HS6.9

Precipitation is a key component of the water cycle and an important forcing data for hydrological simulations and forecasts  and other applications. Having high quality data of precipitation at the watershed scale is challenging. Many methods are used to estimate precipitation such as rain gauges, remote sensing, and reanalysis. Among these, rain gauge provides the most accurate estimate of precipitation, but its scarcely available in remote areas. This in turn badly affects hydrological studies and operational applications. However, the advent of remote sensing offers an opportunity to estimate precipitation in remote areas. The main objective of this study is to evaluate the reliability and the usefulness  satellite precipitation products for hydrological modelling and forecasting. The study was carried out on 7 contrasting catchments located in Eastern Canada. Five gridded daily satellite precipitation products (SPP) including CMORPH, PERSIANN-CDR, CHIRPS, TMPA and GPM were first compared against ERA-5 daily precipitation product used as reference over the 2001-2015 period. Each precipitation product was then used to calibrate a lumped and a semi-distributed version of the GR4J model. Temperature data required by the hydrological models was from ERA-5. Calibration covered a 10-year period (2001-2010), while validation was on a 5-year period (2011-2015). Four scenarios were considered. First, both GR4J models were calibrated using ERA5 and satellites products separately as inputs. Second, SPP were used during the summer period and ERA5 precipitation was used for the remaining seasons separately as input to calibrate the lumped model. Third, the lumped GR4J model was calibrated only during summer seasons using precipitation of each SPP as forcing data. Lastly, the mean of SPP products was used as forcing data to calibrate lumped GR4J model for the first scenario. Evaluation of the reliability of the SPP demonstrate that the GPM product shows highest correlation for daily precipitation compared to reference data (ERA5) with a correlation coefficient of 0.73 for Androscoggin watershed for duration of 2001 to 2015.Moreover, the results depict that all SPP tend to underestimate daily precipitation compared to reference data. Preliminary results also show that the lumped and the semi-distributed two versions of GR4J give comparable results for the first scenario, with NSE values ranging between 0.480 and 0.86 for calibration and 0.357 and 0.86 for validation, respectively. This is followed by the last (0.586 < NSE < 0.809), second (0.0.470 < NSE < 0.85) and the third scenario(0.249<NSE< 0.809) during calibration for the lumped model. Similarly, the NSE values ranging from 0.50 to 0.77 ,0.59 to 0.81 and 0.293 to 0.68 for the last, second and the third scenario for validation respectively. In addition, the third scenario illustrates that CMORPH product performs well in the summer period whereas all the other SPP outperform CMORPH during the spring and winter seasons. In conclusion, merging the 3 SPP contribute to the improvement of the performance of GR4J lumped model. The next step will be to implement short-term forecasting experiments for a subset of the catchments that were already calibrated and validated with the five SPP.

How to cite: Kouki, S. and Leconte, R.: Evaluation of reliability and the added value of satellite precipitation products in hydrological modelling calibration and forecasting in remote areas of northern Canada, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6091, https://doi.org/10.5194/egusphere-egu22-6091, 2022.

Precipitation with high spatio-temporal resolution is one of the critical components of meteorological forcing for hydrological modeling. Its accurate measurement required a large number of rain gauges that are limited for many regions, especially highly elevated domains with a complex and mountainous topography, such as the eastern part of Turkey. On the other hand, open access Gridded Precipitation Datasets (GPDs) varying in spatial and temporal resolutions deliver alternative sources in data-scarce regions. However, their hydrological utilities are to be assessed in different basins to make adequate knowledge for both the developers and end-users. Hence, this study was carried out to investigate the spatio-temporal stability and hydrological utility of four GPDs (MSWEPv2.8, CHIRPSv2.0, ERA5, and IMERGHHFv06) over the upper Euphrates (Karasu) River Basin in the eastern part of Turkey. The accuracy of selected GPDs compared to observed precipitation is expressed in the form of Kling–Gupta Efficiency (KGE), while Hanssen–Kuiper (HK) skill score was utilized to address the detectability strength of GPDs for five different precipitation intensities. Moreover, the hydrological utility of GPDs is evaluated by employing a conceptual hydrologic model under KGE and Nash–Sutcliffe Efficiency (NSE) statistical metrics. Overall, MSWEPv2.8 shows the highest performance (median KGE of 0.34) for the direct comparison with observed precipitation followed by CHIRPSv2.0 (median KGE of 0.34) and ERA5 (median KGE of 0.08) where IMERGHHFv06 shows low performance (median KGE of 0.02) comparatively. Furthermore, CHIRPSv2.0 shows a stable performance for streamflow prediction compared to other Gridded precipitation datasets for the entire period (2015 – 2019), considering two different scenarios. These findings provide guidance for selecting appropriate GPD for the particular region of interest.

