show that all of these uncertainty sources are relevant for design flood estimation and that the importance of the individual uncertainty sources is catchment dependent. Our results also demonstrate that substantial uncertainty is introduced already in the first step of the model chain because commonly used calibration procedures do not take into account the reproduction of flood volumes. Researchers should be aware of such deficiencies when performing bivariate flood frequency analysis on modeled discharge time series and should aim to tailor model calibration procedures to the problem at hand

Future shifts in extreme flow regimes in Alpine regions

Extreme low and high flows can have negative economic, social, and ecological effects and are expected to become more severe in many regions due to climate change. Besides low and high flows, the whole flow regime, i.e., annual hydrograph comprised of monthly mean flows, is subject to changes. Knowledge on future changes in flow regimes is important since regimes contain information on both extremes and conditions prior to the dry and wet seasons. Changes in individual low- and high-flow characteristics as well as flow regimes under mean conditions have been thoroughly studied. In contrast, little is known about changes in extreme flow regimes. We here propose two methods for the estimation of extreme flow regimes and apply them to simulated discharge time series for future climate conditions in Switzerland. The first method relies on frequency analysis performed on annual flow duration curves. The second approach performs frequency analysis of the discharge sums of a large set of stochastically generated annual hydrographs. Both approaches were found to produce similar 100-year regime estimates when applied to a data set of 19 hydrological regions in Switzerland. Our results show that changes in both extreme low- and high-flow regimes for rainfall-dominated regions are distinct from those in melt-dominated regions. In rainfall-dominated regions, the minimum discharge of low-flow regimes decreases by up to 50 %, whilst the reduction is 25 % for high-flow regimes. In contrast, the maximum discharge of low- and high-flow regimes increases by up to 50 %. In melt-dominated regions, the changes point in the other direction than those in rainfall-dominated regions. The minimum and maximum discharges of extreme regimes increase by up to 100 % and decrease by less than 50  %, respectively. Our findings provide guidance in water resource planning and management and the extreme regime estimates are a valuable basis for climate impact studies

A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow

Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging, particularly under nonstationary conditions and if extremes are correlated in space. The goal of this study is to implement a space&ndash;time model for the projection of seasonal streamflow extremes that considers the nonstationarity (interannual variability) and spatiotemporal dependence of high flows. We develop a space&ndash;time model to project seasonal streamflow extremes for several lead times up to 2 months, using a Bayesian hierarchical modeling (BHM) framework. This model is based on the assumption that streamflow extremes (3 d maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates describing the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatiotemporal variability and uncertainty. We apply this modeling framework to predict 3 d maximum streamflow in spring (May&ndash;June) at seven gauges in the Upper Colorado River basin (UCRB) with 0- to 2-month lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections &ndash; El Ni&ntilde;o&ndash;Southern Oscillation, Atlantic Multidecadal Oscillation, and Pacific Decadal Oscillation &ndash; as potential covariates for 3 d spring maximum streamflow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space&ndash;time variability in extreme streamflow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations, thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatiotemporal modeling framework helps in the planning of seasonal adaptation and preparedness measures as predictions of extreme spring streamflows become available 2 months before actual flood occurrence.</p

Elasticity curves describe streamflow sensitivity to precipitation across the entire flow distribution

Streamflow elasticity is a simple approximation of how responsive a river is to precipitation. It is represented as a ratio of the expected percentage change in streamflow for a 1 % change in precipitation. Typically estimated for the annual median streamflow, we here propose a new concept in which streamflow elasticity is estimated across the full range of streamflow percentiles in a large-sample context. This “elasticity curve” can be used to develop a more complete depiction of how streamflow responds to precipitation. We find three different elasticity curve types which characterize this relationship at the annual and seasonal timescales in the USA, based on two statistical modelling approaches, a panel regression which facilitates causal inference and a single catchment model which allows for consideration of static attributes. Type A describes catchments where low flows are the least and high flows are the most responsive to precipitation. The majority of catchments at the annual, winter, and fall timescales exhibit this behavior. Type B describes catchments where the response is relatively consistent across the flow distribution. At the seasonal timescale, many catchments experience a consistent level of response across the flow regime. This is especially true in snow-fed catchments during cold months, when the actual elasticity skews towards zero for all flow percentiles while precipitation is held in storage. Consistent response is also seen across the majority of the country during spring when streamflow is comparatively stable and in summer when evaporation demand is high and soil moisture is low. Finally, Type C describes catchments where low flows are the most responsive to precipitation change. These catchments are dominated by highly flashy low flow behavior. We show that the curve type varies separately from the magnitude of the elasticity. Finally, we demonstrate that available water storage is likely the key control which determines curve type

