Search Results (11)

Integrating numerical weather forecasts that provide ensemble precipitation forecasts, land surface hydrological modeling that resolves surface and subsurface hydrological processes, and artificial intelligence techniques that correct the forecast bias, known as the “meteo-hydro-AI” approach, has emerged as a popular flood forecast method. However, its performance during extreme flood events across different interval basins has received less attention. Here, we evaluated the meteo-hydro-AI approach for forecasting extreme flood events from headwater to downstream sub-basins in the Luo River basin during 2010–2017, with forecast lead times up to 7 days. The proposed meteo-hydro approach based on ECMWF weather forecasts and the Conjunctive Surface-Subsurface Process version 2 land surface model with a spatial resolution of 1 km captured the flood hydrographs quite well. Compared with the ensemble streamflow prediction (ESP) approach based on initial conditions, the meteo-hydro approach increased the Nash-Sutcliffe efficiency of streamflow forecasts at the three outlet stations by 0.27–0.82, decreased the root-mean-squared-error by 22–49%, and performed better in reliability and discrimination. The meteo-hydro-AI approach showed marginal improvement, which suggested further evaluations with larger samples of extreme flood events should be carried out. This study demonstrated the potential of the integrated meteo-hydro-AI approach for ensemble forecasting of extreme flood events. Full article

This modeling study intended to solve a part of the global scientific problem related to increased concentrations of carbon dioxide in the atmosphere via emissions from terrestrial ecosystems that, along with anthropogenic emissions, make notable contributions to the processes of climate change on the planet. The main stream of CO₂ from natural terrestrial ecosystems is related to the activation of biological processes, such as the production/destruction of plant biomass. In this study, the Wetland-DNDC computer simulation model with a focus on nitrogen and carbon biogeochemical cycles was used to study the effect of hydrothermal conditions on greenhouse gas fluxes in West Siberian peatlands. The study was implemented on the site of the world’s largest pristine wetland/peatland system, the Great Vasyugan Mire (GVM). The study was carried out based on data from permanent measurements at meteo stations and our own in situ measurements of hydrological and thermal parameters on sites, which allowed for testing different scenarios of changes in environmental conditions (temperature, precipitation, groundwater level) together with a change in GHG fluxes. The study revealed the air temperature and the level of groundwater as the main drivers controlling CO₂ fluxes. The study of different scenarios of change in annual air temperature revealed the threshold of change in the wetland/peatland ecosystem from carbon sink to carbon source to the atmosphere to happen with an increase in the average annual air temperature by 3 °C with reference to the average annual air temperature values in 2019. Also, we found that the wetland/peatland ecosystem turned to act as an active carbon sink with about 7 cm increase in annual groundwater level, compared with its base level of −21 cm. Full article

Because of the ongoing changing climate, extreme rainfall events’ frequency at the global scale is expected to increase, thus resulting in high social and economic impacts. A Meteo/Hydro/Hydraulic forecasting chain combining heterogeneous observational data sources is a crucial component for an Early Warning System and is a fundamental asset for Civil Protection Authorities to correctly predict these events, their effects, and put in place anticipatory actions. During the last week of October 2021 an intense Mediterranean hurricane (Apollo) affected many Mediterranean countries (Tunisia, Algeria, Malta, and Italy) with a death toll of seven people. The CIMA Meteo/Hydro/Hydraulic forecasting chain, including the WRF model, the hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D, was operated in real-time to predict the Apollo weather evolution as well as its hydrological and hydraulic impacts, in support of the early warning activities of the Italian Civil Protection Department. The WRF model assimilating radar data and in situ weather stations showed very good predictive capability for rainfall timing and location over eastern Sicily, thus supporting accurate river flow peak forecasting with the hydrological model Continuum. Based on WRF predictions, the daily automatic system for water detection (AUTOWADE) run using Sentinel 1 data was anticipated with respect to the scheduled timing to quickly produce a flood monitoring map. Ad hoc tasking of the COSMO-SkyMed satellite constellation was also performed to overcome the S1 data latency in eastern Sicily. The resulting automated operational mapping of floods and inland waters was integrated with the subsequent execution of the hydraulic model TELEMAC-2D to have a complete representation of the flooded area with water depth and water velocity. Full article

