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

Abstract

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.

The significance of monitoring high mountain environments to detect heavy precipitation hotspots: a case study in Gredos, Central Spain

“…Caillaud et al (2021) observed that AROME was able to represent extreme precipitation in the northwestern Mediterranean, including the southern French Alps, at daily and hourly scale; although it did underestimate precipitation for very high intensities (over 200 mm day −1 ). El Khalki et al (2020) confirmed the good performance of AROME model for predicting intense precipitation amounts resulting in flash-floods in Morocco in comparison with other forecast models. They both highlighted that the high-resolution, and the explicitly resolved deep convection, represent clear improvements for intense precipitation forecast compared to other models.…”

Evaluation of the GPM-IMERG Precipitation Product for Flood Modeling in a Semi-Arid Mountainous Basin in Morocco

“…It provides daily soil surface moisture with a spatial resolution of 0.25 degrees [31,45,46]. The product has been validated by [47], and recently validated in the High Atlas of Marrakech by [18,48] for flood modeling. The ESA-CCI product shows in the mountainous area some missing values [49] that are related to a filter that is applied to the ESA-CCI product in order to ensure the data quality [45].…”

“…Several studies have shown that this parameter can be related to different soil moisture indicators, obtained from field measurements [53][54][55], or soil moisture models or from satellite data [56] to reproduce the CN of flood events. In the present study, the approach previously used by [18,48] in a similar basin of South Morocco is implemented: the ESA-CCI soil moisture is used to estimate the initial soil moisture condition. All the model parameters including the CN are first calibrated.…”

Meteorological - Hydrological Coupling Flood Forecast and Error Propagation Characteristics Based on Radar Data Assimilation in Small- to Medium Sized River Basin：A Case Study of Zhanghe River Basin in China

“…The primary reasons are as follows: (1) Due to the nonlinearity and complexity of the atmosphere, numerical weather forecasting is less precise for small to medium-sized river basins compared to larger basins [4,[25][26][27]. (2) With the increase in resolution, it is more difficult to match meteorological and hydrological coupling in scale [6][7][8]. (3) Due to geographical conditions, the distribution of rainfall is uneven, and the time of concentration has a greater influence on small to medium basins than in larger basins.…”

“…Combined with the rapid confluence of such watersheds, this lack of data means that forecast lead times are short and prediction accuracy is low. One approach to generating longer-term flood forecasts, and thereby improving flood control and disaster mitigation in small-and medium-sized rivers basins, is by introducing precipitation forecasts during the lead time and using high-precision meteorological and hydrological information to carry out flood forecasting and early warning work [2][3][4][5][6][7][8][9]. Therefore, increasing attention has been paid to flood forecasting based on the coupling of numerical prediction models with hydrological models.…”