A hindcast study of the Piedmont 1994 flood: the CIMA Research Foundation hydro-meteorological forecasting chain

AbstractBetween the 4th and the 6th of November 1994, Piedmont and the western part of Liguria (two regions in north-western Italy) were hit by heavy rainfalls that caused the flooding of the Po, the Tanaro rivers and several of their tributaries, causing 70 victims and the displacement of over 2000 people. At the time of the event, no early warning system was in place and the concept of hydro-meteorological forecasting chain was in its infancy, since it was still limited to a reduced number of research applications, strongly constrained by coarse-resolution modelling capabilities both on the meteorological and the hydrological sides. In this study, the skills of the high-resolution CIMA Research Foundation operational hydro-meteorological forecasting chain are tested in the Piedmont 1994 event. The chain includes a cloud-resolving numerical weather prediction (NWP) model, a stochastic rainfall downscaling model, and a continuous distributed hydrological model. This hydro-meteorological chain is tested in a set of operational configurations, meaning that forecast products are used to initialise and force the atmospheric model at the boundaries. The set consists of four experiments with different options of the microphysical scheme, which is known to be a critical parameterisation in this kind of phenomena. Results show that all the configurations produce an adequate and timely forecast (about 2 days ahead) with realistic rainfall fields and, consequently, very good peak flow discharge curves. The added value of the high resolution of the NWP model emerges, in particular, when looking at the location of the convective part of the event, which hit the Liguria region.

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Using a precipitation nowcasting algorithm and 3DVAR assimilation in a cloud resolving Numerical Weather Prediction system to enhance a hydrometeorological nowcasting chain

10.5194/egusphere-egu2020-18229 ◽

2020 ◽

Author(s):

Maria Laura Poletti ◽

Martina Lagasio ◽

Francesco Silvestro ◽

Antonio Parodi ◽

Flavio Pignone ◽

...

Keyword(s):

Lead Time ◽

Hydrological Model ◽

Weather Prediction ◽

Warning System ◽

Reliability Function ◽

Distributed Hydrological Model ◽

Rainfall Prediction ◽

Standard Application ◽

Nwp Model ◽

Model Continuum

The use of the best input for an hydrometeorological chain is one of the key elements to improve the discharge prediction in the framework of early warning system. This fact gains in importance in a region such as Liguria Region , where the presence of many catchments with very small drained area and response time in the order of few hours make the prediction of severe events a critical point.The work main scope is to exploit both observations and modelling sources to improve the discharge prediction in small catchments with lead time of 2-8 hours. To pursue this aim in this study the output from the nowcasting technique PhaSt, a spectral-based nowcasting procedure, is used together with the rainfall prediction of WRF NWP model with an hourly cycling 3DVAR data assimilation procedure to produce rainfall scenarios; the continuous distributed hydrological model Continuum, transforms these latter in streamflow scenarios. The connection between the forecasting models outputs is performed through the so called blending &#160;technique, that tries to combine the rainfall fields according to their reliability function of the lead time. The blending has been modified with respect to the standard application using the information retrieved from the NWPS about the total volume on the domain considered and in terms of location of the rainfall structures. The whole chain is applied on some case events of 2014 all over Liguria Region, northern Italy.

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Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts

Hydrology and Earth System Sciences ◽

10.5194/hess-23-3823-2019 ◽

2019 ◽

Vol 23 (9) ◽

pp. 3823-3841 ◽

Cited By ~ 7

Author(s):

Maria Laura Poletti ◽

Francesco Silvestro ◽

Silvio Davolio ◽

Flavio Pignone ◽

Nicola Rebora

Keyword(s):

Data Assimilation ◽

Hydrological Model ◽

Weather Prediction ◽

Distributed Hydrological Model ◽

Streamflow Prediction ◽

Flood Prediction ◽

Meteorological Model ◽

Distributed Approach ◽

Nwp Model ◽

Small Catchments

Abstract. Forecasting flash floods some hours in advance is still a challenge, especially in environments made up of many small catchments. Hydrometeorological forecasting systems generally allow for predicting the possibility of having very intense rainfall events on quite large areas with good performances, even with 12–24 h of anticipation. However, they are not able to predict the exact rainfall location if we consider portions of a territory of 10 to 1000 km2 as the order of magnitude. The scope of this work is to exploit both observations and modelling sources to improve the discharge prediction in small catchments with a lead time of 2–8 h. The models used to achieve the goal are essentially (i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations, (ii) a non-hydrostatic high-resolution numerical weather prediction (NWP) model and (iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other. For this purpose observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information as input to the nowcasting technique in terms of a predicted rainfall volume trend; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events that occurred in the Liguria region (Italy) first to produce a standard analysis on predefined basin control sections and then using a distributed approach that exploits the capabilities of the employed hydrological model. The results obtained for these three analysed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is observed less, and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.

