scholarly journals Evaluation of two hydrometeorological ensemble strategies for flash-flood forecasting over a catchment of the eastern Pyrenees

2020 ◽  
Vol 20 (2) ◽  
pp. 425-450 ◽  
Author(s):  
Hélène Roux ◽  
Arnau Amengual ◽  
Romu Romero ◽  
Ernest Bladé ◽  
Marcos Sanz-Ramos
Keyword(s):  
Mesoscale Model ◽  
Flash Flood ◽  
Ensemble Prediction ◽  
Flood Warning ◽  
Eastern Pyrenees ◽  
Streamflow Forecasts ◽  
Prediction Systems ◽  
Rainfall Runoff Model ◽  
Alert Threshold ◽  
Threshold Exceedance

Abstract. This study aims at evaluating the performances of flash-flood forecasts issued from deterministic and ensemble meteorological prognostic systems. The hydrometeorological modeling chain includes the Weather Research and Forecasting Model (WRF) forcing the rainfall-runoff model MARINE dedicated to flash floods. Two distinct ensemble prediction systems accounting for (i) perturbed initial and lateral boundary conditions of the meteorological state and (ii) mesoscale model physical parameterizations have been implemented on the Agly catchment of the eastern Pyrenees with three subcatchments exhibiting different rainfall regimes. Different evaluations of the performance of the hydrometeorological strategies have been performed: (i) verification of short-range ensemble prediction systems and corresponding streamflow forecasts, for a better understanding of how forecasts behave; (ii) usual measures derived from a contingency table approach, to test an alert threshold exceedance; and (iii) overall evaluation of the hydrometeorological chain using the continuous rank probability score, for a general quantification of the ensemble performances. Results show that the overall discharge forecast is improved by both ensemble strategies with respect to the deterministic forecast. Threshold exceedance detections for flood warning also benefit from large hydrometeorological ensemble spread. There are no substantial differences between both ensemble strategies on these test cases in terms of both the issuance of flood warnings and the overall performances, suggesting that both sources of external-scale uncertainty are important to take into account.

scholarly journals Evaluation of two hydrometeorogical ensemble strategies for flash flood forecasting over a catchment of the eastern Pyrenees

2019 ◽  
Author(s):  
Hélène Roux ◽  
Arnau Amengual ◽  
Romu Romero ◽  
Ernest Bladé ◽  
Marcos Sanz-Ramos
Keyword(s):  
Mesoscale Model ◽  
Flash Flood ◽  
Ensemble Prediction ◽  
Flood Warning ◽  
Eastern Pyrenees ◽  
Meteorological Modeling ◽  
Prediction Systems ◽  
Rainfall Runoff Model ◽  
Alert Threshold ◽  
Threshold Exceedance

Abstract. This study aims at evaluating the performances of flash flood forecasts issued from deterministic and ensemble meteorological prognostic systems. The hydro-meteorological modeling chain includes the Weather Research and Forecasting model (WRF) forcing the rainfall-runoff model MARINE dedicated to flash flood. Two distinct ensemble prediction systems accounting for (i) perturbed initial and lateral boundary conditions of the meteorological state and (ii) mesoscale model physical parameterizations, have been implemented on the Agly catchment of the Eastern Pyrenees with three sub-catchments exhibiting different rainfall regimes. Different evaluations of the performance of the hydrometeorological strategies have been performed: (i) verification of short-range ensemble prediction systems and corresponding stream flow forecasts, for a better understanding of how forecasts behave, (ii) usual measures derived from a contingency table approach, to test an alert threshold exceedance, and (iii) overall evaluation of the hydro-meteorological chain using the Continuous Rank Probability Score, for a general quantification of the ensemble performances. Results show that the overall discharge forecast is improved by both ensemble strategies with respect to the deterministic forecast. Threshold exceedance detections for flood warning also benefit from large hydro-meteorological ensemble spread. There are no substantial differences between both ensemble strategies on these test cases in terms both of the issuance of flood warnings and the overall performances, suggesting that both sources of external-scale uncertainty are important to take into account.

scholarly journals Evaluation of bias-correction methods for ensemble streamflow volume forecasts

2007 ◽  
Vol 11 (2) ◽  
pp. 939-950 ◽  
Author(s):  
T. Hashino ◽  
A. A. Bradley ◽  
S. S. Schwartz
Keyword(s):  
Decision Making ◽  
Bias Correction ◽  
Ensemble Prediction ◽  
Streamflow Prediction ◽  
Historical Simulation ◽  
Probabilistic Forecasts ◽  
Central United States ◽  
Streamflow Forecasts ◽  
Prediction Systems ◽  
Forecast Quality

Abstract. Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for a north-central United States basin (Des Moines River, Iowa), issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.

