Naira Chaouch

Flood and soil wetness monitoring over the Mackenzie River Basin using AMSR-E 37 GHz brightness temperature

The proposed approach aims to estimate the flood extent and soil wetness using AMSR-E passive microwave data. The approach is applied over the Mackenzie River Basin, which is situated in northwestern Canada. The methodology is based on the polarization ratio index (PR), which is computed using AMSR-E 37 GHz, vertically and horizontally polarized brightness temperature values. The water surface fraction (WSF), which represents the fraction of flooded soil, was derived on a pixel-per-pixel basis. The fractional vegetation cover was added to the WSF calculation in order to take into account the temporal variation of the vegetation shading effect. The WSF derived from AMSR-E data, WSF(AMSR-E), was compared to those derived from the Moderate-resolution Imaging Spectroradiometer Terra instrument (MODIS-Terra) images (250 m), WSF(MODIS). A rating curve relationship was developed between the observed discharge and WSF(MODIS). It was noted that the WSF obtained from AMSR-E images systematically exceed those from MODIS, as they are formed from a combination of different contributions, including open water surface, flooded area and wetlands, which are abundant in the northern climates. Therefore, a wetness index was defined based on the difference between passive microwave and visible image responses. This index was able to qualitatively describe the temporal evolution of the wetness over the Mackenzie River Basin. The availability of discharge observations and passive microwave data leads to the definition of a consistent wetness index and soil moisture monitoring over the Mackenzie River Basin. A satisfactory agreement was noted between the wetness index, the precipitation, and the temperature values. The wetness index agrees well with the measured soil moisture.

On the Use of Satellite Passive Microwave Data for Estimating Surface Soil Wetness in the Mackenzie River Basin

A method is presented to obtain dynamic estimates of basin wetness (BWI) and fractional water surface (FWS) indices at the scale of the Mackenzie River Basin using SSM/I remotely sensed brightness temperature measurements. The approach accounts for the seasonal evolution of the vegetation state and the basin surface heterogeneity. Results demonstrate that the approach can filter out the vegetation effect and produce reasonable estimates of FWS at the basin scale. However, the low resolution of SSM/I and other passive microwave sensors precludes the use of this approach for monitoring soil wetness at a smaller scale. Also, the FWS cannot distinguish moisture effects from open water bodies from that of soil surface. A methodology based on the combined use of passive microwave and visible data, along with topographic information, has been developed to separate the open water from the soil surface component in estimating the BWI, and to downscale the index at the digital elevation models scale using a topographic index (TI) which is continuously adjusted to account for vegetation growth. When applied to the Peace-Athabasca-Delta area, this method improved the correlation between soil wetness and precipitation measured at a meteorological station, compared to an approach based on a time invariant TI.

Near real time flood monitoring over the Mackenzie River Basin using passive microwave data

The potential of a rating curve model for flood monitoring is examined using SSM/I passive microwave images and flow discharge data. Flooded areas are estimated by a linear combination of brightness temperatures measured by the SSM/I sensor in the 19, 37 and 85 GHz channels on each pixel at the reception of each new image. NOAA-AVHRR images are used to validate the estimated flooded areas. The used rating curve model is based on an existing correlation between flooded areas and measured discharge. However, a time lag between these two variables can be observed. Thus, the rating curve model was modified by the introduction of a lag term that can vary depending on flooding intensity and basin features. Hence, the lag term is computed dynamically using a crosscorrelation function between the flooded area and the discharge vectors. The rating curve model is based on two empirical parameters that depend on the river morphology. To overcome this dependency, the model was linked to a Kalman filter to dynamically estimate the empirical parameters according to the forecast errors at each time step. With the Kalman filter, the dynamic rating curve model continuously readjusts its parameters to satisfy the non-stationary behavior of hydrological processes. The model is thus sufficiently flexible and adapted to various conditions. Simulations have been carried out over the Mackenzie River Basin (1.8 million km2), in North-West Canada, during the summer seasons of 1998 and 1999. The predicted flooded areas agree well with those derived from the NOAA-AVHRR images. This implies that a combination of SSM/I passive microwave data and discharge data shows an interesting potential in flood monitoring

Assessing the Performance of a Northern Gulf of Mexico Tidal Model Using Satellite Imagery

Tidal harmonic analysis simulations along with simulations spanning four specific historical time periods in 2003 and 2004 were conducted to test the performance of a northern Gulf of Mexico tidal model. A recently developed method for detecting inundated areas based on integrated remotely sensed data (i.e., Radarsat-1, aerial imagery, LiDAR, Landsat 7 ETM+) was applied to assess the performance of the tidal model. The analysis demonstrates the applicability of the method and its agreement with traditional performance assessment techniques such as harmonic resynthesis and water level time series analysis. Based on the flooded/non-flooded coastal areas estimated by the integrated remotely sensed data, the model is able to adequately reproduce the extent of inundation within four sample areas from the coast along the Florida panhandle, correctly identifying areas as wet or dry over 85% of the time. Comparisons of the tidal model inundation to synoptic (point-in-time) inundation areas generated from the remotely sensed data generally agree with the results of the traditional performance assessment techniques. Moreover, this approach is able to illustrate the spatial distribution of model inundation accuracy allowing for targeted refinement of model parameters.