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Reconstructing Flood Events in Mediterranean Coastal Areas Using Different Reanalyses and High-Resolution Meteorological Models

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0270.1 ◽

2020 ◽

Vol 21 (8) ◽

pp. 1865-1887

Author(s):

A. Senatore ◽

S. Davolio ◽

L. Furnari ◽

G. Mendicino

Keyword(s):

High Resolution ◽

Climate Models ◽

Hydrological Model ◽

Numerical Models ◽

Heavy Precipitation ◽

High Impact ◽

Added Value ◽

Distributed Hydrological Model ◽

The Impact ◽

Small Catchments

AbstractReliable reanalysis products can be exploited to drive mesoscale numerical models and generate high-resolution reconstructions of high-impact weather events. Within this framework, regional weather and climate models may greatly benefit from the recent release of the ERA5 product, an improvement to the ERA-Interim dataset. In this study, two different convection-permitting models driven by these two reanalysis datasets are used to reproduce three heavy precipitation events affecting a Mediterranean region. Moreover, different sea surface temperature (SST) initializations are tested to assess how higher-resolution SST fields improve the simulation of high-impact events characterized by strong air–sea interactions. Finally, the coupling with a distributed hydrological model allows evaluating the impact at the ground, specifically assessing the possible added value of the ERA5 dataset for the high-resolution simulation of extreme hydrometeorological events over the Calabria region (southern Italy). Results, based on the comparison against multiple-source precipitation observations, show no clear systematic benefit to using the ERA5 dataset; moreover, intense convective activity can introduce uncertainties masking the signal provided by the boundary conditions of the different reanalyses. The effect of the high-resolution SST fields is even more difficult to detect. The uncertainties propagate and amplify along the modeling chain, where the spatial resolution increases up to the hydrological model. Nevertheless, even in very small catchments, some of the experiments provide reasonably accurate results, suggesting that an ensemble approach could be suitable to cope with uncertainties affecting the overall meteo-hydrological chain, especially for small catchments.

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Application of the HBV hydrological model in a flash flood case in Slovenia

Natural Hazards and Earth System Science ◽

10.5194/nhess-10-2713-2010 ◽

2010 ◽

Vol 10 (12) ◽

pp. 2713-2725 ◽

Cited By ~ 29

Author(s):

M. G. Grillakis ◽

I. K. Tsanis ◽

A. G. Koutroulis

Keyword(s):

Hydrological Model ◽

Flash Flood ◽

Warning System ◽

Weather Forecast ◽

Radar Data ◽

Added Value ◽

Distributed Hydrological Model ◽

Flood Warning ◽

Forecast Models ◽

Flood Warning System

Abstract. An atmospheric depression passed over northwest Slovenia on 18 September 2007 producing precipitation that exceeded 300 mm/d and a 100-year return period runoff in Zelezniki tributary. The resultant flash flood in the study area, which consisted of five basins, was simulated with the conceptual distributed hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning). The model was calibrated and validated with past rainfall – runoff events with satisfactory results producing values of Nash – Sutcliffe coefficient between 0.82 and 0.96. The validated model was applied to the flash flood case with stream gauge failure, driven by spatiotemporal precipitation produced by a set of rain gauges and radar data. The model delivered satisfactory results on three out of five basin outlets while the other two had stream gauge failure during the event. The internal basin dynamics of the most affected area in Zelezniki, was successfully tested in eight of its sub-basins by comparing the peak discharges with the ones evaluated by the slope-conveyance method during a detailed intensive post event campaign. The added value of this method is in the reduced uncertainty in peak discharge estimation and event interpretation and in an effective flash flood warning system for the study area when it is combined with radar nowcasts and operational high resolution short range weather forecast models.

