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 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>

What is the impact of earth observation and in-situ data assimilation on seasonal hydrological forecast quality?

2021 ◽  
Author(s):  
Jude Lubega Musuuza ◽  
Louise Crochemore ◽  
Ilias G. Pechlivanidis
Keyword(s):  
Snow Water Equivalent ◽  
Seasonal Forecasts ◽  
Meteorological Forcing ◽  
In Situ Data ◽  
Snow Water ◽  
Reservoir Inflow ◽  
The Impact ◽  
Forecast Quality

<p>Earth Observations (EO) have become popular in hydrology because they provide information in locations where direct measurements are either unavailable or prohibitively expensive to make. Recent scientific advances have enabled the assimilation of EOs into hydrological models to improve the estimation of initial states and fluxes which can further lead to improved  forecasting of different variables. When assimilated, the data exert additional controls on the quality of the forecasts; it is hence important to apportion the effects according to model forcings and the assimilated datasets. Here,  we investigate the hydrological response and seasonal predictions over the snowmelt driven Umeälven catchment in northern Sweden. The HYPE hydrological model is driven  by two meteorological forcings: (i) a downscaled GCM meteorological product based on the bias-adjusted ECMWF SEAS5 seasonal forecasts, and (ii) historical meteorological data based on the Extended Streamflow Prediction (ESP) technique. Six datasets are assimilated consisting of four EO products (fractional snow cover, snow water equivalent, and the actual and potential evapotranspiration) and two in-situ measurements (discharge and reservoir inflow). We finally assess the impacts of the meteorological forcing data and the assimilated EO and in-situ data on the quality of streamflow and reservoir inflow seasonal forecasting skill for the period 2001-2015. The results show that all assimilations generally improve the skill but the improvement varies depending on the season and assimilated variable. The lead times until when the data assimilations influence the forecast quality are also different for different datasets and seasons; as an example, the impact from assimilating snow water equivalent persists for more than 20 weeks during the spring. We finally show that the assimilated datasets exert more control on the forecasting skill than the meteorological forcing data, highlighting the importance of initial hydrological conditions for this snow-dominated river system.</p>

scholarly journals Numerical experiments in calculating snow water equivalent and estimating their impact on the formation of spring floods

2020 ◽  
Vol 163 ◽  
pp. 06003
Author(s):  
Evgenii Churiulin ◽  
Vladimir Kopeykin ◽  
Natalia Frolova ◽  
Inna Krylenko
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Hydrological Models ◽  
In Situ Data ◽  
Use Of Data ◽  
Complex Approach ◽  
Snow Water ◽  
Seasonal Snow Cover

Seasonal snow cover has a significant impact on forming spring floods. Sparse snow course-measuring network does not meet the requirements of modern tasks related to the technologies of numerical weather prediction (NWP) systems and runoff formation models. Moreover, insufficient volume of hydrometeorological data creates a need to improve spring floods forecasting methods by means of available modern hydrometeorological information related to snow cover. To work out an efficient solution to the issue of initial snow data preparation we need a complex approach including the use of data from satellite, atmospheric models, physical-mathematical models of snow cover and insitu information. This approach will provide modern NWP and hydrological models with reliable initial data on snow cover (snow water equivalent – SWE, snow density – SD). The main purpose of our investigation is related to approbation of satellite data and development of snow cover calculation methods for NWP and hydrological models. Numerous SWE and SD experiments have been performed in order to achieve this aim. A regional snow data assimilation system for COSMORu was implemented during the research. Moreover, a new method of hydrological modelling of spring floods based on ECOMAG model with initial information from COSMO-Ru, SnoWE and in-situ data has been proposed and tested.

scholarly journals A Landsat-Era Sierra Nevada Snow Reanalysis (1985–2015)