Reactive oxygen-related diseases: therapeutic targets and emerging clinical indications

SIGNIFICANCE Enhanced levels of reactive oxygen species (ROS) have been associated with different disease states. Most attempts to validate and exploit these associations by chronic antioxidant therapies have provided disappointing results. Hence, the clinical relevance of ROS is still largely unclear. RECENT ADVANCES We are now beginning to understand the reasons for these failures, which reside in the many important physiological roles of ROS in cell signaling. To exploit ROS therapeutically, it would be essential to define and treat the disease-relevant ROS at the right moment and leave physiological ROS formation intact. This breakthrough seems now within reach. CRITICAL ISSUES Rather than antioxidants, a new generation of protein targets for classical pharmacological agents includes ROS-forming or toxifying enzymes or proteins that are oxidatively damaged and can be functionally repaired. FUTURE DIRECTIONS Linking these target proteins in future to specific disease states and providing in each case proof of principle will be essential for translating the oxidative stress concept into the clinic. Antioxid. Redox Signal. 23, 1171-1185

CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland

We present CAMELS-CH (Catchment Attributes and MEteorology for Large-sample Studies – Switzerland), a large-sample hydro-meteorological data set for hydrologic Switzerland in central Europe. This domain covers 331 basins within Switzerland and neighboring countries. About one-third of the catchments are located in Austria, France, Germany and Italy. As an Alpine country, Switzerland covers a vast diversity of landscapes, including mountainous environments, karstic regions, and several strongly cultivated regions, along with a wide range of hydrological regimes, i.e., catchments that are glacier-, snow- or rain dominated. Similar to existing data sets, CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes. CAMELS-CH (Höge et al., 2023; available at https://doi.org/10.5281/zenodo.7784632) encompasses 40 years of data between 1 January 1981 and 31 December 2020, including daily time series of stream flow and water levels, and of meteorological data such as precipitation and air temperature. It also includes daily snow water equivalent data for each catchment starting from 2 September 1998. Additionally, we provide annual time series of land cover change and glacier evolution per catchment. The static catchment attributes cover location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers. This Swiss data set complements comparable publicly accessible data sets, providing data from the “water tower of Europe”

Performance of the First ANTARES Detector Line

In this paper we report on the data recorded with the first Antares detector line. The line was deployed on the 14th of February 2006 and was connected to the readout two weeks later. Environmental data for one and a half years of running are shown. Measurements of atmospheric muons from data taken from selected runs during the first six months of operation are presented. Performance figures in terms of time residuals and angular resolution are given. Finally the angular distribution of atmospheric muons is presented and from this the depth profile of the muon intensity is derived.Comment: 14 pages, 9 figure

Time calibration of the ANTARES neutrino telescope

The ANTARES deep-sea neutrino telescope comprises a three-dimensional array of photomultipliers to detect the Cherenkov light induced by upgoing relativistic charged particles originating from neutrino interactions in the vicinity of the detector. The large scattering length of light in the deep sea facilitates an angular resolution of a few tenths of a degree for neutrino energies exceeding 10 TeV. In order to achieve this optimal performance, the time calibration procedures should ensure a relative time calibration between the photomultipliers at the level of similar to 1 ns. The methods developed to attain this level of precision are described

Performance of the front-end electronics of the ANTARES neutrino telescope

ANTARES is a high-energy neutrino telescope installed in the Mediterranean Sea at a depth of 2475 m. It consists of a three-dimensional array of optical modules, each containing a large photomultiplier tube. A total of 2700 front-end ASICs named Analogue Ring Samplers (ARS) process the phototube signals, measure their arrival time, amplitude and shape as well as perform monitoring and calibration tasks. The ARS chip processes the analogue signals from the optical modules and converts information into digital data. All the information is transmitted to shore through further multiplexing electronics and an optical link. This paper describes the performance of the ARS chip; results from the functionality and characterization tests in the laboratory are summarized and the long-term performance in the apparatus is illustrated.Comment: 20 pages, 22 figures, published in Nuclear Instruments and Methods