The sequence of heavy precipitation and flash flooding of 12 and 13 September 2019 in eastern Spain. Part II: A hydro-meteorological predictability analysis based on convection-permitting ensemble strategies

2021 ◽  
Author(s):  
A. Amengual ◽  
A. Hermoso ◽  
D. S. Carrió ◽  
V. Homar
Keyword(s):  
Dynamical Downscaling ◽  
Flash Flood ◽  
Deep Convection ◽  
Heavy Precipitation ◽  
Western Mediterranean ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Catchment Scale ◽  
Flash Flooding ◽  
Flood Warning

AbstractOn 12 and 13 September 2019, widespread flash flooding caused devastating effects across eastern Spain. Within the framework of the HyMeX program, this study examines predictability of the long-lasting heavy precipitation episode (HPE) conducive to flash flooding. A set of short-range, convection-permitting ensemble prediction systems (EPSs) is built to cope with different sources of meteorological uncertainty. Specifically, the performance of an Ensemble Kalman Filter, tailored bred vectors and stochastic model parameterizations is compared to more standard ensemble generation techniques such as dynamical downscaling and multiple physics. Results indicate EPS focusing on sampling model uncertainties related to parameterization of subgrid process are skillful, especially when deep convection and its interaction with complex orography are important. Furthermore, representation of small-scale thermodynamical aspects is improved through data assimilation, leading to an enhanced forecasting skill as well. Nevertheless, predictability remains relatively low at the catchment scale in terms of exceedance probabilities in cumulative precipitation and peak discharge. The analysis presented herein could serve as a basis for the future implementation of real-time flash flood warning systems based on skillful meteorological EPSs over small-to-medium, semi-arid watersheds in eastern Spain and, by extension, over the flood-prone Western Mediterranean region.

scholarly journals Evaluation of bias-correction methods for ensemble streamflow volume forecasts

2006 ◽  
Vol 3 (2) ◽  
pp. 561-594 ◽  
Author(s):  
T. Hashino ◽  
A. A. Bradley ◽  
S. S. Schwartz
Keyword(s):  
Decision Making ◽  
Bias Correction ◽  
Ensemble Prediction ◽  
Streamflow Prediction ◽  
Historical Simulation ◽  
Probabilistic Forecasts ◽  
Streamflow Forecasts ◽  
Operational Decision Making ◽  
Prediction Systems ◽  
Forecast Quality

Abstract. Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for the Des Moines River, issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.

scholarly journals Multiphysics superensemble forecast applied to Mediterranean heavy precipitation situations

2010 ◽  
Vol 10 (11) ◽  
pp. 2371-2377 ◽  
Author(s):  
M. Vich ◽  
R. Romero
Keyword(s):  
Mesoscale Model ◽  
Heavy Precipitation ◽  
Western Mediterranean ◽  
Ensemble Prediction ◽  
Training Phase ◽  
Past Performance ◽  
Ensemble Prediction System ◽  
Control Member ◽  
Prediction Systems ◽  
Precipitation Events

Abstract. The high-impact precipitation events that regularly affect the western Mediterranean coastal regions are still difficult to predict with the current prediction systems. Bearing this in mind, this paper focuses on the superensemble technique applied to the precipitation field. Encouraged by the skill shown by a previous multiphysics ensemble prediction system applied to western Mediterranean precipitation events, the superensemble is fed with this ensemble. The training phase of the superensemble contributes to the actual forecast with weights obtained by comparing the past performance of the ensemble members and the corresponding observed states. The non-hydrostatic MM5 mesoscale model is used to run the multiphysics ensemble. Simulations are performed with a 22.5 km resolution domain (Domain 1 in http://mm5forecasts.uib.es) nested in the ECMWF forecast fields. The period between September and December 2001 is used to train the superensemble and a collection of 19~MEDEX cyclones is used to test it. The verification procedure involves testing the superensemble performance and comparing it with that of the poor-man and bias-corrected ensemble mean and the multiphysic EPS control member. The results emphasize the need of a well-behaved training phase to obtain good results with the superensemble technique. A strategy to obtain this improved training phase is already outlined.

scholarly journals A Radar Simulator for High-Resolution Nonhydrostatic Models

2006 ◽  
Vol 23 (8) ◽  
pp. 1049-1067 ◽  
Author(s):  
Olivier Caumont ◽  
Véronique Ducrocq ◽  
Guy Delrieu ◽  
Marielle Gosset ◽  
Jean-Pierre Pinty ◽  
...  
Keyword(s):  
High Resolution ◽  
Mesoscale Model ◽  
Flash Flood ◽  
Weather Prediction ◽  
Building Blocks ◽  
Vertical Gradient ◽  
Beam Bending ◽  
Radar Simulator ◽  
Prediction Systems ◽  
Validation Tool

Abstract A full radar simulator for high-resolution (1–5 km) nonhydrostatic models has been developed within the research nonhydrostatic mesoscale atmospheric (Meso-NH) model. This simulator is made up of building blocks, each of which describes a particular physical process (scattering, beam bending, etc.). For each of these blocks, several formulations have been implemented. For instance, the radar simulator offers the possibility to choose among Rayleigh, Rayleigh–Gans, Mie, or T-matrix scattering methods, and beam bending can be derived from an effective earth radius or can depend on the vertical gradient of the refractive index of air. Moreover, the radar simulator is fully consistent with the microphysical parameterizations used by the atmospheric numerical model. Sensitivity experiments were carried out using different configurations for the simulator. They permitted the specification of an observation operator for assimilation of radar reflectivities by high-resolution nonhydrostatic numerical weather prediction systems, as well as for their validation. A study of the flash flood of 8–9 September 2002 in southeastern France, which was well documented with volumetric data from an S-band radar, serves to illustrate the capabilities of the radar simulator as a validation tool for a mesoscale model.

