Ensemble Discharge Research Articles

Anthropogenic climate change has changed frequency of past flood during 2010-2013

The ongoing increases in anthropogenic radiative forcing have changed the global water cycle and are expected to lead to more intense precipitation extremes and associated floods. However, given the limitations of observations and model simulations, evidence of the impact of anthropogenic climate change on past extreme river discharge is scarce. Here, a large ensemble numerical simulation revealed that 64% (14 of 22 events) of floods analyzed during 2010-2013 were affected by anthropogenic climate change. Four flood events in Asia, Europe, and South America were enhanced within the 90% likelihood range. Of eight snow-induced floods analyzed, three were enhanced and four events were suppressed, indicating that the effects of climate change are more likely to be seen in the snow-induced floods. A global-scale analysis of flood frequency revealed that anthropogenic climate change enhanced the occurrence of floods during 2010-2013 in wide area of northern Eurasia, part of northwestern India, and central Africa, while suppressing the occurrence of floods in part of northeastern Eurasia, southern Africa, central to eastern North America and South America. Since the changes in the occurrence of flooding are the results of several hydrological processes, such as snow melt and changes in seasonal and extreme precipitation, and because a climate change signal is often not detectable from limited observation records, large ensemble discharge simulation provides insights into anthropogenic effects on past fluvial floods.

Discharge Interval method for uncertain flood forecasts using a flood model chain: city of Kulmbach

Abstract Real-time flood forecasting can help authorities in providing reliable warnings to the public. Ensemble prediction systems (EPS) have been progressively used for operational flood forecasting by European hydrometeorological agencies in recent years. This process, however, is non-deterministic such that uncertainty sources need to be considered before issuing forecasts. In this study, a new methodology for flood forecasting named Discharge Interval method is proposed. This method uses at least one historical event hindcast data, run in several ensembles and selects a pair of best ensemble discharge results for every certain discharge level. Later, the method uses the same parameter settings of the chosen ensemble discharge pair to forecast any certain flood discharge level. The methodology was implemented within the FloodEvac tool. The tool can handle calibration/validation of the hydrological model (LARSIM) and produces real-time flood forecasts with the associated uncertainty of the flood discharges. The proposed methodology is computationally efficient and suitable for real-time forecasts with uncertainty. The results using the Discharge Interval method were found comparable to the 90th percentile forecasted discharge range obtained with the Ensemble method.

Multi-Scenario Model Predictive Control Based on Genetic Algorithms for Level Regulation of Open Water Systems under Ensemble Forecasts

Operational water resources management needs to adopt operational strategies to re-allocate water resources by manipulating hydraulic structures. Model Predictive Control (MPC) has been shown to be a promising technique in this context. However, we still need to advance MPC in the face of hydrological uncertainties. This study makes the first attempt to combine Multi-Scenario MPC (MSMPC) with a Genetic Algorithm (GA) to find Pareto optimal solutions for a multi-scenario operational water resources management problem. Then three performance metrics are adopted to select the solution to be implemented. In order to assess the performance of the proposed approach, a case study of the North Sea Canal in the Netherlands is carried out, in which ensemble discharge forecasts are used. Compared with classic MSMPC approaches that deal with uncertainty by the weighted sum approach, GA-MSMPC can better fulfill management goals although it may also be computationally expensive. With the rapid development of multi-objective evolutionary algorithms, our study suggests the potential of GA-MSMPC to deal with a wide range of operational water management problems in the future.

How the Internally Organized Direction Sense Is Used to Navigate

Head-direction cells preferentially discharge when the head points in a particular azimuthal direction, are hypothesized to collectively function as a single neural system for a unitary direction sense, and are believed to be essential for navigating extra-personal space by functioning like a compass. We tested these ideas by recording medial entorhinal cortex (MEC) head-direction cells while rats navigated on a familiar, continuously rotating disk that dissociates the environment into two spatial frames: one stationary and one rotating. Head-direction cells degraded directional tuning referenced to either of the externally referenced spatial frames, but firing rates, sub-second cell-pair action potential discharge relationships, and internally referenced directional tuning were preserved. MEC head-direction cell ensemble discharge collectively generates a subjective, internally referenced unitary representation of direction that, unlike a compass, is inconsistently registered to external landmarks during navigation. These findings indicate that MEC-based directional information is subjectively anchored, potentially providing for navigation without a stable externally anchored direction sense.