This paper presents a system for supporting hydropower production on mountainous areas. The system couples the outputs of a numerical weather prediction model and a snow melting and accumulation temperature-based model. Several procedures are presented for interpolating meteorological variables and calibrating and validating model parameters that can be generalized to any other mountainous area where the estimation of current and forecasted snow water equivalent and melting amount is required. The system reliability has been assessed through the validation of three components: spatial interpolation of meteorological data, mathematical modeling, and quantitative meteorological forecast. The results show that good accuracy of meteorological data spatial interpolation can be achieved when the data from snow gauges are used for assessing the precipitation lapse rate at higher altitudes, and the temperature lapse rate is computed from data at each time step. The temperature-based hydrological model proved to be effective in simulating lake inflow water volume and energy production. No clear result has been found for snow melt forecast due to the difficulties in providing reliable quantitative weather forecast in complex alpine area. Full article

Despite the numerous contributions available in the literature about the wind-induced bias of rainfall intensity measurements, adjustments based on collection efficiency curves are rarely applied operationally to rain records obtained from catching-type rain gauges. The many influencing variables involved and the variability of the results of field experiments do not facilitate the widespread application of adjustment algorithms. In this paper, a Lagrangian particle tracking model is applied to the results of computational fluid dynamic simulations of the airflow field surrounding a rain gauge to derive a simple formulation of the collection efficiency curves as a function of wind speed. A new parameterization of the influence of rainfall intensity is proposed. The methodology was applied to a cylindrical gauge, which has the typical outer shape of tipping-bucket rain gauges, as a representative specimen of most operational measurement instruments. The wind velocity is the only ancillary variable required to calculate the adjustment, together with the measured rainfall intensity. Since wind is commonly measured by operational weather stations, its use adds no relevant burden to the cost of meteo-hydrological networks. Full article

Climate change is a major contemporary phenomenon with multiple consequences. In urban areas, it exacerbates the urban heat island phenomenon. It impacts the health of the inhabitants and the sensation of thermal discomfort felt in urban areas. Thus, it is necessary to estimate as well as possible the air temperature at any point of a territory, in particular in view of the ongoing rationalization of the network of fixed meteorological stations of Météo-France. Understanding the air temperature is increasingly in demand to input quantitative models related to a wide range of fields, such as hydrology, ecology, or climate change studies. This study thus proposes to model air temperature, measured during four mobile campaigns carried out during the summer months, between 2016 and 2019, in Lyon (France), in clear sky weather, using regression models based on 33 explanatory variables from traditionally used data, data from remote sensing by LiDAR (Light Detection and Ranging), or Landsat 8 satellite acquisition. Three types of statistical regression were experimented: partial least square regression, multiple linear regression, and a machine learning method, the random forest regression. For example, for the day of 30 August 2016, multiple linear regression explained 89% of the variance for the study days, with a root mean square error (RMSE) of only 0.23 °C. Variables such as surface temperature, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) have a strong impact on the estimation model. This study contributes to the emergence of urban cooling systems. The solutions available vary. For example, they may include increasing the proportion of vegetation on the ground, facades, or roofs, increasing the number of basins and water bodies to promote urban cooling, choosing water-retaining materials, humidifying the pavement, increasing the number of public fountains and foggers, or creating shade with stretched canvas. Full article

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. Full article

Recent studies have highlighted the need for high resolution rainfall measurements for better modelling of urban and peri-urban catchment responses. In this work, we used a fully-distributed model called “Multi-Hydro” to study small-scale rainfall variability and its hydrological impacts. The catchment modelled is a semi-urban area located in the southwest region of Paris, an area that has been previously partially validated. At this time, we make some changes to the model, henceforth using its drainage system globally, and we investigate the influence of small-scale rainfall variability by modelling three rainfall events with two different rainfall data inputs: the C-band radar data provided by Météo-France at a 1 km × 1 km × 5 min resolution, and the new X-band radar (recently installed at Ecole des Ponts, France) data at a resolution of 250 m × 250 m × 3.41 min, thereby presenting the gains of better resolution (with the help of Universal Multifractals). Finally, we compare the Multi-Hydro hydrological results with those obtained using an operational semi-distributed model called “Optim Sim” over the same area to revalidate Multi-Hydro modelling, and discuss the model’s limitations and the impacts of data quality and resolution, observing the difficulties associated with semi-distributed models when accounting the spatial variability of weather radar data. This work concludes that it may be useful in future to improve rainfall data acquisition, aiming for better spatio-temporal resolution (now achieved by the weather dual-polarized X-band radars) and data quality when considering small-scale rainfall variability, and to merge deterministic, fully-distributed and stochastic models into a hybrid model which would be capable of taking this small-scale rainfall variability into account. Full article

