scholarly journals Validation of the AROME, ALADIN and WRF Meteorological Models for Flood Forecasting in Morocco

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 437 ◽  
Author(s):  
El Mahdi El Khalki ◽  
Yves Tramblay ◽  
Arnau Amengual ◽  
Victor Homar ◽  
Romualdo Romero ◽  
...  
Keyword(s):  
Developing Countries ◽  
Soil Moisture ◽  
International Development ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Added Value ◽  
Flood Events ◽  
African Countries ◽  
Antecedent Soil Moisture ◽  
Meteorological Models

Flash floods are common in small Mediterranean watersheds and the alerts provided by real-time monitoring systems provide too short anticipation times to warn the population. In this context, there is a strong need to develop flood forecasting systems in particular for developing countries such as Morocco where floods have severe socio-economic impacts. In this study, the AROME (Application of Research to Operations at Mesoscale), ALADIN (Aire Limited Dynamic Adaptation International Development) and WRF (Weather Research and Forecasting) meteorological models are evaluated to forecast flood events in the Rheraya and Ourika basin located in the High-Atlas Mountains of Morocco. The model evaluation is performed by comparing for a set of flood events the observed and simulated probabilities of exceedances for different precipitation thresholds. In addition, two different flood forecasting approaches are compared: the first one relies on the coupling of meteorological forecasts with a hydrological model and the second one is a based on a linear relationship between event rainfall, antecedent soil moisture and runoff. Three different soil moisture products (in-situ measurements, European Space Agency’s Climate Change Initiative ESA-CCI remote sensing data and ERA5 reanalysis) are compared to estimate the initial soil moisture conditions before flood events for both methods. Results showed that the WRF and AROME models better simulate precipitation amounts compared to ALADIN, indicating the added value of convection-permitting models. The regression-based flood forecasting method outperforms the hydrological model-based approach, and the maximum discharge is better reproduced when using the WRF forecasts in combination with ERA5. These results provide insights to implement robust flood forecasting approaches in the context of data scarcity that could be valuable for developing countries such as Morocco and other North African countries.

scholarly journals The suitability of remotely sensed soil moisture for improving operational flood forecasting

2014 ◽  
Vol 18 (6) ◽  
pp. 2343-2357 ◽  
Author(s):  
N. Wanders ◽  
D. Karssenberg ◽  
A. de Roo ◽  
S. M. de Jong ◽  
M. F. P. Bierkens
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Remotely Sensed ◽  
Added Value ◽  
Lead Times ◽  
Validation Data ◽  
Data Set ◽  
Discharge Data

Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

Comparison of two flood forecasting approaches over High Atlas Mountains basins in Morocco

2021 ◽  
Author(s):  
El Khalki El Mahdi ◽  
Yves Tramblay ◽  
Arnau Amengual ◽  
Victor Homar ◽  
Romualdo Romero ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Regression Model ◽  
International Development ◽  
Flash Flood ◽  
Flood Forecasting ◽  
Least Squares Regression ◽  
Antecedent Soil Moisture ◽  
High Atlas ◽  
Atlas Mountains ◽  
High Atlas Mountains

<p>This study aims to compare flood forecasting approaches adapted to the context of Morocco, for two catchments (Rheraya and Ourika) located in the High Atlas Mountains. We evaluated the performances of flash-flood forecasts using two approaches; one relying on event-based hydrological modelling, and the second, a generalized least squares regression model linking event rainfall, antecedent soil moisture and runoff. The meteorological forecasts considered were provided by the AROME (Application of Research to Operations at Mesoscale), ALADIN (Aire Limited Dynamic Adaptation International Development) and WRF (Weather Research and Forecasting) models. For both approaches, three soil moisture data sources (in-situ measurements, ESA-CCI remote sensing data and ERA5 reanalysis) were compared to estimate the initial soil wetness conditions before flood events. Results showed that the AROME and WRF models better simulate precipitation amounts than ALADIN, mostly due to their better ability to reproduce convective events. The comparison between the two flood forecasting approaches showed that the regression model outperforms the hydrological model-based approach, due to fewer calibration parameters and a better robustness. The best results were obtained with the combination of the WRF forecasts with antecedent soil moisture from ERA5. This type of approach needs to be tested in other basins of North Africa where data are available, in order to develop flood forecasting in these regions, which are strongly vulnerable to flash floods.</p>

scholarly journals Hydrological Evaluation of Satellite Soil Moisture Data in Two Basins of Different Climate and Vegetation Density Conditions