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Coupled hydro-meteorological modelling on a HPC platform for high-resolution extreme weather impact study

Hydrology and Earth System Sciences ◽

10.5194/hess-20-4707-2016 ◽

2016 ◽

Vol 20 (12) ◽

pp. 4707-4715 ◽

Cited By ~ 1

Author(s):

Dehua Zhu ◽

Shirley Echendu ◽

Yunqing Xuan ◽

Mike Webster ◽

Ian Cluckie

Keyword(s):

High Resolution ◽

Hydrological Model ◽

Weather Prediction ◽

Effective Interaction ◽

Extreme Weather ◽

Added Value ◽

Forecast Horizon ◽

Radar Rainfall ◽

Weather Impact ◽

Forecast Lead Time

Abstract. Impact-focused studies of extreme weather require coupling of accurate simulations of weather and climate systems and impact-measuring hydrological models which themselves demand larger computer resources. In this paper, we present a preliminary analysis of a high-performance computing (HPC)-based hydrological modelling approach, which is aimed at utilizing and maximizing HPC power resources, to support the study on extreme weather impact due to climate change. Here, four case studies are presented through implementation on the HPC Wales platform of the UK mesoscale meteorological Unified Model (UM) with high-resolution simulation suite UKV, alongside a Linux-based hydrological model, Hydrological Predictions for the Environment (HYPE). The results of this study suggest that the coupled hydro-meteorological model was still able to capture the major flood peaks, compared with the conventional gauge- or radar-driving forecast, but with the added value of much extended forecast lead time. The high-resolution rainfall estimation produced by the UKV performs similarly to that of radar rainfall products in the first 2–3 days of tested flood events, but the uncertainties particularly increased as the forecast horizon goes beyond 3 days. This study takes a step forward to identify how the online mode approach can be used, where both numerical weather prediction and the hydrological model are executed, either simultaneously or on the same hardware infrastructures, so that more effective interaction and communication can be achieved and maintained between the models. But the concluding comments are that running the entire system on a reasonably powerful HPC platform does not yet allow for real-time simulations, even without the most complex and demanding data simulation part.

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Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts

10.5194/hess-2019-75 ◽

2019 ◽

Author(s):

Maria Laura Poletti ◽

Francesco Silvestro ◽

Silvio Davolio ◽

Flavio Pignone ◽

Nicola Rebora

Keyword(s):

Data Assimilation ◽

Hydrological Model ◽

Weather Prediction ◽

Distributed Hydrological Model ◽

Streamflow Prediction ◽

Flood Prediction ◽

Meteorological Model ◽

Distributed Approach ◽

Nwp Model ◽

Small Catchments

Abstract. Forecasting flash floods with anticipation of some hours is still a challenge especially in environments made by a collection of small catchments. Hydrometeorological forecasting systems generally allow to predict the possibility of having very intense rainfall events on quite large areas with good performances even with 12–24 hours of anticipation. However, they are not able to predict exactly rainfall location if we consider portions of territory of 10 to 103 km2 as order of magnitude. The scope of this work is to exploit both observations and modeling sources to improve the discharge prediction in small catchments with time horizon of 2–8 hours. The models used to achieve the goal are essentially three i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations; ii) a non hydrostatic high-resolution numerical weather prediction (NWP) model; iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other: observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information in input to a nowcasting technique; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events occurred on Liguria Region (Italy) first to produce a standard analysis on predefined basin control sections, then using a distributed approach that exploit the capabilities of the employed hydrological model. The results obtained for these three analyzed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is rarely observed and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.

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Toward a Weather-Based Forecasting System for Fire Blight and Downy Mildew

Atmosphere ◽

10.3390/atmos9120484 ◽

2018 ◽

Vol 9 (12) ◽

pp. 484 ◽

Cited By ~ 2

Author(s):

Ana Firanj Sremac ◽

Branislava Lalić ◽

Milena Marčić ◽

Ljiljana Dekić

Keyword(s):

Downy Mildew ◽

Fire Blight ◽

Meteorological Data ◽

Weather Prediction ◽

Weather Forecast ◽

Atmospheric Model ◽

Plasmopara Viticola ◽

Forecasting System ◽

Nwp Model ◽

The Impact

The aim of this research is to present a weather-based forecasting system for apple fire blight (Erwinia amylovora) and downy mildew of grapevine (Plasmopara viticola) under Serbian agroecological conditions and test its efficacy. The weather-based forecasting system contains Numerical Weather Prediction (NWP) model outputs and a disease occurrence model. The weather forecast used is a product of the high-resolution forecast (HRES) atmospheric model by the European Centre for Medium-Range Weather Forecasts (ECMWF). For disease modelling, we selected a biometeorological system for messages on the occurrence of diseases in fruits and vines (BAHUS) because it contains both diseases with well-known and tested algorithms. Several comparisons were made: (1) forecasted variables for the fifth day are compared against measurements from the agrometeorological network at seven locations for three months (March, April, and May) in the period 2012–2018 to determine forecast efficacy; (2) BAHUS runs driven with observed and forecast meteorology were compared to test the impact of forecasted meteorological data; and (3) BAHUS runs were compared with field disease observations to estimate system efficacy in plant disease forecasts. The BAHUS runs with forecasted and observed meteorology were in good agreement. The results obtained encourage further development, with the goal of fully utilizing this weather-based forecasting system.