2016 ◽  
Vol 17 (4) ◽  
pp. 1203-1221 ◽  
Author(s):  
Steven A. Margulis ◽  
Gonzalo Cortés ◽  
Manuela Girotto ◽  
Michael Durand
Keyword(s):  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Reanalysis Dataset ◽  
In Situ Data ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water ◽  
Fully Bayesian ◽  
Mean Square Errors

Abstract A newly developed state-of-the-art snow water equivalent (SWE) reanalysis dataset over the Sierra Nevada (United States) based on the assimilation of remotely sensed fractional snow-covered area data over the Landsat 5–8 record (1985–2015) is presented. The method (fully Bayesian), resolution (daily and 90 m), temporal extent (31 years), and accuracy provide a unique dataset for investigating snow processes. The verified dataset (based on a comparison with over 9000 station years of in situ data) exhibited mean and root-mean-square errors less than 3 and 13 cm, respectively, and correlation greater than 0.95 compared with in situ SWE observations. The reanalysis dataset was used to characterize the peak SWE climatology to provide a basic accounting of the stored snowpack water in the Sierra Nevada over the last 31 years. The pixel-wise peak SWE volume over the domain was found to be 20.0 km3 on average with a range of 4.0–40.6 km3. The ongoing drought in California contains the two lowest snowpack years (water years 2014 and 2015) and three of the four driest years over the examined record. It was found that the basin-average peak SWE, while underestimating the total water storage in snowpack over the year, accurately captures the interannual variability in stored snowpack water. However, the results showed that the assumption that 1 April SWE is representative of the peak SWE can lead to significant underestimation of basin-average peak SWE both on an average (21% across all basins) and on an interannual basis (up to 98% across all basin years).

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.

Simulated suspended sediment flows in South America using hydrological-hydrodynamic modeling

2021 ◽  
Author(s):  
Hugo Fagundes ◽  
Fernando Fan ◽  
Rodrigo Paiva ◽  
Vinicius Siqueira ◽  
Diogo Buarque ◽  
...  
Keyword(s):  
South America ◽  
Model Performance ◽  
Suspended Sediments ◽  
Sediment Supply ◽  
Regional Studies ◽  
In Situ Data ◽  
Sediment Flow ◽  
Spatio Temporal ◽  
Negative Impacts

<p>Suspended sediments (SS) have an important role in the maintenance of several ecosystems by supplying them with nutrients. On the other hand, erosion and sediment transport can carry pollutants and pesticides, contributing to the negative impacts on the aquatic biota. Besides that, sediment supply for the rivers is often a driver to geomorphologic changes occurring in the rivers. Erosion and sediment rates in South America are considerably high in comparison to northern continents in the world. In this study we modeled the natural (non affected by reservoirs) spatio-temporal dynamic of suspended sediments in South America, including deposition rates in floodplain areas, using the sediment continental model MGB-SED SA. The model performance was evaluated aga inst 595 in-situ stations; 80 sites using results from regional studies; and 51 sites using results from a global sediment model. For most places, model performance analysis shows a better agreement between simulated and observed (in-situ) data than when results were compared to regional studies and a global model data. A better representation of sediment flow in rivers and floodplains was possible due to the use of hydrodynamic river routing. Based on MGB-SED SA estimates, South America delivers to the oceans 1.00×10<sup>9</sup> t/year of SS. The bigger suppliers are the Amazon (4.36×10<sup>8</sup> t/year), Orinoco (1.37×10<sup>8</sup> t/year), La Plata (1.11×10<sup>8</sup> t/year), and Magdalena (3.26×10<sup>7</sup>) rivers. Around 12% (2.40×10<sup>8</sup> t/year) of SS loads reaching the rivers are stored in the floodplains, showing the importance of these regions.  </p>

regionalization of the potential to increase rainfall-runoff model performance by multi-objective calibration using modis data over Austria

