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

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.

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

2019 ◽  
Vol 23 (9) ◽  
pp. 3823-3841 ◽  
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.

scholarly journals Propagation of hydro-meteorological uncertainty in a model cascade framework to inundation prediction

2015 ◽  
Vol 19 (7) ◽  
pp. 2981-2998 ◽  
Author(s):  
J. P. Rodríguez-Rincón ◽  
A. Pedrozo-Acuña ◽  
J. A. Breña-Naranjo
Keyword(s):  
Hydrodynamic Model ◽  
Hydrological Model ◽  
Extreme Event ◽  
Weather Prediction ◽  
Uncertainty Assessment ◽  
Distributed Hydrological Model ◽  
Flood Prediction ◽  
Meteorological Model ◽  
Ensemble Technique ◽  
Rainfall Prediction

Abstract. This investigation aims to study the propagation of meteorological uncertainty within a cascade modelling approach to flood prediction. The methodology was comprised of a numerical weather prediction (NWP) model, a distributed rainfall–runoff model and a 2-D hydrodynamic model. The uncertainty evaluation was carried out at the meteorological and hydrological levels of the model chain, which enabled the investigation of how errors that originated in the rainfall prediction interact at a catchment level and propagate to an estimated inundation area and depth. For this, a hindcast scenario is utilised removing non-behavioural ensemble members at each stage, based on the fit with observed data. At the hydrodynamic level, an uncertainty assessment was not incorporated; instead, the model was setup following guidelines for the best possible representation of the case study. The selected extreme event corresponds to a flood that took place in the southeast of Mexico during November 2009, for which field data (e.g. rain gauges; discharge) and satellite imagery were available. Uncertainty in the meteorological model was estimated by means of a multi-physics ensemble technique, which is designed to represent errors from our limited knowledge of the processes generating precipitation. In the hydrological model, a multi-response validation was implemented through the definition of six sets of plausible parameters from past flood events. Precipitation fields from the meteorological model were employed as input in a distributed hydrological model, and resulting flood hydrographs were used as forcing conditions in the 2-D hydrodynamic model. The evolution of skill within the model cascade shows a complex aggregation of errors between models, suggesting that in valley-filling events hydro-meteorological uncertainty has a larger effect on inundation depths than that observed in estimated flood inundation extents.

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

2020 ◽  
Author(s):  
Antonio Parodi ◽  
Martina Lagasio ◽  
Agostino N. Meroni ◽  
Flavio Pignone ◽  
Francesco Silvestro ◽  
...  
Keyword(s):  
High Resolution ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Warning System ◽  
Atmospheric Model ◽  
Added Value ◽  
Distributed Hydrological Model ◽  
Nwp Model ◽  
North Western ◽  
Discharge Curves

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.

scholarly journals Predictive Capability of a High-Resolution Hydrometeorological Forecasting Framework Coupling WRF Cycling 3DVAR and Continuum

2019 ◽  
Vol 20 (7) ◽  
pp. 1307-1337 ◽  
Author(s):  
Martina Lagasio ◽  
Francesco Silvestro ◽  
Lorenzo Campo ◽  
Antonio Parodi
Keyword(s):  
Flash Flood ◽  
Weather Prediction ◽  
Precipitation Forecast ◽  
Distributed Hydrological Model ◽  
Predictive Capability ◽  
Flood Prediction ◽  
Convective Systems ◽  
Data Intensive ◽  
Streamflow Forecasts ◽  
Nwp Model

Abstract The typical complex orography of the Mediterranean coastal areas support the formation of the so-called back-building mesoscale convective systems (MCS) producing torrential rainfall often resulting in flash floods. As these events are usually very small-scaled and localized, they are hardly predictable from a hydrometeorological standpoint, frequently causing a significant amount of fatalities and socioeconomic damage. Liguria, a northwestern Italian region, is characterized by small catchments with very short hydrological response time and is thus extremely prone to the impacts of back-building MCSs. Indeed, Liguria has been hit by three intense back-building MCSs between 2011 and 2014, causing a total death toll of 20 people and several hundred millions of euros of damages. Consequently, it is necessary to use hydrometeorological forecasting frameworks coupling the finescale numerical weather prediction (NWP) outputs with rainfall–runoff models to provide timely and accurate streamflow forecasts. Concerning the aforementioned back-building MCS episodes that recently occurred in Liguria, this work assesses the predictive capability of a hydrometeorological forecasting framework composed by a kilometer-scale cloud-resolving NWP model (WRF), including a 6-h cycling 3DVAR assimilation of radar reflectivity and conventional weather stations data, a rainfall downscaling model [Rainfall Filtered Autoregressive Model (RainFARM)], and a fully distributed hydrological model (Continuum). A rich portfolio of WRF 3DVAR direct and indirect reflectivity operators has been explored to drive the meteorological component of the proposed forecasting framework. The results confirm the importance of rapidly refreshing and data intensive 3DVAR for improving the quantitative precipitation forecast, and, subsequently, the flash flood prediction in cases of back-building MCS events.

scholarly journals Precipitation Forecast Contribution Assessment in the Coupled Meteo-Hydrological Models