Optimal Quantitative Precipitation Estimation (QPE) of rainfall is crucial to the accuracy of hydrological models, especially over urban catchments. Small-to-medium size towns are often equipped with sparse rain gauge networks that struggle to capture the variability in rainfall over high spatiotemporal resolutions. X-band Local Area Weather Radars (LAWRs) provide a cost-effective solution to meet this challenge. The Clermont Auvergne metropolis monitors precipitation through a network of 13 rain gauges with a temporal resolution of 5 min. 5 additional rain gauges with a 6-minute temporal resolution are available in the region, and are operated by the national weather service Météo-France. The LaMP (Laboratoire de Météorologie Physique) laboratory’s X-band single-polarized weather radar monitors precipitation as well in the region. In this study, three geostatistical interpolation techniques—Ordinary kriging (OK), which was applied to rain gauge data with a variogram inferred from radar data, conditional merging (CM), and kriging with an external drift (KED)—are evaluated and compared through cross-validation. The performance of the inverse distance weighting interpolation technique (IDW), which was applied to rain gauge data only, was investigated as well, in order to evaluate the effect of incorporating radar data on the QPE’s quality. The dataset is comprised of rainfall events that occurred during the seasons of summer 2013 and winter 2015, and is exploited at three temporal resolutions: 5, 30, and 60 min. The investigation of the interpolation techniques performances is carried out for both seasons and for the three temporal resolutions using raw radar data, radar data corrected from attenuation, and the mean field bias, successively. The superiority of the geostatistical techniques compared to the inverse distance weighting method was verified with an average relative improvement of 54% and 31% in terms of bias reduction for kriging with an external drift and conditional merging, respectively (cross-validation). KED and OK performed similarly well, while CM lagged behind in terms of point measurement QPE accuracy, but was the best method in terms of preserving the observations’ variance. The correction schemes had mixed effects on the multivariate geostatistical methods. Indeed, while the attenuation correction improved KED across the board, the mean field bias correction effects were marginal. Both radar data correction schemes resulted in a decrease of the ability of CM to preserve the observations variance, while slightly improving its point measurement QPE accuracy. Full article

This paper presents a comparison between C-band and X-band radar data over an instrumented and regulated catchment of the Paris region. We study the benefits of polarimetry and the respective hydrological impacts with the help of rain gauge and flow measurements using a semi-distributed hydrological model. Both types of radar confirm the high spatial variability of the rainfall down to their space resolution (1 km and 250 m, respectively). Therefore, X-band radar data underscore the limitations of simulations using a semi-distributed model with sub-catchments of an average size of 2 km. The use of the polarimetric capacity of the Météo-France C-band radar was limited to corrections of the horizontal reflectivity, and its rainfall estimates are adjusted with the help of a rain gauge network. On the contrary, no absolute calibration and scanning optimisation were performed for the polarimetric X-band radar of the Ecole des Ponts ParisTech (hereafter referred to as the ENPC X-band radar). In spite of this and the fact that the catchment is much closer to the C-band radar than to the X-band radar (average distance of 15 km vs. 35 km, respectively), the latter seems to perform at least as well as the former, but with a higher spatial resolution. This was best highlighted with the help of a multifractal analysis, which also shows that the X-band radar was able to pick up a few rainfall extremes that were smoothed out by the C-band radar. Full article

Soil moisture is a key parameter in different environmental applications, suchas hydrology and natural risk assessment. In this paper, surface soil moisture mappingwas carried out over a basin in France using satellite synthetic aperture radar (SAR)images acquired in 2006 and 2007 by C-band (5.3 GHz) sensors. The comparisonbetween soil moisture estimated from SAR data and in situ measurements shows goodagreement, with a mapping accuracy better than 3%. This result shows that themonitoring of soil moisture from SAR images is possible in operational phase. Moreover,moistures simulated by the operational Météo-France ISBA soil-vegetation-atmospheretransfer model in the SIM-Safran-ISBA-Modcou chain were compared to radar moistureestimates to validate its pertinence. The difference between ISBA simulations and radarestimates fluctuates between 0.4 and 10% (RMSE). The comparison between ISBA andgravimetric measurements of the 12 March 2007 shows a RMSE of about 6%. Generally,these results are very encouraging. Results show also that the soil moisture estimatedfrom SAR images is not correlated with the textural units defined in the European Soil Geographical Database (SGDBE) at 1:1000000 scale. However, dependence was observed between texture maps and ISBA moisture. This dependence is induced by the use of the texture map as an input parameter in the ISBA model. Even if this parameter is very important for soil moisture estimations, radar results shown that the textural map scale at 1:1000000 is not appropriate to differentiate moistures zones. Full article