Estimating uncertainty of streamflow measurements with moving-boat acoustic Doppler current profilers

ABSTRACTA theoretical and a semi-empirical model are presented for estimating the uncertainty of streamflow measurements made with an acoustic Doppler current profiler (ADCP) mounted on a moving platform. Both models are based on the statistical analysis of ADCP ensemble discharge time series collected during a transect; therefore, they account for all random error sources associated with the measurement at a site. The theoretical model is developed based on the law of propagation of variance; it explores the theoretical relationship between the variables involved in the problem. The semi-empirical model is developed based on the theory of dimensional analysis; it explores the empirical relationship between the variables. The semi-empirical model is calibrated using 205 transect datasets and verified with an additional 382 transect datasets. It provides a useful tool for the uncertainty analysis and uncertainty-based measurement quality control of moving-boat ADCP streamflow measurements.

How the Internally-Organized Direction Sense Is Used to Navigate

Head-direction cells preferentially discharge when the head points in a particular azimuthal direction, are hypothesized to collectively function as a single neural system for a unitary direction sense, and are believed to be essential for navigating extra-personal space by functioning like a compass in GPS-based navigation. We tested these ideas by recording medial entorhinal cortex (MEC) head-direction cells while rats navigated on a familiar, continuously rotating disk that dissociates the environment into two spatial frames, one stationary, one rotating. Head-direction cells degraded directional tuning referenced to either of the externally referenced spatial frames, but firing rates, sub-second coordinated relationships of action potential discharge, and internally-referenced directional tuning were preserved. MEC head-direction cell ensemble discharge collectively generates a subjective, internally-referenced unitary representation of direction that unlike a compass, is inconsistently registered to external landmarks during navigation. These findings indicate that MEC-based directional information contributes to staged, etak-like navigation rather than GPS-like navigation.

Case study for investigating groundwater and the future of mountain spring discharges in Southern Italy

Groundwater extraction is used to alleviate drought in many habitats. However, widespread drought decreases spring discharge and there is a need to integrate climate change research into resource management and action. Accurate estimates of groundwater discharge may be valuable in improving decision support systems of hydrogeological resource exploitation. The present study performs a forecast for groundwater discharge in Aquifer’s Cervialto Mountains (southern Italy). A time series starting in 1883 was the basis for long-term predictions. An Ensemble Discharge Prediction (EDisP) was applied, and the progress of the discharge ensemble forecast was inferred with the aid of an Exponential Smoothing (ES) model initialized at different annual times. EDisP-ES hindcast model experiments were tested, and discharge plume-patterns forecast was assessed with horizon placed in the year 2044. A 46-year cycle pattern was identified by comparing simulations and observations, which is essential for the forecasting purpose. ED is P-ES performed an ensemble mean path for the coming decades that indicates a discharge regime within ± 1 standard deviation around the mean value of 4.1 m3 s−1. These fluctuations are comparable with those observed in the period 1961–1980 and further back, with change-points detectable around the years 2025 and 2035. Temporary drought conditions are expected after the year 2030.

Evaluation of an ensemble of regional hydrological models in 12 large-scale river basins worldwide

In regional climate impact studies, good performance of regional models under present/historical climate conditions is a prerequisite for reliable future projections. This study aims to investigate the overall performance of 9 hydrological models for 12 large-scale river basins worldwide driven by the reanalysis climate data from the Water and Global Change (WATCH) project. The results serve as the basis of the application of regional hydrological models for climate impact assessment within the second phase of the Inter-Sectoral Impact Model Intercomparison project (ISI-MIP2). The simulated discharges by each individual hydrological model, as well as the ensemble mean and median series were compared against the observed discharges for the period 1971–2001. In addition to a visual comparison, 12 statistical criteria were selected to assess the fidelity of model simulations for monthly hydrograph, seasonal dynamics, flow duration curves, extreme floods and low flows. The results show that most regional hydrological models reproduce monthly discharge and seasonal dynamics successfully in all basins except the Darling in Australia. The moderate flow and high flows (0.02–0.1 flow exceedance probabilities) are also captured satisfactory in many cases according to the performance ratings defined in this study. In contrast, the simulation of low flow is problematic for most basins. Overall, the ensemble discharge statistics exhibited good agreement with the observed ones except for extremes in particular basins that need further scrutiny to improve representation of hydrological processes. The performances of both the conceptual and process-based models are comparable in all basins.

Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls

Abstract. This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) are used as the input data to a rainfall–runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolutions. A distributed rainfall–runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km resolution JMA-NHM ensemble simulation are more appropriate than the 10 km resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s−1, a threshold value for flood control. The inflows with the 10 km resolution ensemble rainfall are all considerably smaller than the observations, while at least one simulated discharge out of 11 ensemble members with the 2 km resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls shows much better results than the original ensemble discharge simulations.

Evaluation of two convection‐permitting ensemble systems in the HyMeX Special Observation Period (SOP1) framework

This study compares and evaluates two convection‐permitting ensemble systems based on Consortium for Small‐scale Modeling (COSMO) and Applications of Research to Operations at Mesoscale (AROME) models in the HyMeX framework. The performance of both AROME‐EPS and COSMO‐H2‐EPS (where EPS denotes Ensemble Prediction System) is assessed over the whole HyMeX special observation period SOP1. Afterwards, an analysis of the predictability of two heavy precipitation events observed during the intense observation period IOP16a of the first HyMeX special observation period SOP1 on 26 October 2012 is also undertaken. Ensemble discharge forecasts were carried out to reinforce the quantitative precipitation forecast evaluation.A probabilistic evaluation is conducted over a 53 day period of the HyMeX SOP1. AROME‐EPS has a more discriminating behaviour than COSMO‐H2‐EPS, especially when comparing both ensembles over a verification domain including a strong variability in precipitation events. AROME‐EPS still has slightly better reliability, but the statistical resolution is nearly the same for both convection‐permitting ensemble prediction systems (CPEPS).For the specific case of heavy precipitation occurring over the Var region (southeastern France), the fine‐scale surface precipitation prediction is strongly sensitive to the good behaviour of a surface low pressure simulated by the ensembles, focusing strong low‐level moisture towards Var. The convergence between strong moistened southerly low‐level inflow and northerly cold air blowing from the Po Valley is a key factor controlling the predictability of the heavy precipitation episode over the Liguria region (northwestern Italy). The two convection‐permitting ensembles, though different in their characteristics, exhibit a good amount of probabilistic skill in forecasting heavy precipitation at a relatively high spatial and temporal resolution. Therefore they can be regarded as promising tools for operational forecasts.

Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

Abstract. The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash–Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A better precipitation product does not guarantee a better discharge simulation because of interactions. It is also found that a good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, suggesting that, although the satellite-based precipitation products are not as accurate as the gauge-based products, they could have better performance in discharge simulations when appropriately combined with hydrological models. This information is revealed for the first time and very beneficial for precipitation product applications.

Hydrological modelling using ensemble satellite rainfall estimates in a sparsely gauged river basin: The need for whole-ensemble calibration

The potential for satellite rainfall estimates to drive hydrological models has been long understood, but at the high spatial and temporal resolutions often required by these models the uncertainties in satellite rainfall inputs are both significant in magnitude and spatiotemporally autocorrelated. Conditional stochastic modelling of ensemble observed fields provides one possible approach to representing this uncertainty in a form suitable for hydrological modelling. Previous studies have concentrated on the uncertainty within the satellite rainfall estimates themselves, sometimes applying ensemble inputs to a pre-calibrated hydrological model. This approach does not account for the interaction between input uncertainty and model uncertainty and in particular the impact of input uncertainty on model calibration. Moreover, it may not be appropriate to use deterministic inputs to calibrate a model that is intended to be driven by using an ensemble. A novel whole-ensemble calibration approach has been developed to overcome some of these issues.This study used ensemble rainfall inputs produced by a conditional satellite-driven stochastic rainfall generator (TAMSIM) to drive a version of the Pitman rainfall-runoff model, calibrated using the whole-ensemble approach. Simulated ensemble discharge outputs were assessed using metrics adapted from ensemble forecast verification, showing that the ensemble outputs produced using the whole-ensemble calibrated Pitman model outperformed equivalent ensemble outputs created using a Pitman model calibrated against either the ensemble mean or a theoretical infinite-ensemble expected value.Overall, for the verification period the whole-ensemble calibration provided a mean RMSE of 61.7% of the mean wet season discharge, compared to 83.6% using a calibration based on the daily mean of the ensemble estimates. Using a Brier’s Skill Score to assess the performance of the ensemble against a climatic estimate, the whole-ensemble calibration provided a positive score for the main range of discharge events. The equivalent score for calibration against the ensemble mean was negative, indicating it showed no skill versus the climatic estimate.