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Lu Zhuo ◽  
Dawei Han
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Spatial Difference ◽  
Vegetation Density ◽  
Modis Ndvi ◽  
Basin Scale ◽  
Microwave Sensors ◽  
Validation Tool

Accurate soil moisture information is very important for real-time flood forecasting. Although satellite soil moisture observations are useful information, their validations are generally hindered by the large spatial difference with the point-based measurements, and hence they cannot be directly applied in hydrological modelling. This study adopts a widely applied operational hydrological model Xinanjiang (XAJ) as a hydrological validation tool. Two widely used microwave sensors (SMOS and AMSR-E) are evaluated, over two basins (French Broad and Pontiac) with different climate types and vegetation covers. The results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland), while both products perform poorly in the French Broad basin (forest). The MODIS NDVI thresholds of 0.81 and 0.64 (for cropland and forest basins, resp.) are very effective in dividing soil moisture datasets into “denser” and “thinner” vegetation periods. As a result, in the cropland, the statistical performance is further improved for both satellites (i.e., improved to NSE = 0.74, RMSE = 0.0059 m and NSE = 0.58, RMSE = 0.0066 m for SMOS and AMER-E, resp.). The overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soil moisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.

scholarly journals The suitability of remotely sensed soil moisture for improving operational flood forecasting

2013 ◽  
Vol 10 (11) ◽  
pp. 13783-13816 ◽  
Author(s):  
N. Wanders ◽  
D. Karssenberg ◽  
A. de Roo ◽  
S. M. de Jong ◽  
M. F. P. Bierkens
Keyword(s):  
Soil Moisture ◽  
Flood Forecasting ◽  
Remotely Sensed ◽  
Added Value ◽  
Validation Dataset ◽  
Lead Times ◽  
Distributed Hydrological Model ◽  
Danube Basin ◽  
Discharge Data ◽  
Soil Moisture Data

Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model for flood predictions with lead times up to 10 days. For this study, satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF, are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

scholarly journals The benefits of pre- and postprocessing streamflow forecasts for an operational flood-forecasting system of 119 Norwegian catchments

2021 ◽  
Author(s):  
Trine J. Hegdahl ◽  
Kolbjørn Engeland ◽  
Ingelin Steinsland ◽  
Andrew Singleton
Keyword(s):  
Lead Time ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Flood Forecasting ◽  
Added Value ◽  
Flood Events ◽  
Ensemble Forecasts ◽  
Processing Scheme ◽  
Temperature And Precipitation ◽  
Streamflow Forecasts

Abstract. The novelty of this study is to evaluate the univariate and the combined effects of including both precipitation and temperature forecasts in the preprocessing together with the postprocessing of streamflow for forecasting of floods as well as all streamflow values for a large sample of catchments. A hydrometeorological forecasting chain in an operational flood forecasting setting with 119 Norwegian catchments was used. This study evaluates the added value of pre- and postprocessing methods for ensemble forecasts in a hydrometeorological forecasting chain in an operational flood forecasting setting with 119 Norwegian catchments. Two years of ECMWF ensemble forecasts of temperature (T) and precipitation (P) with a lead-time up to 9 days were used to force the operational hydrological HBV model to establish streamflow forecasts. Two approaches to preprocess the temperature and precipitation forecasts were tested. 1) An existing approach applied to the gridded forecasts using quantile mapping for temperature and a Bernoulli-gamma distribution for precipitation. 2) Bayesian model averaging (BMA) applied to catchment average values of temperature and precipitation. BMA was also used for postprocessing catchment streamflow forecasts. Ensemble forecasts of streamflow were generated for a total of fourteen schemes based on combinations of raw, preprocessed, and postprocessed forecasts in the hydrometeorological forecasting chain. The aim of this study is to assess which pre- and postprocessing approaches should be used to improve streamflow and flood forecasts and look for regional or seasonal patterns in preferred approaches. The forecasts were evaluated for two datasets: i) all streamflows and ii) flood events with streamflow above mean annual flood. Evaluations were based on reliability, continuous ranked probability score (CRPS) and -skill score (CRPSS). For the flood dataset, the critical success index (CSI) was used. Evaluations based on all streamflow data showed that postprocessing improved the forecasts only up to a lead-time of two to three days, whereas preprocessing T and P using BMA improved the forecasts for 50 %–90 % of the catchments beyond three days lead-time. However, for flood events, the added value of pre- and postprocessing is smaller. Preprocessing of P and T gave better CRPS for marginally more catchments compared to the other schemes. Based on CSI, we found that many of the forecast schemes perform equally well. Further, we found large differences in the ability to issue warnings between spring and autumn floods. There was almost no ability to predict autumn floods beyond 3 days, whereas the spring floods had predictability up to 9 days for many events and catchments. The results indicate that the ensemble forecasts have problems in predicting correct autumn precipitation, and the uncertainty is larger for heavy autumn precipitation compared to spring events when temperature driven snow melt is important. To summarize we find that the flood forecasts benefit from most pre-and postprocessing schemes, although the best processing approaches depend on region, catchment, and season, and that the processing scheme should be tailored to each catchment, lead time, season and the purpose of the forecasting.

