scholarly journals Assessment of satellite-derived rainfall and its use in the ACRU agro-hydrological model

Water SA ◽  
2020 ◽  
Vol 46 (4 October) ◽  
Author(s):  
S Suleman ◽  
KT Chetty ◽  
DJ Clark ◽  
E Kapangaziwiri
Keyword(s):  
Satellite Data ◽  
Hydrological Model ◽  
Model Performance ◽  
Rain Gauge ◽  
Relative Volume ◽  
Rainfall Occurrence ◽  
Occurrence Patterns ◽  
Inform Decision Making

Unfortunately, for various reasons, in-situ rain gauge networks are diminishing, especially in southern Africa, resulting in sparse networks whose records give a poor representation of rainfall occurrence, patterns and magnitudes. Hydrological models are used to inform decision making; however, model performance is directly linked to the quality of input data, such as rainfall. Therefore, the use of satellite-derived rainfall is being increasingly advocated as a viable alternative or supplement. The aim of this study was to evaluate the representativeness of satellite-derived rainfall and its utility in the ACRU agro-hydrological model to simulate streamflow magnitudes, distributions and patterns. The satellite-derived rainfall products selected for use in this study were TRMM3B42, FEWSARC2.0, FEWSRFE2.0, TAMSAT 3.0 and GPM-IMERG4. The satellite rainfall products were validated against available historical observed records and then were used to drive simulations using the ACRU agro-hydrological model in the upper uMngeni, upper uThukela and upper and central Breede catchments in South Africa. At the daily timescale, satellite-derived and observed rainfall were poorly correlated and variable among locations. However, monthly, seasonal and yearly rainfall totals and simulated streamflow volumes were in closer agreement with historical observations than the daily correlations; more so in the upper uMngeni and uThukela than in the upper and central Breede (e.g. FEWSARC2.0 and FEWSRFE2.0, producing relative volume errors of 3.18%, 4.63%, −5.07% and 2.54%, 9.54%, −1.67%, respectively, at Gauges V2E002, 0268883 and 02396985). Therefore, the satellite-derived rainfall shows promise for use in applications operating at coarser temporal scales than at finer daily ones. Complex topographical rainfall generation and varying weather systems, e.g. frontal rainfall, affected the accuracy of satellite-derived product estimates. This study focused on utilising the wealth of available raw satellite data; however, it is clear that the raw satellite data need to be corrected for bias and/or downscaled to provide more accurate results.

scholarly journals Synergy between satellite observations and model simulations during extreme events

2018 ◽  
Author(s):  
Anne Wiese ◽  
Joanna Staneva ◽  
Johannes Schultz-Stellenfleth ◽  
Arno Behrens ◽  
Luciana Fenoglio-Marc ◽  
...  
Keyword(s):  
Satellite Data ◽  
Extreme Events ◽  
Wave Model ◽  
Coastal Areas ◽  
In Situ Measurements ◽  
Model Simulations ◽  
Significant Wave ◽  
Operational Analysis

Abstract. In this study, the quality of wind and wave data provided by the new Sentinel-3A satellite is evaluated. We focus on coastal areas, where altimeter data are of lower quality than those for the open ocean. The satellite data of Sentinel-3A, Jason-2 and CryoSat-2 are assessed in a comparison with in situ measurements and spectral wave model (WAM) simulations. The sensitivity of the wave model to wind forcing is evaluated using data with different temporal and spatial resolution, such as ERA-Interim and ERA5 reanalyses, ECMWF operational analysis and short-range forecasts, German Weather Service (DWD) forecasts and regional atmospheric model simulations -coastDat. Numerical simulations show that both the wave model forced using the ERA5 reanalyses and that forced using the ECMWF operational analysis/forecast demonstrate the best capability over the whole study period, as well as during extreme events. To further estimate the variance of the significant wave height of ensemble members for different wind forcings, especially during extreme events, an empirical orthogonal function (EOF) analysis is performed. Intercomparisons between remote sensing and in situ observations demonstrate that the overall quality of the former is good over the North Sea and Baltic Sea throughout the study period, although the significant wave heights estimated based on satellite data tend to be greater than the in situ measurements by 7 cm to 26 cm. The quality of all satellite data near the coastal area decreases; however, within 10 km off the coast, Sentinel-3A performs better than the other two satellites. Analyses in which data from satellite tracks are separated in terms of onshore and offshore flights have been carried out. No substantial differences are found when comparing the statistics for onshore and offshore flights. Moreover, no substantial differences are found between satellite tracks under various metocean conditions. Furthermore, the satellite data quality does not depend on the wind direction relative to the flight direction. Thus, the quality of the data obtained by the new Sentinel-3A satellite over coastal areas is improved compared to that of older satellites.

scholarly journals Climate-dependent propagation of precipitation uncertainty into the water cycle

2020 ◽  
Author(s):  
Ali Fallah ◽  
Sungmin O ◽  
Rene Orth
Keyword(s):  
Hydrological Model ◽  
Water Cycle ◽  
State Of The Art ◽  
Rain Gauge ◽  
Multiple Sources ◽  
Current State ◽  
Wide Range ◽  
The Impact ◽  
Conceptual Hydrological Model