How to cite: Hafizi, H. and Sorman, A. A.: Evaluating the hydrological utility of four gridded precipitation datasets for streamflow prediction in a mountainous basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12151, https://doi.org/10.5194/egusphere-egu22-12151, 2022.

Soil moisture (SM) measurements over large areas are vital for many operational applications such as flood forecasting, irrigation scheduling, and drought monitoring. Although obtaining SM over extensive areas was difficult until recently, the advent of satellite remote sensing technologies such as passive microwave satellites (e.g., SMOS and SMAP) opened a new way. Nevertheless, the utilization of SM products of these satellites is often impeded because of their coarse spatial resolution (i.e., about 40 km). A number of studies have been attempted to improve the coarse resolution satellite SM products via downscaling. However, despite of many downscaling efforts, subsequent use of downscaled satellite SM products for operational applications has not yet been fully explored. Thus, the objective of this study is to evaluate the value of SMAP SM in enhancing short-term streamflow forecast skills. The random forest machine learning technique was used to downscaled SMAP SM from 36 km to a range of resolutions from 1 to 9 km (i.e., 9, 3, and 1 km). Thereafter, a host of experiments were carried out to update a physically-based distributed hydrological model through direct ingestion of the original SMAP SM (e.g., 36 km), SMAP enhanced SM (i.e., 9 km), and downscaled SMAP SM at different spatial resolutions (e.g., 9, 3 and 1 km). A non-updated model was used as a benchmark for comparison. The result shows that the downscaled SMAP SM has presented better spatial detail than its corresponding native resolution and updating the model state with SMAP SM products (i.e., with the native and downscaled products) shows promising potential for improving short term flood forecasting. Finally, this will in turn helps in better water resources management.

How to cite: Wakigari, S. A. and Leconte, R.: Evaluation of the value of spatially improved SMAP soil moisture products in enhancing streamflow forecast skills, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3318, https://doi.org/10.5194/egusphere-egu22-3318, 2022.

Streamflow forecasts suffer from errors in the initial conditions of the catchment-scale soil moisture distribution. In this research, we evaluate the potential of improving streamflow simulations through the assimilation of Sentinel-1 backscatter data into a land surface model. Our modeling setup consists of the Noah-MP land surface model coupled to the HYMAP river routing model and the 'Water Cloud Model' (WCM), which acts as backscatter observation operator, integrated into the NASA Land Information System. The system was set up at 1 km resolution for two contrasting catchments in Belgium: i) the Demer catchment dominated by agriculture and low topographic gradients, and ii) the Ourthe catchment dominated by mixed forests and high topographic gradients. Surface soil moisture and leaf area index (LAI) dynamically simulated by Noah-MP in an open-loop run were used to calibrate the parameters of the WCM using a Bayesian objective function and Sentinel-1 backscatter data processed to 1 km spatial resolution for the period 2015-2021. We present results of a suite of data assimilation experiments obtained from an ensemble Kalman filter that updates both soil moisture and LAI. We tested the use of (i) WCM parameters that were calibrated using backscatter data from all Sentinel-1 orbits simultaneously or using data from each Sentinel-1 orbit separately, (ii) backscatter observations with or without seasonal bias correction, (iii) backscatter observations in VV and VH polarization separately or combined. The different data assimilation experiments are evaluated with leaf area index from optical remote sensing, microwave-based soil moisture retrievals and streamflow measurements.

Preliminary results indicate substantial differences between the different data assimilation experiments. For the Ourthe catchment, streamflow skill improvement was highest when simultaneously assimilating VV and VH observations without bias correction but using orbit-specific WCM parameters. For soil moisture and LAI, however, the highest skill was obtained by assimilating only VV observations. For the Demer catchment, assimilating observations without seasonal bias correction led to a skill degradation for streamflow while the impact of data assimilation was neutral when applying rescaled observations. Over this agriculturally dominated area, evaluation with soil moisture and LAI generally indicated the highest degradation. Difficulties in the Demer catchment might be related to crop rotation practices typical for the region that causes an interannual variability in backscatter dynamics not well accounted for by a static set of WCM parameters.

How to cite: Bechtold, M., Modanesi, S., Lievens, H., Brangers, I., Getirana, A., Gruber, A., Massari, C., and De Lannoy, G.: Assimilation of Sentinel-1 backscatter into a land surface model for soil moisture and leaf area index updating: Impact on streamflow simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9642, https://doi.org/10.5194/egusphere-egu22-9642, 2022.