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Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction

Hydrology and Earth System Sciences ◽

10.5194/hess-13-293-2009 ◽

2009 ◽

Vol 13 (3) ◽

pp. 293-303 ◽

Cited By ~ 42

Author(s):

Y. Xuan ◽

I. D. Cluckie ◽

Y. Wang

Keyword(s):

High Resolution ◽

Short Range ◽

River Flow ◽

Hydrological Model ◽

Uncertainty Propagation ◽

Weather Forecast ◽

Distributed Model ◽

Distributed Hydrological Model ◽

Weather System ◽

Considerable Uncertainty

Abstract. Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.

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Application of a distributed hydrological model to the design of a road inundation warning system for flash flood prone areas

Natural Hazards and Earth System Science ◽

10.5194/nhess-10-805-2010 ◽

2010 ◽

Vol 10 (4) ◽

pp. 805-817 ◽

Cited By ~ 29

Author(s):

P.-A. Versini ◽

E. Gaume ◽

H. Andrieu

Keyword(s):

Cross Sections ◽

Hydrological Model ◽

Flash Flood ◽

Warning System ◽

Flash Floods ◽

Probability Of Detection ◽

Distributed Hydrological Model ◽

Rainfall Runoff ◽

Erosion Processes ◽

Stage Of Development

Abstract. This paper presents an initial prototype of a distributed hydrological model used to map possible road inundations in a region frequently exposed to severe flash floods: the Gard region (South of France). The prototype has been tested in a pseudo real-time mode on five recent flash flood events for which actual road inundations have been inventoried. The results are promising: close to 100% probability of detection of actual inundations, inundations detected before they were reported by the road management field teams with a false alarm ratios not exceeding 30%. This specific case study differs from the standard applications of rainfall-runoff models to produce flood forecasts, focussed on a single or a limited number of gauged river cross sections. It illustrates that, despite their lack of accuracy, hydro-meteorological forecasts based on rainfall-runoff models, especially distributed models, contain valuable information for flood event management. The possible consequences of landslides, debris flows and local erosion processes, sometimes associated with flash floods, were not considered at this stage of development of the prototype. They are limited in the Gard region but should be taken into account in future developments of the approach to implement it efficiently in other areas more exposed to these phenomena such as the Alpine area.

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A 20-year reforecast study combining high-resolution hydrological modelling, ensemble forecasting and data assimilation for the 12 largest tributaries of the Rhine

10.5194/egusphere-egu2020-3338 ◽

2020 ◽

Author(s):

Bart van Osnabrugge ◽

Maarten Smoorenburg ◽

Remko Uijlenhoet ◽

Albrecht Weerts

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Hydrological Model ◽

Forecast Skill ◽

Ensemble Forecasting ◽

Added Value ◽

River Rhine ◽

Medium Term ◽

Initial State ◽

The Impact

There is an ongoing trend in hydrological forecasting towards both spatially distributed (gridded) models, ensemble forecasting and data assimilation techniques to improve forecasts&#8217; initial states. While in the last years those different aspects have been investigated separately, there are only few studies where the three techniques are combined: ensemble forecasts with state updating of a gridded hydrological model. Additionally, the studies that have addressed this combination of techniques either focus on a small area, a short study period, or both. We here aim to fill this knowledge gap with a 20-year data assimilation and ensemble reforecast experiment with a high resolution gridded hydrological model (wflow_hbv, 1200x1200m) of the full Rhine basin (160 000 km2). To put the impact of state updating in an operational forecasting context, the data assimilation results were compared with AR post-processing as used by the Dutch Forecasting Centre (WMCN).This data assimilation and reforecast experiment was conducted for the twelve main tributaries of the river Rhine. The effect on forecast skill of state updating with the Asynchronous Ensemble Kalman Filter (AEnKF) and AR error correction are compared for medium-term (15-day) forecasts over a period of 20 years (1996 to 2016). State updating improved the initial state for all subbasins and resulted in lasting skill score increase. AR also improved the forecast skill, but the forecast skill with AR did not always converge towards the uncorrected model skill, and instead can deteriorate for longer lead times. AR correction outperformed the AEnKF state updating for the first two days, after which state updating became more effective and outperformed AR. We conclude that state updating has more potential for medium-term hydrological forecasts than the operational AR procedure.Further research is underway to investigate the importance, or added value, of long-term reforecasts as opposed to studies covering a short time span which are often more feasible and therefore more often found in literature.

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