Abstract. Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modelling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in > 200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) combination of multiple sources (MSWEP V2) and (3) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.

scholarly journals Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions

2020 ◽  
Vol 12 (5) ◽  
pp. 811 ◽  
Author(s):  
Jude Lubega Musuuza ◽  
David Gustafsson ◽  
Rafael Pimentel ◽  
Louise Crochemore ◽  
Ilias Pechlivanidis
Keyword(s):  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Filter Method ◽  
Ensemble Size ◽  
In Situ Data ◽  
Predictive Skill ◽  
Reservoir Inflow

The assimilation of different satellite and in situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we used the European-scale Hydrological Predictions for the Environment hydrological model in the Umeälven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimised model performance when the ensemble Kalman filter method is used. We further assessed the effect of assimilating different satellite products; namely, snow water equivalent, fractional snow cover, and actual and potential evapotranspiration, as well as in situ measurements of river discharge and local reservoir inflows. We finally investigated the combinations of those products that improved model predictions of the target variables and how the model performance varied through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without evapotranspiration products improved the model performance.

scholarly journals Improving the Distributed Hydrological Model Performance in Upper Huai River Basin: Using Streamflow Observations to Update the Basin States via the Ensemble Kalman Filter

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Yongwei Liu ◽  
Wen Wang ◽  
Yiming Hu ◽  
Wei Cui
Keyword(s):  
Kalman Filter ◽  
River Basin ◽  
Ensemble Kalman Filter ◽  
Hydrological Model ◽  
Model Performance ◽  
Real Data ◽  
Distributed Hydrological Model ◽  
Huai River Basin ◽  
Huai River

This study investigates the capability of improving the distributed hydrological model performance by assimilating the streamflow observations. Incorrectly estimated model states will lead to discrepancies between the observed and estimated streamflow. Consequently, streamflow observations can be used to update the model states, and the improved model states will eventually benefit the streamflow predictions. This study tests this concept in upper Huai River basin. We assimilate the streamflow observations sequentially into the Soil and Water Assessment Tool (SWAT) using the ensemble Kalman filter (EnKF) to update the model states. Both synthetic experiments and real data application are used to demonstrate the benefit of this data assimilation scheme. The experiment shows that assimilating the streamflow observations at interior sites significantly improves the streamflow predictions for the whole basin. Assimilating the catchment outlet streamflow improves the streamflow predictions near the catchment outlet. In real data case, the estimated streamflow at the catchment outlet is significantly improved by assimilating the in situ streamflow measurements at interior gauges. Assimilating the in situ catchment outlet streamflow also improves the streamflow prediction of one interior location on the main reach. This may demonstrate that updating model states using streamflow observations can constrain the flux estimates in distributed hydrological modeling.

Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China

2013 ◽  
Vol 505 ◽  
pp. 1-12 ◽  
Author(s):  
Hongliang Xu ◽  
Chong-Yu Xu ◽  
Hua Chen ◽  
Zengxin Zhang ◽  
Lu Li
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Rain Gauge ◽  
Humid Region

scholarly journals Climate-dependent propagation of precipitation uncertainty into the water cycle

2020 ◽  
Author(s):  
Ali Fallah Maraghi ◽  
Sungmin Oh ◽  
Rene Orth
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Water Cycle ◽  
State Of The Art ◽  
Rain Gauge ◽  
Multiple Sources ◽  
Current State ◽  
Wide Range ◽  
The Impact

<p>Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modeling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in >200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR) and (3) combination of multiple sources (MSWEP V2). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984-2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.</p>

scholarly journals Impact of satellite and in situ data assimilation on hydrological predictions

2020 ◽  
Author(s):  
Jude Lubega Musuuza ◽  
Louise Crochemore ◽  
David Gustafsson ◽  
Rafael Pimentel ◽  
Ilias Pechlivanidis
Keyword(s):  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Ensemble Size ◽  
In Situ Data ◽  
Predictive Skill ◽  
Improve Model ◽  
Reservoir Inflow

<p>The assimilation of different satellite and in-situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we use the European-scale Hydrological Predictions for the Environment hydrological model in the Umeälven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimises model performance. We further assess the effect of assimilating different satellite products namely snow water equivalent, fractional snow cover, and actual and potential evapotranspiration; as well as in situ measurements of river discharge and local reservoir inflows. We finally investigate the combinations of those products that improve model predictions of the target variables and how the model performance varies through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without ET products improves the model performance.</p>

scholarly journals Rainfall-Runoff Modelling of the West Rapti Basin, Nepal

2020 ◽  
Vol 2 (1) ◽  
pp. 99-107
Author(s):  
Bibek Thapa ◽  
Anusha Danegulu ◽  
Naresh Suwal ◽  
Surabhi Upadhyay ◽  
Bikesh Manandhar ◽  
...  
Keyword(s):  
River Basin ◽  
Water Resource Management ◽  
Hydrological Model ◽  
Model Performance ◽  
Rain Gauge ◽  
Army Corps ◽  
Distributed Hydrological Model ◽  
Rainfall Runoff ◽  
The West ◽  
Us Army