Dynamic estimation of free water surface coverage from a basin wetness index of the Mackenzie River basin using SSM/I measurements

This work is an investigation to obtain dynamic estimates of free water surface (FWS) coverage in the Mackenzie River basin in northwest Canada. The method used in this study is based on the basin wetness index (BWI), which is computed using the brightness temperature of remotely sensed special sensor microwave imager (SSM/I) data in the 19, 37, and 85 GHz channels. In its basic formulation, the BWI uses two empirical parameters that are constant in time, but they may vary in space depending on the surface type. Hence, a preliminary classification exercise is necessary to apply the BWI. The temporal evolution of the vegetation state and the basin heterogeneity suggest that these parameters could vary in both time and space. An alternative approach is therefore proposed that allows for a reassessment of the empirical constants at the reception of each new image. This approach allows the preliminary classification step to be eliminated. The variability of the parameters over time will account for the evolution of the vegetation cover and improve the BWI sensitivity to the surface wetness. The index was computed for each pixel (625 km2) on a daily basis for the summer seasons from 1997 to 2000 over the entire surface of the Mackenzie River basin (1.8 × 106 km2), which makes up roughly 20% of the area of Canada. The FWS was computed based on the BWI estimates. The reliability of this approach is assessed by analyzing the agreement of the FWS variability with the fluctuations of the climatological and hydrological conditions.

A synergetic use of active microwave observations, optical images and topography data for improved flood mapping in the Gulf of Mexico

This work proposes a method for detecting variation in water front between low and high tide conditions in the Gulf of Mexico area using high resolution satellite imagery. Radarsat 1, Landsat images and aerial photography from the Apalachicola region in Florida were used to test the proposed algorithm. A change detection approach was implemented through the analysis of RGB false color composites. In order to alleviate the effect of the inherent speckle in the SAR images ancillary data were used. The flood prone area was a priori delineated through the determination of lower and higher water contour lines with Landsat images and digital elevation model. This technique improved the performance of the proposed algorithm with respect to the detection technique using the entire scene.

Analysis of an Extreme Weather Event in a Hyper Arid Region UsingWRF-Hydro Coupling, Station, and Satellite data

This study investigates an extreme weather event that impacted the United Arab Emirates (UAE) in March 2016, using the Weather Research and Forecasting (WRF) model version 3.7.1 coupled with its hydrological modeling extension package (WRF-Hydro). Six-hourly forecasted forcing records at 0.5 spatial resolution, obtained from the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS), are used to drive the three nested downscaling domains of both standalone WRF and coupled WRF–WRF-Hydro configurations for the recent flood-triggering storm. Ground and satellite observations over the UAE are employed to validate the model results. The model performance was assessed using precipitation from the Global Precipitation Measurement (GPM) mission (30 min, 0.1 product), soil moisture from the Advanced Microwave Scanning Radiometer 2 (AMSR2; daily, 0.1 product) and the NOAA Soil Moisture Operational Products System (SMOPS; 6-hourly, 0.25 product), and cloud fraction retrievals from the Moderate Resolution Imaging Spectroradiometer Atmosphere product (MODATM; daily, 5 km product). The Pearson correlation coefficient (PCC), relative bias (rBIAS), and root-mean-square error (RMSE) are used as performance measures. Results show reductions of 24 % and 13 % in RMSE and rBIAS measures, respectively, in precipitation forecasts from the coupled WRF–WRF-Hydro model configuration, when compared to standalone WRF. The coupled system also shows improvements in global radiation forecasts, with reductions of 45 % and 12 % for RMSE and rBIAS, respectively. Moreover, WRF-Hydro was able to simulate the spatial distribution of soil moisture reasonably well across the study domain when compared to AMSR2derived soil moisture estimates, despite a noticeable dry and wet bias in areas where soil moisture is high and low. Temporal and spatial variabilities of simulated soil moisture compare well to estimates from the NOAA SMOPS product, which indicates the model’s capability to simulate surface drainage. Finally, the coupled model showed a shallower planetary boundary layer (PBL) compared to the standalone WRF simulation, which is attributed to the effect of soil moisture feedback. The demonstrated improvement, at the local scale, implies that WRF-Hydro coupling may enhance hydrological and meteorological forecasts in hyper-arid environments. Published by Copernicus Publications on behalf of the European Geosciences Union. 1130 Y. Wehbe et al.: Analysis of an extreme weather event in a hyper-arid region