scholarly journals A Comparison of Ensemble Strategies for Flash Flood Forecasting: The 12 October 2007 Case Study in Valencia, Spain

2017 ◽  
Vol 18 (4) ◽  
pp. 1143-1166 ◽  
Author(s):  
A. Amengual ◽  
D. S. Carrió ◽  
G. Ravazzani ◽  
V. Homar
Keyword(s):  
Early Warning ◽  
Flash Flood ◽  
Convective Precipitation ◽  
Flood Event ◽  
Ensemble Prediction ◽  
Hydrological Response ◽  
Rainfall Runoff ◽  
The Mediterranean ◽  
Prediction Systems ◽  
Physical Parameterizations

Abstract On 12 October 2007, several flash floods affected the Valencia region, eastern Spain, with devastating impacts in terms of human, social, and economic losses. An enhanced modeling and forecasting of these extremes, which can provide a tangible basis for flood early warning procedures and mitigation measures over the Mediterranean, is one of the fundamental motivations of the international Hydrological Cycle in the Mediterranean Experiment (HyMeX) program. The predictability bounds set by multiple sources of hydrological and meteorological uncertainty require their explicit representation in hydrometeorological forecasting systems. By including local convective precipitation systems, short-range ensemble prediction systems (SREPSs) provide a state-of-the-art framework to generate quantitative discharge forecasts and to cope with different sources of external-scale (i.e., external to the hydrological system) uncertainties. The performance of three distinct hydrological ensemble prediction systems (HEPSs) for the small-sized Serpis River basin is examined as a support tool for early warning and mitigation strategies. To this end, the Flash-Flood Event–Based Spatially Distributed Rainfall–Runoff Transformation–Water Balance (FEST-WB) model is driven by ground stations to examine the hydrological response of this semiarid and karstic catchment to heavy rains. The use of a multisite and novel calibration approach for the FEST-WB parameters is necessary to cope with the high nonlinearities emerging from the rainfall–runoff transformation and heterogeneities in the basin response. After calibration, FEST-WB reproduces with remarkable accuracy the hydrological response to intense precipitation and, in particular, the 12 October 2007 flash flood. Next, the flood predictability challenge is focused on quantitative precipitation forecasts (QPFs). In this regard, three SREPS generation strategies using the WRF Model are analyzed. On the one side, two SREPSs accounting for 1) uncertainties in the initial conditions (ICs) and lateral boundary conditions (LBCs) and 2) physical parameterizations are evaluated. An ensemble Kalman filter (EnKF) is also designed to test the ability of ensemble data assimilation methods to represent key mesoscale uncertainties from both IC and subscale processes. Results indicate that accounting for diversity in the physical parameterization schemes provides the best probabilistic high-resolution QPFs for this particular flash flood event. For low to moderate precipitation rates, EnKF and pure multiple physics approaches render undistinguishable accuracy for the test situation at larger scales. However, only the multiple physics QPFs properly drive the HEPS to render the most accurate flood warning signals. That is, extreme precipitation values produced by these convective-scale precipitation systems anchored by complex orography are better forecast when accounting just for uncertainties in the physical parameterizations. These findings contribute to the identification of ensemble strategies better targeted to the most relevant sources of uncertainty before flash flood situations over small catchments.

scholarly journals Rainfall Threshold for Flash Flood Warning Based on Model Output of Soil Moisture: Case Study Wernersbach, Germany

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1061
Author(s):  
Thanh Thi Luong ◽  
Judith Pöschmann ◽  
Rico Kronenberg ◽  
Christian Bernhofer
Keyword(s):  
Soil Moisture ◽  
Flash Flood ◽  
Rainfall Threshold ◽  
Probability Of Detection ◽  
Water Levels ◽  
Critical Success ◽  
Flood Warning ◽  
Critical Discharge ◽  
Critical Success Index

Convective rainfall can cause dangerous flash floods within less than six hours. Thus, simple approaches are required for issuing quick warnings. The flash flood guidance (FFG) approach pre-calculates rainfall levels (thresholds) potentially causing critical water levels for a specific catchment. Afterwards, only rainfall and soil moisture information are required to issue warnings. This study applied the principle of FFG to the Wernersbach Catchment (Germany) with excellent data coverage using the BROOK90 water budget model. The rainfall thresholds were determined for durations of 1 to 24 h, by running BROOK90 in “inverse” mode, identifying rainfall values for each duration that led to exceedance of critical discharge (fixed value). After calibrating the model based on its runoff, we ran it in hourly mode with four precipitation types and various levels of initial soil moisture for the period 1996–2010. The rainfall threshold curves showed a very high probability of detection (POD) of 91% for the 40 extracted flash flood events in the study period, however, the false alarm rate (FAR) of 56% and the critical success index (CSI) of 42% should be improved in further studies. The proposed adjusted FFG approach has the potential to provide reliable support in flash flood forecasting.

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