2020 ◽  
Author(s):  
Martin Kubáň ◽  
Patrik Sleziak ◽  
Adam Brziak ◽  
Kamila Hlavčová ◽  
Ján Szolgay
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Model Performance ◽  
State Variables ◽  
Rainfall Runoff ◽  
Multi Objective ◽  
Snow Water ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>A multi-objective calibration of the parameters of conceptual hydrologic models has the potential to improve the consistency of the simulated model states, their representativeness with respect to catchment states and thereby to reduce the uncertainty in the estimation of hydrological model outputs. Observed in-situ or remotely sensed state variables, such as the snow cover distribution, snow depth, snow water equivalent and soil moisture were often considered as additional information in such calibration strategies and subsequently utilized in data assimilation for operational streamflow forecasting. The objective of this paper is to assess the effects of the inclusion of MODIS products characterizing soil moisture and the snow water equivalent in a multi-objective calibration strategy of an HBV type conceptual hydrological model under the highly variable physiographic conditions over the whole territory of Austria.</p><p>The methodology was tested using the Technical University of Vienna semi-distributed rainfall-runoff model (the TUW model), which was calibrated and validated in 213 Austrian catchments. For calibration we use measured data from the period 2005 to 2014. Subsequently, we simulated discharges, soil moisture and snow water equivalents based on parameters from the multi-objective calibration and compared these with the respective MODIS values. In general, the multi-objective calibration improved model performance when compared to results of model parametrisation calibrated only on discharge time series. Sensitivity analyses indicate that the magnitude of the model efficiency is regionally sensitive to the choice of the additional calibration variables. In the analysis of the results we indicate ranges how and where the runoff, soil moisture and snow water equivalent simulation efficiencies were sensitive to different setups of the multi-objective calibration strategy over the whole territory of Austria. It was attempted to regionalize the potential to increase of the overall model performance and the improvement in the consistency of the simulation of the two-state variables. Such regionalization may serve model users in the selection which remotely sensed variable or their combination is to be preferred in local modelling studies.</p>

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.

scholarly journals Assessment of the accuracy of snow water equivalent calculation with the use of global numerical weather prediction models and SnoWE snowpack model (by the example of the Kama River basin)

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Andrey N. Shikhov ◽  
◽  
Evgenii V. Churiulin ◽  
Rinat K. Abdullin ◽  
◽  
...  
Keyword(s):  
River Basin ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Entire Area ◽  
Numerical Weather ◽  
In Situ Data ◽  
Snow Water

The paper discusses the results of snow cover formation and snowmelt modeling in the Kama river basin (S = 507 km2) using two approaches previously developed by the authors. The first one is the SnoWE snowpack model developed at the Hydrometeorological Center of the Russian Federation and used in quasi-operational mode since 2015, and the second is GIS-based empirical technique which was previously implemented for the Kama river basin. Both methods are based on a combination of numerical weather prediction (NWP) models data with operational synoptic observations at the weather stations. The study was performed for the winter seasons 2018/19 and 2019/20. To assess the reliability of simulated snow water equivalent (SWE), we obtained in-situ data from 68 locations (snow survey routes) distributed over the entire area of ​​the river basin. As a result of the study, the main advantages and limitations of two methods for SWE calculation were identified. As for the maximum values of SWE, the root mean square error (RMSE) of simulated SWE ranges from 14% to 28% of the average observed SWE according to in-situ data. It was found, that the SnoWE model more reliably reproduces SWE in the lowland part of the river basin. Simultaneously, SWE was substantially underestimated according to the SnoWE model in the northern and mountainous parts of the basin,. The second method provides a more realistic estimate of the spatial distribution of SWE over the area, as well as a higher accuracy of calculation for its northern part of the river basin. The main drawback of the method is the substantial overestimation of the intensity of snowmelt and snow sublimation. Consequently, the accuracy of SWE calculations sharply decreases in the spring season. Wherein, SWE calculation accuracy in the winter season 2019/20 was substantially lower than in 2018/19 due to frequent thaws.

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