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 34 ◽  
Author(s):  
Aida Jabbari ◽  
Jae-Min So ◽  
Deg-Hyo Bae
Keyword(s):  
Lead Time ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Model Performance ◽  
Precipitation Forecast ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Point Scale ◽  
Meteorological Model ◽  
Temporal And Spatial

A numerical weather prediction and a rainfall-runoff model employed to evaluate precipitation and flood forecast for the Imjin River (South and North Korea). The real-time precipitation at point and catchment scales evaluated to select proper hydrological model to couple with atmospheric model. As a major limitation of previous studies, temporal and spatial resolutions of hydrological model are smaller than those of meteorological model. Here, through high resolution of temporal (10 min) and spatial (1 km × 1 km), the optimal resolution determined. The results showed Weather Research and Forecasting (WRF) model underestimated precipitation in point and catchment assessment and its skill was relatively higher for catchment than point scale, as illustrated by the lower Root Mean Square Error (RMSE) of 59.67, 160.48, 68.49 for the catchment and 84.49, 212.80 and 91.53 for the point scale in the events 2002, 2007 and 2011, respectively. The findings led to choose the semi-distributed hydrological model. The variations in temporal and spatial resolutions illustrated accuracy decrease; additionally, the optimal spatial resolution obtained at 8 km and temporal resolution did not affect the inherent inaccuracy of the results. Lead-time variation demonstrated that lead-time dependency was almost negligible below 36 h. With reference to this study, comparisons of model performance provided quantitative knowledge for understanding credibility and restrictions of meteo-hydrological models.

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

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

<p>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.</p><p>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  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.</p>

scholarly journals Effects of Increasing Horizontal Resolution in a Convection-Permitting Model on Flood Forecasting: The 2011 Dramatic Events in Liguria, Italy

2015 ◽  
Vol 16 (4) ◽  
pp. 1843-1856 ◽  
Author(s):  
Silvio Davolio ◽  
Francesco Silvestro ◽  
Piero Malguzzi
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Flood Forecasting ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Flood Prediction ◽  
Meteorological Model ◽  
The Impact ◽  
Atmospheric Simulations

Abstract Coupling meteorological and hydrological models is a common and standard practice in the field of flood forecasting. In this study, a numerical weather prediction (NWP) chain based on the BOLogna Limited Area Model (BOLAM) and the MOdello LOCale in Hybrid coordinates (MOLOCH) was coupled with the operational hydrological forecasting chain of the Ligurian Hydro-Meteorological Functional Centre to simulate two major floods that occurred during autumn 2011 in northern Italy. Different atmospheric simulations were performed by varying the grid spacing (between 1.0 and 3.0 km) of the high-resolution meteorological model and the set of initial/boundary conditions driving the NWP chain. The aim was to investigate the impact of these parameters not only from a meteorological perspective, but also in terms of discharge predictions for the two flood events. The operational flood forecasting system was thus used as a tool to validate in a more pragmatic sense the quantitative precipitation forecast obtained from different configurations of the NWP system. The results showed an improvement in flood prediction when a high-resolution grid was employed for atmospheric simulations. In turn, a better description of the evolution of the precipitating convective systems was beneficial for the hydrological prediction. Although the simulations underestimated the severity of both floods, the higher-resolution model chain would have provided useful information to the decision-makers in charge of protecting citizens.

scholarly journals Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy

2014 ◽  
Vol 18 (10) ◽  
pp. 3923-3936 ◽  
Author(s):  
X. Xie ◽  
S. Meng ◽  
S. Liang ◽  
Y. Yao
Keyword(s):  
Real Time ◽  
Land Surface ◽  
Hydrological Model ◽  
Similarity Measures ◽  
State Parameter ◽  
Model Parameters ◽  
Prediction Errors ◽  
Distributed Hydrological Model ◽  
Streamflow Prediction ◽  
Short Period

Abstract. The challenge of streamflow predictions at ungauged locations is primarily attributed to various uncertainties in hydrological modelling. Many studies have been devoted to addressing this issue. The similarity regionalization approach, a commonly used strategy, is usually limited by subjective selection of similarity measures. This paper presents an application of a partitioned update scheme based on the ensemble Kalman filter (EnKF) to reduce the prediction uncertainties. This scheme performs real-time updating for states and parameters of a distributed hydrological model by assimilating gauged streamflow. The streamflow predictions are constrained by the physical rainfall-runoff processes defined in the distributed hydrological model and by the correlation information transferred from gauged to ungauged basins. This scheme is successfully demonstrated in a nested basin with real-world hydrological data where the subbasins have immediate upstream and downstream neighbours. The results suggest that the assimilated observed data from downstream neighbours have more important roles in reducing the streamflow prediction errors at ungauged locations. The real-time updated model parameters remain stable with reasonable spreads after short-period assimilation, while their estimation trajectories have slow variations, which may be attributable to climate and land surface changes. Although this real-time updating scheme is intended for streamflow predictions in nested basins, it can be a valuable tool in separate basins to improve hydrological predictions by assimilating multi-source data sets, including ground-based and remote-sensing observations.

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