scholarly journals Operational flood-forecasting in the Piemonte region – development and verification of a fully distributed physically-oriented hydrological model

2009 ◽  
Vol 17 ◽  
pp. 111-117 ◽  
Author(s):  
D. Rabuffetti ◽  
G. Ravazzani ◽  
S. Barbero ◽  
M. Mancini
Keyword(s):  
Cross Sections ◽  
System Reliability ◽  
Hydrological Model ◽  
Warning System ◽  
Flood Forecasting ◽  
Large River ◽  
Distributed Hydrological Model ◽  
Flood Events ◽  
Discharge Time ◽  
Po River

Abstract. A hydrological model for real time flood forecasting to Civil Protection services requires reliability and rapidity. At present, computational capabilities overcome the rapidity needs even when a fully distributed hydrological model is adopted for a large river catchment as the Upper Po river basin closed at Ponte Becca (nearly 40 000 km2). This approach allows simulating the whole domain and obtaining the responses of large as well as of medium and little sized sub-catchments. The FEST-WB hydrological model (Mancini, 1990; Montaldo et al., 2007; Rabuffetti et al., 2008) is implemented. The calibration and verification activities are based on more than 100 flood events, occurred along the main tributaries of the Po river in the period 2000–2003. More than 300 meteorological stations are used to obtain the forcing fields, 10 cross sections with continuous and reliable discharge time series are used for calibration while verification is performed on about 40 monitored cross sections. Furthermore meteorological forecasting models are used to force the hydrological model with Quantitative Precipitation Forecasts (QPFs) for 36 h horizon in "operational setting" experiments. Particular care is devoted to understanding how QPF affects the accuracy of the Quantitative Discharge Forecasts (QDFs) and to assessing the QDF uncertainty impact on the warning system reliability. Results are presented either in terms of QDF and of warning issues highlighting the importance of an "operational based" verification approach.

scholarly journals Accuracy assessment of real-time flood forecasting of coupled hydrological and mesoscale meteorological models

2018 ◽  
Author(s):  
Aida Jabbari ◽  
Jae-Min So ◽  
Deg-Hyo Bae
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
Lead Time ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Flood Forecasting ◽  
Coupled Models ◽  
Temporal And Spatial ◽  
Meteorological Models

Abstract. Hydro-meteorological predictions are important for water management plans, which include providing early flood warnings and preventing flood damages. This study evaluates the real-time precipitation of an atmospheric model at the point and catchment scales to select the proper hydrological model to couple with the atmospheric model. Furthermore, a variety of tests were conducted to quantify the accuracy assessments of coupled models to provide details on the maximum spatial and temporal resolutions and lead times in a real-time forecasting system. As a major limitation of previous studies, the temporal and spatial resolutions of the hydrological model are smaller than those of the meteorological model. Here, through ultra-fine scale of temporal (10 min) and spatial resolution (1 km × 1 km), we determined the optimal resolution. A numerical weather prediction model and a rainfall runoff model were employed to evaluate real-time flood forecasting for the Imjin River (South and North Korea). The comparison of the forecasted precipitation and the observed precipitation indicated that the Weather Research and Forecasting (WRF) model underestimated precipitation. The skill of the model was relatively higher for the catchment than for the point scale, as illustrated by the lower RMSE value, which is important for a semi-distributed hydrological model. The variations in temporal and spatial resolutions illustrated a decrease in accuracy; additionally, the optimal spatial resolution obtained at 8 km and the 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 our case study, comparisons of model performance provided quantitative knowledge for understanding the credibility and restrictions of hydro-meteorological models.