A hydrological model helps in understanding, predicting, and managing water resources. The HEC-HMS (Centre for Hydrological Engineering - Hydrological Modelling Systems, US Army Corps of Engineers) is one of the hydrological models used to simulate rainfall-runoff and routing processes in diverse geographical areas. In this study, a semi-distributed hydrological model was developed using HEC-HMS for the West-Rapti river basin. The model was calibrated and validated at each outlet of sub-basins and used to simulate the outflow of each sub-basins of the West Rapti river basin. A total of eight rain gauge stations, five meteorological stations, and three hydrological stations, within the basin, were used. The simulated results closely matched the observed flows at the three gauging stations. The Nash-Sutcliffe Efficiency indicated the good model performance of the simulated streamflow with the observed flow at two stations and satisfactory model fit at one station. The performance based on percentage bias and root mean square error was good. This model provides a reference to study water balance, water resource management, and flooding control of the West Rapti basin and can be replicated in other basins.

scholarly journals Synergy of wind wave model simulations and satellite observations during extreme events

Ocean Science ◽  
2018 ◽  
Vol 14 (6) ◽  
pp. 1503-1521 ◽  
Author(s):  
Anne Wiese ◽  
Joanna Staneva ◽  
Johannes Schulz-Stellenfleth ◽  
Arno Behrens ◽  
Luciana Fenoglio-Marc ◽  
...  
Keyword(s):  
Satellite Data ◽  
Extreme Events ◽  
Wave Model ◽  
Coastal Areas ◽  
Wind Forcing ◽  
Model Simulations ◽  
Significant Wave ◽  
Operational Analysis

Abstract. In this study, the quality of wave data provided by the new Sentinel-3A satellite is evaluated and the sensitivity of the wave model to wind forcing is tested. We focus on coastal areas, where altimeter data are of lower quality and wave modelling is more complex than for the open ocean. In the first part of the study, the sensitivity of the wave model to wind forcing is evaluated using data with different temporal and spatial resolution, such as ERA-Interim and ERA5 reanalyses, the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis and short-range forecasts, German Weather Service (DWD) forecasts and regional atmospheric model simulations (coastDat). Numerical simulations show that the wave model forced using the ERA5 reanalyses and that forced using the ECMWF operational analysis/forecast demonstrate the best capability over the whole study period, as well as during extreme events. To further estimate the variance of the significant wave height of ensemble members for different wind forcings, especially during extreme events, an empirical orthogonal function (EOF) analysis is performed. In the second part of the study, the satellite data of Sentinel-3A, Jason-2 and CryoSat-2 are assessed in comparison with in situ measurements and spectral wave model (WAM) simulations. Intercomparisons between remote sensing and in situ observations demonstrate that the overall quality of the former is good over the North Sea and Baltic Sea throughout the study period, although the significant wave heights estimated based on satellite data tend to be greater than the in situ measurements by 7 to 26 cm. The quality of all satellite data near the coastal area decreases; however, within 10 km off the coast, Sentinel-3A performs better than the other two satellites. Analyses in which data from satellite tracks are separated in terms of onshore and offshore flights have been carried out. No substantial differences are found when comparing the statistics for onshore and offshore flights. Moreover, no substantial differences are found between satellite tracks under various metocean conditions. Furthermore, the satellite data quality does not depend on the wind direction relative to the flight direction. Thus, the quality of the data obtained by the new Sentinel-3A satellite over coastal areas is improved compared to that of older satellites.

scholarly journals Satellite data assimilation to improve forecasts of volcanic ash concentrations

2016 ◽  
Author(s):  
Guangliang Fu ◽  
Hai-Xiang Lin ◽  
Arnold Heemink ◽  
Arjo Segers ◽  
Fred Prata ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Satellite Data ◽  
Volcanic Ash ◽  
Three Dimensional ◽  
Forecast Accuracy ◽  
Improve Model ◽  
Abstract Data ◽  
Assimilation Scheme

Abstract. Data assimilation is a powerful tool that requires available observations to improve model forecast accuracy. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations into the assimilation scheme. However, these satellite-retrieved data are often two-dimensional (2D), and cannot be easily combined with a three-dimensional (3D) volcanic ash model to continuously improve the volcanic ash state in a data assimilation system. By integrating available data including ash mass loadings, cloud top heights and thickness information, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2D volcanic ash mass loadings to 3D concentrations at the top layer of the ash cloud. Ensemble-based data assimilation is used to continuously assimilate the extracted measurements of ash concentrations. The results show that satellite data assimilation can force the volcanic ash state to match the satellite observations, and that it improves the forecast of the ash state. Comparison with highly accurate aircraft in-situ measurements shows that the effective duration of the improved volcanic ash forecasts is about a half day. It is shown that an effective half-day ash forecast significantly improves the quality of the advice given to aviation over continental Europe.

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