Key Features of Human Episodic Recollection in the Cross-Episode Retrieval of Rat Hippocampus Representations of Space

Author SummarySince Plato and the early cognitive psychologists we have believed that recollection is not a straightforward replay of stored experience; memory can be modified by the subject's experience and or state of mind, sometimes generating knowledge that was never itself experienced. We show in rats that direct neurophysiological evidence that episodic recollection is not a verbatim replay of stored experience; rather retrieval is a constructive process—an intermingling of the stored memory, environmental circumstances, and the subject's state of mind. To monitor internal, neural representations, we recorded the action potential discharge within a network of hippocampal neurons while rats performed two familiar active place avoidance tasks: one where the arena was stable and one where it rotated. Neural representations of the stable and rotating conditions were distinct. However, during the rotating task we intermittently observed intervals that we call “cross-episode retrieval” because discharge resembled the representation of the stable task. The incidence of cross-episode retrieval was higher when the angular position of the rotating arena was similar to that in the stable condition, demonstrating that retrieval is influenced by the environment. Cross-episode retrieval was also more likely when discharge represented the position of the rat in the stationary room rather than when it represented positions that rotate with the arena; this demonstrated that retrieval is influenced by internal cognitive variables that are encoded by hippocampal discharge—a state of mind. Thus novel, key features of constructive human episodic memory can be observed in rat hippocampal discharge.

Hydrological ensemble prediction systems

Hydrological ensemble prediction systems

Sensitivity of hydrological ensemble forecasts to different sources and temporal resolutions of probabilistic quantitative precipitation forecasts: flash flood case studies in the Cévennes‐Vivarais region (Southern France)

AbstractThis article analyses the performance of an integrated hydrological ensemble prediction system adapted to small to mid‐sized catchments (100–600 km2) situated in the Cévennes‐Vivarais region (Southern France) and characterized by short lag times (3–12 h). In this framework, flood forecasts need hourly Probabilistic Quantitative Precipitation Forecasts (PQPF) so as to provide early warning with 24–72 h of anticipation. Here, two sources of PQPF at daily and subdaily (6 h) meteorological time steps are considered: Ensemble Prediction Systems from the European Centre for Medium‐range Weather Forecast (ECMWF) and analogy‐based PQPF provided in real‐time at the Laboratoire d'étude des Transferts en Hydrologie et Environnement. The two PQPF are firstly disaggregated to respect the required hydrological hourly time step, through either the use of a stochastic rainfall generator or the application of a multimodel approach. Then, disaggregated PQPF are used as input to a hydrological model, which is called TOPSIMPL, to provide hourly ensemble discharge forecasts up to 48 h ahead. Illustration and evaluation of ensemble discharge forecasts issued in near real‐time conditions are given for some recently observed flash flood events. It is shown that hourly discharge forecasts are first‐order conditioned by the accuracy of PQPF at daily or subdaily time step. Six‐hour ensemble prediction systems correctly reproduce the rainfall temporal dynamics, whereas daily analogy‐based PQPF are less underdispersive in terms of rainfall amounts. As a result, the merging of the two sources of PQPF substantially increases the performance of discharge forecasts, the contribution of a more sophisticated hourly rainfall generator becoming marginal. Copyright © 2012 John Wiley & Sons, Ltd.

Probabilistic evaluation of ensemble discharge nowcasts in two nested Alpine basins prone to flash floods

AbstractThe quality of hydrological discharge simulations depends to a great extent on the uncertainties in the meteorological input and the model parameterization. To quantify these uncertainties, we adopt ensemble techniques in a 4‐year nowcast experiment for two nested flash‐flood–prone basins in the southern Swiss Alps.The spatiotemporal uncertainties in the weather radar quantitative precipitation estimates (QPE) were accounted for by applying an ensemble of 25 radar fields. To account for uncertainties in model parameterization, a Monte Carlo experiment was run to find 26 equifinal model realizations. The resulting parameter ensemble, consisting of 26 members, was run with precipitation input obtained from interpolated pluviometer data and with the deterministic operational weather radar QPE.To produce the discharge nowcast, the PREcipitation‐Runoff‐EVApotranspiration HRU Model (PREVAH) was used. PREVAH was calibrated for the main catchment Verzasca. The results for the sub‐catchment Pincascia are an independent internal verification of the nowcasting system.The three ensemble nowcasts and the two deterministic nowcasts are evaluated for a 4‐year time series and for two events included in that period. The event analysis shows no clear superiority for either pluviometer‐based or radar‐based nowcasts. The performance for single events depends heavily on the storm characteristics. However, the evaluation of the 4‐year nowcast shows that pluviometer‐based nowcasts outperform radar‐based nowcasts in the gauged and calibrated catchment and that there is added value in the application of parameter ensembles.For the small, ungauged catchment, the results achieved by the radar‐based nowcasts are superior to the pluviometer‐based nowcasts. Especially the radar ensemble proves to be of significant advantage for flash flood nowcasts in such catchments. Copyright © 2012 John Wiley & Sons, Ltd.