Synchronicity and drivers of river flooding

2020 ◽  
Author(s):  
Wouter Berghuijs ◽  
James Kirchner
Keyword(s):  
Soil Moisture ◽  
Heavy Precipitation ◽  
Flood Forecasting ◽  
Drainage Basins ◽  
Antecedent Soil Moisture ◽  
River Flooding ◽  
Flood Risks ◽  
The Us ◽  
Order Of Magnitude ◽  
Precipitation Events

<p>When rivers flood, surrounding rivers often flood at the same time. When large precipitation events occur, floods do not always occur. Here we explore the drivers and synchronicity of river flooding. Using flood data from thousands of European and US rivers, we demonstrate that the <em>flood synchrony scale</em>—the distance over which multiple rivers flood near synchronously—far exceeds the size of individual drainage basins and varies regionally by more than an order of magnitude. Regions of large flood synchrony scales are mostly uncorrelated with regions of large precipitation synchrony scales; across most of Europe and the US few floods are caused by the biggest rainfall peaks. Instead, most floods are caused by the concurrence of heavy precipitation with high antecedent soil moisture. Risk finance, flood forecasting, and interpretations of flood trends can benefit from accounting for what drives flooding and how flood risks extend beyond the borders of individual drainage basins.</p>

scholarly journals Extreme floods in Europe: going beyond observations using reforecast ensemble pooling

2021 ◽  
Author(s):  
Manuela I. Brunner ◽  
Louise Slater
Keyword(s):  
Sample Size ◽  
Central Europe ◽  
Extreme Events ◽  
Hydrological Model ◽  
Model Performance ◽  
Added Value ◽  
Lead Times ◽  
Flood Events ◽  
New Approach ◽  
Extreme Flood

Abstract. Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System (EFAS) to increase the sample size available to estimate the frequency of extreme local and regional flood events. We assess the added value of such pooling, determine where in Central Europe one might expect the most extreme events, and evaluate how event extremeness is related to physiographic and meteorological catchment characteristics. We work with a set of 234 catchments from the Global Runoff Data Center for which performance of simulated floods is satisfactory when compared to observed streamflow. We pool EFAS-simulated flood events for 10 perturbed ensemble members and lead times from 22 to 46 days, where flood events are only weakly dependent (< 0.25 average correlation across lead times). The resulting large ensemble (130 time series instead of one) enables analyses of very extreme events, which occur less than twice a century. We demonstrate that such ensemble pooling produces more robust estimates with considerably reduced uncertainty bounds (by ~80 % on average) than observation-based estimates but may equally introduce biases arising from the simulated meteorology and hydrological model. Our results show that specific flood return levels are highest in steep and wet regions and are comparably low in regions with strong flow regulation through dams. Furthermore, our pooled flood estimates indicate that the probability of regional flooding is higher in Central Europe and Great Britain than in Scandinavia. We conclude that reforecast ensemble pooling is an efficient approach to increase sample size and to derive robust local and regional flood estimates in regions with sufficient hydrological model performance.

Hydrological causes of extreme flood events and dependence on flood type

2020 ◽  
Author(s):  
Andreas Schumann ◽  
Svenja Fischer ◽  
Phillip Bühler
Keyword(s):  
Soil Moisture ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Flood Events ◽  
Antecedent Soil Moisture ◽  
Flood Peak ◽  
Extreme Flood ◽  
Spatial Differences ◽  
Two Factors ◽  
Alpine Catchments

&lt;p&gt;Extreme flood events can occur due to manifold combinations of different generating factors. A differentiation into flood types helps to distinguish between the main runoff generating processes and the shape of the flood wave. However, the genesis of extreme flood events cannot always be explained by the flood type only. In a first step, flood peak and flood volume are classified to determine their extremity by a robust classification based on moments. Extreme cases of runoff generating processes like the amount of event precipitation, runoff coefficient and antecedent soil moisture are detected by their deviation from the population distribution. With this, we then analyse significant coherences between the drivers of extreme runoff generating processes and the extreme flood characteristics. It turns out, that the different flood types show very different coherences between these two factors. Moreover, many extreme peaks cannot be explained by either of these factors. Instead, the spatial and temporal distribution of precipitation plays the most important role, especially for floods caused by short and medium rain. In a second step, these two factors are included in the coherence analyses, where significant dependencies of the extremity of the flood peak on these are detected. The approach is applied to several basins in Germany and Austria, including alpine, mountainous and flatland catchments. For these, significant spatial differences in the coherences occur. In the alpine catchments e.g. the soil moisture has much more impact on the extremity of floods than for flatland catchments.&lt;/p&gt;

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