Uncertainty Model for In Situ Quality Control of Stationary ADCP Open-Channel Discharge Measurement

This paper presents a relative standard uncertainty (RSU) model for stationary acoustic Doppler current profiler (ADCP) open-channel discharge measurement. The model is a combination of the Type A and Type B evaluation of uncertainty and complies with the latest methodology for evaluating uncertainty in measurements implemented by the International Organization for Standardization. The Type A uncertainty component in the model accounts for the site-specific measurement conditions such as ambient turbulence; ADCP pitch, roll, and heading variations/errors; and the system noise of the ADCP used at the site. It is determined by statistical analysis of ADCP subsection ensemble discharge data. The model provides an in situ tool for quality control of a stationary ADCP discharge measurement.

Human Muscle Spindles Act as Forward Sensory Models

Modern theories of motor control incorporate forward models that combine sensory information and motor commands to predict future sensory states. Such models circumvent unavoidable neural delays associated with on-line feedback control. Here we show that signals in human muscle spindle afferents during unconstrained wrist and finger movements predict future kinematic states of their parent muscle. Specifically, we show that the discharges of type Ia afferents are best correlated with the velocity of length changes in their parent muscles approximately 100-160 ms in the future and that their discharges vary depending on motor sequences in a way that cannot be explained by the state of their parent muscle alone. We therefore conclude that muscle spindles can act as "forward sensory models": they are affected both by the current state of their parent muscle and by efferent (fusimotor) control, and their discharges represent future kinematic states. If this conjecture is correct, then sensorimotor learning implies learning how to control not only the skeletal muscles but also the fusimotor system.

Attention-Like Modulation of Hippocampus Place Cell Discharge

Hippocampus place cell discharge is an important model system for understanding cognition, but evidence is missing that the place code is under the kind of dynamic attentional control characterized in primates as selective activation of one neural representation and suppression of another, competing representation. We investigated the apparent noise ("overdispersion") in the CA1 place code, hypothesizing that overdispersion results from discharge fluctuations as spatial attention alternates between distal cues and local/self-motion cues. The hypothesis predicts that: (1) preferential use of distal cues will decrease overdispersion; (2) global, attention-like states can be decoded from ensemble discharge such that both the discharge rates and the spatial firing patterns of individual cells will be distinct in the two states; (3) identifying attention-like states improves reconstructions of the rat's path from ensemble discharge. These predictions were confirmed, implying that a covert, dynamic attention-like process modulates discharge on a approximately 1 s time scale. We conclude the hippocampus place code is a dynamic representation of the spatial information in the immediate focus of attention.

A data assimilation approach to discharge estimation from space

AbstractRiver discharge is currently monitored by a diminishing network of gauges, which provide a spatially incomplete picture of global discharges. This study assimilated water level information derived from a fused satellite Synthetic Aperture Radar (SAR) image and digital terrain model (DTM) with simulations from a coupled hydrological and hydrodynamic model to estimate discharge in an un‐gauged basin scenario. Assimilating water level measurements led to a 79% reduction in ensemble discharge uncertainty over the coupled hydrological hydrodynamic model alone. Measurement bias was evident, but the method still provided a means of improving estimates of discharge for high flows. The study demonstrates the potential of currently available synthetic aperture radar imagery to reduce discharge uncertainty in un‐gauged basins when combined with model simulations in a data assimilation framework, where sufficient topographic data are available. The work is timely because in the near future the launch of satellite radar missions will lead to a significant increase in the volume of data available for space‐borne discharge estimation. Copyright © 2009 John Wiley & Sons, Ltd.